Metagenomic and metabolomic profiling of dried shrimp (Litopenaeus Vannamei) prepared by a procedure traditional to the south China coastal area

Metagenomic and metabolomic profiling of dried shrimp (Litopenaeus Vannamei) prepared by a procedure traditional to the south China coastal area | 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 Metagenomic and metabolomic profiling of dried shrimp (Litopenaeus Vannamei) prepared by a procedure traditional to the south China coastal area Mingjia Yu, Jiannan Liu, Junjia Chen, Chuyi Lin, Shiqing Deng, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3206216/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Jan, 2024 Read the published version in International Microbiology → Version 1 posted 4 You are reading this latest preprint version Abstract Sun-drying is a traditional process for preparing dried shrimp in coastal area of South China, but its impacts on nutrition and the formation of flavor-contributory substances in dried shrimp remain largely unknown. This study aimed to examine the effects of the production process on the microbiota and metabolites in dried shrimp. 16S rDNA amplicon sequencing was employed to identify 170 operational taxonomic units (OTUs), with Vibrio , Photobacterium , and Shewanella emerging as the primary pathogenic bacteria in shrimp samples. Lactococcus lactis was identified as the principal potential probiotic to accrue during the dried shrimp production process, and found to contribute significantly to the development of desirable shrimp flavors. LC-MS-based analyses of dried shrimp sample metabolomes revealed a notable increase in compounds associated with unsaturated fatty acid biosynthesis, arachidonic acid metabolism, amino acid biosynthesis, and flavonoid and flavanol biosynthesis throughout the drying process. Subsequent exploration of the relationship between metabolites and bacterial flora highlighted the predominant coexistence of Bifidobacterium , Clostridium , and Photobacterium contributing heterocyclic compounds and metabolites of organic acids and their derivatives. Conversely, Arthrobacter and Staphylococcus were found to inhibit each other, primarily in the presence of heterocyclic compounds. This comprehensive investigation provides valuable insights into the dynamic changes in the microbiota and metabolites of dried shrimps spanning different drying periods, which we expect to contribute to enhancing production techniques and safety measures for dried shrimp processing. sun-drying shrimp microbiota metabolites pathogens nutrition Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Litopenaeus Vannamei is one of the most widely farmed shrimp species in China, and one of the world's major aquatic products (Liao and Chien, 2011 ). It is favored by consumers for its taste and nutritional value. The drying of fresh shrimp can extend its shelf-life and effectively reduce its transport cost (Lin et al., 2022 ). The main drying methods include vacuum freeze-drying, heat-pump-drying, hot-air drying, and sun-drying (Hernández Becerra et al., 2014 ; Sun et al., 2022 ). Different drying methods may yield the different flavors in dried shrimp. Sun-drying is the most popular and most economical drying method in South China (Akintola, 2015 ; Deng et al., 2015 ; El Hage et al., 2018 ). Raw shrimp of the L. vannamei species represent a complex ecosystem that harbors diverse, active microorganisms. Vibrio , Pseudomonas , Photobacterium , and Acinetobacter were the most abundant microorganisms genera in shrimp (Cornejo-Granados et al., 2017 ). These genera include multiple pathogens and probiotics. Vibrio parahaemolyticus expresses PirA/B toxins and that can lead to acute hepatopancreatic necrosis disease (AHPND), is one of the most common pathogens among shrimps (Joshi et al., 2014 ; Silvia et al., 2014 ). At present, changes in the probiotic flora and associated metabolites of shrimp in response to sun-drying remain unknown. The presence of the main nutritional and flavor components of dried shrimp, including unsaturated fatty acids, arachidonic acids, amino acids, flavonoids and flavanols, and to what extent these components are influenced by retained microorganisms are unknown. To understand changes in the microbial community and what microbial species are primarily retained during shrimp drying, and their relationship with the nutritional and flavor components of dried shrimp, we analyzed the microbiota and metabolites changes in dried shrimp during three periods. The results provide valuable information with application to the food industry. Materials and methods Dried shrimp preparation Freshwater shrimp purchased from a local market were prepared by removing the internal organs and peeling off the shells. The shrimp meat was soaked in a mild saline solution for ten minutes (salt: water = 3:1000). Excess moisture was then drained from the shrimp meat, which was placed under the sun for three consecutive days. Samples were collected at three different time points: before sun drying (ME0), after one day of sun drying (ME1), and after 3 days of sun drying (ME3). Collected samples were stored at -80°C for further microbiological analysis, 16s rDNA sequencing, and metabolite detection. Microbiological analysis Ten grams of each shrimp sample was homogenized for 2.5 minutes in a sterile bag containing 90 mL of physiological saline solution. A one mL aliquot of the homogenized solution was used to prepare a series of 10-fold dilutions in physiological saline. Plate streaking was carried out using a volume of 100 µL from each sample dilution, followed by incubation at 36 ± 1°C for 48 h for subsequent counting. DNA extraction and 16s rDNA sequencing A one mL aliquot of each homogenized solution was used for 16s rDNA sequencing. Five biological replicates were sequenced in each experimental group. Total microbial genomic DNA was extracted from shrimp samples using a FastDNA® Spin kit for soil (MP Biomedicals, Irvine, CA, USA) according to the manufacturer’s instructions. DNA quality and concentration were determined by 1.0% agarose gel electrophoresis and NanoDrop2000 spectrophotometry (Thermo Fisher Scientific, Waltham, MA, USA). Samples were kept at -80 ℃ prior to further use. The bacterial 16S rDNA gene was amplified with primer pairs 341F (5'-CCTAYGGGRBGCASCAG-3') and 806R (5'-GGACTACNNGGGTATCTAAT-3') using a T100 Thermal Cycler PCR thermocycler (BIO-RAD, Hercules, CA, USA)(Guo et al., 2017 ). The PCR reaction mixture included 10 µL 2 × mix, 0.8 µL of each primer (5 µM), 10 ng/µL of template DNA, and ddH 2 O to a final volume of 20 µL. PCR amplification cycling conditions were as follows: initial denaturation at 95 ℃ for 3 min, followed by 27 cycles of denaturing at 95 ℃ for 30 s, annealing at 55 ℃ for 30 s and extension at 72 ℃ for 45 s, followed by a single extension at 72 ℃ for 10 min, followed by indefinite holding at 10 ℃. PCR products were extracted from 2% agarose gels purified using a PCR Clean-Up Kit (YuHua, Shanghai, China) according to manufacturer’s instructions, and quantified using Qubit 4.0 (Thermo Fisher Scientific, Waltham, MA, USA). Purified amplicons were pooled in equimolar amounts and paired-end sequenced on an Illumina PE250 platform (Illumina, San Diego, CA, USA) according to the standard protocols by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). Bioinformatics analysis Paired-end reads were quality filtered using FastP (v0.18.0) (Chen et al., 2018 ), and merged using FLASH (v1.2.11) (Magoč and Salzberg, 2011 ). Operational taxonomic units (OTUs) were clustered with a 97% similarity cut-off using UPARSE (Edgar, 2013 ), and chimeric sequences were removed using UCHIME (v9.2.64) (Edgar et al., 2011 ). Bacterial annotation of OTUs was performed using the SILVA database v132 (Pruesse et al., 2007 ). Estimation of alpha diversity including richness and shannon indices, principal coordinate analysis (PCoA), and analysis of similarities (ANOSIM) were performed using vegan (v 2.5.3) (Stevens and Wagner, 2010 ). Indicator taxa were identified at the genus level using the Labdsv package (v2.0-1) (Roberts, 2013 ). BugBase as used to predict the potential functions of microorganisms in samples from the three periods (Ward et al., 2017 ). Metabolites extraction and LC-MS analysis Untargeted metabolomics was used to conduct metabolic profiling of the samples, with five biological replicates per group. Samples were stored in a -80 ℃ freezer thawed on ice, and ground with liquid nitrogen. A 400 µL solution (Methanol: Water = 7:3, v/v) containing an internal standard was added to 20 mg of ground sample, shaken at 1500 rpm for 5 min, and centrifuged at 12000 rpm for 10 min at 4 ℃. Supernatants (300 µL) were collected and stored at -20 ℃ for 30 minutes. Samples were then centrifuged at 12000 rpm for 3 min at 4 ℃. Finally, 200 µL of each supernatant was collected for LC-MS analysis. An ACQUITY UPLC BEH system supplied with a C18 1.8 µm column (2.1 mm × 100 mm; Waters) was utilized. The extracts were eluted with a gradient starting from 5–90% mobile phase B (0.1% formic acid in acetonitrile) linearly over 11 minutes, held for 1 minute, then returned to 5% mobile phase B within 0.1 min and held for 1.9 min before rapidly returning to starting conditions. Mass spectrometry was performed using a TripleTOF 6600 mass spectrometer (AB SCIEX, Foster City, CA, USA) interfaced with a heated electrospray ionization source. Data acquisition was performed using the information-dependent acquisition (IDA) mode with Analyst TF 1.7.1 Software (AB SCIEX). The LC-MS data file was converted to mzML format using ProteoWizard software (Chambers et al., 2012 ). Subsequently, the XCMS program (v3.6.1) was employed for peak extraction, peak alignment, and retention time correction (Smith et al., 2006 ). Peak areas were corrected using the "SVR" method. Any peaks with a detection rate lower than 50% in each sample group were excluded. Metabolic identification information was obtained by searching a combination of the laboratory's self-built database, integrated public databases, an AI database, and metDNA. Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) was performed using the OPLSR.Anal function from the MetaboAnalystR package (v1.0.1) in R software (Chong et al., 2019 ). Differential analysis among the three sample groups (ME1_vs_ME0, ME3_vs_ME0, and ME3_vs_ME1) was performed, and metabolites with Variable Importance in Projection (VIP) > 1 and a P < 0.05 (determined by Student's t-test) were considered statistically significant. Differential metabolites were annotated using the KEGG database (Ogata et al., 1999 ) ( http://www.kegg.jp/kegg/compound/ ), and mapped to KEGG pathways ( http://www.kegg.jp/kegg/pathway.html ). Differential abundance skeys (DA Skey) were calculated for each KEGG pathway. The Short Time-series Expression Miner (STEM) tool (Ernst and Bar-Joseph, 2006 ) was used for temporal metabolomic analysis. Metabolite profiles were selected based on a significance level of P < 0.05. KEGG pathway enrichment analysis was performed using clusterProfiler (v4.7.1) (Yu et al., 2012 ). Interaction analysis of key microbiota and metabolites Spearman’s correlation coefficient was calculated between key microbial communities, and between key microbial communities and differential metabolites. Significant correlations ( P < 0.05) were used to construct an interaction network and were visualized using Cytoscape (v3.9) (Shannon et al., 2003 ). Statistical analysis The pH level and total viable count (TVC) during production of dried shrimp were statistically examined based on Duncan’s test, and P < 0.05 indicated a significant difference. Results and discussion Bacterial enumeration During dried shrimp production, as the shrimp dehydrated, their volume gradually shrank (Fig. 1 A). The pH level significantly decreased at ME1 stage and slightly increased thereafter (Fig. 1 B). TVC significantly increased at ME3 stage (Fig. 1 C). Diversity of the bacterial community in dried shrimp An average of 79,985 paired-end reads per sample were obtained, and the numbers of taxon tags ranged from 45,685 to 134,259 among the samples with 73,329 taxon tags per sample (Table S1). These tags were grouped into 270 microbial Operational Taxonomic Units (OTUs) (Table S1). The number of OTUs in ME0 period samples was the highest, and a total of 170 OTUs was shared by all three periods (Fig. 2 A). The Richness (Fig. 2 B) and Shannon (Fig. 2 C) indices indicated higher α-diversity in the ME0 period than in the latter two periods. PCoA analysis also suggested that samples within groups tended to cluster together, while the ME0 period was distinct from the latter two stages (Fig. 2 D). During dried shrimp production, the similarity between samples within groups gradually decreased, indicating a trend towards unstable community structures, and significant differences were observed in the community structures among different sample groups (Fig. 2 E). A previous study demonstrated the importance of the initial microbial load found on ready-to-eat foods. However, it should be noted that the microbiological load of these foods at the point of sale can be influenced by several factors, including processing, storage, and display conditions (Angelidis et al., 2006 ; Beuchat and Ryu, 1997 ). Microbial community composition and changes during preparation In the ME0 period, the predominant bacterial phyla in the samples were Bacteroidetes , Actinobacteria , and Proteobacteria , while in the latter two periods, Proteobacteria was the predominant phylum, and the relative abundance of Firmicutes was increased (Fig. 3 A). At the genus level, the ME0 samples showed a high abundance of Psychrobacter , while the ME1 samples mainly included Vibrio and Photobacterium genera, and the genera Vibrio , Shewanella , Lactococcus , and Photobacterium were more abundant in the ME3 samples (Fig. 3 B). A previous study showed that Firmicutes and Proteobacteria were the predominant phyla during the drying process in dry sausages, consistent with our results (Hu et al., 2020 ). Lactobacillus sakei is the predominant species in Croatian dry fermented sausages (Zgomba Maksimovic et al., 2018 ). Indicator species analysis indicated that the abundance of Rhodobacter , Flavobacterium , Pseudoalteromonas , Psychrobacter , and Loktanella genera was significantly higher in the ME0 period than in the latter two stages, while Aliivibrio and Vibrio were more abundant in the latter two periods (Fig. 3 C). Photobacterium was significantly more abundant in the ME1 period than in the other two stages, whereas Shewanella was significantly less abundant in the ME1 period than in the other two periods (Fig. 3 C). In particular, Lactococcus gradually increased during shrimp drying process (Fig. 3 C). Vibrio , Photobacterium , and Shewanella are known marine animal pathogens and potential opportunistic human pathogens, whereas Lactococcus are potential beneficial bacteria. A previous study showed that Aliivibrio and Vibrio are important pathogens among seafood and pose a threat to human health (Huehn et al., 2014 ; Neetoo et al., 2022 ; Toranzo et al., 2023 ). Another study showed that Photobacterium and Shewanella were the most prevalent and abundant pathogens among bluespotted seabreams, making up 30.2% and 11.3% of detected pathogens (Itay et al., 2022 ). Lactococcus is the most important beneficial bacteria in dried seafood. One study found that Lactococcus lactis 69, isolated from sun-dried meat, could secrete a heat-stable bacteriocin that exhibited good inhibitory effects againast various pathogens (Biscola et al., 2013 ). Another Lactococcus lactis KTH0-1S isolated from dried Thai shrimp could produce a heat-stable bacteriocin that also inhibits food-borne pathogens and reduces tyramine accumulation (Saelao et al., 2018 , 2017 ). The contribution of Lactococcus to flavor development in fermented meat products was found to be relatively insignificant, primarily owing to its low lipolytic activity (Flores and Toldrá, 2011 ). By contrast, the accumulation of Lactococcus during the drying process in shrimp plays an important role in changes in the shrimp microbiome and flavor. Potential functions of the bacterial community Predicted functions results showed that the aerobic category was significantly higher in the ME0 period than in the latter two periods (Fig. 4 A), and the aerobic category decreased during the shrimp drying process. Facultatively anaerobic (Fig. 4 B), potentially pathogenic (Fig. 4 C), and stress tolerant (Fig. 4 D) categories were significantly higher in the latter two periods, while anaerobic (Fig. 4 E) and biofilm formation (Fig. 4 F) categories did not show significant differences among the three periods. One study showed that aerobic microorganisms such as E. coli O157:H7 are reduced by three logarithms during the process of drying apple slices (Derrickson-Tharrington et al., 2005 ). Another study proved that potential pathogens undergo osmotic stress-tolerances increases to adapt to environmental changes during drying in the food chain (Burgess et al., 2016 ; Sleator and Hill, 2002 ). Metabolomic changes in dried shrimp during preparation A total of 13,713 metabolites were identified in the samples during shrimp drying, with 7,571 metabolites identified at the secondary level (Table S2). Among the differentially accumulated metabolites (DAM), the majority increased during shrimp drying (Fig. 5 A). Pairwise comparison of differentially regulated metabolites was performed across the three periods, and 717 metabolites were found to be differentially regulated (Fig. 5 B). Compared to the ME1 period, the arachidonic acid metabolism, purine metabolism, the sphingolipid signaling pathway, biosynthesis of nucleotide sugars, drug metabolism and other enzymes, the apelin signaling pathway, the pentose phosphate pathway, nicotinate and nicotinamide metabolism, arginine and proline metabolism, the cAMP signaling pathway, and calcium signaling pathway were significantly upregulated in ME0 samples (Fig. 5 C). Compared with the ME1 period, the biosynthesis of unsaturated fatty acids, arachidonic acid metabolism, lysine degradation, necroptosis, phenylalanine metabolism, lysine biosynthesis, fatty acid elongation, propanoate metabolism, glycerophospholipid metabolism, linoleic acid metabolism, valine, leucine and isoleucine biosynthesis, sulfur metabolism, aminoacyl-tRNA biosynthesis, and the prolactin signaling pathways were significantly upregulated in ME3 samples (Fig. 5 D). Biosynthesis of unsaturated fatty acids is an important change during the sun-drying of shrimp. A previous study showed that unsaturated fatty acids (including monounsaturated fatty acids and polyunsaturated fatty acids) were released by Saccharomyces cerevisiae 31 during the fermentation process of fish paste (Chen et al., 2017 ). One study found 43.90% monounsaturated fatty acids, 28.61% polyunsaturated fatty acids, and 27.48% saturated fatty acids among dried shrimps (Sampaio et al., 2006 ). The arachidonic cis-5, 8, 11, 14-Eicosatetraenoic (C20:4 n-6) value in the raw shrimps was increased significantly by smoking and sun-drying (Akintola, 2015 ). Amino acid metabolism also plays a vital role in the drying of shrimp. A previous study showed that tyrosine values increased approximately 70% and 80% respectively in smoked and sun-dried shrimp, which was associated with phenylalanine metabolism and lysine biosynthesis (Akintola, 2015 ; Omolola, 2013). Temporal analysis of DAM was performed by STEM, which identified three significant profiles with an overall increasing trend (Fig. 6 A). The DAM in these significant profiles were mainly enriched in oxidative phosphorylation, cGMP-PKG signaling, flavones and flavonols biosynthesis, biosynthesis of various antibiotics, novobiocin biosynthesis, purine metabolism, AMPK signaling, Type I polyketide structures, riboflavin metabolism, flavonoid biosynthesis, chlorocyclohexane and chlorobenzene degradation, nicotinate and nicotinamide metabolism, and longevity regulating pathway, etc (Fig. 6 B). Flavones and flavonols are important nutriments impacted by food among the fermentation and drying proceses. A previous study showed that the total flavonoid content of green tea was highest after drying at 38 ℃ (Roshanak et al., 2016 ). Interaction of key microbiota and metabolites in dried shrimp Spearman’s correlation analysis was performed on the key indicator microbial community and on differential metabolites (Figures S1 and S2). The majority of key microbial taxa, including Psychrobacter , Shewanella , Lactococcus , Flavobacterium , Rhodobacter , Acinetobacter , Ulvibacter , Tenacibaculum , Leadbetterella , Aequorivita , Staphylococcus , Lactobacillus , Arthrobacter , Paracoccus , Aeromonas , Fluviicola , Olleya , Thiothrix , and Leucothrix , showed positive correlations with each other, and exhibited positive correlations with differentially accumulated metabolites (Figures S1 and S2). By contrast, Vibrio , Allivibrio , Photobacterium , and Bifidobacterium were positively correlated with each other, but their abundances were predominantly negatively correlated with differentially accumulated metabolites (Figures S1 and S2). Based on the results of these correlation analysis, a network depicting microbial interactions and microbial-metabolite interactions was constructed (Fig. 7 ). The results indicate that Bifidobacterium , Clostridium , and Photobacterium are mainly associated with heterocyclic compounds and organic acids and their derivatives metabolites in a co-occurring manner, while Arthrobacter and Staphylococcus were predominantly mutually exclusive with heterocyclic compounds (Fig. 7 ). Previous studies have shown that Bifidobacterium and Photobacterium are associated with heterocyclic compounds (Faridnia et al., 2010 ; Xu et al., 2000 ). Meanwhile, the immobilization of Clostridium was the main effect of organic acids (Dolejš et al., 2014 ; Stinson and Naftulin, 1991 ). One study showed that Arthrobacter mainly degrades heterocyclic compounds (Guo et al., 2019 ). Another study showed that Staphylococcus is primarily exclusive to heterocyclic compounds (Paudel et al., 2017 ). Conclusion We undertook a comprehensive analysis of the variations in the microbiota and metabolites of dried shrimp at three stages of drying. The predominant pathogenic bacteria identified in shrimp samples were Vibrio , Photobacterium , and Shewanella , while Lactococcus lactis , a potential probiotic produced during shrimp drying, played a significant role in the development of shrimp flavors. Subsequent examination of the metabolites present in dried shrimp samples revealed a substantial increase in compounds associated with unsaturated fatty acid biosynthesis, arachidonic acid metabolism, amino acid biosynthesis, and flavonoid and flavanol biosynthesis during the drying process. Finally, an exploration of the interplay between metabolites and bacterial flora demonstrated that Bifidobacterium , Clostridium , and Photobacterium predominantly coexisted with heterocyclic compounds and metabolites of organic acids and their derivatives, whereas Arthrobacter and Staphylococcus primarily inhibited each other, particularly in the presence of heterocyclic compounds. These findings provide valuable insights into the dynamic alterations in the microbiota and metabolites of dried shrimp throughout various stages of drying, contributing to a deeper understanding of shrimp processing and paving the way for improved safety practices in the production of dried shrimp. Declarations Author contribution Conception and design of the work, MY, JL; analysis, MY, JL, JC and MW; Drafting the manuscript, MY, SD and CL; Editing and revising the manuscript, MY and JL. All authors have read and approved the final manuscript. Funding This work was supported by Funds for Guangdong University Innovation Team Project (2021KCXTD081), the Innovation Projects of Colleges and Universities in Guangdong Province (2019GKTSCX120) and Foundation of Foshan Polytechnic (KY201902, KY201903). Availability of data and material The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request. Additional information Supplementary information is available for this paper. Ethics approval Not applicable Competing interests The authors declare no competing interests References Akintola, S.L., 2015. Effects of smoking and sun-drying on proximate, fatty and amino acids compositions of Southern pink shrimp (Penaeus notialis). J. Food Sci. Technol. 52, 2646–2656. https://doi.org/10.1007/s13197-014-1303-0 Angelidis, A.S., Chronis, E.N., Papageorgiou, D.K., Kazakis, I.I., Arsenoglou, K.C., Stathopoulos, G.A., 2006. 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CABI Books. https://doi.org/10.1079/9781780647784.0314 Ward, T., Larson, J., Meulemans, J., Hillmann, B., Lynch, J., Sidiropoulos, D., Spear, J.R., Caporaso, G., Blekhman, R., Knight, R., Fink, R., Knights, D., 2017. BugBase predicts organism-level microbiome phenotypes. bioRxiv 133462. https://doi.org/10.1101/133462 Xu, S., Li, L., Tan, Y., Feng, J., Wei, Z., Wang, L., 2000. Prediction and QSAR Analysis of Toxicity to Photobacterium phosphoreum for a Group of Heterocyclic Nitrogen Compounds. Bull. Environ. Contam. Toxicol. 64, 316–322. https://doi.org/10.1007/s001280000002 Yu, G., Wang, L.-G., Han, Y., He, Q.-Y., 2012. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287. https://doi.org/10.1089/omi.2011.0118 Zgomba Maksimovic, A., Zunabovic-Pichler, M., Kos, I., Mayrhofer, S., Hulak, N., Domig, K.J., Mrkonjic Fuka, M., 2018. Microbiological hazards and potential of spontaneously fermented game meat sausages: A focus on lactic acid bacteria diversity. LWT 89, 418–426. https://doi.org/https://doi.org/10.1016/j.lwt.2017.11.017 Additional Declarations No competing interests reported. Supplementary Files s1.jpg Figure S1. Heatmap representing Spearman correlations among key microbiota. * and ** indicate P < 0.05 and P < 0.01, respectively. s2.jpg Figure S2. Heatmap representing Spearman correlations between key microbiota and differential metabolites. * and ** indicate P < 0.05 and P < 0.01, respectively. Cite Share Download PDF Status: Published Journal Publication published 09 Jan, 2024 Read the published version in International Microbiology → Version 1 posted Editorial decision: Major revision 04 Aug, 2023 Editor assigned by journal 03 Aug, 2023 Submission checks completed at journal 02 Aug, 2023 First submitted to journal 26 Jul, 2023 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3206216","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":223593179,"identity":"5e570dfa-5749-4870-875e-646c3c525ff3","order_by":0,"name":"Mingjia Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYJCCAwwMbDwM7I0P4FwitfAcNmBgSCBSCwRIJBOpxeBGduLhgl98MvwzHzN/LvzBIMd3I4HxcwFeLbkbDs/sY+ORuJ3MJj0jgcFY8kYCs/QMQlp4e4B+uZ1/jJkngSFxw40ENmYeYrTI3zzM/BmopZ44LTw/2HgMbjAzSAO1JBgQ0iJ55i3QlgY2HsMzIL+kSRjOPPOwWRqfFr7juZs/8/w5Zi93HOiwAhsbeb7jyQc/49OicABIMLYdA3OYgbED4jbg0cDAIA+W/lMD0zIKRsEoGAWjABMAAP+pTniHTSfQAAAAAElFTkSuQmCC","orcid":"","institution":"Foshan Polytechnic","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Mingjia","middleName":"","lastName":"Yu","suffix":""},{"id":223593180,"identity":"0b2d8b03-41c2-4947-a0e8-662cc8514051","order_by":1,"name":"Jiannan Liu","email":"","orcid":"","institution":"Foshan Polytechnic","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiannan","middleName":"","lastName":"Liu","suffix":""},{"id":223593181,"identity":"ed4e96e3-3d1c-4740-8730-d93838765f63","order_by":2,"name":"Junjia Chen","email":"","orcid":"","institution":"Foshan Polytechnic","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Junjia","middleName":"","lastName":"Chen","suffix":""},{"id":223593182,"identity":"83901589-f8d3-4aba-ba0e-6196c582db76","order_by":3,"name":"Chuyi Lin","email":"","orcid":"","institution":"Foshan Polytechnic","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chuyi","middleName":"","lastName":"Lin","suffix":""},{"id":223593183,"identity":"991c9add-5d95-4aba-9380-011e20890fe4","order_by":4,"name":"Shiqing Deng","email":"","orcid":"","institution":"Foshan Polytechnic","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shiqing","middleName":"","lastName":"Deng","suffix":""},{"id":223593184,"identity":"09387d8f-32e2-4ce8-a8f0-4eb4ca11c1f4","order_by":5,"name":"Minfu Wu","email":"","orcid":"","institution":"Foshan Polytechnic","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Minfu","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2023-07-26 11:59:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3206216/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3206216/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10123-023-00475-6","type":"published","date":"2024-01-09T15:01:38+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":41248838,"identity":"e63c9640-bf22-4d38-b51f-66ab6706aaed","added_by":"auto","created_at":"2023-08-08 14:46:50","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":729009,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in appearance, pH, and TVC during dried shrimp production. (A) Photos of shrimps before drying (ME0, top left), on day 1 of drying (ME1, top right), and on day 3 of drying (ME3, bottom left). (B) Changes in pH during shrimp drying. (C) Changes in pH during\u003c/p\u003e\n\u003cp\u003eShrimp drying. Different letters indicate significant differences (Duncan’s test at \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"floatimage1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3206216/v1/46e26d0770bd0af7468c74cc.jpg"},{"id":41249650,"identity":"6c85efec-d120-4341-92a6-7f98fd828cad","added_by":"auto","created_at":"2023-08-08 14:54:50","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":366713,"visible":true,"origin":"","legend":"\u003cp\u003eDiversity of the bacterial community during dried shrimp production. (A) OTU upset plot of the three periods, (B) Distribution of richness diversity index, (C) Distribution of Shannon diversity index, (D) Principal Coordinate Analysis (PCoA), (E) Analysis of similarity (ANOSIM) identifying significant differences between the three periods at the genus level.\u003c/p\u003e","description":"","filename":"floatimage2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3206216/v1/9f7aeefb8c6568e3a80f2cd5.jpg"},{"id":41248842,"identity":"f1e02db7-f8ad-425b-a58a-65d27292061b","added_by":"auto","created_at":"2023-08-08 14:46:50","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":550061,"visible":true,"origin":"","legend":"\u003cp\u003eMicrobial community composition and changes during dried shrimp production. (A) Average abundance of taxa at the phylum level in samples from the three periods, (B) Average abundance of taxa at the genus level in samples from the three periods, (C) Bubble plot of indicator species analysis, where larger points indicate higher indicator values.\u003c/p\u003e","description":"","filename":"floatimage3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3206216/v1/ae846f8dca6375577a54fe3d.jpg"},{"id":41248837,"identity":"1d33ac69-292f-49f2-bc86-5374200d6cd6","added_by":"auto","created_at":"2023-08-08 14:46:50","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":345434,"visible":true,"origin":"","legend":"\u003cp\u003eBugBase prediction of microbial phenotypes across the three periods during dried shrimp production, including (A) Aerobic, (B) Facultatively Anaerobic, (C) Potentially Pathogenic, (D) Stress Tolerant, (E) Anaerobic, and (F) Biofilm formation categories.\u003c/p\u003e","description":"","filename":"floatimage4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3206216/v1/5019580d26e496afd1195d4a.jpg"},{"id":41248843,"identity":"9cea433b-f481-4ac8-acfa-b0d5aeadfd1f","added_by":"auto","created_at":"2023-08-08 14:46:50","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":671089,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolomic changes during dried shrimp production. (A) Pairwise comparison of differentially regulated metabolites between the three periods, (B) Venn diagram of differentially regulated metabolites, (C) KEGG enrichment analysis results of differentially accumulated metabolites (DAM) between ME0 and ME1, (D) KEGG enrichment analysis results of differentially regulated metabolites between ME1 and ME3.\u003c/p\u003e","description":"","filename":"floatimage5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3206216/v1/38bb4793127aa314b46b44cc.jpg"},{"id":41249651,"identity":"077c6357-60fc-4787-9e28-749370e13d61","added_by":"auto","created_at":"2023-08-08 14:54:50","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":968245,"visible":true,"origin":"","legend":"\u003cp\u003eSTEM analysis of DAM. (A) The sub-classes of DAM identified by STEM. Profiles were ordered by \u003cem\u003eP\u003c/em\u003e-value. Those highlighted with a colored background are significant profiles. (B) KEGG enrichment analysis of DAM in the 3 significant profiles in (A).\u003c/p\u003e","description":"","filename":"floatimage6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3206216/v1/f4dfe6482c56edbb0a9b0e78.jpg"},{"id":41248845,"identity":"cae07987-36c6-4d61-85cc-1c7d9c993dc5","added_by":"auto","created_at":"2023-08-08 14:46:50","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":695382,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction network of key microbiota and differential metabolites in dried shrimp based on Spearman correlations analysis. Circles represent bacteria, and squares represent metabolites. Red and green edge lines indicate positive and negative correlations, respectively. Node size represents the average abundance of bacteria or metabolites.\u003c/p\u003e","description":"","filename":"floatimage7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3206216/v1/f1b7a0fb7adf48ab92b1e786.jpg"},{"id":49628608,"identity":"0b97c0c2-bdba-4532-b43b-85f7424ed39f","added_by":"auto","created_at":"2024-01-15 15:08:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":989144,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3206216/v1/2bbc9c38-47eb-4550-8c53-2aef1930374c.pdf"},{"id":41248841,"identity":"dd942273-a0fb-44db-89f4-259e801e6ca1","added_by":"auto","created_at":"2023-08-08 14:46:50","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1957994,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1. Heatmap representing Spearman correlations among key microbiota. * and ** indicate \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05 and \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01, respectively.\u003c/p\u003e","description":"","filename":"s1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3206216/v1/9a81d966f3341fe875928ef4.jpg"},{"id":41248840,"identity":"50d4dd48-fb1f-4207-976b-db9ea9e993b8","added_by":"auto","created_at":"2023-08-08 14:46:50","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1739692,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S2. Heatmap representing Spearman correlations between key microbiota and differential metabolites. * and ** indicate \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05 and \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01, respectively.\u003c/p\u003e","description":"","filename":"s2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3206216/v1/8a72c3dda5e8fcc4a76dab4a.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metagenomic and metabolomic profiling of dried shrimp (Litopenaeus Vannamei) prepared by a procedure traditional to the south China coastal area","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cem\u003eLitopenaeus Vannamei\u003c/em\u003e is one of the most widely farmed shrimp species in China, and one of the world's major aquatic products (Liao and Chien, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). It is favored by consumers for its taste and nutritional value. The drying of fresh shrimp can extend its shelf-life and effectively reduce its transport cost (Lin et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The main drying methods include vacuum freeze-drying, heat-pump-drying, hot-air drying, and sun-drying (Hern\u0026aacute;ndez Becerra et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Different drying methods may yield the different flavors in dried shrimp. Sun-drying is the most popular and most economical drying method in South China (Akintola, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Deng et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; El Hage et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRaw shrimp of the \u003cem\u003eL. vannamei\u003c/em\u003e species represent a complex ecosystem that harbors diverse, active microorganisms. \u003cem\u003eVibrio\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003ePhotobacterium\u003c/em\u003e, and \u003cem\u003eAcinetobacter\u003c/em\u003e were the most abundant microorganisms genera in shrimp (Cornejo-Granados et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These genera include multiple pathogens and probiotics. \u003cem\u003eVibrio parahaemolyticus\u003c/em\u003e expresses PirA/B toxins and that can lead to acute hepatopancreatic necrosis disease (AHPND), is one of the most common pathogens among shrimps (Joshi et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Silvia et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). At present, changes in the probiotic flora and associated metabolites of shrimp in response to sun-drying remain unknown.\u003c/p\u003e \u003cp\u003eThe presence of the main nutritional and flavor components of dried shrimp, including unsaturated fatty acids, arachidonic acids, amino acids, flavonoids and flavanols, and to what extent these components are influenced by retained microorganisms are unknown. To understand changes in the microbial community and what microbial species are primarily retained during shrimp drying, and their relationship with the nutritional and flavor components of dried shrimp, we analyzed the microbiota and metabolites changes in dried shrimp during three periods. The results provide valuable information with application to the food industry.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDried shrimp preparation\u003c/h2\u003e \u003cp\u003eFreshwater shrimp purchased from a local market were prepared by removing the internal organs and peeling off the shells. The shrimp meat was soaked in a mild saline solution for ten minutes (salt: water\u0026thinsp;=\u0026thinsp;3:1000). Excess moisture was then drained from the shrimp meat, which was placed under the sun for three consecutive days. Samples were collected at three different time points: before sun drying (ME0), after one day of sun drying (ME1), and after 3 days of sun drying (ME3). Collected samples were stored at -80\u0026deg;C for further microbiological analysis, 16s rDNA sequencing, and metabolite detection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMicrobiological analysis\u003c/h2\u003e \u003cp\u003eTen grams of each shrimp sample was homogenized for 2.5 minutes in a sterile bag containing 90 mL of physiological saline solution. A one mL aliquot of the homogenized solution was used to prepare a series of 10-fold dilutions in physiological saline. Plate streaking was carried out using a volume of 100 \u0026micro;L from each sample dilution, followed by incubation at 36\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C for 48 h for subsequent counting.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction and 16s rDNA sequencing\u003c/h2\u003e \u003cp\u003eA one mL aliquot of each homogenized solution was used for 16s rDNA sequencing. Five biological replicates were sequenced in each experimental group. Total microbial genomic DNA was extracted from shrimp samples using a FastDNA\u0026reg; Spin kit for soil (MP Biomedicals, Irvine, CA, USA) according to the manufacturer\u0026rsquo;s instructions. DNA quality and concentration were determined by 1.0% agarose gel electrophoresis and NanoDrop2000 spectrophotometry (Thermo Fisher Scientific, Waltham, MA, USA). Samples were kept at -80 ℃ prior to further use. The bacterial 16S rDNA gene was amplified with primer pairs 341F (5'-CCTAYGGGRBGCASCAG-3') and 806R (5'-GGACTACNNGGGTATCTAAT-3') using a T100 Thermal Cycler PCR thermocycler (BIO-RAD, Hercules, CA, USA)(Guo et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The PCR reaction mixture included 10 \u0026micro;L 2 \u0026times; mix, 0.8 \u0026micro;L of each primer (5 \u0026micro;M), 10 ng/\u0026micro;L of template DNA, and ddH\u003csub\u003e2\u003c/sub\u003eO to a final volume of 20 \u0026micro;L. PCR amplification cycling conditions were as follows: initial denaturation at 95 ℃ for 3 min, followed by 27 cycles of denaturing at 95 ℃ for 30 s, annealing at 55 ℃ for 30 s and extension at 72 ℃ for 45 s, followed by a single extension at 72 ℃ for 10 min, followed by indefinite holding at 10 ℃. PCR products were extracted from 2% agarose gels purified using a PCR Clean-Up Kit (YuHua, Shanghai, China) according to manufacturer\u0026rsquo;s instructions, and quantified using Qubit 4.0 (Thermo Fisher Scientific, Waltham, MA, USA).\u003c/p\u003e \u003cp\u003e Purified amplicons were pooled in equimolar amounts and paired-end sequenced on an Illumina PE250 platform (Illumina, San Diego, CA, USA) according to the standard protocols by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatics analysis\u003c/h2\u003e \u003cp\u003ePaired-end reads were quality filtered using FastP (v0.18.0) (Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and merged using FLASH (v1.2.11) (Magoč and Salzberg, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Operational taxonomic units (OTUs) were clustered with a 97% similarity cut-off using UPARSE (Edgar, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and chimeric sequences were removed using UCHIME (v9.2.64) (Edgar et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Bacterial annotation of OTUs was performed using the SILVA database v132 (Pruesse et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Estimation of alpha diversity including richness and shannon indices, principal coordinate analysis (PCoA), and analysis of similarities (ANOSIM) were performed using vegan (v 2.5.3) (Stevens and Wagner, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Indicator taxa were identified at the genus level using the Labdsv package (v2.0-1) (Roberts, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). BugBase as used to predict the potential functions of microorganisms in samples from the three periods (Ward et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMetabolites extraction and LC-MS analysis\u003c/h2\u003e \u003cp\u003eUntargeted metabolomics was used to conduct metabolic profiling of the samples, with five biological replicates per group. Samples were stored in a -80 ℃ freezer thawed on ice, and ground with liquid nitrogen. A 400 \u0026micro;L solution (Methanol: Water\u0026thinsp;=\u0026thinsp;7:3, v/v) containing an internal standard was added to 20 mg of ground sample, shaken at 1500 rpm for 5 min, and centrifuged at 12000 rpm for 10 min at 4 ℃. Supernatants (300 \u0026micro;L) were collected and stored at -20 ℃ for 30 minutes. Samples were then centrifuged at 12000 rpm for 3 min at 4 ℃. Finally, 200 \u0026micro;L of each supernatant was collected for LC-MS analysis.\u003c/p\u003e \u003cp\u003eAn ACQUITY UPLC BEH system supplied with a C18 1.8 \u0026micro;m column (2.1 mm \u0026times; 100 mm; Waters) was utilized. The extracts were eluted with a gradient starting from 5\u0026ndash;90% mobile phase B (0.1% formic acid in acetonitrile) linearly over 11 minutes, held for 1 minute, then returned to 5% mobile phase B within 0.1 min and held for 1.9 min before rapidly returning to starting conditions. Mass spectrometry was performed using a TripleTOF 6600 mass spectrometer (AB SCIEX, Foster City, CA, USA) interfaced with a heated electrospray ionization source. Data acquisition was performed using the information-dependent acquisition (IDA) mode with Analyst TF 1.7.1 Software (AB SCIEX).\u003c/p\u003e \u003cp\u003eThe LC-MS data file was converted to mzML format using ProteoWizard software (Chambers et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Subsequently, the XCMS program (v3.6.1) was employed for peak extraction, peak alignment, and retention time correction (Smith et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Peak areas were corrected using the \"SVR\" method. Any peaks with a detection rate lower than 50% in each sample group were excluded. Metabolic identification information was obtained by searching a combination of the laboratory's self-built database, integrated public databases, an AI database, and metDNA.\u003c/p\u003e \u003cp\u003eOrthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) was performed using the OPLSR.Anal function from the MetaboAnalystR package (v1.0.1) in R software (Chong et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Differential analysis among the three sample groups (ME1_vs_ME0, ME3_vs_ME0, and ME3_vs_ME1) was performed, and metabolites with Variable Importance in Projection (VIP)\u0026thinsp;\u0026gt;\u0026thinsp;1 and a \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (determined by Student's t-test) were considered statistically significant. Differential metabolites were annotated using the KEGG database (Ogata et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.kegg.jp/kegg/compound/\u003c/span\u003e\u003cspan address=\"http://www.kegg.jp/kegg/compound/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and mapped to KEGG pathways (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.kegg.jp/kegg/pathway.html\u003c/span\u003e\u003cspan address=\"http://www.kegg.jp/kegg/pathway.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Differential abundance skeys (DA Skey) were calculated for each KEGG pathway. The Short Time-series Expression Miner (STEM) tool (Ernst and Bar-Joseph, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) was used for temporal metabolomic analysis. Metabolite profiles were selected based on a significance level of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. KEGG pathway enrichment analysis was performed using clusterProfiler (v4.7.1) (Yu et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eInteraction analysis of key microbiota and metabolites\u003c/h2\u003e \u003cp\u003eSpearman\u0026rsquo;s correlation coefficient was calculated between key microbial communities, and between key microbial communities and differential metabolites. Significant correlations (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were used to construct an interaction network and were visualized using Cytoscape (v3.9) (Shannon et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe pH level and total viable count (TVC) during production of dried shrimp were statistically examined based on Duncan\u0026rsquo;s test, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated a significant difference.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBacterial enumeration\u003c/h2\u003e \u003cp\u003eDuring dried shrimp production, as the shrimp dehydrated, their volume gradually shrank (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The pH level significantly decreased at ME1 stage and slightly increased thereafter (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). TVC significantly increased at ME3 stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDiversity of the bacterial community in dried shrimp\u003c/h2\u003e \u003cp\u003eAn average of 79,985 paired-end reads per sample were obtained, and the numbers of taxon tags ranged from 45,685 to 134,259 among the samples with 73,329 taxon tags per sample (Table S1). These tags were grouped into 270 microbial Operational Taxonomic Units (OTUs) (Table S1).\u003c/p\u003e \u003cp\u003eThe number of OTUs in ME0 period samples was the highest, and a total of 170 OTUs was shared by all three periods (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The Richness (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) and Shannon (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) indices indicated higher α-diversity in the ME0 period than in the latter two periods. PCoA analysis also suggested that samples within groups tended to cluster together, while the ME0 period was distinct from the latter two stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). During dried shrimp production, the similarity between samples within groups gradually decreased, indicating a trend towards unstable community structures, and significant differences were observed in the community structures among different sample groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). A previous study demonstrated the importance of the initial microbial load found on ready-to-eat foods. However, it should be noted that the microbiological load of these foods at the point of sale can be influenced by several factors, including processing, storage, and display conditions (Angelidis et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Beuchat and Ryu, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMicrobial community composition and changes during preparation\u003c/h2\u003e \u003cp\u003eIn the ME0 period, the predominant bacterial phyla in the samples were \u003cem\u003eBacteroidetes\u003c/em\u003e, \u003cem\u003eActinobacteria\u003c/em\u003e, and \u003cem\u003eProteobacteria\u003c/em\u003e, while in the latter two periods, \u003cem\u003eProteobacteria\u003c/em\u003e was the predominant phylum, and the relative abundance of \u003cem\u003eFirmicutes\u003c/em\u003e was increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). At the genus level, the ME0 samples showed a high abundance of \u003cem\u003ePsychrobacter\u003c/em\u003e, while the ME1 samples mainly included \u003cem\u003eVibrio\u003c/em\u003e and \u003cem\u003ePhotobacterium\u003c/em\u003e genera, and the genera \u003cem\u003eVibrio\u003c/em\u003e, \u003cem\u003eShewanella\u003c/em\u003e, \u003cem\u003eLactococcus\u003c/em\u003e, and \u003cem\u003ePhotobacterium\u003c/em\u003e were more abundant in the ME3 samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). A previous study showed that Firmicutes and Proteobacteria were the predominant phyla during the drying process in dry sausages, consistent with our results (Hu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). \u003cem\u003eLactobacillus sakei\u003c/em\u003e is the predominant species in Croatian dry fermented sausages (Zgomba Maksimovic et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIndicator species analysis indicated that the abundance of \u003cem\u003eRhodobacter\u003c/em\u003e, \u003cem\u003eFlavobacterium\u003c/em\u003e, \u003cem\u003ePseudoalteromonas\u003c/em\u003e, \u003cem\u003ePsychrobacter\u003c/em\u003e, and \u003cem\u003eLoktanella\u003c/em\u003e genera was significantly higher in the ME0 period than in the latter two stages, while \u003cem\u003eAliivibrio\u003c/em\u003e and \u003cem\u003eVibrio\u003c/em\u003e were more abundant in the latter two periods (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). \u003cem\u003ePhotobacterium\u003c/em\u003e was significantly more abundant in the ME1 period than in the other two stages, whereas \u003cem\u003eShewanella\u003c/em\u003e was significantly less abundant in the ME1 period than in the other two periods (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). In particular, \u003cem\u003eLactococcus\u003c/em\u003e gradually increased during shrimp drying process (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). \u003cem\u003eVibrio\u003c/em\u003e, \u003cem\u003ePhotobacterium\u003c/em\u003e, and \u003cem\u003eShewanella\u003c/em\u003e are known marine animal pathogens and potential opportunistic human pathogens, whereas \u003cem\u003eLactococcus\u003c/em\u003e are potential beneficial bacteria. A previous study showed that \u003cem\u003eAliivibrio\u003c/em\u003e and \u003cem\u003eVibrio\u003c/em\u003e are important pathogens among seafood and pose a threat to human health (Huehn et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Neetoo et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Toranzo et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Another study showed that \u003cem\u003ePhotobacterium\u003c/em\u003e and \u003cem\u003eShewanella\u003c/em\u003e were the most prevalent and abundant pathogens among bluespotted seabreams, making up 30.2% and 11.3% of detected pathogens (Itay et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). \u003cem\u003eLactococcus\u003c/em\u003e is the most important beneficial bacteria in dried seafood. One study found that \u003cem\u003eLactococcus lactis\u003c/em\u003e 69, isolated from sun-dried meat, could secrete a heat-stable bacteriocin that exhibited good inhibitory effects againast various pathogens (Biscola et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Another \u003cem\u003eLactococcus lactis\u003c/em\u003e KTH0-1S isolated from dried Thai shrimp could produce a heat-stable bacteriocin that also inhibits food-borne pathogens and reduces tyramine accumulation (Saelao et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The contribution of Lactococcus to flavor development in fermented meat products was found to be relatively insignificant, primarily owing to its low lipolytic activity (Flores and Toldr\u0026aacute;, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). By contrast, the accumulation of \u003cem\u003eLactococcus\u003c/em\u003e during the drying process in shrimp plays an important role in changes in the shrimp microbiome and flavor.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePotential functions of the bacterial community\u003c/h2\u003e \u003cp\u003ePredicted functions results showed that the aerobic category was significantly higher in the ME0 period than in the latter two periods (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), and the aerobic category decreased during the shrimp drying process. Facultatively anaerobic (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), potentially pathogenic (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), and stress tolerant (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD) categories were significantly higher in the latter two periods, while anaerobic (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE) and biofilm formation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF) categories did not show significant differences among the three periods. One study showed that aerobic microorganisms such as \u003cem\u003eE. coli\u003c/em\u003e O157:H7 are reduced by three logarithms during the process of drying apple slices (Derrickson-Tharrington et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Another study proved that potential pathogens undergo osmotic stress-tolerances increases to adapt to environmental changes during drying in the food chain (Burgess et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sleator and Hill, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMetabolomic changes in dried shrimp during preparation\u003c/h2\u003e \u003cp\u003eA total of 13,713 metabolites were identified in the samples during shrimp drying, with 7,571 metabolites identified at the secondary level (Table S2). Among the differentially accumulated metabolites (DAM), the majority increased during shrimp drying (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Pairwise comparison of differentially regulated metabolites was performed across the three periods, and 717 metabolites were found to be differentially regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Compared to the ME1 period, the arachidonic acid metabolism, purine metabolism, the sphingolipid signaling pathway, biosynthesis of nucleotide sugars, drug metabolism and other enzymes, the apelin signaling pathway, the pentose phosphate pathway, nicotinate and nicotinamide metabolism, arginine and proline metabolism, the cAMP signaling pathway, and calcium signaling pathway were significantly upregulated in ME0 samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Compared with the ME1 period, the biosynthesis of unsaturated fatty acids, arachidonic acid metabolism, lysine degradation, necroptosis, phenylalanine metabolism, lysine biosynthesis, fatty acid elongation, propanoate metabolism, glycerophospholipid metabolism, linoleic acid metabolism, valine, leucine and isoleucine biosynthesis, sulfur metabolism, aminoacyl-tRNA biosynthesis, and the prolactin signaling pathways were significantly upregulated in ME3 samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Biosynthesis of unsaturated fatty acids is an important change during the sun-drying of shrimp. A previous study showed that unsaturated fatty acids (including monounsaturated fatty acids and polyunsaturated fatty acids) were released by \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e 31 during the fermentation process of fish paste (Chen et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). One study found 43.90% monounsaturated fatty acids, 28.61% polyunsaturated fatty acids, and 27.48% saturated fatty acids among dried shrimps (Sampaio et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The arachidonic cis-5, 8, 11, 14-Eicosatetraenoic (C20:4 n-6) value in the raw shrimps was increased significantly by smoking and sun-drying (Akintola, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Amino acid metabolism also plays a vital role in the drying of shrimp. A previous study showed that tyrosine values increased approximately 70% and 80% respectively in smoked and sun-dried shrimp, which was associated with phenylalanine metabolism and lysine biosynthesis (Akintola, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Omolola, 2013).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTemporal analysis of DAM was performed by STEM, which identified three significant profiles with an overall increasing trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The DAM in these significant profiles were mainly enriched in oxidative phosphorylation, cGMP-PKG signaling, flavones and flavonols biosynthesis, biosynthesis of various antibiotics, novobiocin biosynthesis, purine metabolism, AMPK signaling, Type I polyketide structures, riboflavin metabolism, flavonoid biosynthesis, chlorocyclohexane and chlorobenzene degradation, nicotinate and nicotinamide metabolism, and longevity regulating pathway, \u003cem\u003eetc\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Flavones and flavonols are important nutriments impacted by food among the fermentation and drying proceses. A previous study showed that the total flavonoid content of green tea was highest after drying at 38 ℃ (Roshanak et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eInteraction of key microbiota and metabolites in dried shrimp\u003c/h2\u003e \u003cp\u003eSpearman\u0026rsquo;s correlation analysis was performed on the key indicator microbial community and on differential metabolites (Figures S1 and S2). The majority of key microbial taxa, including \u003cem\u003ePsychrobacter\u003c/em\u003e, \u003cem\u003eShewanella\u003c/em\u003e, \u003cem\u003eLactococcus\u003c/em\u003e, \u003cem\u003eFlavobacterium\u003c/em\u003e, \u003cem\u003eRhodobacter\u003c/em\u003e, \u003cem\u003eAcinetobacter\u003c/em\u003e, \u003cem\u003eUlvibacter\u003c/em\u003e, \u003cem\u003eTenacibaculum\u003c/em\u003e, \u003cem\u003eLeadbetterella\u003c/em\u003e, \u003cem\u003eAequorivita\u003c/em\u003e, \u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eArthrobacter\u003c/em\u003e, \u003cem\u003eParacoccus\u003c/em\u003e, \u003cem\u003eAeromonas\u003c/em\u003e, \u003cem\u003eFluviicola\u003c/em\u003e, \u003cem\u003eOlleya\u003c/em\u003e, \u003cem\u003eThiothrix\u003c/em\u003e, and \u003cem\u003eLeucothrix\u003c/em\u003e, showed positive correlations with each other, and exhibited positive correlations with differentially accumulated metabolites (Figures S1 and S2). By contrast, \u003cem\u003eVibrio\u003c/em\u003e, \u003cem\u003eAllivibrio\u003c/em\u003e, \u003cem\u003ePhotobacterium\u003c/em\u003e, and \u003cem\u003eBifidobacterium\u003c/em\u003e were positively correlated with each other, but their abundances were predominantly negatively correlated with differentially accumulated metabolites (Figures S1 and S2). Based on the results of these correlation analysis, a network depicting microbial interactions and microbial-metabolite interactions was constructed (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The results indicate that \u003cem\u003eBifidobacterium\u003c/em\u003e, \u003cem\u003eClostridium\u003c/em\u003e, and \u003cem\u003ePhotobacterium\u003c/em\u003e are mainly associated with heterocyclic compounds and organic acids and their derivatives metabolites in a co-occurring manner, while \u003cem\u003eArthrobacter\u003c/em\u003e and \u003cem\u003eStaphylococcus\u003c/em\u003e were predominantly mutually exclusive with heterocyclic compounds (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Previous studies have shown that \u003cem\u003eBifidobacterium and Photobacterium\u003c/em\u003e are associated with heterocyclic compounds (Faridnia et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Meanwhile, the immobilization of \u003cem\u003eClostridium\u003c/em\u003e was the main effect of organic acids (Dolejš et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Stinson and Naftulin, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). One study showed that \u003cem\u003eArthrobacter\u003c/em\u003e mainly degrades heterocyclic compounds (Guo et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Another study showed that \u003cem\u003eStaphylococcus\u003c/em\u003e is primarily exclusive to heterocyclic compounds (Paudel et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe undertook a comprehensive analysis of the variations in the microbiota and metabolites of dried shrimp at three stages of drying. The predominant pathogenic bacteria identified in shrimp samples were \u003cem\u003eVibrio\u003c/em\u003e, \u003cem\u003ePhotobacterium\u003c/em\u003e, and \u003cem\u003eShewanella\u003c/em\u003e, while \u003cem\u003eLactococcus lactis\u003c/em\u003e, a potential probiotic produced during shrimp drying, played a significant role in the development of shrimp flavors. Subsequent examination of the metabolites present in dried shrimp samples revealed a substantial increase in compounds associated with unsaturated fatty acid biosynthesis, arachidonic acid metabolism, amino acid biosynthesis, and flavonoid and flavanol biosynthesis during the drying process. Finally, an exploration of the interplay between metabolites and bacterial flora demonstrated that \u003cem\u003eBifidobacterium\u003c/em\u003e, \u003cem\u003eClostridium\u003c/em\u003e, and \u003cem\u003ePhotobacterium\u003c/em\u003e predominantly coexisted with heterocyclic compounds and metabolites of organic acids and their derivatives, whereas \u003cem\u003eArthrobacter\u003c/em\u003e and \u003cem\u003eStaphylococcus\u003c/em\u003e primarily inhibited each other, particularly in the presence of heterocyclic compounds. These findings provide valuable insights into the dynamic alterations in the microbiota and metabolites of dried shrimp throughout various stages of drying, contributing to a deeper understanding of shrimp processing and paving the way for improved safety practices in the production of dried shrimp.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design of the work, MY, JL; analysis, MY, JL, JC and MW; Drafting the manuscript, MY, SD and CL; Editing and revising the manuscript, MY and JL. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Funds for Guangdong University Innovation Team Project (2021KCXTD081), the Innovation Projects of Colleges and Universities in Guangdong Province (2019GKTSCX120) and Foundation of Foshan Polytechnic (KY201902, KY201903).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary information is available for this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkintola, S.L., 2015. 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LWT 89, 418\u0026ndash;426. https://doi.org/https://doi.org/10.1016/j.lwt.2017.11.017\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"intm","sideBox":"Learn more about [International Microbiology](https://www.springer.com/journal/10123)","snPcode":"10123","submissionUrl":"https://submission.nature.com/new-submission/10123/3","title":"International Microbiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"sun-drying shrimp, microbiota, metabolites, pathogens, nutrition","lastPublishedDoi":"10.21203/rs.3.rs-3206216/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3206216/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSun-drying is a traditional process for preparing dried shrimp in coastal area of South China, but its impacts on nutrition and the formation of flavor-contributory substances in dried shrimp remain largely unknown. This study aimed to examine the effects of the production process on the microbiota and metabolites in dried shrimp. 16S rDNA amplicon sequencing was employed to identify 170 operational taxonomic units (OTUs), with \u003cem\u003eVibrio\u003c/em\u003e, \u003cem\u003ePhotobacterium\u003c/em\u003e, and \u003cem\u003eShewanella\u003c/em\u003e emerging as the primary pathogenic bacteria in shrimp samples. \u003cem\u003eLactococcus\u003c/em\u003e lactis was identified as the principal potential probiotic to accrue during the dried shrimp production process, and found to contribute significantly to the development of desirable shrimp flavors. LC-MS-based analyses of dried shrimp sample metabolomes revealed a notable increase in compounds associated with unsaturated fatty acid biosynthesis, arachidonic acid metabolism, amino acid biosynthesis, and flavonoid and flavanol biosynthesis throughout the drying process. Subsequent exploration of the relationship between metabolites and bacterial flora highlighted the predominant coexistence of \u003cem\u003eBifidobacterium\u003c/em\u003e, \u003cem\u003eClostridium\u003c/em\u003e, and \u003cem\u003ePhotobacterium\u003c/em\u003e contributing heterocyclic compounds and metabolites of organic acids and their derivatives. Conversely, \u003cem\u003eArthrobacter\u003c/em\u003e and \u003cem\u003eStaphylococcus\u003c/em\u003e were found to inhibit each other, primarily in the presence of heterocyclic compounds. This comprehensive investigation provides valuable insights into the dynamic changes in the microbiota and metabolites of dried shrimps spanning different drying periods, which we expect to contribute to enhancing production techniques and safety measures for dried shrimp processing.\u003c/p\u003e","manuscriptTitle":"Metagenomic and metabolomic profiling of dried shrimp (Litopenaeus Vannamei) prepared by a procedure traditional to the south China coastal area","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-08-08 14:46:45","doi":"10.21203/rs.3.rs-3206216/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-08-04T16:53:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-08-03T08:28:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-08-02T17:33:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Microbiology","date":"2023-07-26T11:55:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"intm","sideBox":"Learn more about [International Microbiology](https://www.springer.com/journal/10123)","snPcode":"10123","submissionUrl":"https://submission.nature.com/new-submission/10123/3","title":"International Microbiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7bcf09d3-741a-4313-8143-07d69611c663","owner":[],"postedDate":"August 8th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-01-15T15:04:44+00:00","versionOfRecord":{"articleIdentity":"rs-3206216","link":"https://doi.org/10.1007/s10123-023-00475-6","journal":{"identity":"international-microbiology","isVorOnly":false,"title":"International Microbiology"},"publishedOn":"2024-01-09 15:01:38","publishedOnDateReadable":"January 9th, 2024"},"versionCreatedAt":"2023-08-08 14:46:45","video":"","vorDoi":"10.1007/s10123-023-00475-6","vorDoiUrl":"https://doi.org/10.1007/s10123-023-00475-6","workflowStages":[]},"version":"v1","identity":"rs-3206216","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3206216","identity":"rs-3206216","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}