The effects of Bdellovibrio and like organisms (BALOs) and Lactobacillus salivarius on changes in gut microbial biodiversity and their potential role on Shrimp (Litopenaeus vannamei) Postlarvae

The effects of Bdellovibrio and like organisms (BALOs) and Lactobacillus salivarius on changes in gut microbial biodiversity and their potential role on Shrimp (Litopenaeus vannamei) Postlarvae | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The effects of Bdellovibrio and like organisms (BALOs) and Lactobacillus salivarius on changes in gut microbial biodiversity and their potential role on Shrimp (Litopenaeus vannamei) Postlarvae Farhana Najnine, Meng Wang, Hongcao Han, Junpeng Cai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4319520/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract White-leg shrimp ( Litopenaeus vannamei) is the most important species in the shrimp industry, with its production virtually all from aquaculture. Nevertheless, it frequently suffers from diseases, indicating the inadequacy of existing control measures. Recent, evidence has shown that probiotics play an important role in disease control by maintaining the composition of the gut microbiota. The aim of the present work was to evaluate the probiotic potential of Bdellovibrio and like organisms (BALOs) strain BDN-1F2 and Lactobacillus salivarius strain GZPH2 in rearing shrimp postlarvae (PL) with a control group without any treatment. The results showed that compared to BDN-1F2, GZPH2 only had a better effect on length growth while BDN-1F2 performed significantly better in weight gain and survival rate ( P < 0.05). The 16S high throughput sequencing results showed that BDN-1F2 was significantly more effective than GZPH2 at building a healthy gut microbial communities, including the reduction of pathogenic bacterial taxa Gammaproteobacteria / Vibrionales and the increment of beneficial bacterial taxa Alphaproteobacteria/Rhodobacteraceae. Furthermore, BDN-1F2 also lowered Firmicutes/Bacteroidetes ratios, which theoretically supported better PL growth performance in this group. In addition, the relative abundances of predicted functional microbial genes involving amino acid metabolism and carbohydrate were very high in BDN-1F2 group than in groups GZPH2 and control. To our knowledge, this is the first report to compare between two probiotics, BALOs strain BDN-1F2 and Lactobacillus salivarius strain GZPH2 and an investigation of changes in intestinal microbial community structure as well as their effects on shrimp survival and growth performance. Shrimp Probiotic Bdellovibrio and like organisms Lactobacillus salivarius Gut microbiota Metabolism Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Shrimp is one of the most commonly consumed crustaceans as a good source of healthy protein, calcium, and various essential compounds for the human body while low in calories and fat (Oksuz et al. 2019). The demand of shrimp has been remarkably increased in the past two decades. In this concern, shrimp farmers have tried to increase their production rates. Among the shrimp species, white-leg shrimp Litopenaeus vannamei ( Li. vannamei ) is one of the most cultured shrimp species around the world due to its rapid growth, disease tolerance, high stocking density tolerance, particularly low dietary protein requirement. According to the FAO statistical data in 2018, the production of Li . vannamei culture has increased from 1.32 million tons in 2004 to 4.96 million tons in 2018 (FAO, 2020 ). Although the production of cultured shrimp has intensified to meet the growing demand but shrimp farm industry globally faces numerous challenges due to various infectious disease outbreak (Diwan et al. 2021 ). Shrimp diseases are caused by bacteria and viruses. Bacterial diseases, mainly vibriosis is one of the most severe threat to a wide range of cultured shrimp species that affect the whole shrimp industry (Jayasree et al. 2006 ). Vibrio is responsible for several shrimp diseases such as “luminous bacterial disease” by V. harveyi , “white faeces disease (WFD)” by V. vulnificus, V. fluvialis, V. parahaemolyticus, V. alginolyticus, V. damselae, V. mimicus and V. cholera , “Loose shell syndrome (LSS)”by V. harve yi, V. parahaemolyticus, V. alginolyticus , “white gut disease (WGD)” by V. harveyi, V. alginolyticus, V. agnuillarum , “early mortality syndrome/acute hepatopancreatic necrosis disease (EMS/AHPND)” by V. parahaemolyticus etc. (Tran et al. 2013 ; Kumar et al. 2020 ; Kumar et al. 2021 ; Chellapandian et al. 2021 ; Haifa-Haryani et al. 2022 ). Although different bacteria other than vibrio , like Shewanella , could also cause similar AHPND diseases (Prachumwat et al. 2020 ). A new emerging vibriosis called “translucent post-larvae disease" (TPD) or "glass post-larvae disease (GPD)” occurred by V. parahaemolyticus ( Zou et al. 2020 ). A large proportion of shrimp farmers are using chemical compounds or antibiotics to prevent or treat disease outbreaks. Uncontrolled and widespread use of chemicals or antibiotics in shrimp farms might result in the deposition of residue in the shrimp body or may cause the acceleration of antibiotic resistance (Zalewska et al. 2021 ). Recently, antibiotic resistance is a booming concern for human and animal health. Due to prioritizing human and aquatic animal health or aqua-ecosystem scientific community seeks alternatives to reduce the abuse of antibiotics in aquaculture. Various studies have been conducted in order to use of probiotics and reported that probiotics can balancing the gut microbiota or invasion pathogens cells or modulating the immune system by producing antimicrobial substances like Bacteriocins (Bermudez-Brito et al. 2012 ). As a result, probiotics have received special attention from researchers seeking alternatives to the use of chemical compounds or antibiotics. Many recent studies, various gram-negative and gram-positive bacterial species such as lactic acid bacteria (LAB), Bacillus , Vibrio , photosynthetic bacteria, yeast and/or their mixtures have been used as probiotics in shrimp farming (Ringø et al. 2020 ; Abdel-Tawwab et al. 2020 ; Ezzedine et al. 2021) that have positive effects on the change of diversity of gut microbial composition. As a LAB species, Lactobacillus salivarius ( La. salivarius ) is a well-characterized antimicrobial substances producer probiotic commonly isolated from the gut of human and animals that does not show any negative effects on the host body. Nevertheless, the application of La . salivarius in aquaculture is scarce. Talpur et al. ( 2011 ) applied three species of LAB ( La. plantarum, La. rhamnosus, La. salivarius ) in the blue swimming crab ( Portunus pelagicus ) hatchery trials challenge experiments. They found that La. salivarius increased the survival rate of blue swimming crabs by 53.33% over non-inoculated control (43.33%) trials. Another study conducted by Ljubobratovic et al. ( 2017 ) on juvenile pike-perch ( Sander lucioperca ) found that the combination of La. salivarius BGHO1/ La. reuteri BGGO6-55 had a positive effect on fish growth, skeletal development and survival rate by pathogenic bacteria Vibrio spp. All these results show that La. salivarius is beneficial for cultured organisms, but no research has yet been done on white leg shrimp. BALOs are obligate, aerobic, rapidly motile, gram-negative, predatory bacteria that prey on a broad range of Gram-negative and some Gram-positive bacteria (Li et al. 2014 ; Inoue et al. 2022 ). Because of their intrinsic ability to lyse prey’s cells, BALOs have been considered as a biological agent or alternative to control aquatic pathogens (Qi et al. 2009 ; Ezzedine et al. 2021). Ecologically, BALOs are ubiquitous in nature, including various kinds of waters (Waite et al. 2020 ) as well as the guts of some mammals and farmed organisms (Najnine et al. 2020 ). Several researchers reported that BALOs can control vibrio such as V. alginolyticus (Wen et al. 2014 ), V. Cholerae (Cao et al. 2015 ), V. parahaemolyticus (AHPND Vp) (Kongrueng et al. 2017 ) and other bacterial disease red body disease Proteus penneri (Cao et al. 2014 ) in shrimp farm. Additionally, Li et al. ( 2014 ) revealed that BDHSH06 reduced the total number of bacterial and Vibrio , increased the survival rate, body length, and weight of black tiger shrimp ( Pe. monodon). In general, probiotics are considered to improve host immunity, growth and survival rate by shaping the gut microbial community. Many individual studies have been done in both potentially novel probiotics. However, no comparison has been made yet between BALOs and La. Salivarius in shrimp postlarvae (PL) rearing. Hence, the present study aimed to comparison between BALOs and La. salivarius and evaluate their activity on growth parameter, gut micriobial structure and their function as well as effect against pathogenic Vibrio bacteria in white leg shrimp ( Li. vannamei ) PL rearing. Results Water quality parameters Throughout the experimental period, water temperature, DO and pH did not show any significant differences among all the groups ( p < 0.05, Table 1 ), with water temperature in the range of 23.8 to 25°C, DO in the range of 3.75 to 5.82 mg/L and pH in the range of 7.2 to 8.0, respectively. With regard to NO 2 -N, NO 3 -N and NH 3 -N contents, they were significantly higher in group L than in groups C and F ( P < 0.05, Table 1 ), viz., 3–4 times higher of NO 2 -N (0.11 ± 0.04 mg/L in group L vs. 0.02 ± 0.01 mg/L and 0.03 ± 0.01 mg/L in groups C and F, respectively), 5–7 times higher of NO 3 -N (18.7 ± 5.37 mg/L in group L vs. 2.80 ± 1.18 mg/L and 3.80 ± 1.8 mg/L in groups C and F, respectively), and 2–4 times higher of NH 3 -N (2.45 ± 0.44 mg/L in group L vs. 0.6 ± 0.30 mg/L and 1.21 ± 0.49 mg/L in groups C and F group, respectively). PL Survival rate and growth parameters PL survival rate in group F was 95 ± 5.00%, significantly higher than those in groups C and L, which were 89.00 ± 8.00%, 89 ± 3.00%, respectively ( P < 0.05, Table 2 ). At the start of the 7-day test, average BL of PL in group L (10.2 ± 0.31 mm) was significantly shorter than those in groups C (11.2 ± 0.21 mm) and F (11.2 ± 0.50 mm) ( P < 0.05, Table 2 ), albeit all PL were randomly assigned to each group. At the end of the test, average BL in groups F (11.8 ± 0.64 mm) and L (11.3 ± 0.61 mm) were significantly longer than in control (group C, 10.6 ± 0.30 mm) ( P < 0.05, Table 2 ). PTLG in groups C, L and F were − 5.00 ± 1.00%, 11.0 ± 7.00% and 5.00 ± 7.00%, respectively, with the highest gain in group L. With regard to average PL BW, at the start of the test, they displayed significant differences among them ( p 0.05, Table 2 ), with BW of 0.100 ± 0.01 g, 0.104 ± 0.01 g, and 0.121 ± 0.02 g in groups C, L and F, respectively. PTWG in groups C, L and F were 8.10 ± 6.74%, 38.67 ± 34.00% and 44.04 ± 32.23%, respectively, with the highest gain in group F. SGR, they displayed significant differences among three groups ( p < 0.05, Table 2 ), with group F the highest (0.53 ± 0.40), and group C the lowest (0.11 ± 0.10). Total cultivable bacterial counts (TCBC), total cultivable Vibrios c ounts (TCVC), total cultivable LAB counts (TCLC), and total BALOs counts (TBC) in PL Gut samples Over the 7-day test period, TCBC in PL in all three groups showed a similar trend, i.e., that is, the number of bacteria increased first and then decreased with no significant difference among them at the end of the test ( p > 0.05, Table 3 ). Again, TCVC in PL in groups C and L increased first then decreased again, while in group F, it was decreased entire all the experimental period (Table 3 ). TCVC in three groups had no significant differences ( p > 0.05) at day 0, but it was significantly higher in group L than in group F at day 7 ( p < 0.05, Table 3 ). TCLC and TBC were only detected in group L and F, respectively, but all the results saw that their number had been reduced gradually (Table 3 ).To more accurately reflect the compositions of Vibrios on TCBS plates, we enumerated yellow, green as well as black colonies in separate. Yellow Vibrio , green Vibrio and black colonies in PL gut samples In the TCBS plates, we found three types of bacterial colonies: yellow, green and black. On the basis of colony color morphology, we categorized the yellow colony-forming Vibrio as yellow Vibrio , the green colony-forming Vibrio as green Vibrio and the black colony-forming bacteria as Black colonies (Talib et al. 2017 ). Throughout the 7-day test period, Yellow Vibrio counts in PL decreased from day 0 to day 7, with no significant difference in all three groups initially (Table 4 ). Nevertheless, at days 3 and 7, its counts were significantly higher in groups L and F than in group C ( p < 0.05). While green Vibrio counts were decreased in group F throughout the test period, it was increased slightly in groups C and L. Overall, its counts were significantly higher in groups C and L than in group F ( p < 0.05). Black colony counts in PL were reduced to zero in all three groups at the end of the test, with its initial counts higher in groups C and L than in group F at day 0. Black colonies gone undetected at day 3 in groups C and L, but were still detected in group F. Illumina High-throughput sequencing analysis MiSeq sequencing results Over the 7-day test period, we sampled and sequenced PL gut microbiota at three time points in all three groups, i.e., day 0 (samples C0, L0 and F0 for groups C, L and F, respectively), day 3 (samples C3, L3 and F3 for groups C, L and F, respectively) and day 7 (samples C7, L7 and F7 for groups C, L and F, respectively). All sampling was done in triplicates. Hence, in total, 27 samples were sequenced. After optimization and quality control, 1,184,087 high-quality sequences were obtained. The number of sequences in different groups varied from 39,100 ± 11,214 in sample C0 to 50,595 ± 2,980 in sample F3 (Table 5 ). As sequences of 97% similarity or higher were grouped as an OTU, OTU clustering of non-repetitive sequences yielded a total of 2,165 OUTs. The number of OUTs in different groups ranged from 151 ± 104 in C3 to 441 ± 263 in F3 (Table 5 ). Coverage is the sequencing depth index, a value of 1.00 or near 1.00 means all or nearly all the species in a sample has been sequenced, as is the case in this study (Table 5 ). This confirmed that the sequencing results here represented the real situation of each sample. Diversities of bacterial communities As pointed out by Cao et al. ( 2020 ), alpha diversity of a microbiota includes Shannon and Simpson indices, which reflect the extents of community diversities. While a higher Shannon index indicates a higher diversity, a higher number of Simpson index indicates the otherwise. Alpha diversity also includes ACE index and Chao1 index, which reflect the extents of community richness. A higher number indicates more richness. While the Shannon index (Table 5 ) in control (group C) first decreased from 2.91 ± 0.24 at day 0 to 1.99 ± 1.24 at day 3, then increased to 3.10 ± 0.28 at day 7. In group L, it was first increased slightly, from 2.86 ± 0.32 at day 0 to 2.93 ± 0.28 at day 3, and then again slightly decreased, to 2.48 ± 0.49 at day 7. In group F, it increased from 3.08 ± 0.10 at day 0 to 3.45 ± 0.42 at day 3, then stayed there at day 7 (3.42 ± 0.20). The trend of changes of Simpson indices was opposite to Shannon indices (Table 5 ). Over all, Shannon indices in all three groups showed no significant differences ( p > 0.05) at day 0, but with significant differences at days 3 and 7 ( p < 0.05, Table 5 ). Regarding the richness of the community, the highest richness was found in sample F3 (451.04 ± 263.91), while the lowest richness in sample C3 (162.91 ± 111.31), with the rest in between (Table 5 ). To explore the structure variance of microbial community among all groups, β-diversity were analyzed. Non-metric multidimensional scaling (NMDS) analysis (Fig. 1 a) was conducted at the OUT levels and ANOSIM (permutation_number: 999) was used to test significant difference among in all samples of three groups. The two-dimensional scatter plot showed samples of each group were significantly separated (stress: 0.1, R = 0.3514, p = 0.001) in the NMDS diagram, indicating significant differences in gut microbial community composition among the groups. Dots of different colors or shapes represent different groups of samples, and the closer the two sample points are, the more similar the species composition of the two samples. The horizontal and vertical coordinates represent the relative distance and have no practical significance. The clustering patterns were observed only in F0 samples, they were highly similar and less variance microbial communities. The NMDS result also showed that C0, L, and F0 were nearly similar with less variance microbial communities. The high discrimination with low variation was observed in C3 samples and they did not cluster independently or distinctly. The high discrimination and the high variance were observed in F7 compared to other groups and confirmed the unique development of the microbial community. The results of NMDS analysis showed stress value is 0.1. It is generally considered that stress < 0.2 can be represented by the two-dimensional dot diagram of NMDS, and its graph has certain explanatory significance, when stress < 0.1, it can be regarded as a good sorting. Taxonomic compositions and changes of PL microbial communities By using high throughput sequencing analysis, 36 phyla were detected in PL microbiota in total, where only 13 core phyla were commonly shared among three PL groups viz. Proteobacteria , Actinobacteria , Bacteroidetes , Firmicutes , Patescibacteria , Tenericutes , Gemmatimonadetes , Cyanobacteria , unclassified_k__norank_d__Bacteria , Verrucomicrobi a, Acidobacteria , Chloroflexi , and Planctomycetes (Fig. 1 b). Among them Proteobacteria , Actinobacteria , Bacteroidetes , and Firmicutes were the dominant phyla with relative abundance ≥ 1%, account for 96.9–98.52% of the totals which is consistent with the results of the circos samples and species relationship map (Fig. 1 c) reflects the distribution of dominant species in each group at the phylum levels. Proteobacteria , with an initial relative abundance of 75.37 ± 1.66%, 69.35 ± 8.91%, 75.27 ± 3.06% in groups C, L and F, respectively, increased to 85.92 ± 3.17% in group L (14% increment), and slightly decreased to 71.74 ± 1.10%, 72.22 ± 0.49% in groups C (3.45% reduction) and F (4.05% reduction) at the end of the test, respectively. Actinobacteria , with an initial relative abundance of 11.96 ± 3.52%, 13.39 ± 3.12% and 10.49 ± 1.45% in groups C, L and F, respectively, increased to 15.17 ± 4.00% and 12.84 ± 6.07% in groups C (26.84% increment) and F (22.40% increment), and decreased to 7.87 ± 3.88% in group L (41.23% reduction) at the end of the test, respectively. Bacteroidetes , with an initial relative abundance of 8.51 ± 2.18%, 8.43 ± 3.79% and 8.51 ± 4.15% in groups C, L and F, respectively, reduced to 6.03 ± 1.49% and 5.08 ± 1.46% in groups C (29.14% reduction) and L (39.74% reduction), and increased to 10.98 ± 6.29% in group F (29.03% increment) at the end of the test, respectively. Firmicutes , with an initial relative abundance of 3.23 ± 1.37%, 8.11 ± 9.66% and 5.01 ± 2.13% in groups C, L and F, respectively, increased to 4.95 ± 3.46% in group C (53.25% increment), and decreased to 0.76 ± 0.47%, 1.31 ± 0.74% in groups L (90.63% reduction) and F (73.85% reduction) at the end of the test, respectively. At the order level, there were 241 orders detected in PL microbiota in total, but only 9 with a relative abundance ≥ 5% at a time, viz., Betaproteobacteriales , Vibrionales , Pseudomonadales , Rhodobacterales , Rhizobiales , Sphingomonadales , Corynebacteriales , Flavobacteriales , and Bacillales (Fig. 2 ). Betaproteobacteriales , Vibrionales and Pseudomonadales all belonged to class Gammaproteobacteria of phylum Proteobacteria . Over the test period, the relative abundance of Betaproteobacteriales reduced by 30.97% in group L (from 23.26 ± 6.62% at day 0 to 17.76 ± 11.36% at day 7) and 23.16% in group F (from 20.34 ± 3.14% at day 0 to 15.63 ± 1 4.86% at day 7), while in group C, first it was increased by 36.01% (from 25.55 ± 10.81% at day 0 to 34.75 ± 47.21% at day 3), and then decreased by 14.79% (29.61 ± 10.74%) at day 7. The relative abundance of Vibrionales in control (group C) first increased by 11.17 times (from 1.87 ± 1.24% at day 0 to 22.76 ± 27.90% at day 3), then decreased by 42.09% (13.18 ± 8.45%) at day 7; in group L, it first increased by 13.38 times (from 1.70 ± 1.35% at day 0 to 24.44 ± 7.74% at day 3), then further again increased by 31.47% (32.13 ± 25.93% at day 7); in group F, it first increased by 2.68 times (from 5.44 ± 1.57% at day 0 to 20.00 ± 11.39% at day 3), then decreased by 45.2% (10.96 ± 2.28%) at day 7. The relative abundance of Pseudomonadales decreased by 55.64% (from 19.14 ± 6.34% at day 0 to 8.49 ± 4.39% at day 7) in control (group C), 50.15% (from 24.21 ± 14.05% at day 0 to 12.07 ± 7.47% at day 7) in group L and 77.16% (from 33.40 ± 1.58% at day 0 to 7.63 ± 0.75% at day 7) in group F, correspondingly. Rhodobacterales , Rhizobiales and Sphingomonadales belonged to class Alphaproteobacteria of phylum Proteobacteria (Fig. 2 ). Over the test period, Rhodobacterales relative abundance in control (group C) first decreased by 14.03% (from 14.40 ± 5.22% at day 0 to 12.38 ± 13.87% at day 3), then increased by 40.47% (17.39 ± 6.34% at day 7); in group L, its abundance first increased by 48.80% (from 12.09 ± 3.97% at day 0 to 17.99 ± 6.46% at day 3), then decreased by 18.68% (14.63 ± 1.65% at day 7); in group F, its abundance first increased by 9.85% (from 10.56 ± 0.61% at day 0 to 11.60 ± 3.53% at day 3) and then further increased by 1.33 times (27.07 ± 7.40% at day 7). Rhizobiales relative abundance in control (group C) decreased by 93.98% (from 11.46 ± 9.71% at day 0 to 0.68 ± 0.48% at day 7); in group L, it first decreased by 83.60% (from 4.45 ± 3.66% at day 0 to 0.73 ± 0.56% at day 3), then increased by 57.53% (1.15 ± 1.07% at day 7); in group F, it decreased by 38.71% (from 2.48 ± 0.30% at day 0 to 1.52 ± 0.69% at day 3), then increased by 36.18% (2.07 ± 0.49% at day 7). Sphingomonadales relative abundance in control (group C) first decreased by 76.86% (1.21 ± 0.80% at day 0 to 0.28 ± 0.25% at day 3), then increased by 67.44% ( 0.86 ± 0.13% at day 7); in group L, its relative abundance first decreased by 76.58% (1.11 ± 1.12% at day 0 to 0.26 ± 0.17% at day 3), then increased by 27.04 times (7.29 ± 7.17% at day 7); in group F, its relative abundance decreased slightly, by 28.26% at first (0.46 ± 0.29% at day 0 to 0.33 ± 0.06% at day 3), then increased by 3.42 times (1.46 ± 0.51% at day 7). Corynebacteriales belonged to phylum Actinobacteria (Fig. 2 ). In control (group C), its relative abundance first decreased by 44.94% (10.48 ± 4.44% at day 0 to 5.77 ± 3.83% at day 3), then increased by 116.46% (12.49 ± 4.87% at day 7); in group L, its relative abundance decreased gradually, by 42.51% (from 11.88 ± 4.07% at day 0 to 6.83 ± 4.41% at day 7); in group F, its relative abundance decreased slightly (25.87%) and stayed there (from 9.16 ± 1.62% at day 0 to 6.79 ± 6.73% at day 7). Flavobacteriales belonged to phylum Bacteroidetes . In control (group C), its relative abundance first increased by 23.05% (from 3.34 ± 0.71% to 4.11 ± 4.24% at day 3), then decreased by 20.44% (3.27 ± 0.81% at day 7); in group L, its relative abundance decreased by 48.73% and stayed there (from 5.11 ± 2.81% at day 0 to 2.62 ± 1.29% at day 7); in group F, its relative abundance increased by 25.57% and stayed there (from 4.38 ± 1.72% at day 0 to 5.50 ± 3.49% at day 7). Bacillales belonged to phylum Firmicutes . In control (group C), its relative abundance first decreased by 91.15% (from 2.60 ± 1.45% at day 0 to 0.23 ± 0.25% at day 3), then increased 4.09 times (1.17 ± 1.12% at day 7); in group L, its relative abundance decreased by 92.24% (from 7.60 ± 11.79% at day 0 to 0.59 ± 0.65% at day 7); in group F, its relative abundance first stayed there (4.67 ± 2.41% at days 0 and 3), then decreased by 85.87% (0.66 ± 0.20% at day 7). At the genus level, there were 926 genera detected in PL microbiota in total, but only 38 genera had a relative abundance ≥ 1%, with Ralstonia , Vibrio , Paracoccus , Rhodococcus , Pseudomonas , Acinetobacter , and unclassified_f__Rhodobacteraceae being the dominant genera (relative abundance ≥ 10%) (Fig. 3 ). The significant differences (Kruskal-Wallis H test) anlysis at genus data showed that the relative abundance of Vibrio, Paracoccus, unclassified_f__Rhodobacteraceae , Yangia , Staphylococcus , Microbacterium , norank_f__NS9_marine_group , Bacillus , Roseovarius, Taeseokella, Brevundimonas significantly changed at p ≤ 0.05 and Acinetobacter , unclassified_o__Chitinophagales , and Nitratireductor significantly changed at p ≤ 0.01 among three PL groups (Fig. 4 ). Vibrio , and Acinetobacter both belong to class Gammaproteobacteria . The relative abundance of Vibrio increased significantly ( p ≤ 0.05) in all PL groups C (from 3. 1.87 ± 1.24% to 22.74 ± 27.87%), F (from 5.43 ± 1.56% to 19.9 ± 11.26%) and L (from 1.70 ± 1.35% to 24.43 ± 7.73%) from day 0 to day 3. However, on day 7, the relative abundance of Vibrio significantly ( p ≤ 0.05) decreased again in groups F (10.74 ± 2.213%) and C (13.14 ± 8.407%), whereas in group L significantly ( P ≤ 0.05) increased and the abundance rate was higher (31.81 ± 25.7%) than in groups F and C. Initially, the relative abundance of Acinetobacter was higher in all PL groups C, F and L (13.89.98%, 28.06 ± 1.51%, and 16.26 ± 10.84%, respectively) and significantly decreased ( p ≤ 0.01) at day 3 (1.83 ± 1.67%, 1.63 ± 0.52%, and 1.80 ± 1.08%, respectively) where on day 7, it slightly increased in groups C and F but decreased in group L. Beneficial genera Paracoccus , unclassified_f__Rhodobacteraceae , Yangia , Erythrobacter , Brevundimonas , Roseovarius and Nitratireductor belong to class Alphaproteobacteria significantly increased in group F (Fig. 4 ) than in groups L and C. On the starting of this experiments, the highest relative abundance of Paracoccus was in group C (12.18 ± 4.053%) than in groups L (9.21 ± 3.56%) and F (6.656 ± 1.49%). On the day 3, the abundance rate of Paracoccus significantly decreased ( p ≤ 0.05) in groups C (3.05 ± 3.28%) and F (3.73 ± 1.19%) but slightly increased ( p ≤ 0.05) in group L (9.69 ± 4.56%). The opposite trend happened on day 7, the relative abundance of Paracoccus significantly increased ( p ≤ 0.05) in groups F (6.92 ± 2.39%) and C (3.86 ± 0.63%) and significantly decreased ( p ≤ 0.05) in group L (4.34 ± 3.3%). The relative abundance of unclassified_f__Rhodobacteraceae increased significantly ( p ≤ 0.05) from day 0 to day 3 and the abundance rate was higher in group L (from 1.95 ± 1.29% to 6.35 ± 2.85%) than in groups C (from 1.29 ± 0.75% to 4.75 ± 4.76%) and group F (from 2.18 ± 0.45% to 4.90 ± 2.42%). On the day 7, the relative abundance of unclassified_f__Rhodobacteraceae significantly increased ( p ≤ 0.05) in groups F (14.93 ± 4.614%) and C (10.46 ± 4.26%) whereas significantly decreased ( p ≤ 0.05) in group L (5.48 ± 3.13%). The highest relative abundance of Yangia showed in group C (from 0.56 ± 0.47% to 3.91 ± 5.06%) at day 3, but it decreased again at day 7 (2.25 ± 0.68%). However, Yangia increased significantly ( p ≤ 0.05) in groups F and L from day 0 to day 7 and the highest relative abundance was in group L (4.95 ± 0.19%) at end of the experiments. The initial abundance of Roseovarius was very low during the experiment it was significantly increased ( p ≤ 0.05) and the highest abundance was observed in group F (2.03 ± 1.12%) on the day 7. Initially, the relative abundance Nitratireductor was only significantly ( p ≤ 0.01) higher in group F but slightly decreased on the day 3 and again significantly increased ( p ≤ 0.01) on the day 7. Staphylococcus and Microbacterium belong to phylum Firmicutes. Initially, Staphylococcus was significantly ( p ≤ 0.05) higher in group L (7.29 ± 11.98%) than groups F (4.426 ± 2.26%) and C (2.304 ± 1.471%) and significantly decreased ( p ≤ 0.05) in all groups at the end of the test where the lowest relative abundance was in group F (0.19 ± 0.18%) than group C (0.2 ± 0.06%) and L (0.57 ± 0.64%). At start of the test, the relative abundance of Microbacterium was very low in all groups. Entire of the test, the abundance of Microbacterium significantly increased ( p ≤ 0.05) in groups F (from 0.75 ± 0.18% to 3.75 ± 1.16%) and C (from 0.66 ± 0.28% to 1.49 ± 0.359%) but significantly decreased ( p ≤ 0.05) in group L (from 0.87 ± 0.39% to 0.66 ± 0.41%). Bacillus , Taeseokella and norank_f__NS9_marine_group belong to Phylum Bacteroidetes . The relative abundance of Taeseokella and norank_f__NS9_marine_group significantly ( p ≤ 0.05) decreased entire of the test and both Taeseokella and norank_f__NS9_marine_group were absent in groups L from day 3 to day 7. Initially, Bacillus relative abundance was extremely low, then significantly increased ( p ≤ 0.05) in group F (from 0.09 ± 0.08% to 4.17 ± 7.04%) and decreased ( p ≤ 0.05) in groups C (0.16 ± 0.04% to 0.13 ± 0.09%) and L (from 0.28 ± 0.35% to 0.03 ± 0.02%) from day 0 to day 3. On day 7, the relative abundance of Bacillus significantly increased ( p ≤ 0.05) in group C (0.92 ± 1.09%) and decreased ( p ≤ 0.05) in groups F (0.41 ± 0.09%) and L (0.006 ± 0.008%). The genera unclassified_o__Chitinophagales was only present initially in all PL groups then significantly decreased ( p ≤ 0.01) and absent on the day 3 and day 7. Species univariate correlation networks To explore potential interactions between top 50 genera of the PL gut microbial community, species univariate correlation network diagram (Figs. 5 a-c) were drawn (Networkx software) by calculating the correlation between species and the nodes in the network graph were species-node nodes. The correlation coefficients such as Spearman rank between species were calculated to reflect the correlation between species. By default, species with p < 0.05 are shown. The size of the nodes in the graph represents the abundance of species, and different colors represent different Phylum. The color of the connecting line indicates positive and negative correlations, red indicates a positive correlation, and green indicates a negative correlation. The thickness of the line indicates the magnitude of the correlation coefficient, and the thicker the line indicates the higher the correlation between species. The more lines there are, the closer the connection between one genera to another. The univariate network of groups C, L, and F contains 356 (Fig. 5 a), 322 (Fig. 5 b), and 370 (Fig. 5 c) nodes, respectively. The clustering coefficient represents the complexity of the network and strong interactions among microorganisms. Three of the univariate networks showed the higher ratios of positive correlations in group C (87%) than in groups L (66%) and F (57%) and oppositely the higher ratios of negative correlations were shown in group F (43%) than in groups L (34%) and C (13%). The univariate network of groups L and F showed the highest clustering coefficient (positive coefficient 1 and negative coefficient − 0.9), followed by group C network (positive coefficient 0.9 and negative coefficient − 0.8), indicating that microbial interactions are strongest in two probiotic groups than control. Overall, the univariate network in group F exhibited greater complexity with different genera and correlations compared with groups L and C. The univariate networks of three PL groups also showed that pairs like, Rhodococcus vs. Ralstonia , unclassified_o__Chitinophagales vs. Marinomonas , Winogradskyella vs. norank_f__norank_o__Absconditabacteriales_SR1 , norank_f__NS9_marine_group vs. Acinetobacter , norank_f__NS9_marine_group vs. norank_f__norank_o__Absconditabacteriales_SR1 , norank_f__Beggiatoaceae vs. Staphylococcus , Aquibacter vs. Yangia , Haliea vs. unclassified_f__Rhodobacteraceae , Yangia vs. unclassified_f__Rhodobacteraceae , Yangia vs. Roseovarius , Acinetobacter vs. Pseudoalteromonas , Flavobacterium vs. Pseudoalteromonas had strong positive correlation (coefficient ≥ 0.5, p < 0.001), whereas pairs like Enhydrobacter vs. Leucobacter , Gemmobacter vs. Lactobacillus , Pseudomonas vs. Staphylococcus , Algoriphagus vs. Staphylococcus , Winogradskyella vs. Staphylococcus , unclassified_f__Rhodobacteraceae vs. Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium , Algoriphagus vs. Staphylococcus , Yangia vs. unclassified_c__Gammaproteobacteria , unclassified_f__Rhodobacteraceae vs. Brevundimonas , Alcanivorax vs. Brevundimonas , Rhodovulum vs. unclassified_o__Chitinophagales had strong negative correlation (coefficient ≤ − 0.5, p < 0.0001) (Table S1 ). LEfSe multi-level species difference discrimination analysis The differential enrichment of bacterial taxa in groups C, L and F was analyzed by LEfSe, and the differences in microbiota were apparent (Figs. 6 a-c). At day 0, four different taxa viz. one class, one family and two genera were enriched in group C; two genera were enriched in group F and only one genus was enriched in group L (Fig. 6 a). At day 3, no taxa was enriched in group C; two orders, three families and five genera were enriched in group L while three orders, five families and 12 genera were enriched in group F (Fig. 6 b). At day 7, three families and one genus were enriched in group C; two genera were enriched in group L; three orders, nine families and 17 genera were enriched in group F (Fig. 6 c). Correlation analysis between gut microbita and growth performance The correlation heatmap diagram (Fig. 7 ) showed the correlation among most abundant 25 genera with body length (BL), body weight (BW) and survival rate (SR). Data showed that there was not significantly correlation between body length and PL gut bacteria. But, the body weight had a significantly positive correlation with Algoriphagus at P ≤ 0.05 and Yangia at P ≤ 0.01 and had a significantly negative correlation with unclassified_o__Chitinophagales , Taeseokella and norank_f__NS9_marine_group at P ≤ 0.05. The survival rate had a significantly positive correlation with Flavobacterium and Brevundimonas at P ≤ 0.05; Paracoccus and Staphylococcus at P ≤ 0.01; Acinetobacter , Taeseokella , norank_f__NS9_marine_group and unclassified_o__Chitinophagales at P ≤ 0.001, where had a significantly negative correlation with Vibrio at P ≤ 0.05; Yangia and Algoriphagus at p ≤ 0.01. BugBase phenotype prediction The functions of the bacterial community were annotated by the BugBase phenotype prediction platforms, and the proportion trend of bacteria at the genus level was observed among three groups C, L, and F (Fig. 8 a-i). Bugbase analysis confirmed the existence of opportunistic pathogens in all analyzed bacterial taxa within all PL samples (Fig. 8 f) It also confirmed the existence and relative abundance distribution of genera associated with aerobic, anaerobic, facultatively anaerobic, gram-negative, gram-positive, containing mobile components, forming biofilms, and stress tolerance phenotype in all PL samples. The aerobic genera of all specimens were mainly Rhodococcus , Ralstonia , Pseudomonas , and Acinetobacter (Fig. 8 a) while the most abundant anaerobic genera was n orank_f__NS9_marine_group (Fig. 8 b). Moreover, the most abundant facultatively anaerobic three genera Paracoccus , unclassified_f__Rhodobacteraceae , and Yangia (Fig. 8 c) were also dominant as biofilms producers (Fig. 8 g) in PL samples. The Gram-negative bacterial genera Ralstonia , Pseudomonas , and Acinetobacter (Fig. 8 d) were dominant in both potentially pathogenic and stress tolerance response phenotype (Fig. 8 f and h). The Gram-negative non-fermenters bacterial genus Ralstonia was also the dominant taxa in mobile elements container phenotype, but the proportion was only higher in group C than in groups L and F (Fig. 8 i). On the other hand, the proportion of gram-positive bacteria maintained the lowest abundance in all groups of samples (Fig. 8 e). PICRUSt2 functional prediction To evaluate the functional and metabolic potentials of PL gut microbiota and their possible alteration by BALOs and LAB, PICRUSt analysis was conducted based on the database of COG (Fig. 9 a) and KEGG. A total of 24 Level 2 COG functions were annotated, except Nuclear structure [Y]. An average of 3139627.22 ± 525012.02 Function unknown [S] and 3033662.55 ± 535713.66 Amino acid transport and metabolism were the highest predicted functions in all sample of shrimp PL groups. We found the maximum Amino acid transport and metabolism [E] (3827724) and Function unknown [S] (3690139) was on the day 7 in group F. In addition, the predicted functional abundance was higher in most groups such as 2255911.11 ± 388555.17 Energy production and conversion [C], 3033662.55 ± 535713.66 Amino acid transport and metabolism [E], 767317.11 ± 136379.16 Nucleotide transport and metabolism [F] 1843613.44 ± 379348 Carbohydrate transport and metabolism [G], 1151164 ± 192031.91 Coenzyme transport and metabolism [H], 1646612.11 ± 283582.61 Lipid transport and metabolism [I] and 2128881.44 ± 372617.18 Inorganic ion transport and metabolism [P], etc., all of which were related to the metabolism of basic life. In the functional annotations of KEGG, pathways Level 1 (Fig. 9 b) categorized in six groups viz. Metabolism, Environmental Information Processing, Genetic Information Processing, Function unknown, Cellular Processes, Human Diseases, Organismal Systems. Metabolism (51.17%) was the most overrepresented level 1 pathway across all groups and there were no significant differences among groups. Furthermore, the second highest predicted functional pathway at KEGG Level 1 was Environmental Information Processing (15.72%) followed by Genetic Information Processing (13.80%), Function unknown (13.45%), Cellular Processes (3.72%), Human Diseases (1.30%), Organismal Systems (0.84%) among all shrimp PL groups. In the functional annotations of KEGG, 303 Level 2 KEGG pathways of all samples were predicted, where 237 pathways were classified and 66 pathways were unclassified. Within classified pathways 128 Level 2 KEGG pathways were related to Metabolism where mainly 22 Amino Acids metabolism pathways, 18 Xenobiotics Biodegradation and Metabolism, 15 Biosynthesis of other Secondary Metabolites, 14 Carbohydrate Metabolism pathways, 14 Lipid Metabolism pathways, 12 Metabolism of Cofactors and Vitamins and others. KEGG level 2 predicate functional pathway analysis (Fig. 9 c) showed initially, the Amino Acids Metabolism was higher in group L than groups F and C. On the day 3, both group C and L decreased but decrease rate was highest in group C than L, however, it was slightly increase in group F. On the day 7, Amino Acids Metabolism rate again increased in group C and F but slightly decreased again in group L. Next, the Spearman’s correlation analyse was done between metabolism (Amino Acid Metabolism, Carbohydrate Metabolism, and Lipid Metabolism) most differently distributed Kyoto Encyclopedia of Genes and Genomes (KEGG) level 3 pathways and top 25 bacteria genera with relative abundance > 1% were performed. (Fig. 9 d). The heatmap data showed that the most of the (KEGG) level 3 pathways related to Amino Acid Mmetabolism, Carbohydrate Metabolism, and Lipid Metabolism had significantly positive correlation with four genera Brevundimonas , Flavobacterium , Microbacterium and Paracoccus. Staphylococcus , norank_f__NS9_marine_group , Taeseokella , unclassified_o__Chitinophagales and Nitratireductor showed significantly positive correlation ( p ≤ 0.05) with Lipid Metabolism where Algoriphagus , Roseovarius , Vibrio and Yangia showed significantly negative correlation ( p ≤ 0.05) with Lipid Metabolism especially, Fatty Acid Metabolism, Synthesis and Degradation of Ketone Bodies, Linoleic Acid Metabolism, Ether Lipid Metabolism, Steroid Biosynthesis, Primary Bbile Aacid Biosynthesis and Secondary Bile Acid Biosynthesis. On the other hand Vibrio , Algoriphagus and unclassified_f__Rhodobacteraceaeand showed significantly positive correlation ( p ≤ 0.05) and unclassified_o__Chitinophagales and Acinetobacter showed significantly negative correlation ( p ≤ 0.05) with Carbohydrate Metabolism in particularly, Amino Sugar and Nucleotide Sugar Metabolism, and Starch and Sucrose Metabolism. Table 1 Average of water quality parameters in groups C, L and F during the experiment Water quality parameters Group C Group L Group F pH 7.7 ± 0.34 a 7.6 ± 0.25 a 7.6 ± 0.32 a Temperature (°C) 24.7 ± 0.40 a 24.6 ± 0.38 a 24.7 ± 0.39 a NO 2 -N (mg/L) 0.02 ± 0.01 b 0.11 ± 0.04 a 0.03 ± 0.01 b NO 3 -N (mg/L) 2.80 ± 1.18 b 18.7 ± 5.37 a 3.80 ± 1.80 b NH 3 -N (mg/L) 0.60 ± 0.30 b 2.45 ± 0.44 a 1.21 ± 0.49 b DO (mg/L) 5.03 ± 0.60 a 5.11 ± 0.70 a 5.50 ± 0.41 a Data were shown as mean ± standard deviation (n = 3). Different letters in the same row indicate significant difference (p 0.05). Table 2 Average of shrimp PL productive parameters in groups C, L and F during the experiment Productive parameters Group C Group L Group F SR (%) 89 ± 8.00 b 89 ± 3.00 b 95 ± 5.00 a BLi (mm) 11.2 ± 0.21 a 10.2 ± 0.31 b 11.2 ± 0.50 a BLf (mm) 10.6 ± 0.30 b 11.3 ± 0.61 a 11.8 ± 0.64 a TLG (mm) − 0.6 ± 0.09 b 1.1 ± 0.59 a 0.6 ± 0.50 ac PTLG (%) − 5.0 ± 1.00 c 11.0 ± 7.00 a 5.0 ± 7.00 b BWi (g) 0.092 ± 0.00 a 0.075 ± 0.01 c 0.084 ± 0.02 b BWf (g) 0.100 ± 0.01 a 0.104 ± 0.01 a 0.121 ± 0.02 a TWG (g) 0.008 ± 0.01 c 0.029 ± 0.01 b 0.037 ± 0.03 a PTWG (%) 8.10 ± 6.74 c 38.67 ± 34.00 b 44.04 ± 32.23 a SGR (%/day) 0.11 ± 0.10 b 0.42 ± 0.21 a 0.53 ± 0.40 a Data were shown as mean ± standard deviation (n = 3), based on the data of fifteen shrimp PL in each replicate, forty-five PL each group each time in total. Different letters in the same row indicate significant difference (p 0.05). Here, SR (survival rate), BLi (denotes initial body length), BLf (final body length), BWi (initial body weight) BWf (final body weight), TLG (total length gain) PTLG (percentage total length gain), TWG (total weight gain), PTWG (percentage total weight gain), SGR (specific growth rate). Table 3 TCBC, TCVC, TCLC and TBC in shrimp PL Group C L F TCBC (log CFU/g) Day 0 6.61 ± 6.65 b 6.50 ± 6.31 b 7.64 ± 6.85 a Day 3 7.50 ± 7.31 a 7.21 ± 6.93 a 7.78 ± 7.05 a Day 7 6.73 ± 5.82 a 7.18 ± 7.06 a 6.63 ± 5.76 a TCVC (log CFU/g) Day 0 6.37 ± 5.93 a 6.45 ± 6.03 a 6.53 ± 6.33 a Day 3 6.39 ± 5.96 b 7.03 ± 7.04 a 6.11 ± 5.35 b Day 7 6.07 ± 4.96 ab 6.37 ± 6.51 a 5.61 ± 5.28 b TCLC (log CFU/g) Day 0 NAD 4.01 ± 3.61 NAD Day 3 NAD 3.43 ± 3.04 NAD Day 7 NAD 2.44 ± 2.34 NAD TBC (log PFU/g) Day 0 NAD NAD 4.48 ± 4.36 a Day 3 NAD NAD 4.39 ± 3.88 a Day 7 NAD NAD 4.09 ± 3.93 a All data were shown as mean ± standard deviation (n = 3). Different letters in the same row indicate significant difference (p 0.05). TCBC denotes total cultivable bacterial counts. TCVC denotes total cultivable Vibrio counts. TCLC denotes total cultivable LAB counts. TBC denotes total BALOs counts. ND denotes not detected. Table 4 Yellow Vibrio , green Vibrio and black colony counts grown on the TCBS plate Colonies on TCBS Group Day 0 Day 3 Day 7 Yellow Vibrio (log CFU/g) C 6.06 ± 5.94 a 4.54 ± 4.31 b 0.00 b L 6.17 ± 5.94 a 5.55 ± 5.70 a 5.96 ± 6.11 a F 5.62 ± 5.51 a 5.55 ± 5.01 a 5.39 ± 5.43 a Green Vibrio (log CFU/g) C 5.93 ± 5.73 a 6.12 ± 5.86 b 6.07 ± 4.96 a L 6.13 ± 5.34 a 7.02 ± 7.05 a 6.15 ± 6.28 a F 6.47 ± 6.26 a 5.97 ± 5.21 b 5.22 ± 5.02 b Black colonies (log CFU/g) C 5.64 ± 4.29 a 0.00 b 0.00 L 5.00 ± 5.10 a 0.00 b 0.00 F 4.12 ± 4.13 b 3.35 ± 3.50 a 0.00 All data were shown as mean ± standard deviation (n = 3). Different letters in the same column indicate significant difference ( p 0.05). Table 5 Diversity and richness indices relative to each shrimp PL sample in groups C, L and F Group Sequence Number OTUs Number Shannon Index Simpson Index ACE Index Chao1 Index Coverage (%) Day 0 C0 39100 ± 11214 227 ± 45 2.91 ± 0.24 a 0.11 ± 0.02 a 235.78 ± 46.79 a 244.59 ± 37.45 a 99.93 ± 0.03 L0 46412 ± 7185 195 ± 40 2.86 ± 0.32 a 0.11 ± 0.03 a 201.57 ± 41.93 a 203.74 ± 31.22 a 99.96 ± 0.02 F0 45296 ± 4935 205 ± 19 3.08 ± 0.10 a 0.10 ± 0.02 a 211.91 ± 21.44 a 220.68 ± 17.86 a 99.95 ± 0.01 Day 3 C3 41303 ± 4752 151 ± 104 1.99 ± 1.24 c 0.36 ± 0.26 a 162.91 ± 111.31 c 169.32 ± 95.34 c 99.93 ± 0.05 L3 46092 ± 3274 305 ± 63 2.93 ± 0.28 b 0.11 ± 0.02 b 312.94 ± 59.21 b 318.71 ± 48.06 b 99.95 ± 0.02 F3 50595 ± 2980 441 ± 263 3.45 ± 0.42 a 0.08 ± 0.03 c 451.04 ± 263.91 a 467.74 ± 213.63 a 99.91 ± 0.05 Day 7 C7 41801 ± 9055 254 ± 113 3.10 ± 0.28 b 0.09 ± 0.02 b 259.35 ± 113.74 a 281.07 ± 92.07 a 99.96 ± 0.01 L7 39948 ± 2968 186 ± 24 2.48 ± 0.49 c 0.12 ± 0.03 a 201.24 ± 18.71 b 231.64 ± 34.09 b 99.93 ± 0.03 F7 44150 ± 11883 241 ± 24 3.42 ± 0.20 a 0.08 ± 0.02 b 246.81 ± 16.95 a 261.73 ± 91.45 a 99.94 ± 0.01 Results were presented as mean ± standard deviation (n = 3);Different letters in the same column at the same day indicate significant difference ( p 0.05). Materials and methods BALOs strain and its cultivation was adopted from Cao et al. ( 2020 ), as given below Preparation of host strain Gram negative Citrobacter amanolaticus strain TC (GenBank accession number, MN956654) was isolated from salty water and used as a host for propagating BALOs. It was proven to be non-haemolytic (data not shown). Strain TC was grown in nutrient broth (NB: Guangdong Huankai Biotechnology Co., Ltd) for 13–15 h at 30°C with shaking at 200 rev/min (rpm) to reach the late exponential phase. Then it was harvested by centrifugation at 5,000 rpm for 10 min at 4°C and resuspended with sterile phosphate buffered saline (PBS: 28 mmol/L NaH 2 PO 4 , 72 mmol/L Na 2 HPO 4 , pH 7.2) to the final concentration 1 × 10 10 CFU/mL. Stored at 4°C before use. Preparation of Bdellovibrionales strain BDN-1F2 (BALOs) at free swimming stage BDN-1F2 was a mutant of wild type BDN-1 after Co 60 mutagenesis (data not shown). Wild type BDN-1 was identified as a strain of Bdellovibrionales (GenBank accession number, MK159102) which is closely related to Bdellovibrionales strain BDSH06 (GenBank accession number, EF011103) (Chen 2019 ). BDN-1F2 was kept as plaques on 15‰ DNB (Dilute Nutrient Broth: 0.8 g nutrient broth, 0.5 g casein hydrolysate, 0.1 g yeast extract, 15 g NaCl, 15g agar, 1 L distilled water, and pH 7.2) double-layer agar plate at 4°C before use. A single plaque was picked up from a freshly grown double-layer agar plate with a sterile inoculation loop and inoculated into an Erlenmeyer flask that contained 50 ml of DNB liquid medium and 1 mL of suspended host strain (Merrifield et al. 2010 ) and kept for shaking at 200 rpm for 72 h at 28°C. Next, the remnant hosts were pelleted to 5,000 rpm for 20 minutes at 4°C, and the supernant was filtered through a 0.45-µm-pore-shaped membrane filter to release it from the remaining hosts and debris. Then the filtrate was centrifuged at 16,000 rpm and pellets were resuspended with sterile PBS to achieve a final concentration 7.16 × 10 9 plaque forming units (PFU)/mL. Kept at 4°C before use. Preparation of Lactobacillus salivarius strain GZPH2 La. salivarius strain GZPH2 was isolated from commercially available pickles in Guangzhou, patterned and deposited at the China Type Culture Collection (CCTCC) with the deposit number CCTCC M 2014598. It was proven to be non-haemolytic ability (data not shown). GZPH2 was grown in 50 mL MRS broth (De Man–Rogosa–Sharpe agar/MRS: 20 g peptone, 1 g yeast extract, 1 gsodium acetate, 0.4g Di-ammonium hydrogen citrate, 0.04 g manganese sulphate, 3 g monosodium glutamate (MSG), 8 g beef extract, 20 g dextrose, 2 g Di-potassium hydrogen phosphate, 0.2 g magnesium sulphate hepthydrate, 1 ml tween 80, 15g NaCl, 1 L distilled water, and pH 6.5–6.7) for 24 hours with shaking at 200 rpm at 37°C. To determine its concentration, GZPH2 culture was diluted in a ten-fold series dilution and the appropriate dilutions were spread on MRS agar plates. After the advent of colonies, plates numbering colonies between 30 and 300 were counted and, and expressed as colony forming units (CFU)/mL. PL shrimp rearing experiment The PL test was conducted for 7 days in the laboratory at around 28°C following a procedure similar to Cao et al. ( 2020 ), as given below: Briefly, Preparation for saltwater of 15‰ in salinity, 15 g salt (NaCl) was dissolved in one liter of tap water, then the mixture was boiled for 5 min to reduce possible microbes/ Vibrios contamination from water, and normally cooled it at room temperature. After that, the saline water was aerated and dissolved oxygen (DO) concentration was brought back to 5 ppm or above with an air pump fitted with 0.22 µm air-sterilization filter. The PL shrimp was cultured using nine plastic tanks with capacity of eight liters of water. The plastic cultured tanks were disinfected with 0.1% KMnO 4 and thoroughly rinsed with sterile saltwater. Then, four liters of prepared saline water was poured to each tank. Throughout the duration of the experiment, the aeration was done by placing two air-stones in each tank, with 0.22 µm membrane filter to filter out any possible bacterial contaminants in the aeration process. Postlarval shrimp ( Li. vannamei ) of PL7-8 were collected from a shrimp hatchery in Guangdong province. They were first-generation PL shrimp and maintained at 30‰ salinity. Prior to packing, the salinity was lowered from 30‰ to 15‰ to meet our experimental need. The PL7-8 were visually healthy without any apparent signs of diseases. After acclimatization, in total 585 PL were randomly divided into 3 groups: group C (control without any treatment), group L (treatment with La. salivarius strain GZPH2), and group F (treatment with BALOs species Bdellovibrionales strain BDN-1F2) with three replicates and stocked 65 PL into each tanks. Three tanks were considered as test tanks group F with the addition of free swimming BDN-1F2 directly once to water to a final concentration of appropriately 1 × 10 4 PFU/mL at the start of the test. Three tanks were considered as test tanks group L, where each time feeding 1 mL GZPH2 culture (10 8 CFU/mL) GZPH2 was mixed with shrimp feed to a final concentration of approximately 5 × 10 5 CFU/mL in rearing water. The rest were used as controls (group c) with no BDN-1F2 or GZPH2 addition. During the experimental period, PL were fed three times a day, with 0.5 mg shrimp flakes (a powder form high-protein diet contained 45% protein, 6% fat, 5% calcium, 1.2% phosphate, 1.4% lysine, 8% water, 16% ash, 3% crude fiber) per 10 shrimp each time. During the 7-day PL culture period, tank water was not exchanged. Water samples were taken at day 0, 3, 5 and 7 to check the water parameters. Shrimp samples were taken at day 0, 3 and 7 for PL growth parameters and gut microbiota analysis. The number of dead shrimp was recorded and the survival rates were calculated at the end of the test. Water quality parameters and PL growth parameters During the test, pH, water temperature and DO level were directly measured every morning. Then, 100 mL water sample was taken for assaying nitrite (NO2-N), nitrate (NO3-N), ammonia-N (NH3_N ) with Zerui reagent test kit (Zerui Chemistry Technology, Shanghai, China). On PL sampling day (days 0, 3, 7), randomly 15 PL were collected from each tank, with water on shrimp surface blotted with sterile filter paper. Body length (BL) of PL was measured, one by one, with electronic vernier calipers (accuracy 0.1 mm ), and body weight (BW) was weighed together with an electronic analytical balance (min. 0.001 g), as they were too light to be weighed individually. PL survival rate (SR), total length gain (TLG), percentage total length gain (PTLG, %), total weight gain (TWG), percentage total weight gain (PTWG, %), and specific growth rate (SGR) were calculated at the end of the test, according to the following formulae: SR (%) = 100 × N i ∕ N 0 TLG (mm) = L i − L 0 PTLG (%) = 100 × (L i − L 0 ) ∕ L 0 TWG (mg) = W i − W 0 PTWG (%) = 100 × (W i − W 0 ) ∕W 0 SGR (%/day) = 100 × (W i − W 0 ) ∕Number of test days Where, N i is the total number of PL alive at the end of test, N 0 is the total number of PL at the start of the test; L i is the PL final mean body length, L 0 is the PL initial mean body length; W i is the PL final mean body weight, W 0 is the PL initial mean body weight.) Conventional bacterial plate counts After the measurement of BL and BW, various cultivable bacterial counts were assayed. In each group, all 15 shrimp were weighed together, but rinsed separately, first with 75% ethanol, then with sterile distilled water to remove possible bacteria on body surfaces (Zheng et al. 2017 ). As PL7-8 shrimp were very tiny, it was practically impossible to dissect for the intestines. Hence, the bodies of 15 PL in each replicate/sample were homogenized altogether using a sterile grinder with 1 mL sterile PBS (pH 7.2), then divided into two parts, one for traditional bacteriological analysis as specified below and the other for 16S high-throughput sequencing and stored at -20°C before use. Bacterial enumeration was performed in triplicate. Total cultivable bacterial counts (TCBC) and total cultivable Vibrio counts (TCVC) were obtained by spread plate method after incubation at 28°C for 24 h. Total cultivable lactic acid bacteria (LAB) counts (TCLC) were also done by spread plate method by incubation at 37°C for 72 h. For TCBC counts, marine 2216E (5 g peptone, 1 g yeast extract, 0.01 g ferric phosphate, 15 g sodium chloride, 15 g agar, 1 L distilled water, pH 7.6–7.8) was used. For TCVC counts, Thiosulfate Citrate Bile Salts medium (TCBS: Guangdong Huankai Biotechnology Co., Ltd) of 15‰ in salinity was used. For TCLC counts MRS medium with 1.5% agar in a Petri dish was used. A series of 10-fold (10 0 , 10 − 1 , 10 − 2 , …) dilutions were made with sterile 15‰ saltwater and 0.1 mL each dilution was spread onto appropriate culture medium, those plates having 30–300 colonies were counted, expressed as CFU/mg for PL shrimp samples. For total BALOs counts (TBC), double-layer plating technique was used, viz., 500 µL appropriately diluted sample and 500 µL of the host (strain TC) suspension (1 × 10 9 CFU/mL) were mixed with 3 mL of liquefied overlay agar (DNB medium containing 0.8% agar) that was kept in a thermostatic water bath at 50°C. The mixture was briefly vortexed to mix before being poured over the surface of a bottom layer agar plate containing DNB medium with 1.5% agar in a Petri dish (90 mm in diameter). Plates were incubated at 28°C for 3–5 days until clear circular plaques appeared. Each plaque was counted as PFU, expressing as PFU/mg for PL samples. DNA extraction and PCR amplification Microbial community genomic DNA was extracted from homogenized samples using the E.Z.N.A.® soil DNA Kit (Omega Bio-tek, Norcross, GA, U.S.) according to manufacturer’s instructions. DNA extract was checked on 1% agarose gel, and DNA concentration and purity were determined with NanoDrop 2000 UV-vis spectrophotometer (Thermo Scientific, Wilmington, USA). Sample named with group name and combination of collection day i.e., day 0 (samples C0, L0 and F0 for groups C, L and F, respectively), day 3 (samples C3, L3 and F3 for groups C, L and F, respectively) and day 7 (samples C7, L7 and F7 for groups C, L and F, respectively). All sampling was done in triplicates. Afterwards, genomic DNA was used as templates for PCR to amplify the hypervariable V3-V4 region of the bacterial 16S ribosomal RNA gene with primer pairs 338F 5'-ACTCCTACGGGAGGCAGCAG-3' and 806R 5'-GGACTACHVGGGTWTCTAAT-3' by an ABI GeneAmp® 9700 PCR thermocycler (ABI, CA, USA). PCR amplification contained template DNA 10 ng, 5 × FastPfu Buffer 4 µL, 2.5 mM dNTPs 2 µL, forward primer (5 µM) 0.8 µL, reverse primer (5 µM) 0.8 µL, TransStart FastPfu DNA Polymerase 0.4 µL, BSA 0.2 µL and finally added ddH 2 O up to 20 µL. PCR amplification was performed as follows: initial denaturation at 95°C for 3 min, followed by 28 cycles of denaturing at 95°C for 30 s, annealing at 55°C for 30 s and extension at 72°C for 45 s, and single extension at 72°C for 10 min, and end at 4°C. PCR reactions were performed in triplicate and mix 3 replicate PCR products together. PCR products were checked with 2% agarose gel and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to manufacturer’s instructions and quantified using Quantus™ Fluorometer (Promega,Wisconsin, USA). Illumina MiSeq sequencing Construction of Miseq library using NEXTFLEX Rapid DNA-Seq Kit (Bioo Scientific, Texas, USA). Purified amplicons were pooled in equimolar and paired-end sequenced (2 × 300) on an Illumina MiSeq platform (Illumina, San Diego, USA) according to the standard protocols by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). Data analysis Statistical analysis Results were presented as mean ± standard deviation from triplicate experiments. Statistically significant differences were determined by one-way ANOVA, p < 0.05 and Tukey tests was used to evaluate the significance level using IBM SPSS statistical software version 26 ( New York, USA). Spearman correlation coefficient analyses, heatmap and histogram were performed with IBM SPSS statistical software version 26 (New York, USA) and R package software. Processing of sequencing data and analysis The raw 16S rRNA gene sequencing reads were de-multiplexed, quality-filtered by Trimmomatic and merged by FLASH with the following criteria: (i) 300 bp reads were truncated at any site receiving an average quality score of < 20 over a 50 bp sliding window, and the truncated reads shorter than 50 bp were discarded, reads containing ambiguous characters were also discarded; (ii) only overlapping sequences longer than 10 bp were assembled according to their overlapped sequence. The maximum mismatch ratio of overlap region is 0.2. Reads that could not be assembled were discarded; (iii) samples were distinguished according to the barcode and primers, and the sequence direction was adjusted, exact barcode matching, 2 nucleotide mismatch in primer matching. Operational taxonomic units (OTUs) with 97% similarity cutoff value were clustered using UPARSE (version 7.1, http://drive5.com/uparse/ ), and chimeric sequences were identified and removed. The taxonomy of each OTU representative sequence was analyzed by RDP Classifier ( http://rdp.cme.msu.edu/ ) against the SILVA database ( http://www.arb-silva.de/ ) using confidence threshold of 70%. Alpha diversity, species composition and interactions analysis at different taxonomic levels were based on the homogenized OTU table. Alpha diversity reflects taxonomic richness and community diversities, including sequencing depth index (Coverage), ACE index, Chao 1 index, Shannon index and Simpson index. Alpha diversity and species composition analysis were analyzed using QIIME 1.9.1. Non-metric multidimensional scaling analysis (NMDS) by using software QIIME (2020.2.0) calculated the beta diversity distance matrix at the OTU level, and the R vegan (2.4.3) software package performs NMDS analysis and mapping. Species composition analysis reflects dominant species and their relative abundance. Species and community composition analyses are represented by venn, circos and bar plots. The venn diagram used to evaluate the unique and shared species in among three groups of all samples by using R (version 3.3.1) tools for statistics and graphing. The circos sample-species relationship circle chart was drew by using circos-0.67-7 software ( http://circos.ca/ ) to know the distribution proportion of dominant species in each sample as well as the distribution proportion of each dominant species in different samples. The bar diagram of the gut microbial community drew to know two aspects of information: (1) what types of microorganisms are contained in each sample at the taxonomic level; (2) the relative abundance of each microorganism in the sample, to understand the composition of the community structure of different samples at each taxonomic level. In order to identify the gut microbial communities with significant differences in relative abundance of species among the all PL samples, Kruskal-Wallis H test and Tukey-Kramer tests was used to evaluate the significance level. The presence of different species among different PL groups were analyzed by the Linear discriminant analysis (LDA) effect size (LEfSe), logarithmic LDA score of 2 and used the nonparametric factorial Kruskal–Wallis (KW) sum-rank test to identify the most differently abundant species by using LEfSe software ( http://huttenhower.sph.harvard.edu/galaxy/root?tool_id=lefse_upload ). Single-factor network analysis was performed using R igraph package (version 3.2) and networkx software with Kamada-Kawai algorithm. The top 50 species in total abundance at genus level data were selected, and the correlation coefficients such as Spearman rank between species were calculated to reflect the correlation between species. By default, species with p < 0.05 were shown. Functional analysis Prediction of microbial phenotypes present in microbiome sample were annotated by BugBase functional prediction platforms ( https://bugbase.cs.umn.edu/index.html ). BugBase first normalizes the OUT by the predicted 16S rRNA copy number and then predicts the microbial phenotype using the pre-computed file provided. The phenotypic types include Gram positive, Gram negative, biofilms forming, potentially pathogenic, mobile element containing, oxygen utilizing (including aerobic, anaerobic, facultatively anaerobic) and oxidative stress tolerance. Prediction of microbial metabolic functions was performed using prediction tool, viz., the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) software package (version 1.0.0). Firstly, the OTU abundance table was standardized by PICRUSt (the PICRUSt process stored the COG information and KO information corresponding to the greengene id), that is, the effect of the number of copies of the 16S marker gene in the species genome was removed. Then, the COG family information and KEGG Ortholog (KO) information corresponding to the OTU were obtained through the green gene id corresponding to each OTU. The abundance and KO abundance of each COG were calculated. According to the information of the COG database, the description information of each COG and its functional information parsed from the eggNOG database to obtain the functional abundance spectrum. Based on the information in the KEGG database, KO and Pathway information obtained, and the abundance of each functional classes calculated based on the OTU abundance. In addition, for the pathway, PICRUSt used to obtain the information of three levels of the metabolic pathway, and the abundance table of each level obtained separately. However, the bar plot of predicted functions on the basis of COG with PICRUSt and the relative abundance different types of metabolism (KEGG pathway level 2) of bacterial communities using PICRUSt2 analysis of PL samples was drawn by using SRPLOT ( www.bioinformatics.com.cn ). Discussion Since the dawn of probiotic concept in aquaculture (Gatesoupe 1999 ), its definition has been expanded as such “A probiotic can be seen as a live, dead or component of a microbial cell, which is administered via the feed or to the rearing water, benefiting the host by improving disease resistance, health status, growth performance, feed utilization, stress response or general vigour, which is achieved via improving the hosts microbial balance or the microbial balance of the ambient environment” (Hai et al. 2015; Merrifield et al. 2010 ). In line with this definition, it is clear that La. salivarius strain GZPH2 and Bdellovibrionales strain BDN-1F2 PL can be recognized as probiotics as they have demonstrated beneficial effects by significantly improving white-leg shrimp postlarvae growth performances, albeit at different aspects and degrees between them (Table 2 ). Compared to control, with the exception of SR that GZPH2 showed no improvement, it had significantly better effects in all other aspects related to growth performance, i.e., PTLG, TLG, PTWG, TWG, SGR, with the latter being 2.82 times higher (p < 0.05). However, when compared to BDN-1F2, GZPH2 showed a better effect only in PTLG and TLG, while in all other aspects, including PTWG, TWG, SGR and SR, BDN-1F2 performed significantly better (p < 0.05, Table 2 ). Consistent with our current findings, Nguyen et al. ( 2018 ) revealed that Lactobacillus strain has positive effects on the growth and resistance of Li. vannamei against V. parahaemolyticus which causes acute hepatopancreatic necrosis disease (AHPND). Another study also showed that the combination of La. salivarius BGHO1/ La. reuteri BGGO6-55 had a positive effect on juvenile pike-perch ( Sander lucioperca ) growth, while improved survival (Ljubobratovic et al. 2017 ). On the other hand, some studies reveled that BALOs also showed better effect on shrimp growth performance. A previous study was done by Li et al. ( 2014 ) where, BDHSH06 had been applied to an 85-day rearing of black tiger shrimp ( Penaeus monodon ) and was found to significantly enhance its growth and survival and alter bacterial community structures in its rearing water. With the addition of BDHSH06, total bacterial and Vibrio numbers were significantly reduced (P < 0.05) by 1.3 to 4.5 log CFU/mL and CFU/g in both water and shrimp intestines, respectively, compared to those in the control. Similarly, Wen et al. ( 2014 ) s hown that Bacteriovorax DA5 has positive effect on white shrimp ( Li. vannamei ) and it significantly improved the survival rate and metamorphic rates by controlling vibriosis. Biodiversity is generally recognized as a main determinant of ecosystem functioning (Johnke et al. 2020 ), which could be better reflected by Shannon index values (Chen et al. 2015). It is generally recognized that a healthier and more robust microbial community has a higher biodiversity (and thus Shannon index) than an unhealthy one (Rungrassamee et al. 2016 ; Chen et al. 2017 ). To distinguish between healthy and unhealthy shrimp, the gut microbiota of shrimp at PL7-15, a value above 2.0 for a healthy state, was tentatively proposed, while below this value, it was considered an unhealthy state (Cao et al. 2020 ). Based on this criterion, it is obvious that at the start of the test, PL in all groups were in a healthy state, as their Shannon index values were well above 2.0 (Table 5 ). However, at day 3, this value in group C dropped to 1.99 ± 1.24, indicating a not-so-healthy state even though it recovered to 3.10 ± 0.28 at day 7 (Table 5 ). In groups L and F, PL gut microbiota stayed in a healthy state throughout the test period, albeit with the highest values at day 3. Within these two groups, group F had higher Shannon index values than group L, implying higher diversities, and more robust and healthier gut microbiota (Table 5 ). The reduction of Shannon index in control at day 3 could be due to the effect of salinity changes Cao et al. ( 2020 ) as PL were maintained at 30‰ seawater in the shrimp hatchery and its salinity was lowered to 15‰ prior to packing. Meanwhile, the rise of Shannon index in groups L and F at day 3 once again demonstrated the protective effect of BDN-1F2 and GZPH2 in counteracting the unfavorable impacts brought about by the salinity changes. As gut microbiota serves as a virtual endocrine organ (Clarke et al. 2014 ) and are known to promote juvenile growth, development and survival in Drosophila melanogaster (Erkosar et al. 2017 ), an unhealthy state would certainly undermine its growth. This is exactly the case in this study as PL grew most slowly in control (SGR at 0.11 ± 0.10%/day), and fastest in group F (SGR at 0.53 ± 0.40%/day), with group L in between (SGR at 0.42 ± 0.21% /day) (Table 2 ). It is generally recognized that the growth of host is strongly associated with the gut microbiota (Tarnecki et al. 2017 ). Previous studies have proven that the ratio of Bacteroidetes vs. Firmicutes (B/F ratio) is a growth indicator, the higher growth rate with the lower ratio (Jia 2017 ; Wang et al. 2020 ). Here, it is evident that PL in groups F and L would grow faster than those in control as B/F ratios at days 0 and 3 in groups L and F were 1.04 and 1.70; and 1.12 and 1.31; respectively, both were lower than those in control (days 0 and 3, ratios of 2.63 and 19.14, respectively), albeit at day 7, B/F ratio in control (1.22) was lower than those in groups L (6.68) and F (8.39) (Fig. 1 c). Similar to others’ findings Cao et al. ( 2020 ), Proteobacteria was a major component in PL gut microbiota in all groups, ranging from 69.35 ± 8.91% to 85.92 ± 3.17% (Fig. 1 c). Nevertheless, if we look into the two dominant and mutually antagonistic groupings in Proteobacteria , viz., Gammaproteobacteria and Alphaproteobacteria , the effects of BDN-1F2 and GZPH2 are clear albeit different. BDN-1F2 in group F reduced Gammaproteobacteria relative abundance by 32.21% while increased Alphaproteobacteria relative abundance by 1.11 times, GZPH2 in group L simultaneously increased the relative abundances of both Gammaproteobacteria and Alphaproteobacteria , by 22.30% and by 28.86% respectively; in control, the relative abundance of Gammaproteobacteria first increased by 46.71% then decreased by 25.70% while its Alphaproteobacteria relative abundance was first reduced by 50.80% and then increased by 44.54%. As G ammaproteobacteria is generally recognized to be associated with diseased or retarded growth organisms, including shrimps (Xiong et al. 2015 ), and Alphaproteobacteria with healthy or normal/faster growth shrimps (Chen et al. 2017 ), it is once again natural that PL in control grew much slower than those in groups F and L. Meanwhile, it seems that while BDN-1F2 possess healthy capabilities to reduce Gammaproteobacteria while promoting Alphaproteobacteria , the ability of GZPH2 to contain Gammaproteobacteria and to promote Alphaproteobacteria is both weaker. Within the class Gammaproteobacteria , Vibrionales, Pseudomonadales and and Betaproteobacteriales , were the dominant orders (a relative abundance ≥ 5% at a time) (Fig. 4 ). Once again, the effects of BDN-1F2 and GZPH2 on these orders were evident. With regard to order Vibrionales , especially genera Vibrio (Figs. 2 and 3 ), it seems that GZPH2 did not exert reduction effect on it, as Vibrio increment was higher in group L than in groups C and F at day 3, and reaching higher relative abundance at the end of the test. On the other hand TCBS plate counting results also showed, TCVC in PL was lower in group F than in groups C and L (Table 3 ), whereas green Vibrio counts in PL was significantly lower in group F than in groups C and L ( p < 0.05, Table 4 ). It previously confirmed by Chen et al. ( 2019 ) that BDN-1F2, a mutant of wild type BDN-1 after Co 60 mutagenesi could lyse 27 out of 30 tested bacteria, including 9 strains of V. alginolyticus , 3 strains of V. parahaemolyticus , 4 strains of V. cholerae , 11 strains of Pseudomonas sp., with 93.3% lysis rate on 16 strains of vibrio. We can conclude that because of lysis ability of BDN-1F2, the number of Vibrio in group BDN-1F2 was lower than in groups control and GZPH2. Recently, Yang et al. (2023) reveled that Bdellovibrio sp. exhibited a certain lysis effect on the selected aquatic pathogens including Vibrio fluvialis , Vibrio anguillarum , Vibrio cholerae and significantly reduce the mortality rate of Carassius auratus caused by the infections with A. vironii. Even we also found significantly negative correlation between survival rate with Vibrio (Fig. 7 ). It is generally recognized that gut microbes are highly diverse and their interactions is very complex, which basically depend on microbial biodiversity and environmental factor in gut. However, explaining microbial interactions is challenging and largely dependent on correlation-based network analysis (Milici et al. 2016 ). In microbial interactions network has three types of centrality metrics viz. degree centrality, closeness centrality and betweenness centrality. Among the centrality metrics, betweenness centrality measures the extent to which a given nodes/species is located within the shortest paths between other pairs of nodes/species in a network (Brandes 2001 ) which specifically used to explore organisms relation with broad host or partner ranges (Toju et al. 2017 ). The genus with higher betweenness values were considered as keystone species in the genus network diagram (Vinothkumar et al. 2021 ). In this study, the microbial interactions network (Figs. 5 a-c; Table S2) data revealed that betweenness value of Vibrio was highest in group L (0.05056) than group C (0.00914) and F (0.00401). The microbial correlation network (Figs. 5 a-c; Table S2) data also showed that Vibrio was positively correlated (coefficient ≥ 0.5) to Yangia and unclassified_f__Rhodobacteraceae and negatively correlated (coefficient ≤ − 0.5) to unclassified_o__Chitinophagales , Bacillus , Flavobacterium , Staphylococcus and Acinetobacter (Fig. 5 a-c). On the other hand, the relative abundance of Yangia increased in group L than group F, whereas unclassified_o__Chitinophagales , Bacillus , Flavobacterium , Staphylococcus and Acinetobacter decreased. As a result, from day 0 to day 7 in both group L, where Acinetobacter along with Staphylococcus decreased but Vibrio and Yangia increased. This result reveled that BDN-1F2 was better than GZPH2 to control Vibrio species in PL rearing. Several result support our this report that BALOs can control vibrio such as Vibrio sp. (Li et al. 2014 ), V. Cholerae (Cao et al. 2015 ), V. parahaemolyticus (Cheng et al. 2008 ). Regarding Pseudomonadales where Acinetobacter and Pseudomonas were the dominant genera (Figs. 2 and 3 ), still, BDN-1F2 showed better reduction effect (77.16% reduction) than GZPH2 (50.15%) as compared to control (55.64% reduction), leaving the relative abundance of Pseudomonadales at 7.63 ± 0.75%, 12.07 ± 7.47% and 8.49 ± 4.39%, respectively, at the end of the test. While the relative abundance of Betaproteobacteriales was reduced in groups L (30.97%) and F (23.16%); in control, it first increased by 36.01% and then again decreased by 14.79% at the end of the test. Within the order Betaproteobacteriales , Ralstonia , found as a dominant genera in control (Fig. 3 ). The microbial interactions network (Fig. 5 a-c; Table S2) showed that Ralstonia was positively correlated (coefficient ≥ 0.5) to Staphylococcus , Acinetobacter and negatively correlated (coefficient ≤ − 0.5) to Pseudomonas. The highest betweenness value of Ralstonia was found in group C (0.05591) than group L (0.0329) where in group F betweenness value of Ralstonia was zero. The highest betweenness value of Staphylococcus and Pseudomonas , was found in group F (0.04167 and 0.05361, respectively) than group L ( 0 and 0.02513 respectively) and C (0.007 and 0.01992, respectively) where, betweenness value of Acinetobacter was the highest in group L than in groups F and C (0.1201, 0.01216 and 0.00325, respectively). We can conclude that this may be one reason why the relative abundance of Ralstonia was higher in the control group whereas Pseudomonas was higher in the GZPH2 group. The networks data also showed the higher ratios of positive correlations in group C (87%) and L (66%) than F (57%) and oppositely the higher ratios of negative correlations were shown in group F (43%) than in groups L (34%) and C (13%). This result reveled that BDN-1F2 helped to build up a balance positive and negative microbial interactions ratio which helped to reduce the potential pathogenic species Vibrio , Pseudomonas , Staphylococcus and Ralstonia for shrimp PL cultivation. A possible explanation could be attributed to BALOs are obligate predatory bacteria that selectively prey on a broad range of Gram-negative bacteria, including Pseudomonas , Staphylococcus and Vibrio (Najnine et al. 2020 ; Saralegui et al. 2022 ). In the free-living attack phase BALOs enter into a prey cell periplasm, and there by grow forming a structure known as the bdelloplast, after completing the growth stage of new progeny’s they lysed prey cell and released new progeny to attack new prey again (Sockett and Lambert 2004 ). The Bugbase phenotypic function prediction bar plots (Fig. 8 d) data showed that the relative abundance of gram negative bacteria Acinetobacter decreased from day 0 to day 3 in all PL groups, simultaneously the relative abundance of another gram negative bacteria Pseudomonas increased at day 3. On day 7, the relative abundance of Pseudomonas increased again in group L but decreased in groups F and C where the reduction rate was higher in group F. The pair Acinetobacter vs. Pseudomonas showed strongly negative correlatatio (coefficient ≤ − 0.5) (Fig. 5 c) as a result when Acinetobacter decreased, Pseudomonas increaesd. Previous study was done by Saralegui et al. ( 2022 ) showed that Bdellovibrio bacteriovorus has ability to prey on pathogenic bacteria Pseudomonas aeruginosa (Cai et al. 2009 ). So, we could predict that the reduction rate of Gram negative bacteria Pseudomonas in BDN-1F2 was higher because of lysis ability of BALOs. As to the reason(s) why GZPH2 was not effective in this occasion even though in vitro testing demonstrated its anti-vibrio effect (Guo et al. 2015 ), it could be due to the much higher contents of nitrate (4.92–6.68 times), nitrite (3.67–5.5 times) and ammonia (2.03–4.08 times) in the rearing water when compared to both control and group F (Table 1 ). Such high contents of these nitrogen salts could aid the growth of Gammaproteobacteria and Vibrionales/Vibrionaceae , as well as Pseudoalteromonadaceae (Huang et al. 2020 ) thus making the reduction less effective, although these nitrogen salts were around the safety levels as proposed by Valencia-Casta et al. (2018), viz., for salinities of 1 and 3 g/L, 0.54 and 0.81 mg/L for total ammonia-N, 0.17 and 0.25 mg/L for NO 2 -N, and 5.6 and 21.5 mg/L for NO 3 -N, respectively. This tempts us to suggest that the beneficial effects of La. salivarius strain GZPH2 could be even better if these nitrogen salts are lower. Bugbase phenotypic function prediction analysis (Fig. 8 h) at genus level among top 25 genera data showed that stress-related all genera Ralstonia , Pseudomonas , Vibrio , Achromobacter , Alcaligenes , Alcanivorax , Stenotrophomonas , norank_f__Beggiatoaceae , and Haliea were belonged to Gammaproteobacteria among all PL groups. Bugbase phenotypic function data also showed that potentially pathogenic (Fig. 8 f) maximum genera were belonged to Proteobacteria / Gammaproteobacteria viz. Ralstonia, Pseudomonas, Vibrio, Achromobacter , and Burkholderia-Caballeronia-Paraburkholderia , and only one genera Proteobacteria / Alphaproteobacteria viz. Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium , one genera was belonged to Firmicutes viz. Staphylococcus and one genera was belonged to Bacteroidetes viz. norank_f__NS9_marine_group. We could be concluded that Gammaproteobacteria were the dominant class of stress-related and potentially pathogenic genera of white leg shrimp ( Litopenaeus vannamei ) post larvae. Visualizations of Bugbase phenotypic function prediction bar plots (Fig. 8 f) further showed some differences in the relative abundance of stress-related and opportunistic pathogen taxa among three PL groups. The relative abundances of stress-related and potentially pathogenic genera Ralstonia , Pseudomonas , and Vibrio were lower in group F than in groups L and C. However, the relative abundances of stress-related four genera were higher in group F than groups C and L viz. Achromobacter (0.77 ± 0.45%, 0.01 ± 0.01% and 0.01 ± 0.01%, respectively), Alcaligenes (0.094 ± 0.042%, 0.01 ± 0.01%, and 0, respectively), Alcanivorax (0.94 ± 0.84%, 0%, and 0%, respectively) and Haliea (0.73 ± 0.39%, 0.12 ± 0.05%, and 0%, respectively) (Fig. 3 ). Here, GZPH2 showed better effect than BDN-1F2 to decrease Achromobacter,Alcaligenes, Alcanivorax , and Haliea. Although, Pseudomonas, Alcaligenes , and Alcanivorax are stress-related genera, but there are still some evidence of their beneficial effects such as Alcanivorax helped in hydrocarbon degrading (Zadjelovic et al. 2020), Pseudomonas helped in denitrification (Tran et al. 2019 ; He et al. 2019 ) and Alcaligenes also helped in denitrification (Joo et al. 2005 b). Previous studies also demonstrated that a healthier and more robust microbial community has a higher biodiversity than an unhealthy one (Rungrassamee et al. 2016 ; Chen et al. 2017 ). The overall trend indicated that the relative abundance of potentially pathogenic genera was higher in the group C as well as the microbial community structure was unstable. Furthermore, the biodiversity diversity of potentially pathogenic taxa was balanced as well as the relative abundances of potentially pathogenic genera was lower in group F than in groups L and C (Fig. 8 f). Although, several studies have shown that Ralstonia is a potentially beneficial genera in fish gut microbial community and play a crucial role to change the intestinal microbial structure (Wu et al. 2021 ). The most surprising thing is, sometimes the absence of some bacteria can be pathogenic. Yu et al. (2022) found that, the abundance of Ralstonia in translucent diseased shrimp PL gut was significantly lower than healthy Shrimp PL while Vibrio and Mycoplasmataceae were higher in number and mentioned it might be one of the reason for the occurrence of translucent diseased in host. In this current experiment, we also found similar results that Ralstonia abundance suppresses the abundance of other bacteria in control group. As a result the microbial biodiversity of control group was lower than that of the BDN-1F2 and GZPH2 groups. Within the class Alphaproteobacteria , Rhodobacterales , Rhizobiales and Sphingomonadales were the dominant orders (Fig. 2 ). With respect to Rhodobacterales (Fig. 2 ), both GZPH2 and BDN-1F2 showed positive promotion effects. While the relative abundance of Rhodobacterales in control decreased 14.03% in the first 3 days, and increased 40.47% later on; the trends of changes in groups L and F were just the opposite, viz., increased 48.80% and 9.85%, respectively. Though it decreased again 18.68% in group L at the end of test, it increased 1.33 times, to 27.07 ± 0.49% at day 7 in group F. Again, data here showed that BDN-1F2 had better promotion effect on Rhodobacterales than GZPH2 could do. Regarding Rhizobiales (Fig. 2 ), though its relative abundance in three groups all initially decreased, the extents were different. In control, it decreased 93.98% throughout the test period. In group L, it first decreased 83.60% after that again increased 57.53% at day 7. Similarly, in group F, it first decreased but only by 38.71%, then increased 36.18% at day 7. Regarding Sphingomonadales (Fig. 2 ), the pattern of changes in three groups is similar to Rhizobiales , but with different extents. That is, in control, it decreased 76.86% and then increased 67.44% at the end of the test. In group L, it initially decreased 76.58% and then increased 27.04 times. In group F, it deceased 28.26% at first and then increased 3.42 times. As both Rhodobacterales and Rhizobiales are shown to be beneficial (Xiong et al. 2017 ; Chen et al. 2017 ), and Sphingomonadales is aerobic anoxygenic phototrophs (photoheterotrophs), with a variety of physiological features and carotenoid pigments, including astaxanthin (Siddaramappa et al. 2018 ). Some could even detoxify a fungal toxin, fumonisin (Li et al. 2021 ). Therefore, their increments in groups F and L should be beneficial. According to Bugbase phenotypic function analysis (Fig. 8 g) data, biofilms producer all genera Paracoccus , unclassified_f__Rhodobacteraceae , Yangia , Nautella , Gemmobacter , Rhodovulum , Roseovarius belonged to Rhodobacterales family, and Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium and Nitratireductor belonged to Rhizobiaceae family. Initially, the relative abundances of biofilms former genera Paracoccus and Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium were higher in the groups C and L than in group F. On the other hand, the relative abundances of Nautella, Nitratireductor , and Pseudochrobactrum were higher in the group F than in groups L and C. On the day 3, the relative abundances of Paracoccus decreased in groups F and C but increased in group L. Although, the relative abundances of Paracoccus decreased in group L where slightly increased in group C and F. On the day 7, the relative abundance of unclassified_f__Rhodobacteraceae , Nitratireductor , and Roseovarius significantly increased in group F than group L and group C (Fig. 4 , Fig. 8 g). Overall data reveled that enhancement of biofilms producing bacteria BDN-1F2 showed better performance than GZPH2. Bacterial biofilms are complex communities of bacteria held together by self-generated extracellular polymeric matrix as well as increase survival rate by improving the defense system, increase nutrients availability and cellular communication and transfer of genetic material (Tremblay et al. 2014 ). In our experiment, we found most biofilm-producing genera under the Alphaproteobacteria class. The so far discovered, Alphaproteobacteria class in particularly the Rhodobacteraceae family is the dominant taxa for biofilm community formation than the Gammaproteobacteria class, which support our findings (Elifantz et al. 2013 ). A retrospective 16S rRNA gene study conducted by Elifantz et al. ( 2013 ) identified primary colonies of biofilms and the results showed that the Alphaproteobacteria class represented 30–70% of the bacterial community whereas Gammaproteobacteria accounted for only up to 10% of the community. Another study has reported that Rhodobacteraceae is a major core gut microbial taxa which help to create a stable microbial community in shrimp gut (Dong et al. 2023). In addition, some taxa belong to Alphaproteobacteria , help to denitrification Paracoccus (Zhao et al. 2020 ), Nitratireductor (Ye et al. 2020 ) and sulphur-oxidizing Paracoccus (Jaffer et al. 2019a ) which were significantly higher in group F than group L and C. Possibility it could be the reason of the lower contents of nitrate, nitrite and ammonia in rearing water of group F compared to both control and group L (Table 1 ). Again, data here showed that BDN-1F2 had a better promotion effect on building healthy microbial community structures in the PL gut than GZPH2. The application of BDN-1F2 in the rearing of shrimp PL might help solve the existing so-called translucent disease in China (Zou et al. 2020 ) albeit more tests at the production level should be carried out. Actinobacteria are well known as a group of secondary metabolites producers and tend to be playing beneficial roles (Binda et al. 2018 ). However, its relative abundance decreased in group L by 41.23%, while increased 26.84% in control and 22.40% in group F (Fig. 1 c). With in phylum Actinobacteria , the relative abundance of Microbacterium was significantly changed among three PL groups and the abundance rate was higher in group F than in groups C and L. Similar patents of changes also occurred to Bacillales of phylum Firmicutes (Fig. 2 ). That is, their relative abundance decreased 92.24% and 85.87% in groups L and F, respectively, while in control, it first decreased by 91.15% and then increased 4.09 times. Though it is generally recognized to be beneficial, Bacilli (from the class to the family level) has been shown to be dominant in slow-growing shrimp intestines while Vibrio was dominant in the intestine of the fast-growing shrimp in outdoor ponds (Duan et al. 2020 ). In line with the suggestion of Duan et al. ( 2020 ), higher abundance of Actinobacteria and/or Bacillales could divert more energy for defence and leave less for growth, thus leading to poorer growth performance. This is exactly the case here as shrimp PL grew slower in control as compared to group L and F even though the latter had higher abundance of Vibrionales / Vibrio and lower abundance of Actinobacteria and/or Bacillales . Flavobacteriales belonged to phylum Bacteroidetes . In control, its relative abundance first increased 23.05% and then decreased 20.44%, whereas it decreased 48.73% in group L and increased 25.57% in group F (Fig. 2 ). As Flavobacteriales was identified as a shrimp gut keystone taxon associated with diseased shrimp, and its infectious disease causing potential was significantly and positively associated with its relative abundance (Dai et al. 2020 ), therefore, its increase would naturally endanger shrimp PL health. In this sense, it seems that GZPH2 could do better than BDN-1F2 here. Over the 7-day test period, the differential enrichment of bacterial taxa in groups C, L and F was analyzed by LEfSe (Figs. 6 a-c) and found four taxa were enriched in control, viz., f__Leuconostocaceae and g__Weissella , f__Dysgonomonadaceae , and f__Corynebacteriaceae . While the first two belong to LABs, obviously being beneficial to shrimp PL; the third taxon has been shown capable of degrading various polysaccharides (Murakami et al. 2018 ), and the last taxon has been found to be symbionts in the guts of salmon (Hartviksen et al. 2014 ). Two taxa were enriched in group L, viz., g_Martelella and g_Muricauda . Both could be considered beneficial as the former is within Rhizobiales (Xiong et al. 2017 ; Chen et al. 2017 ), while the latter could produce antioxidant pigments like zeaxanthin (Prabhu et al. 2014 ). In group F, we could classify the enriched taxa into 3 types, viz., beneficial, potential pathogenic, and functionally neutral or unknowns. The beneficial taxa enriched included f__Microbacteriaceae and g__Microbacterium (proposed as probiotics (Hipólito-Morales et al. 2009 ), f_ _Demequinaceae and g__Demequina (bioactive producers (Subramani and Sipkema 2019 ), f__norank_o__Saccharimonadales and g__norank_f__norank_o__Saccharimonadales (epibiotic living (He et al. 2014 ), f_ _Family_XII_o__Bacillales (known probiotics), g__Nitratireductor (denitrification (Sánchez et al. 2013 ), g_ _Exiguobacterium (proposed as probiotics (Subramani and Sipkema 2019 ), and g__Alcaligenes nitrification (Sánchez et al. 2013 ), g_ _Corynebacterium (symbiont in the guts of salmon (Hartviksen et al. 2014 ). The potentially pathogenic taxa enriched included f__Shewanellaceae and g__Shewanella (Prachumwat et al. 2020 ), f_ _Pseudoalteromonadaceae and g__Pseudoalteromonas (Zheng et al. 2016 ), g_ _Nautella (Zheng et al. 2016 ), g_ _Roseovarius (Travers et al. 2015 ) g_ _Myroides (opportunistic pathogens for human (Schroettner et al. 2014), and even g__Haloferula (higher in abundance associated with lower shrimp body weight (Fan et al. 2019 ). The functionally neutral or unknowns included f__Alcanivoracaceae and g__Alcanivorax (hydrocarbon-degrading bacteria (Yakimov et al. 2019 ), f_ _Halieaceae and g__Haliea (alkene and ethylene-assimilating bacteria (Suzuki et al. 2019 ) f_ _Rubritaleaceae (associated with live feed in yellowtail kingfish (Walburn et al. 2019 ), g_ _Phenylobacterium (prevalent in water of mixed fish culture (Zeng et al. 2020 ) and g_ _Maritalea . If we look into the details of the relative abundances of these potentially pathogenic taxa enriched in three groups (Figs. 6 a-c), they are as follows: f__Shewanellaceae and g__Shewanella , both with 0%, 0.01 ± 0.01% and 1.28 ± 1.00% in control, groups L and F, respectively; f__Pseudoalteromonadaceae and g__Pseudoalteromonas , both with 0%, 0%, 0.01 ± 0.01% in control, groups L and F, respectively; g__Nautella with 0%, 0%, 0.03 ± 0.01% in control, groups L and F, respectively; g__Roseovarius with 0.36 ± 0.08%, 0.14 ± 0.01%, 2.07 ± 0.93% in control, groups L and F, respectively; g__Myroides with 0%, 0%, 0.33 ± 0.24% in control, groups L and F, respectively; g__Haloferula with 0.09 ± 0.05%, 0.01 ± 0.01%, 0.24 ± 0.11% in control, groups L and F, respectively. Hereby, it is quite clear that as beneficial bacteria, GZPH2 and BDN-1F2 they didn’t just raise the abundances of other beneficial bacteria (as in beneficial taxa enriched), but also some of (potential) pathogenic bacteria (as in pathogenic taxa enriched, like Shewanellaceae and Shewanella ), while keeping others in check (taxa with little changes, like Ralstonia ) and reducing the overall abundances of (potential) pathogenic bacteria as a whole at the community level (Fig. 3 ). The rises in abundance of (potential) pathogens may be favourable to the hosts and their environment in some instances as they could provide complementary and/or necessary ecological functions for the ecosystem. In group F, over the 7-day test period, the relative abundance of Shewanella was rising, from 0.37 ± 0.12% at day 0 to 0.45 ± 0.09% at day 3, then further up to 1.28 ± 0.22%, while that of norank_f__NS9_marine_group was from 0.61 ± 0.06% at day 3 to 0.92 ± 0.09% at day 7, also rising albeit slightly (Fig. 3 ). Therefore, the rise of abundance of fermentative bacteria genus Shewanella or norank_f__NS9_marine_group should help lower NH 3 -N and NO 2 -N concentrations in the environment (Yoon et al. 2015 ; Zhang et al. 2022 ; Shi et al. 2023 ) and thus create better conditions for the PL to grow. This is exactly the case here as NH 3 -N and NO 2 -N in group F was 1.21 ± 0.49 mg/L and 0.03 ± 0.01 mg/L, much lower than that in group L (Table 1 ). As changes of a structure would bring on changes of functions. Here, the addition of BDN-1F2 and GZPH2 to the rearing of PL has strengthened various ecological functions in its gut microbiota, as predicted when compared to control, even though most functional changes were with no statistical significance. The gut microbiota plays a potential role in host nutrition by producing numerous metabolites, such as free fatty acids, amino acids, and vitamins, are found in the host intestine which are equally vital for host intestinal homeostasis and gut microbial community structure (Postler et al. 2017). Postler et al. (2017) reveled that microorganisms produce three basic types of metabolites namely metabolites that are produced by gut microbes from dietary components, metabolites that are produced by the host and biochemically modified by gut microbes, and metabolites that are synthesized de novo by gut microbes. Hence, the PL gut microbial composition obtained by 16S rRNA gene sequencing was used to predict microbial function using COG and KEGG metabolic pathways that were involved, and the differences between different samples and groups were analyzed. Based on exploring the proportions of each COG function (Level 2) (Fig. 9 a) and KEGG metabolic pathway (level 1/2/3) (Fig. 9 b-d), we found some discrepancies among three PL groups C, L and F. Overall microbial function pathway analysis result showed that the relative abundances of functional genes involving metabolism were very high in all PL groups including amino acid metabolism, carbohydrate metabolism and so on (Fig. 9 a-c). According to the relative abundances of functional genes involving metabolism data, we found amino acid metabolism and carbohydrate metabolism increased in group F (13.86% and 22.61%, respectively) where, decreased in groups C (0.69% and 0.56%, respectively) and L (17.56% and 16.39%, respectively) from the beginning to end of experiment (Fig. 9 c). According to Rosas et al. ( 2000 ), shrimp have a limited ability to metabolize carbohydrates; alternatively, shrimp use protein as a source of energy and growth. Chuntapa et al. ( 1999 ) reported that protein is important nutrients for optimal growth and survival of juvenile tiger shrimp and found the optimal protein:energy (P:E) ratios 150 and 146 mg protein/kcal, respectively. Amino acids are organic molecules that form a protein when combined together with other amino acids. At the end of our experiment, amino acids metabolism related functional genes was higher in group F on day 7 than groups C and L (Fig. 9 c). Amino acids play an important role in the structure and metabolism of all living organisms. Shrimp cannot synthesize all amino acids but they need several amino acid to increase their immune function, survival rate as well as growth performance (Simon et al. 2021 ). Specifically, arginine, proline and glutamate have been demonstrated to regulate immune defense (Shao et al. 2023 ). The essential amino acids (EAAs): arginine, methionine, valine, threonine, isoleucine, leucine, lysine, histidine, phenylalanine, and tryptophan are must acquire through shrimp diet, all of which are not synthesized de novo by eukaryotic cells (NRC 2011 ). The gut microbiota can de novo synthesize some essential amino acids that contribute to the host's amino acid homeostasis (Metges 2000 ). In this study, we found PL gut microbiome had large enrichment of genes involved in the metabolism and biosynthesis of the essential amino acids and other amino acids viz. pyruvate family (valine, leucine, and isoleucine), aspartate family (lysine, threonine, methionine), aromatic family (phenylalanine, tyrosine and tryptophan), serine family (serine, glycine, cysteine) and histidine (Fig. 9 d). Some findings are supported our result that the gut microbiome had large enrichment of genes involved in the metabolism and biosynthesis of amino acids. Gill et al. ( 2006 ) found that the gut microbiome had large enrichment of genes involved in the biosynthesis of leucine, isoleucine, lysine, phenylalanine, tyrosine, tryptophan, and valine as well as enrichment in genes associated with the metabolism of alanine, aspartate, glutamate, histidine, methionine, glycine, serine and threonine. Another findings showed that gut microbiome had large enrichment of genes involved in pathways such as the biosynthesis of lysine, phenylalanine, tyrosine, tryptophan, valine, leucine and isoleucine compared to the host genome (Qin et al. 2010 ). We also found that the essential amino acids and other amino acids metabolism and biosynthesis had significantly positive correlation with PL gut microbiota Paracoccus, Flavobacterium , Brevundimonas , Microbacterium, Nitratireductor and norank_f__Mycoplasmataceae and pyruvate family and lysine degradation had significant negative correlation with Vibrio and Yangia (Fig. 9 d). In this test we found, the relative abundance of Paracoccus, Brevundimonas , Microbacterium ( p < 0.05) and Nitratireductor ( p < 0.01) were significantly higher in group F on day 7 than other samples (Fig. 4 ) and also found higher amino acids metabolism in this sample (Fig. 9 d). Oppositely, in group L, the relative abundance of Vibrio and Yangia was significantly higher ( p < 0.05) and Microbacterium was significantly lower ( p < 0.05) than in groups F and L as well as Brevundimonas and Nitratireductor were absent on the day 7 (Fig. 4 ) which negatively affected the amino acids metabolism. As a result, we can conclude that the presence or absence of certain gut microbiota has been shown to significantly change amino acids metabolism and biosynthesis in PL groups: BDN-1F2, GZPH2 and control. Lipid, an important group of nutrients are essential component of living beings including aquatic animals. It is recognized that gut microbiota improved the accumulation of lipids in the host gut by enhancing lipid metabolism (Ringø et al. 2022 ). In this study, we found, the lipid metabolism related functional genes was slightly higher in group C than in groups F and L (1390422, 1386260, and 1346231, respectively) at end of test (Fig. 9 c). Instead, the enrichment of relative abundances of lipid metabolism-related functional genes was higher in group L than in groups F and C (77.32%, 10.29% and 6.52%, respectively) from the beginning to end of experiment (Fig. 9 c). We observed that the presence of specific gut bacteria and their relative abundance strongly affected the lipid metabolism in host intestine. We found that Yangi a, Vibrio, Algoriphagus , and Roseovarius had a significantly negative correlation with lipid metabolism and biosynthesis especially, fatty acid metabolism, synthesis and degradation of ketone bodies, linoleic acid metabolism, ether lipid metabolism, steroid biosynthesis, primary bile acid biosynthesis and secondary bile acid biosynthesis, in contrast, Acinetobacter , unclassified_o__Chitinophagales , and Staphylococcus they had a significantly positive correlation (Fig. 9 d). Furthermore, fatty acid metabolism, linoleic acid metabolism, ether lipid metabolism and steroid biosynthesis showed significantly positive correlation with Brevundimonas, Paracoccus, Rhodococcus and Taeseokella but negative correlation with Roseovarius (Fig. 9 d). Furthermore, primary bile acid biosynthesis and secondary bile acid biosynthesis had significantly negative correlation with Unclassified__f__Rhodobacteraceae ( p < 0.05) and Sphingolipid metabolism had significantly negative correlation with unclassified_o__Chitinophagales ( p < 0.001) and Acinetobacter ( p < 0.05) (Fig. 9 d).. We observe that the relative abundance of Acinetobacter, unclassified_o__Chitinophagales ( P < 0.01), Unclassified__f__Rhodobacteraceae, Brevundimonas , Roseovarius and Paracoccus ( P < 0.05) was significantly higher in group F than in groups L and C (Fig. 4 ). Conversely, the relative abundance of Vibrio and Yangia ( P < 0.05) was significantly increased in group L than in groups F and C from day 0 to day 7 (Fig. 4 ). We found in this test, the change of lipid metabolism and biosynthesis significantly correlated the relative abundance of PL microbiome (Fig. 9 d). Although, we found the relative abundance of lipid metabolism functional genes was relatively higher in BDN-1F2 group (2.97%) than GZPH2 but the relative abundance enrichment percentage was 7.51 times higher in group GZPH2 than group BDN-1F2 and 11.85 times higher from control (Fig. 9 c). Similar results was reported by Salas-Leiva et al. ( 2020 ) that lipid metabolism of longfin yellowtail ( Seriola rivoliana ) juvenile was strongly correlated with the gut microbiotal metabolic contribution and found biosynthesis of fatty acids, glycerolipid, glycerophospholipid, secondary bile acid, and sphingolipid were affected by gut microbial community. Several previous studies reveled that the regulation of lipid metabolism helps to increase the host body length and weight (Joyce et al. 2015; Zhou et al. 2017 ). Similar result we also found in this experiment, lipid metabolism along with the body length, body weight and survival rate of PL was the higher in groups BDN-1F2 and GZPH2 than control. In this study, we found significantly positive correlations between the PL gut microbiota such as viz. Brevundimonas , Flavobacterium , Microbacterium and Paracoccus with carbohydrate metabolism especially glycolysis or gluconeogenesis, glyoxylate and dicarboxylate metabolism, pentose and glucuronate interconversions and inositol phosphate metabolism (Fig. 9 d). It has been previously shown that the intestinal microbiota of longfin yellowtail juveniles, mainly dominated by Proteobacteria , Firmicutes , Bacteroides , Cyanobacteria , and Actinobacteria , exhibited a contribution to carbohydrate and amino acid metabolism (Salas-Leiva et al. 2020 ). Another experimental result also reveled that the gut bacteria of Nile tilapia were positively correlated with carbohydrate metabolism (Wu et al. 2021 ). In this study, the abundance rate of Brevundimonas , Flavobacterium , Microbacterium and Paracoccus was significantly (p < 0.05) higher in group F than in groups L and C which strongly increased the carbohydrate metabolism in group F than in groups C and L. Carbohydrates occupy an important place in metabolism due to the energy source of various biosynthetic pathways. The pitiful scenario is that the utilization of carbohydrates is very poor in most of the fish species and crustaceans including shrimp. Several studies have shown that high levels of dietary carbohydrates can cause metabolic diseases in fish due to poor carbohydrate metabolism, especially in carnivorous fish species (Stone 2003 ). Recently, many aquaculture scientists have tried to improve carbohydrate utilization and explore new technologies to prevent metabolic diseases related to fish carbohydrate metabolism, one of them is the application of functional intestinal microorganisms or the use of probiotics, which is a new emerging technology (Serra et al. 2019 ). Here, we found between BDN-1F2 and GZPH2, BDN-1F2 showed a effect result than GZPH2 on Carbohydrate metabolism by changing the gut microbial community structure. On the basis of our finding, we can suggest that BDN-1F2 as a probiotic of shrimp PL rearing for enhancing carbohydrate utilization. It will be possible to spare dietary protein which can be an achievement to decrease the amount of nitrogen waste. Conclusions To sum up the above, we could conclude here that as both Bdellovibrio and like organisms (BALOs) strain BDN-1F2 and Lactobacillus salivarius strain GZPH2 could improve the overall growth performance of white-leg shrimp PL, via the changes of composition/structure and the functionality of a microbial community, they have met the criteria set out for aquaculture probiotics and should therefore be used as probiotics. Between these two kind of probiotics compare to GZPH2, BDN-1F2 showed better effect to build up a healthy microbial biodiversity by controlling the relative abundance of potentially pathogenic taxa including Vibrio, Pseudomonas, Staphylococcus, Ralstonia as well as enrichment of beneficial taxa including Paracoccus , unclassified_f__Rhodobacteraceae , Brevundimonas , Flavobacterium , Microbacterium and Nitratireductor . In addition, BDN-1F2 showed better effect on metabolism basically amino acid metabolism and carbohydrate metabolism than GZPH2. The application of BDN-1F2 in the rearing of shrimp PL might help to reduce protein content by increasing the carbohydrate portion in the shrimp PL feed albeit more tests at the production level should be carried out. Declarations Data Availability Statement The raw 16S DNA sequencing data were deposited in the NCBI Sequence Read Archive (SRA) under the accession number PRJNA694360 (SRP303063 or SRR13517589-SRR13517618). Ethics Statement This study was carried out in accordance with the recommendations of Animal Ethics Committee of Guangdong Province, China. Conflict of Interest Statement Authors declare that no commercial or financial conflict. Acknowledgement Authors thank the following two organizations for the financial support: Science and Technology Department of Guangdong Province of China (2016A020222002), and ProBioti Biotech (Guangzhou) Company Limited. 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(2020) Determination of the infectious agent of translucent post-larva disease (TPD) in Penaeus vannamei . Pathogens 9(9):741 https://doi.org/10.3390/pathogens9090741 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 29 Apr, 2024 Submission checks completed at journal 29 Apr, 2024 First submitted to journal 24 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4319520","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":296899715,"identity":"b229639f-243d-42c7-9327-ba7a1fb0bd63","order_by":0,"name":"Farhana Najnine","email":"","orcid":"","institution":"South China University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Farhana","middleName":"","lastName":"Najnine","suffix":""},{"id":296899718,"identity":"91cbf174-a7bc-4c80-9f34-06ee11b4eaf8","order_by":1,"name":"Meng Wang","email":"","orcid":"","institution":"South China University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Meng","middleName":"","lastName":"Wang","suffix":""},{"id":296899720,"identity":"26cd66e6-7792-4cec-9b88-f14b5ca6d085","order_by":2,"name":"Hongcao Han","email":"","orcid":"","institution":"South China University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Hongcao","middleName":"","lastName":"Han","suffix":""},{"id":296899722,"identity":"63713fbf-3f24-4934-ad38-d2e92681a7f3","order_by":3,"name":"Junpeng Cai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYBAC9gYeBgMgzcMPFWBsIKSF5wBUiyRI6QFitYCBwQGitbD3HiguqLGTMT5/+Jn0BwYb2Q0HmJ89wKuF51yC8YxjyTxmN9LMJA4wpBlvOMBmboBPi71EjoExbwMzUAsPG1DL4cQNB4AMvLbIvwFpqecx7j8D0vKfCC0SPCAth3kMGHJAWg4QoYUnD+SX4zwSN9KMLc4YJBvPPMxmhl8L+9ljxgU11fb8/Ycf3qiosJPtO978DK8WIGAzRrBBQcVMQD1IyWPCakbBKBgFo2BEAwBLbULjk3xh4wAAAABJRU5ErkJggg==","orcid":"","institution":"South China University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Junpeng","middleName":"","lastName":"Cai","suffix":""}],"badges":[],"createdAt":"2024-04-24 16:24:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4319520/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4319520/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55792472,"identity":"547ba50b-8362-4d9b-b511-1b9fc7fde63c","added_by":"auto","created_at":"2024-05-03 09:49:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1475954,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea \u003c/strong\u003eThe two-dimensional scatter plot of non-metric multidimensional scaling analysis (NMDS) of PL gut microbial community analysis. The nine color dots represent the samples of three PL groups C, L and F. The closer the two sample points are the more similar the species composition of the two samples. Stress \u0026lt; 0.2 indicates indicates that the graph has certain explanatory significance. Statistical analysis of ANOSIM application showed significant differences (\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001) among all PL samples.\u003cstrong\u003e b \u003c/strong\u003eVenn Diagram at the phylum level of the three PL groups C, L and F \u003cstrong\u003ec \u003c/strong\u003eCircos samples and species relationship map at the phylum level. In this figure, the small semicircle (left semicircle) represents the species composition in the sample; the color of the outer ribbon represents the group from which it comes, the color of the inner ribbon represents the species, and the length represents the relative abundance of the species in the corresponding sample. The large semicircle (right semicircle) represents the distribution of species in different samples at the phylum/class level. The outer color band represents the species, the inner color band represents three PL groups C, L and F and the length represents the distribution proportion of the sample in a species.. (Here, C0, L0 and F0 represent PL samples at day 0; C3, L3 and F3 represent PL samples at day 3; C7, L7 and F7 represent PL samples at day 7).\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-4319520/v1/28a75dfec0a0ef65691e6377.png"},{"id":55792473,"identity":"ef01efaa-d573-4c3b-bdfe-5f65eec98472","added_by":"auto","created_at":"2024-05-03 09:49:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":117231,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance of different bacterial orders within PL microbiota in groups C, L and F. A relative abundance of less than 1% was defined as others. This distribution barplot was drawn with R software.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-4319520/v1/bf4f008df988c4495bcd75df.png"},{"id":55792471,"identity":"607f9b9b-1d1c-48ed-8e7f-088d976ec40f","added_by":"auto","created_at":"2024-05-03 09:49:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":155259,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance of different bacterial genera within PL microbiota in groups C, L and F. A relative abundance of less than 1% was defined as others. This distribution barplot was drawn with R software.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-4319520/v1/d29327f0fba9d009ea1177bd.png"},{"id":55792469,"identity":"b835f0c1-17ea-4c10-a872-93048dc84698","added_by":"auto","created_at":"2024-05-03 09:49:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":577309,"visible":true,"origin":"","legend":"\u003cp\u003eSignificant difference test between groups at the genus level. In this Figure, the y-axis represents the species name at the genus level, the x-axis represents the average relative abundance in different groups of species, and the columns with different colors represent three PL groups C, L and F. On the far right is the P-value, *0.01 \u0026lt;\u003cem\u003e p\u003c/em\u003e≤ 0.05, ** 0.001\u0026lt; \u003cem\u003ep\u003c/em\u003e ≤ 0.01, *** \u003cem\u003ep\u003c/em\u003e ≤ 0.001.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-4319520/v1/eeba3d0dcf6ed7a32e784a87.png"},{"id":55792470,"identity":"7104f704-cbd6-4ad0-aaac-bf3224c27ba4","added_by":"auto","created_at":"2024-05-03 09:49:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":343614,"visible":true,"origin":"","legend":"\u003cp\u003eThe species correlation network diagram at genus level (top 50 genus). The species correlation network diagram of all samples of: \u003cstrong\u003ea\u003c/strong\u003e group\u003cstrong\u003e \u003c/strong\u003eC (C0, C3 and C7),\u003cstrong\u003e b \u003c/strong\u003egroup\u003cstrong\u003e \u003c/strong\u003eL (L0, L3 and L7) and \u003cstrong\u003ec\u003c/strong\u003e group F (F0, F3 and F7). The size of the nodes in the graph represents the abundance of species, and different colors represent different species. The color of the connecting line indicates positive and negative correlations, red indicates positive correlation, and green indicates negative correlation. The thickness of the line indicates the magnitude of the correlation coefficient, and the thicker the line, the higher the correlation between species. The more lines there are, the closer the connection between the nodes.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-4319520/v1/1f5c25118abddc1a29262e55.png"},{"id":55792475,"identity":"b8a2244e-3cd6-4c4f-aaab-574da0b6a54d","added_by":"auto","created_at":"2024-05-03 09:49:17","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":486478,"visible":true,"origin":"","legend":"\u003cp\u003eBarplot of\u003cstrong\u003e \u003c/strong\u003eLinear discriminant analysis Effect Size (LEfSe). LEfSe comparison of microbiota among groups C, L and F: \u003cstrong\u003ea \u003c/strong\u003eat day 0. \u003cstrong\u003eb\u003c/strong\u003e at day 3. \u003cstrong\u003ec\u003c/strong\u003eat day 7. LDA score of LEfSe-PICRUSt, i.e., the length of column, represents the effect size of bacterial lineages. Columns in red indicate bacterial taxa enriched in group C, columns in blue indicate bacterial taxa enriched in group L, columns in green indicate bacterial taxa enriched in group F. Only taxa that linear discriminate analysis (LDA) value above 2 and with significance (\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05) were shown. This figure was drawn using LEfSe software.\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-4319520/v1/c5211d501a30c765b0d25085.png"},{"id":55792477,"identity":"efded134-b6bd-4c8c-9de3-9987b9ddcc24","added_by":"auto","created_at":"2024-05-03 09:49:17","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":406502,"visible":true,"origin":"","legend":"\u003cp\u003eThe Spearman correlation heatmap diagram shows the correlation between growth performance and top 25 genus. The X-axis and Y-axis are growth performance factors body length (BL), body weight (BW), survival rate (SR) and species, respectively, and the correlation \u003cem\u003er\u003c/em\u003e and \u003cem\u003ep\u003c/em\u003evalues are calculated. The \u003cem\u003er\u003c/em\u003e value is displayed in different colors in the figure, and the \u003cem\u003ep\u003c/em\u003e value is marked with * 0.01 \u0026lt; \u003cem\u003ep\u003c/em\u003e ≤ 0.05，** 0.001 \u0026lt;\u003cem\u003e p\u003c/em\u003e ≤ 0.01，*** \u003cem\u003ep \u003c/em\u003e≤ 0.001.\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-4319520/v1/f7f4fbb517ae69e3229de8c7.png"},{"id":55792474,"identity":"c51731ad-7960-49c1-bcfd-dc17d82d0e00","added_by":"auto","created_at":"2024-05-03 09:49:16","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":954628,"visible":true,"origin":"","legend":"\u003cp\u003eBugBase functional analysis of genus level among three PL groups C, L and F. The bar plot showing the relative abundance of \u003cstrong\u003ea \u003c/strong\u003eaerobic bacteria, \u003cstrong\u003eb\u003c/strong\u003e anaerobic bacteria, \u003cstrong\u003ec \u003c/strong\u003efacultatively anaerobic bacteria, \u003cstrong\u003ed\u003c/strong\u003e Gram-negative bacteria, \u003cstrong\u003ee\u003c/strong\u003e Gram-positive bacteria, \u003cstrong\u003ef\u003c/strong\u003e potentially pathogenic bacteria, \u003cstrong\u003eg\u003c/strong\u003e biofilm forming bacteria, \u003cstrong\u003eh\u003c/strong\u003e stress tolerance phenotype bacteria, \u003cstrong\u003ei\u003c/strong\u003e containing mobile components bacteria on the basis of BugBase functional analysis. The abscissa indicates the samples name and the ordinate indicates the relative abundance. Each color gradient indicates one genus.\u003c/p\u003e","description":"","filename":"Fig.8.png","url":"https://assets-eu.researchsquare.com/files/rs-4319520/v1/a75f5ea5329d82ebd0758981.png"},{"id":55792476,"identity":"8dcb3b98-756a-4ac7-bfb4-be7d85420b35","added_by":"auto","created_at":"2024-05-03 09:49:17","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1499180,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea \u003c/strong\u003eRelative abundance of predicted functions on the basis of COG with PICRUSt. The abscissa is the relative abundance of different function, the ordinate is the name of the pathway level 2 function. The different color block represents the samples of three PL groups. \u003cstrong\u003eb \u003c/strong\u003eThe heatmap shows the functional categories (KEGG pathway level 1) of bacterial communities using PICRUSt2 analysis of PL samples. The abscissa is the name of the samples of different groups of shrimp PL, the ordinate is the name of the pathway level 1 function, and the color gradient of the color block shows the changes of different functional abundances in the samples, and the legend is the value represented by the color gradient. \u003cstrong\u003ec \u003c/strong\u003eThe bar plot shows the relative abundance different types of metabolism (KEGG pathway level 2) of bacterial communities using PICRUSt2 analysis of PL samples. The abscissa is the relative abundance of different different types of metabolism; the ordinate is the name of different different types of metabolism, and the color block represent the samples of different PL groups. \u003cstrong\u003ed \u003c/strong\u003eThe correlation heatmap diagram shows the correlation between top 25 genus and different types of metabolism (amino acids metabolism, lipid metabolism and carbohydrate metabolism) at KEGG pathway level 3 of PL samples. The X-axis and Y-axis are the name of different types of metabolism at KEGG pathway level 3 with KO ID number and species, respectively, and the correlation \u003cem\u003er \u003c/em\u003eand \u003cem\u003ep\u003c/em\u003e values are calculated. The \u003cem\u003er\u003c/em\u003evalue is displayed in different colors in the figure, and the \u003cem\u003ep\u003c/em\u003e value is marked with * 0.01 \u0026lt; \u003cem\u003eP\u003c/em\u003e ≤ 0.05，** 0.001 \u0026lt; \u003cem\u003eP\u003c/em\u003e ≤ 0.01，***\u003cem\u003e P\u003c/em\u003e ≤ 0.001.\u003c/p\u003e","description":"","filename":"Fig.9.png","url":"https://assets-eu.researchsquare.com/files/rs-4319520/v1/07f841f622fe8b0bb04e019c.png"},{"id":55792468,"identity":"2364e073-4242-44fb-82c5-f4f88a74efa9","added_by":"auto","created_at":"2024-05-03 09:49:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1540219,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4319520/v1/c618d148-388e-4647-b753-f7add10bea05.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The effects of Bdellovibrio and like organisms (BALOs) and Lactobacillus salivarius on changes in gut microbial biodiversity and their potential role on Shrimp (Litopenaeus vannamei) Postlarvae","fulltext":[{"header":"Introduction","content":"\u003cp\u003eShrimp is one of the most commonly consumed crustaceans as a good source of healthy protein, calcium, and various essential compounds for the human body while low in calories and fat (Oksuz et al. 2019). The demand of shrimp has been remarkably increased in the past two decades. In this concern, shrimp farmers have tried to increase their production rates. Among the shrimp species, white-leg shrimp \u003cem\u003eLitopenaeus vannamei\u003c/em\u003e (\u003cem\u003eLi. vannamei\u003c/em\u003e) is one of the most cultured shrimp species around the world due to its rapid growth, disease tolerance, high stocking density tolerance, particularly low dietary protein requirement. According to the FAO statistical data in 2018, the production of \u003cem\u003eLi\u003c/em\u003e. \u003cem\u003evannamei\u003c/em\u003e culture has increased from 1.32\u0026nbsp;million tons in 2004 to 4.96\u0026nbsp;million tons in 2018 (FAO, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although the production of cultured shrimp has intensified to meet the growing demand but shrimp farm industry globally faces numerous challenges due to various infectious disease outbreak (Diwan et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eShrimp diseases are caused by bacteria and viruses. Bacterial diseases, mainly vibriosis is one of the most severe threat to a wide range of cultured shrimp species that affect the whole shrimp industry (Jayasree et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). \u003cem\u003eVibrio\u003c/em\u003e is responsible for several shrimp diseases such as \u0026ldquo;luminous bacterial disease\u0026rdquo; by \u003cem\u003eV. harveyi\u003c/em\u003e, \u0026ldquo;white faeces disease (WFD)\u0026rdquo; by \u003cem\u003eV. vulnificus, V. fluvialis, V. parahaemolyticus, V. alginolyticus, V. damselae, V. mimicus\u003c/em\u003e and \u003cem\u003eV. cholera\u003c/em\u003e, \u0026ldquo;Loose shell syndrome (LSS)\u0026rdquo;by \u003cem\u003eV. harve\u003c/em\u003eyi, \u003cem\u003eV. parahaemolyticus, V. alginolyticus\u003c/em\u003e, \u0026ldquo;white gut disease (WGD)\u0026rdquo; by \u003cem\u003eV. harveyi, V. alginolyticus, V. agnuillarum\u003c/em\u003e, \u0026ldquo;early mortality syndrome/acute hepatopancreatic necrosis disease (EMS/AHPND)\u0026rdquo; by \u003cem\u003eV. parahaemolyticus\u003c/em\u003e etc. (Tran et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kumar et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kumar et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chellapandian et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Haifa-Haryani et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although different bacteria other than \u003cem\u003evibrio\u003c/em\u003e, like \u003cem\u003eShewanella\u003c/em\u003e, could also cause similar AHPND diseases (Prachumwat et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A new emerging vibriosis called \u0026ldquo;translucent post-larvae disease\" (TPD) or \"glass post-larvae disease (GPD)\u0026rdquo; occurred by \u003cem\u003eV. parahaemolyticus\u003c/em\u003e ( Zou et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A large proportion of shrimp farmers are using chemical compounds or antibiotics to prevent or treat disease outbreaks. Uncontrolled and widespread use of chemicals or antibiotics in shrimp farms might result in the deposition of residue in the shrimp body or may cause the acceleration of antibiotic resistance (Zalewska et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Recently, antibiotic resistance is a booming concern for human and animal health. Due to prioritizing human and aquatic animal health or aqua-ecosystem scientific community seeks alternatives to reduce the abuse of antibiotics in aquaculture. Various studies have been conducted in order to use of probiotics and reported that probiotics can balancing the gut microbiota or invasion pathogens cells or modulating the immune system by producing antimicrobial substances like Bacteriocins (Bermudez-Brito et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). As a result, probiotics have received special attention from researchers seeking alternatives to the use of chemical compounds or antibiotics. Many recent studies, various gram-negative and gram-positive bacterial species such as lactic acid bacteria (LAB), \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003eVibrio\u003c/em\u003e, photosynthetic bacteria, yeast and/or their mixtures have been used as probiotics in shrimp farming (Ring\u0026oslash; et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Abdel-Tawwab et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ezzedine et al. 2021) that have positive effects on the change of diversity of gut microbial composition.\u003c/p\u003e \u003cp\u003eAs a LAB species, \u003cem\u003eLactobacillus salivarius\u003c/em\u003e (\u003cem\u003eLa. salivarius\u003c/em\u003e) is a well-characterized antimicrobial substances producer probiotic commonly isolated from the gut of human and animals that does not show any negative effects on the host body. Nevertheless, the application of \u003cem\u003eLa\u003c/em\u003e. \u003cem\u003esalivarius\u003c/em\u003e in aquaculture is scarce. Talpur et al. (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) applied three species of LAB (\u003cem\u003eLa. plantarum, La. rhamnosus, La. salivarius\u003c/em\u003e) in the blue swimming crab (\u003cem\u003ePortunus pelagicus\u003c/em\u003e) hatchery trials challenge experiments. They found that \u003cem\u003eLa. salivarius\u003c/em\u003e increased the survival rate of blue swimming crabs by 53.33% over non-inoculated control (43.33%) trials. Another study conducted by Ljubobratovic et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) on juvenile pike-perch (\u003cem\u003eSander lucioperca\u003c/em\u003e) found that the combination of \u003cem\u003eLa. salivarius\u003c/em\u003e BGHO1/\u003cem\u003eLa. reuteri\u003c/em\u003e BGGO6-55 had a positive effect on fish growth, skeletal development and survival rate by pathogenic bacteria \u003cem\u003eVibrio\u003c/em\u003e spp. All these results show that \u003cem\u003eLa. salivarius\u003c/em\u003e is beneficial for cultured organisms, but no research has yet been done on white leg shrimp. BALOs are obligate, aerobic, rapidly motile, gram-negative, predatory bacteria that prey on a broad range of Gram-negative and some Gram-positive bacteria (Li et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Inoue et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Because of their intrinsic ability to lyse prey\u0026rsquo;s cells, BALOs have been considered as a biological agent or alternative to control aquatic pathogens (Qi et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ezzedine et al. 2021). Ecologically, BALOs are ubiquitous in nature, including various kinds of waters (Waite et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) as well as the guts of some mammals and farmed organisms (Najnine et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Several researchers reported that BALOs can control \u003cem\u003evibrio\u003c/em\u003e such as \u003cem\u003eV. alginolyticus\u003c/em\u003e (Wen et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), V. \u003cem\u003eCholerae\u003c/em\u003e (Cao et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), V. \u003cem\u003eparahaemolyticus\u003c/em\u003e (AHPND Vp) (Kongrueng et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and other bacterial disease red body disease \u003cem\u003eProteus penneri\u003c/em\u003e (Cao et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) in shrimp farm. Additionally, Li et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) revealed that BDHSH06 reduced the total number of bacterial and \u003cem\u003eVibrio\u003c/em\u003e, increased the survival rate, body length, and weight of black tiger shrimp (\u003cem\u003ePe. monodon).\u003c/em\u003e In general, probiotics are considered to improve host immunity, growth and survival rate by shaping the gut microbial community. Many individual studies have been done in both potentially novel probiotics. However, no comparison has been made yet between BALOs and \u003cem\u003eLa. Salivarius\u003c/em\u003e in shrimp postlarvae (PL) rearing. Hence, the present study aimed to comparison between BALOs and \u003cem\u003eLa. salivarius\u003c/em\u003e and evaluate their activity on growth parameter, gut micriobial structure and their function as well as effect against pathogenic \u003cem\u003eVibrio\u003c/em\u003e bacteria in white leg shrimp (\u003cem\u003eLi. vannamei\u003c/em\u003e) PL rearing.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eWater quality parameters\u003c/h2\u003e\n \u003cp\u003eThroughout the experimental period, water temperature, DO and pH did not show any significant differences among all the groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), with water temperature in the range of 23.8 to 25\u0026deg;C, DO in the range of 3.75 to 5.82 mg/L and pH in the range of 7.2 to 8.0, respectively. With regard to NO\u003csub\u003e2\u003c/sub\u003e-N, NO\u003csub\u003e3\u003c/sub\u003e-N and NH\u003csub\u003e3\u003c/sub\u003e-N contents, they were significantly higher in group L than in groups C and F (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), viz., 3\u0026ndash;4 times higher of NO\u003csub\u003e2\u003c/sub\u003e-N (0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 mg/L in group L vs. 0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 mg/L and 0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 mg/L in groups C and F, respectively), 5\u0026ndash;7 times higher of NO\u003csub\u003e3\u003c/sub\u003e-N (18.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.37 mg/L in group L vs. 2.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18 mg/L and 3.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8 mg/L in groups C and F, respectively), and 2\u0026ndash;4 times higher of NH\u003csub\u003e3\u003c/sub\u003e-N (2.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44 mg/L in group L vs. 0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30 mg/L and 1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49 mg/L in groups C and F group, respectively).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003ePL Survival rate and growth parameters\u003c/h2\u003e\n \u003cp\u003ePL survival rate in group F was 95\u0026thinsp;\u0026plusmn;\u0026thinsp;5.00%, significantly higher than those in groups C and L, which were 89.00\u0026thinsp;\u0026plusmn;\u0026thinsp;8.00%, 89\u0026thinsp;\u0026plusmn;\u0026thinsp;3.00%, respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). At the start of the 7-day test, average BL of PL in group L (10.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31 mm) was significantly shorter than those in groups C (11.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21 mm) and F (11.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50 mm) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), albeit all PL were randomly assigned to each group. At the end of the test, average BL in groups F (11.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64 mm) and L (11.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61 mm) were significantly longer than in control (group C, 10.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30 mm) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). PTLG in groups C, L and F were \u0026minus;\u0026thinsp;5.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00%, 11.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.00% and 5.00\u0026thinsp;\u0026plusmn;\u0026thinsp;7.00%, respectively, with the highest gain in group L. With regard to average PL BW, at the start of the test, they displayed significant differences among them (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), viz., 0.092\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 g, 0.075\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 g, and 0.084\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 g, in groups C, L and F, respectively. However, their differences were gone at the end of the 7-day test period (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), with BW of 0.100\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 g, 0.104\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 g, and 0.121\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 g in groups C, L and F, respectively. PTWG in groups C, L and F were 8.10\u0026thinsp;\u0026plusmn;\u0026thinsp;6.74%, 38.67\u0026thinsp;\u0026plusmn;\u0026thinsp;34.00% and 44.04\u0026thinsp;\u0026plusmn;\u0026thinsp;32.23%, respectively, with the highest gain in group F. SGR, they displayed significant differences among three groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), with group F the highest (0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40), and group C the lowest (0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTotal cultivable bacterial counts (TCBC), total cultivable\u003c/strong\u003e \u003cstrong\u003eVibrios c\u003c/strong\u003e\u003cstrong\u003eounts (TCVC), total cultivable LAB counts (TCLC), and total BALOs counts (TBC) in PL Gut samples\u003c/strong\u003e\u003c/p\u003eOver the 7-day test period, TCBC in PL in all three groups showed a similar trend, i.e., that is, the number of bacteria increased first and then decreased with no significant difference among them at the end of the test (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Again, TCVC in PL in groups C and L increased first then decreased again, while in group F, it was decreased entire all the experimental period (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). TCVC in three groups had no significant differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) at day 0, but it was significantly higher in group L than in group F at day 7 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). TCLC and TBC were only detected in group L and F, respectively, but all the results saw that their number had been reduced gradually (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).To more accurately reflect the compositions of \u003cem\u003eVibrios\u003c/em\u003e on TCBS plates, we enumerated yellow, green as well as black colonies in separate.\u003cp\u003e\u003cstrong\u003eYellow\u003c/strong\u003e \u003cstrong\u003eVibrio\u003c/strong\u003e, \u003cstrong\u003egreen\u003c/strong\u003e \u003cstrong\u003eVibrio\u003c/strong\u003e \u003cstrong\u003eand black colonies in PL gut samples\u003c/strong\u003e\u003c/p\u003eIn the TCBS plates, we found three types of bacterial colonies: yellow, green and black. On the basis of colony color morphology, we categorized the yellow colony-forming \u003cem\u003eVibrio\u003c/em\u003e as yellow \u003cem\u003eVibrio\u003c/em\u003e, the green colony-forming \u003cem\u003eVibrio\u003c/em\u003e as green \u003cem\u003eVibrio\u003c/em\u003e and the black colony-forming bacteria \u003cem\u003eas Black colonies\u003c/em\u003e (Talib et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Throughout the 7-day test period, Yellow \u003cem\u003eVibrio\u003c/em\u003e counts in PL decreased from day 0 to day 7, with no significant difference in all three groups initially (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Nevertheless, at days 3 and 7, its counts were significantly higher in groups L and F than in group C (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). While green \u003cem\u003eVibrio\u003c/em\u003e counts were decreased in group F throughout the test period, it was increased slightly in groups C and L. Overall, its counts were significantly higher in groups C and L than in group F (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Black colony counts in PL were reduced to zero in all three groups at the end of the test, with its initial counts higher in groups C and L than in group F at day 0. Black colonies gone undetected at day 3 in groups C and L, but were still detected in group F.\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eIllumina High-throughput sequencing analysis\u003c/h2\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003eMiSeq sequencing results\u003c/h2\u003e\n \u003cp\u003eOver the 7-day test period, we sampled and sequenced PL gut microbiota at three time points in all three groups, i.e., day 0 (samples C0, L0 and F0 for groups C, L and F, respectively), day 3 (samples C3, L3 and F3 for groups C, L and F, respectively) and day 7 (samples C7, L7 and F7 for groups C, L and F, respectively). All sampling was done in triplicates. Hence, in total, 27 samples were sequenced. After optimization and quality control, 1,184,087 high-quality sequences were obtained. The number of sequences in different groups varied from 39,100\u0026thinsp;\u0026plusmn;\u0026thinsp;11,214 in sample C0 to 50,595\u0026thinsp;\u0026plusmn;\u0026thinsp;2,980 in sample F3 (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). As sequences of 97% similarity or higher were grouped as an OTU, OTU clustering of non-repetitive sequences yielded a total of 2,165 OUTs. The number of OUTs in different groups ranged from 151\u0026thinsp;\u0026plusmn;\u0026thinsp;104 in C3 to 441\u0026thinsp;\u0026plusmn;\u0026thinsp;263 in F3 (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Coverage is the sequencing depth index, a value of 1.00 or near 1.00 means all or nearly all the species in a sample has been sequenced, as is the case in this study (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). This confirmed that the sequencing results here represented the real situation of each sample.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eDiversities of bacterial communities\u003c/h2\u003e\n \u003cp\u003eAs pointed out by Cao et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), alpha diversity of a microbiota includes Shannon and Simpson indices, which reflect the extents of community diversities. While a higher Shannon index indicates a higher diversity, a higher number of Simpson index indicates the otherwise. Alpha diversity also includes ACE index and Chao1 index, which reflect the extents of community richness. A higher number indicates more richness. While the Shannon index (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) in control (group C) first decreased from 2.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24 at day 0 to 1.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24 at day 3, then increased to 3.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28 at day 7. In group L, it was first increased slightly, from 2.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32 at day 0 to 2.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28 at day 3, and then again slightly decreased, to 2.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49 at day 7. In group F, it increased from 3.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10 at day 0 to 3.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42 at day 3, then stayed there at day 7 (3.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20). The trend of changes of Simpson indices was opposite to Shannon indices (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Over all, Shannon indices in all three groups showed no significant differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) at day 0, but with significant differences at days 3 and 7 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Regarding the richness of the community, the highest richness was found in sample F3 (451.04\u0026thinsp;\u0026plusmn;\u0026thinsp;263.91), while the lowest richness in sample C3 (162.91\u0026thinsp;\u0026plusmn;\u0026thinsp;111.31), with the rest in between (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eTo explore the structure variance of microbial community among all groups, \u0026beta;-diversity were analyzed. Non-metric multidimensional scaling (NMDS) analysis (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea) was conducted at the OUT levels and ANOSIM (permutation_number: 999) was used to test significant difference among in all samples of three groups. The two-dimensional scatter plot showed samples of each group were significantly separated (stress: 0.1, R\u0026thinsp;=\u0026thinsp;0.3514, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) in the NMDS diagram, indicating significant differences in gut microbial community composition among the groups. Dots of different colors or shapes represent different groups of samples, and the closer the two sample points are, the more similar the species composition of the two samples. The horizontal and vertical coordinates represent the relative distance and have no practical significance. The clustering patterns were observed only in F0 samples, they were highly similar and less variance microbial communities. The NMDS result also showed that C0, L, and F0 were nearly similar with less variance microbial communities. The high discrimination with low variation was observed in C3 samples and they did not cluster independently or distinctly. The high discrimination and the high variance were observed in F7 compared to other groups and confirmed the unique development of the microbial community. The results of NMDS analysis showed stress value is 0.1. It is generally considered that stress\u0026thinsp;\u0026lt;\u0026thinsp;0.2 can be represented by the two-dimensional dot diagram of NMDS, and its graph has certain explanatory significance, when stress\u0026thinsp;\u0026lt;\u0026thinsp;0.1, it can be regarded as a good sorting.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eTaxonomic compositions and changes of PL microbial communities\u003c/h2\u003e\n \u003cp\u003eBy using high throughput sequencing analysis, 36 phyla were detected in PL microbiota in total, where only 13 core phyla were commonly shared among three PL groups viz. \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eActinobacteria\u003c/em\u003e, \u003cem\u003eBacteroidetes\u003c/em\u003e, \u003cem\u003eFirmicutes\u003c/em\u003e, \u003cem\u003ePatescibacteria\u003c/em\u003e, \u003cem\u003eTenericutes\u003c/em\u003e, \u003cem\u003eGemmatimonadetes\u003c/em\u003e, \u003cem\u003eCyanobacteria\u003c/em\u003e, \u003cem\u003eunclassified_k__norank_d__Bacteria\u003c/em\u003e, \u003cem\u003eVerrucomicrobi\u003c/em\u003ea, \u003cem\u003eAcidobacteria\u003c/em\u003e, \u003cem\u003eChloroflexi\u003c/em\u003e, and \u003cem\u003ePlanctomycetes\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb). Among them \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eActinobacteria\u003c/em\u003e, \u003cem\u003eBacteroidetes\u003c/em\u003e, and \u003cem\u003eFirmicutes\u003c/em\u003e were the dominant phyla with relative abundance\u0026thinsp;\u0026ge;\u0026thinsp;1%, account for 96.9\u0026ndash;98.52% of the totals which is consistent with the results of the circos samples and species relationship map (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ec) reflects the distribution of dominant species in each group at the phylum levels. \u003cem\u003eProteobacteria\u003c/em\u003e, with an initial relative abundance of 75.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.66%, 69.35\u0026thinsp;\u0026plusmn;\u0026thinsp;8.91%, 75.27\u0026thinsp;\u0026plusmn;\u0026thinsp;3.06% in groups C, L and F, respectively, increased to 85.92\u0026thinsp;\u0026plusmn;\u0026thinsp;3.17% in group L (14% increment), and slightly decreased to 71.74\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10%, 72.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49% in groups C (3.45% reduction) and F (4.05% reduction) at the end of the test, respectively. \u003cem\u003eActinobacteria\u003c/em\u003e, with an initial relative abundance of 11.96\u0026thinsp;\u0026plusmn;\u0026thinsp;3.52%, 13.39\u0026thinsp;\u0026plusmn;\u0026thinsp;3.12% and 10.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45% in groups C, L and F, respectively, increased to 15.17\u0026thinsp;\u0026plusmn;\u0026thinsp;4.00% and 12.84\u0026thinsp;\u0026plusmn;\u0026thinsp;6.07% in groups C (26.84% increment) and F (22.40% increment), and decreased to 7.87\u0026thinsp;\u0026plusmn;\u0026thinsp;3.88% in group L (41.23% reduction) at the end of the test, respectively. \u003cem\u003eBacteroidetes\u003c/em\u003e, with an initial relative abundance of 8.51\u0026thinsp;\u0026plusmn;\u0026thinsp;2.18%, 8.43\u0026thinsp;\u0026plusmn;\u0026thinsp;3.79% and 8.51\u0026thinsp;\u0026plusmn;\u0026thinsp;4.15% in groups C, L and F, respectively, reduced to 6.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49% and 5.08\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46% in groups C (29.14% reduction) and L (39.74% reduction), and increased to 10.98\u0026thinsp;\u0026plusmn;\u0026thinsp;6.29% in group F (29.03% increment) at the end of the test, respectively. \u003cem\u003eFirmicutes\u003c/em\u003e, with an initial relative abundance of 3.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37%, 8.11\u0026thinsp;\u0026plusmn;\u0026thinsp;9.66% and 5.01\u0026thinsp;\u0026plusmn;\u0026thinsp;2.13% in groups C, L and F, respectively, increased to 4.95\u0026thinsp;\u0026plusmn;\u0026thinsp;3.46% in group C (53.25% increment), and decreased to 0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47%, 1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74% in groups L (90.63% reduction) and F (73.85% reduction) at the end of the test, respectively.\u003c/p\u003e\n \u003cp\u003eAt the order level, there were 241 orders detected in PL microbiota in total, but only 9 with a relative abundance\u0026thinsp;\u0026ge;\u0026thinsp;5% at a time, viz., \u003cem\u003eBetaproteobacteriales\u003c/em\u003e, \u003cem\u003eVibrionales\u003c/em\u003e, \u003cem\u003ePseudomonadales\u003c/em\u003e, \u003cem\u003eRhodobacterales\u003c/em\u003e, \u003cem\u003eRhizobiales\u003c/em\u003e, \u003cem\u003eSphingomonadales\u003c/em\u003e, \u003cem\u003eCorynebacteriales\u003c/em\u003e, \u003cem\u003eFlavobacteriales\u003c/em\u003e, and \u003cem\u003eBacillales\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). \u003cem\u003eBetaproteobacteriales\u003c/em\u003e, \u003cem\u003eVibrionales\u003c/em\u003e and \u003cem\u003ePseudomonadales\u003c/em\u003e all belonged to class \u003cem\u003eGammaproteobacteria\u003c/em\u003e of phylum \u003cem\u003eProteobacteria\u003c/em\u003e. Over the test period, the relative abundance of \u003cem\u003eBetaproteobacteriales\u003c/em\u003e reduced by 30.97% in group L (from 23.26\u0026thinsp;\u0026plusmn;\u0026thinsp;6.62% at day 0 to 17.76\u0026thinsp;\u0026plusmn;\u0026thinsp;11.36% at day 7) and 23.16% in group F (from 20.34\u0026thinsp;\u0026plusmn;\u0026thinsp;3.14% at day 0 to 15.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1 4.86% at day 7), while in group C, first it was increased by 36.01% (from 25.55\u0026thinsp;\u0026plusmn;\u0026thinsp;10.81% at day 0 to 34.75\u0026thinsp;\u0026plusmn;\u0026thinsp;47.21% at day 3), and then decreased by 14.79% (29.61\u0026thinsp;\u0026plusmn;\u0026thinsp;10.74%) at day 7. The relative abundance of \u003cem\u003eVibrionales\u003c/em\u003e in control (group C) first increased by 11.17 times (from 1.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24% at day 0 to 22.76\u0026thinsp;\u0026plusmn;\u0026thinsp;27.90% at day 3), then decreased by 42.09% (13.18\u0026thinsp;\u0026plusmn;\u0026thinsp;8.45%) at day 7; in group L, it first increased by 13.38 times (from 1.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35% at day 0 to 24.44\u0026thinsp;\u0026plusmn;\u0026thinsp;7.74% at day 3), then further again increased by 31.47% (32.13\u0026thinsp;\u0026plusmn;\u0026thinsp;25.93% at day 7); in group F, it first increased by 2.68 times (from 5.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.57% at day 0 to 20.00\u0026thinsp;\u0026plusmn;\u0026thinsp;11.39% at day 3), then decreased by 45.2% (10.96\u0026thinsp;\u0026plusmn;\u0026thinsp;2.28%) at day 7. The relative abundance of \u003cem\u003ePseudomonadales\u003c/em\u003e decreased by 55.64% (from 19.14\u0026thinsp;\u0026plusmn;\u0026thinsp;6.34% at day 0 to 8.49\u0026thinsp;\u0026plusmn;\u0026thinsp;4.39% at day 7) in control (group C), 50.15% (from 24.21\u0026thinsp;\u0026plusmn;\u0026thinsp;14.05% at day 0 to 12.07\u0026thinsp;\u0026plusmn;\u0026thinsp;7.47% at day 7) in group L and 77.16% (from 33.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58% at day 0 to 7.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75% at day 7) in group F, correspondingly.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eRhodobacterales\u003c/em\u003e, \u003cem\u003eRhizobiales\u003c/em\u003e and \u003cem\u003eSphingomonadales\u003c/em\u003e belonged to class \u003cem\u003eAlphaproteobacteria\u003c/em\u003e of phylum \u003cem\u003eProteobacteria\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Over the test period, \u003cem\u003eRhodobacterales\u003c/em\u003e relative abundance in control (group C) first decreased by 14.03% (from 14.40\u0026thinsp;\u0026plusmn;\u0026thinsp;5.22% at day 0 to 12.38\u0026thinsp;\u0026plusmn;\u0026thinsp;13.87% at day 3), then increased by 40.47% (17.39\u0026thinsp;\u0026plusmn;\u0026thinsp;6.34% at day 7); in group L, its abundance first increased by 48.80% (from 12.09\u0026thinsp;\u0026plusmn;\u0026thinsp;3.97% at day 0 to 17.99\u0026thinsp;\u0026plusmn;\u0026thinsp;6.46% at day 3), then decreased by 18.68% (14.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.65% at day 7); in group F, its abundance first increased by 9.85% (from 10.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61% at day 0 to 11.60\u0026thinsp;\u0026plusmn;\u0026thinsp;3.53% at day 3) and then further increased by 1.33 times (27.07\u0026thinsp;\u0026plusmn;\u0026thinsp;7.40% at day 7). \u003cem\u003eRhizobiales\u003c/em\u003e relative abundance in control (group C) decreased by 93.98% (from 11.46\u0026thinsp;\u0026plusmn;\u0026thinsp;9.71% at day 0 to 0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48% at day 7); in group L, it first decreased by 83.60% (from 4.45\u0026thinsp;\u0026plusmn;\u0026thinsp;3.66% at day 0 to 0.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56% at day 3), then increased by 57.53% (1.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07% at day 7); in group F, it decreased by 38.71% (from 2.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30% at day 0 to 1.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69% at day 3), then increased by 36.18% (2.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49% at day 7). \u003cem\u003eSphingomonadales\u003c/em\u003e relative abundance in control (group C) first decreased by 76.86% (1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80% at day 0 to 0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25% at day 3), then increased by 67.44% ( 0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13% at day 7); in group L, its relative abundance first decreased by 76.58% (1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12% at day 0 to 0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17% at day 3), then increased by 27.04 times (7.29\u0026thinsp;\u0026plusmn;\u0026thinsp;7.17% at day 7); in group F, its relative abundance decreased slightly, by 28.26% at first (0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29% at day 0 to 0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06% at day 3), then increased by 3.42 times (1.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51% at day 7).\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eCorynebacteriales\u003c/em\u003e belonged to phylum \u003cem\u003eActinobacteria\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). In control (group C), its relative abundance first decreased by 44.94% (10.48\u0026thinsp;\u0026plusmn;\u0026thinsp;4.44% at day 0 to 5.77\u0026thinsp;\u0026plusmn;\u0026thinsp;3.83% at day 3), then increased by 116.46% (12.49\u0026thinsp;\u0026plusmn;\u0026thinsp;4.87% at day 7); in group L, its relative abundance decreased gradually, by 42.51% (from 11.88\u0026thinsp;\u0026plusmn;\u0026thinsp;4.07% at day 0 to 6.83\u0026thinsp;\u0026plusmn;\u0026thinsp;4.41% at day 7); in group F, its relative abundance decreased slightly (25.87%) and stayed there (from 9.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62% at day 0 to 6.79\u0026thinsp;\u0026plusmn;\u0026thinsp;6.73% at day 7). \u003cem\u003eFlavobacteriales\u003c/em\u003e belonged to phylum \u003cem\u003eBacteroidetes\u003c/em\u003e. In control (group C), its relative abundance first increased by 23.05% (from 3.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71% to 4.11\u0026thinsp;\u0026plusmn;\u0026thinsp;4.24% at day 3), then decreased by 20.44% (3.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81% at day 7); in group L, its relative abundance decreased by 48.73% and stayed there (from 5.11\u0026thinsp;\u0026plusmn;\u0026thinsp;2.81% at day 0 to 2.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29% at day 7); in group F, its relative abundance increased by 25.57% and stayed there (from 4.38\u0026thinsp;\u0026plusmn;\u0026thinsp;1.72% at day 0 to 5.50\u0026thinsp;\u0026plusmn;\u0026thinsp;3.49% at day 7). \u003cem\u003eBacillales\u003c/em\u003e belonged to phylum \u003cem\u003eFirmicutes\u003c/em\u003e. In control (group C), its relative abundance first decreased by 91.15% (from 2.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45% at day 0 to 0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25% at day 3), then increased 4.09 times (1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12% at day 7); in group L, its relative abundance decreased by 92.24% (from 7.60\u0026thinsp;\u0026plusmn;\u0026thinsp;11.79% at day 0 to 0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65% at day 7); in group F, its relative abundance first stayed there (4.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.41% at days 0 and 3), then decreased by 85.87% (0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20% at day 7).\u003c/p\u003e\n \u003cp\u003eAt the genus level, there were 926 genera detected in PL microbiota in total, but only 38 genera had a relative abundance\u0026thinsp;\u0026ge;\u0026thinsp;1%, with \u003cem\u003eRalstonia\u003c/em\u003e, \u003cem\u003eVibrio\u003c/em\u003e, \u003cem\u003eParacoccus\u003c/em\u003e, \u003cem\u003eRhodococcus\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eAcinetobacter\u003c/em\u003e, and \u003cem\u003eunclassified_f__Rhodobacteraceae\u003c/em\u003e being the dominant genera (relative abundance\u0026thinsp;\u0026ge;\u0026thinsp;10%) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The significant differences (Kruskal-Wallis H test) anlysis at genus data showed that the relative abundance of \u003cem\u003eVibrio, Paracoccus, unclassified_f__Rhodobacteraceae\u003c/em\u003e, \u003cem\u003eYangia\u003c/em\u003e, \u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eMicrobacterium\u003c/em\u003e, \u003cem\u003enorank_f__NS9_marine_group\u003c/em\u003e, \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003eRoseovarius, Taeseokella, Brevundimonas\u003c/em\u003e significantly changed at \u003cem\u003ep\u0026thinsp;\u0026le;\u0026thinsp;0.05\u003c/em\u003e and \u003cem\u003eAcinetobacter\u003c/em\u003e, \u003cem\u003eunclassified_o__Chitinophagales\u003c/em\u003e, and \u003cem\u003eNitratireductor\u003c/em\u003e significantly changed at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.01 among three PL groups (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). \u003cem\u003eVibrio\u003c/em\u003e, and \u003cem\u003eAcinetobacter\u003c/em\u003e both belong to class \u003cem\u003eGammaproteobacteria\u003c/em\u003e. The relative abundance of \u003cem\u003eVibrio\u003c/em\u003e increased significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) in all PL groups C (from 3. 1.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24% to 22.74\u0026thinsp;\u0026plusmn;\u0026thinsp;27.87%), F (from 5.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56% to 19.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.26%) and L (from 1.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35% to 24.43\u0026thinsp;\u0026plusmn;\u0026thinsp;7.73%) from day 0 to day 3. However, on day 7, the relative abundance of \u003cem\u003eVibrio\u003c/em\u003e significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) decreased again in groups F (10.74\u0026thinsp;\u0026plusmn;\u0026thinsp;2.213%) and C (13.14\u0026thinsp;\u0026plusmn;\u0026thinsp;8.407%), whereas in group L significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) increased and the abundance rate was higher (31.81\u0026thinsp;\u0026plusmn;\u0026thinsp;25.7%) than in groups F and C. Initially, the relative abundance of \u003cem\u003eAcinetobacter\u003c/em\u003e was higher in all PL groups C, F and L (13.89.98%, 28.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51%, and 16.26\u0026thinsp;\u0026plusmn;\u0026thinsp;10.84%, respectively) and significantly decreased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.01) at day 3 (1.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.67%, 1.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52%, and 1.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08%, respectively) where on day 7, it slightly increased in groups C and F but decreased in group L.\u003c/p\u003e\n \u003cp\u003eBeneficial genera \u003cem\u003eParacoccus\u003c/em\u003e, \u003cem\u003eunclassified_f__Rhodobacteraceae\u003c/em\u003e, \u003cem\u003eYangia\u003c/em\u003e, \u003cem\u003eErythrobacter\u003c/em\u003e, \u003cem\u003eBrevundimonas\u003c/em\u003e, \u003cem\u003eRoseovarius\u003c/em\u003e and \u003cem\u003eNitratireductor\u003c/em\u003e belong to class \u003cem\u003eAlphaproteobacteria\u003c/em\u003e significantly increased in group F (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) than in groups L and C. On the starting of this experiments, the highest relative abundance of \u003cem\u003eParacoccus\u003c/em\u003e was in group C (12.18\u0026thinsp;\u0026plusmn;\u0026thinsp;4.053%) than in groups L (9.21\u0026thinsp;\u0026plusmn;\u0026thinsp;3.56%) and F (6.656\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49%). On the day 3, the abundance rate of \u003cem\u003eParacoccus\u003c/em\u003e significantly decreased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) in groups C (3.05\u0026thinsp;\u0026plusmn;\u0026thinsp;3.28%) and F (3.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19%) but slightly increased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) in group L (9.69\u0026thinsp;\u0026plusmn;\u0026thinsp;4.56%). The opposite trend happened on day 7, the relative abundance of \u003cem\u003eParacoccus\u003c/em\u003e significantly increased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) in groups F (6.92\u0026thinsp;\u0026plusmn;\u0026thinsp;2.39%) and C (3.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63%) and significantly decreased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) in group L (4.34\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3%). The relative abundance of \u003cem\u003eunclassified_f__Rhodobacteraceae\u003c/em\u003e increased significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) from day 0 to day 3 and the abundance rate was higher in group L (from 1.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29% to 6.35\u0026thinsp;\u0026plusmn;\u0026thinsp;2.85%) than in groups C (from 1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75% to 4.75\u0026thinsp;\u0026plusmn;\u0026thinsp;4.76%) and group F (from 2.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45% to 4.90\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42%). On the day 7, the relative abundance of \u003cem\u003eunclassified_f__Rhodobacteraceae\u003c/em\u003e significantly increased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) in groups F (14.93\u0026thinsp;\u0026plusmn;\u0026thinsp;4.614%) and C (10.46\u0026thinsp;\u0026plusmn;\u0026thinsp;4.26%) whereas significantly decreased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) in group L (5.48\u0026thinsp;\u0026plusmn;\u0026thinsp;3.13%). The highest relative abundance of \u003cem\u003eYangia\u003c/em\u003e showed in group C (from 0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47% to 3.91\u0026thinsp;\u0026plusmn;\u0026thinsp;5.06%) at day 3, but it decreased again at day 7 (2.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68%). However, \u003cem\u003eYangia\u003c/em\u003e increased significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) in groups F and L from day 0 to day 7 and the highest relative abundance was in group L (4.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19%) at end of the experiments. The initial abundance of \u003cem\u003eRoseovarius\u003c/em\u003e was very low during the experiment it was significantly increased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) and the highest abundance was observed in group F (2.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12%) on the day 7. Initially, the relative abundance \u003cem\u003eNitratireductor\u003c/em\u003e was only significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.01) higher in group F but slightly decreased on the day 3 and again significantly increased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.01) on the day 7.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eStaphylococcus\u003c/em\u003e and \u003cem\u003eMicrobacterium\u003c/em\u003e belong to phylum \u003cem\u003eFirmicutes.\u003c/em\u003e Initially, \u003cem\u003eStaphylococcus\u003c/em\u003e was significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) higher in group L (7.29\u0026thinsp;\u0026plusmn;\u0026thinsp;11.98%) than groups F (4.426\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26%) and C (2.304\u0026thinsp;\u0026plusmn;\u0026thinsp;1.471%) and significantly decreased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) in all groups at the end of the test where the lowest relative abundance was in group F (0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18%) than group C (0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06%) and L (0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64%). At start of the test, the relative abundance of \u003cem\u003eMicrobacterium\u003c/em\u003e was very low in all groups. Entire of the test, the abundance of \u003cem\u003eMicrobacterium\u003c/em\u003e significantly increased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) in groups F (from 0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18% to 3.75\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16%) and C (from 0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28% to 1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.359%) but significantly decreased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) in group L (from 0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39% to 0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41%). \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003eTaeseokella\u003c/em\u003e and \u003cem\u003enorank_f__NS9_marine_group\u003c/em\u003e belong to Phylum \u003cem\u003eBacteroidetes\u003c/em\u003e. The relative abundance of \u003cem\u003eTaeseokella\u003c/em\u003e and \u003cem\u003enorank_f__NS9_marine_group\u003c/em\u003e significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) decreased entire of the test and both \u003cem\u003eTaeseokella\u003c/em\u003e and \u003cem\u003enorank_f__NS9_marine_group\u003c/em\u003e were absent in groups L from day 3 to day 7. Initially, \u003cem\u003eBacillus\u003c/em\u003e relative abundance was extremely low, then significantly increased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) in group F (from 0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08% to 4.17\u0026thinsp;\u0026plusmn;\u0026thinsp;7.04%) and decreased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) in groups C (0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04% to 0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09%) and L (from 0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35% to 0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02%) from day 0 to day 3. On day 7, the relative abundance of \u003cem\u003eBacillus\u003c/em\u003e significantly increased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) in group C (0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09%) and decreased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) in groups F (0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09%) and L (0.006\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008%). The genera \u003cem\u003eunclassified_o__Chitinophagales\u003c/em\u003e was only present initially in all PL groups then significantly decreased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.01) and absent on the day 3 and day 7.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eSpecies univariate correlation networks\u003c/h2\u003e\n \u003cp\u003eTo explore potential interactions between top 50 genera of the PL gut microbial community, species univariate correlation network diagram (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea-c) were drawn (Networkx software) by calculating the correlation between species and the nodes in the network graph were species-node nodes. The correlation coefficients such as Spearman rank between species were calculated to reflect the correlation between species. By default, species with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 are shown. The size of the nodes in the graph represents the abundance of species, and different colors represent different Phylum. The color of the connecting line indicates positive and negative correlations, red indicates a positive correlation, and green indicates a negative correlation. The thickness of the line indicates the magnitude of the correlation coefficient, and the thicker the line indicates the higher the correlation between species. The more lines there are, the closer the connection between one genera to another. The univariate network of groups C, L, and F contains 356 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea), 322 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb), and 370 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec) nodes, respectively. The clustering coefficient represents the complexity of the network and strong interactions among microorganisms. Three of the univariate networks showed the higher ratios of positive correlations in group C (87%) than in groups L (66%) and F (57%) and oppositely the higher ratios of negative correlations were shown in group F (43%) than in groups L (34%) and C (13%). The univariate network of groups L and F showed the highest clustering coefficient (positive coefficient 1 and negative coefficient \u0026minus;\u0026thinsp;0.9), followed by group C network (positive coefficient 0.9 and negative coefficient \u0026minus;\u0026thinsp;0.8), indicating that microbial interactions are strongest in two probiotic groups than control. Overall, the univariate network in group F exhibited greater complexity with different genera and correlations compared with groups L and C. The univariate networks of three PL groups also showed that pairs like, \u003cem\u003eRhodococcus\u003c/em\u003e vs. \u003cem\u003eRalstonia\u003c/em\u003e, \u003cem\u003eunclassified_o__Chitinophagales\u003c/em\u003e vs. \u003cem\u003eMarinomonas\u003c/em\u003e, \u003cem\u003eWinogradskyella\u003c/em\u003e vs. \u003cem\u003enorank_f__norank_o__Absconditabacteriales_SR1\u003c/em\u003e, \u003cem\u003enorank_f__NS9_marine_group\u003c/em\u003e vs. \u003cem\u003eAcinetobacter\u003c/em\u003e, \u003cem\u003enorank_f__NS9_marine_group\u003c/em\u003e vs. \u003cem\u003enorank_f__norank_o__Absconditabacteriales_SR1\u003c/em\u003e, \u003cem\u003enorank_f__Beggiatoaceae\u003c/em\u003e vs. \u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eAquibacter\u003c/em\u003e vs. \u003cem\u003eYangia\u003c/em\u003e, \u003cem\u003eHaliea\u003c/em\u003e vs. \u003cem\u003eunclassified_f__Rhodobacteraceae\u003c/em\u003e, \u003cem\u003eYangia\u003c/em\u003e vs. \u003cem\u003eunclassified_f__Rhodobacteraceae\u003c/em\u003e, \u003cem\u003eYangia\u003c/em\u003e vs. \u003cem\u003eRoseovarius\u003c/em\u003e, \u003cem\u003eAcinetobacter\u003c/em\u003e vs. \u003cem\u003ePseudoalteromonas\u003c/em\u003e, \u003cem\u003eFlavobacterium\u003c/em\u003e vs. \u003cem\u003ePseudoalteromonas\u003c/em\u003e had strong positive correlation (coefficient\u0026thinsp;\u0026ge;\u0026thinsp;0.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas pairs like \u003cem\u003eEnhydrobacter\u003c/em\u003e vs. \u003cem\u003eLeucobacter\u003c/em\u003e, \u003cem\u003eGemmobacter\u003c/em\u003e vs. \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e vs. \u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eAlgoriphagus\u003c/em\u003e vs. \u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eWinogradskyella\u003c/em\u003e vs. \u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eunclassified_f__Rhodobacteraceae\u003c/em\u003e vs. \u003cem\u003eAllorhizobium-Neorhizobium-Pararhizobium-Rhizobium\u003c/em\u003e, \u003cem\u003eAlgoriphagus\u003c/em\u003e vs. \u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eYangia\u003c/em\u003e vs. \u003cem\u003eunclassified_c__Gammaproteobacteria\u003c/em\u003e, \u003cem\u003eunclassified_f__Rhodobacteraceae\u003c/em\u003e vs. \u003cem\u003eBrevundimonas\u003c/em\u003e, \u003cem\u003eAlcanivorax\u003c/em\u003e vs. \u003cem\u003eBrevundimonas\u003c/em\u003e, \u003cem\u003eRhodovulum\u003c/em\u003e vs. \u003cem\u003eunclassified_o__Chitinophagales\u003c/em\u003e had strong negative correlation (coefficient\u0026thinsp;\u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;0.5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eLEfSe multi-level species difference discrimination analysis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe differential enrichment of bacterial taxa in groups C, L and F was analyzed by LEfSe, and the differences in microbiota were apparent (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea-c). At day 0, four different taxa viz. one class, one family and two genera were enriched in group C; two genera were enriched in group F and only one genus was enriched in group L (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea). At day 3, no taxa was enriched in group C; two orders, three families and five genera were enriched in group L while three orders, five families and 12 genera were enriched in group F (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb). At day 7, three families and one genus were enriched in group C; two genera were enriched in group L; three orders, nine families and 17 genera were enriched in group F (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ec).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eCorrelation analysis between gut microbita and growth performance\u003c/h2\u003e\n \u003cp\u003eThe correlation heatmap diagram (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e) showed the correlation among most abundant 25 genera with body length (BL), body weight (BW) and survival rate (SR). Data showed that there was not significantly correlation between body length and PL gut bacteria. But, the body weight had a significantly positive correlation with \u003cem\u003eAlgoriphagus\u003c/em\u003e at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05 and \u003cem\u003eYangia\u003c/em\u003e at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.01 and had a significantly negative correlation with \u003cem\u003eunclassified_o__Chitinophagales\u003c/em\u003e, \u003cem\u003eTaeseokella\u003c/em\u003e and \u003cem\u003enorank_f__NS9_marine_group\u003c/em\u003e at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05. The survival rate had a significantly positive correlation with \u003cem\u003eFlavobacterium\u003c/em\u003e and \u003cem\u003eBrevundimonas\u003c/em\u003e at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05; \u003cem\u003eParacoccus\u003c/em\u003e and \u003cem\u003eStaphylococcus\u003c/em\u003e at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.01; \u003cem\u003eAcinetobacter\u003c/em\u003e, \u003cem\u003eTaeseokella\u003c/em\u003e, \u003cem\u003enorank_f__NS9_marine_group\u003c/em\u003e and \u003cem\u003eunclassified_o__Chitinophagales\u003c/em\u003e at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.001, where had a significantly negative correlation with \u003cem\u003eVibrio\u003c/em\u003e at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05; \u003cem\u003eYangia\u003c/em\u003e and \u003cem\u003eAlgoriphagus\u003c/em\u003e at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.01.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eBugBase phenotype prediction\u003c/h2\u003e\n \u003cp\u003eThe functions of the bacterial community were annotated by the BugBase phenotype prediction platforms, and the proportion trend of bacteria at the genus level was observed among three groups C, L, and F (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ea-i). Bugbase analysis confirmed the existence of opportunistic pathogens in all analyzed bacterial taxa within all PL samples (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ef) It also confirmed the existence and relative abundance distribution of genera associated with aerobic, anaerobic, facultatively anaerobic, gram-negative, gram-positive, containing mobile components, forming biofilms, and stress tolerance phenotype in all PL samples. The aerobic genera of all specimens were mainly \u003cem\u003eRhodococcus\u003c/em\u003e, \u003cem\u003eRalstonia\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, and \u003cem\u003eAcinetobacter\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ea) while the most abundant anaerobic genera was n\u003cem\u003eorank_f__NS9_marine_group\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eb). Moreover, the most abundant facultatively anaerobic three genera \u003cem\u003eParacoccus\u003c/em\u003e, \u003cem\u003eunclassified_f__Rhodobacteraceae\u003c/em\u003e, and \u003cem\u003eYangia\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ec) were also dominant as biofilms producers (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eg) in PL samples. The Gram-negative bacterial genera \u003cem\u003eRalstonia\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, and \u003cem\u003eAcinetobacter\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ed) were dominant in both potentially pathogenic and stress tolerance response phenotype (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ef and h). The Gram-negative non-fermenters bacterial genus \u003cem\u003eRalstonia\u003c/em\u003e was also the dominant taxa in mobile elements container phenotype, but the proportion was only higher in group C than in groups L and F (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ei). On the other hand, the proportion of gram-positive bacteria maintained the lowest abundance in all groups of samples (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ee).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003ePICRUSt2 functional prediction\u003c/h2\u003e\n \u003cp\u003eTo evaluate the functional and metabolic potentials of PL gut microbiota and their possible alteration by BALOs and LAB, PICRUSt analysis was conducted based on the database of COG (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ea) and KEGG. A total of 24 Level 2 COG functions were annotated, except Nuclear structure [Y]. An average of 3139627.22\u0026thinsp;\u0026plusmn;\u0026thinsp;525012.02 Function unknown [S] and 3033662.55\u0026thinsp;\u0026plusmn;\u0026thinsp;535713.66 Amino acid transport and metabolism were the highest predicted functions in all sample of shrimp PL groups. We found the maximum Amino acid transport and metabolism [E] (3827724) and Function unknown [S] (3690139) was on the day 7 in group F. In addition, the predicted functional abundance was higher in most groups such as 2255911.11\u0026thinsp;\u0026plusmn;\u0026thinsp;388555.17 Energy production and conversion [C], 3033662.55\u0026thinsp;\u0026plusmn;\u0026thinsp;535713.66 Amino acid transport and metabolism [E], 767317.11\u0026thinsp;\u0026plusmn;\u0026thinsp;136379.16 Nucleotide transport and metabolism [F] 1843613.44\u0026thinsp;\u0026plusmn;\u0026thinsp;379348 Carbohydrate transport and metabolism [G], 1151164\u0026thinsp;\u0026plusmn;\u0026thinsp;192031.91 Coenzyme transport and metabolism [H], 1646612.11\u0026thinsp;\u0026plusmn;\u0026thinsp;283582.61 Lipid transport and metabolism [I] and 2128881.44\u0026thinsp;\u0026plusmn;\u0026thinsp;372617.18 Inorganic ion transport and metabolism [P], etc., all of which were related to the metabolism of basic life.\u003c/p\u003e\n \u003cp\u003eIn the functional annotations of KEGG, pathways Level 1 (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eb) categorized in six groups viz. Metabolism, Environmental Information Processing, Genetic Information Processing, Function unknown, Cellular Processes, Human Diseases, Organismal Systems. Metabolism (51.17%) was the most overrepresented level 1 pathway across all groups and there were no significant differences among groups. Furthermore, the second highest predicted functional pathway at KEGG Level 1 was Environmental Information Processing (15.72%) followed by Genetic Information Processing (13.80%), Function unknown (13.45%), Cellular Processes (3.72%), Human Diseases (1.30%), Organismal Systems (0.84%) among all shrimp PL groups. In the functional annotations of KEGG, 303 Level 2 KEGG pathways of all samples were predicted, where 237 pathways were classified and 66 pathways were unclassified. Within classified pathways 128 Level 2 KEGG pathways were related to Metabolism where mainly 22 Amino Acids metabolism pathways, 18 Xenobiotics Biodegradation and Metabolism, 15 Biosynthesis of other Secondary Metabolites, 14 Carbohydrate Metabolism pathways, 14 Lipid Metabolism pathways, 12 Metabolism of Cofactors and Vitamins and others. KEGG level 2 predicate functional pathway analysis (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ec) showed initially, the Amino Acids Metabolism was higher in group L than groups F and C. On the day 3, both group C and L decreased but decrease rate was highest in group C than L, however, it was slightly increase in group F. On the day 7, Amino Acids Metabolism rate again increased in group C and F but slightly decreased again in group L.\u003c/p\u003e\n \u003cp\u003eNext, the Spearman\u0026rsquo;s correlation analyse was done between metabolism (Amino Acid Metabolism, Carbohydrate Metabolism, and Lipid Metabolism) most differently distributed Kyoto Encyclopedia of Genes and Genomes (KEGG) level 3 pathways and top 25 bacteria genera with relative abundance\u0026thinsp;\u0026gt;\u0026thinsp;1% were performed. (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ed). The heatmap data showed that the most of the (KEGG) level 3 pathways related to Amino Acid Mmetabolism, Carbohydrate Metabolism, and Lipid Metabolism had significantly positive correlation with four genera \u003cem\u003eBrevundimonas\u003c/em\u003e, \u003cem\u003eFlavobacterium\u003c/em\u003e, \u003cem\u003eMicrobacterium\u003c/em\u003e and \u003cem\u003eParacoccus. Staphylococcus\u003c/em\u003e, \u003cem\u003enorank_f__NS9_marine_group\u003c/em\u003e, \u003cem\u003eTaeseokella\u003c/em\u003e, \u003cem\u003eunclassified_o__Chitinophagales\u003c/em\u003e and \u003cem\u003eNitratireductor\u003c/em\u003e showed significantly positive correlation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) with Lipid Metabolism where \u003cem\u003eAlgoriphagus\u003c/em\u003e, \u003cem\u003eRoseovarius\u003c/em\u003e, \u003cem\u003eVibrio\u003c/em\u003e and \u003cem\u003eYangia\u003c/em\u003e showed significantly negative correlation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) with Lipid Metabolism especially, Fatty Acid Metabolism, Synthesis and Degradation of Ketone Bodies, Linoleic Acid Metabolism, Ether Lipid Metabolism, Steroid Biosynthesis, Primary Bbile Aacid Biosynthesis and Secondary Bile Acid Biosynthesis. On the other hand \u003cem\u003eVibrio\u003c/em\u003e, \u003cem\u003eAlgoriphagus and unclassified_f__Rhodobacteraceaeand\u003c/em\u003e showed significantly positive correlation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) and \u003cem\u003eunclassified_o__Chitinophagales\u003c/em\u003e and \u003cem\u003eAcinetobacter\u003c/em\u003e showed significantly negative correlation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) with Carbohydrate Metabolism in particularly, Amino Sugar and Nucleotide Sugar Metabolism, and Starch and Sucrose Metabolism.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAverage of water quality parameters in groups C, L and F during the experiment\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003eWater quality parameters\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eGroup C\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eGroup L\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eGroup F\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003epH\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e7.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eTemperature (\u0026deg;C)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e24.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e24.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e24.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eNO\u003csub\u003e2\u003c/sub\u003e-N (mg/L)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eNO\u003csub\u003e3\u003c/sub\u003e-N (mg/L)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e18.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.37\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.80\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eNH\u003csub\u003e3\u003c/sub\u003e-N (mg/L)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eDO (mg/L)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eData were shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (n\u0026thinsp;=\u0026thinsp;3). Different letters in the same row indicate significant difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and same letters indicate no difference (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAverage of shrimp PL productive parameters in groups C, L and F during the experiment\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003eProductive parameters\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eGroup C\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eGroup L\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eGroup F\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSR (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e89\u0026thinsp;\u0026plusmn;\u0026thinsp;8.00\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e89\u0026thinsp;\u0026plusmn;\u0026thinsp;3.00\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e95\u0026thinsp;\u0026plusmn;\u0026thinsp;5.00\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eBLi (mm)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e11.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e10.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e11.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eBLf (mm)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e10.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e11.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e11.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eTLG (mm)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026minus;\u0026thinsp;0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003csup\u003eac\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ePTLG (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026minus;\u0026thinsp;5.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003csup\u003ec\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e11.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.00\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.00\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eBWi (g)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.092\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.075\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003ec\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.084\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eBWf (g)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.100\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.104\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.121\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eTWG (g)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.008\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003ec\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.029\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.037\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ePTWG (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e8.10\u0026thinsp;\u0026plusmn;\u0026thinsp;6.74\u003csup\u003ec\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e38.67\u0026thinsp;\u0026plusmn;\u0026thinsp;34.00\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e44.04\u0026thinsp;\u0026plusmn;\u0026thinsp;32.23\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSGR (%/day)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eData were shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (n\u0026thinsp;=\u0026thinsp;3), based on the data of fifteen shrimp PL in each replicate, forty-five PL each group each time in total. Different letters in the same row indicate significant difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas same letters indicate no difference (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Here, SR (survival rate), BLi (denotes initial body length), BLf (final body length), BWi (initial body weight) BWf (final body weight), TLG (total length gain) PTLG (percentage total length gain), TWG (total weight gain), PTWG (percentage total weight gain), SGR (specific growth rate).\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTCBC, TCVC, TCLC and TBC in shrimp PL\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eGroup\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eC\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eL\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eF\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003eTCBC\u003cbr\u003e(log CFU/g)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eDay 0\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e6.61\u0026thinsp;\u0026plusmn;\u0026thinsp;6.65\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e6.50\u0026thinsp;\u0026plusmn;\u0026thinsp;6.31\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e7.64\u0026thinsp;\u0026plusmn;\u0026thinsp;6.85\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eDay 3\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e7.50\u0026thinsp;\u0026plusmn;\u0026thinsp;7.31\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e7.21\u0026thinsp;\u0026plusmn;\u0026thinsp;6.93\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e7.78\u0026thinsp;\u0026plusmn;\u0026thinsp;7.05\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eDay 7\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e6.73\u0026thinsp;\u0026plusmn;\u0026thinsp;5.82\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e7.18\u0026thinsp;\u0026plusmn;\u0026thinsp;7.06\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e6.63\u0026thinsp;\u0026plusmn;\u0026thinsp;5.76\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003eTCVC\u003cbr\u003e(log CFU/g)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eDay 0\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e6.37\u0026thinsp;\u0026plusmn;\u0026thinsp;5.93\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e6.45\u0026thinsp;\u0026plusmn;\u0026thinsp;6.03\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e6.53\u0026thinsp;\u0026plusmn;\u0026thinsp;6.33\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eDay 3\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e6.39\u0026thinsp;\u0026plusmn;\u0026thinsp;5.96\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e7.03\u0026thinsp;\u0026plusmn;\u0026thinsp;7.04\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e6.11\u0026thinsp;\u0026plusmn;\u0026thinsp;5.35\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eDay 7\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e6.07\u0026thinsp;\u0026plusmn;\u0026thinsp;4.96\u003csup\u003eab\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e6.37\u0026thinsp;\u0026plusmn;\u0026thinsp;6.51\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.61\u0026thinsp;\u0026plusmn;\u0026thinsp;5.28\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003eTCLC\u003cbr\u003e(log CFU/g)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eDay 0\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eNAD\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e4.01\u0026thinsp;\u0026plusmn;\u0026thinsp;3.61\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eNAD\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eDay 3\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eNAD\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3.43\u0026thinsp;\u0026plusmn;\u0026thinsp;3.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eNAD\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eDay 7\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eNAD\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.44\u0026thinsp;\u0026plusmn;\u0026thinsp;2.34\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eNAD\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003eTBC\u003cbr\u003e(log PFU/g)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eDay 0\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eNAD\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eNAD\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e4.48\u0026thinsp;\u0026plusmn;\u0026thinsp;4.36\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eDay 3\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eNAD\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eNAD\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e4.39\u0026thinsp;\u0026plusmn;\u0026thinsp;3.88\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eDay 7\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eNAD\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eNAD\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e4.09\u0026thinsp;\u0026plusmn;\u0026thinsp;3.93\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eAll data were shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (n\u0026thinsp;=\u0026thinsp;3). Different letters in the same row indicate significant difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas same letters indicate no difference (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). TCBC denotes total cultivable bacterial counts. TCVC denotes total cultivable \u003cem\u003eVibrio\u003c/em\u003e counts. TCLC denotes total cultivable LAB counts. TBC denotes total BALOs counts. ND denotes not detected.\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eYellow \u003cem\u003eVibrio\u003c/em\u003e, green \u003cem\u003eVibrio\u003c/em\u003e and black colony counts grown on the TCBS plate\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003eColonies on TCBS\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eGroup\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eDay 0\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eDay 3\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eDay 7\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003eYellow\u0026nbsp;\u003cem\u003eVibrio\u003c/em\u003e(log CFU/g)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eC\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e6.06\u0026thinsp;\u0026plusmn;\u0026thinsp;5.94\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e4.54\u0026thinsp;\u0026plusmn;\u0026thinsp;4.31\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.00\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eL\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e6.17\u0026thinsp;\u0026plusmn;\u0026thinsp;5.94\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.55\u0026thinsp;\u0026plusmn;\u0026thinsp;5.70\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.96\u0026thinsp;\u0026plusmn;\u0026thinsp;6.11\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eF\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.62\u0026thinsp;\u0026plusmn;\u0026thinsp;5.51\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.55\u0026thinsp;\u0026plusmn;\u0026thinsp;5.01\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.39\u0026thinsp;\u0026plusmn;\u0026thinsp;5.43\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003eGreen \u003cem\u003eVibrio\u003c/em\u003e\u003cbr\u003e(log CFU/g)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eC\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.93\u0026thinsp;\u0026plusmn;\u0026thinsp;5.73\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e6.12\u0026thinsp;\u0026plusmn;\u0026thinsp;5.86\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e6.07\u0026thinsp;\u0026plusmn;\u0026thinsp;4.96\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eL\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e6.13\u0026thinsp;\u0026plusmn;\u0026thinsp;5.34\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e7.02\u0026thinsp;\u0026plusmn;\u0026thinsp;7.05\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e6.15\u0026thinsp;\u0026plusmn;\u0026thinsp;6.28\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eF\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e6.47\u0026thinsp;\u0026plusmn;\u0026thinsp;6.26\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.97\u0026thinsp;\u0026plusmn;\u0026thinsp;5.21\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.22\u0026thinsp;\u0026plusmn;\u0026thinsp;5.02\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003eBlack colonies\u003cbr\u003e(log CFU/g)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eC\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.64\u0026thinsp;\u0026plusmn;\u0026thinsp;4.29\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.00\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eL\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e5.00\u0026thinsp;\u0026plusmn;\u0026thinsp;5.10\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.00\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eF\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e4.12\u0026thinsp;\u0026plusmn;\u0026thinsp;4.13\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3.35\u0026thinsp;\u0026plusmn;\u0026thinsp;3.50\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.00\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eAll data were shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (n\u0026thinsp;=\u0026thinsp;3). Different letters in the same column indicate significant difference (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas same letters indicate no difference (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDiversity and richness indices relative to each shrimp PL sample in groups C, L and F\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eGroup\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eSequence\u003cbr\u003eNumber\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eOTUs\u003cbr\u003eNumber\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eShannon\u003cbr\u003eIndex\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eSimpson\u003cbr\u003eIndex\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eACE\u003cbr\u003eIndex\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eChao1\u003cbr\u003eIndex\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eCoverage (%)\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003eDay 0\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eC0\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e39100\u0026thinsp;\u0026plusmn;\u0026thinsp;11214\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e227\u0026thinsp;\u0026plusmn;\u0026thinsp;45\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e235.78\u0026thinsp;\u0026plusmn;\u0026thinsp;46.79\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e244.59\u0026thinsp;\u0026plusmn;\u0026thinsp;37.45\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e99.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eL0\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e46412\u0026thinsp;\u0026plusmn;\u0026thinsp;7185\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e195\u0026thinsp;\u0026plusmn;\u0026thinsp;40\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e201.57\u0026thinsp;\u0026plusmn;\u0026thinsp;41.93\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e203.74\u0026thinsp;\u0026plusmn;\u0026thinsp;31.22\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e99.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eF0\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e45296\u0026thinsp;\u0026plusmn;\u0026thinsp;4935\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e205\u0026thinsp;\u0026plusmn;\u0026thinsp;19\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e211.91\u0026thinsp;\u0026plusmn;\u0026thinsp;21.44\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e220.68\u0026thinsp;\u0026plusmn;\u0026thinsp;17.86\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e99.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003eDay 3\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eC3\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e41303\u0026thinsp;\u0026plusmn;\u0026thinsp;4752\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e151\u0026thinsp;\u0026plusmn;\u0026thinsp;104\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e1.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24\u003csup\u003ec\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e162.91\u0026thinsp;\u0026plusmn;\u0026thinsp;111.31\u003csup\u003ec\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e169.32\u0026thinsp;\u0026plusmn;\u0026thinsp;95.34\u003csup\u003ec\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e99.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eL3\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e46092\u0026thinsp;\u0026plusmn;\u0026thinsp;3274\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e305\u0026thinsp;\u0026plusmn;\u0026thinsp;63\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e312.94\u0026thinsp;\u0026plusmn;\u0026thinsp;59.21\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e318.71\u0026thinsp;\u0026plusmn;\u0026thinsp;48.06\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e99.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eF3\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e50595\u0026thinsp;\u0026plusmn;\u0026thinsp;2980\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e441\u0026thinsp;\u0026plusmn;\u0026thinsp;263\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003ec\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e451.04\u0026thinsp;\u0026plusmn;\u0026thinsp;263.91\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e467.74\u0026thinsp;\u0026plusmn;\u0026thinsp;213.63\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e99.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003eDay 7\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eC7\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e41801\u0026thinsp;\u0026plusmn;\u0026thinsp;9055\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e254\u0026thinsp;\u0026plusmn;\u0026thinsp;113\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e259.35\u0026thinsp;\u0026plusmn;\u0026thinsp;113.74\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e281.07\u0026thinsp;\u0026plusmn;\u0026thinsp;92.07\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e99.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eL7\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e39948\u0026thinsp;\u0026plusmn;\u0026thinsp;2968\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e186\u0026thinsp;\u0026plusmn;\u0026thinsp;24\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e2.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003csup\u003ec\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e201.24\u0026thinsp;\u0026plusmn;\u0026thinsp;18.71\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e231.64\u0026thinsp;\u0026plusmn;\u0026thinsp;34.09\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e99.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eF7\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e44150\u0026thinsp;\u0026plusmn;\u0026thinsp;11883\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e241\u0026thinsp;\u0026plusmn;\u0026thinsp;24\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e3.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003eb\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e246.81\u0026thinsp;\u0026plusmn;\u0026thinsp;16.95\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e261.73\u0026thinsp;\u0026plusmn;\u0026thinsp;91.45\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"char\"\u003e99.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eResults were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (n\u0026thinsp;=\u0026thinsp;3);Different letters in the same column at the same day indicate significant difference (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas same letters indicate no difference (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eBALOs strain and its cultivation was adopted from Cao et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), as given below\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ch2\u003ePreparation of host strain\u003c/h2\u003e\n\u003cp\u003eGram negative \u003cem\u003eCitrobacter amanolaticus\u003c/em\u003e strain TC (GenBank accession number, MN956654) was isolated from salty water and used as a host for propagating BALOs. It was proven to be non-haemolytic (data not shown). Strain TC was grown in nutrient broth (NB: Guangdong Huankai Biotechnology Co., Ltd) for 13\u0026ndash;15 h at 30\u0026deg;C with shaking at 200 rev/min (rpm) to reach the late exponential phase. Then it was harvested by centrifugation at 5,000 rpm for 10 min at 4\u0026deg;C and resuspended with sterile phosphate buffered saline (PBS: 28 mmol/L NaH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e, 72 mmol/L Na\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e, pH 7.2) to the final concentration 1 \u0026times; 10\u003csup\u003e10\u003c/sup\u003e CFU/mL. Stored at 4\u0026deg;C before use.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreparation of\u003c/strong\u003e \u003cstrong\u003eBdellovibrionales\u003c/strong\u003e \u003cstrong\u003estrain BDN-1F2 (BALOs) at free swimming stage\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBDN-1F2 was a mutant of wild type BDN-1 after Co\u003csup\u003e60\u003c/sup\u003e mutagenesis (data not shown). Wild type BDN-1 was identified as a strain of \u003cem\u003eBdellovibrionales\u003c/em\u003e (GenBank accession number, MK159102) which is closely related to \u003cem\u003eBdellovibrionales\u003c/em\u003e strain BDSH06 (GenBank accession number, EF011103) (Chen \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). BDN-1F2 was kept as plaques on 15\u0026permil; DNB (Dilute Nutrient Broth: 0.8 g nutrient broth, 0.5 g casein hydrolysate, 0.1 g yeast extract, 15 g NaCl, 15g agar, 1 L distilled water, and pH 7.2) double-layer agar plate at 4\u0026deg;C before use. A single plaque was picked up from a freshly grown double-layer agar plate with a sterile inoculation loop and inoculated into an Erlenmeyer flask that contained 50 ml of DNB liquid medium and 1 mL of suspended host strain (Merrifield et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) and kept for shaking at 200 rpm for 72 h at 28\u0026deg;C. Next, the remnant hosts were pelleted to 5,000 rpm for 20 minutes at 4\u0026deg;C, and the supernant was filtered through a 0.45-\u0026micro;m-pore-shaped membrane filter to release it from the remaining hosts and debris. Then the filtrate was centrifuged at 16,000 rpm and pellets were resuspended with sterile PBS to achieve a final concentration 7.16 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e plaque forming units (PFU)/mL. Kept at 4\u0026deg;C before use.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreparation of\u003c/strong\u003e \u003cstrong\u003eLactobacillus salivarius\u003c/strong\u003e \u003cstrong\u003estrain GZPH2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLa. salivarius\u003c/em\u003e strain GZPH2 was isolated from commercially available pickles in Guangzhou, patterned and deposited at the China Type Culture Collection (CCTCC) with the deposit number CCTCC M 2014598. It was proven to be non-haemolytic ability (data not shown). GZPH2 was grown in 50 mL MRS broth (De Man\u0026ndash;Rogosa\u0026ndash;Sharpe agar/MRS: 20 g peptone, 1 g yeast extract, 1 gsodium acetate, 0.4g Di-ammonium hydrogen citrate, 0.04 g manganese sulphate, 3 g monosodium glutamate (MSG), 8 g beef extract, 20 g dextrose, 2 g Di-potassium hydrogen phosphate, 0.2 g magnesium sulphate hepthydrate, 1 ml tween 80, 15g NaCl, 1 L distilled water, and pH 6.5\u0026ndash;6.7) for 24 hours with shaking at 200 rpm at 37\u0026deg;C. To determine its concentration, GZPH2 culture was diluted in a ten-fold series dilution and the appropriate dilutions were spread on MRS agar plates. After the advent of colonies, plates numbering colonies between 30 and 300 were counted and, and expressed as colony forming units (CFU)/mL.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003ePL shrimp rearing experiment\u003c/h2\u003e\n \u003cp\u003eThe PL test was conducted for 7 days in the laboratory at around 28\u0026deg;C following a procedure similar to Cao et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), as given below:\u003c/p\u003e\n \u003cp\u003eBriefly, Preparation for saltwater of 15\u0026permil; in salinity, 15 g salt (NaCl) was dissolved in one liter of tap water, then the mixture was boiled for 5 min to reduce possible microbes/\u003cem\u003eVibrios\u003c/em\u003e contamination from water, and normally cooled it at room temperature. After that, the saline water was aerated and dissolved oxygen (DO) concentration was brought back to 5 ppm or above with an air pump fitted with 0.22 \u0026micro;m air-sterilization filter. The PL shrimp was cultured using nine plastic tanks with capacity of eight liters of water. The plastic cultured tanks were disinfected with 0.1% KMnO\u003csub\u003e4\u003c/sub\u003e and thoroughly rinsed with sterile saltwater. Then, four liters of prepared saline water was poured to each tank. Throughout the duration of the experiment, the aeration was done by placing two air-stones in each tank, with 0.22 \u0026micro;m membrane filter to filter out any possible bacterial contaminants in the aeration process. Postlarval shrimp (\u003cem\u003eLi. vannamei\u003c/em\u003e) of PL7-8 were collected from a shrimp hatchery in Guangdong province. They were first-generation PL shrimp and maintained at 30\u0026permil; salinity. Prior to packing, the salinity was lowered from 30\u0026permil; to 15\u0026permil; to meet our experimental need. The PL7-8 were visually healthy without any apparent signs of diseases. After acclimatization, in total 585 PL were randomly divided into 3 groups: group C (control without any treatment), group L (treatment with \u003cem\u003eLa. salivarius\u003c/em\u003e strain GZPH2), and group F (treatment with BALOs species \u003cem\u003eBdellovibrionales\u003c/em\u003e strain BDN-1F2) with three replicates and stocked 65 PL into each tanks. Three tanks were considered as test tanks group F with the addition of free swimming BDN-1F2 directly once to water to a final concentration of appropriately 1 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e PFU/mL at the start of the test. Three tanks were considered as test tanks group L, where each time feeding 1 mL GZPH2 culture (10\u003csup\u003e8\u003c/sup\u003e CFU/mL) GZPH2 was mixed with shrimp feed to a final concentration of approximately 5 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e CFU/mL in rearing water. The rest were used as controls (group c) with no BDN-1F2 or GZPH2 addition. During the experimental period, PL were fed three times a day, with 0.5 mg shrimp flakes (a powder form high-protein diet contained 45% protein, 6% fat, 5% calcium, 1.2% phosphate, 1.4% lysine, 8% water, 16% ash, 3% crude fiber) per 10 shrimp each time. During the 7-day PL culture period, tank water was not exchanged. Water samples were taken at day 0, 3, 5 and 7 to check the water parameters. Shrimp samples were taken at day 0, 3 and 7 for PL growth parameters and gut microbiota analysis. The number of dead shrimp was recorded and the survival rates were calculated at the end of the test.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eWater quality parameters and PL growth parameters\u003c/h2\u003e\n \u003cp\u003eDuring the test, pH, water temperature and DO level were directly measured every morning. Then, 100 mL water sample was taken for assaying nitrite (NO2-N), nitrate (NO3-N), ammonia-N (NH3_N ) with Zerui reagent test kit (Zerui Chemistry Technology, Shanghai, China). On PL sampling day (days 0, 3, 7), randomly 15 PL were collected from each tank, with water on shrimp surface blotted with sterile filter paper. Body length (BL) of PL was measured, one by one, with electronic vernier calipers (accuracy 0.1 mm ), and body weight (BW) was weighed together with an electronic analytical balance (min. 0.001 g), as they were too light to be weighed individually. PL survival rate (SR), total length gain (TLG), percentage total length gain (PTLG, %), total weight gain (TWG), percentage total weight gain (PTWG, %), and specific growth rate (SGR) were calculated at the end of the test, according to the following formulae:\u003c/p\u003e\n \u003cp\u003eSR (%)\u0026thinsp;=\u0026thinsp;100 \u0026times; N\u003csub\u003ei\u003c/sub\u003e ∕ N\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003eTLG (mm)\u0026thinsp;=\u0026thinsp;L\u003csub\u003ei\u003c/sub\u003e \u0026minus; L\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003ePTLG (%)\u0026thinsp;=\u0026thinsp;100 \u0026times; (L\u003csub\u003ei\u003c/sub\u003e \u0026minus; L\u003csub\u003e0\u003c/sub\u003e) ∕ L\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003eTWG (mg)\u0026thinsp;=\u0026thinsp;W\u003csub\u003ei\u003c/sub\u003e \u0026minus; W\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003ePTWG (%)\u0026thinsp;=\u0026thinsp;100 \u0026times; (W\u003csub\u003ei\u003c/sub\u003e \u0026minus; W\u003csub\u003e0\u003c/sub\u003e) ∕W\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003eSGR (%/day)\u0026thinsp;=\u0026thinsp;100 \u0026times; (W\u003csub\u003ei\u003c/sub\u003e \u0026minus; W\u003csub\u003e0\u003c/sub\u003e) ∕Number of test days\u003c/p\u003e\n \u003cp\u003eWhere, N\u003csub\u003ei\u003c/sub\u003e is the total number of PL alive at the end of test, N\u003csub\u003e0\u003c/sub\u003e is the total number of PL at the start of the test; L\u003csub\u003ei\u003c/sub\u003e is the PL final mean body length, L\u003csub\u003e0\u003c/sub\u003e is the PL initial mean body length; W\u003csub\u003ei\u003c/sub\u003e is the PL final mean body weight, W\u003csub\u003e0\u003c/sub\u003e is the PL initial mean body weight.)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eConventional bacterial plate counts\u003c/h2\u003e\n \u003cp\u003eAfter the measurement of BL and BW, various cultivable bacterial counts were assayed. In each group, all 15 shrimp were weighed together, but rinsed separately, first with 75% ethanol, then with sterile distilled water to remove possible bacteria on body surfaces (Zheng et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). As PL7-8 shrimp were very tiny, it was practically impossible to dissect for the intestines. Hence, the bodies of 15 PL in each replicate/sample were homogenized altogether using a sterile grinder with 1 mL sterile PBS (pH 7.2), then divided into two parts, one for traditional bacteriological analysis as specified below and the other for 16S high-throughput sequencing and stored at -20\u0026deg;C before use. Bacterial enumeration was performed in triplicate. Total cultivable bacterial counts (TCBC) and total cultivable \u003cem\u003eVibrio\u003c/em\u003e counts (TCVC) were obtained by spread plate method after incubation at 28\u0026deg;C for 24 h. Total cultivable lactic acid bacteria (LAB) counts (TCLC) were also done by spread plate method by incubation at 37\u0026deg;C for 72 h. For TCBC counts, marine 2216E (5 g peptone, 1 g yeast extract, 0.01 g ferric phosphate, 15 g sodium chloride, 15 g agar, 1 L distilled water, pH 7.6\u0026ndash;7.8) was used. For TCVC counts, Thiosulfate Citrate Bile Salts medium (TCBS: Guangdong Huankai Biotechnology Co., Ltd) of 15\u0026permil; in salinity was used. For TCLC counts MRS medium with 1.5% agar in a Petri dish was used. A series of 10-fold (10\u003csup\u003e0\u003c/sup\u003e, 10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, \u0026hellip;) dilutions were made with sterile 15\u0026permil; saltwater and 0.1 mL each dilution was spread onto appropriate culture medium, those plates having 30\u0026ndash;300 colonies were counted, expressed as CFU/mg for PL shrimp samples. For total BALOs counts (TBC), double-layer plating technique was used, viz., 500 \u0026micro;L appropriately diluted sample and 500 \u0026micro;L of the host (strain TC) suspension (1 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e CFU/mL) were mixed with 3 mL of liquefied overlay agar (DNB medium containing 0.8% agar) that was kept in a thermostatic water bath at 50\u0026deg;C. The mixture was briefly vortexed to mix before being poured over the surface of a bottom layer agar plate containing DNB medium with 1.5% agar in a Petri dish (90 mm in diameter). Plates were incubated at 28\u0026deg;C for 3\u0026ndash;5 days until clear circular plaques appeared. Each plaque was counted as PFU, expressing as PFU/mg for PL samples.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eDNA extraction and PCR amplification\u003c/h2\u003e\n \u003cp\u003eMicrobial community genomic DNA was extracted from homogenized samples using the E.Z.N.A.\u0026reg; soil DNA Kit (Omega Bio-tek, Norcross, GA, U.S.) according to manufacturer\u0026rsquo;s instructions. DNA extract was checked on 1% agarose gel, and DNA concentration and purity were determined with NanoDrop 2000 UV-vis spectrophotometer (Thermo Scientific, Wilmington, USA). Sample named with group name and combination of collection day i.e., day 0 (samples C0, L0 and F0 for groups C, L and F, respectively), day 3 (samples C3, L3 and F3 for groups C, L and F, respectively) and day 7 (samples C7, L7 and F7 for groups C, L and F, respectively). All sampling was done in triplicates. Afterwards, genomic DNA was used as templates for PCR to amplify the hypervariable V3-V4 region of the bacterial 16S ribosomal RNA gene with primer pairs 338F 5\u0026apos;-ACTCCTACGGGAGGCAGCAG-3\u0026apos; and 806R 5\u0026apos;-GGACTACHVGGGTWTCTAAT-3\u0026apos; by an ABI GeneAmp\u0026reg; 9700 PCR thermocycler (ABI, CA, USA). PCR amplification contained template DNA 10 ng, 5 \u0026times; FastPfu Buffer 4 \u0026micro;L, 2.5 mM dNTPs 2 \u0026micro;L, forward primer (5 \u0026micro;M) 0.8 \u0026micro;L, reverse primer (5 \u0026micro;M) 0.8 \u0026micro;L, TransStart FastPfu DNA Polymerase 0.4 \u0026micro;L, BSA 0.2 \u0026micro;L and finally added ddH\u003csub\u003e2\u003c/sub\u003eO up to 20 \u0026micro;L. PCR amplification was performed as follows: initial denaturation at 95\u0026deg;C for 3 min, followed by 28 cycles of denaturing at 95\u0026deg;C for 30 s, annealing at 55\u0026deg;C for 30 s and extension at 72\u0026deg;C for 45 s, and single extension at 72\u0026deg;C for 10 min, and end at 4\u0026deg;C. PCR reactions were performed in triplicate and mix 3 replicate PCR products together. PCR products were checked with 2% agarose gel and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to manufacturer\u0026rsquo;s instructions and quantified using Quantus\u0026trade; Fluorometer (Promega,Wisconsin, USA).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eIllumina MiSeq sequencing\u003c/h2\u003e\n \u003cp\u003eConstruction of Miseq library using NEXTFLEX Rapid DNA-Seq Kit (Bioo Scientific, Texas, USA). Purified amplicons were pooled in equimolar and paired-end sequenced (2 \u0026times; 300) on an Illumina MiSeq platform (Illumina, San Diego, USA) according to the standard protocols by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003eData analysis\u003c/h2\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eResults were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation from triplicate experiments. Statistically significant differences were determined by one-way ANOVA, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and Tukey tests was used to evaluate the significance level using IBM SPSS statistical software version 26 ( New York, USA). Spearman correlation coefficient analyses, heatmap and histogram were performed with IBM SPSS statistical software version 26 (New York, USA) and R package software.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003eProcessing of sequencing data and analysis\u003c/h2\u003e\n \u003cp\u003eThe raw 16S rRNA gene sequencing reads were de-multiplexed, quality-filtered by Trimmomatic and merged by FLASH with the following criteria: (i) 300 bp reads were truncated at any site receiving an average quality score of \u0026lt;\u0026thinsp;20 over a 50 bp sliding window, and the truncated reads shorter than 50 bp were discarded, reads containing ambiguous characters were also discarded; (ii) only overlapping sequences longer than 10 bp were assembled according to their overlapped sequence. The maximum mismatch ratio of overlap region is 0.2. Reads that could not be assembled were discarded; (iii) samples were distinguished according to the barcode and primers, and the sequence direction was adjusted, exact barcode matching, 2 nucleotide mismatch in primer matching. Operational taxonomic units (OTUs) with 97% similarity cutoff value were clustered using UPARSE (version 7.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://drive5.com/uparse/\u003c/span\u003e\u003c/span\u003e), and chimeric sequences were identified and removed. The taxonomy of each OTU representative sequence was analyzed by RDP Classifier (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rdp.cme.msu.edu/\u003c/span\u003e\u003c/span\u003e) against the SILVA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.arb-silva.de/\u003c/span\u003e\u003c/span\u003e) using confidence threshold of 70%. Alpha diversity, species composition and interactions analysis at different taxonomic levels were based on the homogenized OTU table. Alpha diversity reflects taxonomic richness and community diversities, including sequencing depth index (Coverage), ACE index, Chao 1 index, Shannon index and Simpson index. Alpha diversity and species composition analysis were analyzed using QIIME 1.9.1. Non-metric multidimensional scaling analysis (NMDS) by using software QIIME (2020.2.0) calculated the beta diversity distance matrix at the OTU level, and the R vegan (2.4.3) software package performs NMDS analysis and mapping. Species composition analysis reflects dominant species and their relative abundance. Species and community composition analyses are represented by venn, circos and bar plots. The venn diagram used to evaluate the unique and shared species in among three groups of all samples by using R (version 3.3.1) tools for statistics and graphing. The circos sample-species relationship circle chart was drew by using circos-0.67-7 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://circos.ca/\u003c/span\u003e\u003c/span\u003e) to know the distribution proportion of dominant species in each sample as well as the distribution proportion of each dominant species in different samples. The bar diagram of the gut microbial community drew to know two aspects of information: (1) what types of microorganisms are contained in each sample at the taxonomic level; (2) the relative abundance of each microorganism in the sample, to understand the composition of the community structure of different samples at each taxonomic level. In order to identify the gut microbial communities with significant differences in relative abundance of species among the all PL samples, Kruskal-Wallis H test and Tukey-Kramer tests was used to evaluate the significance level. The presence of different species among different PL groups were analyzed by the Linear discriminant analysis (LDA) effect size (LEfSe), logarithmic LDA score of 2 and used the nonparametric factorial Kruskal\u0026ndash;Wallis (KW) sum-rank test to identify the most differently abundant species by using LEfSe software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://huttenhower.sph.harvard.edu/galaxy/root?tool_id=lefse_upload\u003c/span\u003e\u003c/span\u003e). Single-factor network analysis was performed using R igraph package (version 3.2) and networkx software with Kamada-Kawai algorithm. The top 50 species in total abundance at genus level data were selected, and the correlation coefficients such as Spearman rank between species were calculated to reflect the correlation between species. By default, species with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were shown.\u003c/p\u003e\n \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\n \u003ch2\u003eFunctional analysis\u003c/h2\u003e\n \u003cp\u003ePrediction of microbial phenotypes present in microbiome sample were annotated by BugBase functional prediction platforms (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bugbase.cs.umn.edu/index.html\u003c/span\u003e\u003c/span\u003e). BugBase first normalizes the OUT by the predicted 16S rRNA copy number and then predicts the microbial phenotype using the pre-computed file provided. The phenotypic types include Gram positive, Gram negative, biofilms forming, potentially pathogenic, mobile element containing, oxygen utilizing (including aerobic, anaerobic, facultatively anaerobic) and oxidative stress tolerance. Prediction of microbial metabolic functions was performed using prediction tool, viz., the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) software package (version 1.0.0). Firstly, the OTU abundance table was standardized by PICRUSt (the PICRUSt process stored the COG information and KO information corresponding to the greengene id), that is, the effect of the number of copies of the 16S marker gene in the species genome was removed. Then, the COG family information and KEGG Ortholog (KO) information corresponding to the OTU were obtained through the green gene id corresponding to each OTU. The abundance and KO abundance of each COG were calculated. According to the information of the COG database, the description information of each COG and its functional information parsed from the eggNOG database to obtain the functional abundance spectrum. Based on the information in the KEGG database, KO and Pathway information obtained, and the abundance of each functional classes calculated based on the OTU abundance. In addition, for the pathway, PICRUSt used to obtain the information of three levels of the metabolic pathway, and the abundance table of each level obtained separately. However, the bar plot of predicted functions on the basis of COG with PICRUSt and the relative abundance different types of metabolism (KEGG pathway level 2) of bacterial communities using PICRUSt2 analysis of PL samples was drawn by using SRPLOT (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.bioinformatics.com.cn\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSince the dawn of probiotic concept in aquaculture (Gatesoupe \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e), its definition has been expanded as such \u0026ldquo;A probiotic can be seen as a live, dead or component of a microbial cell, which is administered via the feed or to the rearing water, benefiting the host by improving disease resistance, health status, growth performance, feed utilization, stress response or general vigour, which is achieved via improving the hosts microbial balance or the microbial balance of the ambient environment\u0026rdquo; (Hai et al. 2015; Merrifield et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). In line with this definition, it is clear that \u003cem\u003eLa. salivarius\u003c/em\u003e strain GZPH2 and \u003cem\u003eBdellovibrionales\u003c/em\u003e strain BDN-1F2 PL can be recognized as probiotics as they have demonstrated beneficial effects by significantly improving white-leg shrimp postlarvae growth performances, albeit at different aspects and degrees between them (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Compared to control, with the exception of SR that GZPH2 showed no improvement, it had significantly better effects in all other aspects related to growth performance, i.e., PTLG, TLG, PTWG, TWG, SGR, with the latter being 2.82 times higher (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, when compared to BDN-1F2, GZPH2 showed a better effect only in PTLG and TLG, while in all other aspects, including PTWG, TWG, SGR and SR, BDN-1F2 performed significantly better (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Consistent with our current findings, Nguyen et al. (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) revealed that \u003cem\u003eLactobacillus\u003c/em\u003e strain has positive effects on the growth and resistance of \u003cem\u003eLi. vannamei\u003c/em\u003e against \u003cem\u003eV. parahaemolyticus\u003c/em\u003e which causes acute hepatopancreatic necrosis disease (AHPND). Another study also showed that the combination of \u003cem\u003eLa. salivarius\u003c/em\u003e BGHO1/\u003cem\u003eLa. reuteri\u003c/em\u003e BGGO6-55 had a positive effect on juvenile pike-perch (\u003cem\u003eSander lucioperca\u003c/em\u003e) growth, while improved survival (Ljubobratovic et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). On the other hand, some studies reveled that BALOs also showed better effect on shrimp growth performance. A previous study was done by Li et al. (\u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) where, BDHSH06 had been applied to an 85-day rearing of black tiger shrimp (\u003cem\u003ePenaeus monodon\u003c/em\u003e) and was found to significantly enhance its growth and survival and alter bacterial community structures in its rearing water. With the addition of BDHSH06, total bacterial and \u003cem\u003eVibrio\u003c/em\u003e numbers were significantly reduced (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) by 1.3 to 4.5 log CFU/mL and CFU/g in both water and shrimp intestines, respectively, compared to those in the control. Similarly, Wen et al. (\u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) \u003cstrong\u003es\u003c/strong\u003ehown that \u003cem\u003eBacteriovorax\u003c/em\u003e DA5 has positive effect on white shrimp (\u003cem\u003eLi. vannamei\u003c/em\u003e) and it significantly improved the survival rate and metamorphic rates by controlling vibriosis.\u003c/p\u003e\n\u003cp\u003eBiodiversity is generally recognized as a main determinant of ecosystem functioning (Johnke et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), which could be better reflected by Shannon index values (Chen et al. 2015). It is generally recognized that a healthier and more robust microbial community has a higher biodiversity (and thus Shannon index) than an unhealthy one (Rungrassamee et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Chen et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). To distinguish between healthy and unhealthy shrimp, the gut microbiota of shrimp at PL7-15, a value above 2.0 for a healthy state, was tentatively proposed, while below this value, it was considered an unhealthy state (Cao et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Based on this criterion, it is obvious that at the start of the test, PL in all groups were in a healthy state, as their Shannon index values were well above 2.0 (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). However, at day 3, this value in group C dropped to 1.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24, indicating a not-so-healthy state even though it recovered to 3.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28 at day 7 (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). In groups L and F, PL gut microbiota stayed in a healthy state throughout the test period, albeit with the highest values at day 3. Within these two groups, group F had higher Shannon index values than group L, implying higher diversities, and more robust and healthier gut microbiota (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The reduction of Shannon index in control at day 3 could be due to the effect of salinity changes Cao et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) as PL were maintained at 30\u0026permil; seawater in the shrimp hatchery and its salinity was lowered to 15\u0026permil; prior to packing. Meanwhile, the rise of Shannon index in groups L and F at day 3 once again demonstrated the protective effect of BDN-1F2 and GZPH2 in counteracting the unfavorable impacts brought about by the salinity changes. As gut microbiota serves as a virtual endocrine organ (Clarke et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) and are known to promote juvenile growth, development and survival in \u003cem\u003eDrosophila melanogaster\u003c/em\u003e (Erkosar et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), an unhealthy state would certainly undermine its growth. This is exactly the case in this study as PL grew most slowly in control (SGR at 0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10%/day), and fastest in group F (SGR at 0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40%/day), with group L in between (SGR at 0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21% /day) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIt is generally recognized that the growth of host is strongly associated with the gut microbiota (Tarnecki et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Previous studies have proven that the ratio of \u003cem\u003eBacteroidetes\u003c/em\u003e vs. \u003cem\u003eFirmicutes\u003c/em\u003e (B/F ratio) is a growth indicator, the higher growth rate with the lower ratio (Jia \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wang et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e ). Here, it is evident that PL in groups F and L would grow faster than those in control as B/F ratios at days 0 and 3 in groups L and F were 1.04 and 1.70; and 1.12 and 1.31; respectively, both were lower than those in control (days 0 and 3, ratios of 2.63 and 19.14, respectively), albeit at day 7, B/F ratio in control (1.22) was lower than those in groups L (6.68) and F (8.39) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ec).\u003c/p\u003e\n\u003cp\u003eSimilar to others\u0026rsquo; findings Cao et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), \u003cem\u003eProteobacteria\u003c/em\u003e was a major component in PL gut microbiota in all groups, ranging from 69.35\u0026thinsp;\u0026plusmn;\u0026thinsp;8.91% to 85.92\u0026thinsp;\u0026plusmn;\u0026thinsp;3.17% (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ec). Nevertheless, if we look into the two dominant and mutually antagonistic groupings in \u003cem\u003eProteobacteria\u003c/em\u003e, viz., \u003cem\u003eGammaproteobacteria\u003c/em\u003e and \u003cem\u003eAlphaproteobacteria\u003c/em\u003e, the effects of BDN-1F2 and GZPH2 are clear albeit different. BDN-1F2 in group F reduced \u003cem\u003eGammaproteobacteria\u003c/em\u003e relative abundance by 32.21% while increased \u003cem\u003eAlphaproteobacteria\u003c/em\u003e relative abundance by 1.11 times, GZPH2 in group L simultaneously increased the relative abundances of both \u003cem\u003eGammaproteobacteria\u003c/em\u003e and \u003cem\u003eAlphaproteobacteria\u003c/em\u003e, by 22.30% and by 28.86% respectively; in control, the relative abundance of \u003cem\u003eGammaproteobacteria\u003c/em\u003e first increased by 46.71% then decreased by 25.70% while its \u003cem\u003eAlphaproteobacteria\u003c/em\u003e relative abundance was first reduced by 50.80% and then increased by 44.54%. As G\u003cem\u003eammaproteobacteria\u003c/em\u003e is generally recognized to be associated with diseased or retarded growth organisms, including shrimps (Xiong et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), and \u003cem\u003eAlphaproteobacteria\u003c/em\u003e with healthy or normal/faster growth shrimps (Chen et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), it is once again natural that PL in control grew much slower than those in groups F and L. Meanwhile, it seems that while BDN-1F2 possess healthy capabilities to reduce \u003cem\u003eGammaproteobacteria\u003c/em\u003e while promoting \u003cem\u003eAlphaproteobacteria\u003c/em\u003e, the ability of GZPH2 to contain \u003cem\u003eGammaproteobacteria\u003c/em\u003e and to promote \u003cem\u003eAlphaproteobacteria\u003c/em\u003e is both weaker.\u003c/p\u003e\n\u003cp\u003eWithin the class \u003cem\u003eGammaproteobacteria\u003c/em\u003e, \u003cem\u003eVibrionales, Pseudomonadales\u003c/em\u003eand and \u003cem\u003eBetaproteobacteriales\u003c/em\u003e, were the dominant orders (a relative abundance\u0026thinsp;\u0026ge;\u0026thinsp;5% at a time) (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Once again, the effects of BDN-1F2 and GZPH2 on these orders were evident. With regard to order \u003cem\u003eVibrionales\u003c/em\u003e, especially genera \u003cem\u003eVibrio\u003c/em\u003e (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), it seems that GZPH2 did not exert reduction effect on it, as \u003cem\u003eVibrio\u003c/em\u003e increment was higher in group L than in groups C and F at day 3, and reaching higher relative abundance at the end of the test. On the other hand TCBS plate counting results also showed, TCVC in PL was lower in group F than in groups C and L (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), whereas green \u003cem\u003eVibrio\u003c/em\u003e counts in PL was significantly lower in group F than in groups C and L (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). It previously confirmed by Chen et al. (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) that BDN-1F2, a mutant of wild type BDN-1 after Co\u003csup\u003e60\u003c/sup\u003e mutagenesi could lyse 27 out of 30 tested bacteria, including 9 strains of \u003cem\u003eV. alginolyticus\u003c/em\u003e, 3 strains of \u003cem\u003eV. parahaemolyticus\u003c/em\u003e, 4 strains of \u003cem\u003eV. cholerae\u003c/em\u003e, 11 strains of \u003cem\u003ePseudomonas\u003c/em\u003e sp., with 93.3% lysis rate on 16 strains of vibrio. We can conclude that because of lysis ability of BDN-1F2, the number of \u003cem\u003eVibrio\u003c/em\u003e in group BDN-1F2 was lower than in groups control and GZPH2. Recently, Yang et al. (2023) reveled that \u003cem\u003eBdellovibrio\u003c/em\u003e sp. exhibited a certain lysis effect on the selected aquatic pathogens including \u003cem\u003eVibrio fluvialis\u003c/em\u003e, \u003cem\u003eVibrio anguillarum\u003c/em\u003e, \u003cem\u003eVibrio cholerae\u003c/em\u003e and significantly reduce the mortality rate of \u003cem\u003eCarassius auratus\u003c/em\u003e caused by the infections with \u003cem\u003eA. vironii.\u003c/em\u003e Even we also found significantly negative correlation between survival rate with \u003cem\u003eVibrio\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIt is generally recognized that gut microbes are highly diverse and their interactions is very complex, which basically depend on microbial biodiversity and environmental factor in gut. However, explaining microbial interactions is challenging and largely dependent on correlation-based network analysis (Milici et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). In microbial interactions network has three types of centrality metrics viz. degree centrality, closeness centrality and betweenness centrality. Among the centrality metrics, betweenness centrality measures the extent to which a given nodes/species is located within the shortest paths between other pairs of nodes/species in a network (Brandes \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e) which specifically used to explore organisms relation with broad host or partner ranges (Toju et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). The genus with higher betweenness values were considered as keystone species in the genus network diagram (Vinothkumar et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this study, the microbial interactions network (Figs. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea-c; Table S2) data revealed that betweenness value of \u003cem\u003eVibrio\u003c/em\u003e was highest in group L (0.05056) than group C (0.00914) and F (0.00401). The microbial correlation network (Figs. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea-c; Table S2) data also showed that \u003cem\u003eVibrio\u003c/em\u003e was positively correlated (coefficient\u0026thinsp;\u0026ge;\u0026thinsp;0.5) to \u003cem\u003eYangia\u003c/em\u003e and \u003cem\u003eunclassified_f__Rhodobacteraceae\u003c/em\u003e and negatively correlated (coefficient\u0026thinsp;\u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;0.5) to \u003cem\u003eunclassified_o__Chitinophagales\u003c/em\u003e, \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003eFlavobacterium\u003c/em\u003e, \u003cem\u003eStaphylococcus\u003c/em\u003e and \u003cem\u003eAcinetobacter\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea-c). On the other hand, the relative abundance of \u003cem\u003eYangia\u003c/em\u003e increased in group L than group F, whereas \u003cem\u003eunclassified_o__Chitinophagales\u003c/em\u003e, \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003eFlavobacterium\u003c/em\u003e, \u003cem\u003eStaphylococcus\u003c/em\u003e and \u003cem\u003eAcinetobacter\u003c/em\u003e decreased. As a result, from day 0 to day 7 in both group L, where \u003cem\u003eAcinetobacter\u003c/em\u003e along with \u003cem\u003eStaphylococcus\u003c/em\u003e decreased but \u003cem\u003eVibrio\u003c/em\u003e and \u003cem\u003eYangia\u003c/em\u003e increased. This result reveled that BDN-1F2 was better than GZPH2 to control \u003cem\u003eVibrio\u003c/em\u003e species in PL rearing. Several result support our this report that BALOs can control \u003cem\u003evibrio\u003c/em\u003e such as \u003cem\u003eVibrio\u003c/em\u003e sp. (Li et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e), V. \u003cem\u003eCholerae\u003c/em\u003e (Cao et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), V. \u003cem\u003eparahaemolyticus\u003c/em\u003e (Cheng et al. \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eRegarding \u003cem\u003ePseudomonadales\u003c/em\u003e where \u003cem\u003eAcinetobacter\u003c/em\u003e and \u003cem\u003ePseudomonas\u003c/em\u003e were the dominant genera (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), still, BDN-1F2 showed better reduction effect (77.16% reduction) than GZPH2 (50.15%) as compared to control (55.64% reduction), leaving the relative abundance of \u003cem\u003ePseudomonadales\u003c/em\u003e at 7.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75%, 12.07\u0026thinsp;\u0026plusmn;\u0026thinsp;7.47% and 8.49\u0026thinsp;\u0026plusmn;\u0026thinsp;4.39%, respectively, at the end of the test. While the relative abundance of \u003cem\u003eBetaproteobacteriales\u003c/em\u003e was reduced in groups L (30.97%) and F (23.16%); in control, it first increased by 36.01% and then again decreased by 14.79% at the end of the test. Within the order \u003cem\u003eBetaproteobacteriales\u003c/em\u003e, \u003cem\u003eRalstonia\u003c/em\u003e, found as a dominant genera in control (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The microbial interactions network (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea-c; Table S2) showed that \u003cem\u003eRalstonia\u003c/em\u003e was positively correlated (coefficient\u0026thinsp;\u0026ge;\u0026thinsp;0.5) to \u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eAcinetobacter\u003c/em\u003e and negatively correlated (coefficient\u0026thinsp;\u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;0.5) to \u003cem\u003ePseudomonas.\u003c/em\u003e The highest betweenness value of \u003cem\u003eRalstonia\u003c/em\u003e was found in group C (0.05591) than group L (0.0329) where in group F betweenness value of \u003cem\u003eRalstonia\u003c/em\u003e was zero. The highest betweenness value of \u003cem\u003eStaphylococcus\u003c/em\u003e and \u003cem\u003ePseudomonas\u003c/em\u003e, was found in group F (0.04167 and 0.05361, respectively) than group L ( 0 and 0.02513 respectively) and C (0.007 and 0.01992, respectively) where, betweenness value of \u003cem\u003eAcinetobacter\u003c/em\u003e was the highest in group L than in groups F and C (0.1201, 0.01216 and 0.00325, respectively). We can conclude that this may be one reason why the relative abundance of \u003cem\u003eRalstonia\u003c/em\u003e was higher in the control group whereas \u003cem\u003ePseudomonas\u003c/em\u003e was higher in the GZPH2 group.\u003c/p\u003e\n\u003cp\u003eThe networks data also showed the higher ratios of positive correlations in group C (87%) and L (66%) than F (57%) and oppositely the higher ratios of negative correlations were shown in group F (43%) than in groups L (34%) and C (13%). This result reveled that BDN-1F2 helped to build up a balance positive and negative microbial interactions ratio which helped to reduce the potential pathogenic species \u003cem\u003eVibrio\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eStaphylococcus\u003c/em\u003e and \u003cem\u003eRalstonia\u003c/em\u003e for shrimp PL cultivation.\u003c/p\u003e\n\u003cp\u003eA possible explanation could be attributed to BALOs are obligate predatory bacteria that selectively prey on a broad range of Gram-negative bacteria, including \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eStaphylococcus\u003c/em\u003e and \u003cem\u003eVibrio\u003c/em\u003e (Najnine et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Saralegui et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the free-living attack phase BALOs enter into a prey cell periplasm, and there by grow forming a structure known as the bdelloplast, after completing the growth stage of new progeny\u0026rsquo;s they lysed prey cell and released new progeny to attack new prey again (Sockett and Lambert \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e). The Bugbase phenotypic function prediction bar plots (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ed) data showed that the relative abundance of gram negative bacteria \u003cem\u003eAcinetobacter\u003c/em\u003e decreased from day 0 to day 3 in all PL groups, simultaneously the relative abundance of another gram negative bacteria \u003cem\u003ePseudomonas\u003c/em\u003e increased at day 3. On day 7, the relative abundance of \u003cem\u003ePseudomonas\u003c/em\u003e increased again in group L but decreased in groups F and C where the reduction rate was higher in group F. The pair \u003cem\u003eAcinetobacter vs. Pseudomonas\u003c/em\u003e showed strongly negative correlatatio (coefficient\u0026thinsp;\u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;0.5) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec) as a result when \u003cem\u003eAcinetobacter\u003c/em\u003e decreased, \u003cem\u003ePseudomonas\u003c/em\u003e increaesd. Previous study was done by Saralegui et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) showed that \u003cem\u003eBdellovibrio bacteriovorus\u003c/em\u003e has ability to prey on pathogenic bacteria \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e (Cai et al. \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). So, we could predict that the reduction rate of Gram negative bacteria \u003cem\u003ePseudomonas\u003c/em\u003e in BDN-1F2 was higher because of lysis ability of BALOs.\u003c/p\u003e\n\u003cp\u003eAs to the reason(s) why GZPH2 was not effective in this occasion even though in vitro testing demonstrated its anti-vibrio effect (Guo et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), it could be due to the much higher contents of nitrate (4.92\u0026ndash;6.68 times), nitrite (3.67\u0026ndash;5.5 times) and ammonia (2.03\u0026ndash;4.08 times) in the rearing water when compared to both control and group F (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Such high contents of these nitrogen salts could aid the growth of \u003cem\u003eGammaproteobacteria\u003c/em\u003e and \u003cem\u003eVibrionales/Vibrionaceae\u003c/em\u003e, as well as \u003cem\u003ePseudoalteromonadaceae\u003c/em\u003e (Huang et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) thus making the reduction less effective, although these nitrogen salts were around the safety levels as proposed by Valencia-Casta et al. (2018), viz., for salinities of 1 and 3 g/L, 0.54 and 0.81 mg/L for total ammonia-N, 0.17 and 0.25 mg/L for NO\u003csub\u003e2\u003c/sub\u003e-N, and 5.6 and 21.5 mg/L for NO\u003csub\u003e3\u003c/sub\u003e-N, respectively. This tempts us to suggest that the beneficial effects of \u003cem\u003eLa. salivarius\u003c/em\u003e strain GZPH2 could be even better if these nitrogen salts are lower.\u003c/p\u003e\n\u003cp\u003eBugbase phenotypic function prediction analysis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eh) at genus level among top 25 genera data showed that stress-related all genera \u003cem\u003eRalstonia\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eVibrio\u003c/em\u003e, \u003cem\u003eAchromobacter\u003c/em\u003e, \u003cem\u003eAlcaligenes\u003c/em\u003e, \u003cem\u003eAlcanivorax\u003c/em\u003e, \u003cem\u003eStenotrophomonas\u003c/em\u003e, \u003cem\u003enorank_f__Beggiatoaceae\u003c/em\u003e, and \u003cem\u003eHaliea\u003c/em\u003e were belonged to \u003cem\u003eGammaproteobacteria\u003c/em\u003e among all PL groups. Bugbase phenotypic function data also showed that potentially pathogenic (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ef) maximum genera were belonged to \u003cem\u003eProteobacteria\u003c/em\u003e/\u003cem\u003eGammaproteobacteria\u003c/em\u003e viz. \u003cem\u003eRalstonia, Pseudomonas, Vibrio, Achromobacter\u003c/em\u003e, and \u003cem\u003eBurkholderia-Caballeronia-Paraburkholderia\u003c/em\u003e, and only one genera \u003cem\u003eProteobacteria\u003c/em\u003e/\u003cem\u003eAlphaproteobacteria viz. Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium\u003c/em\u003e, one genera was belonged to \u003cem\u003eFirmicutes viz. Staphylococcus\u003c/em\u003e and one genera was belonged to \u003cem\u003eBacteroidetes\u003c/em\u003e viz. \u003cem\u003enorank_f__NS9_marine_group.\u003c/em\u003e We could be concluded that \u003cem\u003eGammaproteobacteria\u003c/em\u003e were the dominant class of stress-related and potentially pathogenic genera of white leg shrimp (\u003cem\u003eLitopenaeus vannamei\u003c/em\u003e) post larvae.\u003c/p\u003e\n\u003cp\u003eVisualizations of Bugbase phenotypic function prediction bar plots (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ef) further showed some differences in the relative abundance of stress-related and opportunistic pathogen taxa among three PL groups. The relative abundances of stress-related and potentially pathogenic genera \u003cem\u003eRalstonia\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, and \u003cem\u003eVibrio\u003c/em\u003e were lower in group F than in groups L and C. However, the relative abundances of stress-related four genera were higher in group F than groups C and L viz. \u003cem\u003eAchromobacter\u003c/em\u003e (0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45%, 0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01% and 0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01%, respectively), \u003cem\u003eAlcaligenes\u003c/em\u003e (0.094\u0026thinsp;\u0026plusmn;\u0026thinsp;0.042%, 0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01%, and 0, respectively), \u003cem\u003eAlcanivorax\u003c/em\u003e (0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84%, 0%, and 0%, respectively) and \u003cem\u003eHaliea\u003c/em\u003e (0.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39%, 0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05%, and 0%, respectively) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Here, GZPH2 showed better effect than BDN-1F2 to decrease \u003cem\u003eAchromobacter,Alcaligenes, Alcanivorax\u003c/em\u003e, and \u003cem\u003eHaliea.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAlthough, \u003cem\u003ePseudomonas, Alcaligenes\u003c/em\u003e, and \u003cem\u003eAlcanivorax\u003c/em\u003e are stress-related genera, but there are still some evidence of their beneficial effects such as \u003cem\u003eAlcanivorax\u003c/em\u003e helped in hydrocarbon degrading (Zadjelovic et al. 2020), \u003cem\u003ePseudomonas\u003c/em\u003e helped in denitrification (Tran et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; He et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) and \u003cem\u003eAlcaligenes\u003c/em\u003e also helped in denitrification (Joo et al. \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003eb). Previous studies also demonstrated that a healthier and more robust microbial community has a higher biodiversity than an unhealthy one (Rungrassamee et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Chen et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). The overall trend indicated that the relative abundance of potentially pathogenic genera was higher in the group C as well as the microbial community structure was unstable. Furthermore, the biodiversity diversity of potentially pathogenic taxa was balanced as well as the relative abundances of potentially pathogenic genera was lower in group F than in groups L and C (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ef). Although, several studies have shown that \u003cem\u003eRalstonia\u003c/em\u003e is a potentially beneficial genera in fish gut microbial community and play a crucial role to change the intestinal microbial structure (Wu et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The most surprising thing is, sometimes the absence of some bacteria can be pathogenic. Yu et al. (2022) found that, the abundance of \u003cem\u003eRalstonia\u003c/em\u003e in translucent diseased shrimp PL gut was significantly lower than healthy Shrimp PL while \u003cem\u003eVibrio\u003c/em\u003e and \u003cem\u003eMycoplasmataceae\u003c/em\u003e were higher in number and mentioned it might be one of the reason for the occurrence of translucent diseased in host. In this current experiment, we also found similar results that \u003cem\u003eRalstonia\u003c/em\u003e abundance suppresses the abundance of other bacteria in control group. As a result the microbial biodiversity of control group was lower than that of the BDN-1F2 and GZPH2 groups.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eWithin the class \u003cem\u003eAlphaproteobacteria\u003c/em\u003e, \u003cem\u003eRhodobacterales\u003c/em\u003e, \u003cem\u003eRhizobiales\u003c/em\u003e and \u003cem\u003eSphingomonadales\u003c/em\u003e were the dominant orders (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). With respect to \u003cem\u003eRhodobacterales\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), both GZPH2 and BDN-1F2 showed positive promotion effects. While the relative abundance of \u003cem\u003eRhodobacterales\u003c/em\u003e in control decreased 14.03% in the first 3 days, and increased 40.47% later on; the trends of changes in groups L and F were just the opposite, viz., increased 48.80% and 9.85%, respectively. Though it decreased again 18.68% in group L at the end of test, it increased 1.33 times, to 27.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49% at day 7 in group F. Again, data here showed that BDN-1F2 had better promotion effect on \u003cem\u003eRhodobacterales\u003c/em\u003e than GZPH2 could do.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eRegarding \u003cem\u003eRhizobiales\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), though its relative abundance in three groups all initially decreased, the extents were different. In control, it decreased 93.98% throughout the test period. In group L, it first decreased 83.60% after that again increased 57.53% at day 7. Similarly, in group F, it first decreased but only by 38.71%, then increased 36.18% at day 7. Regarding \u003cem\u003eSphingomonadales\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), the pattern of changes in three groups is similar to \u003cem\u003eRhizobiales\u003c/em\u003e, but with different extents. That is, in control, it decreased 76.86% and then increased 67.44% at the end of the test. In group L, it initially decreased 76.58% and then increased 27.04 times. In group F, it deceased 28.26% at first and then increased 3.42 times.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eAs both \u003cem\u003eRhodobacterales\u003c/em\u003e and \u003cem\u003eRhizobiales\u003c/em\u003e are shown to be beneficial (Xiong et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Chen et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), and \u003cem\u003eSphingomonadales\u003c/em\u003e is aerobic anoxygenic phototrophs (photoheterotrophs), with a variety of physiological features and carotenoid pigments, including astaxanthin (Siddaramappa et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Some could even detoxify a fungal toxin, fumonisin (Li et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, their increments in groups F and L should be beneficial. According to Bugbase phenotypic function analysis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eg) data, biofilms producer all genera \u003cem\u003eParacoccus\u003c/em\u003e, \u003cem\u003eunclassified_f__Rhodobacteraceae\u003c/em\u003e, \u003cem\u003eYangia\u003c/em\u003e, \u003cem\u003eNautella\u003c/em\u003e, \u003cem\u003eGemmobacter\u003c/em\u003e, \u003cem\u003eRhodovulum\u003c/em\u003e, \u003cem\u003eRoseovarius\u003c/em\u003e belonged to \u003cem\u003eRhodobacterales\u003c/em\u003e family, and \u003cem\u003eAllorhizobium-Neorhizobium-Pararhizobium-Rhizobium\u003c/em\u003e and \u003cem\u003eNitratireductor\u003c/em\u003e belonged to \u003cem\u003eRhizobiaceae\u003c/em\u003e family. Initially, the relative abundances of biofilms former genera \u003cem\u003eParacoccus\u003c/em\u003e and \u003cem\u003eAllorhizobium-Neorhizobium-Pararhizobium-Rhizobium\u003c/em\u003e were higher in the groups C and L than in group F. On the other hand, the relative abundances of \u003cem\u003eNautella, Nitratireductor\u003c/em\u003e, and \u003cem\u003ePseudochrobactrum\u003c/em\u003e were higher in the group F than in groups L and C. On the day 3, the relative abundances of \u003cem\u003eParacoccus\u003c/em\u003e decreased in groups F and C but increased in group L. Although, the relative abundances of \u003cem\u003eParacoccus\u003c/em\u003e decreased in group L where slightly increased in group C and F. On the day 7, the relative abundance of \u003cem\u003eunclassified_f__Rhodobacteraceae\u003c/em\u003e, \u003cem\u003eNitratireductor\u003c/em\u003e, and \u003cem\u003eRoseovarius\u003c/em\u003e significantly increased in group F than group L and group C (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eg). Overall data reveled that enhancement of biofilms producing bacteria BDN-1F2 showed better performance than GZPH2.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eBacterial biofilms are complex communities of bacteria held together by self-generated extracellular polymeric matrix as well as increase survival rate by improving the defense system, increase nutrients availability and cellular communication and transfer of genetic material (Tremblay et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). In our experiment, we found most biofilm-producing genera under the \u003cem\u003eAlphaproteobacteria\u003c/em\u003e class. The so far discovered, \u003cem\u003eAlphaproteobacteria\u003c/em\u003e class in particularly the \u003cem\u003eRhodobacteraceae\u003c/em\u003e family is the dominant taxa for biofilm community formation than the \u003cem\u003eGammaproteobacteria\u003c/em\u003e class, which support our findings (Elifantz et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). A retrospective 16S rRNA gene study conducted by Elifantz et al. (\u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e) identified primary colonies of biofilms and the results showed that the \u003cem\u003eAlphaproteobacteria\u003c/em\u003e class represented 30\u0026ndash;70% of the bacterial community whereas \u003cem\u003eGammaproteobacteria\u003c/em\u003e accounted for only up to 10% of the community. Another study has reported that \u003cem\u003eRhodobacteraceae\u003c/em\u003e is a major core gut microbial taxa which help to create a stable microbial community in shrimp gut (Dong et al. 2023). In addition, some taxa belong to \u003cem\u003eAlphaproteobacteria\u003c/em\u003e, help to denitrification \u003cem\u003eParacoccus\u003c/em\u003e (Zhao et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), \u003cem\u003eNitratireductor\u003c/em\u003e (Ye et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) and sulphur-oxidizing \u003cem\u003eParacoccus\u003c/em\u003e (Jaffer et al. \u003cspan class=\"CitationRef\"\u003e2019a\u003c/span\u003e) which were significantly higher in group F than group L and C. Possibility it could be the reason of the lower contents of nitrate, nitrite and ammonia in rearing water of group F compared to both control and group L (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Again, data here showed that BDN-1F2 had a better promotion effect on building healthy microbial community structures in the PL gut than GZPH2. The application of BDN-1F2 in the rearing of shrimp PL might help solve the existing so-called translucent disease in China (Zou et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) albeit more tests at the production level should be carried out.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eActinobacteria\u003c/em\u003e are well known as a group of secondary metabolites producers and tend to be playing beneficial roles (Binda et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, its relative abundance decreased in group L by 41.23%, while increased 26.84% in control and 22.40% in group F (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ec). With in phylum \u003cem\u003eActinobacteria\u003c/em\u003e, the relative abundance of \u003cem\u003eMicrobacterium\u003c/em\u003e was significantly changed among three PL groups and the abundance rate was higher in group F than in groups C and L. Similar patents of changes also occurred to \u003cem\u003eBacillales\u003c/em\u003e of phylum \u003cem\u003eFirmicutes\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). That is, their relative abundance decreased 92.24% and 85.87% in groups L and F, respectively, while in control, it first decreased by 91.15% and then increased 4.09 times. Though it is generally recognized to be beneficial, \u003cem\u003eBacilli\u003c/em\u003e (from the class to the family level) has been shown to be dominant in slow-growing shrimp intestines while \u003cem\u003eVibrio\u003c/em\u003e was dominant in the intestine of the fast-growing shrimp in outdoor ponds (Duan et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). In line with the suggestion of Duan et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), higher abundance of \u003cem\u003eActinobacteria\u003c/em\u003e and/or \u003cem\u003eBacillales\u003c/em\u003e could divert more energy for defence and leave less for growth, thus leading to poorer growth performance. This is exactly the case here as shrimp PL grew slower in control as compared to group L and F even though the latter had higher abundance of \u003cem\u003eVibrionales\u003c/em\u003e/\u003cem\u003eVibrio\u003c/em\u003e and lower abundance of \u003cem\u003eActinobacteria\u003c/em\u003e and/or \u003cem\u003eBacillales\u003c/em\u003e. \u003cem\u003eFlavobacteriales\u003c/em\u003e belonged to phylum \u003cem\u003eBacteroidetes\u003c/em\u003e. In control, its relative abundance first increased 23.05% and then decreased 20.44%, whereas it decreased 48.73% in group L and increased 25.57% in group F (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). As \u003cem\u003eFlavobacteriales\u003c/em\u003e was identified as a shrimp gut keystone taxon associated with diseased shrimp, and its infectious disease causing potential was significantly and positively associated with its relative abundance (Dai et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), therefore, its increase would naturally endanger shrimp PL health. In this sense, it seems that GZPH2 could do better than BDN-1F2 here.\u003c/p\u003e\n\u003cp\u003eOver the 7-day test period, the differential enrichment of bacterial taxa in groups C, L and F was analyzed by LEfSe (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea-c) and found four taxa were enriched in control, viz., \u003cem\u003ef__Leuconostocaceae\u003c/em\u003e and \u003cem\u003eg__Weissella\u003c/em\u003e, \u003cem\u003ef__Dysgonomonadaceae\u003c/em\u003e, and \u003cem\u003ef__Corynebacteriaceae\u003c/em\u003e. While the first two belong to LABs, obviously being beneficial to shrimp PL; the third taxon has been shown capable of degrading various polysaccharides (Murakami et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), and the last taxon has been found to be symbionts in the guts of salmon (Hartviksen et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). Two taxa were enriched in group L, viz., \u003cem\u003eg_Martelella\u003c/em\u003e and \u003cem\u003eg_Muricauda\u003c/em\u003e. Both could be considered beneficial as the former is within \u003cem\u003eRhizobiales\u003c/em\u003e (Xiong et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Chen et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), while the latter could produce antioxidant pigments like zeaxanthin (Prabhu et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). In group F, we could classify the enriched taxa into 3 types, viz., beneficial, potential pathogenic, and functionally neutral or unknowns. The beneficial taxa enriched included \u003cem\u003ef__Microbacteriaceae\u003c/em\u003e and \u003cem\u003eg__Microbacterium\u003c/em\u003e (proposed as probiotics (Hip\u0026oacute;lito-Morales et al. \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e), f_\u003cem\u003e_Demequinaceae\u003c/em\u003e and \u003cem\u003eg__Demequina\u003c/em\u003e (bioactive producers (Subramani and Sipkema \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), \u003cem\u003ef__norank_o__Saccharimonadales\u003c/em\u003e and \u003cem\u003eg__norank_f__norank_o__Saccharimonadales\u003c/em\u003e (epibiotic living (He et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e), f_\u003cem\u003e_Family_XII_o__Bacillales\u003c/em\u003e (known probiotics), \u003cem\u003eg__Nitratireductor\u003c/em\u003e (denitrification (S\u0026aacute;nchez et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e), g_\u003cem\u003e_Exiguobacterium\u003c/em\u003e (proposed as probiotics (Subramani and Sipkema \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), and \u003cem\u003eg__Alcaligenes\u003c/em\u003e nitrification (S\u0026aacute;nchez et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e), g_\u003cem\u003e_Corynebacterium\u003c/em\u003e (symbiont in the guts of salmon (Hartviksen et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). The potentially pathogenic taxa enriched included \u003cem\u003ef__Shewanellaceae\u003c/em\u003e and \u003cem\u003eg__Shewanella\u003c/em\u003e (Prachumwat et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), f_\u003cem\u003e_Pseudoalteromonadaceae\u003c/em\u003e and \u003cem\u003eg__Pseudoalteromonas\u003c/em\u003e (Zheng et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e), g_\u003cem\u003e_Nautella\u003c/em\u003e (Zheng et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e), g_\u003cem\u003e_Roseovarius\u003c/em\u003e (Travers et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) g_\u003cem\u003e_Myroides\u003c/em\u003e (opportunistic pathogens for human (Schroettner et al. 2014), and even \u003cem\u003eg__Haloferula\u003c/em\u003e (higher in abundance associated with lower shrimp body weight (Fan et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). The functionally neutral or unknowns included \u003cem\u003ef__Alcanivoracaceae\u003c/em\u003e and \u003cem\u003eg__Alcanivorax\u003c/em\u003e (hydrocarbon-degrading bacteria (Yakimov et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), f_\u003cem\u003e_Halieaceae\u003c/em\u003e and \u003cem\u003eg__Haliea\u003c/em\u003e (alkene and ethylene-assimilating bacteria (Suzuki et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) f_\u003cem\u003e_Rubritaleaceae\u003c/em\u003e (associated with live feed in yellowtail kingfish (Walburn et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), g_\u003cem\u003e_Phenylobacterium\u003c/em\u003e (prevalent in water of mixed fish culture (Zeng et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) and g_\u003cem\u003e_Maritalea\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eIf we look into the details of the relative abundances of these potentially pathogenic taxa enriched in three groups (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea-c), they are as follows: \u003cem\u003ef__Shewanellaceae\u003c/em\u003e and \u003cem\u003eg__Shewanella\u003c/em\u003e, both with 0%, 0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01% and 1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00% in control, groups L and F, respectively; \u003cem\u003ef__Pseudoalteromonadaceae\u003c/em\u003e and \u003cem\u003eg__Pseudoalteromonas\u003c/em\u003e, both with 0%, 0%, 0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01% in control, groups L and F, respectively; \u003cem\u003eg__Nautella\u003c/em\u003e with 0%, 0%, 0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01% in control, groups L and F, respectively; \u003cem\u003eg__Roseovarius\u003c/em\u003e with 0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08%, 0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01%, 2.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93% in control, groups L and F, respectively; \u003cem\u003eg__Myroides\u003c/em\u003e with 0%, 0%, 0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24% in control, groups L and F, respectively; \u003cem\u003eg__Haloferula\u003c/em\u003e with 0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05%, 0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01%, 0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11% in control, groups L and F, respectively. Hereby, it is quite clear that as beneficial bacteria, GZPH2 and BDN-1F2 they didn\u0026rsquo;t just raise the abundances of other beneficial bacteria (as in beneficial taxa enriched), but also some of (potential) pathogenic bacteria (as in pathogenic taxa enriched, like \u003cem\u003eShewanellaceae\u003c/em\u003e and \u003cem\u003eShewanella\u003c/em\u003e), while keeping others in check (taxa with little changes, like \u003cem\u003eRalstonia\u003c/em\u003e) and reducing the overall abundances of (potential) pathogenic bacteria as a whole at the community level (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The rises in abundance of (potential) pathogens may be favourable to the hosts and their environment in some instances as they could provide complementary and/or necessary ecological functions for the ecosystem. In group F, over the 7-day test period, the relative abundance of \u003cem\u003eShewanella\u003c/em\u003e was rising, from 0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12% at day 0 to 0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09% at day 3, then further up to 1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22%, while that of \u003cem\u003enorank_f__NS9_marine_group\u003c/em\u003e was from 0.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06% at day 3 to 0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09% at day 7, also rising albeit slightly (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Therefore, the rise of abundance of fermentative bacteria genus \u003cem\u003eShewanella or norank_f__NS9_marine_group\u003c/em\u003e should help lower NH\u003csub\u003e3\u003c/sub\u003e-N and NO\u003csub\u003e2\u003c/sub\u003e-N concentrations in the environment (Yoon et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shi et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) and thus create better conditions for the PL to grow. This is exactly the case here as NH\u003csub\u003e3\u003c/sub\u003e-N and NO\u003csub\u003e2\u003c/sub\u003e-N in group F was 1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49 mg/L and 0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 mg/L, much lower than that in group L (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). As changes of a structure would bring on changes of functions. Here, the addition of BDN-1F2 and GZPH2 to the rearing of PL has strengthened various ecological functions in its gut microbiota, as predicted when compared to control, even though most functional changes were with no statistical significance.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe gut microbiota plays a potential role in host nutrition by producing numerous metabolites, such as free fatty acids, amino acids, and vitamins, are found in the host intestine which are equally vital for host intestinal homeostasis and gut microbial community structure (Postler et al. 2017). Postler et al. (2017) reveled that microorganisms produce three basic types of metabolites namely metabolites that are produced by gut microbes from dietary components, metabolites that are produced by the host and biochemically modified by gut microbes, and metabolites that are synthesized \u003cem\u003ede novo\u003c/em\u003e by gut microbes. Hence, the PL gut microbial composition obtained by 16S rRNA gene sequencing was used to predict microbial function using COG and KEGG metabolic pathways that were involved, and the differences between different samples and groups were analyzed. Based on exploring the proportions of each COG function (Level 2) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ea) and KEGG metabolic pathway (level 1/2/3) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eb-d), we found some discrepancies among three PL groups C, L and F. Overall microbial function pathway analysis result showed that the relative abundances of functional genes involving metabolism were very high in all PL groups including amino acid metabolism, carbohydrate metabolism and so on (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ea-c). According to the relative abundances of functional genes involving metabolism data, we found amino acid metabolism and carbohydrate metabolism increased in group F (13.86% and 22.61%, respectively) where, decreased in groups C (0.69% and 0.56%, respectively) and L (17.56% and 16.39%, respectively) from the beginning to end of experiment (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ec).\u003c/p\u003e\n\u003cp\u003eAccording to Rosas et al. (\u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e), shrimp have a limited ability to metabolize carbohydrates; alternatively, shrimp use protein as a source of energy and growth. Chuntapa et al. (\u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e) reported that protein is important nutrients for optimal growth and survival of juvenile tiger shrimp and found the optimal protein:energy (P:E) ratios 150 and 146 mg protein/kcal, respectively. Amino acids are organic molecules that form a protein when combined together with other amino acids. At the end of our experiment, amino acids metabolism related functional genes was higher in group F on day 7 than groups C and L (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ec). Amino acids play an important role in the structure and metabolism of all living organisms. Shrimp cannot synthesize all amino acids but they need several amino acid to increase their immune function, survival rate as well as growth performance (Simon et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Specifically, arginine, proline and glutamate have been demonstrated to regulate immune defense (Shao et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). The essential amino acids (EAAs): arginine, methionine, valine, threonine, isoleucine, leucine, lysine, histidine, phenylalanine, and tryptophan are must acquire through shrimp diet, all of which are not synthesized de novo by eukaryotic cells (NRC \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). The gut microbiota can de novo synthesize some essential amino acids that contribute to the host\u0026apos;s amino acid homeostasis (Metges \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e). In this study, we found PL gut microbiome had large enrichment of genes involved in the metabolism and biosynthesis of the essential amino acids and other amino acids viz. pyruvate family (valine, leucine, and isoleucine), aspartate family (lysine, threonine, methionine), aromatic family (phenylalanine, tyrosine and tryptophan), serine family (serine, glycine, cysteine) and histidine (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ed). Some findings are supported our result that the gut microbiome had large enrichment of genes involved in the metabolism and biosynthesis of amino acids. Gill et al. (\u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e) found that the gut microbiome had large enrichment of genes involved in the biosynthesis of leucine, isoleucine, lysine, phenylalanine, tyrosine, tryptophan, and valine as well as enrichment in genes associated with the metabolism of alanine, aspartate, glutamate, histidine, methionine, glycine, serine and threonine. Another findings showed that gut microbiome had large enrichment of genes involved in pathways such as the biosynthesis of lysine, phenylalanine, tyrosine, tryptophan, valine, leucine and isoleucine compared to the host genome (Qin et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). We also found that the essential amino acids and other amino acids metabolism and biosynthesis had significantly positive correlation with PL gut microbiota \u003cem\u003eParacoccus, Flavobacterium\u003c/em\u003e, \u003cem\u003eBrevundimonas\u003c/em\u003e, \u003cem\u003eMicrobacterium, Nitratireductor\u003c/em\u003e and \u003cem\u003enorank_f__Mycoplasmataceae\u003c/em\u003e and pyruvate family and lysine degradation had significant negative correlation with \u003cem\u003eVibrio\u003c/em\u003e and \u003cem\u003eYangia\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ed). In this test we found, the relative abundance of \u003cem\u003eParacoccus, Brevundimonas\u003c/em\u003e, \u003cem\u003eMicrobacterium\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and \u003cem\u003eNitratireductor\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were significantly higher in group F on day 7 than other samples (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) and also found higher amino acids metabolism in this sample (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ed). Oppositely, in group L, the relative abundance of \u003cem\u003eVibrio\u003c/em\u003e and \u003cem\u003eYangia\u003c/em\u003e was significantly higher (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and \u003cem\u003eMicrobacterium\u003c/em\u003e was significantly lower (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) than in groups F and L as well as \u003cem\u003eBrevundimonas\u003c/em\u003e and \u003cem\u003eNitratireductor\u003c/em\u003e were absent on the day 7 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) which negatively affected the amino acids metabolism. As a result, we can conclude that the presence or absence of certain gut microbiota has been shown to significantly change amino acids metabolism and biosynthesis in PL groups: BDN-1F2, GZPH2 and control.\u003c/p\u003e\n\u003cp\u003eLipid, an important group of nutrients are essential component of living beings including aquatic animals. It is recognized that gut microbiota improved the accumulation of lipids in the host gut by enhancing lipid metabolism (Ring\u0026oslash; et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this study, we found, the lipid metabolism related functional genes was slightly higher in group C than in groups F and L (1390422, 1386260, and 1346231, respectively) at end of test (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ec). Instead, the enrichment of relative abundances of lipid metabolism-related functional genes was higher in group L than in groups F and C (77.32%, 10.29% and 6.52%, respectively) from the beginning to end of experiment (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ec). We observed that the presence of specific gut bacteria and their relative abundance strongly affected the lipid metabolism in host intestine. We found that \u003cem\u003eYangi\u003c/em\u003ea, \u003cem\u003eVibrio, Algoriphagus\u003c/em\u003e, and \u003cem\u003eRoseovarius\u003c/em\u003e had a significantly negative correlation with lipid metabolism and biosynthesis especially, fatty acid metabolism, synthesis and degradation of ketone bodies, linoleic acid metabolism, ether lipid metabolism, steroid biosynthesis, primary bile acid biosynthesis and secondary bile acid biosynthesis, in contrast, \u003cem\u003eAcinetobacter\u003c/em\u003e, \u003cem\u003eunclassified_o__Chitinophagales\u003c/em\u003e, and \u003cem\u003eStaphylococcus\u003c/em\u003ethey had a significantly positive correlation (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ed). Furthermore, fatty acid metabolism, linoleic acid metabolism, ether lipid metabolism and steroid biosynthesis showed significantly positive correlation with \u003cem\u003eBrevundimonas, Paracoccus, Rhodococcus\u003c/em\u003e and \u003cem\u003eTaeseokella\u003c/em\u003e but negative correlation with \u003cem\u003eRoseovarius\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ed). Furthermore, primary bile acid biosynthesis and secondary bile acid biosynthesis had significantly negative correlation with \u003cem\u003eUnclassified__f__Rhodobacteraceae\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and Sphingolipid metabolism had significantly negative correlation with \u003cem\u003eunclassified_o__Chitinophagales\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and \u003cem\u003eAcinetobacter\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ed).. We observe that the relative abundance of \u003cem\u003eAcinetobacter, unclassified_o__Chitinophagales\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), \u003cem\u003eUnclassified__f__Rhodobacteraceae, Brevundimonas\u003c/em\u003e, \u003cem\u003eRoseovarius\u003c/em\u003e and \u003cem\u003eParacoccus\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was significantly higher in group F than in groups L and C (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Conversely, the relative abundance of \u003cem\u003eVibrio\u003c/em\u003e and \u003cem\u003eYangia\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was significantly increased in group L than in groups F and C from day 0 to day 7 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). We found in this test, the change of lipid metabolism and biosynthesis significantly correlated the relative abundance of PL microbiome (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ed). Although, we found the relative abundance of lipid metabolism functional genes was relatively higher in BDN-1F2 group (2.97%) than GZPH2 but the relative abundance enrichment percentage was 7.51 times higher in group GZPH2 than group BDN-1F2 and 11.85 times higher from control (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ec). Similar results was reported by Salas-Leiva et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) that lipid metabolism of longfin yellowtail (\u003cem\u003eSeriola rivoliana\u003c/em\u003e) juvenile was strongly correlated with the gut microbiotal metabolic contribution and found biosynthesis of fatty acids, glycerolipid, glycerophospholipid, secondary bile acid, and sphingolipid were affected by gut microbial community. Several previous studies reveled that the regulation of lipid metabolism helps to increase the host body length and weight (Joyce et al. 2015; Zhou et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Similar result we also found in this experiment, lipid metabolism along with the body length, body weight and survival rate of PL was the higher in groups BDN-1F2 and GZPH2 than control.\u003c/p\u003e\n\u003cp\u003eIn this study, we found significantly positive correlations between the PL gut microbiota such as viz. \u003cem\u003eBrevundimonas\u003c/em\u003e, \u003cem\u003eFlavobacterium\u003c/em\u003e, \u003cem\u003eMicrobacterium\u003c/em\u003e and \u003cem\u003eParacoccus\u003c/em\u003e with carbohydrate metabolism especially glycolysis or gluconeogenesis, glyoxylate and dicarboxylate metabolism, pentose and glucuronate interconversions and inositol phosphate metabolism (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ed). It has been previously shown that the intestinal microbiota of longfin yellowtail juveniles, mainly dominated by \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eFirmicutes\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eCyanobacteria\u003c/em\u003e, and \u003cem\u003eActinobacteria\u003c/em\u003e, exhibited a contribution to carbohydrate and amino acid metabolism (Salas-Leiva et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Another experimental result also reveled that the gut bacteria of Nile tilapia were positively correlated with carbohydrate metabolism (Wu et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this study, the abundance rate of \u003cem\u003eBrevundimonas\u003c/em\u003e, \u003cem\u003eFlavobacterium\u003c/em\u003e, \u003cem\u003eMicrobacterium\u003c/em\u003e and \u003cem\u003eParacoccus\u003c/em\u003e was significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) higher in group F than in groups L and C which strongly increased the carbohydrate metabolism in group F than in groups C and L. Carbohydrates occupy an important place in metabolism due to the energy source of various biosynthetic pathways. The pitiful scenario is that the utilization of carbohydrates is very poor in most of the fish species and crustaceans including shrimp. Several studies have shown that high levels of dietary carbohydrates can cause metabolic diseases in fish due to poor carbohydrate metabolism, especially in carnivorous fish species (Stone \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e). Recently, many aquaculture scientists have tried to improve carbohydrate utilization and explore new technologies to prevent metabolic diseases related to fish carbohydrate metabolism, one of them is the application of functional intestinal microorganisms or the use of probiotics, which is a new emerging technology (Serra et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Here, we found between BDN-1F2 and GZPH2, BDN-1F2 showed a effect result than GZPH2 on Carbohydrate metabolism by changing the gut microbial community structure. On the basis of our finding, we can suggest that BDN-1F2 as a probiotic of shrimp PL rearing for enhancing carbohydrate utilization. It will be possible to spare dietary protein which can be an achievement to decrease the amount of nitrogen waste.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eTo sum up the above, we could conclude here that as both \u003cem\u003eBdellovibrio\u003c/em\u003e and like organisms (BALOs) strain BDN-1F2 and \u003cem\u003eLactobacillus salivarius\u003c/em\u003e strain GZPH2 could improve the overall growth performance of white-leg shrimp PL, via the changes of composition/structure and the functionality of a microbial community, they have met the criteria set out for aquaculture probiotics and should therefore be used as probiotics. Between these two kind of probiotics compare to GZPH2, BDN-1F2 showed better effect to build up a healthy microbial biodiversity by controlling the relative abundance of potentially pathogenic taxa including \u003cem\u003eVibrio, Pseudomonas, Staphylococcus, Ralstonia\u003c/em\u003e as well as enrichment of beneficial taxa including \u003cem\u003eParacoccus\u003c/em\u003e, \u003cem\u003eunclassified_f__Rhodobacteraceae\u003c/em\u003e, \u003cem\u003eBrevundimonas\u003c/em\u003e, \u003cem\u003eFlavobacterium\u003c/em\u003e, \u003cem\u003eMicrobacterium\u003c/em\u003e and \u003cem\u003eNitratireductor\u003c/em\u003e. In addition, BDN-1F2 showed better effect on metabolism basically amino acid metabolism and carbohydrate metabolism than GZPH2. The application of BDN-1F2 in the rearing of shrimp PL might help to reduce protein content by increasing the carbohydrate portion in the shrimp PL feed albeit more tests at the production level should be carried out.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw 16S DNA sequencing data were deposited in the NCBI Sequence Read Archive (SRA) under the accession number PRJNA694360 (SRP303063\u0026nbsp;or SRR13517589-SRR13517618).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis study was carried out in accordance with the recommendations of Animal Ethics Committee of Guangdong Province, China.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eAuthors declare that no commercial or financial conflict.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eAuthors thank the following two organizations for the financial support: Science and Technology Department of Guangdong Province of China (2016A020222002), and ProBioti Biotech (Guangzhou) Company Limited.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdel‐Tawwab M, Khalil RH, Nour AA, Elkhayat BK, Khalifa E, Abdel‐Latif HM (2020) Effects of \u003cem\u003eBacillus subtilis\u003c/em\u003e-fermented rice bran on water quality, performance, antioxidants/oxidants, and immunity biomarkers of White leg shrimp (\u003cem\u003eLitopenaeus vannamei\u003c/em\u003e) reared at different salinities with zero water exchange. 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Pathogens 9(9):741 https://doi.org/10.3390/pathogens9090741\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"","identity":"aquaculture-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"10499","submissionUrl":"https://submission.nature.com/new-submission/10499/3","title":"Aquaculture International","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Shrimp, Probiotic, Bdellovibrio and like organisms, Lactobacillus salivarius, Gut microbiota, Metabolism","lastPublishedDoi":"10.21203/rs.3.rs-4319520/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4319520/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWhite-leg shrimp (\u003cem\u003eLitopenaeus vannamei)\u003c/em\u003e is the most important species in the shrimp industry, with its production virtually all from aquaculture. Nevertheless, it frequently suffers from diseases, indicating the inadequacy of existing control measures. Recent, evidence has shown that probiotics play an important role in disease control by maintaining the composition of the gut microbiota. The aim of the present work was to evaluate the probiotic potential of \u003cem\u003eBdellovibrio\u003c/em\u003e and like organisms (BALOs) strain BDN-1F2 and \u003cem\u003eLactobacillus salivarius\u003c/em\u003e strain GZPH2 in rearing shrimp postlarvae (PL) with a control group without any treatment. The results showed that compared to BDN-1F2, GZPH2 only had a better effect on length growth while BDN-1F2 performed significantly better in weight gain and survival rate (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The 16S high throughput sequencing results showed that BDN-1F2 was significantly more effective than GZPH2 at building a healthy gut microbial communities, including the reduction of pathogenic bacterial taxa \u003cem\u003eGammaproteobacteria\u003c/em\u003e/\u003cem\u003eVibrionales\u003c/em\u003e and the increment of beneficial bacterial taxa \u003cem\u003eAlphaproteobacteria/Rhodobacteraceae.\u003c/em\u003e Furthermore, BDN-1F2 also lowered \u003cem\u003eFirmicutes/Bacteroidetes\u003c/em\u003e ratios, which theoretically supported better PL growth performance in this group. In addition, the relative abundances of predicted functional microbial genes involving amino acid metabolism and carbohydrate were very high in BDN-1F2 group than in groups GZPH2 and control. To our knowledge, this is the first report to compare between two probiotics, BALOs strain BDN-1F2 and \u003cem\u003eLactobacillus salivarius\u003c/em\u003e strain GZPH2 and an investigation of changes in intestinal microbial community structure as well as their effects on shrimp survival and growth performance.\u003c/p\u003e","manuscriptTitle":"The effects of Bdellovibrio and like organisms (BALOs) and Lactobacillus salivarius on changes in gut microbial biodiversity and their potential role on Shrimp (Litopenaeus vannamei) Postlarvae","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-03 09:49:09","doi":"10.21203/rs.3.rs-4319520/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-04-29T18:24:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-29T05:44:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Aquaculture International","date":"2024-04-24T16:22:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"","identity":"aquaculture-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"10499","submissionUrl":"https://submission.nature.com/new-submission/10499/3","title":"Aquaculture International","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"bea4ca57-4608-4c5f-b8a9-1f1659849d92","owner":[],"postedDate":"May 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-05-03T09:49:09+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-03 09:49:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4319520","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4319520","identity":"rs-4319520","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}