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Article

Functional miRNA-mRNA Regulatory Modules in the Head Kidney of Pelteobagrus vachellii in Response to Aeromonas veronii Infection

by
Feiyang Li
1,
Xiaoyun Wu
1,
Yeyu Chen
1,
Qiaolin Zou
1,
Pengcheng Li
1,
Mingjiang Song
1,
Quan Gong
1,
Ya Liu
1,
Jiansheng Lai
1,
Luyun Ni
1,* and
Jun Wang
2,*
1
Fisheries Research Institute, Sichuan Academy of Agricultural Sciences (Sichuan Fisheries Research Institute), Chengdu 611731, China
2
Key Laboratory of Sichuan Province for Fishes Conservation and Utilization in the Upper Reaches of the Yangtze River, Neijiang Normal University, Neijiang 641000, China
*
Authors to whom correspondence should be addressed.
Fishes 2025, 10(10), 530; https://doi.org/10.3390/fishes10100530
Submission received: 29 August 2025 / Revised: 10 October 2025 / Accepted: 16 October 2025 / Published: 18 October 2025
(This article belongs to the Special Issue Physiological Response Mechanisms of Aquatic Animals to Stress)
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Versions Notes

Abstract

Aeromonas veronii is a major pathogen threatening freshwater aquaculture, yet the molecular mechanisms of Pelteobagrus vachellii’s immune response to this infection remain unclear. This study integrated histopathology, mRNA-seq and small RNA-seq to investigate P. vachellii’s response to A. veronii at 48 h post-challenge. Histopathologically, infection induced gill epithelial detachment, hepatocyte swelling with cytoplasmic vacuolation, and melanomacrophage centers (MMCs) in the mid-kidney (histological assessment of the head kidney was not feasible due to sampling limitations associated with its small size). Transcriptomic analysis identified 1210 differentially expressed genes (DEGs) in the head kidney (819 downregulated, 391 upregulated), significantly enriched in 11 immune pathways (e.g., NF-κB, Th17 cell differentiation, Complement and coagulation cascades), with key immune genes (e.g., IL-1β, TCRα, CCL4) upregulated. Gene Set Enrichment Analysis (GSEA) revealed activation of the proteasome, ribosome and oxidative phosphorylation pathways, and suppression of the autophagy-animal, FoxO and AMPK pathways. Small RNA-seq identified 544 known and 958 novel miRNAs in the head kidney, with 42 downregulated and 36 upregulated differentially expressed miRNAs (DE miRNAs). The miRNA-mRNA network showed that DE miRNAs (e.g., miR-101-y/z, miR-132-z, miR-3167-y) negatively regulated immune-related target genes (IL-1R1, IRF4, IκBα) in core immune pathways. Collectively, this study clarifies the pathological and miRNA-mRNA regulatory modules of P. vachellii head kidney against A. veronii infection, providing valuable information that enables the further analyses of the defense mechanisms of P. vachellii against A. veronii infection.
Keywords:
Pelteobagrus vachellii; Aeromonas veronii; mRNA; miRNA; immune response
Key Contribution: This study innovatively integrates histopathology, mRNA-seq, and small RNA-seq to systematically clarify the pathological changes and the regulatory mechanisms of the head kidney of Pelteobagrus vachellii in response to Aeromonas veronii infection at 48 h post-challenge. It not only fills a gap in our understanding of the antibacterial immunity of P. vachellii but also provides crucial molecular insights for developing disease control strategies in freshwater aquaculture.

1. Introduction

The commercially significant freshwater teleost, Pelteobagrus vachellii, commonly known as the Darkbarbel catfish, is widely farmed in China due to its nutritional value and high consumer demand [1]. This species, a close relative of the yellow catfish (Pelteobagrus fulvidraco) within the Bagridae family, exhibits a faster growth rate compared to the yellow catfish [1]. Growing consumer demand has led to a significant increase in its market value. Nevertheless, the expansion of intensive aquaculture and the degradation of aquatic environments have significantly increased the prevalence of diseases in P. vachellii [1]. In comparison to congeneric fish species such as the yellow catfish or channel catfish, the immunity of P. vachellii to infection is relatively underexplored. Prior investigations have studied the molecular characterization and expression of complement factor I in P. vachellii during Aeromonas hydrophila infection, as well as conducted iTRAQ-based analysis of immune-related proteins in the liver of P. vachellii specimens infected with Edwardsiella ictaluri [2,3].
Among pathogenic agents, Aeromonas veronii, a Gram-negative opportunistic bacterium, has emerged as a major threat to freshwater aquaculture species—infection with this pathogen causes systemic tissue damage, inflammation, and high mortality in various fish and crustaceans [4]. Fish diseases caused by A. veronii infection have been recorded in several species, including channel catfish (Ictalurus punctatus) [5], African catfish (Clarias gariepinus) [6], koi carp (Cyprinus carpio koi) [7] and Chinese mitten crab (Eriocheir sinensis) [8], underscoring its wide-ranging pathogenicity and significant economic implications.
MicroRNAs (miRNAs) are small non-coding RNAs (20–24 nt) that negatively modulate gene expression through binding to the 3′-untranslated region (UTR) of target mRNAs, playing crucial roles in the development of the immune system and responses to pathogens [9]. miRNAs play a pivotal role in regulating responses to viral and bacterial infections, stress, and immune reactions across diverse organisms [10,11]. In teleosts, these molecules govern immune pathways during bacterial infections. For example, regulatory miRNAs could either facilitate immune evasion by Edwardsiella tarda or enhance immune response during infection [12]. An example is miR-122, which regulates interleukin-15 (IL-15) to enhance resistance against A. hydrophila in Epinephelus coioides [13]. Currently, integrated mRNA-miRNA analysis is commonly employed for investigating mRNA-miRNA interaction through paired expression profiles across various species [14,15]. However, the pathogenic mechanisms of P. vachellii following A. veronii infection and the regulatory patterns of the host’s immune responses are still not thoroughly understood. Moreover, the response profiles of functional miRNA-mRNA regulatory modules in P. vachellii following A. veronii infection have not yet been established.
Thus, this study integrated histopathological examination, mRNA sequencing, and small RNA sequencing to analyze the immune responses of the head kidney of P. vachellii to A. veronii infection at 48 h post-challenge (the head kidney, an anterior, lymphoid portion of the fish kidney that performs hematopoietic and immune functions). Our aim was to characterize the immune mechanisms activated in the fish upon bacterial infection. Differentially expressed mRNAs and miRNAs expressed in response to A. veronii infection were identified, followed by functional enrichment and GSEA. This study also revealed numerous mRNA-miRNA interactions, shedding light on the molecular mechanisms that govern genes involved in the immune response of P. vachellii infected with A. veronii.

2. Materials and Methods

2.1. Fish Preparation

The P. vachellii specimens utilized in this study were sourced from the Fisheries Research Institute, Sichuan Academy of Agricultural Sciences (Sichuan Fisheries Research Institute) in Chengdu, Sichuan, China. A total of 60 healthy P. vachellii were collected in March 2023 for the bacterial challenge assay. The fish were of uniform size, weighing 20.15 ± 1.08 g. Before the experiment, a two-week acclimatization period was conducted in recirculating water tanks with a temperature of 27 ± 0.5 °C, a pH of 7.5 ± 0.3, and a 12 h light/12 h dark cycle. Adequate oxygen levels were maintained throughout acclimatization, with the fish being fed a commercial feed containing 44% protein, in the form of a puffed compound feed (Tongwei Co., Ltd., Chengdu, China), twice daily. All individuals were confirmed to be uninjured before the experiment and were fasted for 24 h prior to its initiation.
The protocols for animal handling followed in this study were reviewed and approved by the Institutional Animal Care and Use Committee of Fisheries Research Institute, Sichuan Academy of Agricultural Sciences (Sichuan Fisheries Research Institute) in Chengdu, Sichuan, China (Approval No.: 20220217001A; Approval Date: 17 February 2022). All experimental procedures were carried out in compliance with the guidelines sanctioned by this committee.

2.2. Bacterial Challenge and Sample Collection

The A. veronii strain used in this study was isolated from diseased P. vachellii and maintained in our laboratory. The bacteria were cultured in Luria–Bertani (LB) medium at 28 °C for 24 h with agitation at 180 rpm. Subsequently, the bacteria were harvested via centrifugation at 6000× g for 5 min, washed once with 0.1 mM of pH 7.4 sterile phosphate-buffered saline (PBS), and subjected to a second course of centrifugation. Following the protocol outlined by Zhai et al., bacterial pellets were resuspended in PBS at a concentration of 1 × 107 CFU/mL [16]. Sixty fish were then randomly allocated into two groups, with each group consisting of thirty individuals. One group served as the experimental group (S), receiving an intraperitoneal injection of 0.1 mL of the A. veronii suspension, while the other group was designated as the control group (C), administered an equal volume of PBS via intraperitoneal injection. At 48 h post infection, the intestine, liver, gill, mid-kidney, and head kidney samples were collected from both the experimental and control groups for subsequent analysis.

2.3. Histopathological Analysis

Tissues sourced from the intestine, liver, gill, and mid-kidney were procured from both the control and experimental P. vachellii groups. Notably, the histological analysis of the head kidney was disregarded due to its minute size, which posed challenges in preserving tissue integrity during sampling. Following collection, the samples were promptly fixed in 4% paraformaldehyde (PFA) solution at room temperature for 24 h for histological preservation. After fixation, the tissues underwent a series of ethanol dehydration steps for water removal. Subsequently, the dehydrated tissues were embedded in paraffin wax to generate tissue blocks, which were then sectioned into 5 μm slices using a microtome (RM2235; Leica, Wetzlar, Germany). These sections were stained with hematoxylin and eosin (H&E) according to established histological procedures. Post-staining, slides were air-dried at room temperature upon being embedded in neutral resin. Observation was performed with an optical microscope (model DM500; Leica, Wetzlar, Germany), and image acquisition was carried out at magnifications of 10×, 20×, and 40×, using the Leica Microsystem imaging system (model DM1000; Leica, Wetzlar, Germany).

2.4. RNA Extraction

The head kidney, identified as a cranial, dark-red lymphoid mass immediately posterior to the gill chamber and distinguishable from the excretory trunk (posterior) kidney, was dissected and sampled. Total RNA was extracted from the head kidney of both the control and experimental groups (n = 3) using TRIzol reagent. Total RNA was extracted from the head kidney of both the control and experimental groups (n = 3) using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) following the manufacturer’s protocol. RNA quality, including degradation, purity, and integrity, was assessed using 1% agarose gel electrophoresis and the Agilent Bioanalyzer 2100 system (Agilent Technologies, Palo Alto, CA, USA).

2.5. mRNA Sequencing and Data Analysis

After total RNA extraction, eukaryotic mRNA was enriched with oligo(dT) magnetic beads, while prokaryotic mRNA underwent rRNA depletion using the Ribo-Zero™ Magnetic Kit (Epicentre, Madison, WI, USA) to remove ribosomal RNA contaminants. The enriched mRNA was fragmented with random hexamers and reverse-transcribed into first-strand cDNA. Subsequently, second-strand cDNA synthesis was performed using DNA Polymerase I, RNase H, and deoxynucleotide triphosphates (dNTPs). Following end repair, Illumina sequencing adapters were ligated to the cDNA fragments. After purification and amplification, 150 bp paired-end sequencing was performed using the Illumina NovaSeq 6000 platform (Illumina, San Diego, CA, USA) by Novogene Co., Ltd. (Beijing, China).
The raw data were filtered using fastp to eliminate adapter sequences, reads with over 10% unknown nucleotides, and low-quality reads (q-value ≤ 20), resulting in clean reads [17]. These clean reads were aligned to the P. vachellii reference genome using HISAT2 [18,19]. Transcript reconstruction was carried out with Stringtie based on the HISAT2 alignment. Gene expression levels for each sample were calculated using RSEM [20]. Functional annotation was performed by comparing them against various databases, including the NCBI non-redundant protein (http://www.ncbi.nlm.nih.gov, accessed on 12 August 2025), Swiss-Prot (http://www.expasy.ch/sprot, accessed on 12 August 2025), KEGG (http://www.genome.jp/kegg, accessed on 12 August 2025), and COG/KOG (http://www.ncbi.nlm.nih.gov/COG, accessed on 12 August 2025) databases using BLASTx with an E-value threshold of 1 × 10−5. Differential gene expression between the control and experimental groups was quantified using TPM values via RSEM and DESeq2, identifying differentially expressed genes (DEGs) with |fold change| (FC) > 1.5, and p < 0.05 [20,21]. Subsequently, GO functional enrichment and KEGG pathway enrichment analyses of these genes were conducted using the clusterProfiler package in R, with results visualized through the ggplot2 package [22]. Key immune-related genes showing differential expression were analyzed, and their relative expression levels were graphed using GraphPad Prism version 9.

2.6. Gene Set Enrichment Analysis (GSEA) of RNA-Seq Data

GSEA of RNA-seq data revealed significant pathways associated with the biological processes under investigation, seeking to elucidate the differential expression of genes with a critical influence on cellular functions and disease mechanisms following A. veronii infection. To identify differential GO terms and pathways, both GSEA and MSigDB were employed to compare the control group with the A. veronii-infected group. Genes were prioritized by ranking the expression matrix via the Signal2Noise method. After that, enrichment scores (ESs), p-values, and FDR values were calculated using default parameters. Normalization was performed to obtain |NES|, and gene sets within pathways that satisfied the criteria of |NES| > 1, NOM p-value < 0.05, and FDR q-value < 0.25 were considered significant. Notably, analysis outcomes with higher credibility are associated with larger absolute NES values and lower FDR values.

2.7. Small RNA Sequencing and Data Analysis

Six small RNA libraries were constructed from the control and A. veronii-infected groups. Small RNAs were extracted from total RNA and then ligated to 3′ and 5′ adapters. The resulting ligation products underwent reverse transcription through PCR amplification to form a cDNA library, which was subsequently sequenced using the Illumina HiSeq™ 2500 Analyzer.
Raw reads underwent quality filtering by eliminating low-quality reads, reads lacking 3′ adapters, reads containing 5′ adapters, reads with polyA sequences, and reads shorter than 18 nucleotides in length. Clean tags were derived from clean reads by further removing rRNA, scRNA, snoRNA, snRNA, tRNA, and sequences matching exons, introns, or repetitive regions. The remaining clean tags were compared against the miRBase database to identify known miRNAs. Novel miRNAs were identified based on genomic locations and predicted hairpin structures using miRDeep2 software [23].
The miRNAs in each sample were summarized, and the expression level of each miRNA was quantified as TPMs (tags per million). Differential analysis to compare the control and A. veronii-infected groups was conducted using edgeR [24], considering miRNAs with |FC| ≥ 1.5 and p < 0.05 to be differentially expressed (DE) miRNAs. Target gene prediction was performed using Miranda (v3.3a) and TargetScan (Version: 7.0) [25,26], with the intersection of results considered to make miRNA target gene predictions. Additionally, miRNA target genes were annotated through GO functional enrichment and KEGG pathway analysis.

2.8. Establishment of miRNA-mRNA Target Network

Based on the analysis of differential expression patterns of miRNAs and their target genes, as well as the evaluation of their targeting relationships, the Pearson correlation coefficient (PCC) was calculated to quantify their expression correlation. MiRNA target pairs with a PCC < −0.7 and p < 0.05 were considered to be co-expressed negatively correlated pairs, with all included RNAs showing differential expression. Subsequently, a miRNA-target network was constructed following the aforementioned steps and visualized using Cytoscape software (v3.6.0) (http://www.cytoscape.org/ accessed on 12 August 2025).

2.9. Quantitative Real Time-PCR (qRT-PCR) Validation

qRT-PCR was conducted to validate the transcriptome profiling results using an ABI 7500 FAST real-time PCR system (Applied Biosystems, Foster City, CA, USA). The RNA samples used for validation were identical to those isolated from head kidney tissues. Primers for both the target and reference genes were designed with Primer Premier 5, and their sequences are shown in Supplementary Table S1. Each assay was conducted in triplicate for each sample. Amplification reactions were performed using 2× SG Fast qPCR Master Mix (Sangon, Shanghai, China) under the following conditions: initial denaturation at 95 °C for 3 min, followed by 45 cycles of denaturation at 95 °C for 15 s, annealing at 60 °C for 30 s, and extension at 72 °C for 30 s. β-actin was served as the housekeeping gene, and the relative gene expression levels were determined using the 2−ΔΔCt method [27].

3. Results

3.1. Histopathology Characteristics of P. vachellii Challenged with A. veronii

Histopathological analysis was performed to assess the effects of A. veronii challenge on P. vachellii. In the control group, intestinal mucosal epithelial cells were arranged in an orderly manner, whereas no obvious pathological changes were observed in the intestine samples of A. veronii-infected group (Figure 1A–D). Liver tissues in the control group presented a relatively intact architecture with densely distributed hepatocytes; in contrast, hepatocytes in the A. veronii-infected group exhibited swelling, increased cellular volume, cytoplasmic vacuolation, and predominantly centrally or eccentrically located nuclei (Figure 1E–H). Gill tissues in the control group displayed a normal histological structure without significant pathological alterations, while localized detachment of epithelial cells from gill filaments and lamellae was observed in the A. veronii-infected group (Figure 1I–L). In the control group, the mid-kidney exhibited a normal histological architecture, whereas in the A. veronii-infected group, renal tubular epithelial cells were enlarged, with melanomacrophage centers (MMCs) being observed (Figure 1M–P).

3.2. mRNA Sequencing of P. vachellii Challenged with A. veronii

Six libraries from the control and A. veronii-infected P. vachellii were generated using Illumina 2500 sequencing. As shown in Table 1, clean reads were obtained from raw reads after removing low-quality reads, with the Q20 rates all exceeding 97%, the Q30 rates all exceeding than 92%, and the GC contents ranging from 44.63% to 46.68%. The clean reads were aligned to the reference genome using HISAT2 software, and the proportions of reads successfully mapped to the P. vachellii reference genome were all above 92% (Table 2). These results indicate that the sequencing and assembly quality of the P. vachellii head kidney transcriptome is satisfactory.
PCA demonstrated distinct separation between the control (C) and A. veronii infection (S) groups based on transcriptional profiles (Figure 2A). The Venn diagram illustrates 554 genes specific to group C, 417 genes specific to group S, and 14,567 genes common to both groups (Figure 2B). The volcano plot highlights 819 down-regulated and 391 up-regulated genes (Figure 2C). GO analysis revealed the enrichment of biological processes such as hematopoiesis, immune system development, and cell differentiation regulation. Key terms included hematopoiesis, immune organ development, and the positive regulation of cell differentiation following A. veronii infection (Figure 2D). KEGG analysis indicated involvement in pathways such as MicroRNAs in cancer, the NF-kappa B signaling pathway, Hepatitis B, and Th17 cell differentiation (Figure 2E).
We subsequently quantified the number of DEGs within the subcategories of KEGG pathways. The immune system subcategory within organismal systems exhibited a notable number of DEGs across various categories (Figure 3A). A total of 114 DEGs were significantly enriched in 11 immune-related signaling pathways, including Th17 cell differentiation, the complement and coagulation cascades, Th1 and Th2 differentiation, IL-17 signaling pathway, the chemokine signaling pathway, antigen processing and presentation, the B-cell receptor signaling pathway, and the T-cell receptor signaling pathway (Figure 3B). The expression profiles of several immune-related DEGs were depicted. Post-infection, the expression of IL-1β, IL-1R1, TCRα, TCRβ, CCL4, IκBα, STAT1, and HSP90 was significant up-regulated (Figure 3C).

3.3. GSEA of DEGs

GSEA evaluated the enrichment of signaling pathways in both the control and infection groups. The enrichment plots displayed the significant activation of the proteasome (KO03050), ribosome (KO03010), and oxidative phosphorylation (KO00190) pathways, while the autophagy-animal (KO04140), foxO signaling pathway (KO04068), and AMPK signaling pathway (KO04152) were notably suppressed (Figure 4A,B). Heatmaps in Figure 4C–H illustrate the expression levels of selected DEGs associated with these pathways. Within the proteasome pathway, several genes such as PSMA5, PSMC6, and PSMD14 were significantly upregulated in the head kidney of A. veronii-infected P. vachellii. Similarly, in the ribosome pathway, genes such as RPLP0, MRPS14, and MRPL33 exhibited elevated expression in the A. veronii-infected group. Notably, genes including COX15, TCIRG1, and CYC were significantly upregulated in the oxidative phosphorylation pathway post-infection. Conversely, key genes such as ATG4, PDPK1, ULK1, and CTSD were down-regulated in the autophagy-animal pathway following A. veronii infection. Similar downregulation was observed in the foxO signaling pathway, with genes such as SMAD3, FOXO3, and FOXO4 showing decreased expression levels. In the AMPK signaling pathway, several genes including AKT2 and TSC2 exhibited significant downregulation in the A. veronii-infected group.

3.4. miRNA Sequencing of P. vachellii Challenged with A. veronii

PCA revealed a clear separation between the healthy control and A. veronii-infected groups, indicating significant differences in small RNA expression profiles between the two groups (Figure 5A). Small RNA sequencing of six libraries (derived from the head kidney tissues of control and infected P. vachellii) generated an average of 12,384,953 bp clean reads and 12,260,678 bp high-quality reads per library (Table 3). The distribution of small RNA tag counts showed a distribution peaking in the mid-size range, with different small RNA types (e.g., known miRNAs, novel miRNAs, rRNAs) distinguished by color coding to visualize their relative abundances across samples (Figure 5B). In total, 544 known miRNAs and 958 novel miRNAs were identified (Supplementary Table S2). Differential expression analysis followed by Venn diagram visualization showed that 95 miRNAs were specifically expressed in the C group, while 61 miRNAs were uniquely expressed in the S group (Figure 5C). Furthermore, compared with the C group, the S group had 42 significantly downregulated miRNAs and 36 significantly upregulated miRNAs (Figure 5D). The top 20 most abundant known miRNAs and novel miRNAs are shown separately in Figure 5E and Figure 5F, respectively.

3.5. Interaction Analysis of mRNA and miRNA

miRNA-mRNA pairs with a PCC < −0.7 and a significance level of p < 0.05 were identified as negatively correlated co-expression pairs, all exhibiting differential expression. Subsequently, all immune genes within the immune system category of the KEGG pathway were chosen to establish a miRNA-target gene network (shown in Figure 6, which focuses solely on known miRNAs). In this network, nodes represent miRNAs and their target genes, with edges denoting negative co-expression relationships. The analysis revealed that multiple immune genes were regulated by one or more miRNAs. For example, interferon regulatory factor 4 (IRF4), a key regulator of immune cell differentiation and fate determination, was found to be negatively regulated by miR-132-z. Likewise, interleukin-1 receptor type 1 (IL1R1) was concurrently negatively regulated by miR-101-y and miR-101-z. Additionally, IκBα, a central negative regulator of the NF-κB signaling pathway, was targeted by miR-3167-y.

3.6. qRT-PCR Validation

To examine the expression levels in P. vachellii after A. veronii infection, six genes were selected for validation using qRT-PCR. The internal reference gene for data normalization in this study was β-actin. The comparison of the qRT-PCR results with the RNA-seq expression data, as depicted in Figure 7, revealed alignment with the transcription analysis findings.

4. Discussion

4.1. Histopathological Characteristics of A. veronii Infection

Histopathological examinations were conducted on both healthy and A. veronii-infected P. vachellii, covering the intestine, liver, gill, and mid-kidney. Performing histological assessment of the head kidney was not feasible due to sampling limitations associated with its small size. Following A. veronii infection, the shedding of gill epithelial cells was observed, potentially compromising the gills’ barrier function and increasing susceptibility to secondary pathogens. In the liver, hepatocyte swelling and cytoplasmic vacuolization were evident, suggesting the disruption of liver cell metabolic processes by the infection. MMCs are linked to infectious diseases, chronic inflammation, contaminated water, and tissue degradation [28]. The detection of MMCs in the mid-kidney indicates immune activation in reaction to the pathogen. These pathological manifestations bear resemblance to those reported in A. veronii-infected Micropterus salmoides, where tissue damage in organs such as the liver, kidney, and gill occurs [29]. Similarly, Macrobrachium rosenbergii infected with A. veronii displayed degeneration at the distal end of the gill filament [30], consistent with gill epithelial detachment observed in P. vachellii.

4.2. Regulatory Patterns of Pathways Revealed by DEGs Enrichment Analysis

The immune response of P. vachellii to A. veronii infection was investigated via head kidney transcriptome analysis. A total of 1210 DEGs were identified, with infected individuals exhibiting more downregulated genes than controls. This suggests that A. veronii infection predominantly suppresses gene expression, potentially disrupting biological functions. These results align with previous transcriptome studies on P. fulvidraco infected with A. veronii [31]. In contrast, Mastacembelus armatus exhibited a higher number of upregulated genes than downregulated genes following A. veronii infection [32]. Such differences may stem from variations in post-infection timing, highlighting divergent patterns in immune response patterns to bacterial challenges across host species.
Enrichment analysis of the DEGs revealed significant enrichment in immune-related pathways in infected P. vachellii, including the NF-κB pathway, Th17 cell differentiation, the complement and coagulation cascades, Th1 and Th2 differentiation, the IL-17 signaling pathway, the chemokine signaling pathway, antigen processing and presentation, the B-cell receptor signaling pathway, and the T-cell receptor signaling pathway. These results indicate that post-infection remodeling of the host immune system and pathogen-induced alterations in fish immune pathways occurred, which is consistent with prior observations of altered immune-related pathways in Cyprinus carpio koi [7] and Channa argus [33] upon pathogen exposure. The NF-κB signaling pathway, known for its pivotal role in inflammatory responses and immune regulation, was notably enriched post-infection in our study, consistent with research on other fish species infected with pathogenic bacteria. This activation implies active initiation of inflammatory responses to counter bacterial invasion, as observed in P. fulvidraco infected with E. tarda [34]. Mechanistically, NF-κB typically resides in inactive form in the cytoplasm bound to the inhibitory protein IκB. Pathogen stimulation activates IκB kinase (IKK), inducing IκB phosphorylation and degradation [35]. This releases NF-κB dimers, which translocate to the nucleus to drive transcription of pro-inflammatory genes such as TNF-α and IL-1β [36]. As a key pro-inflammatory cytokine, IL-1β recruits immune cells to infection sites and enhances host defense. Concordantly, our study observed upregulated IL-1β and IL-1R1 expression, confirming active inflammatory initiation against bacterial invasion.
The enrichment of DEGs in the antigen processing and presentation pathway, as well as the T/B cell receptor (TCR/BCR) signaling pathway, marks the onset of adaptive immune responses. Antigen processing and presentation to the immune effector cells that fight pathogens are key in the adaptive immune response. Significant upregulation of TCRα and TCRβ directly reflects T-cell activation, while BCR pathway DEGs lay the groundwork for subsequent B cell proliferation, differentiation, and antibody production [37,38]. Furthermore, the upregulation of CCL4 in the chemokine signaling pathway reinforces immune cell recruitment: as a chemokine, CCL4 specifically attracts macrophages and T-cells to accumulate in head kidney tissue, amplifying local immune responses. IκBα, a negative regulator of NF-κB, may prevent inflammatory storms via “feedback inhibition” to limit excessive NF-κB activation [39]. Conversely, upregulated STAT1 likely cross-regulates with the NF-κB pathway, jointly maintaining inflammatory balance and immune precision. Additionally, HSP90 upregulation may ensure efficient immune signal transduction by facilitating proper folding of immune-related proteins, which is consistent with observations in channel catfish following bacterial infections [40]. Collectively, these observations suggest that key immune pathways in teleosts have an evolutionarily conserved nature and can mount responses to pathogen threats.

4.3. Regulatory Patterns of Pathways Revealed by GSEA

GSEA further refined the response mechanisms of the head kidney tissue in P.vachellii at 48 h post-A. veronii infection. The results showed that multiple pathways including the proteasome, ribosome, and oxidative phosphorylation pathways were significantly activated. The proteasome, as the core executor of the intracellular ubiquitin proteasome system (UPS), can amplify pro-inflammatory responses by regulating the degradation of NF-κB inhibitors [41]. The ribosome, as the site of protein synthesis, plays a critical role in biological activities [42]. Thus, it can be inferred that the activation of the ribosome pathway provides a foundation for the synthesis of immune-related proteins. In contrast, GSEA demonstrated significant inhibition of pathways such as autophagy-animal, FoxO, and AMPK in the head kidney of P. vachellii at 48 h post-A. veronii infection. These repressions may reflect an active metabolic reprogramming that balances immune efficacy with cell survival. Autophagy, a conserved intracellular degradation process, is crucial in eliminating intracellular pathogens by sequestering them into autophagosomes for lysosomal degradation. The suppression of autophagy, indicated by the downregulation of ATG4, ULK1, and CTSD, can impede bacterial degradation, facilitating A. veronii persistence within macrophages. Similar autophagy inhibition has been observed in other fish bacterium interaction models, correlating with increased bacterial loads and short-term host cell survival. In Paralichthys olivaceus infected with lymphocystis disease virus (LCDV), the autophagy pathway was suppressed, leading to lymphocystis cell formation primarily through apoptosis suppression [43]. Similarly, in hybrid sturgeon infected with Nocardia seriolae and white sturgeon iridovirus (WSIV), the downregulation of the autophagy pathway resulted in apoptosis inhibition, enabling pathogens to evade the host antiviral response [44]. This study suggests that autophagy pathway inhibition by A. veronii may serve as an immune evasion strategy or a host self-protective mechanism to prevent the excessive apoptosis of immune cells. AMPK, functioning as a “cellular energy switch” activated under energy deficiency [45], showed alterations along with oxidative phosphorylation pathways, indicating changes in the host’s energy allocation following A. veronii infection. By enhancing oxidative phosphorylation, the host rapidly generates ATP and channels it preferentially to energy-demanding key immune responses, implying that this energy redistribution may represent an adaptive strategy in teleosts for combating acute bacterial infections.

4.4. Infection-Responsive Functions of the miRNA-mRNA Regulatory Network

miRNAs inhibit and destabilize mRNAs to carry out their functions, playing pivotal roles in a wide array of biological processes, including essential cellular functions and stress responses [46,47]. Their importance in both immune system development and immune responses has been well-documented [48]. In this study, 544 known miRNAs and 958 novel miRNAs were characterized, revealing 42 significantly downregulated and 36 significantly upregulated miRNAs. Notably, the number of downregulated differentially expressed miRNAs exceeded the number of upregulated ones, aligning with the expression pattern observed in the transcriptome. In the miRNA-mRNA regulatory network in the head kidney tissue of P. vachellii following A. veronii infection, several miRNAs, including miR-101 and miR-122, play pivotal roles. For example, miR-101 functions as a negative regulator of immune responses in miiuy croaker (Miichthys miiuy) [49]. Upon exposure to lipopolysaccharide (LPS) and Vibrio harveyi, the expression of miR-20-1 and miR-101a is upregulated, leading to the suppression of LPS-induced pro-inflammatory cytokines. In particular, miR-101a demonstrates inhibitory effects in response to LPS stimulation by targeting TRAF6. In this study, miR-101-y and miR-101-z were found to directly target IL-1R1, a gene that exhibited significant upregulation following A. veronii infection. The suppression of miR-101-y/z relieves its inhibition of IL-1R1, thereby potentiating NF-κB-driven inflammation.
Additionally, miR-122 reportedly plays a crucial role in immune responses [50,51]. Specifically, studies have demonstrated that miR-122 mediates the immune response to A. hydrophila infection in the orange-spotted grouper (E. coioides) by regulating IL-15 [13]. In this study, miR-122 was also found to be involved in regulating the immune response of P. vachellii after A. veronii infection. Additionally, a large number of novel miRNAs were identified in P. vachellii following A. veronii infection. While the complete validation of these newly identified miRNAs is pending completing, their target genes are predominantly associated with immune pathways, indicating the potential involvement of these novel miRNAs in the precise regulation of anti-infective responses in P. vachellii. Collectively, both known and novel miRNAs orchestrate the defensive response in the head kidney of P. vachellii against A. veronii infection by targeting essential immune genes.

4.5. Limitations and Future Perspectives

This study, by integrating histopathology, mRNA-seq, and small RNA-seq techniques, clarified the pathological characteristics, differentially expressed genes/miRNAs, and core regulatory pathways in the head kidney of P. vachellii at 48 h post-infection with A. veronii, providing key evidence for deciphering the antibacterial immune mechanism of this fish. However, this study still has obvious limitations: it only focuses on a single infection time point (48 h), failing to reveal the temporal dynamic process of the immune response (such as the activation order of pathways and the regulatory timeliness of miRNAs). The 958 identified novel miRNAs and predicted miRNA-mRNA regulatory pairs are only based on negative correlation analysis, lacking direct functional validation such as dual-luciferase reporter assays and gain/loss-of-function experiments. To address the above limitations, future studies can move research forward by considering four aspects: supplementing multi-time point analysis (12 h, 24 h, 72 h, and 96 h) to clarify the dynamic rules of the immune response and verifying the biological functions of novel miRNAs and regulatory pairs through target-binding validation, cellular, and in vivo functional experiments, laying a solid foundation for miRNA-guided disease prevention in aquaculture.

5. Conclusions

This study examined the response of P. vachellii head kidney to A. veronii infection at 48 h using histopathology, mRNA/small RNA sequencing, and functional analyses. Histopathological analysis revealed A. veronii-induced gill epithelial detachment, hepatocyte swelling with vacuolation, and mid-kidney MMCs, confirming its pathogenicity. The histological assessment of the head kidney was not feasible due to sampling limitations associated with its small size. Transcriptomic analysis identified 1210 differentially expressed genes (DEGs; 819 downregulated, 391 upregulated) enriched in 11 immune pathways (e.g., NF-κB, Th17 cell differentiation, complement and coagulation cascades), with key genes (eg., IL-1β, TCRα, TCRβ, CCL4) being upregulated. GSEA revealed the activation of the proteasome, ribosome, and oxidative phosphorylation pathways, along with the suppression of the autophagy, FoxO, and AMPK pathways. Small RNA sequencing revealed 544 known and 958 novel miRNAs. Among these, 78 differentially expressed miRNAs were found to regulate genes associated with the immune system in the context of A. veronii infection in P. vachellii. The study also identified their differentially expressed target genes and constructed an interaction network. These results indicate the significant role played by miRNAs in immune responses in teleosts, resembling higher vertebrates. Our study may offer valuable insights into the molecular regulatory mechanisms of miRNA-mRNA interactions during host–pathogen interactions, offering valuable information for further analyses of the defense mechanisms of P. vachellii against A. veronii infection.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/fishes10100530/s1, Table S1 Gene-specific primers for qRT-PCR; Table S2. The information of the identified known miRNAs and novel miRNAs.

Author Contributions

F.L.: Formal analysis; Investigation; Validation; Visualization; Writing—original draft; Writing—review and editing. X.W.: Formal analysis; Resources. Y.C.: Investigation; Methodology; Resource. Q.Z.: Methodology; Resources. P.L.: Investigation; M.S.: Validation; Q.G.: Resources; Funding acquisition; Y.L.: Formal analysis; J.L.: Data Curation; L.N.: Conceptualization; Supervision; Writing—original draft; Writing—review and editing; J.W.: Supervision; Writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

Financial support for this work was provided by the Key Laboratory of Sichuan Province for Fishes Conservation and Utilization in the Upper Reaches of the Yangtze River, Neijiang Normal University, Neijiang 641112, China (NJTCSC22-2).

Institutional Review Board Statement

The protocols for animal handling followed in this study were reviewed and approved by the Institutional Animal Care and Use Committee of Fisheries Research Institute, Sichuan Academy of Agricultural Sciences (Sichuan Fisheries Research Institute) in Chengdu, Sichuan, China (Approval No.: 20220217001A; Approval Date: 17 February 2022). All experimental operations were carried out in compliance with the guidelines sanctioned by this committee.

Informed Consent Statement

Not applicable.

Data Availability Statement

The RNA-seq data from this study have been deposited in the Genome Sequence Archive (GSA, https://ngdc.cncb.ac.cn/gsa/, accessed on 29 August 2025) at the China National Center for Bioinformation (CNCB) and can be accessed.

Conflicts of Interest

The authors declare that they have no competing interests. The funders were not involved in the study’s design, data collection, analysis, interpretation, manuscript writing, or publication decision.

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Figure 1. Histopathology characteristics of P. vachelli challenged with A. veronii. (A–D) intestine; (E–H) liver; (I–L) gill; (M–P) mid-kidney. GF. gill filament; GL. gill lamella; He. Hepatocyte; ML. mucous layer; MMC. melanomacrophage centers; SM. serous membrane. The yellow arrow indicates swollen renal tubular epithelial cells.
Figure 1. Histopathology characteristics of P. vachelli challenged with A. veronii. (A–D) intestine; (E–H) liver; (I–L) gill; (M–P) mid-kidney. GF. gill filament; GL. gill lamella; He. Hepatocyte; ML. mucous layer; MMC. melanomacrophage centers; SM. serous membrane. The yellow arrow indicates swollen renal tubular epithelial cells.
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Figure 2. Transcriptomic profiles of head-kidney from control (C) and A. veronii-infected (S) P. vachelli. (A) PCA plot showing separation of gene expression profiles between groups. (B) Venn diagram of unique and common expressed genes. (C) Volcano plot of DEGs. (D) Top 20 GO terms enriched in DEGs. (E) Top 20 KEGG pathways enriched in DEGs.
Figure 2. Transcriptomic profiles of head-kidney from control (C) and A. veronii-infected (S) P. vachelli. (A) PCA plot showing separation of gene expression profiles between groups. (B) Venn diagram of unique and common expressed genes. (C) Volcano plot of DEGs. (D) Top 20 GO terms enriched in DEGs. (E) Top 20 KEGG pathways enriched in DEGs.
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Figure 3. Immune-related transcriptomic profiles of head-kidney from control and A. veronii-infected P. vachelli. (A) Distribution of gene counts across functional categories, highlighting the prominence of immune system genes. (B) All immune system pathways identified. (C) Expression levels of selected key immune genes in C and S groups. * indicates significant differences (p < 0.05).
Figure 3. Immune-related transcriptomic profiles of head-kidney from control and A. veronii-infected P. vachelli. (A) Distribution of gene counts across functional categories, highlighting the prominence of immune system genes. (B) All immune system pathways identified. (C) Expression levels of selected key immune genes in C and S groups. * indicates significant differences (p < 0.05).
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Figure 4. GSEA of head-kidney transcriptomes from control and A. veronii-infected P. vachelli. (A) GSEA enrichment plots for the top 3 positive pathways: proteasome (KO03050), ribosome (KO03010), and oxidative phosphorylation (KO00190). (B) GSEA enrichment plots for the top 3 negative pathways: Autophagy-animal pathway (KO04140), FoxO signaling pathway (KO04068), and AMPK signaling pathway (KO04152). (C–H) Heatmaps of DEGs related to the above pathways. (C) for the proteasome pathway, (D) for the ribosome pathway, (E) for the oxidative phosphorylation pathway, (F) for the autophagy-animal pathway, (G) for the foxO signaling pathway, and (H) for the AMPK signaling pathway. Colors represent relative gene expression levels across different samples, with red for high expression and blue for low expression.
Figure 4. GSEA of head-kidney transcriptomes from control and A. veronii-infected P. vachelli. (A) GSEA enrichment plots for the top 3 positive pathways: proteasome (KO03050), ribosome (KO03010), and oxidative phosphorylation (KO00190). (B) GSEA enrichment plots for the top 3 negative pathways: Autophagy-animal pathway (KO04140), FoxO signaling pathway (KO04068), and AMPK signaling pathway (KO04152). (C–H) Heatmaps of DEGs related to the above pathways. (C) for the proteasome pathway, (D) for the ribosome pathway, (E) for the oxidative phosphorylation pathway, (F) for the autophagy-animal pathway, (G) for the foxO signaling pathway, and (H) for the AMPK signaling pathway. Colors represent relative gene expression levels across different samples, with red for high expression and blue for low expression.
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Figure 5. Small RNA sequencing analysis of head-kidney from control and A. veronii-infected P. vachelli. (A) PCA plot showing separation of small—RNA expression profiles between groups. (B) Distribution of small RNA tag counts. (C) Venn diagram of unique and common small RNAs in C and S groups. (D) Volcano plot of differentially expressed miRNAs. (E) The top 20 known miRNAs showing maximum abundance and log2(fc) values. (F) The top 20 novel miRNAs showing maximum abundance and log2(fc) values.
Figure 5. Small RNA sequencing analysis of head-kidney from control and A. veronii-infected P. vachelli. (A) PCA plot showing separation of small—RNA expression profiles between groups. (B) Distribution of small RNA tag counts. (C) Venn diagram of unique and common small RNAs in C and S groups. (D) Volcano plot of differentially expressed miRNAs. (E) The top 20 known miRNAs showing maximum abundance and log2(fc) values. (F) The top 20 novel miRNAs showing maximum abundance and log2(fc) values.
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Figure 6. A miRNA-mRNA network constructed from immune genes in the immune system category of the KEGG pathway. Blue nodes represent known miRNAs, and pink nodes represent target genes. Edges between nodes indicate negatively correlated co-expression relationships (Pearson correlation coefficient < −0.7 and p < 0.05).
Figure 6. A miRNA-mRNA network constructed from immune genes in the immune system category of the KEGG pathway. Blue nodes represent known miRNAs, and pink nodes represent target genes. Edges between nodes indicate negatively correlated co-expression relationships (Pearson correlation coefficient < −0.7 and p < 0.05).
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Figure 7. qRT-PCR validation for RNA-seq data. (A) FPKM values. (B) mRNA expression levels. qRT-PCR data are presented as the means ± SEM (n = 3). * indicates significant differences (p < 0.05).
Figure 7. qRT-PCR validation for RNA-seq data. (A) FPKM values. (B) mRNA expression levels. qRT-PCR data are presented as the means ± SEM (n = 3). * indicates significant differences (p < 0.05).
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Table 1. Transcriptome sequencing and assembly data of head-kidney from control and infected P. vachellii.
Table 1. Transcriptome sequencing and assembly data of head-kidney from control and infected P. vachellii.
SampleRaw Data (bp)Clean Data (bp)Q20 Rate (%)Q30 Rate (%)GC Rate (%)
C15,645,586,3005,571,126,69097.1892.66 46.36
C26,824,948,7006,739,799,76497.3492.98 45.69
C37,758,962,7007,668,867,70497.3292.13 46.22
S16,953,786,7006,887,975,00897.4092.16 46.68
S25,732,555,4005,658,284,30397.1392.19 44.63
S35,706,639,3005,651,435,86297.3292.23 45.99
Table 2. The mapping results of RNA-seq and the reference genome.
Table 2. The mapping results of RNA-seq and the reference genome.
SampleUnmapped (%)Unique Mapped (%)Multiple Mapped (%)Total Mapped (%)
C17.21 87.07 5.71 92.79
C26.83 86.98 6.19 93.17
C36.60 86.64 6.76 93.40
S15.58 89.10 5.32 94.42
S27.00 86.41 6.59 93.00
S35.89 87.67 6.43 94.11
Table 3. miRNA sequencing data of head-kidney from control and infected P. vachellii.
Table 3. miRNA sequencing data of head-kidney from control and infected P. vachellii.
SampleClean Reads (bp)High Quality Reads (bp)High Quality Read Ratio (%)Clean Tags (bp)Clean Tag Ratio (%)
C111,762,03811,640,19798.96 10,815,58991.95
C211,633,86911,509,10998.93 10,244,79588.06
C313,613,36313,489,00399.09 11,080,41881.39
S112,737,63712,621,54699.09 11,672,26291.64
S210,950,70410,829,96298.90 9,401,90985.86
S313,612,10413,474,25398.99 11,721,45286.11
Note: Clean reads refer to sequences obtained by filtering raw small RNA-seq reads to remove low-quality reads, adapter-deficient/abnormal reads, polyA-containing reads, and sequences shorter than 18 nt. Clean tags were derived from clean reads by further removing rRNA, scRNA, snoRNA, snRNA, tRNA, and sequences matching exons, introns, or repetitive regions.
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Li, F.; Wu, X.; Chen, Y.; Zou, Q.; Li, P.; Song, M.; Gong, Q.; Liu, Y.; Lai, J.; Ni, L.; et al. Functional miRNA-mRNA Regulatory Modules in the Head Kidney of Pelteobagrus vachellii in Response to Aeromonas veronii Infection. Fishes 2025, 10, 530. https://doi.org/10.3390/fishes10100530

AMA Style

Li F, Wu X, Chen Y, Zou Q, Li P, Song M, Gong Q, Liu Y, Lai J, Ni L, et al. Functional miRNA-mRNA Regulatory Modules in the Head Kidney of Pelteobagrus vachellii in Response to Aeromonas veronii Infection. Fishes. 2025; 10(10):530. https://doi.org/10.3390/fishes10100530

Chicago/Turabian Style

Li, Feiyang, Xiaoyun Wu, Yeyu Chen, Qiaolin Zou, Pengcheng Li, Mingjiang Song, Quan Gong, Ya Liu, Jiansheng Lai, Luyun Ni, and et al. 2025. "Functional miRNA-mRNA Regulatory Modules in the Head Kidney of Pelteobagrus vachellii in Response to Aeromonas veronii Infection" Fishes 10, no. 10: 530. https://doi.org/10.3390/fishes10100530

APA Style

Li, F., Wu, X., Chen, Y., Zou, Q., Li, P., Song, M., Gong, Q., Liu, Y., Lai, J., Ni, L., & Wang, J. (2025). Functional miRNA-mRNA Regulatory Modules in the Head Kidney of Pelteobagrus vachellii in Response to Aeromonas veronii Infection. Fishes, 10(10), 530. https://doi.org/10.3390/fishes10100530

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