Characterization of Immune Response Against Mycobacterium marinum Infection in Coho Salmon (Oncorhynchus kisutch)

Abstract

Mycobacterium marinum is an opportunistic pathogen prevalent in aquatic environments, causing significant morbidity in fish, including Coho salmon (Oncorhynchus kisutch), a species increasingly cultured in Chinese salmonid aquaculture. This study investigated the immune response of Coho salmon to M. marinum infection and the bacterial proliferation dynamics in the liver and kidney. Transcriptome analysis revealed 5028 differentially expressed genes (DEGs) in the kidney and 3419 DEGs in the liver at 6 weeks post-infection. Gene Ontology and KEGG enrichment analysis highlighted pathways such as cytokine–cytokine receptor interaction, metabolic pathways, and Toll-like receptor signaling in the kidney, while the DEGs in the liver were enriched in metabolic pathways, immune system processes, and stress and defense responses. The temporal expression profiling of 15 immune-related genes, including acute-phase proteins (serum amyloid A-5 and hepcidin), cytokines (TNF-α, IL-1β, IL-17A), chemokines (CXCL13 and CCL19), pattern recognition receptors (Toll-like receptor 13), and other immune-related genes, showed significant upregulation against M. marinum infection, with stronger responses in the liver. Furthermore, it was found that there was a progressive proliferation of M. marinum in the infected liver and kidney from approximately 2.5 log₁₀ cfu/g at week 2 to about 6 log₁₀ cfu/g by 6 weeks, with a significantly higher load in the liver. These findings provide critical insights into the immune mechanisms of Coho salmon against M. marinum and the pathogen’s tissue-specific proliferation, offering a foundation for developing targeted control strategies against M. marinum in aquaculture.

1. Introduction

Mycobacterium marinum is a slow-growing, opportunistic, nontuberculous mycobacterium that is commonly found in aquatic environments [1]. This bacterium is known to cause a potentially serious skin infection called aquarium granuloma, particularly in individuals with compromised immune systems or those with open wounds [2]. In fish, M. marinum can cause a systemic granulomatous disease characterized by skin lesions, a loss of scales, and ulcerations, leading to significant morbidity and mortality in affected aquatic species [3,4]. These infections may present as either acute or chronic conditions, and they can also remain asymptomatic for extended periods before advancing to clinically apparent disease [5]. M. marinum infections have been reported in a wide range of fish species, both in wild and captive populations [6]. The prevalence of infection can be as high as 40–50% in some fish species, particularly in aquaculture settings [7], which leads to significant production and economic losses in the fish farming industry. Also, amphibians and reptiles could be infected by M. marinum, with reports of infections in frogs, salamanders, and turtles [8], posing a potential threat to the health of aquatic animals.

Salmonid aquaculture, particularly rainbow trout (Oncorhynchus mykiss), has significantly expanded in China in recent years, driven by high market demand [9,10]. However, the intensification of aquaculture, including the use of recirculating aquaculture systems (RASs), creates environments favorable for disease outbreaks and opportunistic pathogen proliferation [11,12,13]. While rainbow trout farming dominates, other salmonids like Coho salmon (Oncorhynchus kisutch) and Atlantic salmon (Salmo salar) are also increasingly cultured [14]. With the rapid development of salmonid farming in China, disease issues have become increasingly prominent, and several diseases have affected the salmonid aquaculture industry, causing heavy economic losses, including bacterial infections such as Vibriosis and Aeromonas, viral diseases like infectious pancreatic necrosis, viral hemorrhagic septicemia, and infectious hematopoietic necrosis, and parasitic infections such as Gyrodactylus and Ichthyopthirius [15,16]. M. marinum, as one of the opportunistic pathogens, was found to be prevalent in Chinese salmonid aquaculture, including Coho salmon cultured in RASs. Despite extensive research, effective treatment for mycobacteriosis in fish remains elusive due to the lack of potent and efficacious interventions [17]. To date, no systematic transcriptomic analysis of salmonid immune responses to M. marinum has been reported. Understanding the immune mechanisms in Coho salmon (a species now increasingly farmed) is therefore of great value, as it could guide management practices and the development of potential vaccine strategies against mycobacteriosis in salmonids.

Understanding the immune response of Coho salmon to M. marinum is crucial for developing effective vaccines and therapies. This study utilizes transcriptome profiling to analyze the immune response of Coho salmon to M. marinum infection. The resulting transcriptome profiles will provide valuable information on Coho salmon gene sequences and differentially expressed genes (DEGs) following infection, offering insights into disease mechanisms and guiding future prevention and control strategies.

2. Materials and Methods

2.1. Bacterial Challenge and Sample Collection

A total of 100 healthy Coho salmon (50 ± 10 g) were purchased from a marine farm in Shandong province, China. The fish were maintained in tanks containing aerated sand-filtered seawater at 17 ± 0.5 °C for one week prior to processing. The M. marinum strain used in this study was isolated from the liver of Coho salmon naturally infected with mycobacteriosis, and its virulence was validated by a challenge experiment in healthy Coho salmon. The M. marinum was cultured at 28 °C on solid-phase Middlebrook 7H10 Agar for 10 days. For infection, the concentration of M. marinum was determined by colony-forming unit (CFU) counting on Middlebrook 7H10 agar and then adjusted to 5 × 10⁶ CFU/mL, and each fish was intraperitoneally injected with 100 μL of bacteria suspension, the livers and kidneys were separately sampled from three infected fish at 0, 2, 4, and 6 weeks after infection for gene expression analysis and bacterial load examination, and the tissues sampled at 0 and 6 weeks were used for RNA-seq analysis. For RNA-seq, tissues from 15 fish were sampled at each time point, and every 5 individual samples were pooled to create one composite sample. The samples were quickly frozen with liquid nitrogen and stored at −80 °C. Before injection, the fish were anesthetized with 100 mg/mL of MS-222 (Sigma-Aldrich, City of Saint Louis, MO, USA). For sampling, the fish were over-anesthetized with 300 mg/mL of MS-222.

2.2. RNA Extraction, Library Construction, and Sequencing

Total RNA was extracted using the Trizol reagent kit (Invitrogen, Carlsbad, CA, USA), according to the manufacturer’s protocol. RNA quality was assessed on an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA) and checked using RNase-free agarose gel electrophoresis. After total RNA was extracted, eukaryotic mRNA was enriched by Oligo (dT) beads, while prokaryotic mRNA was enriched by removing rRNA by Ribo-ZeroTM Magnetic Kit (Epicentre, Madison, WI, USA). Then, the enriched mRNA was fragmented into short fragments using fragmentation buffer and reverse transcripted into cDNA with random primers. Second-strand cDNA was synthesized by DNA polymerase I, RNase H, dNTP, and buffer. Then, the cDNA fragments were purified with the QiaQuick PCR extraction kit (Qiagen, Venlo, The Netherlands), and end-repaired poly(A) was added and ligated to Illumina sequencing adapters. The ligation products were size selected by agarose gel electrophoresis, PCR amplified, and sequenced using Illumina HiSeq2500 by Gene Denovo Biotechnology Co. (Guangzhou, China).

2.3. Basic Bioinformatics Analysis

Reads obtained from the sequencing machines include raw reads containing adapters or low-quality bases, which will affect the following assembly and analysis. Thus, to obtain high-quality clean reads, reads were further filtered by fastp (version 0.18.0). The parameters were set and performed as follows: (1) removing reads containing adapters; (2) removing reads containing more than 10% of unknown nucleotides (N); (3) removing low-quality reads containing more than 50% of low-quality (Q-value ≤ 20) bases. The reference genome was obtained from the NCBI database (GCA_002021735.2). An index was built using hisat2-build from HISAT2 (v2.2.4) to facilitate read alignment. Paired-end clean reads were mapped to the indexed genome using hisat2, generating SAM files. The mapped reads of each sample were assembled by using StringTie v1.3.1 in a reference-based approach. For each transcription region, an FPKM value was calculated to quantify its expression abundance and variations, using StringTie software. RNA differential expression analysis was performed by DESeq2 software between two different groups (healthy and infected fish at week 6). The healthy fish sampled before infection was used as the control. The genes/transcripts with the parameter of a false discovery rate (FDR) below 0.05 and absolute fold change ≥2 were considered differentially expressed genes/transcripts.

2.4. GO and Pathway Enrichment and Analysis

All DEGs were mapped to Gene Ontology (GO) terms in the Gene Ontology database (http://www.geneontology.org/ (accessed on 30 May 2024)), gene numbers were calculated for every term, and significantly enriched GO terms in DEGs compared to the genome background were defined by a hypergeometric test. GO annotations of transcripts were obtained by searching against the non-redundant database using the Blast2GO program [18], and the GO functional classifications were carried out using WEGO software [19]. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were assigned to the assembled transcripts using the online KEGG Automatic Annotation Server (http://www.genome.jp/kegg/kaas/ (accessed on 30 May 2024)). The bi-directional best-hit (BBH) method was used to obtain KEGG orthology assignments.

2.5. qPCR Analysis of Immune-Related Genes

Based on the results of transcriptome analysis, 15 genes related to Mycobacterium marinum infection (Serum amyloid A-5, hepcidin, N-acetylmuramoyl-L-alanine amidase (NAMLAA), mannose-binding protein C, Toll-like receptor 13, pentraxin-3 (PTX3), tumor necrosis factor α (TNF-α), Hemicentin-2, interleukin-1β (IL-1β), interleukin-17A (IL-17A), C-X-C motif chemokine 13, C-C motif chemokine 19, cis-aconitate decarboxylase, and claudin-1) were selected for time-course expression analysis using qRT-PCR. SYBR GreenIMaster (Roche, Basel, Switzerland) was used as a reagent and real-time PCR was carried out in the LightCycler^® 480 II System (Roche, Basel, Switzerland). To normalize expression data, β-actin was used as an internal control gene. The expression levels of these genes were analyzed by the 2^−△△Ct method, and the primers used are listed in

Table 1. Primers used for the qPCR analysis of selected genes.

Gene Name	Sequences (5′-3′)	Primer Source (Genbank No.)
Serum amyloid A-5	F:GCGACATGAAGGATGCCAAC	XM_020478634
	R:TCCTCATGTCCTCGACCACT
Cis-aconitate decarboxylase	F:CTCCAACGAGGCTCAGAATATC	XM_031789418
	R: TGAGAGCGTTCCAGAGAGA
N-acetylmuramoyl-L-alanine amidase	F:CTGTGGTGAGGGAAGGAATAAA	XM_020486665
	R:CACTGACACCGTGGGATAATAG
Mannose-binding protein C	F:CTTTGACGATGCTGTTCAGTTC	XM_020469364
	R:CCTTCAAAGACCTGAGTGAGAG
Toll-like receptor 13	F:AGGGACTGTGTCATCTACCTATC	XM_031792536
	R:GTTTGGGTGCAGGTCCTTAATA
Hepcidin	F:TTCAGGTTCAAGCGTCAGAG	XM_031790947
	R:CAGGCAGGTCCTCAGAATTT
Pentraxin-3	F:GGACACAGGCTATCCAACTTATC	XM_020471418
	R:CATCTCTGCTGATGCCACTT
Tumor necrosis factor α	F:GGCGAGCATACCACTCCTCT	Zhang et al., 2023 [20]
	R:TCGGACTCAGCATCACCGTA
Hemicentin-2	F:GTCAGTCCGTCTGGTGAAATAG	XM_020469630
	R:CCCGTCCGTTCTTCATTCTT
Interleukin-1β	F:GCGACATGGTGCGTTTCCTTTT	Zhang et al., 2023 [20]
	R:TGTCTACCGGTTTGGTGTAGTCCT
Interleukin-17A	F:GGTCTTTGGACGGAGAGTAATG	XM_020477819
	R:GAGAGACCTGGTGGCTTTATG
C-X-C motif chemokine 13	F:ACACTACTTGTTGGTCGTACTG	XM_020477664
	R:GAAACCTGGTGGGAGAGATAAA
C-C motif chemokine 19	F:CCACAGAGTGGTGAAGTCATAC	XM_020458446
	R:GCACACAGTCTAAGGTTCTTCT
Claudin-1	F:CTTGCTTTGATCGGTCTTGTTG	XM_020480182
	R:GTTACCGTGCTCTCTGTGTAAG
β-actin	F:CCAAAGCCAACAGGGAGAA	Zhang et al., 2023 [20]
	R:AGGGACAACACCGCCTGGAT

.

2.6. Quantitative PCR for Determining Mycobacterial Loads

The infected Coho salmon were euthanized at designated time points (2, 4, and 6 weeks post-infection) using MS-222 ( Sigma-Aldrich, City of Saint Louis, MO, USA). The liver and kidney were aseptically excised, rinsed in sterile phosphate-buffered saline (PBS) to remove blood, and placed into pre-weighed 1.5 mL tubes. Tissue weights were recorded for normalization. Then, the samples were homogenized and processed using the QIAamp DNA Microbiome kit (Qiagen, Venlo, The Netherlands), according to the manufacturer’s instructions. Extracted DNA was measured with a NanoDrop 8000 spectrophotometer (Thermo Scientific, Waltham, MA, USA). For Quantitative qPCR, the primers targeting the M. marinum 16S-23S rRNA ITS region (forward: 5′-CACCACGAGAAACACTCCAA-3′, reverse: 5′-ACATCCCGAAAC CAACAGAG-3′) were used in 20 µL reactions. The qPCR amplification system and conditions were performed as previously described [21]. All the samples were run in triplicate on an Applied Biosystems 7500 system. A standard curve (10¹–10⁷ CFU) was prepared from M. marinum DNA, and the amplification program was followed by a melting curve. The bacterial load was calculated as the CFU per gram of tissue.

3. Results

3.1. Analysis of Sample Relationships and Differentially Expressed Genes

To ensure the greater reliability of the results of differential gene analysis between samples, the correlation analysis of sample tissues was carried out. The PCA results demonstrated that the positions of three parallel sample points were similar within the groups, and there was an obvious grouping trend among the four distinct groups (Figure 1A). Additionally, the results of the correlation heat map indicated that the correlation coefficient of parallel samples in each group was greater than 0.99 (Figure 1B), suggesting that the experimental treatment had excellent repeatability, and there were significant differences in gene expression between kidney and liver tissues post-infection.

The DEGs in the kidney and liver tissues of the infected group and the control group were both analyzed, and genes with log2 > 2 and FDR < 0.05 were selected as DEGs. As depicted in Figure 2, compared with the control group, 5028 differential genes were identified in the kidney tissues after infection with Mycobacterium marinum, including 1739 upregulated genes and 3289 downregulated genes. A total of 3419 differential genes were identified in the liver tissue, including 2167 upregulated genes and 1252 downregulated genes.

3.2. GO Enrichment of the DEGs in Kidney and Liver

The DEGs were annotated to Gene Ontology (GO) terms and subjected to enrichment analysis across three categories: Biological Process (BP), Molecular Function (MF), and Cellular Component (CC). The top 20 GO terms were ranked based on their FDR values. As seen in Figure 3A, the DEGs in the kidney were predominantly associated with processes such as bioadhesion, cell adhesion, carboxylic acid metabolism, oxoacid metabolism, organic acid metabolism, motility, cell motility, cell localization, extracellular structure organization, response to external stimuli, subcellular component motility, monobiotic processes, cell–cell adhesion, and cell migration. Enriched cellular components included the extracellular region, extracellular matrix, extracellular matrix structural components, proteinaceous extracellular matrix, membrane-bounded vesicles, and vesicles. As seen in Figure 3B, the DEGs in liver were mainly enriched in biological processes such as organic acid metabolism, carboxylic acid metabolism, oxoacid metabolism, small-molecule metabolic processes, single-organism metabolic processes, stress responses, immune system processes, bacterial response, defense response, response to biological stimuli, interactions with other organisms, monocarboxylic acid metabolism, response to external biological stimuli, coenzyme metabolism, and biological quality control. Enriched cellular components included membrane-bounded vesicles, vesicles, and lysosomal vacuoles.

3.3. KEGG Enrichment of the DEGs in the Kidney and Liver

The KEGG enrichment analysis identified the top 20 signaling pathways based on FDR values. As shown in Figure 4A, in response to M. marinum infection, DEGs were classified into six major categories: metabolism, human diseases, organismal systems, cellular processes, genetic information processing, and environmental information processing. In kidney tissues (Figure 4B), the most significantly enriched pathways included cytokine–cytokine receptor interaction, metabolic pathways, epithelial cell signaling in Helicobacter pylori infection, amino metabolisms, viral protein–cytokine and cytokine receptor interaction, carbon metabolism, extracellular matrix (ECM)–receptor interaction, toxoplasmosis, amino acid biosynthesis, malaria, amebiasis, the TNF signaling pathway, cell adhesion molecules, Toll-like receptor signaling, and so on. In the liver (Figure 4C), the most enriched pathways included metabolic pathways, carbon metabolism, epithelial cell signaling in Helicobacter pylori infection, the role of AGE-RAGE signaling in diabetic complications, fluid shear stress and atherosclerosis, rheumatoid arthritis, phagosomes, cytokine–cytokine receptor interaction, glycolysis/gluconeogenesis, malaria, viral protein–cytokine and cytokine receptor interaction, amino acid biosynthesis, the TNF signaling pathway, osteoclast differentiation, glycine, serine, threonine metabolism, tuberculosis, bile acid secretion, insulin resistance, leishmaniasis, and the NF-κB signaling pathway.

3.4. Temporal Expression Characteristics of 15 Genes Related to Mycobacterium marinum Infection

Based on the results of transcriptomic analysis, we selected 15 significantly differentially expressed genes closely related to mycobacterial infection and immune responses to further study their dynamic expression following Mycobacterium marinum infection across six weeks. The results demonstrated that the 15 selected genes involved in bacterial infection, including acute-phase proteins, pattern recognition receptors, inflammatory cytokines, and other immune-related genes, were all upregulated in the liver and kidney, which was generally consistent with the results obtained from the transcriptome analysis (Figure 5 and Figure 6). The expression levels of acute-phase proteins, including serum amyloid A-5 and hepcidin, exhibited quicker and stronger responses in the liver than in the kidney following infection. Innate immunity-related proteins, including mannose-binding protein C, Toll-like receptor 13, and NAMLAA, exhibited a significant increase and reached peak levels at 2 or 4 weeks post-infection, maintaining elevated levels for 6 weeks. Cytokines, including TNF-α, IL-1β, IL-11, and IL-17A, displayed dynamic regulation in response to M. marinum infection. In the liver, TNF-α and IL-1β showed significant upregulation at 2 weeks post-infection, peaking at 4 weeks. IL-17A expression progressively increased, reaching its highest level at 6 weeks post-infection. IL-11 displayed a modest upregulation at 4 weeks post-infection, with relatively minor changes overall. In the kidney, the expression trends of these cytokines were generally consistent with those in the liver but with reduced magnitudes. Two chemokines, C-X-C motif chemokine 13 and C-C motif chemokine 19, also exhibited a significant increase and peaked at 4 or 6 weeks post-infection, both in the liver and kidney. The expression of other immune-related proteins, including PTX3, Hemectin-2, and claudin-1, displayed diverse patterns following infection. Cis-aconitate decarboxylase, a metabolic enzyme, showed a gradual increase within 6 weeks after infection and reached the peak level at the sixth week.

3.5. The Proliferation Dynamic of M. marinum in Liver and Kidney

A linear standard curve was successfully developed using quantitative PCR (qPCR) technology to enable the precise detection and quantification of M. marinum. The relationship between the logarithm of bacterial colonies and the cycle threshold (Ct) value was modeled by the linear equation y = −3.34x + 38.18. This curve exhibited a strong negative correlation, with a p-value of 0.998, confirming excellent linearity and reliability (Figure 7A). The bacterial load of M. marinum in the liver and kidney of Coho salmon was quantified at 2, 4, and 6 weeks post-infection based on the established standard curve, revealing a progressive increase in bacterial proliferation over time in both organs (Figure 1B). At 2 and 4 weeks post-infection, the bacterial loads were around 2.5 and 5 log₁₀ CFU/g in the two tissues, respectively, and no significant difference was found between the liver and kidney. At week 6, the bacterial load peaked significantly, with the liver at approximately 6.5 log₁₀ cfu/g, showing a significantly higher load compared to that in the kidney with 5.6 log₁₀ cfu/g.

4. Discussion

Mycobacterium species are resilient bacteria found in aquatic environments that cause chronic infections and pose a zoonotic risk to aquaculture practitioners [22,23,24,25]. M. marinum threatens aquatic animals, leading to disease outbreaks, growth impairment, and mortality, especially in intensive farming, causing significant economic losses [26,27]. This study provided a comprehensive analysis of the immune response of Coho salmon to M. marinum infection, shedding light on the complex interplay between the host and pathogen. By employing transcriptome profiling and qPCR-based quantification, we also elucidated key aspects of the Coho salmon’s defense mechanisms and the pathogen’s proliferation dynamics in vivo. These findings have the potential to inform the development of future vaccines or immunotherapies for mycobacteriosis in fish.

This study investigated the transcriptional response of Coho salmon to the infection with M. marinum, focusing on gene expression changes in kidney and liver tissues. Principal component analysis (PCA) and correlation analysis demonstrated excellent reproducibility within experimental groups, validating the experimental design and sample consistency [28]. The substantial number of DEGs identified in both kidney and liver tissues highlights the profound impact of M. marinum infection on multiple physiological systems in Coho salmon. The predominance of upregulated genes in both tissues suggests a robust activation of immune responses, alongside significant alterations in metabolic processes and other physiological functions. For instance, the enrichment of Gene Ontology terms related to immune system processes, such as cytokine production and pathogen recognition, indicates a strong immune activation, while terms associated with carboxylic acid metabolism and energy metabolism reflect a metabolic reprogramming to support the host’s defense efforts. Similarly, pathways related to tissue repair and stress responses suggest a coordinated systemic response to mitigate infection-induced damage. These findings suggested the complex, multifaceted response of Coho salmon to M. marinum infection, involving an interplay of immune, metabolic, and physiological adaptations to counter the pathogen and maintain homeostasis. The disparity in the number of up- and downregulated genes between the tissues implies distinct roles for these organs in the overall immune response. The larger number of downregulated immune genes in the kidney suggests a potential pathogen-driven immunosuppression or regulatory response [29]. The significant differences observed in gene expression between kidney and liver tissues suggest the tissue-specific nature of the host response to M. marinum infection.

Gene Ontology enrichment analysis revealed distinct functional categories enriched in each tissue. In the kidney, GO terms related to cell adhesion, extracellular matrix (ECM), and carboxylic acid metabolism were significantly enriched. Cell adhesion and ECM are crucial for tissue repair and remodeling. During infection, damaged tissues need to be repaired, and the ECM often needs to be restructured to facilitate immune cell infiltration and tissue healing. The enrichment of these terms suggests that the organism is actively engaging in tissue repair mechanisms [30,31]. During infection, cells often need to adapt their metabolic processes to meet the increased energy demands of the immune response and to produce necessary molecules for fighting the infection. Therefore, carboxylic acid metabolism enrichment indicates a shift in cellular metabolism [32]. The enrichment of GO terms related to immune system processes, stress response, and metabolic processes in the liver suggests the liver’s critical function as both a metabolic and immune organ. It was documented that the liver plays a crucial role in the systemic immune response and energy metabolism during a pathogen’s infection [33]. The liver also plays an important role in immune functions and homeostasis [34]. These results collectively point to a coordinated, systemic response orchestrated by the liver to address the challenges posed by infection. KEGG pathway analysis further elucidated the complex interplay of signaling pathways involved in the response to M. marinum. The significant enrichment of pathways involved in cytokine–cytokine receptor interaction, various metabolic pathways, and Toll-like receptor signaling in both tissues confirms the activation of both innate and adaptive immune responses [35,36]. In the kidney, the enrichment of pathways related to cytokine–cytokine receptor interaction, metabolic processes, and Toll-like receptor signaling suggests a coordinated effort to activate immune cells, modulate metabolism to support the immune response, and recognize pathogen-associated molecular patterns [37,38]. On the other hand, the liver exhibited enrichment in immune system processes, stress responses, and NF-κB signaling, indicating a more pronounced inflammatory response and the activation of cellular stress pathways [39,40].

The temporal expression profiling of 15 selected immune-related genes provided valuable insights into the kinetics of the immune response. The upregulation of acute-phase proteins, cytokines, chemokines, and pattern recognition receptors confirms the activation of both innate and adaptive immune mechanisms. The quicker and stronger responses observed in the liver for acute-phase proteins suggest a more rapid mobilization of systemic defenses in this tissue. The upregulation of acute-phase proteins (APPs) such as serum amyloid A-5 and hepcidin, particularly prominent in the liver, highlights the pivotal role of APPs in the early stages of the host’s immune response. This early hepatic response aligns with the liver’s function as a primary site for APP production [41,42,43]. The quicker and stronger response of these proteins in the liver compared to the kidney suggests a coordinated, organ-specific prioritization of defense mechanisms, necessary for mitigating pathogen spread [44]. The significant increase in the expression of pattern recognition receptors, such as mannose-binding protein C and Toll-like receptor 13, reflects their integral role in pathogen recognition and the activation of downstream immune pathways. PRRs are the first line of defense in the innate immune system. Their upregulation suggests the active surveillance and detection of pathogens, initiating the immune response [45,46,47]. The sustained elevation of these receptors up to six weeks post-infection highlights their importance not just in immediate pathogen detection but also in prolonged defense, facilitating the recruitment and activation of other immune components, and maintaining a prolonged state of alertness against the pathogen. The dynamic regulation of cytokines, with TNF-α and IL-1β showing early upregulation and IL-17A increasing progressively, suggests a sequential activation of different inflammatory pathways. The timing of these peaks suggests an orchestrated effort to mediate inflammation and direct adaptive immune responses as seen in other aquatic species infected with mycobacteria [48]. Interestingly, IL-11 showed only modest upticks, which could indicate a more nuanced role, possibly in regulating excessive inflammation to prevent tissue damage, a balancing act essential during prolonged infections [49].

The upregulation of chemokines, notably C-X-C motif chemokine 13 and C-C motif chemokine 19, plays a crucial role in the host’s immune response against Mycobacterium marinum infection in fish. These chemotactic cytokines are instrumental in orchestrating leukocyte recruitment, a hallmark of the immune response to mycobacterial infections [50,51]. The observed peak expression of these chemokines at later stages of infection suggests a sustained immune response, potentially indicative of ongoing leukocyte recruitment necessary for pathogen containment and clearance. The varied expression patterns of proteins like PTX3 and claudin-1 further reflected the multifaceted nature of the host immune response. As a pattern recognition molecule, PTX3 plays a crucial role in the identification and opsonization of pathogens, facilitating their clearance by phagocytes [52]. In fish, PTX3 has also been shown to be involved in the acute-phase response and possesses antimicrobial properties [53]. The increased expression of PTX3 in M. marinum infection suggests its involvement in pathogen recognition and the orchestration of innate immune responses, potentially contributing to the containment of mycobacterial spread. The upregulation of claudin-1, a tight junction protein, suggests significant alterations in epithelial barriers as part of the host defense strategy. Claudins play a crucial role in maintaining the integrity of epithelial and endothelial barriers, which are critical first lines of defense against pathogen invasion [54]. In fish, the modulation of claudin expression has been observed in response to various pathogens [55,56]. The increased expression of claudin in M. marinum infection may represent an attempt to enhance barrier function, potentially limiting mycobacterial dissemination and protecting underlying tissues.

The qPCR-based quantification of M. marinum loads revealed a progressive proliferation of the pathogen in both the liver and kidney over time. This finding is consistent with previous studies investigating mycobacterial infections in other fish species, such as zebrafish (Danio rerio), where M. marinum infection typically shows a gradual increase in bacterial burden over time [57]. Interestingly, the bacterial loads in the liver and kidney were similar at 2 and 4 weeks post-infection, but diverged significantly by week 6, with a higher bacterial load in the liver. This tissue-specific difference contrasts with some studies in other fish species. For instance, in goldfish (Carassius auratus), M. marinum infection often results in higher bacterial loads in the kidney compared to the liver [58]. The higher liver burden in Coho salmon at later stages of infection could indicate a species-specific tropism of M. marinum or differences in tissue-specific immune responses. Several factors may contribute to the higher bacterial load observed in the liver of Coho salmon at 6 weeks post-infection. Firstly, the rich vascularization and extensive blood supply in the liver might provide an ideal microenvironment for M. marinum proliferation, as the bacterium is known to infect macrophages and persist intracellularly [59]. Secondly, the route of infection, likely intraperitoneal injection in this study, might also facilitate the bacterial dissemination to the liver. In addition, differences in experimental conditions, such as bacterial strain and infection dose, could also influence tissue tropism. The difference between Coho salmon and other fish species also highlights the need for species-specific research in aquaculture. While zebrafish and goldfish provide valuable models for studying mycobacterial infections, their tissue tropism patterns may not fully represent those in commercially important salmonids. These findings highlight the importance of tailored diagnostic and therapeutic approaches based on the unique physiological and immunological characteristics of Coho salmon.

5. Conclusions

In summary, this study provides a comprehensive characterization of the immune response of Coho salmon to Mycobacterium marinum infection. By integrating transcriptome profiling and qPCR-based quantification, we have identified key immune genes and pathways, elucidated the tissue-specific dynamics of the immune response, and revealed the pathogen’s proliferation dynamics in vivo. These findings contribute to our understanding of the host–pathogen interaction and provide a foundation for developing targeted disease control strategies against mycobacterial infections.