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Immunoinformatics and Pathology informatics (13) Genome Analysis and Visualization (14)

Published on in Vol 4 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42421, first published .
The Differentially Expressed Genes Responsible for the Development of T Helper 9 Cells From T Helper 2 Cells in Various Disease States: Immuno-Interactomics Study

The Differentially Expressed Genes Responsible for the Development of T Helper 9 Cells From T Helper 2 Cells in Various Disease States: Immuno-Interactomics Study

The Differentially Expressed Genes Responsible for the Development of T Helper 9 Cells From T Helper 2 Cells in Various Disease States: Immuno-Interactomics Study

Authors of this article:

Manoj Khokhar1 Author Orcid Image ;   Purvi Purohit1 Author Orcid Image ;   Ashita Gadwal1 Author Orcid Image ;   Sojit Tomo1 Author Orcid Image ;   Nitin Kumar Bajpai2 Author Orcid Image ;   Ravindra Shukla3 Author Orcid Image
Article Authors Cited by (5) Tweetations (6) Metrics

    Original Paper

    1Department of Biochemistry, All India Institute of Medical Sciences Jodhpur, Jodhpur, India

    2Department of Nephrology, All India Institute of Medical Sciences Jodhpur, Jodhpur, India

    3Department of Endocrinology and Metabolism, All India Institute of Medical Sciences Jodhpur, Jodhpur, India

    Corresponding Author:

    Purvi Purohit, MSc, PhD

    Department of Biochemistry

    All India Institute of Medical Sciences Jodhpur

    Basni Industrial Area Phase-2

    Jodhpur, 342005

    India

    Phone: 91 09928388223

    Email: dr.purvipurohit@gmail.com


    Background: T helper (Th) 9 cells are a novel subset of Th cells that develop independently from Th2 cells and are characterized by the secretion of interleukin (IL)-9. Studies have suggested the involvement of Th9 cells in variable diseases such as allergic and pulmonary diseases (eg, asthma, chronic obstructive airway disease, chronic rhinosinusitis, nasal polyps, and pulmonary hypoplasia), metabolic diseases (eg, acute leukemia, myelocytic leukemia, breast cancer, lung cancer, melanoma, pancreatic cancer), neuropsychiatric disorders (eg, Alzheimer disease), autoimmune diseases (eg, Graves disease, Crohn disease, colitis, psoriasis, systemic lupus erythematosus, systemic scleroderma, rheumatoid arthritis, multiple sclerosis, inflammatory bowel disease, atopic dermatitis, eczema), and infectious diseases (eg, tuberculosis, hepatitis). However, there is a dearth of information on its involvement in other metabolic, neuropsychiatric, and infectious diseases.

    Objective: This study aims to identify significant differentially altered genes in the conversion of Th2 to Th9 cells, and their regulating microRNAs (miRs) from publicly available Gene Expression Omnibus data sets of the mouse model using in silico analysis to unravel various pathogenic pathways involved in disease processes.

    Methods: Using differentially expressed genes (DEGs) identified from 2 publicly available data sets (GSE99166 and GSE123501) we performed functional enrichment and network analyses to identify pathways, protein-protein interactions, miR-messenger RNA associations, and disease-gene associations related to significant differentially altered genes implicated in the conversion of Th2 to Th9 cells.

    Results: We extracted 260 common downregulated, 236 common upregulated, and 634 common DEGs from the expression profiles of data sets GSE99166 and GSE123501. Codifferentially expressed ILs, cytokines, receptors, and transcription factors (TFs) were enriched in 7 crucial Kyoto Encyclopedia of Genes and Genomes pathways and Gene Ontology. We constructed the protein-protein interaction network and predicted the top regulatory miRs involved in the Th2 to Th9 differentiation pathways. We also identified various metabolic, allergic and pulmonary, neuropsychiatric, autoimmune, and infectious diseases as well as carcinomas where the differentiation of Th2 to Th9 may play a crucial role.

    Conclusions: This study identified hitherto unexplored possible associations between Th9 and disease states. Some important ILs, including CCL1 (chemokine [C-C motif] ligand 1), CCL20 (chemokine [C-C motif] ligand 20), IL-13, IL-4, IL-12A, and IL-9; receptors, including IL-12RB1, IL-4RA (interleukin 9 receptor alpha), CD53 (cluster of differentiation 53), CD6 (cluster of differentiation 6), CD5 (cluster of differentiation 5), CD83 (cluster of differentiation 83), CD197 (cluster of differentiation 197), IL-1RL1 (interleukin 1 receptor-like 1), CD101 (cluster of differentiation 101), CD96 (cluster of differentiation 96), CD72 (cluster of differentiation 72), CD7 (cluster of differentiation 7), CD152 (cytotoxic T lymphocyte–associated protein 4), CD38 (cluster of differentiation 38), CX3CR1 (chemokine [C-X3-C motif] receptor 1), CTLA2A (cytotoxic T lymphocyte–associated protein 2 alpha), CTLA28, and CD196 (cluster of differentiation 196); and TFs, including FOXP3 (forkhead box P3), IRF8 (interferon regulatory factor 8), FOXP2 (forkhead box P2), RORA (RAR-related orphan receptor alpha), AHR (aryl-hydrocarbon receptor), MAF (avian musculoaponeurotic fibrosarcoma oncogene homolog), SMAD6 (SMAD family member 6), JUN (Jun proto-oncogene), JAK2 (Janus kinase 2), EP300 (E1A binding protein p300), ATF6 (activating transcription factor 6), BTAF1 (B-TFIID TATA-box binding protein associated factor 1), BAFT (basic leucine zipper transcription factor), NOTCH1 (neurogenic locus notch homolog protein 1), GATA3 (GATA binding protein 3), SATB1 (special AT-rich sequence binding protein 1), BMP7 (bone morphogenetic protein 7), and PPARG (peroxisome proliferator–activated receptor gamma, were able to identify significant differentially altered genes in the conversion of Th2 to Th9 cells. We identified some common miRs that could target the DEGs. The scarcity of studies on the role of Th9 in metabolic diseases highlights the lacunae in this field. Our study provides the rationale for exploring the role of Th9 in various metabolic disorders such as diabetes mellitus, diabetic nephropathy, hypertensive disease, ischemic stroke, steatohepatitis, liver fibrosis, obesity, adenocarcinoma, glioblastoma and glioma, malignant neoplasm of stomach, melanoma, neuroblastoma, osteosarcoma, pancreatic carcinoma, prostate carcinoma, and stomach carcinoma.

    JMIR Bioinform Biotech 2023;4:e42421

    doi:10.2196/42421

    Keywords

    Th9 cells; Th2 cells; autoimmune diseases; DEGs; interleukins 


    CD4+ T helper (Th) cells have been classified into different subsets based on the cytokine profile that each subset secretes and their distinct role in regulating immunity and inflammation. Previous studies have shown that immune cells play a role in various metabolic [1-3] and infectious [3-7] diseases. Th9 cells are a subset of CD4+ Th cells that develop from naïve T cells and release interleukin (IL)-9. The generation of Th9 cells from naïve Th0 cells requires a Th2 state as an intermediate. While both Th2 and Th9 cells express PU.1 (spleen focus forming virus [SFFV] proviral integration oncogenes), IRF4 (interferon regulatory factor 4), and GATA3 (GATA binding protein 3), the latter have upregulated expression of IRF4 and suppressed PU.1. The Th2 cells, generated during Th0 cell differentiation, further evolve into Th9 cells in the presence of activated Smad3/Smad4 and IRF4 pathways. The prolonged transforming growth factor beta (TGFβ) stimulation transforms the Th2 cells into Th9 cells and alters the cytokine secretion pattern from an IL-4–dominant phenotype to an IL-9–dominant one [8]. Th9 cells produce IL-9, which is crucial in regulating autoimmune and allergic reactions [9]. Various other cytokines also affect the development of Th9 cells and IL-9 production. IL-23 inhibits IL-9 production, whereas IL-1 and IL-33 stimulate the production of IL-9 in T cells [10,11]. Similarly, IL-25 stimulates the release of IL-9 from T cells [12]. In addition, costimulatory receptors, such as OX40, have been found to be a stimulant for the development of Th9 cells [13]. Thus, the development of Th9 cells is a result of integrating multiple positive and negative signals in the form of cytokines and costimulation from surface receptors.

    Th9 cells can manifest differently in various diseases. Th9 cells have been demonstrated to incite allergic airway disease [14]. Th9 cells have also been implicated in tumor immunity [10]. Interestingly, the evolution of Th2 to Th9 cells does influence the pathophysiology of multiple diseases. The nitric oxide–mediated airway inflammation has been attributed to the inducing effect of nitric oxide on the development of Th9 cells [15]. The tricarboxylic acid cycle metabolite succinate stimulates Th9 cell differentiation and leads to Th9 cell–mediated tumor regression. Similarly, Th9 differentiation resulting from IL-35 stimulation accentuates the inflammatory process and leads to an immunoglobulin (Ig) class switch toward IgG4 in IgG4-related diseases [16].

    Unfortunately, the experimental approach to Th9 cells has been riddled with difficulty, because a selective deficiency model for Th9 lineage has not yet been defined. In addition, factors needed to develop Th9 cells such as IL-4 and IRF4 are required to develop other Th subsets [17]. Our study aimed to compare the transcriptome of Th2 and Th9 cells to identify the pattern of changes in the expression of various genes when the Th2 cells get differentiated into Th9 cells. We also aimed to assess these genes, which are markedly altered in the transition of Th2 to Th9 cells, in various other diseases to enlist the possible diseases in which Th9 cells may play a crucial role.


    Expression Profiling: Gene Expression Omnibus Assay to Data Mining for Th2 to Th9 Cells Differentiation

    We performed a search in the Gene Expression Omnibus (GEO) database using several keywords, including “Healthy Control,” “Wild Type,” “Mice,” “Mus musculus,” “Th9,” “Th2,” and “Expression profiling by array” from January 1, 2012, to December 17, 2020, and selected 2 gene series expressions (GSEs) data for further study: GSE99166 and GSE123501. GSE99166 contained 4 samples of Th2 wild-type cells (GSM2634701, GSM2634702, GSM2634711, and GSM2634712) and 5 samples of Th9 wild-type cells (GSM2634695, GSM2634703, GSM2634704, GSM2634713, and GSM2634714) from the spleen. GSE123501 contained 2 samples of Th2 wild-type cells (GSM3505597 and GSM3505602) and another 2 samples of Th9 wild-type cells (GSM350598 and GSM3505603) from the spleen (Figure 1).

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    Figure 1. Flow diagram illustrating the data collection process and the number of data sets considered for inclusion. Th: T helper; Treg: T regulatory cell.

    Assortment and Identification of Codifferentially Expressed Messenger RNAs From the Spleen (2 Different) Data Sets

    The differentially expressed genes (DEGs) were obtained from the 13 samples of 2 different data sets (GSE99166 and GSE123501) using the GREIN (GEO RNA-seq Experiments Interactive Navigator) platform (BD2K-LINCS Data Coordination and Integration Center). This interactive online web tool analyses GEO RNA-seq data [18]. The DEGs extracted from the data sets comprised genes from Th2 and Th9 cells. As we wanted to assess the alteration of genes during the conversion of Th2 to Th9 cells, the analysis was performed with DEGs of Th2 cells as the standard to which DEGs of Th9 cells were compared. The workflow for the data processing and analysis is portrayed in Figure 2.

    The DEGs were considered upregulated when the expression of genes in Th9 cells was higher than that in Th2 cells. The cutoff for the selection was kept at P<.05, and overlapping DEGs between 2 data sets (GSE99166 and GSE123501) on comparison of Th2 and Th9 cells were identified by the Venn diagram tool [19,20]. In addition, the common upregulated, downregulated, and oppositely regulated DEGs of these 2 data sets (GSE99166 and GSE123501) were identified. The fold change expression distribution was visualized by a heat map and violin plot using the Linear Models for the Microarray Data (limma) Package of R (R Foundation for Statistical Computing) and Orange Data Mining (University of Ljubljana) [21,22].

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    Figure 2. Flowchart of the data processing and analysis for Th2 to Th9 cells differentiation. DEG: differentially expressed gene; KEGG: Kyoto Encyclopedia of Genes and Genomes; mRNA: messenger RNA; Th: T helper.

    Functional Enrichment of Gene Ontology for Common, Regulated DEGs

    The codifferential genes were divided into 3 parts, namely, (1) common upregulated, (2) common downregulated, and (3) common, oppositely regulated. The top ranked ontological features of all DEGs were analyzed with STRING. The Gene Ontology (GO) terms included the following 3 categories: biological processes, cellular components, and molecular functions. The significant GO terms regulating genes are presented in a radar graph with a negative log10 (false discovery rate). We defined P<.05 as a significant value.

    Kyoto Encyclopedia of Genes and Genomes Pathway Analysis of Top Ranked Significant, Common, Regulated DEGs

    We searched the functionally significant Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways for top ranked significantly altered DEGs using the STRING and WikiPathways databases. We identified important genes participating in each pathway, and selected the top 7 pathways based on negative log10 (false discovery rate) and P values (<.05) that were important for further study.

    Genes Assortment and Construction of a Protein-Protein Interaction Network of the Top Enriched Pathways

    We downloaded the complete gene list of the top ranked 7 individual pathways with an interaction network from the KEGG database. We revisualized and constructed the pathway with the help of Cytoscape (Cytoscape Team/Institute for Systems Biology; an open-source software platform for visualizing complex networks and integrating these with any type of attribute data) [23] and marked the DEGs that play a significant role in the differentiation of Th2 to Th9 cells.

    Identification of Top Regulatory MicroRNAs Involved in the Th2 to Th9 Differentiation Pathways

    The top 10 microRNAs (miRs) that targeted the hub genes were predicted by the well-established miR target prediction database miRNet version 22.0 [24], with special emphasis on the selected organism. Default values for the degree of interaction and betweenness were selected. Common miRs and their targeted messenger RNAs (mRNAs) of all groups were sorted by the Venn diagram.

    Construction of a Gene-Disease–Based Genomic Pathway Interaction Network

    The DEGs that were identified to play a significant role in Th2 to Th9 differentiation were further analyzed for their involvement in various pathways pertaining to specific diseases using DisGeNET (IBI Group) [25], a discovery platform that describes genes, transcription factors (TFs), chemokines, and IL in association with various specific diseases.

    Ethical Considerations

    The study was approved by the Institutional Ethics Committee of All India Institute of Medical Sciences (AIIMS) Jodhpur (certificate reference number AIIMS/IEC/2019-20/792).


    Assortment of Significant DEGs in the Differentiation of Th2 to Th9 Cells

    The Mus musculus (C57BL/6) mRNA expression profiles of GSE99167 and GSE123501, which were selected for this study, included the expression profiles of Th2 and Th9 cells obtained from the spleen. We extracted and compared mice spleen samples from 2 different studies to identify genes that are involved in the differentiation of Th2 to Th9 cells. In both groups, 254 common mRNAs were identified, and 634 common DEGs were identified, of which 236 were downregulated and 260 were upregulated. We performed a quality assessment of the selected samples for our expression profiles (Figures 3A-3I; see Tables S1 and S2 in Multimedia Appendix 1, and Multimedia Appendix 2 for larger version of figures).

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    Figure 3. Differential mRNA expression of the 2 data sets (GSE99166 and GSE123501) for Th2 to Th9 differentiation: Venn diagrams (A-C) of the 3 data sets' total common, downregulated, and upregulated DEGs; volcano plot for the data sets GSE99166 (D) and GSE123501 (E); and heat maps of common downregulated (F) and common upregulated (G) DEGs extracted from the data sets consisting of genes from Th2 and Th9 cells. FCE levels are displayed in ascending order from blue to yellow. Violin plots (H and I) showing FCE distribution of both data sets for common upregulated and common downregulated DEGs. DEG: differentially expressed gene; FCE: fold change expression; mRNA: messenger RNA; Th: T helper.

    Identification and Assortment of Codifferentially Expressed ILs, Cytokines, Receptors, and TFs

    Our analysis identified genes encoding various ILs and receptors whose differential expression may determine the differentiation of Th2 to Th9 cells. Some important ILs identified were CCL1 (chemokine [C-C motif] ligand 1), CCL20 (chemokine [C-C motif] ligand 20), IL-13, IL-4, IL-12A, and IL-9. The important receptors identified in our analysis were IL-12RB1, IL-4RA (interleukin 4 receptor alpha), CD53 (cluster of differentiation 53), CD6 (cluster of differentiation 6), CD5 (cluster of differentiation 5), CD83 (cluster of differentiation 83), CD197 (cluster of differentiation 197), IL-1RL1 (interleukin 1 receptor-like 1), CD101 (cluster of differentiation 101), CD96 (cluster of differentiation 96), CD72 (cluster of differentiation 72), CD7 (cluster of differentiation 7), CD152 (cytotoxic T lymphocyte–associated protein 4), CD38 (cluster of differentiation 38), CX3CR1 (chemokine [C-X3-C motif] receptor 1), CTLA2A (cytotoxic T lymphocyte–associated protein 2 alpha), CTLA28, and CD196 (cluster of differentiation 196). In addition, the differential expression of various TFs such as FOXP3 (forkhead box P3), IRF8 (interferon regulatory factor 8), FOXP2 (forkhead box P2), RORA (RAR-related orphan receptor alpha), AHR (aryl-hydrocarbon receptor), MAF (avian musculoaponeurotic fibrosarcoma oncogene homolog), SMAD6 (SMAD family member 6), JUN (Jun proto-oncogene), JAK2 (Janus kinase 2), EP300 (E1A binding protein p300), ATF6 (activating transcription factor 6), BTAF1 (B-TFIID TATA-box binding protein associated factor 1), BAFT (basic leucine zipper transcription factor), NOTCH1 (neurogenic locus notch homolog protein 1), GATA3, SATB1 (special AT-rich sequence binding protein 1), BMP7 (bone morphogenetic protein 7), and PPARG (peroxisome proliferator–activated receptor gamma) may influence the differentiation of Th2 to Th9 cells. The expression of the aforementioned immune regulators is represented by a heat map and Venn diagram in Figures 4A-4F (also see Tables S3-S5 in Multimedia Appendix 1, and Multimedia Appendix 2 for larger version of figures).

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    Figure 4. Differential mRNA expression of the 2 data sets (GSE99166 and GSE123501) for Th2 to Th9 differentiation: (A, B) interleukin, cytokines/chemokines; (C, D) immune regulating transcription factor; (E, F) immune receptor heat maps and violin plot of all, downregulated, and upregulated DEGs; (G, J) enrichment analysis of common DEGs; (G) Gene Ontology cellular component terms; (H) Gene Ontology molecular function terms; (I) Gene Ontology biological process terms; and (J) the KEGG pathway. The radar plot or circular area graph values are represented in the -log10 (FDR). DEG: differentially expressed gene; FCE: fold change expression; FDR: false discovery rate; KEGG: Kyoto Encyclopedia of Genes and Genomes; mRNA: messenger RNA; Th: T helper.

    Functional Enrichment and KEGG Pathway Analysis of DEGs Involved in the Transition of Th2 to Th9 Cells

    A GO analysis of DEGs classified them into 3 functional classes (Figures 4G-4I; see Multimedia Appendix 2 for larger versions of figures): cellular component, biological process, and molecular function.

    The enrichments for the 3 DEG classes with significantly altered expression are shown in Tables S6-S8 in Multimedia Appendix 1. In the KEGG pathway enrichment analysis, the identified genes were enriched in various KEGG pathways such as cytokines-cytokines interaction, Th1 and Th2 cell differentiation, inflammatory bowel disease (IBD), Th17 cell differentiation, the Fc epsilon RI signaling pathway, the T-cell receptor signaling pathway, and pathways in cancer (Figure 4J and Tables S9 and S10 in Multimedia Appendix 1; see Multimedia Appendix 2 for larger version of figures).

    Construction of the Protein-Protein Interaction Network of DEGs Involved in the Transition of Th2 to Th9 Cells

    We downloaded the complete protein-protein interaction (PPI) network of the identified KEGG pathways from the KEGG database. The Cytoscape software was used for the construction of the network. The significantly altered DEGs of cytokines, chemokines, receptors, and TFs were highlighted in the respective networks. Our analysis of the KEGG pathway enrichment and PPI network demonstrated that the genes that had a significantly altered expression in Th9 cells when compared with Th2 cells also played a significant role in other immune regulating pathways. These affected pathways were mainly involved in cytokines-cytokines interaction, Th1 and Th2 differentiation, CTLA4 (cytotoxic T lymphocyte–associated protein 4) regulation, T-cell receptor signaling, Fc epsilon signaling, Th17 cell differentiation, IBD, and cancer. The concurrent presence of these genes in the aforementioned pathways highlights the significance of the differentiation of Th2 to Th9 in diseases where these pathways are affected. The role of the identified DEGs in these pathways and their interaction with other genes has been depicted in Figures 5-7. See Multimedia Appendix 2 for larger images.

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    Figure 5. Illustration of the Th2 to Th9 differentiation, mainly the 7 pathways involved in this regulatory mechanism: (A) heat maps expression (the upper section of the heat map shows FCE values represented by varying color densities); (B) PPIs networks (top significant DEGs of the network are illustrated in cyan); (C) common posttranscriptional regulatory microRNA pathways—(1) cytokine-cytokine receptor interaction, (2) Th1 and Th2 cell differentiation, and (3) inflammatory bowel disease. FCE: fold change expression; PPI: protein-protein interaction; Th: T helper.
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    Figure 6. Illustration of the Th2 to Th9 differentiation, mainly the 7 pathways involved in this regulatory mechanism: (A) heat maps expression (the upper section of the heat map shows FCE values represented by varying color densities); (B) PPIs networks (top significant DEGs of the network are illustrated in cyan); (C) common posttranscriptional regulatory microRNA pathways—(4) Th17 cell differentiation, (5) Fc epsilon and RI pathway, (6) T-cell receptor signaling pathway. DEG: differentially expressed gene; FCE: fold change expression; PPI: protein-protein interaction; Th: T helper.

    Assessment of Gene Similarity in Pathways Identified in the KEGG Pathway Enrichment Analysis

    We performed a gene similarity analysis to find similar genes in all the 7 KEGG pathways identified with the help of the Venn diagram and calculate the percentage of similarity among the genes that were altered. We observed that 7/13 (54%) genes were similar between the “Th1 and Th2 cell differentiation” and “IBD” pathways, whereas 7/14 (50%) genes were similar between the “Th1 and Th2 cell differentiation” and “Th17 cell differentiation” pathways (Figure 7A; see Multimedia Appendix 2 for larger images).

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    Figure 7. Illustration of the Th2 to Th9 differentiation, mainly the 7 pathways involved in this regulatory mechanism: (A) heat maps expression (the upper part of the heat map shows FCE values represented by varying color densities); (B) PPIs networks (top significant DEGs of the network are presented in cyan); (C) common posttranscriptional regulatory microRNA pathways; (D) gene or DEGs similarity in regulatory pathways. DEG: differentially expressed gene; FCE: fold change expression; PPI: protein-protein interaction; Th: T helper.

    Prediction of miRs That Target the DEGs Involved in the Transition of Th2 to Th9 cells

    To explore the posttranscriptional regulation of the identified DEGs, we predicted the miRs that could target the identified DEGs. We identified the following 53 common miRs that could target the DEGs listed in our analysis: let-7b-5p, let-7f-5p, let-7g-5p, let-7i-5p, miR-1-3p, miR-101-3p, miR-103a-3p, miR-107, miR-10a-5p, miR-10b-5p, miR-122-5p, miR-124-3p, miR-129-3p, miR-130a-3p, miR-133a-3p, miR-139-5p, miR-147a, miR-155-5p, miR-16-5p, miR-17-5p, miR-186-5p, miR-195-5p, miR-200b-3p, miR-20a-3p, miR-20a-5p, miR-20b-5p, miR-21-3p, miR-21-5p, miR-22-3p, miR-224-5p, miR-24-3p, miR-26a-5p, miR-26b-5p, miR-27-5p, miR-27a-3p, miR-302a, miR-30a-5p, miR-30c-5p, miR-30d-5p, miR-320a, miR-34-5p, miR-374-5p, miR-426, miR-429, miR-618, miR-6499-3p, miR-93-5p, miR-98-5p, miR-103a-3p, miR-139-5p, miR-147a, miR-195-5p, and miR-27a-5p.

    Identification of Diseases Associated With Dysregulation of the Identified miRs and DEGs

    Subsequent to the identification of pathways affected as a result of the alteration of DEGs found in our analysis, we further searched for possible diseases whose pathogenesis is affected by alterations in these pathways. We listed the diseases where alterations in the aforementioned 7 pathways have already been documented, and these were as follows: metabolic diseases (eg, diabetes mellitus, diabetic nephropathy, hyperactive behavior, hypertensive disease, ischemic stroke, steatohepatitis, liver fibrosis, obesity), allergic and pulmonary diseases (eg, asthma, chronic obstructive airway disease, chronic rhinosinusitis, nasal polyps, pulmonary hypoplasia, hay fever), carcinomas (eg, acute leukemia and myelocytic leukemia, B-cell lymphomas, lymphoma, adenocarcinoma, breast carcinoma, carcinoma of the lung, cervical cancer, colorectal carcinoma, glioblastoma and glioma, liver carcinoma, malignant neoplasm of the stomach, melanoma, neuroblastoma, osteosarcoma, pancreatic carcinoma, prostate carcinoma, stomach carcinoma), neuropsychiatric disorders (eg, mental depression, schizophrenia, Alzheimer disease), autoimmune diseases (eg, Graves disease, Crohn disease, colitis, psoriasis, systemic lupus erythematosus [SLE], systemic scleroderma, rheumatoid arthritis, multiple sclerosis [MS], IBD, atopic dermatitis, eczema), and infectious diseases (eg, sepsis, septicemia, tuberculosis, hepatitis, herpes simplex infections, malaria; Figure 8).

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    Figure 8. The diagram illustrates the molecular regulatory process of Th2 to Th9 differentiation in mouse spleen. This regulatory mechanism is regulated by 7 major pathways controlled by some specific immune regulatory transcription factors, receptors, and cytokines/chemokines. The standard and specific microRNAs play a crucial role in the posttranscriptional regulatory mechanism. These 7 pathways regulate DEGs misexpression involved in some critical diseases. DEG: differentially expressed gene; KEGG: Kyoto Encyclopedia of Genes and Genomes; Th: T helper.

    Principal Findings

    In this study, we compared 2 different data sets (GSE99166 and GSE123501) that have compared the mRNA expression in Th2 and Th9 cells. We identified common DEGs that have significantly altered expression between Th2 and Th9 cells from these 2 data sets. Sequential assessment of the DEGs and miRs that had significantly altered expression between Th2 and Th9 cells allows to identify disease states that affect the differentiation process. Although this analysis does not answer whether differentiation of Th2 to Th9 is the cause or the effect of the disease state, it does unravel the possibility of hitherto unknown associations between various diseases and the process of differentiation of Th2 to Th9 cells. Our analysis indicates that differentiation of Th2 to Th9 may play a crucial role via the alteration of DEGs (Table 1) and miRs (Table 2) in various metabolic diseases, allergic and pulmonary diseases, carcinomas, neuropsychiatric disorders, autoimmune diseases, and infectious diseases. In concordance with the existing literature, it was revealed that Th9 cells might play a major role in erythematosus, MS, IBDs, and psoriasis. The role of Th9 cells in autoimmune disease has already been explored in multiple studies [26], including in Graves disease [27], Crohn disease [28-30], psoriasis [31], SLE [32-35], systemic scleroderma [36], rheumatoid arthritis [37-40], MS [26,36,41,42], IBD [26,29,30,43], and atopic dermatitis/eczema [44], which have demonstrated an increased level of differentiation of Th2 to Th9 cells. Th9 cells and IL-9 have been observed in peripheral blood mononuclear cells and synovial fluid from patients with rheumatoid arthritis. Toll-like receptor 2 (TLR2) stimulates naïve CD4+ T cells for IL-9 secretion and Th9 differentiation by increasing the expression of TFs BATF and PU.1. TLR2 activation results in increased expression of IL-33 and its receptor ST2, augmenting IL-9 gene expression and Th9 cell development [45]. Similarly, in patients with SLE, Th9 cell differentiation is suppressed by repression of IRF4 expression [46]. Although the role of Th9 has been explored in experimental models of MS and IBD, there is insufficient evidence regarding its role in humans. Th9 cells have been shown to play a pathogenic role in experimental autoimmune encephalomyelitis, an animal model of MS [47]. However, only limited studies have assessed Th9 cells in human patients with MS. The skin toxicity of Th9 cells makes them a crucial link in the pathophysiology of multiple skin diseases [48]. Our study highlights the possibility of Th9 playing a crucial role in the pathophysiology of various autoimmune skin diseases such as eczema, atopic dermatitis, psoriasis, and dermatitis. A predominant expression of IL-9 from Th9 cells was observed to be a characteristic immunologic signature in psoriatic arthritis [49]. Similarly, IL-9 and PU.1 gene expressions in atopic dermatitis were higher and associated with disease severity [50]. In addition, the Th9 cell percentage in patients with atopic dermatitis correlated with serum IgE levels, highlighting the link between allergy and the development of Th9 cells [51]. Our in silico analysis further reiterated the involvement of Th9 in various autoimmune pathways. The involvement of IL-9 and Th9 cells in allergic response can also be seen in other diseases. One such allergic disease in which Th9 cells have been recently explored is asthma. Patients with allergic asthma have increased peripheral blood Th9 cells and elevated levels of serum IL-9 [51]. SGK1 (serum/glucocorticoid regulated kinase 1) has been shown to enhance the differentiation of Th9 by modulating the nuclear factor kappa B (NF-κB) signaling pathway in patients with asthma [52]. The activation of MAPK (mitogen-activated protein kinase) has also been attributed to the activation of Th9 cells in mice models of asthma [53]. Interestingly, IL-9 and IL-13 have been elevated in patients with chronic obstructive airway disease compared with asthma [54]. However, so far, the Th9 cells have not been explored for their significance in the pathophysiology of chronic obstructive pulmonary disease. Interestingly, apart from asthma, our in silico analysis highlighted chronic obstructive airway disease, tuberculosis, and chronic rhinosinusitis with nasal polyps as major airway diseases in which Th9 cells may play a crucial role. Our findings are in sync with the study of Ye et al [55], which demonstrated tuberculous pleural effusion to be chemotactic for Th9 cells, while pleural mesothelial cells in tuberculosis stimulated the Th9 cell differentiation. This in silico analysis also highlights the possible role of Th9 in neuropsychiatric diseases. Very few studies have explored the role of Th9 in neuropsychiatric disorders. Saresella et al [56] have demonstrated an increase in the activity of Th9 lymphocytes, while postthymic maturation pathways showed an accumulation of differentiated effector T lymphocytes (CD4+). In Alzheimer disease, schizophrenia, and multiple-episode schizophrenia, although IL-9 has been elevated, limited studies have been performed to assess the role of Th9 cells in the pathophysiology of the diseases [56,57]. In addition to the aforementioned diseases, this study identified malignancies as one of the disease states that could be affected by the development of Th9 cells. The role of Th9 cells in modulating immunity in cancer has been widely explored. Th9 cells contribute to antitumor immunity by enhancing the recruitment and activation of mast cells, natural killer cells, CD8 T cells, and dendritic cells in the tumor microenvironment. The antitumor effect of Th9 cells has been documented in various animal studies. Lu et al [58] have demonstrated the protective effects of IL-9 and Th9 on tumor development. The tumor-specific Th9 cells promoted the activation of CD8+ cytotoxic T lymphocytes by recruiting dendritic cells into tumor tissues and subsequently presenting tumor antigens in tumor-draining LNs. Th9 cells in tumor tissues mount an inflammatory response via CTL in a CCL20/CCR6 (chemokine [C-C motif] receptor 6)-dependent manner [59,60]. Wang et al [61] also demonstrated that Th9-enriched CD4+ T cells significantly increased the expansion of activated CD8+ T cells in a manner that was dependent on the expression of IL-9R (interleukin 9 receptor). Th9 thus seems to enhance antitumor immune response through T-cell cytotoxicity and play a crucial role in controlling the progression of cancer [62]. Apart from Th9 cells, the cytokine IL-9 has also been widely explored in cancers. Expression of IL-9 in the serum and circulating CD4+ T cells was significantly upregulated in patients with breast cancer compared with healthy controls [63]. Purwar et al [10] demonstrated that IL-9 depletion in RORγt-deficient mice promoted melanoma growth. Zheng et al [64] demonstrated that Th9 cells produce IL-9 to induce glioma cell apoptosis and inhibit tumor growth. Interestingly, tumor-specific Th9 cells displayed a unique PU.1-TRAF6-NF-kB activation–driven hyperproliferative feature, suggesting a persistence mechanism rather than an antiapoptotic strategy. This equips tumor-specific Th9 cells to become a more effective CD4+ T-cell subset for adoptive cancer therapy [65]. Although Th9 cells play an important role in tumor suppression, they have not been studied in various cancer subtypes. Our analysis suggests a possible role for Th9 in different cancer types such as malignant neoplasm of the stomach, melanoma, neuroblastoma, osteosarcoma, pancreatic carcinoma, and prostate carcinoma. Finally, our study also highlights the possible role of Th9 in different metabolic diseases. Interestingly, to our knowledge, no study has yet explored the role of Th9 in metabolic diseases such as diabetes and obesity. We want to highlight these lacunae to open up newer research attempts that would explore the role of Th9 in metabolic diseases. The insights into the role of Th9 in metabolic diseases would better help delineate the role of immunological dysregulation in developing metabolic diseases.

    Table 1. Role of various differentially expressed genes in the differentiation of T helper 9 cells and production of IL-9a.
    Cytokine or ligandReceptorTranscription factorsEffect on T helper 9 cell differentiationReferences
    IL-6IL-6R and gp130STAT1b and STAT3Both increases and decreases[66,67]
    IL-10IL-10R1c and IL-10R2STAT1 and STAT3Both increases and decreases[68-70]
    IL-23IL-23R and IL-12RB1STAT3Decreases[71,72]
    IL-27IL-27R and gp130STAT1Decreases[73]
    IL-27IL-27RIFN-γdDecreases[74]
    IL-1αIL-1R1 and IL-1RACPNF-κBe, MYD88f, and IRAKgIncreases[75,76]
    IL-1βIL-1R1 and IL-1RACPMYD88, IRAK, NF-κB, STAT1, IL-9, and IRF1hIncreases[77-80]
    IL-2IL-2Rα, IL-2Rβ, and γcSTAT5, IL-9, BCL-6i, IRF4, and GATA3jIncreases[75,81,82]
    IL-4IL-4Rα and γ-chainSTAT6, FOXP3k, IL-9Increases[83-85]
    IL-21IL-21R and γ-chainIL-1β, BCL-6, STAT1, and STAT3Increases[77,82]
    IL-25IL-17RBACT1l and TRAF6?mIncreases[86]
    IL-33IL-1RL1 and IL-1RACPUnknownIncreases[87]
    IFNα and IFNβIFNAR1n and IFNAR2STAT1Increases[69]
    TGFβoTGFβR2SMADp, IL-9, PU.1q, FOXP3Increases[85,88,89]
    TSLPrTSLPRs and IL-7RαSTAT5, IL-9Increases[81]
    Activin AACTRIIt and ALK4uSMAD, TGFβIncreases[90]
    CGRPvN/AwPKAx, NFATC2y, GATA3, and PU.1Increases[91]
    Nitric oxideN/Ap53z, IL-2, STAT5, IL-4Rα, TGFβR2Increases[15]
    TL1AaaDR3bbIL-2, STAT5Increases[92]
    NotchJaggedNICD1ccIncreases[67]
    IFNγIFNGR1dd and IFNGR2eeSTAT1Decreases[73]
    PDL2ffPD1ggSHP2hhDecreases[93]

    aIL: interleukin.

    bSTAT: signal transducer and activator of transcription.

    cILxR: interleukin receptor (where x corresponds to the interleukin number).

    dIFN: interferon.

    eNF-κB: nuclear factor kappa B.

    fMYD88: myeloid differentiation primary response gene 88.

    gIRAK: interleukin-1 receptor-associated kinase 1.

    hIRF: interferon regulatory factor.

    iBCL-6: B-cell leukemia/lymphoma 6.

    jGATA3: GATA binding protein 3.

    kFOXP3: forkhead box P3.

    lACT1: actin-related gene 1.

    mTRAF6: TNF receptor–associated factor 6.

    nIFNAR: interferon (alpha and beta) receptor.

    oTGF: transforming growth factor.

    pSMAD: SMAD family member.

    qPU.1: spleen focus forming virus (SFFV) proviral integration oncogene.

    rTSLP: thymic stromal lymphopoietin.

    sTSLPR: thymic stromal lymphopoietin receptor.

    tACTRII: activin receptor type 2.

    uALK4: activin A receptor, type 1B.

    vCGRP: calcitonin/calcitonin-related polypeptide.

    wN/A: not applicable.

    xPKA: protein kinase A.

    yNFATC2: nuclear factor of activated T cells, cytoplasmic, calcineurin dependent 2.

    zp53: transformation-related protein 53.

    aaTL1A: tumor necrosis factor (ligand) superfamily, member 15.

    bbDR3: death-domain receptor 3 (tumor necrosis factor receptor superfamily).

    ccNICD1: notch1 intracellular domain 1.

    ddIFNGR1: interferon gamma receptor 1.

    eeIFNGR2: interferon gamma receptor 2.

    ffPDL2: programmed cell death 1 ligand 2.

    ggPD1: programmed cell death protein 1.

    hhSHP2: protein tyrosine phosphatase, nonreceptor type 11.

    Table 2. Role of various microRNAs and target genes in the differentiation of Th9a cells in various disease conditions.
    MicroRNAStudy modelType of diseaseLevel of microRNAMolecular target geneDifferentiation of Th9Reference
    miR-145MouseLiver cancerUpregulatedReducing the expression of HIF-1αbIncreased[94]
    miR-155MouseWoundUpregulated Increased c-MAF1c, SOCS1d, CXCL1e, CXCL2f, IL-9Rg/IL-9h, IL-17Ri/IL-17AIncreased[95]
    miR-155Human and mouseAcute graft-versus-host diseaseUpregulatedTNF-αjIncreased[96]
    miR-15b/miR-16MouseN/AkUpregulatedDecreased HIF-2α expressionDecreased the IL-9 level in overexpressed Th9 cells[97]
    miR-493-5pBoth human and mouseAsthmaDownregulatedDecreased FOXO1l expressionDecreased[98]
    miR-143 and miR-145MouseN/AUpregulatedNFATC1m downregulationDecreased[99]
    miR-155HumanMethicillin-resistant Staphylococcus aureus pneumoniaUpregulatedDecreased SIRT1nIncreased Th9/IL-9[100]
    miR-148a-3pMouseAllergic rhinitisUpregulatedIncreased IRF4oIncreased[101]

    aTh9: T helper 9.

    bHIF: hypoxia-inducible factor.

    cMAF: avian musculoaponeurotic fibrosarcoma oncogene homolog.

    dSOCS1: suppressor of cytokine signaling 1.

    eCXCL1: chemokine (C-X-C motif) ligand 1.

    fCXCL2: chemokine (C-X-C motif) ligand 2.

    gIL-9R: interleukin 9 receptor.

    hIL: interleukin.

    iIL-17R: interleukin 17 receptor.

    jTNF: tumor necrosis factor.

    kN/A: not applicable.

    lFOXO1: forkhead box O1.

    mNFATC1: nuclear factor of activated T cells, cytoplasmic, calcineurin dependent 1.

    nSIRT1: sirtuin 1.

    oIRF: interferon regulatory factor.

    Limitations

    The main limitation of the study is that the analysis is based on an in silico method where only a few specific wild-type samples from data sets of previous studies were included; therefore, further validation of the identified genes and miRNAs is required in various animal models and human diseases. The data sets were compiled using different arrays on the Affymetrix platform, which may account for some of the variability in the results. However, the functional enrichment for the mRNAs highlighted some significant pathways related to immune regulation and its derangements.

    Conclusions

    This study identified common DEGs of ILs, receptors, and TFs that have significantly altered expression between Th2 and Th9 cells. The KEGG pathway enrichment analysis identified cytokines-cytokines interaction, Th1 and Th2 differentiation, T-cell receptor signaling regulation via CTLA4, Fc epsilon signaling, and Th17 cell differentiation as the significant pathways affected by the identified DEGs. Our study identified hitherto unexplored possible associations between Th9 and disease states. The interactome analysis also identified pathways that are involved in various metabolic diseases, allergic and pulmonary diseases, carcinomas, neuropsychiatric disorders, autoimmune diseases, and infectious diseases, where differentiation of Th2 to Th9 may play a crucial role. The scarcity of studies on the role of Th9 in metabolic diseases highlights the lacunae in this field. Thus, our study provides the rationale for exploring the role of Th9 in various metabolic disorders.

    Acknowledgments

    The authors are grateful to the All India Institute of Medical Sciences Jodhpur for providing the research facility to perform this in silico experiment. MK is supported by a senior research fellowship of The University Grants Commission of India (number NOV2017-361200).

    Data Availability

    Publicly available GEO data sets were used for the analysis in this study. These data sets can be accessed online [102,103].

    Conflicts of Interest

    None declared.

    ‎
    Multimedia Appendix 1

    Fold change expression of the significant differentially expressed genes analyzed.

    DOCX File , 64 KB
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    Multimedia Appendix 2

    Higher resolution images for Figures 3-7.

    DOCX File , 6052 KB
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    ‎
    ACT1: actin-related gene 1
    ACTRII: activin receptor type 2
    AHR: aryl-hydrocarbon receptor
    ALK4: activin A receptor, type 1B
    ATF6: activating transcription factor 6
    BAFT: basic leucine zipper transcription factor
    BCL6: B-cell leukemia/lymphoma 6
    BMP7: bone morphogenetic protein 7
    BTAF1: B-TFIID TATA-box binding protein associated factor 1
    CCL1: chemokine (C-C motif) ligand 1
    CCL20: chemokine (C-C motif) ligand 20
    CCR6: chemokine (C-C motif) receptor 6
    CD: cluster of differentiation
    CGRP: calcitonin/calcitonin-related polypeptide, alpha
    CTLA: cytotoxic T lymphocyte–associated protein
    CX3CR1: chemokine (C-X3-C motif) receptor 1
    CXCL1: chemokine (C-X-C motif) ligand 1
    CXCL2: chemokine (C-X-C motif) ligand 2
    DC: dendritic cell
    DEG: differentially expressed gene
    DR3: death-domain receptor 3 (tumor necrosis factor receptor superfamily)
    EAE: autoimmune encephalomyelitis
    EP300: E1A binding protein p300
    FOXO1: forkhead box O1
    FOXP2: forkhead box P2
    FOXP3: forkhead box P3
    GATA3: GATA binding protein 3
    GEO: Gene Expression Omnibus
    GO: The Gene Ontology
    GREIN: GEO RNA-seq Experiments Interactive Navigator
    HIF: hypoxia-inducible factor
    IBD: inflammatory bowel disease
    IF1: NDV-induced circulating interferon
    IFN: interferon
    IFNAR1: interferon (alpha and beta) receptor 1
    IFNAR2: interferon (alpha and beta) receptor 2
    IFNGR1: interferon gamma receptor 1
    IFNGR2: interferon gamma receptor 2
    IL: interleukin
    IL-1R1: interleukin 1 receptor, type I
    IL-1RL1: interleukin 1 receptor-like 1
    IL-1RL1: interleukin 1 receptor-like 1
    IL-2R: interleukin 2 receptor, alpha chain
    IL-4R: interleukin 4 receptor, alpha
    IL-4RA: interleukin 4 receptor, alpha
    IL-6R: interleukin 6 receptor, alpha
    IL-7R: interleukin 7 receptor
    IL-9R: interleukin 9 receptor
    IL-10R2: interleukin 10 receptor, beta
    IL-12RB1: interleukin 12 receptor, beta 1
    IL-12RB1: interleukin 12 receptor, beta 1
    IL-17R: interleukin 17 receptor A
    IL-17RB: interleukin 17 receptor B
    IL-21R: interleukin 21 receptor
    IL-23R: interleukin 23 receptor
    IRAK: interleukin-1 receptor-associated kinase 1
    IRF1: interferon regulatory factor
    JAK2: Janus kinase 2
    JUN: Jun proto-oncogene
    KEGG: Kyoto Encyclopedia of Genes and Genomes
    MAF: avian musculoaponeurotic fibrosarcoma oncogene homolog
    MAPK: mitogen-activated protein kinase
    MS: multiple sclerosis
    MYD88: myeloid differentiation primary response gene 88
    NFATC1: nuclear factor of activated T cells, cytoplasmic, calcineurin dependent 1
    NFATC2: nuclear factor of activated T cells, cytoplasmic, calcineurin dependent 2
    NF-κB: nuclear factor kappa B
    NICD1: notch1 intracellular domain 1
    NOTCH1: neurogenic locus notch homolog protein 1
    p53: transformation-related protein 53
    PD1: programmed cell death protein 1
    PDL2: programmed cell death 1 ligand 2
    PPARG: peroxisome proliferator–activated receptor gamma
    PPI: protein-protein interaction
    PU.1: spleen focus forming virus (SFFV) proviral integration oncogene
    R2: ribonucleotide reductase M2
    RORA: RAR-related orphan receptor alpha
    SATB1: special AT-rich sequence binding protein 1
    SGK1: serum/glucocorticoid regulated kinase 1
    SHP2: protein tyrosine phosphatase, non-receptor type 11
    SIRT1: sirtuin 1
    SLE: systemic lupus erythematosus
    SMAD3: SMAD family member 3
    SMAD4: SMAD family member 4
    SMAD6: SMAD family member 6
    SOCS1: suppressor of cytokine signaling 1
    STAT: signal transducer and activator of transcription
    TGF: transforming growth factor
    Th: T helper
    TL1A: tumor necrosis factor (ligand) superfamily, member 15
    TLR: Toll-like receptor
    TNF: tumor necrosis factor
    TRAF6: TNF receptor–associated factor 6
    TSLP: thymic stromal lymphopoietin
    TSLPR: thymic stromal lymphopoietin receptor

    Edited by T Leung; submitted 03.09.22; peer-reviewed by H Mohammed, R Pillai, O Rahaman; comments to author 08.11.22; revised version received 18.01.23; accepted 25.01.23; published 23.02.23

    Copyright

    ©Manoj Khokhar, Purvi Purohit, Ashita Gadwal, Sojit Tomo, Nitin Kumar Bajpai, Ravindra Shukla. Originally published in JMIR Bioinformatics and Biotechnology (https://bioinform.jmir.org), 23.02.2023.

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