Harnessing Probiotics to Combat Nonylphenol Toxicity: A Multi-Omics Approach of Gut Microbiome Remodeling in Silurus meridionalis  

Harnessing Probiotics to Combat Nonylphenol Toxicity: A Multi-Omics Approach of Gut Microbiome Remodeling in Silurus meridionalis | 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 Harnessing Probiotics to Combat Nonylphenol Toxicity: A Multi-Omics Approach of Gut Microbiome Remodeling in Silurus meridionalis Deqin Luo, Fanglian Lu, Lian Yang, Zhenbo Gan, Xianbo Zhang, Zhenxin Zhao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8843019/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Background As a ubiquitous environmental endocrine disruptor, nonylphenol (NP) threatens aquatic organisms, driving the need for sustainable mitigation strategies. While probiotics represent promising eco-friendly supplements, their molecular mechanisms against NP toxicity remain unclear. In this study, Silurus meridionalis received a 7-week probiotics ( Bacillus subtilis and Lactobacillus acidophilus ) pretreatment followed by 15-day NP exposure. Integrated metagenomics, transcriptomics, and metabolomics analysis, with qPCR and ELISA validation, to uncover microbial, gene and metabolic responses. Growth performance (SGR, WGR) was concurrently assessed. Result NP exposure significant suppressed WGR and SGR, and induced gut microbiota dysbiosis alongside lipid metabolism disorders in S.meridionalis . Probiotics pretreatment effectively reversed these toxic effects and restored the inhibited WGR and SGR. Multi-omics integration showed that probiotics protection was mediated via a coherent "microbe-host" co-metabolism network across three progressive layers: (1)Microbial Remodeling: enriching beneficial taxa (e.g., Bacteroides eggerthii and Cetobacterium sp.) and enhancing their functional capacity for short-chain fatty acid(SCFAs) synthesis and ethanolamine metabolism; (2) Host Gene Regulation: upregulating key lipid metabolism genes ( ek1 , cept1 , ept1 , mogat2 , abcg2a ) and restoring lipase activity; (3) Metabolic Pathways Activation and Physiological Repair: reactivating the NP‑suppressed Kennedy pathway, thereby promoting critical phospholipid (PE and PC) synthesis and ultimately restoring gut barrier function. These results were further were corroborated by qPCR and ELISA. Conclusion This study systematically elucidates that the probiotics alleviate NP toxicity by remodeling a "microbiota-host Kennedy pathway genes-metabolites (PE and PC)-growth performance" regulatory network. The key mechanism is the beneficial microbiota activating the host Kennedy pathway, restoring gut phospholipid homeostasis and barrier function. These findings provide a theoretical basis for developing targeted, lipid metabolism focused probiotic feed additives in sustainable aquaculture. Nonylphenol Silurus meridionalis Bacillus subtilis Lactobacillus acidophilus Gut microbiota Multi - omics analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Nonylphenol (NP), a widely used industrial compound in surfactant and lubricants [ 1 , 2 ] , poses a significant environmental threat. Its hydrophobicity leads to accumulate in organic-rich sediments, making it a persistent aquatic pollutant [ 3 , 4 ] . As a typical environmental endocrine disruptor, NP induces both acute and chronic toxicity in aquatic organisms. In fish, crustaceans, and mollusks, NP disrupts reproductive hormone synthesis, leading to gonadal abnormalities and imbalanced population sex ratios [ 5 , 6 ] .NP also induced intestinal toxicity, damaging gut epithelium, compromising barrier integrity, and perturbs microbiome homeostasis [ 7 ] . Its resistant long alkyl chain leads to persistent accumulation in gut tissues, exacerbating barrier dysfunction and microbial dysbiosis [ 8 ] . Within antibiotic-free aquaculture, probiotics are redefined as strategic microbial modulators [ 9 , 10 ] . Their function now extends beyond growth promoting to include the enhancement of environmental adaptability through dynamic microbiota-host interactions [ 11 ] . Probiotics transiently colonize the gut mucosa, exerting direct or indirect effects on host health by secretion of metabolites, competition for ecological niches, and modulation of the local microenvironment [ 12 ] . Evidence from various models supports their multifunctional roles. For example, Lactobacillus enhances gut barrier function by activating the hypoxia-inducible factor signaling pathway through lactic acid production [ 13 ] . In Oncorhynchus mykiss , Additionally, improvements in gut morphology and concomitant enhancement of absorptive capacity have been observed dietary Pediococcus acidilactici improves gut morphology and absorptive capacity [ 14 ] . Studies also show that Bacillus subtilis (including its spore form) promotes beneficial lactic acid bacteria, while Lactobacillus acidophilus modulates microbiota composition, improves gut structure, and enhance digestive enzymes activity [ 15 – 17 ] . Furthermore, in Oreochromis niloticus , compound probiotics modify the gut microbiota to enhance nutrient absorption [ 18 ] . In Litopenaeus vannamei , a supplement containing Bacillus subtilis and Lactobacillus acidophilus strengthens the gut barrier, enhances the gut microbiota's carbon utilization, and increases beneficial probiotics populations [ 19 ] . Similarly, in Sus scrofa , the same combination strengthens the mucosal barrier by boosting beneficial microbiota and short-chain fatty acids(SCFAs) [ 20 ] . These findings highlight probiotics' dual role in improving nutrient utilization and enhancing resilience by maintaining a diverse and functional gut microbiota. Although the role of probiotics in promoting gut homeostasis is well documented. However, their ability to reduce environmental pollutants toxicity and the related molecular mechanisms is not well understood. Silurus meridionalis , an economically important species of the family Siluridae, is valued for its high intake ability, strong disease resistance, rapid growth, and nutritional value, making it a vital source of high-quality protein [ 21 – 23 ] . Due to its benthic habit, S. meridionalis is consistently exposed to NP in sediments. It's uncertain if this affects gut balance and whether probiotics can counteract the toxicity, as well as the molecular processes involved. Based on the aforementioned background, this study employed S. meridionalis as a model organism. Utilizing a "probiotic-pretreatment followed by NP-exposure" experimental design, we sought not only to evaluate the mitigation of NP‑induced intestinal toxicity by a Bacillus subtilis and Lactobacillus acidophilus complex but to mechanistically decipher its protective effects. To this end, an integrated multi‑omics approach was used: metagenomics analyzed microbial community and functional pathway changes, transcriptomics examined host genes related to lipid metabolism and intestinal barrier function, and untargeted metabolomics tracked global shifts in the gut metabolome. Finally, through correlating and integrating of these data, a "microbial function – host gene – metabolite – phenotype" regulatory network was constructed, thereby providing a systems‑level perspective on how the probiotic formulation alleviates NP‑induced toxicity. 2. Materials and Methods 2.1 Experimental Feed and Design Bacillus subtilis and Lactobacillus acidophilus used in this study were purchased from Shandong Linyi Yi Antibiotic-Free Biotechnology Co., Ltd. (Shandong, China). with viable counts of 1.0 × 10¹¹ CFU/g and 1.0 × 10¹⁰ CFU/g, respectively. In accordance with the experimental protocol, a stock solution of NP was initially prepared in ethanol and subsequently diluted to achieve the desired working concentrations of 0.1 mg/L and 0.25 mg/L. Simultaneously, equal quantities of two probiotic types were combined to create compound probiotic powder preparations, adhering to a mass ratio of 1.00% relative to the basal feed. These preparations were then dissolved in a neutral buffer solution, maintaining a buffer solution volume (mL) to feed mass (g) ratio of 1:10. The compound probiotics were uniformly sprayed onto the basal feed, thoroughly mixed, and air-dried under ventilated conditions. Fresh feed was prepared daily, portioned appropriately, and stored at − 20°C until required for use. After acclimation to the rearing conditions, the experimental fish were randomly assigned into six groups (20 fish per group, with three replicates per group) as follows: Group C : fed with basal feed only; Group NP1 : fed with basal feed for 7 weeks, followed by exposure to 0.1mg/L NP for 15 days; Group NP2 : fed with basal feed for 7 weeks, followed by exposure to 0.25 mg/L NP for 15 days; Group NP1 + PB : fed with compound probiotic-supplemented feed for 7 weeks, followed by exposure to 0.1 mg/L NP for 15 days; GroupNP2 + PB : fed with compound probiotic-supplemented feed for 7 weeks, followed by exposure to 0.25 mg/L NP for 15 days; Group PB : fed with compound probiotic-supplemented feed for 7 weeks without NP exposure [ 24 – 27 ] . 2.2 Experimental Fish Healthy S. meridionalis used in this study were obtained from a local aquaculture farm in Huishui County, (Guizhou, China). Prior to the experiment, the fish were acclimated for 2 weeks and fed a commercially available S. meridionalis diet to adapt them to the experimental environment. Feeding was performed twice daily (8:00a.m. and 17:30p.m.) to apparent satiation. During the experimental period, water quality was strictly controlled: water temperature was maintained at 25 ± 2°C, dissolved oxygen was kept above 7.0 mg/L, pH was 7.0 ± 0.5, and ammonia nitrogen concentration was maintained below 0.08 mg/L [ 28 – 30 ] . 2.3 Enzyme Activity Measurement and Differentially Expressed Metabolites Validation EK1 and Lipase were measured according to the protocols provided by Jiangsu Saiyan Biotechnology Co., Ltd. (Jiangsu, China). All experimental procedures were performed strictly following the instructions provided with the kits. To assess the reliability of the differential metabolite results, the expression levels of phosphocholine (PC), total phospholipids (PL), and phosphatidylethanolamine (PE) were measured using an enzyme-linked immunosorbent assay (ELISA).The experiment was meticulously conducted in accordance with the operational instructions provided by Jiangsu Saiyan Biotechnology Co., Ltd. (Jiangsu, China). utilizing their reagent kit. A standard curve was constructed and a linear equation was derived based on the concentration and absorbance (OD) of the standard substances. The OD values of the samples were then used to calculate protein concentrations, allowing for the evaluation of the consistency and accuracy of the metabolomic findings. 2.4 Metagenomic Analysis Genomic DNA from gut samples was extracted using a commercial extraction kit provided by Guizhou Saiyan Biotechnology Co., Ltd. (Guizhou, China) The integrity of the extracted DNA was assessed via 1% agarose gel electrophoresis, and only samples with a primary band length of ≥ 10kb were retained to ensure high sequencing quality. After confirming DNA quantity and purity, sequencing was performed on the Illumina Nova Seq high-throughput platform. Independent libraries were constructed for each sample, employing a paired-end sequencing strategy (PE150bp). Raw sequencing reads were initially evaluated using FastQC (v0.12.1) to assess base quality distribution, mean sequence quality, and nucleotide composition. Quality control was subsequently conducted using Cutadapt (v1.17) and fastp (v0.20.0) to remove low-quality reads (length < 50 bp, average quality score < 20, or reads containing ambiguous bases) [ 31 ] . High-quality clean reads were then assembled using MEGAHIT with the “meta-large” preset parameter. A “single-sample pre-assembly—merging of unmapped reads—re-assembly” strategy was applied to achieve efficient large-scale metagenomic assembly and optimization, thereby generating high-quality contigs for subsequent taxonomic annotation and functional analysis. Non-redundant gene sets were constructed using MMseqs2. The predicted sequences were aligned against the GenBank non-redundant (NR) database using BLAST, and hits with E -value ≤ 1e − 5 were retained for species annotation [ 32 ] . Functional annotation was then performed by comparing the predicted genes against GO, KEGG, eggNOG, Pfam, SwissProt, CAZy, VFDB, CYPED, and additional reference databases [ 33 ] . 2.5 Transcriptomic Analysis Total RNA was extracted from gut samples of S. meridionalis using a commercial kit (Guizhou Saiyan Biotechnology Co., Ltd., China). RNA quality was rigorously assessed: purity (OD260/280 ratio of 1.8–2.0) and concentration were determined spectrophotometrically, and integrity was verified by 1% agarose gel electrophoresis and an Agilent 2100 Bioanalyzer (all samples had RNA Integrity Number, RIN > 7.0). Sequencing libraries were constructed from qualified RNA and sequenced on an Illumina HiSeq X Ten platform to generate 150-bp paired-end reads. Raw reads were processed using fastp (v0.23.0) to remove adapters, poly-N sequences, and low-quality bases, yielding high-quality clean reads [ 34 ] . These reads were aligned to the S. meridionalis reference genome using HISAT2 (v2.2.1). Transcript assembly and quantification were performed with StringTie (v1.3.0). For functional annotation, predicted protein sequences were aligned against the NR, Swiss-Prot, and KOG databases using DIAMOND (v2.0.15) with a significance threshold of E-value < 1e − 5. The top-hit protein (highest alignment score) for each transcript was used to assign functional annotations. Gene expression levels were quantified as read counts and FPKM using featureCounts (v2.0.9) and Salmon (v1.10.0), respectively. Differentially expressed genes (DEGs) were identified with DESeq2 (v1.46.0), applying a threshold of adjusted p-value 1) [ 35 ] . Gene Ontology (GO) and KEGG pathway enrichment analyses of DEGs were conducted using clusterProfiler (v4.14.0), with results visualized via ggplot2 (v3.5.1) [ 36 ] . 2.6 Validation of DEGs by Quantitative Real-Time PCR (qPCR) Five differentially expressed genes ( ek1, gpd1l, gpd1b, pgs1, and abcg2a ) were selected for validation using quantitative real-time PCR (qPCR). The total RNA utilized for validation was identical to that used in the transcriptomic analysis and was reverse-transcribed into complementary DNA (cDNA). qPCR assays were conducted employing the SYBR Green method. Primer sequences are listed in Table 1 . The β-actin gene served as an internal reference, and relative gene expression levels were determined using the 2^ −ΔΔCt method. Each gene was analyzed using three biological samples, with each sample assessed in triplicate technical replicates. Data are presented as mean ± standard deviation (SD). Statistical significance among groups was evaluated using one-way analysis of variance (ANOVA) [ 37 ] . Table 1 Primer information Gene Symbol Forward Primer (5′->3′) Reverse Primer (5′->3′) β-actin CCTGACGGACAGGTCATCA CGGATGTCGACATCACACTTCA ek1 CTGAAGGGCATCACCCTTGA CAGGTTCCACAGATGAGCTGGAA gpd1l CGGCTGAGAAGTTCTGCGAAAC CGGTGTCTGCATCATCCACAACA gpd1b CTGACCTCATCACCACCTGCTA TGGCTGGACCCTGAAGCTTCT cdp CCAGACCATCAGGCTCCAATGA CCCCTGGAACTCTGAATGCTG abcg2a CCGTCACCTACAGCACGTCTT GAGCGAAGGAGGTCTGTGGAT 2.7 Metabolomic Analysis Gut samples stored at − 80°C were used for metabolomic analysis. All experimental instruments (such as steel beads and tweezers) were sterilized at high temperature or disinfected with ethanol prior to use to ensure experimental consistency and prevent cross-contamination. Approximately 50mg of each sample was weighed and mixed with 1mL of pre-chilled extraction solvent (methanol: acetonitrile: water = 2:2:1, v/v/v) containing internal standards. Stainless steel beads were added, and samples were mechanically disrupted at 65 Hz for 90 s using a tissue grinder. This was followed by intermittent ultrasonic extraction for 10mins to enhance metabolite recovery. The homogenates were incubated at − 20°C for 1h to precipitate proteins and then centrifuged at 12,000 r/min for 15 min at 4°C. A 50µL aliquot of the supernatant was transferred into a 2 mL centrifuge tube and stored overnight at − 20°C. Samples were centrifuged again under identical conditions to remove residual impurities, and 120 µL of the final supernatant was transferred into pretreated amber vials for subsequent analysis. Metabolomic profiling was performed using a Waters ACQUITY I-Class PLUS ultra-high-performance liquid chromatography (UHPLC) system coupled with a Xevo G2-XS QTOF high-resolution mass spectrometer. Chromatographic separation conditions were as follows: 98% A (0–0.25 min), 2% A (10.0–13.0 min), and 98%A (13.1–14.1 min), where mobile phase A was water containing 0.1% formic acid, and phase B was an acetonitrile/methanol mixture. Raw mass spectrometry data were processed using Progenesis QI software for peak extraction, alignment, de-noising, and quantification. Metabolites were annotated through multiple databases, including HMDB, KEGG, and Lipid Maps, to identify their chemical classes and potential metabolic pathways. Subsequently, significantly altered metabolites were subjected to statistical and pathway enrichment analyses using Python-based bioinformatics toolkits (e.g., pandas, scipy, bioinfokit), enabling elucidation of biologically relevant changes at the metabolic pathway level. 2.8 Statistical analysis The research data were analyzed using OriginPro 2026 and the Windows version of SPSS v27.0 software, and expressed as mean and standard deviation (SD). The statistical assessment of differences was conducted by applying one-way analysis of variance (ANOVA). Statistical significance is defined by a p value less than 0.05. Growth parameters, including weight gain rata (WGR, %) = (final weight – initial weight)/initial weight × 100; specific growth rate (SGR, %/d) = (Ln final weight − Ln initial weight) × 100/days. The data were analyzed on the online tool of Majorbio Cloud Platform ( https://www.majorbio.com/tools ). 3. Results 3.1 Gut Biochemical Indicators and Growth Performance Exposure to NP significantly inhibited the growth and enzymatic activity in S. meridionalis (Fig. 1 ), with key enzymes, EK1 and Lipase showing decreased activity compared to the control group ( p < 0.05). NP1 and NP2 groups also experienced lower SGR and WGR ( p < 0.05). However, the introduction of probiotics significantly alleviated these effects, as the NP1 + PB and NP2 + PB groups showed higher growth and enzyme activity compared to the NP group ( p 0.05). 3.2 Probiotics Supplementation and the Restoration of Microbiota-Gene Association Patterns PB supplementation effectively reverses the negative impact of NP on gut microbiota and host gene expression, as shown by by integrated metagenomic and transcriptomic data. NP exposure (NP1/NP2 groups) significantly reduced in the abundance of beneficial bacteria like Bacteroides eggerthii , Bacteroides acidifaciens , Cetobacterium sp ., and Parabacteroides merdae , while significantly increased the abundance of harmful bacteria such as Escherichia coli and Clostridium . PB supplementation (NP1 + PB/NP2 + PB groups) markedly restored bacterial balance and abundance ( p < 0.05) (Fig. 2A). Correspondingly, transcriptomic analysis revealed that PB supplementation reversed NP-induced gene downregulation and significantly upregulated key lipid metabolism genes (e.g., mogat2 , gpd1l , ept1 , ocln , ek1 , and abcg2a ) (Fig. 2B). Correlation network analysis further clarified that PB reshaped the disrupted "microbiota-gene" interaction network caused by NP. Specifically, the restored beneficial bacteria (especially B. acidifaciens , B.eggertthii , and Parabacteroides merdae ) showed significant positive synergistic correlations with upregulated lipid metabolism genes (mogat2, gpd1l, ept1, ocln, ek1, abcg2a) (Fig. 2C). In summary, PB alleviates NP-induced toxicity by synergistically restoring the composition of gut microbiota and host lipid metabolism gene expression. Figure 2. Probiotics alleviate NP toxicity by enriching beneficial bacteria and upregulating host lipid metabolism genes. (A) NP induced reduction and PB-mediated restoration of beneficial bacteria abundance (e.g., B. eggertthii ).(B) PB significantly upregulated lipid metabolism genes ( mogat2 , gpd1l , ept1 , ocln , abcg2a ). (C) Correlation network analysis reveals a positive synergy between restored beneficial bacteria and upregulated lipid metabolism genes. 3.3 Probiotics Supplementation and the Restoration of Microbiota-Metabolites Association Patterns Among the twenty most significantly enriched gut bacterial taxa, the NP group had more harmful genera (e.g., Clostridium, Escherichia ; p < 0.05), while the NP + PB group contained more beneficial genera (e.g., Parabacteroides, Bacteroides, Cetobacterium ; p < 0.05) (Fig. 3 A). Metabolites in the NP2 + PB group were significantly enriched in the Glycolysis/Gluconeogenesis, Metabolic, Galactose Metabolism pathway, indicating that PB may regulate host energy metabolism (Fig. 3 B). Correlation analysis revealed that NP exposure weakened associations between beneficial bacteria (e.g., Parabacteroides merdae , B. acidifaciens , B. eggerthii and Cetobacterium sp. ) and key metabolites (e.g., PC, PE, Phosphatidic Acid, and Phosphocholine), whereas PB supplementation strengthened these (Fig. 3 C). In summary, PB alleviates NP-induced toxicity by restructuring the gut microbiota and modulating host lipid metabolism. 3.4 Probiotics Supplementation and the Restoration of Transcription-Metabolites Association Patterns Integrating transcriptomic and metabolomic data demonstrated that PB supplementation helps restore host genes and metabolic homeostasis disrupted by NP. Compared to the NP group, the NP + PB group showed increased enrichment of metabolic pathways, particularly in lipid metabolism (Fig. 4 A), with glycerophospholipid metabolism pathway being central (Fig. 4 B). In this group, key lipid metabolism genes like mogat2 and ept1 , along with lipid metabolites such as PC and PE, were significantly increased ( p < 0.05) (Fig. 4 C-D). Network analysis revealed distinct “gene‑metabolite” interaction between the NP2 and NP2 + PB groups. The NP2 + PB group exhibited a strengthened positive‑correlation network with upregulated key genes (including mogat2 , ept1 , ek1 , and ocln ), and phospholipid metabolites (PC, PE and Phosphocholine) compared to NP2 group (Figs. 4 E-F). In summary, the NP2 + PB group showed reconstructed synergistic interactions centered on mogat2 and ept1 connecting host genes and metabolites, thereby reversing NP‑induced glycerophospholipid metabolism suppression. 3.5 Multi-Omics Integrated Analysis: PB alleviates NP-induced gut barrier damage by activating the gut microbiota–Kennedy pathway axis to promote host phospholipid synthesis Integrated analyses of metagenomic, transcriptomic, and metabolomic data, supported by mechanistic investigation, demonstrate that PB restores gut barrier function compromised by NP exposure by remodeling the “microbiota–substrate–host synthesis” axis, thereby driving gut phospholipid synthesis (Fig. 5 ). Specifically, PB increases beneficial bacteria like Bacteroides eggerthii and Cetobacterium sp. . The SCFAs produced by these bacteria facilitate triglyceride (TG) hydrolysis, raising 2-monoacylglycerol (2-MAG) levels. Within enterocytes, 2-MAG is converted to 1,2-diacylglycerol (1,2-DAG) by MOGAT2, which together with Etn, is a precursor for phosphatidylethanolamine (PE) synthesis. PB supplementation coordinately upregulated of core enzymatic genes expression in the Kennedy pathway, driving PE synthesis through three sequential steps: 1) Phosphorylation: Etn is phosphorylated to phosphoethanolamine (P-Etn) by ethanolamine kinase EK1 (encoded by the significantly upregulated gene ek1 ). 2) Cytidylylation: P-Etn is converted to CDP-ethanolamine by the key regulatory enzyme P-Etn cytidylyltransferase (CEPT1; the encoding gene cept1 also exhibited elevated expression). 3) Transfer: CDP-Etn react s with 1,2-DAG to produce PE, a reaction catalyzed by diacylglycerol ethanolamine phosphotransferase (EPT1; concomitantly, ept1 showed increased transcription). The enhanced this pathway flux significantly increased the PE and PC levels, thereby promoting the integration of newly synthesized phospholipids into the enterocyte membrane. This supports the anchoring of tight-junction proteins like ocln , strengthening the gut physical barrier. In summary, PB alleviates NP-induced metabolic and barrier dysfunction by reconstructing a complete metabolic cascade, from beneficial bacteria and substrate provision to the coordinated upregulation of the core Kennedy pathway enzymes, thus systemically promoting the endogenous synthesis of PE and PC. 3.6 Validation of Differentially Expressed Genes (DEGs) via Quantitative Real-Time PCR (qRT-PCR) we assessed the relative mRNA levels of six differentially expressed genes ( ek1, gpd1l, gpd1b, pgs1, and abcg2a ) using qRT-PCR. In the NP1 and NP2 groups, the relative mRNA levels of abcg2a , pgs1 , gpd1l , gpd1b , and ek1 were significantly lower compared to the control group ( p < 0.05). However, following PB supplementation (NP1 + PB and NP2 + PB groups), there was a notable recovery in the expression of these genes ( p 0.05). The findings demonstrated a concordance between the qRT‑PCR results and the transcriptomic analysis (Fig. 6 ). 3.7 Validation of Differential metabolites by ELISA ELISA was employed to quantify the levels of PC, PE, and PL in gut tissue. The ELISA results demonstrated a significant upregulation ( p < 0.05) of PC, PE, and PL concentrations in the NP + PB group compared to Group C. Conversely, a significant downregulation ( p < 0.05) in protein concentrations was observed between NP1 and NP2 groups(Fig. 7 ). The concentrations of PC, PE and PL were consistent with the metabolomic data. 4. Discussion 1. Probiotics as Green Bioregulators: Engineering a Protective Microbiome for Sustainable Aquaculture The advancement of sustainable aquaculture demands innovative, eco-friendly strategies to mitigate the impact of pervasive environmental pollutants like NP. Modulating the gut microbiota to improve health offers a promising alternative to direct chemical interventions [ 38 – 40 ] .Our integrated multi-omics analysis (metagenomics, transcriptomics, and metabolomics) delineates this this probiotic‑mediated process: while NP exposure induced gut dysbiosis, probiotic (PB) supplementation restored microbial homeostasis [ 41 ] . This restoration was characterized by the enrichment of beneficial commensals like B. eggerthii and Cetobacterium sp. , likely associated with increased production of short‑chain fatty acids (SCFAs) like acetate and propionate [ 42 , 43 ] . Thus, our findings demonstrate that probiotics function as targeted “green bioregulators,” actively shaping a protective microbiome that enhances host resilience. This offers a concrete, microbiota-mediated bioremediation strategy for sustainable aquaculture management. 2.Restoration of the Nutrient Assimilation Axis: A Genomic Insight into Enhanced Feed Efficiency Our results indicate that NP exposure inhibits the activity of gut lipase, a critical enzyme for lipid digestion in carnivorous fish. This impairment disrupts the hydrolysis of dietary TG into 2-MAG, the primary form for intestinal fat absorption. Consequently, the uptake of energy and essential fatty acids is compromised, ultimately restricting growth and representing a key bottleneck for sustainable production [ 44 , 45 ] . Crucially, transcriptomic and enzymatic analyses confirmed that PB supplementation restored lipase expression and activity, thereby reinstating efficient luminal lipid digestion and ensuring a sustained supply of 2-MAG [ 46 ] , By directly addressing this digestive bottleneck [ 47 , 48 ] . PB re‑established nutrient assimilation axis, supplying essential substrates for downstream anabolic pathways and directly linking to the recovered growth performance (WGR, SGR). These insights deepen our genomic understanding of how probiotics alleviate NP-induced toxicity and offer a targeted strategy to safeguard nutrient absorption and feed efficiency in aquaculture under pollutant stress. 3. Multi-omics Elucidation of the Probiotic-Mediated Gut Barrier Repair via the CDP-Etn 1,2-MAGPE Metabolic Axis 3. Multi-omics Elucidation of the Probiotic-Mediated Gut Barrier Repair via the CDP-Etn + 1,2-MAG → PE Metabolic Axis NP‑induced gut toxicity also involves direct structural damage. We found that NP specifically inhibits the CDP-Etn + 1,2-MAG → PE axis, reducing PE and PC synthesis. As phospholipids constitute the fundamental matrix of cellular membranes, their reduction directly impairs the integrity and function of gut epithelial cells [ 49 – 51 ] . Importantly, our integrated multi-omics approach precisely revealed the probiotic counteraction: transcriptomic data showed specific upregulation of key genes within this axis (e.g., ek1 , cept1 , ept1 , mogat2 ). Critically, direct enzymatic assays confirmed the restoration of EK1 activity, functionally validating a PB-induced reprogramming of host metabolism. This metabolic recovery led to a rebound in PL, PE and PC levels. The reconstituted PL pool served a dual purpose: primarily, it provided the necessary biochemical substrate for the repair of cellular membranes and organelles; furthermore, it established a lipid microenvironment conducive to the assembly of tight junction complexes [ 52 ] . The concurrent upregulation of key tight junction protein gene ( ocln ) in the NP + PB group molecularly connected this metabolic repair to enhanced barrier function, aligning with and extending previous reports on probiotics [ 53 – 55 ] . Therefore, PB alleviates NP‑induced damage via a complementary dual-axis restoration: it restores the "Lipase → 2-MAG" axis for substrate and energy acquisition and simultaneously reactivates the " CDP-Etn + 1,2-MAG → PE" axis for membrane biosynthesis. This model comprehensively explains the systemic growth recovery (WGR, SGR) and offers a novel case for combating metabolic disruptions, supporting the development of chemical-reduced aquaculture. 5. Conclusion In summary, our multi-omics analysis reveals that probiotic remediation operates through a microbiota-triggered, host-centered metabolic reprogramming. The enrichment of SCFA-producing taxa (e.g., B.eggerthii and Cetobacterium sp .) triggered a repair cascade that concurrently restored luminal lipid digestion and reactivated the host's Kennedy pathway for PE synthesis. This dual restoration was driven by the coordinated upregulation of core genes (e.g., ek1 , cept1 , ept1 , mogat2 ), ensuring functional recovery from nutrient assimilation to membrane assembly. Thus, by re-establishing this critical metabolic flow, probiotics provide a fundamental physiological basis for growth recovery under pollutant stress. Collectively, these insights form an actionable roadmap for employing genomics to optimize microbe-host interactions, thereby advancing strategies for resilient and sustainable green aquaculture. Declarations Acknowledgements The authors express their sincere thanks to the researchers and laboratories that contributed to this study. Authors ’ contributions LDQ: Data curation, Validation, Visualization, Formal analysis, Methodology, Writing – original draft, Writing–review & editing; LDQ, LFL, GZB and YL: Investigation (animal husbandry, sample collection,); DRR: Writing – review & editing, Supervision, funding acquisition; ZZX and ZXB: Resources (experimental fish). All authors read and approved the final manuscript. Funding This work was financially supported by grants from the National Natural Science Foundation of China (32460918), Guizhou Modern Agricultural Industry Technology System of China (GZSTYYCYJSTX-202605) and the Guizhou Provincial Key Technology R&D Program (2024 (No. 079)). Data availability Data is provided within the manuscript. The raw sequence data reported in this study have been deposited in the GSA and OMIX databases at the National Genomics Data Center (NGDC), China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences. The accession numbers for the metagenomic, transcriptomic, and metabolomic data are CRA035524, CRA035794, and OMIX014201 respectively, via the following URLs: https://ngdc.cncb.ac.cn/gsa and https://ngdc.cncb.ac.cn/omix Ethics approval and consent to participate The animal study protocol was approved by the Experimental Animal Ethics Committee of Guizhou University (protocol code EAE-GZU-2024-T103） Competing interests The authors declare no competing interests. References Soares A, Guieysse B, Jefferson B, et al. Nonylphenol in the environment: A critical review on occurrence, fate, toxicity and treatment in wastewaters[J]. Environ Int. 2008;34(7):1033–49. Zhang X, Yan W, Chen X, et al. Long-term 4-nonylphenol exposure drives cervical cell malignancy through MAPK-mediated ferroptosis inhibition[J]. J Hazard Mater. 2024;471:134371. De C, Meena B, Behera DK. B K, et al. Probiotics in fish and shellfish culture: immunomodulatory and ecophysiological responses[J]. 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Probiotic Modulation of Lipid Metabolism Disorders Caused by Perfluorobutanesulfonate Pollution in Zebrafish[J]. Volume 54. Environmental Science & Technology; 2020. pp. 7494–503. 12. Suman, Gaurav P, Joshi M, et al. Toxicogenomic profiling of endocrine disruptor 4-Nonylphenol in male catfish Heteropneustes fossilis with respect to gonads[J]. Sci Rep. 2025;15:14307. Chen X, Yi H, Liu S, et al. Probiotics Improve Eating Disorders in Mandarin Fish (Siniperca chuatsi) Induced by a Pellet Feed Diet via Stimulating Immunity and Regulating Gut Microbiota[J]. Microorganisms. 2021;9(6):1288. Amoah K, Tan B, Zhang S, et al. Host gut-derived Bacillus probiotics supplementation improves growth performance, serum and liver immunity, gut health, and resistive capacity against Vibrio harveyi infection in hybrid grouper (♀Epinephelus fuscoguttatus × ♂Epinephelus lanceolatus)[J]. Anim Nutr. 2023;14:163–84. Desai JK, Trangadia BJ, Patel UD, et al. Neurotoxicity of 4-nonylphenol in adult zebrafish: Evaluation of behaviour, oxidative stress parameters and histopathology of brain[J]. Environ Pollut. 2023;334:122206. Chen S, Zhou Y, Chen Y, et al. fastp: an ultra-fast all-in-one FASTQ preprocessor[J]. Bioinformatics. 2018;34(17):i884–90. Bazinet AL, Ondov BD, Sommer DD, et al. BLAST-based validation of metagenomic sequence assignments[J]. PeerJ. 2018;6:e4892. Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements[J]. Nat Methods. 2015;12(4):357–60. Roberts A, Trapnell C, Donaghey J, et al. Improving RNA-Seq expression estimates by correcting for fragment bias[J]. Genome Biol. 2011;12(3):R22. Pasquier J, Cabau C, Nguyen T, et al. Gene evolution and gene expression after whole genome duplication in fish: the PhyloFish database. BMC Genomics. 2016;17:368. Chen L, Zhang YH, Wang S, et al. Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways[J]. PLoS ONE. 2017;12(9):e0184129. Livak KJ, Schmittgen TD. Method[J] Methods. 2001;25(4):402–8. Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2 – ∆∆CT. Pan J, Lu D, Yu L, et al. Nonylphenol induces depressive behavior in rats and affects gut microbiota: A dose–dependent effect[J]. Environ Pollut. 2024;344:123357. Van Der Hee B, Wells JM. Microbial Regulation of Host Physiology by Short-chain Fatty Acids[J]. Trends Microbiol. 2021;29(8):700–12. Van Der Hee B, Wells JM. Microbial Regulation of Host Physiology by Short-chain Fatty Acids[J]. Trends Microbiol. 2021;29(8):700–12. Pan J, Lu D, Yu L, et al. Nonylphenol induces depressive behavior in rats and affects gut microbiota: A dose–dependent effect[J]. Environ Pollut. 2024;344:123357. Martin-Gallausiaux C, Marinelli L, Blottière HM et al. SCFA: mechanisms and functional importance in the gut[J]. Proceedings of the Nutrition Society, 2021, 80(1): 37–49. González Hernández MA, Canfora EE, Jocken JWE et al. The Short-Chain Fatty Acid Acetate in Body Weight Control and Insulin Sensitivity[J]. Nutrients, 2019, 11(8): 1943. Esteban L, Muñío MDM, Robles A, et al. Synthesis of 2-monoacylglycerols (2-MAG) by enzymatic alcoholysis of fish oils using different reactor types[J]. Biochem Eng J. 2009;44(2–3):271–9. Muñío MDM, Esteban L, Robles A, et al. Synthesis of 2-monoacylglycerols rich in polyunsaturated fatty acids by ethanolysis of fish oil catalyzed by 1,3 specific lipases[J]. Process Biochem. 2008;43(10):1033–9. Wang X, Liu K, Wang Y, et al. Preparation of 2-Arachidonoylglycerol by Enzymatic Alcoholysis: Effects of Solvent and Water Activity on Acyl Migration[J]. Foods. 2022;11(20):3213. Adnan M, Patel M, Hadi S. Functional and health promoting inherent attributes of Enterococcus hirae F2 as a novel probiotic isolated from the digestive tract of the freshwater fish Catla catla[J]. PeerJ. 2017;5:e3085. Salehi M, Bagheri D, Sotoudeh E, et al. The Combined Effects of Propionic Acid and a Mixture of Bacillus spp. Probiotic in a Plant Protein–Rich Diet on Growth, Digestive Enzyme Activities, Antioxidant Capacity, and Immune-Related Genes mRNA Transcript Abundance in Lates calcarifer Fry[J]. Probiotics Antimicrob Proteins. 2023;15(3):655–67. Wu Y, Chen K, Xing G, et al. Phospholipid remodeling is critical for stem cell pluripotency by facilitating mesenchymal-to-epithelial transition[J]. Sci Adv. 2019;5(11):eaax7525. Fowle-Grider R, Rowles JL, Shen I, et al. Dietary fructose enhances tumour growth indirectly via interorgan lipid transfer[J]. Nature. 2024;636(8043):737–44. Yin Y, Sichler A, Ecker J, et al. Gut microbiota promote liver regeneration through hepatic membrane phospholipid biosynthesis[J]. J Hepatol. 2023;78(4):820–35. Abdulqadir R, Engers J, Al-Sadi R. Role of Bifidobacterium in Modulating the Intestinal Epithelial Tight Junction Barrier: Current Knowledge and Perspectives[J]. Curr Developments Nutr. 2023;7(12):102026. Serek P, Oleksy-Wawrzyniak M. The Effect of Bacterial Infections, Probiotics and Zonulin on Intestinal Barrier Integrity[J]. Int J Mol Sci. 2021;22(21):11359. Al-Sadi R, Dharmaprakash V, Nighot P, et al. Bifidobacterium bifidum Enhances the Intestinal Epithelial Tight Junction Barrier and Protects against Intestinal Inflammation by Targeting the Toll-like Receptor-2 Pathway in an NF-κB-Independent Manner[J]. Int J Mol Sci. 2021;22(15):8070. Al-Sadi R, Nighot P, Nighot M, et al. Lactobacillus acidophilus Induces a Strain-specific and Toll-Like Receptor 2–Dependent Enhancement of Intestinal Epithelial Tight Junction Barrier and Protection Against Intestinal Inflammation[J]. Am J Pathol. 2021;191(5):872–84. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 06 May, 2026 Reviews received at journal 30 Mar, 2026 Reviews received at journal 02 Mar, 2026 Reviewers agreed at journal 23 Feb, 2026 Reviewers agreed at journal 23 Feb, 2026 Reviewers invited by journal 17 Feb, 2026 Editor assigned by journal 12 Feb, 2026 Submission checks completed at journal 12 Feb, 2026 First submitted to journal 10 Feb, 2026 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-8843019","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":592595946,"identity":"184aaeaf-fc47-4642-ba63-4d06b7160c0e","order_by":0,"name":"Deqin Luo","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Deqin","middleName":"","lastName":"Luo","suffix":""},{"id":592595947,"identity":"89244072-ab0a-473f-99d1-a50c275eabf4","order_by":1,"name":"Fanglian Lu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Fanglian","middleName":"","lastName":"Lu","suffix":""},{"id":592595948,"identity":"4ca07777-62ce-4c2a-99f6-f8f6788c3da5","order_by":2,"name":"Lian Yang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lian","middleName":"","lastName":"Yang","suffix":""},{"id":592595949,"identity":"02d6a0fa-2dc1-4240-8822-46e966e4000a","order_by":3,"name":"Zhenbo Gan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zhenbo","middleName":"","lastName":"Gan","suffix":""},{"id":592595950,"identity":"82c1a854-f725-499e-a72e-88749bc01cf9","order_by":4,"name":"Xianbo Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xianbo","middleName":"","lastName":"Zhang","suffix":""},{"id":592595951,"identity":"079e8715-ad7f-4397-9832-283d98a74ab9","order_by":5,"name":"Zhenxin Zhao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zhenxin","middleName":"","lastName":"Zhao","suffix":""},{"id":592595952,"identity":"47203f28-1d76-46c2-bfe1-7b20a6e24798","order_by":6,"name":"Ranran Dong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYHACAyC2gTB5SNCSRrqWwyRoMZ99eOPjgl/n7ebPSGB88LaNQd6ckBaZc2nFxjP7bic3zkhgNpzbxmC4s4GAFgkeHjNp3p7bycwSCWzSvG0MCQYHiNNyLplNIoH9N/FaeH4csOMB2sJMpBa2YmPehuQECZ6HzZJzzkkYbiCshXnjY54/dvby7ckHP7wps5EnaAsYMLYxJDYwMDaAjCBGPQj8YbAnVukoGAWjYBSMQAAAmFo2zx3ivJEAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Ranran","middleName":"","lastName":"Dong","suffix":""}],"badges":[],"createdAt":"2026-02-10 16:08:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8843019/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8843019/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102985924,"identity":"35671b39-bd98-4a05-99cc-f51bbc6ad2ec","added_by":"auto","created_at":"2026-02-19 10:17:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1480833,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of different treatment groups on lipase activity, EK1 protein concentration, WGR, and SGR. C: Fed basal feed only; NP1: basal feed 7 weeks+ 0.1 mg/L NP for 15 days; NP2: basal feed 7 weeks+ 0.1 mg/L NP for 15 days; NP2+PB: basal feed 7 weeks+ 0.2mg/L NP for 15 days; PB: Probiotic-supplemented feed for 7 weeks without NP exposure.Note: Different lowercase letters in the table indicate significant differences between groups (\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05), while different uppercase letters indicate highly significant differences (\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01). Groups sharing the same letter are not significantly different.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8843019/v1/21fd47c775243e372e2af271.png"},{"id":103049259,"identity":"9867f70e-2183-41a7-ad18-465ace3ddab1","added_by":"auto","created_at":"2026-02-20 07:39:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2561724,"visible":true,"origin":"","legend":"\u003cp\u003eProbiotics alleviate NP toxicity by enriching beneficial bacteria and upregulating host lipid metabolism genes. (A) NP induced reduction and PB-mediated restoration of beneficial bacteria abundance (e.g., \u003cem\u003eB. eggertthii\u003c/em\u003e).(B) PB significantly upregulated lipid metabolism genes (\u003cem\u003emogat2\u003c/em\u003e, \u003cem\u003egpd1l\u003c/em\u003e, \u003cem\u003eept1\u003c/em\u003e, \u003cem\u003eocln\u003c/em\u003e,\u003cem\u003e abcg2a\u003c/em\u003e). (C) Correlation network analysis reveals a positive synergy between restored beneficial bacteria and upregulated lipid metabolism genes.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8843019/v1/1facf040f6bd497c9cec7c07.png"},{"id":102985931,"identity":"54eb4ac3-6599-49eb-befe-b477fa7936c9","added_by":"auto","created_at":"2026-02-19 10:17:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5048351,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of microbiome and metabolite differences among different treatment groups. (A) Circos plot illustrating the species abundance distribution and inter-group associations among the NP1, NP2, PB, and NP2+PB samples. (B) Differential enrichment of KEGG between the NP2 and NP2+PB groups. (C) Correlation between key microbial species and metabolites revealed by spearman analysis.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8843019/v1/7dabdb16baf69928b3a0cb9b.png"},{"id":103049302,"identity":"6a0bb5e4-76d7-4913-bbff-bf07ea1eed1f","added_by":"auto","created_at":"2026-02-20 07:39:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":8739120,"visible":true,"origin":"","legend":"\u003cp\u003ePB-mediated remodeling of Glycerophospholipid metabolism unveiled through integrated transcriptomic and metabolomic analysis. (A) Dominance of metabolic pathways identified by transcriptome KEGG enrichment analysis. (B)Metabolomic enrichment analysis highlighting glycerophospholipid metabolism. (C) Identification of glycerophospholipid metabolism as the core altered pathway (NP2+PB vs NP2) with the involvement of \u003cem\u003emogat2\u003c/em\u003e. (D) Scatter plot of differentially expressed metabolites , indicating upregulated levels of PC and PE in the NP+PB group. (E, F) Integrated gene-metabolite interaction networks in the NP2+PB group: positive correlations between \u003cem\u003emogat2\u003c/em\u003e/\u003cem\u003eept1\u003c/em\u003eand PC/PE with inclusion of \u003cem\u003eek1\u003c/em\u003e and \u003cem\u003eoculn.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8843019/v1/4552ea92fde84d66d52147f9.png"},{"id":102985929,"identity":"8c2dce95-d94f-420d-b7d4-dd9b6f0266c9","added_by":"auto","created_at":"2026-02-19 10:17:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1513175,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of the mechanism by which PB restores gut barrier integrity through the microbiota–substrate–Kennedy pathway axis. Note: This schematic was generated employing Figdraw 2.0.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8843019/v1/77f35c1bdb226295c8cae125.png"},{"id":102985925,"identity":"57ec9a8b-ad98-42d6-8e91-8d0c34a43a3f","added_by":"auto","created_at":"2026-02-19 10:17:49","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":690791,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of DEGs by qRT-PCR. Different characters (a, b, c, d) show significant difference (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8843019/v1/5d3eb9a3d3488270aa0ab165.png"},{"id":102985928,"identity":"8eec1be0-57cd-41c9-aea1-4a64752894f6","added_by":"auto","created_at":"2026-02-19 10:17:49","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":359437,"visible":true,"origin":"","legend":"\u003cp\u003eProbiotic supplementation restores Kennedy pathway phospholipid homeostasis in the gut of NP-exposed \u003cem\u003eSilurus meridionalis\u003c/em\u003e. (A) Phosphatidylethanolamine(PE), (B) phosphatidylcholine(PC), and (C) total phospholipid (PL) contents.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8843019/v1/bcfd2e9e7036444911974098.png"},{"id":103050835,"identity":"b45ad4cb-01d1-4935-8f8b-3761fcd819ee","added_by":"auto","created_at":"2026-02-20 07:55:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":22655346,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8843019/v1/5672658e-e9bd-4a37-99a7-fc2c0e8cedd4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Harnessing Probiotics to Combat Nonylphenol Toxicity: A Multi-Omics Approach of Gut Microbiome Remodeling in Silurus meridionalis ","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNonylphenol (NP), a widely used industrial compound in surfactant and lubricants \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e, poses a significant environmental threat. Its hydrophobicity leads to accumulate in organic-rich sediments, making it a persistent aquatic pollutant \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. As a typical environmental endocrine disruptor, NP induces both acute and chronic toxicity in aquatic organisms. In fish, crustaceans, and mollusks, NP disrupts reproductive hormone synthesis, leading to gonadal abnormalities and imbalanced population sex ratios\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.NP also induced intestinal toxicity, damaging gut epithelium, compromising barrier integrity, and perturbs microbiome homeostasis\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Its resistant long alkyl chain leads to persistent accumulation in gut tissues, exacerbating barrier dysfunction and microbial dysbiosis\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWithin antibiotic-free aquaculture, probiotics are redefined as strategic microbial modulators\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Their function now extends beyond growth promoting to include the enhancement of environmental adaptability through dynamic microbiota-host interactions\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Probiotics transiently colonize the gut mucosa, exerting direct or indirect effects on host health by secretion of metabolites, competition for ecological niches, and modulation of the local microenvironment\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Evidence from various models supports their multifunctional roles. For example, \u003cem\u003eLactobacillus\u003c/em\u003e enhances gut barrier function by activating the hypoxia-inducible factor signaling pathway through lactic acid production\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. In \u003cem\u003eOncorhynchus mykiss\u003c/em\u003e, Additionally, improvements in gut morphology and concomitant enhancement of absorptive capacity have been observed dietary \u003cem\u003ePediococcus acidilactici\u003c/em\u003e improves gut morphology and absorptive capacity\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Studies also show that \u003cem\u003eBacillus subtilis\u003c/em\u003e (including its spore form) promotes beneficial lactic acid bacteria, while \u003cem\u003eLactobacillus acidophilus\u003c/em\u003e modulates microbiota composition, improves gut structure, and enhance digestive enzymes activity\u003csup\u003e[\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Furthermore, in \u003cem\u003eOreochromis niloticus\u003c/em\u003e, compound probiotics modify the gut microbiota to enhance nutrient absorption \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. In \u003cem\u003eLitopenaeus vannamei\u003c/em\u003e, a supplement containing \u003cem\u003eBacillus subtilis\u003c/em\u003e and \u003cem\u003eLactobacillus acidophilus\u003c/em\u003e strengthens the gut barrier, enhances the gut microbiota's carbon utilization, and increases beneficial probiotics populations\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Similarly, in \u003cem\u003eSus scrofa\u003c/em\u003e, the same combination strengthens the mucosal barrier by boosting beneficial microbiota and short-chain fatty acids(SCFAs)\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. These findings highlight probiotics' dual role in improving nutrient utilization and enhancing resilience by maintaining a diverse and functional gut microbiota. Although the role of probiotics in promoting gut homeostasis is well documented. However, their ability to reduce environmental pollutants toxicity and the related molecular mechanisms is not well understood.\u003c/p\u003e \u003cp\u003e \u003cem\u003eSilurus meridionalis\u003c/em\u003e, an economically important species of the family Siluridae, is valued for its high intake ability, strong disease resistance, rapid growth, and nutritional value, making it a vital source of high-quality protein\u003csup\u003e[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Due to its benthic habit, \u003cem\u003eS. meridionalis\u003c/em\u003e is consistently exposed to NP in sediments. It's uncertain if this affects gut balance and whether probiotics can counteract the toxicity, as well as the molecular processes involved. Based on the aforementioned background, this study employed \u003cem\u003eS. meridionalis\u003c/em\u003e as a model organism. Utilizing a \"probiotic-pretreatment followed by NP-exposure\" experimental design, we sought not only to evaluate the mitigation of NP‑induced intestinal toxicity by a \u003cem\u003eBacillus subtilis\u003c/em\u003e and \u003cem\u003eLactobacillus acidophilus\u003c/em\u003e complex but to mechanistically decipher its protective effects. To this end, an integrated multi‑omics approach was used: metagenomics analyzed microbial community and functional pathway changes, transcriptomics examined host genes related to lipid metabolism and intestinal barrier function, and untargeted metabolomics tracked global shifts in the gut metabolome. Finally, through correlating and integrating of these data, a \"microbial function \u0026ndash; host gene \u0026ndash; metabolite \u0026ndash; phenotype\" regulatory network was constructed, thereby providing a systems‑level perspective on how the probiotic formulation alleviates NP‑induced toxicity.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Experimental Feed and Design\u003c/h2\u003e \u003cp\u003e \u003cem\u003eBacillus subtilis\u003c/em\u003e and \u003cem\u003eLactobacillus acidophilus\u003c/em\u003e used in this study were purchased from Shandong Linyi Yi Antibiotic-Free Biotechnology Co., Ltd. (Shandong, China). with viable counts of 1.0 \u0026times; 10\u0026sup1;\u0026sup1; CFU/g and 1.0 \u0026times; 10\u0026sup1;⁰ CFU/g, respectively. In accordance with the experimental protocol, a stock solution of NP was initially prepared in ethanol and subsequently diluted to achieve the desired working concentrations of 0.1 mg/L and 0.25 mg/L. Simultaneously, equal quantities of two probiotic types were combined to create compound probiotic powder preparations, adhering to a mass ratio of 1.00% relative to the basal feed. These preparations were then dissolved in a neutral buffer solution, maintaining a buffer solution volume (mL) to feed mass (g) ratio of 1:10. The compound probiotics were uniformly sprayed onto the basal feed, thoroughly mixed, and air-dried under ventilated conditions. Fresh feed was prepared daily, portioned appropriately, and stored at \u0026minus;\u0026thinsp;20\u0026deg;C until required for use. After acclimation to the rearing conditions, the experimental fish were randomly assigned into six groups (20 fish per group, with three replicates per group) as follows: \u003cb\u003eGroup C\u003c/b\u003e: fed with basal feed only; \u003cb\u003eGroup NP1\u003c/b\u003e: fed with basal feed for 7 weeks, followed by exposure to 0.1mg/L NP for 15 days; \u003cb\u003eGroup NP2\u003c/b\u003e: fed with basal feed for 7 weeks, followed by exposure to 0.25 mg/L NP for 15 days; \u003cb\u003eGroup NP1\u0026thinsp;+\u0026thinsp;PB\u003c/b\u003e: fed with compound probiotic-supplemented feed for 7 weeks, followed by exposure to 0.1 mg/L NP for 15 days; \u003cb\u003eGroupNP2\u0026thinsp;+\u0026thinsp;PB\u003c/b\u003e: fed with compound probiotic-supplemented feed for 7 weeks, followed by exposure to 0.25 mg/L NP for 15 days; \u003cb\u003eGroup PB\u003c/b\u003e: fed with compound probiotic-supplemented feed for 7 weeks without NP exposure\u003csup\u003e[\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Experimental Fish\u003c/h2\u003e \u003cp\u003eHealthy \u003cem\u003eS. meridionalis\u003c/em\u003e used in this study were obtained from a local aquaculture farm in Huishui County, (Guizhou, China). Prior to the experiment, the fish were acclimated for 2 weeks and fed a commercially available \u003cem\u003eS. meridionalis\u003c/em\u003e diet to adapt them to the experimental environment. Feeding was performed twice daily (8:00a.m. and 17:30p.m.) to apparent satiation. During the experimental period, water quality was strictly controlled: water temperature was maintained at 25\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C, dissolved oxygen was kept above 7.0 mg/L, pH was 7.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5, and ammonia nitrogen concentration was maintained below 0.08 mg/L\u003csup\u003e[\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Enzyme Activity Measurement and Differentially Expressed Metabolites Validation\u003c/h2\u003e \u003cp\u003eEK1 and Lipase were measured according to the protocols provided by Jiangsu Saiyan Biotechnology Co., Ltd. (Jiangsu, China). All experimental procedures were performed strictly following the instructions provided with the kits.\u003c/p\u003e \u003cp\u003eTo assess the reliability of the differential metabolite results, the expression levels of phosphocholine (PC), total phospholipids (PL), and phosphatidylethanolamine (PE) were measured using an enzyme-linked immunosorbent assay (ELISA).The experiment was meticulously conducted in accordance with the operational instructions provided by Jiangsu Saiyan Biotechnology Co., Ltd. (Jiangsu, China). utilizing their reagent kit. A standard curve was constructed and a linear equation was derived based on the concentration and absorbance (OD) of the standard substances. The OD values of the samples were then used to calculate protein concentrations, allowing for the evaluation of the consistency and accuracy of the metabolomic findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Metagenomic Analysis\u003c/h2\u003e \u003cp\u003eGenomic DNA from gut samples was extracted using a commercial extraction kit provided by Guizhou Saiyan Biotechnology Co., Ltd. (Guizhou, China) The integrity of the extracted DNA was assessed via 1% agarose gel electrophoresis, and only samples with a primary band length of \u0026ge;\u0026thinsp;10kb were retained to ensure high sequencing quality. After confirming DNA quantity and purity, sequencing was performed on the Illumina Nova Seq high-throughput platform. Independent libraries were constructed for each sample, employing a paired-end sequencing strategy (PE150bp). Raw sequencing reads were initially evaluated using FastQC (v0.12.1) to assess base quality distribution, mean sequence quality, and nucleotide composition. Quality control was subsequently conducted using Cutadapt (v1.17) and fastp (v0.20.0) to remove low-quality reads (length\u0026thinsp;\u0026lt;\u0026thinsp;50 bp, average quality score\u0026thinsp;\u0026lt;\u0026thinsp;20, or reads containing ambiguous bases)\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. High-quality clean reads were then assembled using MEGAHIT with the \u0026ldquo;meta-large\u0026rdquo; preset parameter. A \u0026ldquo;single-sample pre-assembly\u0026mdash;merging of unmapped reads\u0026mdash;re-assembly\u0026rdquo; strategy was applied to achieve efficient large-scale metagenomic assembly and optimization, thereby generating high-quality contigs for subsequent taxonomic annotation and functional analysis. Non-redundant gene sets were constructed using MMseqs2. The predicted sequences were aligned against the GenBank non-redundant (NR) database using BLAST, and hits with \u003cem\u003eE\u003c/em\u003e-value\u0026thinsp;\u0026le;\u0026thinsp;1e\u0026thinsp;\u0026minus;\u0026thinsp;5 were retained for species annotation\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Functional annotation was then performed by comparing the predicted genes against GO, KEGG, eggNOG, Pfam, SwissProt, CAZy, VFDB, CYPED, and additional reference databases\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Transcriptomic Analysis\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from gut samples of \u003cem\u003eS. meridionalis\u003c/em\u003e using a commercial kit (Guizhou Saiyan Biotechnology Co., Ltd., China). RNA quality was rigorously assessed: purity (OD260/280 ratio of 1.8\u0026ndash;2.0) and concentration were determined spectrophotometrically, and integrity was verified by 1% agarose gel electrophoresis and an Agilent 2100 Bioanalyzer (all samples had RNA Integrity Number, RIN\u0026thinsp;\u0026gt;\u0026thinsp;7.0). Sequencing libraries were constructed from qualified RNA and sequenced on an Illumina HiSeq X Ten platform to generate 150-bp paired-end reads. Raw reads were processed using fastp (v0.23.0) to remove adapters, poly-N sequences, and low-quality bases, yielding high-quality clean reads\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. These reads were aligned to the \u003cem\u003eS. meridionalis\u003c/em\u003e reference genome using HISAT2 (v2.2.1). Transcript assembly and quantification were performed with StringTie (v1.3.0). For functional annotation, predicted protein sequences were aligned against the NR, Swiss-Prot, and KOG databases using DIAMOND (v2.0.15) with a significance threshold of E-value\u0026thinsp;\u0026lt;\u0026thinsp;1e\u0026thinsp;\u0026minus;\u0026thinsp;5. The top-hit protein (highest alignment score) for each transcript was used to assign functional annotations. Gene expression levels were quantified as read counts and FPKM using featureCounts (v2.0.9) and Salmon (v1.10.0), respectively. Differentially expressed genes (DEGs) were identified with DESeq2 (v1.46.0), applying a threshold of adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log₂FC| \u0026gt; 1)\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Gene Ontology (GO) and KEGG pathway enrichment analyses of DEGs were conducted using clusterProfiler (v4.14.0), with results visualized via ggplot2 (v3.5.1)\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Validation of DEGs by Quantitative Real-Time PCR (qPCR)\u003c/h2\u003e \u003cp\u003eFive differentially expressed genes (\u003cem\u003eek1, gpd1l, gpd1b, pgs1, and abcg2a\u003c/em\u003e) were selected for validation using quantitative real-time PCR (qPCR). The total RNA utilized for validation was identical to that used in the transcriptomic analysis and was reverse-transcribed into complementary DNA (cDNA). qPCR assays were conducted employing the SYBR Green method. Primer sequences are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The β-actin gene served as an internal reference, and relative gene expression levels were determined using the 2^\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method. Each gene was analyzed using three biological samples, with each sample assessed in triplicate technical replicates. Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Statistical significance among groups was evaluated using one-way analysis of variance (ANOVA) \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimer information\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene Symbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward Primer (5\u0026prime;-\u0026gt;3\u0026prime;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReverse Primer (5\u0026prime;-\u0026gt;3\u0026prime;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-actin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCTGACGGACAGGTCATCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCGGATGTCGACATCACACTTCA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eek1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTGAAGGGCATCACCCTTGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCAGGTTCCACAGATGAGCTGGAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egpd1l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCGGCTGAGAAGTTCTGCGAAAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCGGTGTCTGCATCATCCACAACA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egpd1b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTGACCTCATCACCACCTGCTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTGGCTGGACCCTGAAGCTTCT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecdp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCAGACCATCAGGCTCCAATGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCCCTGGAACTCTGAATGCTG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eabcg2a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCGTCACCTACAGCACGTCTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGAGCGAAGGAGGTCTGTGGAT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Metabolomic Analysis\u003c/h2\u003e \u003cp\u003eGut samples stored at \u0026minus;\u0026thinsp;80\u0026deg;C were used for metabolomic analysis. All experimental instruments (such as steel beads and tweezers) were sterilized at high temperature or disinfected with ethanol prior to use to ensure experimental consistency and prevent cross-contamination. Approximately 50mg of each sample was weighed and mixed with 1mL of pre-chilled extraction solvent (methanol: acetonitrile: water\u0026thinsp;=\u0026thinsp;2:2:1, v/v/v) containing internal standards. Stainless steel beads were added, and samples were mechanically disrupted at 65 Hz for 90 s using a tissue grinder. This was followed by intermittent ultrasonic extraction for 10mins to enhance metabolite recovery. The homogenates were incubated at \u0026minus;\u0026thinsp;20\u0026deg;C for 1h to precipitate proteins and then centrifuged at 12,000 r/min for 15 min at 4\u0026deg;C. A 50\u0026micro;L aliquot of the supernatant was transferred into a 2 mL centrifuge tube and stored overnight at \u0026minus;\u0026thinsp;20\u0026deg;C. Samples were centrifuged again under identical conditions to remove residual impurities, and 120 \u0026micro;L of the final supernatant was transferred into pretreated amber vials for subsequent analysis. Metabolomic profiling was performed using a Waters ACQUITY I-Class PLUS ultra-high-performance liquid chromatography (UHPLC) system coupled with a Xevo G2-XS QTOF high-resolution mass spectrometer. Chromatographic separation conditions were as follows: 98% A (0\u0026ndash;0.25 min), 2% A (10.0\u0026ndash;13.0 min), and 98%A (13.1\u0026ndash;14.1 min), where mobile phase A was water containing 0.1% formic acid, and phase B was an acetonitrile/methanol mixture. Raw mass spectrometry data were processed using Progenesis QI software for peak extraction, alignment, de-noising, and quantification. Metabolites were annotated through multiple databases, including HMDB, KEGG, and Lipid Maps, to identify their chemical classes and potential metabolic pathways. Subsequently, significantly altered metabolites were subjected to statistical and pathway enrichment analyses using Python-based bioinformatics toolkits (e.g., pandas, scipy, bioinfokit), enabling elucidation of biologically relevant changes at the metabolic pathway level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe research data were analyzed using OriginPro 2026 and the Windows version of SPSS v27.0 software, and expressed as mean and standard deviation (SD). The statistical assessment of differences was conducted by applying one-way analysis of variance (ANOVA). Statistical significance is defined by a p value less than 0.05. Growth parameters, including weight gain rata (WGR, %) = (final weight \u0026ndash; initial weight)/initial weight \u0026times; 100; specific growth rate (SGR, %/d) = (Ln final weight\u0026thinsp;\u0026minus;\u0026thinsp;Ln initial weight) \u0026times; 100/days. The data were analyzed on the online tool of Majorbio Cloud Platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.majorbio.com/tools\u003c/span\u003e\u003cspan address=\"https://www.majorbio.com/tools\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Gut Biochemical Indicators and Growth Performance\u003c/h2\u003e \u003cp\u003eExposure to NP significantly inhibited the growth and enzymatic activity in \u003cem\u003eS. meridionalis\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with key enzymes, EK1 and Lipase showing decreased activity compared to the control group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). NP1 and NP2 groups also experienced lower SGR and WGR (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, the introduction of probiotics significantly alleviated these effects, as the NP1\u0026thinsp;+\u0026thinsp;PB and NP2\u0026thinsp;+\u0026thinsp;PB groups showed higher growth and enzyme activity compared to the NP group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), though not fully reaching control group levels. The PB group showed no significant differences from the control group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Probiotics Supplementation and the Restoration of Microbiota-Gene Association Patterns\u003c/h2\u003e \u003cp\u003ePB supplementation effectively reverses the negative impact of NP on gut microbiota and host gene expression, as shown by by integrated metagenomic and transcriptomic data. NP exposure (NP1/NP2 groups) significantly reduced in the abundance of beneficial bacteria like \u003cem\u003eBacteroides eggerthii\u003c/em\u003e, \u003cem\u003eBacteroides acidifaciens\u003c/em\u003e, \u003cem\u003eCetobacterium sp\u003c/em\u003e., and \u003cem\u003eParabacteroides merdae\u003c/em\u003e, while significantly increased the abundance of harmful bacteria such as \u003cem\u003eEscherichia coli\u003c/em\u003e and \u003cem\u003eClostridium\u003c/em\u003e. PB supplementation (NP1\u0026thinsp;+\u0026thinsp;PB/NP2\u0026thinsp;+\u0026thinsp;PB groups) markedly restored bacterial balance and abundance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;2A). Correspondingly, transcriptomic analysis revealed that PB supplementation reversed NP-induced gene downregulation and significantly upregulated key lipid metabolism genes (e.g., \u003cem\u003emogat2\u003c/em\u003e, \u003cem\u003egpd1l\u003c/em\u003e, \u003cem\u003eept1\u003c/em\u003e, \u003cem\u003eocln\u003c/em\u003e, \u003cem\u003eek1\u003c/em\u003e, and \u003cem\u003eabcg2a\u003c/em\u003e) (Fig.\u0026nbsp;2B). Correlation network analysis further clarified that PB reshaped the disrupted \"microbiota-gene\" interaction network caused by NP. Specifically, the restored beneficial bacteria (especially \u003cem\u003eB. acidifaciens\u003c/em\u003e, \u003cem\u003eB.eggertthii\u003c/em\u003e, and \u003cem\u003eParabacteroides merdae\u003c/em\u003e) showed significant positive synergistic correlations with upregulated lipid metabolism genes (mogat2, gpd1l, ept1, ocln, ek1, abcg2a) (Fig.\u0026nbsp;2C). In summary, PB alleviates NP-induced toxicity by synergistically restoring the composition of gut microbiota and host lipid metabolism gene expression.\u003c/p\u003e \u003cp\u003eFigure 2. Probiotics alleviate NP toxicity by enriching beneficial bacteria and upregulating host lipid metabolism genes. (A) NP induced reduction and PB-mediated restoration of beneficial bacteria abundance (e.g., \u003cem\u003eB. eggertthii\u003c/em\u003e).(B) PB significantly upregulated lipid metabolism genes (\u003cem\u003emogat2\u003c/em\u003e, \u003cem\u003egpd1l\u003c/em\u003e, \u003cem\u003eept1\u003c/em\u003e, \u003cem\u003eocln\u003c/em\u003e, \u003cem\u003eabcg2a\u003c/em\u003e). (C) Correlation network analysis reveals a positive synergy between restored beneficial bacteria and upregulated lipid metabolism genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Probiotics Supplementation and the Restoration of Microbiota-Metabolites Association Patterns\u003c/h2\u003e \u003cp\u003eAmong the twenty most significantly enriched gut bacterial taxa, the NP group had more harmful genera (e.g., \u003cem\u003eClostridium, Escherichia\u003c/em\u003e; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while the NP\u0026thinsp;+\u0026thinsp;PB group contained more beneficial genera (e.g., \u003cem\u003eParabacteroides, Bacteroides, Cetobacterium\u003c/em\u003e; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Metabolites in the NP2\u0026thinsp;+\u0026thinsp;PB group were significantly enriched in the Glycolysis/Gluconeogenesis, Metabolic, Galactose Metabolism pathway, indicating that PB may regulate host energy metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Correlation analysis revealed that NP exposure weakened associations between beneficial bacteria (e.g., \u003cem\u003eParabacteroides merdae\u003c/em\u003e, \u003cem\u003eB. acidifaciens\u003c/em\u003e, \u003cem\u003eB. eggerthii\u003c/em\u003e and \u003cem\u003eCetobacterium sp.\u003c/em\u003e) and key metabolites (e.g., PC, PE, Phosphatidic Acid, and Phosphocholine), whereas PB supplementation strengthened these (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). In summary, PB alleviates NP-induced toxicity by restructuring the gut microbiota and modulating host lipid metabolism.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Probiotics Supplementation and the Restoration of Transcription-Metabolites Association Patterns\u003c/h2\u003e \u003cp\u003eIntegrating transcriptomic and metabolomic data demonstrated that PB supplementation helps restore host genes and metabolic homeostasis disrupted by NP. Compared to the NP group, the NP\u0026thinsp;+\u0026thinsp;PB group showed increased enrichment of metabolic pathways, particularly in lipid metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), with glycerophospholipid metabolism pathway being central (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). In this group, key lipid metabolism genes like \u003cem\u003emogat2\u003c/em\u003e and \u003cem\u003eept1\u003c/em\u003e, along with lipid metabolites such as PC and PE, were significantly increased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-D). Network analysis revealed distinct \u0026ldquo;gene‑metabolite\u0026rdquo; interaction between the NP2 and NP2\u0026thinsp;+\u0026thinsp;PB groups. The NP2\u0026thinsp;+\u0026thinsp;PB group exhibited a strengthened positive‑correlation network with upregulated key genes (including \u003cem\u003emogat2\u003c/em\u003e, \u003cem\u003eept1\u003c/em\u003e, \u003cem\u003eek1\u003c/em\u003e, and \u003cem\u003eocln\u003c/em\u003e), and phospholipid metabolites (PC, PE and Phosphocholine) compared to NP2 group (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eE-F). In summary, the NP2\u0026thinsp;+\u0026thinsp;PB group showed reconstructed synergistic interactions centered on \u003cem\u003emogat2\u003c/em\u003e and \u003cem\u003eept1\u003c/em\u003e connecting host genes and metabolites, thereby reversing NP‑induced glycerophospholipid metabolism suppression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e3.5 Multi-Omics Integrated Analysis: PB alleviates NP-induced gut barrier damage by activating the gut microbiota\u0026ndash;Kennedy pathway axis to promote host phospholipid synthesis\u003c/p\u003e \u003cp\u003eIntegrated analyses of metagenomic, transcriptomic, and metabolomic data, supported by mechanistic investigation, demonstrate that PB restores gut barrier function compromised by NP exposure by remodeling the \u0026ldquo;microbiota\u0026ndash;substrate\u0026ndash;host synthesis\u0026rdquo; axis, thereby driving gut phospholipid synthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Specifically, PB increases beneficial bacteria like \u003cem\u003eBacteroides eggerthii\u003c/em\u003e and \u003cem\u003eCetobacterium sp.\u003c/em\u003e. The SCFAs produced by these bacteria facilitate triglyceride (TG) hydrolysis, raising 2-monoacylglycerol (2-MAG) levels. Within enterocytes, 2-MAG is converted to 1,2-diacylglycerol (1,2-DAG) by MOGAT2, which together with Etn, is a precursor for phosphatidylethanolamine (PE) synthesis. PB supplementation coordinately upregulated of core enzymatic genes expression in the Kennedy pathway, driving PE synthesis through three sequential steps: 1) Phosphorylation: Etn is phosphorylated to phosphoethanolamine (P-Etn) by ethanolamine kinase EK1 (encoded by the significantly upregulated gene \u003cem\u003eek1\u003c/em\u003e). 2) Cytidylylation: P-Etn is converted to CDP-ethanolamine by the key regulatory enzyme P-Etn cytidylyltransferase (CEPT1; the encoding gene \u003cem\u003ecept1\u003c/em\u003e also exhibited elevated expression). 3) Transfer: CDP-Etn \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ereact\u003c/span\u003es with 1,2-DAG to produce PE, a reaction catalyzed by diacylglycerol ethanolamine phosphotransferase (EPT1; concomitantly, \u003cem\u003eept1\u003c/em\u003e showed increased transcription). The enhanced this pathway flux significantly increased the PE and PC levels, thereby promoting the integration of newly synthesized phospholipids into the enterocyte membrane. This supports the anchoring of tight-junction proteins like \u003cem\u003eocln\u003c/em\u003e, strengthening the gut physical barrier. In summary, PB alleviates NP-induced metabolic and barrier dysfunction by reconstructing a complete metabolic cascade, from beneficial bacteria and substrate provision to the coordinated upregulation of the core Kennedy pathway enzymes, thus systemically promoting the endogenous synthesis of PE and PC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Validation of Differentially Expressed Genes (DEGs) via Quantitative Real-Time PCR (qRT-PCR)\u003c/h2\u003e \u003cp\u003ewe assessed the relative mRNA levels of six differentially expressed genes (\u003cem\u003eek1, gpd1l, gpd1b, pgs1, and abcg2a\u003c/em\u003e) using qRT-PCR. In the NP1 and NP2 groups, the relative mRNA levels of \u003cem\u003eabcg2a\u003c/em\u003e, \u003cem\u003epgs1\u003c/em\u003e, \u003cem\u003egpd1l\u003c/em\u003e, \u003cem\u003egpd1b\u003c/em\u003e, and \u003cem\u003eek1\u003c/em\u003e were significantly lower compared to the control group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, following PB supplementation (NP1\u0026thinsp;+\u0026thinsp;PB and NP2\u0026thinsp;+\u0026thinsp;PB groups), there was a notable recovery in the expression of these genes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the PB group, the expression levels of \u003cem\u003eabcg2a\u003c/em\u003e and \u003cem\u003eek1\u003c/em\u003e showed no statistically significant difference compared to the control group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The findings demonstrated a concordance between the qRT‑PCR results and the transcriptomic analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Validation of Differential metabolites by ELISA\u003c/h2\u003e \u003cp\u003eELISA was employed to quantify the levels of PC, PE, and PL in gut tissue. The ELISA results demonstrated a significant upregulation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) of PC, PE, and PL concentrations in the NP\u0026thinsp;+\u0026thinsp;PB group compared to Group C. Conversely, a significant downregulation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in protein concentrations was observed between NP1 and NP2 groups(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The concentrations of PC, PE and PL were consistent with the metabolomic data.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e1. Probiotics as Green Bioregulators: Engineering a Protective Microbiome for Sustainable Aquaculture\u003c/p\u003e \u003cp\u003eThe advancement of sustainable aquaculture demands innovative, eco-friendly strategies to mitigate the impact of pervasive environmental pollutants like NP. Modulating the gut microbiota to improve health offers a promising alternative to direct chemical interventions\u003csup\u003e[\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e.Our integrated multi-omics analysis (metagenomics, transcriptomics, and metabolomics) delineates this this probiotic‑mediated process: while NP exposure induced gut dysbiosis, probiotic (PB) supplementation restored microbial homeostasis\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. This restoration was characterized by the enrichment of beneficial commensals like \u003cem\u003eB. eggerthii\u003c/em\u003e and \u003cem\u003eCetobacterium sp.\u003c/em\u003e, likely associated with increased production of short‑chain fatty acids (SCFAs) like acetate and propionate \u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. Thus, our findings demonstrate that probiotics function as targeted \u0026ldquo;green bioregulators,\u0026rdquo; actively shaping a protective microbiome that enhances host resilience. This offers a concrete, microbiota-mediated bioremediation strategy for sustainable aquaculture management.\u003c/p\u003e\n\u003ch3\u003e2.Restoration of the Nutrient Assimilation Axis: A Genomic Insight into Enhanced Feed Efficiency\u003c/h3\u003e\n\u003cp\u003eOur results indicate that NP exposure inhibits the activity of gut lipase, a critical enzyme for lipid digestion in carnivorous fish. This impairment disrupts the hydrolysis of dietary TG into 2-MAG, the primary form for intestinal fat absorption. Consequently, the uptake of energy and essential fatty acids is compromised, ultimately restricting growth and representing a key bottleneck for sustainable production\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. Crucially, transcriptomic and enzymatic analyses confirmed that PB supplementation restored lipase expression and activity, thereby reinstating efficient luminal lipid digestion and ensuring a sustained supply of 2-MAG\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e, By directly addressing this digestive bottleneck\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. PB re‑established nutrient assimilation axis, supplying essential substrates for downstream anabolic pathways and directly linking to the recovered growth performance (WGR, SGR). These insights deepen our genomic understanding of how probiotics alleviate NP-induced toxicity and offer a targeted strategy to safeguard nutrient absorption and feed efficiency in aquaculture under pollutant stress.\u003c/p\u003e\n\u003ch3\u003e3. Multi-omics Elucidation of the Probiotic-Mediated Gut Barrier Repair via the CDP-Etn 1,2-MAGPE Metabolic Axis\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003e3. Multi-omics Elucidation of the Probiotic-Mediated Gut Barrier Repair via the CDP-Etn\u0026thinsp;\u003cb\u003e+\u003c/b\u003e\u0026thinsp;1,2-MAG\u003cb\u003e\u0026rarr;\u003c/b\u003ePE Metabolic Axis\u003c/div\u003e \u003cp\u003eNP‑induced gut toxicity also involves direct structural damage. We found that NP specifically inhibits the CDP-Etn\u0026thinsp;\u003cb\u003e+\u003c/b\u003e\u0026thinsp;1,2-MAG\u003cb\u003e\u0026rarr;\u003c/b\u003ePE axis, reducing PE and PC synthesis. As phospholipids constitute the fundamental matrix of cellular membranes, their reduction directly impairs the integrity and function of gut epithelial cells\u003csup\u003e[\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. Importantly, our integrated multi-omics approach precisely revealed the probiotic counteraction: transcriptomic data showed specific upregulation of key genes within this axis (e.g., \u003cem\u003eek1\u003c/em\u003e, \u003cem\u003ecept1\u003c/em\u003e, \u003cem\u003eept1\u003c/em\u003e, \u003cem\u003emogat2\u003c/em\u003e). Critically, direct enzymatic assays confirmed the restoration of EK1 activity, functionally validating a PB-induced reprogramming of host metabolism. This metabolic recovery led to a rebound in PL, PE and PC levels. The reconstituted PL pool served a dual purpose: primarily, it provided the necessary biochemical substrate for the repair of cellular membranes and organelles; furthermore, it established a lipid microenvironment conducive to the assembly of tight junction complexes \u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e. The concurrent upregulation of key tight junction protein gene (\u003cem\u003eocln\u003c/em\u003e) in the NP\u0026thinsp;+\u0026thinsp;PB group molecularly connected this metabolic repair to enhanced barrier function, aligning with and extending previous reports on probiotics\u003csup\u003e[\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e. Therefore, PB alleviates NP‑induced damage via a complementary dual-axis restoration: it restores the \"Lipase\u003cb\u003e\u0026rarr;\u003c/b\u003e2-MAG\" axis for substrate and energy acquisition and simultaneously reactivates the \" CDP-Etn\u0026thinsp;\u003cb\u003e+\u003c/b\u003e\u0026thinsp;1,2-MAG\u003cb\u003e\u0026rarr;\u003c/b\u003ePE\" axis for membrane biosynthesis. This model comprehensively explains the systemic growth recovery (WGR, SGR) and offers a novel case for combating metabolic disruptions, supporting the development of chemical-reduced aquaculture.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn summary, our multi-omics analysis reveals that probiotic remediation operates through a microbiota-triggered, host-centered metabolic reprogramming. The enrichment of SCFA-producing taxa (e.g., \u003cem\u003eB.eggerthii\u003c/em\u003e and \u003cem\u003eCetobacterium sp\u003c/em\u003e.) triggered a repair cascade that concurrently restored luminal lipid digestion and reactivated the host's Kennedy pathway for PE synthesis. This dual restoration was driven by the coordinated upregulation of core genes (e.g., \u003cem\u003eek1\u003c/em\u003e, \u003cem\u003ecept1\u003c/em\u003e, \u003cem\u003eept1\u003c/em\u003e, \u003cem\u003emogat2\u003c/em\u003e), ensuring functional recovery from nutrient assimilation to membrane assembly. Thus, by re-establishing this critical metabolic flow, probiotics provide a fundamental physiological basis for growth recovery under pollutant stress. Collectively, these insights form an actionable roadmap for employing genomics to optimize microbe-host interactions, thereby advancing strategies for resilient and sustainable green aquaculture.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their sincere thanks to the researchers and laboratories that contributed to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003cstrong\u003e\u0026rsquo;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLDQ: Data curation, Validation, Visualization, Formal analysis, Methodology, Writing \u0026ndash; original draft, Writing\u0026ndash;review \u0026amp; editing; LDQ, LFL, GZB and YL: Investigation (animal husbandry, sample collection,); DRR: Writing \u0026ndash; review \u0026amp; editing, Supervision, funding acquisition; ZZX and ZXB:\u0026nbsp;Resources (experimental fish). All authors 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 financially supported by grants from the National Natural Science Foundation of China (32460918), Guizhou Modern Agricultural Industry Technology System of China (GZSTYYCYJSTX-202605) and the Guizhou Provincial Key Technology R\u0026amp;D Program (2024 (No. 079)).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript. The raw sequence data reported in this study have been deposited in the GSA and OMIX databases at the National Genomics Data Center (NGDC), China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences. The accession numbers for the metagenomic, transcriptomic, and metabolomic data are CRA035524, CRA035794, and OMIX014201 respectively, via the following URLs: https://ngdc.cncb.ac.cn/gsa and https://ngdc.cncb.ac.cn/omix\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe animal study protocol was approved by the Experimental Animal Ethics Committee of Guizhou University (protocol code EAE-GZU-2024-T103）\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSoares A, Guieysse B, Jefferson B, et al. Nonylphenol in the environment: A critical review on occurrence, fate, toxicity and treatment in wastewaters[J]. 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Lactobacillus acidophilus Induces a Strain-specific and Toll-Like Receptor 2\u0026ndash;Dependent Enhancement of Intestinal Epithelial Tight Junction Barrier and Protection Against Intestinal Inflammation[J]. Am J Pathol. 2021;191(5):872\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e\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":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Nonylphenol, Silurus meridionalis, Bacillus subtilis, Lactobacillus acidophilus, Gut microbiota, Multi - omics analysis","lastPublishedDoi":"10.21203/rs.3.rs-8843019/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8843019/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAs a ubiquitous environmental endocrine disruptor, nonylphenol (NP) threatens aquatic organisms, driving the need for sustainable mitigation strategies. While probiotics represent promising eco-friendly supplements, their molecular mechanisms against NP toxicity remain unclear. In this study, \u003cem\u003eSilurus meridionalis\u003c/em\u003e received a 7-week probiotics (\u003cem\u003eBacillus subtilis\u003c/em\u003e and \u003cem\u003eLactobacillus acidophilus\u003c/em\u003e) pretreatment followed by 15-day NP exposure. Integrated metagenomics, transcriptomics, and metabolomics analysis, with qPCR and ELISA validation, to uncover microbial, gene and metabolic responses. Growth performance (SGR, WGR) was concurrently assessed.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eNP exposure significant suppressed WGR and SGR, and induced gut microbiota dysbiosis alongside lipid metabolism disorders in \u003cem\u003eS.meridionalis\u003c/em\u003e. Probiotics pretreatment effectively reversed these toxic effects and restored the inhibited WGR and SGR. Multi-omics integration showed that probiotics protection was mediated via a coherent \"microbe-host\" co-metabolism network across three progressive layers: (1)Microbial Remodeling: enriching beneficial taxa (e.g., \u003cem\u003eBacteroides eggerthii\u003c/em\u003e and \u003cem\u003eCetobacterium sp.)\u003c/em\u003e and enhancing their functional capacity for short-chain fatty acid(SCFAs) synthesis and ethanolamine metabolism; (2) Host Gene Regulation: upregulating key lipid metabolism genes (\u003cem\u003eek1\u003c/em\u003e, \u003cem\u003ecept1\u003c/em\u003e, \u003cem\u003eept1\u003c/em\u003e, \u003cem\u003emogat2\u003c/em\u003e, \u003cem\u003eabcg2a\u003c/em\u003e) and restoring lipase activity; (3) Metabolic Pathways Activation and Physiological Repair: reactivating the NP‑suppressed Kennedy pathway, thereby promoting critical phospholipid (PE and PC) synthesis and ultimately restoring gut barrier function. These results were further were corroborated by qPCR and ELISA.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study systematically elucidates that the probiotics alleviate NP toxicity by remodeling a \"microbiota-host Kennedy pathway genes-metabolites (PE and PC)-growth performance\" regulatory network. The key mechanism is the beneficial microbiota activating the host Kennedy pathway, restoring gut phospholipid homeostasis and barrier function. These findings provide a theoretical basis for developing targeted, lipid metabolism focused probiotic feed additives in sustainable aquaculture.\u003c/p\u003e","manuscriptTitle":"Harnessing Probiotics to Combat Nonylphenol Toxicity: A Multi-Omics Approach of Gut Microbiome Remodeling in Silurus meridionalis ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-19 10:17:44","doi":"10.21203/rs.3.rs-8843019/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-06T09:59:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-30T12:59:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-02T13:26:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59297650145380988903688631566368877410","date":"2026-02-23T13:11:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"102918495562585296761071423086904751783","date":"2026-02-23T12:37:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-17T08:28:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-12T22:43:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-12T22:43:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2026-02-10T15:03:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d2d2c18f-3a85-4f4a-99a5-a49191cfa771","owner":[],"postedDate":"February 19th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-06T09:59:54+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T10:10:02+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-19 10:17:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8843019","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8843019","identity":"rs-8843019","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}