The effects of thioredoxin peroxidase from Cysticercus cellulosae excretory-secretory antigens on TGF-β signaling pathway and Th17 cells differentiation in Jurkat cells by transcriptomics

doi:10.21203/rs.3.rs-3227306/v1

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Research Article

The effects of thioredoxin peroxidase from Cysticercus cellulosae excretory-secretory antigens on TGF-β signaling pathway and Th17 cells differentiation in Jurkat cells by transcriptomics

https://doi.org/10.21203/rs.3.rs-3227306/v1

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published 14 Dec, 2023

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(1) Background: Thioredoxin peroxidase (TPx) protein from the excretory-secretory antigens (ESAs) of Cysticercus cellulosae (C. cellulosae) has been shown to regulate the differentiation of host Treg and Th17 cells, resulting in an immunosuppressive response dominated by Treg cells. However, the molecular mechanism by which TPx protein from the ESAs of C. cellulosae regulates the imbalance of host Treg/Th17 cell differentiation has not been reported.

(2) Methods: TPx protein from porcine C. cellulosae ESAs was used to stimulate Jurkat cells activated with PMA and Ionomycin at 0, 24, 48, and 72 hours. Transcriptomic analysis was performed to investigate the signaling pathways associated with Jurkat cells differentiation regulated by TPx protein from C. cellulosae ESAs.

(3) Results: Gene Set Enrichment Analysis (GSEA) revealed that TPx protein from porcine C. cellulosae ESAs could induce upregulation of the TGF-β signaling pathway and downregulation of Th17 cell differentiation in Jurkat cells.

(4) Conclusion: TPx protein from porcine C. cellulosae ESAs can activate the TGF-β signaling pathway in Jurkat cells, thereby regulating the differentiation of Treg/Th17 cells and leading to an immunosuppressive response dominated by Treg cells, enabling evasion of the host immune attack. This study provides a foundation for further validation of these pathways and further elucidates the molecular mechanisms underlying immune evasion caused by porcine C. cellulosae.

Cysticercus cellulosae

thioredoxin peroxidase

transcriptomics

T cell immune response

signaling pathways

Taenia solium(T. solium), commonly known as the pork tapeworm, is a neglected zoonotic parasite that ranks first globally in terms of foodborne parasites [1]. Humans serve as the definitive host, harboring the adult tapeworm in their intestines (taeniasis), but can also act as an incidental dead-end intermediate host, where the metacestode larval stage develops in muscles, subcutaneous tissues (cysticercosis, CC), or the central nervous system (neurocysticercosis, NCC) [2]. Cysticercosis is distributed worldwide, with higher prevalence in economically disadvantaged countries and regions in Latin America, Africa, and Asia [3, 4]. The burden of cysticercosis far exceeds that of taeniasis, with NCC being associated with high mortality and morbidity rates. NCC often remains silent for years, but when symptomatic, patients may experience headaches, dizziness, seizures, cognitive and psychiatric disorders, among other symptoms [5, 6]. According to the World Health Organization, NCC is considered a neglected tropical disease, affecting approximately 500,000 people globally and causing 50,000 deaths annually [7]. The porcine tapeworm and the cysticerci form a vicious cycle of mutual infection between humans and pigs [8]. Currently, treatment options for cysticercosis primarily involve medication and surgery, but they have limitations. Researchers worldwide have been actively engaged in the development of an effective cysticercosis vaccine using genetic engineering techniques. However, the complex immunopathogenesis regulated by cysticerci remains a major bottleneck in the development of an effective vaccine [9, 10]. Therefore, the study of host immune regulation induced by cysticerci still faces significant challenges.

The immune response refers to the process by which the immune system recognizes and eliminates antigens following antigen stimulation. It consists of innate immune responses and adaptive immune responses. T cells play a dominant role in parasitic immune responses. Based on their functions, T cells can differentiate into different subtypes, including helper T cells (Th), regulatory T cells (Treg), and cytotoxic T lymphocytes (CTL). According to the cytokines they secrete, Th cells are primarily classified into three types: Th1, Th2, and Th17 cells. Th17 cells, as a subtype of helper T cells, produce cytokines such as interleukin-17A (IL-17A), IL-17F, IL-23, and IL-22. They often play anti-inflammatory and anti-parasitic roles during the early stages of parasitic infections [11, 12]. Treg cells are a subset of T cells that exert strong suppressive effects on both innate and adaptive immunity. During parasitic infections, Treg cells increase in number and exert immune suppressive functions, allowing parasites to evade immune attacks and persist in the host for longer periods [13]. Treg cells and Th17 cells play opposing roles in the immune response to parasitic infections, and the imbalance between the two is observed in various parasitic infection diseases and models. Maintaining the balance of Treg/Th17 cells is crucial for maintaining a stable immune state in the host following parasitic infections [14].

Excretory secretory antigens (ESAs), secreted and excreted by parasites during their long-term parasitic process within the host, play important roles in tissue infiltration, immune invasion, and host-parasite interactions [15]. ESAs can directly modulate the host's T-cell immune response through secretion, which benefits the parasite in evading host immune attacks and surviving within the host [16, 17]. Thioredoxin peroxidase (TPx), belonging to the peroxiredoxin superfamily, is widely expressed in various developmental stages of parasites as well as their ESAs [18]. With the development of molecular biology techniques, TPx proteins have been constructed through genetic engineering in the field of parasitology. These proteins can not only be used for immunodiagnosis of parasitic diseases but also regulate host macrophage polarization and CD4 + T cells subset differentiation, with the potential as candidate vaccine molecules for parasitic diseases [19]. TPx proteins from certain parasites can regulate host CD4 + T cells subset differentiation, thereby evading host immune attacks and promoting worm survival within the host. For example, TPx protein from Fasciola gigantica (Fg) can stimulate peripheral blood mononuclear cells (PBMCs) from healthy goats, inducing the production of Th1, Th2, Th17, and Treg cells [20]. This suggests that TPx proteins in parasites can modulate the host's T-cell immune response to evade host immune attacks. TPx protein from the cysticercus of Taenia solium, found in pigs, exhibits high sensitivity and specificity, serving not only as a specific antigen for diagnosing cysticercosis but also as a candidate vaccine molecule for immunoprevention of cysticercosis [18]. We have previously discovered that TPx protein from the ESAs of the cysticercus of Taenia solium can induce dysregulation of the host's T-cell immune response, imbalance of Treg/Th17 cells, and the generation of an immune inhibitory response primarily composed of Treg cells, thereby exerting an immune evasion effect [21].

TGF-β is an important factor that mediates cellular processes, including cell growth, cell differentiation, cell apoptosis, and cellular homeostasis [22]. Studies have shown that the main downstream effector proteins in the TGF-β signaling pathway are the Smad protein family [23–25]. Type I receptor (TβR-I) and type II receptor (TβR-II), which contain serine/threonine kinase domains, are receptor proteins involved in the TGF-β signaling pathway [26, 27]. TGF-β binds to TβR-I and TβR-II receptors, forming a complex that triggers intracellular signal transduction. TβR-II phosphorylates and activates TβR-I, and the phosphorylated TβR-I further activates downstream Smad2/3 proteins. Phosphorylated R-Smad2 and R-Smad3 form a trimeric complex with Smad4 and translocate to the cell nucleus. They interact with the specific transcription factor retinoic acid-related orphan receptor-γt (ROR-γt) for Th17 cells or the specific marker for Treg cells, forkhead box protein 3 (Foxp3), to regulate gene transcription, ultimately regulating the differentiation of Th17 or Treg cells [28, 29]. Recent studies have shown that parasites can modulate host T-cell immune responses and affect T-cell proliferation and differentiation into different subsets through the TGF-β/Smad signaling pathway during parasitic infections. This pathway plays an important role in the occurrence, development of parasitic diseases, and the interaction between parasites and the host (Fig. 1).

The omics era plays a crucial role in pioneering new approaches in the field of parasitic biology [30]. Among them, transcriptomics is a powerful tool for uncovering regulatory mechanisms and the interaction between parasites and hosts during parasitic infections [31]. Transcriptomic analysis helps us better understand the molecular mechanisms of diseases and has become a research hotspot in the field of parasitology [32–34]. In recent years, researchers have extensively utilized transcriptomic technologies to investigate the molecular mechanisms underlying parasite infections in hosts. By employing transcriptomic techniques, relevant signaling pathways and transcription factors involved in parasite infections can be identified, as these pathways may play a key role in modulating host immune responses to parasites. The Jurkat cell model, which mimics T lymphocyte functions, is widely used in in vitro studies of T-cell signaling transduction, cytokine production, and receptor expression. It also provides important guidance and reference for the treatment and mechanistic research of various infectious diseases [35].Given the complex and incompletely understood mechanism by which TPx protein from the cysticercus of Taenia solium induces immune evasion in porcine T cells, and based on our previous finding that TPx protein from the ESAs of the cysticercus of Taenia solium can regulate host T-cell immune responses, we employed transcriptomic technology and utilized Jurkat cells to further explore the molecular mechanism by which TPx protein from the cysticercus of Taenia solium induces imbalance in the host's Treg/Th17 cell equilibrium through the TGF-β signaling pathway and Th17 cell differentiation. This study aims to lay the foundation for elucidating the molecular mechanisms underlying the imbalance in Treg/Th17 cell equilibrium induced by TPx protein from the cysticercus of Taenia solium in the ESAs.

2.1 Preparation of TPx Protein from the Cysticercus of Taenia solium ESAs

Bioinformatics analysis was employed to predict the hydrophilicity and T-cell antigenic epitopes of TPx protein. The TPx antigen-coding gene was synthesized using whole gene synthesis technology. The gene was then cloned into the pcDNA3.4 vector to construct the recombinant plasmid pcDNA3.4-TPx. The recombinant plasmid was enzymatically digested and sequenced for verification. The plasmid was transfected into Human embryonic kidney (HEK) 293 cells and the expressed TPx protein was purified using a Ni column. The purified protein was analyzed by SDS-PAGE and further confirmed by Western blotting [36]. The recombinant TPx protein was stored at -80°C.

2.2. Activation and Cultivation of Jurkat Cells

The human leukemia T lymphocyte Jurkat cells, Clone E6-1 (referred to as "Jurkat cells" hereafter), were purchased from the Cell Bank of the Chinese Academy of Sciences, Shanghai. The suspended cells were cultured in RPMI 1640 medium (Solarbio Sciences, China) containing 10% fetal bovine serum (Gibco, Australia) and antibiotics (100 µg/ml penicillin and 100 µg/ml streptomycin). The cells were stimulated by adding 50 ng/ml of PMA(Solarbio Sciences, China) and 1 µM final concentration of Ionomycin(MedChemExpress, USA) to promote cell growth for 48 hours and induce activation. Then, TPx protein (50 µg/ml) from the ESAs of pig cysticerci was added to the cells. Jurkat cells were cultured in a 6-well plate at a density of 1×10⁶ cells/well, at 37°C with 5% CO₂. The RPMI 1640 medium was added to the blank control group, and each experimental group consisted of 3 samples. After culturing for 24, 48, and 72 hours, the cells were collected.

2.3. RNA Extraction

After the completion of Jurkat cell culture, the cells were centrifuged at 1500g for 10 minutes at 4°C to remove the culture medium. Then, the cells were resuspended in PBS buffer (Solarbio Sciences, China) and centrifuged again at 1500g for 10 minutes at 4°C, followed by a quick wash. The cell pellets were resuspended in TRIzol (Takara, Japan) at a ratio of 1 ml per 1×10⁶ cells and vigorously pipetted until a clear and non-viscous liquid was formed. The lysate was then transferred to a new centrifuge tube.

2.4. Transcriptome Library Construction and Sequencing

The extracted RNA samples were sent to Shanghai OE Biotech Co., Ltd. for quality inspection. After meeting the requirements, library construction was performed, and the library quality was evaluated. Finally, RNA-seq sequencing and sequence alignment were conducted with the assistance of Shanghai OE Biotech Co., Ltd. The workflow of transcriptome library construction and data analysis is shown in Figs. 2 and 3, respectively.

2.5. Differential Gene Analysis

Based on the transcriptome sequencing results, differential protein-coding genes were identified using DESeq2 software with the screening criteria of p-value < 0.05 and fold change (FC) > 2.0 or < -2.0.

2.6. Bioinformatics Analysis

2.6.1. Gene Ontology (GO) Annotation

GO annotation (http://www.maxquant.org/) was performed based on the UniProt-GOA database (http://www.ebi.ac.uk/GOA/). The differentially expressed genes were categorized into three main categories: Biological Process (BP), Cellular Component (CC), and Molecular Function (MF), to elucidate their biological roles from different perspectives.

2.6.2. KEGG Enrichment Analysis

The differential gene expressions were subjected to pathway enrichment analysis using the KEGG database (https://www.kegg.jp/). The Fisher's exact test was employed, and a p-value < 0.05 was considered statistically significant for pathway enrichment.

2.6.3. Gene Set Enrichment Analysis (GSEA)

GSEA (https://www.gsea-msigdb.org/gsea/index.jsp) is used to assess the distribution trend of genes from a pre-defined gene set in a gene expression dataset sorted by phenotype correlation. It determines whether genes within the gene set are enriched at the top or bottom of the ranked gene list based on phenotype correlation, thereby identifying the impact of co-regulated gene changes on phenotype. It involves three steps: calculation of enrichment score, estimation of the significance level of the enrichment score, and correction for multiple hypothesis testing.

2.6.4. Transcription Factor and Target Gene Prediction Analysis

Transcription factors (TFs), also known as trans-acting factors, are a group of protein molecules that can specifically bind to upstream specific sequences of genes' 5' ends, ensuring the specific strength, time, and spatial expression of target genes. Transcription factors play a crucial role in gene expression regulation by controlling the transcription of genetic information from DNA to RNA. By comparing the distribution of transcription factors between all genes and differentially expressed genes (upregulated and downregulated), transcription factors with significantly different proportions can be identified for further focused analysis of their potential association with the observed differential expression.

2.6.5. Protein-Protein Interaction (PPI) Analysis

Based on the species information annotation in the STRING database (direct associations) or by performing a BLAST search (e-value < 10–10) of the species' gene sequences against the protein sequences in the STRING database (in the absence of direct associations), gene relationships can be obtained to extract the interactions of differentially expressed genes in terms of protein-protein interactions.

3.1 Differential gene expression analysis

As shown in Table 1, statistical analysis revealed a total of 1,278 significantly differentially expressed genes in the 24-hour experimental group compared to the control group. Among them, 297 genes were upregulated, while 981 genes were downregulated (P < 0.05, |log2FC| > 1.0). In comparison to the 24-hour experimental group, the 48-hour experimental group identified a total of 223 significantly differentially expressed genes, including 150 upregulated genes and 73 downregulated genes. Furthermore, compared to the 48-hour experimental group, the 72-hour experimental group identified a total of 327 significantly differentially expressed genes, with 309 genes upregulated and 18 genes downregulated.

Table 1

Number of differentially expressed genes
Compared groups	Regulated type	q-value < 0.05&\|log2FC\|>1.0
24-hour experimental group/0-hour control group	Up-regulated	297
24-hour experimental group/0-hour control group	Down-regulated	981
48-hour experimental group/24-hour experimental group	Up-regulated	150
48-hour experimental group/24-hour experimental group	Down-regulated	73
72-hour experimental group/48-hour experimental group	Up-regulated	309
72-hour experimental group/48-hour experimental group	Down-regulated	18

3.2 Gene Ontology (GO) functional annotation

To elucidate the functional relevance of the differentially expressed genes, GO enrichment analysis was performed on all time points. Compared to the negative control group, the top 30 enriched GO terms for the differentially expressed genes in the 24-hour experimental group are shown in Fig. 4A. These terms are involved in positive regulation of cytoplasmic calcium ion concentration, inflammatory response, DNA-binding transcription factor activity, and others. In comparison to the 24-hour experimental group, the top 20 enriched GO terms for the differentially expressed genes in the 48-hour experimental group are depicted in Fig. 4B. These terms involve cell division, regulation of cyclin-dependent protein serine/threonine kinase activity, DNA-binding transcription factor activity, and more.Furthermore, compared to the 48-hour experimental group, the top 30 enriched GO terms for the differentially expressed genes in the 72-hour experimental group are shown in Fig. 4C. These terms encompass cellular protein metabolic process, cell division, signaling receptor binding, and others. These results suggest that the TPx protein in pig cysticerci can induce changes in Jurkat cells, affecting T cell immune processes, cell differentiation, and signaling molecules.

3.3. Enrichment Analysis of KEGG Signaling Pathways

Enrichment analysis of KEGG signaling pathways was performed on differentially expressed genes at all time points. As shown in Fig. 5A and 5B, compared to the negative control group, the 24-hour experimental group exhibited upregulation in signaling pathways such as TGF-β (hsa:04350), T cell receptor (hsa:04660), and Th1 and Th2 cell differentiation (hsa:04658). Downregulated pathways mainly included GABAergic synapses (hsa:04727) and cAMP signaling pathway in cancer (hsa:04024). As shown in Fig. 5C and 5D, compared to the 24-hour experimental group, the 48-hour experimental group exhibited upregulation in signaling pathways such as cell cycle (hsa:04110) and cellular senescence (hsa:04218). Downregulated pathways included Hippo signaling pathway (hsa:04392), thyroid cancer (hsa:05216), and bladder cancer (hsa:05219) in cancer. As shown in Fig. 5E and 5F, compared to the 48-hour experimental group, the 72-hour experimental group exhibited upregulation in signaling pathways such as Complement and coagulation cascades (hsa:04610), P53 (hsa:04115), and cell cycle (hsa:04110). Downregulated pathways included Hepatitis B (hsa:05161), IL-17 (hsa:047657), and TNF (hsa:01522) signaling pathways. These results indicate that these differentially expressed genes are mainly involved in T cell biological responses, T cell proliferation and differentiation, and cell cycle processes.

3.4. Transcription Factor and Target Gene Prediction Analysis

Transcription factor and target gene prediction analysis was performed on the significantly differentially expressed transcripts shared between the 24-hour experimental group and the control group, the 48-hour experimental group and the 24-hour experimental group, and the 72-hour experimental group and the 48-hour experimental group. This analysis aimed to identify the transcription factors and their target genes associated with the differentially expressed genes. Based on the relationship list between transcription factors and target genes, the differentially expressed target genes corresponding to the differentially expressed transcription factors were extracted. A statistical graph of differentially expressed transcription factor families and their target genes was generated, as shown in Fig. 6. In this analysis, it was found that there was one common differentially expressed target gene among the three groups, which was TGF-β. Additionally, there were six common differentially expressed target genes among the two groups, including SRGAP1, E2F2, S100A1, PIF1, FOS, and CENPA. These results suggest that the TPx protein in ESAs from Cysticercus cellulosae larvae may potentially regulate the immune response of the host by modulating the target gene TGF-β and its associated transcription factors. It has the potential to regulate cell cycle, cell growth, and cell differentiation. Further investigations are needed to determine its relevance to the pathological manifestations of trichinellosis infection.

3.5. GSEA Enrichment Analysis: TPx protein in ESAs from Cysticercus cellulosae larvae activates TGF-β signaling pathway and suppresses Th17 differentiation in Jurkat cells.

To explore the signaling pathways affected by TPx protein in ESAs from Cysticercus cellulosae larvae on Treg/Th17 cell differentiation, we conducted GSEA enrichment analysis based on the KEGG database. Comparing the 24-hour experimental group with the control group, the 48-hour experimental group with the 24-hour experimental group, and the 72-hour experimental group with the 48-hour experimental group, we found that the TGF-β signaling pathway (KEGG: map04350) was consistently upregulated at different time points (Fig. 7A, C, E). Additionally, we observed that the Th17 cell differentiation pathway (KEGG: map04659) was consistently downregulated (Fig. 7B, D, F). These findings suggest that TPx protein in ESAs from Cysticercus cellulosae larvae can activate the TGF-β signaling pathway and suppress Th17 cell differentiation in Jurkat cells.

3.6. Protein-Protein Interaction (PPI) Analysis of Differentially Expressed Proteins

Based on the results from section 3.5, we performed a PPI analysis on the differentially expressed genes enriched in the TGF-β signaling pathway and Th17 cell differentiation. The analysis revealed that in comparison between the 24-hour experimental group and the control group, the 48-hour experimental group and the 24-hour experimental group, and the 72-hour experimental group and the 48-hour experimental group, the genes CD4, SMAD2, SMAD3, TGF-β1, RORC, and IL-2RA showed a higher level of interaction and formed a connected network (Fig. 8). This suggests that these genes may play important roles in the activation of these two pathways.

TSCT is a significant public health issue, and developing countries and regions still face enormous challenges in the prevention and control of TSCT [2, 37]. When the pig cysticercus parasitizes the host, it can secrete and excrete certain products known as ESAs, which play a crucial role in the host-parasite interaction and immune regulation. Previous studies have shown that these antigens possess immunomodulatory properties and can influence the host immune response by inducing the production of immune-suppressive Treg cells, thereby aiding the pig cysticercus in evading the host's immune attacks [38]. The composition of pig cysticercus ESAs is complex, and with the advancement of proteomics research and application in the field of cestodes [39, 40], our research group has conducted proteomic analysis of pig cysticercus ESAs. As a result, a immune-active protein called TPx, which belongs to the peroxiredoxin superfamily, was successfully identified. TPx not only serves as a specific diagnostic antigen and a candidate vaccine antigen, but also induces macrophage polarization and Th immune responses [18]. Further analysis through GO functional annotation revealed that TPx protein is detected in immune system processes and is localized in the cytoplasm. Literature review indicates that TPx protein can bind to cell membrane surface receptors and exert its function intracellularly. TPx protein in pig cysticercus ESAs can stimulate an increase in the number of CD4 + T cells, CD8 + T cells, and CD4 + CD25 + Foxp3 + Treg cells in normal piglet PBMC, inducing T cell immune dysfunction and the generation of negative immune responses [21]. However, current research has only focused on this phenomenon, and the molecular mechanisms by which TPx protein regulates the imbalance of host Treg/Th17 cell equilibrium in pig cysticercus ESAs have not been fully elucidated. Therefore, we used transcriptomics to investigate the molecular mechanisms by which TPx protein in pig cysticercus ESAs regulates piglet Treg/Th17 cell differentiation through the TGF-β signaling pathway.

In the field of parasitology, transcriptomics has become an important tool [32–34]. Differential transcriptomics, as a crucial branch of transcriptomics, provides a new perspective and method for us to gain a deeper understanding of the biological characteristics, parasitic mechanisms, and interactions with the host in parasites. In this study, differential transcriptome analysis was employed to explore the signaling pathways by which TPx protein in pig cysticercus ESAs affects the imbalance of Treg/Th17 cells. The differentially expressed genes were analyzed through GO enrichment and KEGG pathway analysis. It was found that recombinant TPx protein in pig cysticercus can induce changes in T cell immune processes, cell differentiation, and signaling molecules in Jurkat cells. However, through KEGG pathway enrichment, it was discovered that these differentially expressed genes are primarily involved in T cell biological responses, catalytic activity, and metabolic processes, such as positive regulation of cytoplasmic calcium ion concentration, inflammatory responses, DNA-binding transcription factor activity, cell division, regulation of cyclin-dependent protein serine/threonine kinase activity, cellular protein metabolic process, and signaling receptor binding. Therefore, we further conducted GSEA analysis on these differentially expressed genes based on the KEGG database. GSEA enrichment analysis has several advantages in studying signaling pathways. It not only comprehensively considers gene expression patterns but also possesses good biological interpretability and integration, which helps to reveal the functions and interactions of gene sets in specific biological processes.

Treg cells and Th17 cells play opposing roles in the immune response to parasitic infections, and the imbalance between them has been observed in various parasitic infection diseases and models. Maintaining the balance of Treg/Th17 cells is crucial for maintaining the stable immune state of the host following parasitic infection [14]. It has been found that the TGF-β signaling pathway regulates the balance of Treg/Th17 cells through its downstream signaling molecule, the Smad protein. For example, in a mouse model of cysticercosis caused by Echinococcus granulosus (Eg), the percentage of splenic CD4 + Treg cells, Foxp3 mRNA transcription levels, and serum TGF-β1 levels increase. Subsequently, treatment of the model mice with the inhibitor SB-525334 leads to significant reductions in the percentage of splenic CD4 + Treg cells and phosphorylation levels of Smad2/3 proteins compared to the E. granulosus-infected group. This indicates that in the mouse model of cysticercosis, the inhibitor SB-255334 inhibits Smad2/3 phosphorylation, blocks the TGF-β/Smad signaling pathway, reduces IL-10 secretion, and inhibits Treg cell differentiation (Yin et al. 2017). In a mouse model of alveolar echinococcosis caused by Echinococcus multilocularis (Em), it was found that during the early phase of infection (day 2 to day 30), the mRNA expression levels of Smad7 in mouse liver tissue significantly increase, while there is no significant difference in the number and ratio of Treg and Th17 cells in peripheral blood compared to the control group. During the middle phase of infection (day 30 to day 90), the mRNA expression levels of TβRI, TβRII, p-Smad2/3, and IL-17 all significantly increase, and both the number of Treg cells and Th17 cells increase. During the late phase of infection (day 90 to day 270), the number of Treg cells significantly increases, serum TGF-β and IL-10 levels rise, the number of Th17 cells decreases significantly, and the Treg/Th17 cell ratio completely reverses. This suggests that during the early phase of alveolar echinococcosis infection, Smad7 expression is upregulated to block the translocation of Smad2/3 complexes into the nucleus and TGF-β signaling. During the middle phase of infection, the TGF-β/Smad signaling pathway is activated, leading to the upregulation of TβRI, TβRII, p-Smad2/3, and the induction of a large amount of IL-17A by the host, resulting in a predominantly Th17 immune response. In the late phase of infection, the TGF-β/Smad signaling pathway promotes the differentiation of naive T cells into Treg cells, alleviating the Th17 cell-mediated immune response in the later stages. This imbalance in Treg/Th17 cell immune regulation favors the parasite's evasion of the host's immune response in the later stages of infection [42].

In this study, transcription factor and target gene prediction analysis was performed on the differentially expressed transcriptome data to identify and predict the transcription factors and their potential target genes involved in the differential expression. Through comparing the differential data between the 24-hour experimental group and the control group, the 48-hour experimental group and the 24-hour experimental group, and the 72-hour experimental group and the 48-hour experimental group, TGF-β was identified as a common differentially expressed target gene in all three groups, suggesting its potential role in regulating Treg and Th17 cell differentiation. The differential transcription factors, FOXA2, FOXH1, and FOXM1, belong to the Forkhead-box (FOX) gene family. They act as both transcriptional activators and repressors, as well as pioneer factors, interacting with other transcription factors and epigenetic effectors [43, 44]. Forkhead box proteins are important components of the TGF-β/Smad signaling pathway [45]. FOXA2 is important for maintaining the balance of effector cell differentiation in T cells. FOXA2 in thymic epithelial cells (TECs) can promote positive selection of conventional CD4 T cells and regulate the generation and activity of Treg cells [46]. Conditional deletion of FOXA2 in T cells aggravates Th2 inflammation, increases differentiation towards Th2, and inhibits CD4 T cell differentiation towards Th1 in vitro [47]. FOXH1 (also known as FAST, forkhead activin signal transducer) is a transcriptional activation factor that plays an important role as a signaling intermediate of TGF-β family members (such as Activin and Nodal). It is involved in the recruitment of SMAD3, SMAD2, and SMAD4 to form trimeric complexes [48]. The aryl hydrocarbon receptor (AHR), a member of the bHLH-PAS family, is a basic helix-loop-helix transcription factor activated through cognate ligand binding [49]. AHR plays a key regulatory role in the differentiation of Treg and Th17 cells. Studies have found that the expression of AhR in CD4 + T cells leads to a significant increase in the percentage of Treg and related gene transcripts (including Foxp3, IL-10, and CD39), and a significant decrease in the transcription of Th17 cells and related genes (including RORγt, IL-17A, and IL-17F), inducing an imbalance between Th17 and Treg cells [49, 50].

In this study, we also identified through GSEA that the TPx protein in Cysticercus cellulosae ESAs can inhibit the differentiation of Jurkat cells into Th17 cells at 24, 48, and 72 hours time points. In the differentially expressed genes enriched during Th17 cell differentiation, it was found that the TGF-β/Smad signaling pathway can promote the expression of the Foxp3 transcription factor, which in turn inhibits the expression of the ROR-γt transcription factor, thereby suppressing Th17 cell differentiation [29]. This indicates that the TPx protein in Cysticercus cellulosae ESAs is involved in regulating the balance of host Treg/Th17 cells and subsequently modulating the host immune response. These findings provide insights into the pathogenesis of cysticercosis.

The above research is confined to the level of differential transcriptomics. In future studies, we will further investigate the impact of the TPx protein in Cysticercus cellulosae ESAs on the balance of host Treg/Th17 cells using flow cytometry. We will also employ techniques such as inhibitors (ALK5 inhibit or/Smad7) to systematically validate the potential mechanisms through which the TPx protein affects the TGF-β signaling pathway and Th17 cell differentiation. These efforts aim to provide a foundation for understanding the host immune response to Cysticercus cellulosae infection and the immune evasion mechanisms of Cysticercus cellulosae.

As mentioned earlier, the molecular mechanisms through which the TPx protein in cysticercus ESAs regulates the proliferation and differentiation of Treg/Th17 cells have not been explored. In this study, we employed transcriptomic techniques to compare the differential transcriptome data between the experimental and control groups. We identified host gene expression changes associated with cysticercus infection and, through GSEA, discovered that the TPx protein in cysticercus ESAs upregulates the TGF-β/Smad signaling pathway in Jurkat cells at 24, 48, and 72 hours, while inhibiting Th17 cell differentiation. This indicates that the TPx protein in cysticercus ESAs can regulate the balance between Treg/Th17 cells by activating the TGF-β/Smad signaling pathway, thereby promoting the proliferation and differentiation of Jurkat cells into Treg cells and suppressing Th17 cell differentiation.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors without undue reservation.

Ethics Statement

Not applicable.

Author Contributions

X.S, and Q.M conducted the experiments. X.S, M.L, F.Y, and B.Z designed the experiments. X.S analyzed the data. Q.M, M.L, F.Y, and B.Z assisted with the experiments. X.S wrote the manuscript. B.Z revised the manuscript. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Funding

This work was supported by the National Natural Science Foundation of China (No. 81960378) and Project of Basic Research Program of Guizhou Provincial Department of Science and Technology ([2023]-516).

Acknowledgements

We thank Shanghai OE Biotech Co., Ltd. (Shanghai, China) for supporting differential transcriptomics analysis.

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No competing interests reported.

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The effects of thioredoxin peroxidase from Cysticercus cellulosae excretory-secretory antigens on TGF-β signaling pathway and Th17 cells differentiation in Jurkat cells by transcriptomics

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Journal Publication

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Abstract

Figures

1. Introduction

2. Materials and Methods

2.1 Preparation of TPx Protein from the Cysticercus of Taenia solium ESAs

2.2. Activation and Cultivation of Jurkat Cells

2.3. RNA Extraction

2.4. Transcriptome Library Construction and Sequencing

2.5. Differential Gene Analysis

2.6. Bioinformatics Analysis

2.6.1. Gene Ontology (GO) Annotation

2.6.2. KEGG Enrichment Analysis

2.6.3. Gene Set Enrichment Analysis (GSEA)

2.6.4. Transcription Factor and Target Gene Prediction Analysis

2.6.5. Protein-Protein Interaction (PPI) Analysis

3. Results

3.1 Differential gene expression analysis

3.2 Gene Ontology (GO) functional annotation

3.3. Enrichment Analysis of KEGG Signaling Pathways

3.4. Transcription Factor and Target Gene Prediction Analysis

3.6. Protein-Protein Interaction (PPI) Analysis of Differentially Expressed Proteins

4. Discussion

5. Conclusion

Declarations

References

Additional Declarations

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Journal Publication

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