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Article

The Pathology and Splenic Transcriptome Profiling of Trionyx sinensis Challenged with Bacillus cereus

by
Jinbiao Jiao
1,
Jing Chen
1,
Jiayun Yao
1,
Yanli Li
2,
Xuemei Yuan
1,
Lei Huang
1,
Shengqi Su
2,* and
Haiqi Zhang
1,*
1
Agriculture Ministry Key Laboratory of Healthy Freshwater Aquaculture, Key Laboratory of Fish Health and Nutrition of Zhejiang Province, Key Laboratory of Fishery Environment and Aquatic Product Quality and Safety of Huzhou City, Zhejiang Institute of Freshwater Fisheries, Huzhou 313001, China
2
College of Fisheries, Key Laboratory of Freshwater Fish Reproduction and Development (Ministry of Education), Research Center of Fishery Resources and Environment, Southwest University, Chongqing 400715, China
*
Authors to whom correspondence should be addressed.
Fishes 2023, 8(2), 84; https://doi.org/10.3390/fishes8020084
Submission received: 7 December 2022 / Revised: 28 January 2023 / Accepted: 30 January 2023 / Published: 31 January 2023
(This article belongs to the Special Issue Transcriptomics in Aquaculture: Current Status and Applications)
Browse Figures
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Abstract

:
The pathogenic Bacillus cereus strain XS0724 isolated from China can cause high lethality to Trionyx sinensis, but little information is available on their detailed interactions. In this study, histopathologic profiling indicated that B. cereus caused vacuolization and cell necrosis in the liver, spleen, kidney, and intestine. The identification of the virulence factor genes non-hemolytic enterotoxin (Nhe), hemolysin BL (Hbl), and enterotoxin FM (entFM) confirmed bacterial pathogenicity. Splenic transcriptomic sequencing at 96 h post-infection identified various immune-related genes mapped to diverse gene families, including interleukin, complement, chemokine, and interferon. The differentially expressed genes (DEGs) were enriched in 2174 GO terms: 1694 in biological processes, 138 in cellular components, and 342 in molecular functions. Further KEGG enrichment indicated that DEGs were primarily associated with the phagosome, NF-kappa B signaling pathway, and PI3K-Akt signaling pathway. The DEGs and enriched pathways may be involved in the elimination of invasive B. cereus. These data laid the foundation for elucidating the potential molecular mechanisms in this bacterial infection process, and provided robust genetic evidence for subsequent work on resistance genes of T. sinensis.

1. Introduction

Intensive aquaculture has recently led to bacterial diseases and huge economic losses. As key pathogens, pathogenic bacteria are rapidly spread in suitable aquatic environments, which can disrupt the normal function, metabolism, and physiology of aquatic animals by exploiting an arsenal of genes encoding virulence factors [1]. Although antibiotics and vaccines are effective in controlling distinct bacterial diseases, there exist some limitations [2]. Antibiotics risk triggering drug resistance and threatening the safety of aquatic products [3]. Despite the success of bacterial vaccines in recent years, several emerging bacterial diseases are not effectively treated by current commercially available vaccines [4]. Therefore, it is critical to elucidate the pathogenic mechanism at the molecular level for preventing pathogens and developing new strategies in the future.
Chinese soft-shelled turtle Trionyx sinensis is one of the main commercial freshwater-cultured species in China due to its substantial economic, nutritional, medicinal, and ornamental value. In recent years, however, with the deterioration of the water environment, increased breeding density, and degradation of germplasm resources, T. sinensis has suffered severe infectious diseases, including Bacillus cereus [5,6,7,8]. Outbreaks of these pathogens have brought severe economic losses to the aquaculture industry.
B. cereus, a Gram-positive bacteria, is regarded as a facultative anaerobe ubiquitously distributed in soil, water, sewage plants, and food [9]. It functions not only as a probiotic by reinforcing gastrointestinal balance and immune systems [10], but also as a main pathogen of food poisoning due to its virulence factors [11]. Previous studies reported that B. cereus caused diseases in a wide range of aquatic animal species [12,13], as well as head-shaking syndrome characterized by high mortality in T. sinensis [14]. These diseases have caused large economic losses to farmers. B. cereus group isolates carrying virulence factor genes cytK, HblC, NheA, and entFM were detected from diseased T. sinensis in Taiwan [8]. Whole genome sequencing of B. cereus strain SYJ15 from T. sinensis has also been reported [15]. However, little is known about the virulence factors and pathogenicity of the previous B. cereus strain XS0724, and this requires further study. As little is currently known about how T. sinensis responds to B. cereus strain XS0724, it is of great significance to understand the immune mechanism of T. sinensis.
To date, high-throughput transcriptome sequencing has been broadly applied in immune-related gene identification in many aquatic animals under bacterial challenge [16,17,18]. The molecular mechanism identified by RNA-seq of T. sinensis after B. cereus infection has not yet been established. Given that B. cereus is highly pathogenic to T. sinensis and molecular mechanisms of the immune response are limited, histopathological observations were carried out in this study to evaluate histopathologic changes in different organs. In addition, we utilized transcriptomic sequencing to reveal potential regulators and biological responses after pathogenic infection. These results may inform antibacterial immune activities at the transcriptional level and provide a strong theoretical basis for controlling and preventing bacterial disease in T. sinensis, especially against B. cereus.

2. Materials and Methods

2.1. Animal Infection and Sample Collection

T. sinensis individuals, each weighing 750 ± 50 g, were obtained from a commercial turtle farm (Huzhou, China). A total of 20 specific pathogen-free turtles were randomly divided into two groups: infection group (GD, n = 10) and control group (CK, n = 10). Prior to the infection experiments, the two groups were both acclimatized in a 200-L tank with aerated freshwater (28–30 °C) for one week. B. cereus strain XS0724 (Genebank ID: OQ255816) isolated previously [14] was freshly cultivated in tryptic soy broth (TSB) medium (Huankai Co., Ltd., Guangzhou, China) to logarithmic growth phase, and resuspended in phosphate-buffered saline (PBS) at a density of 3.84 × 106 CFU/mL. For the infection group, the turtles were injected intraperitoneally with 200 µL of fresh bacteria, while the control group was inoculated with an equal volume of PBS. At 96 h post-infection (hpi), three turtles were randomly chosen in both groups for sampling. The spleens were collected and flash frozen in liquid nitrogen for RNA extraction. Meanwhile, the livers, spleens, kidneys, and intestines from the same individuals were extracted and immersed in 4% paraformaldehyde at 4 °C for histopathologic analysis.

2.2. Histopathologic Observation

The collected livers, spleens, kidneys, and intestines (see Section 2.1) were fixed in 4% paraformaldehyde for more than 24 h. After dehydration with an ethanol gradient and embedding in paraffin, the tissues were cut into thin sections (4-μm thickness). Finally, the sections were stained with hematoxylin and eosin (HE) following standard procedures, and assessed under a microscope [19].

2.3. Detection of Virulence Factor Genes

The genomic DNA of bacteria was extracted from fresh bacterial cultures with a Bacterial Genomic DNA Extraction kit (Tiangen Biotech, Beijing, China). All PCR reactions were performed with 2 × Taq Master Mix (New England Biolabs, Ipswich, MA, USA). The reactions contained 12.5 μL of 2 × Taq Master Mix, 1 μL of template DNA, 1 μL of forward primer (10 μM), 1 μL of reverse primer (10 μM), and 9.5 μL of ddH2O. The PCR program consisted of one initial denaturation step at 95 °C for 5 min, followed by 35 cycles (denaturation at 95 °C for 30 s, annealing for 30 s, extension at 72 °C), and a final extension at 72 °C for 5 min. The PCR amplification was performed on a Bio-Rad Thermal Cycler (Bio-Rad, Hercules, CA, USA). The annealing temperature and extension time depended on the primers and target fragment lengths (about 1 min/kb), and are detailed in Table 1. PCR products were visualized by electrophoresis on a 1% agarose gel containing GelRed (Biotium, Fremont, CA, USA) to determine the presence of virulence factor genes.

2.4. RNA Extraction, cDNA Library Construction, and Sequencing

The frozen spleen samples (see Section 2.1) were ground in liquid nitrogen and total RNA was extracted using RNAiso Plus Reagent (Takara, Shiga, Japan) following the manufacturer’s instructions. The concentration of RNA was measured using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and RNA integrity was examined by an Agilent 2100 Bioanalyzer system (Agilent Technologies, Santa Clara, CA, USA).
Polyadenylated RNA was isolated from total RNA with Dynabeads Oligo (dT) (Invitrogen, Waltham, MA, USA), fragmented into 300 bp fragments, and then reverse transcribed into first strand cDNA using reverse transcriptase and random primers. The resulting suitable fragments were enriched by PCR amplification to construct the cDNA libraries. The size distribution of the cDNA libraries was validated using an Agilent Bioanalyzer 2100, and cDNA was quantified by qPCR. Sequencing of the complete libraries was performed in 150 bp paired-end mode using an Illumina HiSeq 4000 platform (Illumina, San Diego, CA, USA).

2.5. Raw Data Assembly and Annotation

Raw reads were quality controlled by clipping sequencing adapters and filtering low-quality reads (<Q20) and short reads (length < 50 bp). High-quality clean sequences were de novo assembled to obtain transcripts using Trinity 2.4.0 [24] with the default parameters. After clustering transcripts, the longest transcript was regarded as the unigene for each cluster.
All assembled unigenes were functionally annotated by aligning against public databases: NCBI non-redundant protein sequences (NR) (http://www.ncbi.nlm.nih.gov/, accessed on 17 December 2019), Swissprot (http://www.expasy.ch/sprot, accessed on 17 December 2019), Protein family (Pfam) (http://pfam.xfam.org/, accessed on 17 December 2019), Gene Ontology (GO) (http://geneontology.org/page/go-database, accessed on 17 December 2019), Kyoto Encyclopedia of Genes and Genomes (KEGG) (http://www.genome.jp/kegg, accessed on 17 December 2019), and eggNOG (http://eggnog.embl.de/, accessed on 17 December 2019) using Diamond (version 0.7.9) [25], Blast2go (version 2.5.0) [26], and KAAS (version 2.0) [27].

2.6. Identification and Patterns of DEGs

The fragments per kilobase of transcript per million mapped (FPKM) method was used to normalize and calculate gene expression. The clean read counts were obtained from the transcriptome alignment files using RSEM 1.2.12 software [28]. Differential gene expression analysis between samples was performed using the DESeq R package (1.12.0) with default parameters [29]. p-values were adjusted for multiple testing by the Benjamini–Hochberg procedure [30]. The threshold for defining DEGs was |log2 (fold change)| > 1 and p-value < 0.05. Volcano and MA plots of differentially expressed genes were rendered using ggplot2 R package.

2.7. GO Term and KEGG Pathway Enrichment Analysis of DEGs

In order to clarify the biological processes and functions of DEGs, we mapped all DEGs to the GO and KEGG databases. GO functional enrichment and KEGG pathway enrichment were conducted using the topGO package and KOBAS 2.0 [31] with a threshold p-value < 0.05.

2.8. Analysis of Gene Expression by qPCR

To further validate the transcriptome DEG data, we selected 11 DEGs for qPCR analysis. Total RNA was extracted from frozen spleen samples as described above. RNA was reverse transcribed into cDNA using a Prime Script RT Reagent kit (Takara, Shiga, Japan). All qPCR primers were designed using Primer Premier 5.0 software (Premier Biosoft, Palo Alto, CA, USA) (Table 2). The qPCR reactions were performed with SYBR Green PCR Mastermix (Roche, Basel, Switzerland) using a Light Cycler 480 Real-Time PCR system (Roche, Basel, Switzerland), with the following parameters: 95 °C for 600 s, followed by 45 cycles (95 °C for 10 s, 60 °C for 10 s, and 72 °C for 30 s), and 95 °C for 10 s, 65 °C for 60 s and 97 °C for 1 s. All reactions, including controls, were performed in triplicate from three independently isolated RNA samples. The β-actin gene was utilized as an internal control for normalization [32], and the gene expression levels relative to β-actin gene were quantified using the comparative 2−ΔΔCT method [33]. Consistency between RNA-seq and qPCR was evaluated by linear regression analysis.

2.9. Statistical Analysis

All quantitative data were obtained from at least three independent experiments. The statistical significance of differences was analyzed by Student’s t test using SPSS version 25 (IBM, Armonk, NY, USA). A significance threshold was set at p < 0.05. Graph assembly was visualized by GraphPad Prism version 8 software (GraphPad, San Diego, CA, USA).

3. Results

3.1. Histopathology

Notably, sections generated from all infected tissues showed shared features: necrotic cells, visible vacuolization, and bacterial accumulation in the tissues (Figure 1). The infected liver exhibited structurally disorganized hepatic lobules, hepatocellular necrosis and vacuolation, cell nuclear pyknosis and disappearance, and blurred cell outlines (Figure 1b). The infected spleen was characterized by a dramatic decrease in lymphocytes, lymphoid follicle hyperemia, and severe lymphatic necrosis and depletion (Figure 1d). Lymphocytic infiltration was observed in glomeruli and tubules, the number of renal tubules decreased, and tubules showed necrosis and atrophy (Figure 1f). Compared to the control group, the infected intestinal villi displayed fewer goblet cells and disordered absorptive cells. Structural disorder occurred in the lamina propria of the villi, with injured central chylomicron and loose muscle fibers (Figure 1h).

3.2. Toxigenic Profiles of B. cereus

According to our agarose gel electrophoretogram, B. cereus strain XS0724 carried several typical virulence factor genes, such as NheA, NheB, NheC, HblA, HblC, HblD, and entFM, but lacked cytK and ces. It is worth noting that although bceT primers amplified a significant signal, it clearly did not match (>500 bp) the length of the target product (428 bp) (Figure 2).

3.3. Illumina Sequencing and de Novo Transcriptome Assembly

Qualified RNA was used for the sequencing library construction (Supplement Table S1). A total of 297 million raw reads were generated from the six cDNA libraries. The percentage of Q20 for each library was greater than 97% and Q30 was greater than 93%, indicating that the sequencing data was of high quality. A total of 277 million clean reads were obtained by removing low-quality reads and adaptor-contaminated reads (Table 3). The clean reads were assembled into 581,291 transcripts with a mean length of 904.31 bp and an N50 of 1877 bp. These transcripts were mapped to 348,665 unigenes with a mean length of 617.15 bp and an N50 of 892 bp (Table 4).

3.4. Unigene Annotation

Among all unigenes, a total of 70,704 unigene sequences were annotated in six different databases. As shown in Table 5, 61,527 (17.65%), 25,656 (7.36%), 33,764 (9.68%) 15,315 (4.39%), 59,855 (17.17%), and 30,692 (8.80%) were annotated in the NR database, GO database, KEGG database, Pfam database, eggNOG database, and Swissprot database, respectively, and 7496 (2.15%) unigenes were matched to all databases. The homologous species (top hits upon a database search) distribution based on NR annotation revealed that 43.69% of the unigenes were highly similar to T. sinensis, which was more similar than Chelonia mydas (20.09%), Chrysemys picta bellii (10.99%), and others (Figure 3).
GO analysis can comprehensively clarify the biological function of unigenes. In the GO term annotation, a total of 25,656 unigenes were classified into three main categories: biological process, cellular component, and molecular function. Among the biological process subcategories, cellular process (GO:0009987), single-organism process (GO:0044699), and metabolic process (GO:0008152) were dominant. The predominant subcategories in cellular component were cell (GO:0005623), cell part (GO:0044464), and membrane (GO:0016020). The most abundantly represented subcategories in molecular function were binding (GO:0005488), catalytic activity (GO:0003824), and transporter activity (GO:0005215) (Figure 4a).
To identify the KEGG pathways in T. sinensis, 33,764 annotated unigenes were assigned to multiple terms, primarily involving five broad categories: cellular processes, environmental information processing, genetic information processing, metabolism, and organismal systems. Furthermore, signal transduction, endocrine system, immune system, and transport and catabolism accounted for the main subcategories, containing 4457, 2579, 2409, and 2407 unigenes, respectively (Figure 4b).
The eggNOG annotation has an essential role in the functional description and functional classification of orthologous groups. Overall, a total of 59,855 unigenes were assigned to 26 eggNOG functional categories. Among them, the general function prediction only category (R; 26,773 unigenes) accounted for the major proportion, followed by the function unknown category (S; 13,267 unigenes), and the signal transduction mechanisms category (T; 7129 unigenes) (Table 6).

3.5. Analysis of the DEGs

To explore the mechanisms of the host response to B. cereus infection, DEGs were identified. All DEGs were displayed in a volcano plot (Figure 5a) and an MA plot (Figure 5b). Compared with the control group, a total of 18,593 genes were differentially expressed in the infected group, with 9678 upregulated genes and 8915 downregulated genes. A number of immune-related gene families were annotated and expressed differentially, mainly including immunoglobulins, antimicrobial peptides, nuclear factors, interferons, interleukins, complements, tumor necrosis factors, and chemokines (Table 7).

3.6. GO Analysis and KEGG Enrichment

GO enrichment analysis was further carried out to investigate the biological role of DEGs during B. cereus infection. A total of 3946 DEGs were enriched in 2174 GO terms: 1694 (77.92%) in the biological process category, 138 (6.35%) in the cellular component category, and 342 (15.73%) in the molecular function category. Response to stimulus (GO:0050896), cellular response to stimulus (GO:0051716), and multicellular organismal process (GO:0032501) were the three most enriched biological process GO terms. In the cellular component category, the top three terms were membrane part (GO:0044425), intrinsic component of membrane (GO:0031224), and integral component of membrane (GO:0016021). The three most represented terms enriched in the molecular function domain were receptor activity (GO:0004872), molecular transducer activity (GO:0060089), and signaling receptor activity (GO:0038023) (Figure 6).
For a systematic understanding of how T. sinensis responded to B. cereus, KEGG pathway enrichment analysis was further conducted. Overall, a total of 151 KEGG pathways were significantly enriched (p-value < 0.05), which were mainly involved in four categories: cellular processes, environmental information processing, human diseases, and organismal systems. Phagosome, NF-kappa B signaling pathway, rheumatoid arthritis, and hematopoietic cell lineage exhibited the most prominent enrichment in cellular processes, environmental information processing, human diseases, and organismal systems, respectively (Figure 7a). The top twenty KEGG pathways based on the lowest false discovery rates (FDRs) are shown in a bubble plot (Figure 7b).

3.7. Validation of RNA-Seq Data by qPCR

To further confirm the confidence and accuracy of transcriptome results, 11 immune-related genes were selected for qPCR validation, including six upregulated genes and five downregulated genes. Notably, a high consistency was observed between the transcriptome data and the qPCR results based on the correlation coefficient of 0.9617 and p-value < 0.001 (Figure 8).

4. Discussion

4.1. Pathogenicity of B. cereus

B. cereus is generally regarded as a probiotic, and commonly used as an aquatic feed additive to promote growth and disease resistance [10,34]. However, growing evidence indicates that highly virulent strains could infect multiple aquatic animals, and cause gastrointestinal diseases in humans [35]. Histopathology is an intuitive approach to assessing pathogenic hazards and determining the organ damage mechanisms at the tissue level. Our results revealed an enormous number of necrotic cells and varying degrees of bleeding and bacterial accumulation in the liver, spleen, kidney, and intestine. A previous study found that T. sinensis infected with B. cereus suffered high mortality, accompanied by liver hemorrhage, splenomegaly, and intestinal edema [8]. A safety evaluation of commercial B. cereus probiotics also found that some strains caused sepsis, liver injury, and intestinal inflammation in mice [36]. All these findings confirmed that virulent B. cereus could lead to serious organ damage in hosts, including T. sinensis.
We identified three types of virulence factor genes, Nhes, Hbls, and entFM in B. cereus strain XS0724. These virulence factor genes were also detected in B. cereus previously isolated from T. sinensis with high-mortality in Taiwan, China [8] and parrots in São Paulo, Brazil [37]. Nhe is one of the main members of the enterotoxins, and consists of NheA, NheB, and NheC. The enterotoxins can form an Nhe complex, which mediates holes in cell membranes and triggers cell lysis [38]. In our study, we speculated that cellular necrosis and vacuolization may be closely related to Nhe. As a hemolysin, Hbl is capable of hemolysis and altering vascular permeability [39]. This may be an important factor leading to visceral congestion and swelling found in our study. We also noted congestion in the lungs, liver, heart, kidneys, intestines, and spleens [37]. Highly similar symptoms suggested that the bacteria were indeed attacking the diseased organs. EntFM is involved in bacterial movement and shape, adhesion, biofilm formation, and vacuolization of macrophages [40]. The bceT, cytK, and ces genes have been detected in B. cereus from other sources [8,37,41,42,43]. However, B. cereus strain XS0724 presented an absence of the commonly found bceT, cytK, and ces genes in our study. This revealed differences in virulence factors among B. cereus from different sources, possibly due to specificity in host-pathogen affinity and variability from environmental adaptations [44].

4.2. Immune-Related DEGs

To further understand the response mechanism of immune organs to B. cereus infection, we systematically dissected the transcriptome of the spleen. A total of 18,593 differentially expressed genes were identified, including 9678 upregulated genes and 8915 downregulated genes, which was more than in the lung tissue of T. sinensis infected by Trionyx sinensis hemorrhagic syndrome virus [32]. DEGs showed high diversity, especially in immune-related genes. The diverse genes initially indicated that bacterial infections triggered complex immune activities.
Interleukin-6 (IL-6), a pleiotropic pro-inflammatory cytokine, plays a pivotal role in inducing the proliferation and expansion of lymphocytes [45]. In our experiments, IL-6 mRNA level was significantly upregulated in response to infection. Upon pathogen recognition, the similar up-regulation of IL-6 mRNA level was observed in the spleen leukocytes, indicating that IL-6 mRNA might be involved in host defense against bacterial infection [46]. IL-6 is used as a typical inflammatory biomarker to predict diseases in humans, considering its rapid induction during inflammation and cancer [47,48].
IL-8, also referred to as CXCL8, is responsible for neutrophil recruitment, activation, and chemotaxis [49]. In our results, IL-8 mRNA expression was significantly upregulated in response to B. cereus infection. A previous study found that IL-8 mRNA was constitutively expressed in different tissues, and was upregulated particularly in the spleen after bacterial infection [50]. All these findings suggested that the spleen and IL-8 might contribute to the antimicrobial response. Similar to avian species, T. sinensis can express IL-8 mRNA containing an ELR motif [50], which has been found to promote angiogenesis [51]. We speculated that upregulation of IL-8 mRNA expression may compensate for visceral hemorrhage after infection.
Chemokines are confirmed to stimulate leukocyte migration and activation during an immuno-inflammatory response [52]. CCL20 plays an important role in the migration of Th17 cells and regulatory T cells to inflammatory sites and can also induce IL-17 production [53]. The prominent upregulation of CCL20 mRNA expression with bacterial infection in this study may suggest the activation of the inflammatory response after bacterial infection. CCL20 chemokine displayed antibacterial activities in Micropterus salmoides challenge with Nocardia seriolae [54], which may be relative to its electrostatic surface topology [55]. This may explain the changes of CCL20 mRNA level in our study. CCL20 can only bind to the specific receptor CCR6 to form the CCL20-CCR6 axis [56]. Moreover, B cells with a higher CCR6 expression level showed a higher CCL20 expression level [57]. In this study, we did not find a high expression level of CCR6 mRNA but did for CCL20 mRNA. It was possible that the CCL20-CCR6 axis was not activated. Further research is needed to clarify the detailed mechanism.
The complement system is a critical part of immune reactions and contributes to defense against invading pathogens [58]. Complement activation enhances pathogen lysis, phagocytosis, clearance of apoptotic bodies, and cytokine production [59]. Our results showed that the C1r mRNA level was increased after infection, but C7 and C1q mRNA levels were downregulated, in contradiction with previous reports [60,61]. The binding of C1q to immunoglobulin triggers the subsequent efficient activation of C1, the first component of the complement system [62]. The activation of C7 can lead to the membrane attack complex (MAC) on the target membrane, which ultimately contributes to the disruption of bacterial cell walls, cell lysis, and osmotic imbalance [63]. It could be predicted that C7 might have antibacterial effects in T. sinensis. Our results, however, suggested that T. sinensis had a weak complement response to B. cereus infection. This may be explained by differences in host and pathogen stimuli. Noting previous studies, although the complement system was not significantly activated in this research, it was possible that complement components indirectly stimulated immune responses through inflammation, adaptive immunity, and tissue repair [64,65].

4.3. Immune-Related Pathways

Pathway analysis helps to elucidate molecular immune mechanisms and associations more clearly and purposefully because pathways are the relationship network among genes. In the KEGG enrichment analysis, the primary pathways related to the immune response were phagosome, NF-kappa B signaling pathway, PI3K-Akt signaling pathway, cell adhesion molecules (CAMs), and hematopoietic cell lineage. These enriched pathways may be involved in the antimicrobial and inflammatory responses in the spleen of T. sinensis infected by B. cereus. Similar pathways were also reported in previous studies [66,67,68].
The phagosome signaling pathway is considered as one of the most common mechanisms of eliminating exogenous microorganisms [69]. Phagosomes mainly occur in several specialized phagocytic cells, such as macrophages, monocytes, neutrophils, and dendritic cells (DCs) [70]. In macrophages, mature phagosomes can kill internalized microorganisms, and degrade and remove cell debris [71]. In DCs, phagocytes effectively prevent degradation of the antigenic peptides, allowing the major histocompatibility complex (MHC) to participate in antigen presentation [72]. In rainbow trout, the phagosome pathway was activated and MHC1 was upregulated in the spleen to resist Yersinia ruckeri infection [73]. In addition, it has been reported that phagosomes have a superior ability to present antigens through MHC II by generating degraded exogenous peptides from microorganisms bound to TLR ligands [74]. Therefore, the significant enrichment of the phagosome pathways in this study indicated that the host effectively promoted microbial degradation and stimulated the immune response against microbial invasion.
It is noteworthy that in addition to the phagosome pathway, the NF-κB pathway also participated in the antibacterial response in T. sinensis. The NF-κB pathway is regarded as a canonical proinflammatory signaling pathway [75]. Nuclear factor-κB (NF-κB) is composed of several transcription factors. Under unstimulated conditions, NF-κB is an inactive dimer due to the inhibitor of NF-κB proteins (IκBs) [76]. NF-κB is activated by lipopolysaccharide and proinflammatory cytokines under exogenous B. cereus infection [77], and translocates to the nucleus to promote the transcription of targeted genes [78]. The target genes cover a variety of cytokines, such as interleukins, chemokines, adhesion molecules, acute phase proteins, and inflammatory enzymes [79]. Differentially expressed inflammatory cytokines were found in our results, some of which may be mediated by the pro-inflammatory effects of the NF-κB pathway. The enrichment of the NF-κB pathway suggested that it played a crucial part in the inflammatory response against B. cereus infection. It has been reported that the promoter region of the serum amyloid A (a major acute phase protein) gene in T. sinensis contains an NF-κB binding site [80], suggesting that NF-κB is also critical in regulating the acute phase reaction.

5. Conclusions

In this study, we evaluated the pathogenicity of B. cereus. Histopathological observations and the detection of virulence factor genes confirmed severe damage at the tissue level and revealed pathogenic molecules. Furthermore, for the first time, we presented the transcriptome profile of the spleen in T. sinensis with B. cereus infection, and identified multiple immune-related genes involved in the phagosome, NF-kappa B signaling pathway, PI3K-Akt signaling pathway, and cell adhesion molecules (CAMs). These results contributed to enriching our understanding of the immune mechanisms of T. sinensis against B. cereus, providing opportunities for developing novel strategies in the prevention of this disease in the future.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/fishes8020084/s1, Table S1: Information on RNA samples.

Author Contributions

Conceptualization, S.S. and H.Z.; Methodology, J.J.; Software, J.C.; Validation, H.Z.; Formal Analysis and Investigation, J.J., J.C. and Y.L.; Resources, J.Y.; Data Curation, J.Y.; Writing—Original Draft Preparation, J.J.; Writing—Review and Editing, X.Y. and L.H.; Visualization, X.Y. and L.H.; Supervision, S.S.; Project Administration, S.S.; Funding Acquisition, S.S. and H.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Key Scientific and Technological Grant of Zhejiang for Breeding New Agricultural Varieties (No: 2021C02069), and the Science and Technology Project of Zhejiang Province (No: 2021SNLF026).

Institutional Review Board Statement

Approved by the Animal Research and Ethics Committee of Zhejiang Institute of Freshwater Fisheries; no ethical approval is required for this study.

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw data of RNA-seq has been deposited to the SRA database with the accession number PRJNA924069.

Conflicts of Interest

All the authors declare that they have no conflict of financial or non-financial interest.

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Figure 1. HE-stained histological sections of livers, spleens, kidneys, and intestines. CK and GD represent the control group and infection group, respectively. Scale bars denote a length of 50 (a,b) or 100 μm (c–h). The blue arrows point to bacterial accumulation universally. (a) The normal liver. (b) Collapse in a hepatic lobule (green arrow) and vacuolated cytoplasm due to nuclear pyknosis and karyolysis (black arrow). (c) Lymphoid follicle filled with an enormous number of lymphocytes in the control spleen (thick white arrow). (d) Atrophy and necrosis in a lymphoid follicle (black arrow) and overt hyperemia around lymphatic nodules (green arrow). (e) Healthy glomerulus with a distinct profile (thick black arrow). (f) Tubular necrosis (green arrow), and lymphocytic infiltrates and atrophy in glomeruli (black arrow). (g) The normal intestinal villi with morphologically normal lamina propria (thick white arrow) and goblet cells (thick black arrow). (h) Structural disorder of the lamina propria of the villi, with loose smooth muscle (green arrow) and damaged central chylomicron (black arrow).
Figure 1. HE-stained histological sections of livers, spleens, kidneys, and intestines. CK and GD represent the control group and infection group, respectively. Scale bars denote a length of 50 (a,b) or 100 μm (c–h). The blue arrows point to bacterial accumulation universally. (a) The normal liver. (b) Collapse in a hepatic lobule (green arrow) and vacuolated cytoplasm due to nuclear pyknosis and karyolysis (black arrow). (c) Lymphoid follicle filled with an enormous number of lymphocytes in the control spleen (thick white arrow). (d) Atrophy and necrosis in a lymphoid follicle (black arrow) and overt hyperemia around lymphatic nodules (green arrow). (e) Healthy glomerulus with a distinct profile (thick black arrow). (f) Tubular necrosis (green arrow), and lymphocytic infiltrates and atrophy in glomeruli (black arrow). (g) The normal intestinal villi with morphologically normal lamina propria (thick white arrow) and goblet cells (thick black arrow). (h) Structural disorder of the lamina propria of the villi, with loose smooth muscle (green arrow) and damaged central chylomicron (black arrow).
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Figure 2. Gel electrophoretogram of virulence factor genes. Lane M represents the DL2000 DNA ladder marker. Lanes 1–10 represent NheA, NheB, NheC, HblA, HblC, HblD, bceT, cytK, ces, and entFM, respectively.
Figure 2. Gel electrophoretogram of virulence factor genes. Lane M represents the DL2000 DNA ladder marker. Lanes 1–10 represent NheA, NheB, NheC, HblA, HblC, HblD, bceT, cytK, ces, and entFM, respectively.
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Figure 3. The distribution of species for the best matched unigenes in the NR database. The pie chart shows the top seven species that were highly homologous to the unigene sequences matched in the NR database.
Figure 3. The distribution of species for the best matched unigenes in the NR database. The pie chart shows the top seven species that were highly homologous to the unigene sequences matched in the NR database.
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Figure 4. GO and KEGG category assignments of assembled unigenes. All unigenes were annotated into (a) three GO functional categories and (b) five KEGG categories.
Figure 4. GO and KEGG category assignments of assembled unigenes. All unigenes were annotated into (a) three GO functional categories and (b) five KEGG categories.
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Figure 5. Statistical overview of DEGs. Each red dot represents a significantly upregulated gene, each blue dot represents a significantly downregulated gene, and each gray dot represents a non-significant differential gene. (a) Volcano plot of DEGs. The two vertical dashed lines indicate a fold change threshold of more than 2.0, while the horizontal dashed line indicates a p-value threshold lower than 0.05. (b) MA plot of DEGs. The X-axis represents the logarithm base 2 of the product of the gene expression in the two groups, while the Y-axis represents the logarithm of the fold change (base = 2).
Figure 5. Statistical overview of DEGs. Each red dot represents a significantly upregulated gene, each blue dot represents a significantly downregulated gene, and each gray dot represents a non-significant differential gene. (a) Volcano plot of DEGs. The two vertical dashed lines indicate a fold change threshold of more than 2.0, while the horizontal dashed line indicates a p-value threshold lower than 0.05. (b) MA plot of DEGs. The X-axis represents the logarithm base 2 of the product of the gene expression in the two groups, while the Y-axis represents the logarithm of the fold change (base = 2).
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Figure 6. GO classification of DEGs after B. cereus infection. The top 20 terms of the three GO taxonomies, biological processes, cellular components, and molecular function, are presented.
Figure 6. GO classification of DEGs after B. cereus infection. The top 20 terms of the three GO taxonomies, biological processes, cellular components, and molecular function, are presented.
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Figure 7. Classification and enrichment of DEGs mapped to KEGG pathways. (a) The top 20 KEGG pathways with the lowest p-value assigned in four categories. (b) Bubble plot of the top 20 enriched pathways based on the FDR values and the number of DEGs. The bubble size reflects the number of DEGs; the color of the bubble reflects the FDR, with a lower FDR indicating more significant enrichment.
Figure 7. Classification and enrichment of DEGs mapped to KEGG pathways. (a) The top 20 KEGG pathways with the lowest p-value assigned in four categories. (b) Bubble plot of the top 20 enriched pathways based on the FDR values and the number of DEGs. The bubble size reflects the number of DEGs; the color of the bubble reflects the FDR, with a lower FDR indicating more significant enrichment.
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Figure 8. (a) Comparative analysis of RNA-seq data and qPCR at the transcript level. The standard deviations of the mean values from three qPCR replicates are visualized using error bars. (b) The linear regression of log2(fold-change) between qPCR data and RNA-seq data.
Figure 8. (a) Comparative analysis of RNA-seq data and qPCR at the transcript level. The standard deviations of the mean values from three qPCR replicates are visualized using error bars. (b) The linear regression of log2(fold-change) between qPCR data and RNA-seq data.
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Table
Table 1. The primers used to amplify virulence factor genes.
Table 1. The primers used to amplify virulence factor genes.
GenePrimer Sequence (5′ to 3′)Product Length (bp)Annealing (°C)References
NheATACGCTAAGGAGGGGCA50055[20]
GTTTTATTGCTTCATCGGCT
NheBCAAGCTCCAGTTCATGCGG93558[21]
GATCCCATTGTGTACCATTG
NheCACATCCTTTTGCAGCAGAAC61858[21]
CCACCAGCAATGACCATATC
NblAGCAAAATCTATGAATGCCTA88454[21]
GCATCTGTTCGTAATGTTTT
HblCCCTATCAATACTCTCGCAA69554[21]
TTTCCTTTGTTATACGCTGC
HblDAATCAAGAGCTCTCACGAAT43052[20]
CACCAATTGACCATGCTAAT
bceTTTACATTACCAGGACGTGCTT42855[22]
TGTTTGTGATTGTAATTCAGG
cytKCGACGTCACAAGTTGTAACA56552[21]
CGTGTGTAAATACCCCAGTT
cesGCATTTCGTGAAGCAGAGGT69959[23]
CCCTTTATCCCCTTCGATGT
entFMGTTCGTTCAGGTGCTGGTAC48655[21]
AGCTGGGCCTGTACGTACTT
Table
Table 2. The primers used in qPCR validation.
Table 2. The primers used in qPCR validation.
Primer NameSequence (5′ to 3′)Product Length (bp)
DDN1-FTAAACACAGCGGGATTCAAAC119
DDN1-RGGCAAACACAATGCAGAAGTA
OCSTAMP-FGGTGCTCCTCTCTTTGACTCTGA85
OCSTAMP-RCACAGCCGTGTCTGCCAAG
TNIP1-FTACCTTGTTCCTCATCCGCC132
TNIP1-RTTCAACTACCAACGCCTCCA
IL-8-FCGTTGGAAATGACTTAGGCAG205
IL-8-RTGAAACCACAAAACCCAAGTC
CCL20-FAACTCAGGAAGATGTCACTGTAATG132
CCL20-RGAATCCAGGAACAATGGTCAGT
CRABP2-FCTTTCTAAACGAGGGCAGGAT171
CRABP2-RTTTAAGGGAGCTGTTCAGGGT
PLA2-FAGACTCAAACGAGAGACGGGT118
PLA2-RTAGCAGGGTCGCAATGAGAG
TNFRSF13C-FTATTGCTGGTCTGTCGAGTGAGG86
TNFRSF13C-RCTTCAGTCTCTTACAGGCTTGGC
IgM-FGCAAAGCCAAACACCCAAAT88
IgM-RCGAGCCGCAGACATTTTTCA
EFNA5-FCATTAGAACCAGCAGATGATACCG82
EFNA5-RCAAGACCCTGATGTTTTCTGTGAC
CTSK-FGAAGGAGAACAGGGGCATTGAC92
CTSK-RCGGCTGTAGAACTGGAAAGAGG
β-actin-FGAGACCTGACAGACTACCT156
β-actin-RAGGATGATGAAGCAGCAGT
Table
Table 3. Raw data statistics and filtering.
Table 3. Raw data statistics and filtering.
SampleRaw Read CountN (%)Q20 (%)Q30 (%)Clean Read Count
GD152,733,5140.00112697.1993.1948,898,534
GD255,934,4700.00112697.1793.2751,912,132
GD343,562,6120.00114097.3593.5140,488,066
CK149,459,4800.00114497.3793.5046,319,408
CK247,077,2100.00114197.4393.6244,012,852
CK349,009,1280.00113697.2893.3745,907,658
Raw read count represents the total number of raw reads; N (%) represents the percentage of ambiguous bases; Q20 (%) represents the percentage of raw reads with a base recognition accuracy rate above 99%; Q30 (%) represents the percentage of raw reads with a base recognition accuracy rate above 99.9%; Clean read count represents the total number of clean reads.
Table
Table 4. Summary of transcript assembly.
Table 4. Summary of transcript assembly.
CategoryContigsTranscriptsUnigenes
Total Length (bp)301,124,798525,666,538215,178,805
Sequence Number994,637581,291348,665
Max. Length (bp)48,04848,19248,192
Mean Length (bp)302.75904.31617.15
N50 (bp)4041877892
N50 Sequence Number140,09673,70550,754
N90 (bp)140311255
N90 Sequence Number737,917368,926253,811
GC (%)47.0048.1947.16
N50 (bp): If all sequences are arranged from long to short and accumulated, when the accumulated length exceeds 50% of the total length of all sequences, the corresponding length of the last sequence is the N50; N90 has a similar definition to N50; GC (%) represents the GC content of the sequence.
Table
Table 5. Annotation statistics of unigenes in different databases.
Table 5. Annotation statistics of unigenes in different databases.
DatabaseNumberPercentage (%)
NR61,52717.65
GO25,6567.36
KEGG33,7649.68
Pfam15,3154.39
eggNOG59,85517.17
Swissprot30,6928.80
All databases74962.15
Table
Table 6. The annotated eggNOG functions of unigenes.
Table 6. The annotated eggNOG functions of unigenes.
DescriptionCategoryNumber of Unigenes
RNA processing and modificationA891
Chromatin structure and dynamicsB592
Energy production and conversionC912
Cell cycle control, cell division, chromosome partitioningD745
Amino acid transport and metabolismE660
Nucleotide transport and metabolismF438
Carbohydrate transport and metabolismG859
Coenzyme transport and metabolismH198
Lipid transport and metabolismI888
Translation, ribosomal structure, and biogenesisJ1182
TranscriptionK3420
Replication, recombination, and repairL3983
Cell wall/membrane/envelope biogenesisM140
Cell motilityN31
Posttranslational modification, protein turnover, chaperonesO3251
Inorganic ion transport and metabolismP1015
Secondary metabolite biosynthesis, transport, and catabolismQ443
General function prediction onlyR26,773
Function unknownS13,267
Signal transduction mechanismsT7129
Intracellular trafficking, secretion, and vesicular transportU1561
Defense mechanismsV694
Extracellular structuresW695
UndeterminedX0
Nuclear structureY88
CytoskeletonZ1532
Table
Table 7. Information of immune-related DEGs in response to B. cereus.
Table 7. Information of immune-related DEGs in response to B. cereus.
DescriptionFold Changep-ValueGenebank
Immunoglobulin M heavy chain constant region−2.224.35 × 10−2ACU45376.1
Immunoglobulin D heavy chain constant region−20.271.30 × 10−5ACU45375.1
Immunoglobulin Y heavy chain constant region−169.277.78 × 10−10ACU45374.1
Immunoglobulin superfamily member 8−18.551.09 × 10−8XP_006115285.1
Immunoglobulin superfamily member 3-like−2.723.08 × 10−2XP_014424150.1
Cathepsin D−4.236.27 × 10−5XP_006134990.1
Cathepsin Z−5.791.03 × 10−6XP_006134717.1
Cathepsin W-like−8.214.41 × 10−3XP_006121731.2
Cathepsin S−3.658.45 × 10−3XP_006110499.1
Cathepsin K-like−11.172.53 × 10−2XP_006110501.1
Nuclear factor NF-kappa-B p105 subunit+12.321.17 × 10−2XP_014430902.1
Nuclear factor of activated T-cells, cytoplasmic 1−4.581.59 × 10−5XP_006128931.1
Nuclear factor NF-kappa-B p100 subunit+12.007.84 × 10−3XP_006116288.1
Nuclear factor interleukin-3-regulated protein+2.361.59 × 10−2XP_006131942.1
Interferon kappa-like+7.541.07 × 10−2XP_006123052.1
Interferon-induced protein with tetratricopeptide repeats 5+8.368.70 × 10−9XP_006121507.1
Interferon alpha-inducible protein 27-like protein 2B+4.867.30 × 10−6XP_006113091.1
Interferon gamma receptor 1−4.243.09 × 10−5XP_006112786.2
Stimulator of interferon genes protein−4.332.35 × 10−5XP_014433634.1
Interferon-induced transmembrane protein 1-like−2.371.37 × 10−2XP_006131716.1
Interferon-induced helicase C domain-containing protein 1+2.401.55 × 10−2XP_006114909.1
Interferon regulatory factor 7+5.461.76 × 10−2AHB33440.1
Interferon-induced protein with tetratricopeptide repeats 5-like+2.191.84 × 10−2XP_014426629.1
Interferon gamma receptor 2−2.073.06 × 10−2XP_006126153.1
Interleukin-12 subunit beta+147.936.44 × 10−6XP_006138150.1
Interleukin-8-like+71.181.33 × 10−4XP_006125459.1
Interleukin-6+12.922.61 × 10−3XP_006138413.1
Interleukin-7−3.683.32 × 10−4XP_014433210.1
Interleukin-20 receptor subunit alpha−2.881.61 × 10−3XP_006112787.1
Interleukin-10-like+2.431.31 × 10−2XP_006137332.1
Interleukin-5 receptor subunit alpha-like−2.741.33 × 10−2XP_006135915.1
Interleukin-1 receptor-like 1−21.266.43 × 10−7XP_006127707.1
Interleukin-22 receptor subunit alpha-2−22.781.87 × 10−3XP_006112795.1
Toll-like receptor 9−8.151.25 × 10−7XP_014427285.1
Toll-like receptor 8−6.191.19 × 10−6XP_006122907.1
Toll-like receptor 10−5.224.70 × 10−3XP_014432216.1
Toll-like receptor 5+3.087.33 × 10−3XP_006115662.2
Complement C1r subcomponent+6.856.70 × 10−3XP_006130432.1
Complement receptor type 1-like+3.319.58 × 10−4XP_006137323.1
Complement component C7 isoform X2−36.212.45 × 10−19XP_006139554.1
Complement C1q subcomponent subunit A-like−36.701.08 × 10−18XP_006121776.1
Tumor necrosis factor receptor superfamily member 1B−4.511.30 × 10−5XP_014431383.1
Tumor necrosis factor receptor superfamily member 8−7.211.63 × 10−4XP_006127981.1
Tumor necrosis factor receptor superfamily member 13B−4.351.44 × 10−3XP_006126690.1
Tumor necrosis factor receptor superfamily member 13C−11.183.67 × 10−3XP_006120590.1
Tumor necrosis factor ligand superfamily member 15+2.475.97 × 10−3XP_006137247.1
Tumor necrosis factor receptor superfamily member 17−5.001.23 × 10−2XP_014433201.1
C-C motif chemokine 20-like+68.843.25 × 10−4XP_006119948.1
C-C motif chemokine 4-like+54.472.01 × 10−4XP_006115931.1
C-C chemokine receptor type 9-like−4.244.52 × 10−3XP_014434094.1
C-C chemokine receptor type 7−4.571.11 × 10−4XP_014424601.1
Chemokine-like receptor 1−6.102.98 × 10−5XP_006113767.1
C-C motif chemokine 3-like−7.854.82 × 10−2XP_006115927.1
C-X-C chemokine receptor type 5−16.601.11 × 10−4XP_006121016.1
C-X-C motif chemokine 14−28.462.85 × 10−17XP_006116095.1
For fold change, “+” represents upregulation, “−” represents downregulation.
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MDPI and ACS Style

Jiao, J.; Chen, J.; Yao, J.; Li, Y.; Yuan, X.; Huang, L.; Su, S.; Zhang, H. The Pathology and Splenic Transcriptome Profiling of Trionyx sinensis Challenged with Bacillus cereus. Fishes 2023, 8, 84. https://doi.org/10.3390/fishes8020084

AMA Style

Jiao J, Chen J, Yao J, Li Y, Yuan X, Huang L, Su S, Zhang H. The Pathology and Splenic Transcriptome Profiling of Trionyx sinensis Challenged with Bacillus cereus. Fishes. 2023; 8(2):84. https://doi.org/10.3390/fishes8020084

Chicago/Turabian Style

Jiao, Jinbiao, Jing Chen, Jiayun Yao, Yanli Li, Xuemei Yuan, Lei Huang, Shengqi Su, and Haiqi Zhang. 2023. "The Pathology and Splenic Transcriptome Profiling of Trionyx sinensis Challenged with Bacillus cereus" Fishes 8, no. 2: 84. https://doi.org/10.3390/fishes8020084

APA Style

Jiao, J., Chen, J., Yao, J., Li, Y., Yuan, X., Huang, L., Su, S., & Zhang, H. (2023). The Pathology and Splenic Transcriptome Profiling of Trionyx sinensis Challenged with Bacillus cereus. Fishes, 8(2), 84. https://doi.org/10.3390/fishes8020084

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