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28 February 2026

Integrated Gut Microbiome and Metabolome Analysis in Largemouth Bass (Micropterus salmoides) Following Viral Infection

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1
College of Life Science, Huzhou University, Huzhou 313000, China
2
Key Laboratory of Freshwater Aquatic Animal Genetic and Breeding of Zhejiang Province, Ministry of Agriculture and Rural Affairs, Zhejiang Institute of Freshwater Fisheries, Huzhou 313000, China
3
Shanghai Engineering Research Center of Aquaculture, Shanghai Ocean University, Shanghai 201306, China
*
Authors to whom correspondence should be addressed.
Animals2026, 16(5), 752;https://doi.org/10.3390/ani16050752
This article belongs to the Special Issue Genetic Selection and Breeding for Improved Traits in Aquaculture Animals
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Simple Summary

This study investigates changes in the gut microbiome and metabolome of largemouth bass following infection with largemouth bass virus. We integrated microbiome analysis via 16S rRNA sequencing and metabolomics using ultra-performance liquid chromatography–tandem mass spectrometry to examine how viral infection affects microbial composition and metabolic pathways in largemouth bass gut. The results showed that viral infection caused significant microbial dysbiosis, including reduced microbial diversity and shifts in dominant taxa. Additionally, metabolomic profiling revealed changes in metabolic pathways, particularly those related to metabolism. These changes suggest that the virus disrupts both microbial and host metabolism, potentially exacerbating intestinal damage.

Abstract

Largemouth bass (Micropterus salmoides) is an important aquaculture species increasingly threatened by viral diseases, particularly largemouth bass virus (LMBV), which can cause significant mortality. However, integrative analyses linking LMBV-induced gut microbiota dysbiosis to metabolomic dysfunction are limited. In this study, we profiled the intestinal microbiome and metabolome alterations in largemouth bass following LMBV infection and conducted an integrated analysis. Infected fish showed reduced alpha diversity and significant shifts in community structure, including increased relative abundances of Bacteroidota and Fusobacteriota and a decrease in Proteobacteria. Opportunistic taxa, such as Pseudomonas and Mycobacterium, were enriched after infection. Metabolomic profiling revealed differential metabolites primarily involved in amino acid and carbohydrate metabolism. Integrative correlation analyses further identified significant associations between opportunistic bacteria and putative harmful metabolites, suggesting that LMBV-induced dysbiosis disrupts host metabolic homeostasis and contributes to immune dysfunction. These findings may clarify the microbiota–metabolite landscape during LMBV infection.

1. Introduction

Largemouth bass (Micropterus salmoides) is a fast-growing, resilient freshwater species with desirable flesh quality that has gained prominence in China’s aquaculture industry in recent years [1]. The rapid expansion of production and market demand has accelerated industry growth. However, intensified farming and increasingly complex rearing environments have led to more frequent disease outbreaks [2,3,4]. Largemouth bass virus (LMBV) causes rapidly spreading infections, leading to severe economic losses and hindering the industry’s sustainable development [5]. LMBV was first detected in Lake Weir, Florida, in 1991, and later triggered mass mortality events in regions such as South Carolina [6]. In China, after its initial isolation and identification in Guangdong in 2008, LMBV has spread widely across major farming areas, posing a significant threat to largemouth bass aquaculture [7,8]. LMBV is an enveloped virus with an icosahedral structure, classified in the genus Ranavirus of the family Iridoviridae, and infects various freshwater fish, including perch, carp, and hybrid mandarin [9,10]. The virus targets vital organs, including the liver, spleen, and intestine, causing lesions such as necrosis, hemorrhage, and intestinal lumen dilation, which lead to high mortality rates [11,12]. Thus, understanding LMBV pathogenesis and host antiviral defenses has become a critical priority for aquaculture health management.
The gut is a primary organ for nutrient digestion and absorption, and it also acts as a critical immune barrier against pathogen invasion. As a key mucosal-associated lymphoid tissue (MALT) in fish, the intestine plays vital roles in initiating immune responses, maintaining mucosal homeostasis, and reinforcing defense against pathogenic colonization. Therefore, maintaining its homeostasis is essential for fish health [13]. The gut microbiota, a central component of the intestinal microecosystem, contributes to nutrient metabolism, vitamin biosynthesis, and intestinal development. It also modulates host immunity and limits colonization by opportunistic pathogens [14]. Environmental stressors and pathogen infections can disrupt microbial equilibrium, leading to dysbiosis, impaired immune function, and increased disease susceptibility [15]. Iridovirus infections in fish significantly alter gut microbiota composition and diversity, which are closely linked to disease onset and progression. For example, in sea perch infected with Sea Perch Iridovirus (SPIV), the abundance of Romboutsia decreased, while Lawsonella, Corynebacterium, and Achromobacter increased. This dysbiosis directly compromised the intestinal barrier [16]. Recent work by Kong et al. [17] showed that LMBV induces segment-specific alterations in intestinal immune responses and microbial community dynamics. However, it remains unclear whether these microbiota fluctuations translate into metabolite profile shifts, and the underlying mechanisms require further investigation.
Metabolomics, a key discipline in systems biology, enables comprehensive profiling of small-molecule metabolites, capturing metabolic perturbations caused by environmental challenges or disease states [18]. This approach provides critical insights into pathogenesis, potential biomarkers, and evidence to inform prevention and control strategies. The intestinal metabolome—an integrated functional readout of the gut microecosystem—is influenced by microbial metabolism, host physiology, and environmental factors [19]. Iridovirus infections profoundly disrupt host energy and immune-related metabolism. For example, in groupers infected with Singapore grouper iridovirus (SGIV), carbohydrate, amino acid, and lipid metabolic pathways are significantly perturbed [20].
Although LMBV-induced alterations in the gut microbiome of largemouth bass have been reported [17], these microbial shifts are often closely linked to changes in metabolite profiles [21]. Microbial metabolism produces various small molecules that modulate host physiology and immune function, influencing antiviral defenses. Conversely, the host metabolic state shapes microbial composition and function, creating a dynamic ecological equilibrium [22]. Whether a similar microbiota–metabolite–immunity regulatory axis operates during LMBV infection in bass remains unclear. To address this, we proposed an integrated analysis of the intestinal microbiome and metabolome of largemouth bass following LMBV infection, combining high-throughput 16S rRNA gene sequencing with ultra-performance liquid chromatography–tandem mass spectrometry (UPLC–MS/MS). We compared gut microbial community structures between healthy and infected fish to define LMBV-induced changes in microbial composition and diversity, identifying key taxa associated with infection. In parallel, we profiled infection-induced metabolomic alterations, identified differential metabolites, and performed Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment to reveal LMBV-driven metabolic dysregulation. Using these datasets, we constructed microbiome–metabolome association networks with Pearson correlation to investigate the links between microbial shifts and metabolite abnormalities, clarifying how gut microecological imbalance contributes to LMBV pathogenesis. These findings collectively refine our understanding of fish mucosal immunology from both microbial and metabolic perspectives.

2. Materials and Methods

2.1. Ethics Statement

All experiments involving largemouth bass were approved by the Animal Research and Ethics Committee of Huzhou University (Protocol Code: 2019015). All experimental procedures adhered strictly to the relevant guidelines and regulations.

2.2. Animals and Virus Infection

Healthy largemouth bass used in this experiment were obtained from the Zhejiang Institute of Freshwater Fisheries, with an average body weight of 12.3 ± 2.5 g. The fish were acclimated for 2 weeks in filtered water with constant aeration (temperature: 28 °C, pH: 6.5–8.5, dissolved oxygen: 5–10 mg/L) and fed a commercial diet at 2% of their body weight daily. Briefly, pooled liver and intestinal tissues were subjected to RNA extraction and cDNA reverse transcription, followed by qPCR detection. All cycle threshold (CT) values exceeded 40, and these fish were considered free of LMBV [23]. The qPCR primers for LMBV are listed in Table S1.
The LMBV strain used in this study was sourced from the Zhejiang Institute of Freshwater Fisheries. Viral titers were determined in EPC cells (provided by Shanghai Ocean University) using a modified 50% tissue culture infectious dose (TCID50) assay based on a previous method [24]. Briefly, eight confluent EPC cell wells in 96-well plates were inoculated with 100 μL of 10-fold serial dilutions of the virus (10−1 to 10−8). Cytopathic effect (CPE) was observed daily for 7 days post-infection (DPI), and the viral titer was calculated as TCID50 using the Reed-Muench method [25].
Following acclimation, 60 largemouth bass were randomly divided into two equal groups: control and LMBV-infected. Prior to the main experiment, preliminary studies were conducted to establish the infectious dose. These studies determined that a concentration of 100 μL LMBV (1 × 105 TCID50), administered over 15 days, resulted in a 40–50% mortality rate in largemouth bass. In the formal experiment, the LMBV-infected groups were intraperitoneally injected with 0.1 mL of LMBV at a concentration of 1 × 105 TCID50. The control group received an equivalent volume of sterile 1× Phosphate-Buffered Saline (PBS, pH 7.4, Univ-Bio, Shanghai, China) via intraperitoneal injection. All fish were fasted for 2 days prior to the preliminary experiment and the formal LMBV challenge experiment.
After exposure, both groups were transferred to new freshwater tanks, with the culture water conditions consistent with those previously described, and no feeding was provided thereafter. The number of dead fish was recorded daily to calculate the cumulative survival rate throughout the experiment. Analyses involved plotting the survival fraction versus time for the control and LMBV-infected groups using the Kaplan–Meier method. At 0, 3, 7, 11, and 15 DPI, gut tissues were collected from three surviving fish per group for RT-qPCR analysis to detect LMBV copy numbers. At 15 DPI, gut tissues were sampled from nine surviving fish per group for microbiota analysis. Among these samples, 6 were randomly selected for metabolomics analysis, and intestinal and liver tissues from 3 randomly chosen fish underwent histological analysis. Prior to sampling, largemouth bass were anesthetized with MS-222 at a dose of 70 mg·L−1.

2.3. RT-qPCR Assays

Total RNA was extracted from each gut sample using Trizol (Invitrogen, Carlsbad, CA, USA) following the manufacturer’s protocols. RNA concentration and purity were determined using a Nanodrop2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA), with purity assessed via the A260/A280 ratio. RNA integrity was confirmed by 1% agarose gel electrophoresis. cDNA was synthesized using HiScript Q Select RT SuperMix for qPCR (+gDNA wiper) (TaKaRa, Beijing, China). qPCR was performed using a CFX96 Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA) with SYBR® Green Master Mix (TaKaRa, Beijing, China). The β-actin gene of largemouth bass served as the internal reference gene [26]. Primer sequences were listed in Table S1. Relative mRNA levels were calculated using the 2−ΔΔCt method [27].

2.4. Histological Analysis

Gut and liver tissues were fixed in 10% formalin at 4 °C for 24 h, processed through a graded ethanol series, cleared in xylene, and embedded in paraffin. Paraffin blocks were sectioned at 5 μm using an ultramicrotome (Leica, Wetzlar, Germany). The sections were then mounted on glass slides and stained with hematoxylin and eosin (H&E) [28]. Histomorphological examination was performed using a light microscope (Olympus, Tokyo, Japan).

2.5. Microbiome Analysis

Genomic DNA of organisms was extracted from gut samples using the E.Z.N.A. Soil DNA Kit (Omega Bio-tek, Norcross, GA, USA). Briefly, the procedure involved mechanical homogenization of the intestinal tissue followed by cell lysis. After centrifugation, DNA was purified from the cleared lysate using magnetic bead-based binding, washing, and elution, resulting in a high-purity DNA extract. DNA concentration and purity were assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and 1% agarose gel electrophoresis. The V3–V4 hypervariable region of the 16S rRNA gene was amplified using primers 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) [29] on a T100 Thermal Cycler (Bio-Rad, Hercules, CA, USA). The PCR amplification conditions were as follows: an initial denaturation at 95 °C for 3 min, followed by 40 cycles of denaturation at 95 °C for 30 s, annealing at 55 °C for 30 s, and extension at 72 °C for 45 s. A final extension was performed at 72 °C for 10 min, followed by a final hold at 10 °C until it was terminated manually. PCR amplicons were purified using a PCR Clean-Up Kit (Yuhua, Shanghai, China) according to the manufacturer’s instructions, normalized to equimolar concentrations, and pooled for library preparation. Libraries were sequenced by Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China) on an Illumina NextSeq 2000 platform (Illumina, San Diego, CA, USA).
After demultiplexing, reads were quality-filtered using fastp (v0.19.6) [30] and merged with FLASH (v1.2.11) [31]. High-quality reads were denoised using the DADA2 plugin [32] in QIIME 2 (v2020.2) with recommended parameters to infer amplicon sequence variants (ASVs) at single-nucleotide resolution based on per-sample error models [33]. To minimize depth-related biases in alpha and beta diversity analyses, samples were rarefied to 20,000 reads per sample, achieving an average Good’s coverage of 97.90%. Taxonomy was assigned to ASVs using the QIIME 2 Naive Bayes consensus classifier against the SILVA 16S rRNA reference database (v138).
Functional potential was inferred using PICRUSt2 according to the developer’s documentation: HMMER aligned ASV representative sequences to reference sequences; EPA-NG and GAPPA placed representatives into a reference phylogeny; Castor normalized 16S rRNA gene copy numbers; and MinPath inferred gene family profiles and mapped them to metabolic pathways [34,35]. Alpha diversity was calculated using mothur (v1.30) with observed ASVs, Chao1, ACE, Shannon, and Simpson indices, and rarefaction curves were generated from these metrics. Shared and unique ASVs between groups were visualized using Venn diagrams. Beta diversity was assessed using non-metric multidimensional scaling (NMDS) based on Bray–Curtis dissimilarities to evaluate sample clustering and dispersion. Statistical differences in community structure between control and infected groups were tested with adonis, along with NMDS stress values. Community composition was summarized using stacked bar plots at the phylum and genus levels. KEGG pathway predictions were derived from PICRUSt2 outputs to explore the functional potential of the bacterial communities.

2.6. Metabolomics Analysis

Metabolites were extracted using methanol and 2-chlorobenzalanine. A 20 µL aliquot from each sample was pooled to create a quality control (QC) sample, and the remaining volume was used for LC–MS/MS analysis. Chromatographic separation was conducted on a UHPLC system equipped with an ACQUITY HSS T3 column (100 mm × 2.1 mm, 1.8 µm; Waters, Milford, MA, USA) and coupled with an Orbitrap Exploris 240 mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) at Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China). Mass spectrometry was conducted using an electrospray ionization (ESI) source in both positive and negative ion modes.
Raw LC–MS data were processed with Progenesis QI (Waters, Milford, MA, USA) to generate a three-dimensional feature matrix (samples × features with m/z, retention time, and ion intensity). Peaks corresponding to internal standards and known artifacts were excluded. The remaining features underwent deconvolution, retention-time alignment, and adduct/fragment de-duplication to ensure high-quality feature extraction. Metabolite annotation was carried out by matching databases against HMDB (http://www.hmdb.ca/ [Accessed 16 August 2025]), METLIN (https://metlin.scripps.edu/ [Accessed 16 August 2025]), and the in-house Majorbio Database (Majorbio Biotechnology Co., Ltd., Shanghai, China). Data quality was monitored through QC/QA procedures.
Unsupervised principal component analysis (PCA) and supervised partial least squares–discriminant analysis (PLS-DA) were conducted using the ropls package (v1.6.2) in R, with 7-fold cross-validation to evaluate model stability and reliability. Differential metabolites between groups were mapped to biochemical pathways using KEGG (http://www.genome.jp/kegg/ [Accessed 16 August 2025]). Pathway-level enrichment analyses were performed to determine whether specific functional categories were overrepresented relative to the background metabolome. Metabolites were grouped by shared pathways/biological functions to extend single-metabolite annotations into functionally coherent sets. Statistical enrichment was assessed using the scipy.stats package (Python), and the pathways most associated with the experimental treatments were identified accordingly.

2.7. Correlation Analysis of Gut Bacteria and Metabolites

Pearson correlation coefficients were calculated to assess associations between significantly altered gut bacterial taxa and differential metabolites. Results were visualized as heatmaps, with hierarchical clustering dendrograms for bacteria and metabolites constructed based on their correlation coefficients. Analyses were performed using the SciPy library in Python (v2.7.10). Correlation coefficients close to +1 indicate strong positive associations, while those near −1 indicate strong negative associations. Values close to 0 suggest little to no linear relationship.

3. Results

3.1. Establishment of the LMBV Artificial Infection Model

An LMBV infection model was established via intraperitoneal injection to systematically assess the immune responses of largemouth bass to viral challenge. Approximately 50% mortality was observed in infected fish by 15 DPI, whereas no mortality occurred in the control group (Figure 1A). qPCR analysis showed that viral loads in the gut peaked at 11 DPI (Figure 1B). Clinically, infected fish exhibited typical symptoms, including hepatomegaly, pale liver, and hemorrhaging at the fin base (Figure 1C). H&E images further confirmed significant tissue damage in infected fish. Significant pathological damage was observed in the intestines of the infected group, including abundant epithelial cell debris in the intestinal lumen, widened lamina propria, and prominent inflammatory cell infiltration (Figure 1D). Liver tissues from the infected group exhibited obvious inflammatory cell infiltration, along with hepatocyte shrinkage, necrosis and cell membrane breakdown (Figure S1). Collectively, these results indicate that LMBV successfully breached the intestinal mucosal barrier of largemouth bass, validating the establishment of the viral infection model.
Figure 1. Infection model and pathological analysis. (A) Cumulative survival curves of control and LMBV-infected groups over the experimental period. (B) The copies (log10) of LMBV were measured by qPCR; ** p < 0.01, *** p < 0.001. (C) Representative clinical signs observed following LMBV challenge. (D) H&E-stained intestinal sections from control and infected fish, red arrow: degenerative and necrotic epithelial cells; black arrow: shed epithelial cells; green arrow: lamina propria widening; blue arrow: inflammatory cell infiltration; scale bars, 100 µm.

3.2. LMBV Infection Induces Gut Microbial Dysbiosis

To evaluate the impact of LMBV infection on the abundance and diversity of the gut microbiota in largemouth bass, 16S rRNA sequencing was performed on intestinal samples from both control and infected fish. A total of 1.2 million raw reads were generated from the two groups. Following quality filtering with DADA2, 900,229 high-quality reads were retained for subsequent analysis. The sequences were then clustered into unique amplicon sequence variants (ASVs) with a 97.9% similarity threshold using the DADA2 plugin.
A total of 76 ASVs were shared between the two groups. Notably, the LMBV-infected group exhibited a significantly lower number of unique ASVs compared to the control group (Figure 2A). To evaluate β-diversity, non-metric multidimensional scaling (NMDS) analysis was performed, which revealed a clear separation between the intestinal microbial communities of the control and LMBV-infected groups, suggesting distinct compositional profiles in response to infection (Figure 2B). Furthermore, we compared both the diversity and composition of the gut microbiota between the groups. Alpha diversity was assessed using four indices: community richness (Ace and Chao) and community diversity (Shannon and Simpson). Compared to the control group, the Ace, Chao, and Shannon indices were significantly reduced in the LMBV-infected fish, whereas the Simpson index was elevated (Figure 2C–F).
Figure 2. Gut microbial richness and diversity in largemouth bass following LMBV infection. (A) ASV Venn diagram; (B) NMDS based on Bray–Curtis dissimilarities; (C) ACE index; (D) Shannon index; (E) Chao1 index; (F) Simpson index; ** p < 0.01, *** p < 0.001.

3.3. LMBV Infection Changed Gut Microbial Community Composition

The composition of the gut microbiota at the phylum level was analyzed. In healthy largemouth bass, the intestinal microbiota was primarily dominated by Proteobacteria, Bacteroidota, and Fusobacteriota (Figure 3A). LMBV infection induced significant structural shifts at the phylum level. Specifically, the relative abundance of Proteobacteria was markedly decreased following infection (Figure 3A). Conversely, the abundance of Bacteroidota and Fusobacteriota was significantly elevated in infected fish (Figure 3A). Additionally, the relative abundance of Actinomycetota was notably lower in the infected group than in the controls. At the genus level, LMBV infection led to a reduction in the relative abundances of Cetobacterium, unclassified o_Bacteroidales, norank f_Baresiellaceae, and Plesiomonas. In contrast, the abundances of Methylobacterium, Acinetobacter, Pseudomonas, Mycobacterium, Methyloversatilis, and Agrobacterium were significantly enriched (Figure 3B).
Figure 3. Gut microbiota alterations in largemouth bass following LMBV infection. (A) Relative abundance at the phylum level; (B) relative abundance at the genus level; (C) predicted microbial phenotypes (BugBase); (D) differential KEGG functional profiles between groups; * p < 0.05, ** p < 0.01, *** p < 0.001.
To evaluate the functional consequences LMBV infection has for the gut microbiota, phenotypic prediction analysis was conducted using BugBase. Compared to the control group, the infected group showed a significant enrichment of anaerobic bacteria (Figure 3C), which was associated with an increased abundance of g__norank_f__Barnesiellaceae (Table S2). Additionally, the abundance of potentially pathogenic bacteria was markedly elevated following LMBV infection (Figure 3C). This enrichment correlated with changes in the relative abundance of Pseudomonas, Agrobacterium, Methyloversatilis, Acinetobacter, Methylocystis, and Roseateles (Table S2).
Tax4Fun predicted 248 significantly different functional pathways. Figure 3D illustrates the top ten KEGG pathways ranked by relative abundance. Over half of these pathways were categorized under “metabolism,” including cysteine and methionine metabolism, pyruvate metabolism, purine metabolism, microbial metabolism in diverse environments, biosynthesis of secondary metabolites, and metabolic pathways.

3.4. LMBV Infection Induces Gut Metabolomes Alterations

PCA revealed distinct clustering and significant metabolic differences between the healthy and infected groups (Figure 4A). To further evaluate inter-group variability, PLS-DA was employed. The model demonstrated high predictability (Q2) and strong goodness of fit (R2X, R2Y), indicating that the LMBV infection model was robust and suitable for identifying differentially expressed metabolites (Figure 4B and Figure S2).
Figure 4. Gut metabolite alterations in largemouth bass following LMBV infection. (A) PCA score plot. (B) PLS-DA score plot; the dashed lines connect each sample point to its group centroid, with line length proportional to the sample’s deviation from the group mean. (C) Venn diagram of shared and unique metabolites between groups. (D) Statistical chart of differential expression levels of metabolites. (E) Top 20 KEGG pathway enrichments for differential metabolites.
The Venn diagram revealed 2456 common differential metabolites identified across intergroup comparisons (Figure 4C and Supplementary File S2). Specifically, 893 unique metabolites were detected in the control group, including 108 amino acids, peptides, and analogues; 48 carbohydrates and carbohydrate conjugates; and 24 flavonoid glycosides. Conversely, only 80 unique metabolites were identified in the LMBV-infected group (Figure 4C). Relative to the control group, 1174 metabolites were downregulated, while 322 were upregulated in the infected group (Figure 4D).
The top 20 significantly enriched pathways for differential metabolites are presented in Figure 4E and Table S3. Key enriched pathways included amino sugar and nucleotide sugar metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; lysine degradation; tryptophan metabolism; taurine and hypotaurine metabolism; arginine biosynthesis; and arachidonic acid metabolism.

3.5. Associations Between Microbiota and Metabolites

Pearson correlation coefficient analysis was performed to investigate the functional relationship between the gut microbiota and metabolites. A total of 283 significant genus–metabolite associations were identified. For visualization, eight differentially expressed metabolites exhibiting strong correlations with intestinal microbes were selected and displayed in a heatmap (Figure 5A and Figure S3). Specifically, D-glucosamine 6-phosphate, N-α-benzyl-L-citrulline, 3-hydroxypyruvic acid, and phenylpyruvic acid showed significant positive correlations with the genera Pseudomonas, Mycobacterium, and Acinetobacter. Conversely, L-asparagine, oxaloacetic acid, quinic acid, and N-acetylornithine were negatively correlated with these genera. Furthermore, analysis of metabolomic data revealed that following viral infection, the mass spectrometry intensities of D-glucosamine 6-phosphate (Figure 5B), N-α-benzyl-L-citrulline (Figure 5C), 3-hydroxypyruvic acid (Figure 5D), and phenylpyruvic acid (Figure 5E) were elevated. In contrast, the intensities of L-asparagine (Figure 5F), oxaloacetic acid (Figure 5G), quinic acid (Figure 5H), and N-acetylornithine (Figure 5I) significantly reduced.
Figure 5. Associations of intestinal microbiome with intestinal metabolites following LMBV infection. (A) Correlation between intestinal microbiome and intestinal metabolic indicators mediated following LMBV infection. Significant changes in intestinal metabolites following LMBV infection; specifically, these metabolites were D-Glucosamine 6-Phosphate (B), N-Alpha-Acetyl-L-Citrulline (C), 3-Hydroxybenzoic Acid (D), Phenylpyruvic Acid (E), L-Asparagine (F), Oxalacetic Acid (G), Quinic Acid (H), N-Acetylmethionine (I). The horizontal axis represents the two different groups, and the vertical axis represents the mass spectrum intensity value, *** p < 0.001.

4. Discussion

The gut is a crucial organ, not only controlling digestion and nutrient absorption but also acting as a primary immune barrier, functioning as a MALT [36]. Recent studies indicate that LMBV infection induces dysbiosis of the largemouth bass gut microbiota, thereby affecting anti-viral immune processes [17,37]. It has been hypothesized that microbiota-driven metabolite disruptions may exacerbate intestinal pathology; however, this notion has yet to be experimentally validated. Accordingly, our study aimed to delineate virus-induced alterations in the gut microbiome and metabolome and to interrogate their potential inter-relationships.
Using intraperitoneal injection, we successfully established an LMBV infection model in largemouth bass. qPCR analysis showed that intestinal viral loads peaked at 11 DPI and subsequently declined, indicating stable viral replication during the early phase of infection followed by gradual clearance, presumably mediated by host immune responses. Contradictorily, Kong et al. [17] reported that LMBV copies peaked at 4 DPI, which could be explained by their higher inoculation dose (1 × 107 TCID50). H&E staining revealed marked intestinal pathology in the infected group, including abundant shed epithelial cell debris in the intestinal lumen, widened lamina propria, and prominent inflammatory cell infiltration. In contrast, the control group exhibited an intact mucosal structure and regular epithelial morphology. Collectively, these findings validate the infection model and demonstrate that LMBV can invade the intestine of largemouth bass. As the intestine is a key component of the GALT, this structural compromise likely impairs its critical role in local immunity, thereby weakening the host’s antiviral defenses.
A stable intestinal microbiota serves as a vital biological barrier against pathogenic infection in the host. Alpha and beta diversity are essential parameters for characterizing the structure of microbial communities. In the present study, largemouth bass infected with LMBV showed reductions in the ACE, Chao, and Shannon indices, along with an increase in the Simpson index, indicating a decrease in microbial richness and diversity following infection. These findings are consistent with the observations reported by Kong et al. [17], who also documented a decline in the richness and diversity of intestinal microbiota in largemouth bass after LMBV infection. Similar declines in gut community diversity have been reported in other teleosts following viral challenge, including red hybrid tilapia (Oreochromis spp.) infected with Tilapia lake virus [38] and zebrafish (Danio rerio) infected with spring viremia of carp virus [39], suggesting that viral infection disrupts gut microbial equilibrium in teleosts.
At the phylum level, viral infection may alter the abundance of dominant taxa. Bacteroidota metabolize various gastrointestinal polysaccharides and play key roles in carbohydrate metabolism [40]. In contrast, Proteobacteria participate in the degradation of complex carbon- and nitrogen-containing compounds and are widely considered a hallmark of dysbiosis, with significant abundance shifts during inflammation [41,42,43]. Fusobacteriota constitute a commensal clade that includes opportunistic pathogens, and their abundance has been positively linked to human colorectal cancer [44]. In this study, increased relative abundances of Bacteroidota and Fusobacteriota, along with a decrease in Proteobacteria, suggest that LMBV may reduce microbial richness and diversity while reshaping dominant taxa, which could impact host metabolic processes and health.
At the genus level, the data indicated that several taxa exhibited distinct responses to LMBV infection. The predicted opportunistic genera included Pseudomonas [45] and Mycobacterium [46], which are ubiquitous in aquatic environments and represent potential pathogens in aquaculture species, as well as Methylobacterium [47] and Acinetobacter [48] both important nosocomial pathogens in humans. The altered relative abundances of these bacteria in the intestines of infected largemouth bass, particularly the changes observed in the first two genera, are likely to disrupt the microbial barrier. This disruption could potentially create a permissive environment for secondary bacterial colonization, further compromising intestinal immune homeostasis.
Intestinal metabolites result from the combined metabolic activities of the host and its microbiota, reflecting the impact of microbial contributions to nutrient digestion, absorption, and subsequent metabolic processing. Multivariate analyses, such as PCA and PLS-DA, revealed significant changes in the hindgut metabolomic profiles of largemouth bass after LMBV infection, highlighting a pronounced effect of the virus on host–microbe co-metabolism. Further analysis of differential metabolites revealed that most key features were predicted to be enriched in metabolism-related pathways, such as phenylalanine, tyrosine, and tryptophan biosynthesis; pentose and glucuronate interconversions; lysine degradation; tryptophan metabolism; galactose metabolism; taurine and hypotaurine metabolism; and nucleotide metabolism. The enrichment of these predicted pathways suggests that viral infection in this model may disrupt host metabolism in the intestine, with notable effects on amino acid metabolism and translation-related processes.
Based on our integrated microbiome–metabolome analysis, the data suggest an increase in the abundance of four genera often associated with pathogenicity: Pseudomonas, Mycobacterium, Methylobacterium, and Acinetobacter. Pearson correlation analysis revealed significant positive correlations between four metabolites (D-glucosamine 6-phosphate, N-α-benzyl-L-citrulline, phenylpyruvic acid, and 3-hydroxypyruvic acid) and these opportunistic genera. In contrast, four other metabolites (L-asparagine, oxaloacetic acid, quinic acid, and N-acetylornithine) were significantly negatively correlated. L-Asparagine is the biologically predominant enantiomer of asparagine [49]. As an essential amino acid for fish, sufficient dietary asparagine enhances tolerance to environmental stressors and salinity changes during migration. Conversely, asparagine deficiency reduces feed intake and induces gastrointestinal issues in fish [50]. In mammals, quinic acid is a recognized anti-inflammatory compound with potent radioprotective, anti-inflammatory, and antioxidant activities [51]. It reduces inflammation and mucosal injury in rat models of ulcerative colitis [52] and alleviates inflammatory responses and oxidative stress in a Freund’s complete adjuvant-induced arthritis model [53]. Additionally, quinic acid can ameliorate ulcerative colitis in the rat intestine, a critical MALT, by inhibiting the two key signaling pathways of TLR4-NF-κB and NF-κB-iNOS-NO [54]. We hypothesize that quinic acid may similarly exert anti-inflammatory effects in largemouth bass, and its reduction could potentially trigger inflammatory outbreaks. Phenylalanine is an essential amino acid and a building block of proteins, is catabolized to phenylpyruvic acid as a primary metabolite [55]. Elevated phenylpyruvic acid is often linked to adverse physiological states. It can inhibit glucose-6-phosphate dehydrogenase, impairing NADPH generation and disturbing cellular redox homeostasis [56]. Increased phenylpyruvic acid also impairs wound healing and enhances NLRP3 palmitoylation, thereby promoting inflammasome activation and pro-inflammatory cytokine release [57]. In fish studies, it has been found that the accumulation of phenylpyruvic acid can undergo oxidation, producing toxic and even pathogenic effects [58]. In conclusion, we hypothesize that the predicted reduction in potentially beneficial metabolites, combined with the predicted increase in harmful metabolites, may contribute to the intestinal lesions observed in largemouth bass following LMBV infection. However, it is important to note that correlation analyses between microbiome and metabolome data have limitations, particularly in fish models. Therefore, further experimental studies are needed to validate the relationships between these bacteria and metabolites in LMBV-infected largemouth bass.

5. Conclusions

This study presents a comprehensive analysis of the gut microbiome and metabolome in largemouth bass following LMBV infection, highlighting potential interactions between viral challenge, microbial community shifts, and metabolic pathway alterations. Our data indicate that LMBV infection was associated with disruptions in the gut microbiota, characterized by reduced diversity and a compositional shift toward genera with documented pathogenic potential. Furthermore, LMBV infection appeared to induce notable changes in the metabolome, with a particular focus on amino acid metabolism. Integrative correlation analyses revealed potential links between opportunistic bacterial taxa and metabolites of concern, proposing a model wherein LMBV-associated dysbiosis may perturb host metabolic homeostasis and contribute to immune dysregulation. Collectively, these findings offer a foundation for further investigation into the potential pathogenic mechanisms of LMBV.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ani16050752/s1, Figure S1: Liver tissue section, with black arrows indicating inflammatory cells; Figure S2: PLS-DA permutation test; Figure S3: Correlation between intestinal microbiome and intestinal metabolic indicators mediated following LMBV infection with BH multiple testing; * p < 0.05, ** p < 0.01, *** p < 0.001; Table S1: Primer sequences for qRT-PCR; Table S2: BugBase contribution statistics; Table S3: Top 20 KEGG pathway enrichments for differential metabolites; Supplementary File S2: The detailed metabolites in the metabolome Venn diagram.

Author Contributions

Conceptualization and writing—original draft preparation, H.Y.; data curation and supervision, S.D.; formal analysis, validation and writing—review and editing, L.W.; investigation and methodology, S.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was financially supported by Major Science and Technology Programs for Agriculture (Aquaculture) New Variety Selection of Zhejiang Province (2021C02069-2), Basic Public Welfare Research Program of Zhejiang Province (LGN22C190024) and Zhejiang Provincial Financial Special Project (2025CZZX02).

Institutional Review Board Statement

All experiments involving largemouth bass were approved by the Animal Research and Ethics Committee of Huzhou University (Protocol Code 2019015). All experimental procedures were handled in strict accordance with the relevant guidelines and regulations.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
LMBVLargemouth Bass Virus
UPLC–MS/MSUltra-Performance Liquid Chromatography–Tandem Mass Spectrometry
qPCRQuantitative Polymerase Chain Reaction
RT-qPCRReverse Transcription Quantitative Polymerase Chain Reaction
TCID5050% Tissue Culture Infectious Dose
ASVAmplicon Sequence Variant
NMDSNon-Metric Multidimensional Scaling
PCAPrincipal Component Analysis
PLS-DAPartial Least Squares–Discriminant Analysis
KEGGKyoto Encyclopedia of Genes and Genomes
PICRUSt2Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2
DPIDays Post-Injection
HMDBHuman Metabolome Database
METLINMetabolite Link Database
TNF-αTumor Necrosis Factor-α
HSP70Heat Shock Protein 70
NLRP3NOD-Like Receptor Pyrin Domain-Containing Protein 3
SPIVSea Perch Iridovirus
SVCVSpring Viremia of Carp Virus
MSRVMicropterus salmoides Rhabdovirus

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