An Integrated Bioinformatics Analysis of the Potential Regulatory Effects of miR-21 on T-cell Related Target Genes in Multiple Sclerosis

Background: Overexpression of miR-21 is a characteristic feature of patients with Multiple Sclerosis (MS) and is involved in gene regulation and the expression enhancement of pro-inflammatory factors including IFNγ and TNF-α following stimulation of T-cells via the T Cell Receptor (TCR). In this study, a novel integrated bioinformatics analysis was used to obtain a better understanding of the involvement of miR-21 in the development of MS, its protein biomarker signatures, RNA levels, and drug interactions through existing microarray and RNA-seq datasets of MS. Methods: In order to obtain data on the Differentially Expressed Genes (DEGs) in patients with MS and normal controls, the GEO2R web tool was used to analyze the Gene Expression Omnibus (GEO) datasets, and then Protein-Protein Interaction (PPI) networks of co-expressed DEGs were designed using STRING. A molecular network of miRNA-genes and drugs based on differentially expressed genes was created for T-cells of MS patients to identify the targets of miR-21, that may act as important regulators and potential biomarkers for early diagnosis, prognosis and, potential therapeutic targets for MS. Results: It found that seven genes (NRIP1, ARNT, KDM7A, S100A10, AK2, TGFβR2, and IL-6R) are regulated by drugs used in MS and miR-21. Finally, three overlapping genes (S100A10, NRIP1, KDM7A) were identified between miRNA-gene-drug network and nineteen genes as hub genes which can reflect the pathophysiology of MS. Conclusion: Our findings suggest that miR-21 and MS-related drugs can act synergistically to regulate several genes in the existing datasets, and miR-21 inhibitors have the potential to be used in MS treatment.

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A new family of markers to characterize the human γδ T-cell lineage

10.31219/osf.io/jzcf3 ◽

2019 ◽

Author(s):

Shahan Mamoor

Keyword(s):

T Cells ◽

T Cell ◽

Differentially Expressed Genes ◽

Cell Lineage ◽

Γδ T Cells ◽

Differentially Expressed ◽

Γδ T Cell ◽

Differentiation Markers ◽

Hematopoietic Stem ◽

New Family

Prospective isolation of γδ T lymphocytes demands a comprehensive description of the molecules that distinguish T cells with γδ T-cell receptors (TCRs) (γδ T cells, or Tγδ) from those with αβTCRs (Tαβ). Here I describe some of the most differentially expressed genes in the γδ T cell when compared to the developmentally proximal but lineage-distinct Tαβ CD4+ and CD8+ lymphocytes. These genes encode cluster of differentiation markers, transcription factors, cell surface receptors and non-coding RNAs. As hematopoietic stem cells (HSCs) have been prospectively isolated based on the analysis of differentially expressed genes (1), any combination of these molecules may potentially be used to isolate Tγδ, perhaps even independent of the γδTCR. This description of the most striking identifying features of the Tγδ will be a resource for the isolation of a multi-potent common γδ T-cell progenitor.

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Towards Understanding & Uncovering New Key Players in T-Cell Development upon Aging

Blood ◽

10.1182/blood-2019-127294 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 2482-2482

Author(s):

Hanane Boukarabila ◽

Kalpana Nattamai ◽

Medhanie Assmelash Mulaw ◽

Hartmut Geiger

Keyword(s):

T Cells ◽

T Cell ◽

Differentially Expressed Genes ◽

T Cell Development ◽

Cell Lineage ◽

Cell Development ◽

Differentially Expressed ◽

Rna Seq ◽

Thymic Involution ◽

Total P

Aging-associated immune remodeling (AAIR) leads to an impaired ability to respond to vaccination and combat infections, and is due to many factors acting in concert. Several studies have linked the T-cell decline that occurs with age to thymic involution. However, there is novel and mounting evidence that also aged lymphoid-primed multipotent progenitors (LMPPs) are immune system intrinsic players in AAIR. However, very little is known on molecular and cellular mechanisms by which aging LMPPs could drive this AAIR phenomenon. Deciphering the underlying mechanisms is of crucial importance for developing new therapies to attenuate AAIR. Here, we present new data demonstrating the dysregulated pathways associated with aged LMPPs and the cellular changes in early thymic differentiation events in driving AAIR. To assess the T-lineage potential of aged LMPPs, we performed single cells ex-vivo OP9D assays using LMPPs (Lin-cKit+Sca1+CD34-Flt3hi) from aged (18-20 month-old) and young (8-10 week-old) C57BL/6 animals as controls. The frequencies of T-cell lineage potential in aged LMPPs and young LMPPs at the single cell level were very similar. This result was also validated in vivo by transplantation assays where 5000 aged or young LMPPs were injected into sub-lethally irradiated young recipients followed by T-cell lineage (CD4+ & CD8+) analysis in the peripheral blood (PB) at 4 weeks post transplantation (Mean; Y:12,75 vs A:16:23 % of total, p=0.46). However, aged LMPPs were associated with a dramatic disadvantage of PB T-cells at 4 weeks post injections (Mean; Y:9 vs A:2.3 % of total, p<0.0001) and in the development of all thymic stages of thymocytes, from early double negative stage CD4-CD8-(DN1) (Mean; Y:30 vs A:8 % of parent, p<0.0001) to double positive stage CD4+CD8+(DP) (Mean; Y:53 vs A:15 % of parent, p<0.0001), as well as single positive (SP) CD4+(Mean; Y:54 vs A:13 % of parent, p<0.0001) and CD8+(Mean; Y:45 vs A:10 % of parent, p<0.0001) thymocytes when intravenously transplanted in combination with 1:1 ratio of young LMPPs into young recipients. To overcome a potential homing to the thymus bias of aged LMPPs in competitive transplants, we performed intra-thymic injections of young and aged LMPPs with identical ratios into sub-lethally irradiated young recipients. The analysis of PB at 4 weeks post injections show a dramatic reduction of PB T-cells derived from aged LMPPs in comparison with young LMPPs (Mean; Y:14.8 vs A:8 % of total, p<0.0001). There was primarily a strong disadvantage towards generating DP stage (Mean; Y:46 vs A:28 % of parent, p<0.0001), suggesting that the intra-thymic injections indeed alleviated the dramatic decrease in the early thymocyte stage DN1 (Mean; Y:31 vs A:22 % of parent, p<0.05). This suggests that aged LMPPs confer a T-cell differentiation and maybe an additional homing to the thymus defect. We also performed RNA-Seq analyses on LMPPs from young and aged mice. Unsupervised hierarchical clustering of differentially expressed genes between young and aged LMPPs highlighted a clear dysregulation of only a few pathways that are involved in T-cell development such as Notch signaling. We next correlated our RNA-Seq data with other immunological signatures in attempt to look for more T-cell specific key factors that are differentially expressed between young and aged LMPPs. Importantly, the results show that the data from our RNA-Seq correlated with more than 400 immunological signatures among which 25 were most highly correlated. Interestingly, this correlation has allowed us to curate a list of the top 30 differentially expressed genes between young and aged LMPPs including T-cell specific transcription factors such as Satb1 and Foxo1. Altogether, our findings reveal that the T-cell immune decline that occurs with age is already imprinted in LMPPs within the bone marrow and translates into a dysregulation of signaling pathways that are directly related to T-cell development. Targeting these pathways could open up new perspectives in attenuating AAIR. Disclosures No relevant conflicts of interest to declare.

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Network-based analysis of differentially expressed genes in cerebrospinal fluid (CSF) and blood reveals new candidate genes for multiple sclerosis

PeerJ ◽

10.7717/peerj.2775 ◽

2016 ◽

Vol 4 ◽

pp. e2775 ◽

Cited By ~ 15

Author(s):

Nahid Safari-Alighiarloo ◽

Mostafa Rezaei-Tavirani ◽

Mohammad Taghizadeh ◽

Seyyed Mohammad Tabatabaei ◽

Saeed Namaki

Keyword(s):

Multiple Sclerosis ◽

Cerebrospinal Fluid ◽

Candidate Genes ◽

Differentially Expressed Genes ◽

Expression Profiles ◽

Biological Pathways ◽

Differentially Expressed ◽

Potential Candidate ◽

Functional Modules ◽

Ppi Networks

BackgroundThe involvement of multiple genes and missing heritability, which are dominant in complex diseases such as multiple sclerosis (MS), entail using network biology to better elucidate their molecular basis and genetic factors. We therefore aimed to integrate interactome (protein–protein interaction (PPI)) and transcriptomes data to construct and analyze PPI networks for MS disease.MethodsGene expression profiles in paired cerebrospinal fluid (CSF) and peripheral blood mononuclear cells (PBMCs) samples from MS patients, sampled in relapse or remission and controls, were analyzed. Differentially expressed genes which determined only in CSF (MSvs.control) and PBMCs (relapsevs.remission) separately integrated with PPI data to construct the Query-Query PPI (QQPPI) networks. The networks were further analyzed to investigate more central genes, functional modules and complexes involved in MS progression.ResultsThe networks were analyzed and high centrality genes were identified. Exploration of functional modules and complexes showed that the majority of high centrality genes incorporated in biological pathways driving MS pathogenesis. Proteasome and spliceosome were also noticeable in enriched pathways in PBMCs (relapsevs.remission) which were identified by both modularity and clique analyses. Finally, STK4, RB1, CDKN1A, CDK1, RAC1, EZH2, SDCBP genes in CSF (MSvs.control) and CDC37, MAP3K3, MYC genes in PBMCs (relapsevs.remission) were identified as potential candidate genes for MS, which were the more central genes involved in biological pathways.DiscussionThis study showed that network-based analysis could explicate the complex interplay between biological processes underlying MS. Furthermore, an experimental validation of candidate genes can lead to identification of potential therapeutic targets.

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Identification of differentially expressed genes and signaling pathways in rheumatoid arthritis by integrated bioinformatics analysis

10.21203/rs.3.rs-38219/v1 ◽

2020 ◽

Author(s):

Yanzhi Ge ◽

Li Zhou ◽

Zuxiang Chen ◽

Yingying Mao ◽

Ting Li ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Signaling Pathways ◽

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Bioinformatics Analysis ◽

Kegg Pathway ◽

Differentially Expressed ◽

Receptor Interaction ◽

Effective Prevention ◽

Ppi Networks

Abstract Background The disability rate associated with rheumatoid arthritis (RA) ranks high among inflammatory joint diseases. However, the cause and potential molecular events are as yet not clear. Here, we aimed to identify key genes and pathways involved in RA utilizing integrated bioinformatics analysis and uncover underlying molecular mechanisms. Materials and methods The expression profiles of GSE55235, GSE55457, GSE55584 and GSE77298 were downloaded from the Gene Expression Omnibus database, which contained 76 synovial membrane samples, including 49 RA samples and 27 controls. The microarray datasets were consolidated and differentially expressed genes (DEGs) were acquired and further analyzed by bioinformatics techniques. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were performed using R (version 3.6.1), respectively. The protein-protein interaction (PPI) networks of DEGs were developed utilizing the STRING database. Results A total of 828 DEGs were recognized, with 758 up-regulated and 70 down-regulated. GO and KEGG pathway analyses demonstrated that these DEGs focused primarily on multifactorial binding, transcription activity, cytokin-cytokin receptor interaction and relevant signaling pathways. The 30 most firmly related genes among DEGs were identified from the PPI network. Conclusion This study shows that screening for DEGs and pathways utilizing integrated bioinformatics analyses could aid in the comprehension of the molecular mechanisms involved in RA development. In addition, our study provides valuable data for the effective prevention, diagnosis, treatment and rehabilitation of RA patients as well as providing potential targets for the treatment of RA.

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Identification of potential biomarkers in dengue via integrated bioinformatic analysis

PLoS Neglected Tropical Diseases ◽

10.1371/journal.pntd.0009633 ◽

2021 ◽

Vol 15 (8) ◽

pp. e0009633

Author(s):

Li-Min Xie ◽

Xin Yin ◽

Jie Bi ◽

Huan-Min Luo ◽

Xun-Jie Cao ◽

...

Keyword(s):

Dengue Fever ◽

Differentially Expressed Genes ◽

Target Genes ◽

Bioinformatics Analysis ◽

Bioinformatic Analysis ◽

Denv Infection ◽

Differentially Expressed ◽

Pathogenic Genes ◽

Underlying Mechanisms ◽

Potential Biomarkers

Dengue fever virus (DENV) is a global health threat that is becoming increasingly critical. However, the pathogenesis of dengue has not yet been fully elucidated. In this study, we employed bioinformatics analysis to identify potential biomarkers related to dengue fever and clarify their underlying mechanisms. The results showed that there were 668, 1901, and 8283 differentially expressed genes between the dengue-infected samples and normal samples in the GSE28405, GSE38246, and GSE51808 datasets, respectively. Through overlapping, a total of 69 differentially expressed genes (DEGs) were identified, of which 51 were upregulated and 18 were downregulated. We identified twelve hub genes, including MX1, IFI44L, IFI44, IFI27, ISG15, STAT1, IFI35, OAS3, OAS2, OAS1, IFI6, and USP18. Except for IFI44 and STAT1, the others were statistically significant after validation. We predicted the related microRNAs (miRNAs) of these 12 target genes through the database miRTarBase, and finally obtained one important miRNA: has-mir-146a-5p. In addition, gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were carried out, and a protein–protein interaction (PPI) network was constructed to gain insight into the actions of DEGs. In conclusion, our study displayed the effectiveness of bioinformatics analysis methods in screening potential pathogenic genes in dengue fever and their underlying mechanisms. Further, we successfully predicted IFI44L and IFI6, as potential biomarkers with DENV infection, providing promising targets for the treatment of dengue fever to a certain extent.

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CEBPα Is a Transcriptional Repressor of T-Cell Related Genes Explaining the Myeloid/T-Lymphoid Features of CEBPα-Silenced AML

Blood ◽

10.1182/blood.v118.21.554.554 ◽

2011 ◽

Vol 118 (21) ◽

pp. 554-554

Author(s):

Erdogan Taskesen ◽

Roberto Avellino ◽

Meritxell AlberichJorda ◽

Daniel G. Tenen ◽

Jeroen Ridder de ◽

...

Keyword(s):

Gene Expression ◽

Bone Marrow ◽

T Cell ◽

Differentially Expressed Genes ◽

Target Genes ◽

Differentially Expressed ◽

Lymphoid Leukemia ◽

Hematopoietic Stem ◽

Knock Out ◽

Molecular Abnormalities

Abstract Abstract 554 Acute myeloid leukemia (AML) is a heterogeneous disease of different subtypes characterized by distinct cytogenetic or molecular abnormalities. We recently identified a previously unrecognized subtype with a unique epigenetic feature, i.e. silencing of the gene that encodes CCAAT-enhancer binding protein alpha (CEBPα) by DNA hypermethylation (denoted as the CEBPαsilenced group). The leukemic blast cells of these patients express myeloid as well as T-lymphoid markers. Moreover, gene expression and DNA methylation profiling put these leukemias in between AML and T-lymphoid leukemia (T-ALL). CEBPα is a transcription regulator that is essential for normal neutrophil development. We hypothesize that methylation and consequently silencing of the gene encoding CEBPα is abnormal and an important hit in the transformation of this unique leukemia subtype. The mechanism by which CEBPα silencing plays a role in transformation of cells with myeloid/T-lymphoid features is the aim of our study. We carried out gene expression profiling of the CEBPαsilenced group (n=10) and compared gene expression levels to normal CD34+ bone marrow cells (n=11) and the remaining AML group (n=506) using a three-way ANOVA and a post-hoc test (tukey-kramer method). We detected 686 differentially expressed genes with P < 0.05 after multiple testing. Of these 686 genes, 288 were up- and 401 down-regulated in the CEBPαsilenced group. We next asked the question which of those genes might be bona fide CEBPα targets and whether expression has been altered as the result of CEBPα silencing. We transduced estrogen-inducible C/EBPα in 32D cells and carried out ChIP-on-chip analysis using ER specific antibodies. The analysis yielded a collection of 529 CEBPα target genes that are significant enriched for C/EBPα binding (P < 0.05) using Hypergeometric Analysis of Tiling-arrays (HAT). We hypothesized that the direct target genes of CEBPα, derived from the 32D model system, were also present among deregulated genes in CEBPαsilenced human AMLs. We therefore overlaid the detected direct binding targets of CEBPα in 32D cells, with the differentially expressed genes in the CEBPαsilenced group and identified 49 overlapping genes (P=1×10−7) as putative direct targets. Among these 49 genes, 25 were down-regulated and 24 were upregulated in the primary CEBPαsilenced AML group. Both groups of genes were highly enriched for the CEBPα consensus binding sequence, i.e. 16/25 and 20/24 promoter regions respectively. The 25 downregulated genes, among which ACSL1, MYCT1 or Slc7a11, represent targets that are most likely normally activated by CEBPα, but are not transcribed in CEBPαsilenced human AMLs due to the absence of CEBPα. Among the genes that were upregulated in CEBPαsilenced leukemias are BCL2, CCR9, CEBPG or CD47. These putative target genes are repressed in CEBPα expressing cells and activated when CEBPα is silenced. This observation suggests that the transcription factor CEBPα may also acts as a repressor of gene transcription. We therefore studied the expression of two of those genes, i.e. CEBPG and CCR9 in Lin-Sca+Kit+ (LSK) bone marrow hematopoietic stem cells from wild type versus conditional Mx-Cre/CEBPα knock-out mice. Similar to what we observed in human CEBPαsilenced leukemias, we found that these two genes were switched off in LSK cells from CEBPα knock-out animals. Ingenuity pathway analysis of the 24 upregulated genes detected high enrichment (P < 0.001) of pathways involved with T-cell development. Our data clearly suggest that CEBPα may act as repressor of T-cell related genes through direct promoter interaction. We therefore propose that silencing of CEBPα by promoter hypermethylation is one of the transforming events that driving towards mixed myeloid/T-lymphoid leukemia. Disclosures: No relevant conflicts of interest to declare.

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Sjögren’s Syndrome Minor Salivary Gland CD4+ Memory T Cells Associate with Glandular Disease Features and Have a Germinal Center T Follicular Helper Transcriptional Profile

Journal of Clinical Medicine ◽

10.3390/jcm9072164 ◽

2020 ◽

Vol 9 (7) ◽

pp. 2164 ◽

Cited By ~ 1

Author(s):

Michelle L. Joachims ◽

Kerry M. Leehan ◽

Mikhail G. Dozmorov ◽

Constantin Georgescu ◽

Zijian Pan ◽

...

Keyword(s):

T Cells ◽

Salivary Gland ◽

T Cell ◽

Differentially Expressed Genes ◽

Cd4 T Cells ◽

Memory T Cells ◽

Sjogren's Syndrome ◽

Differentially Expressed ◽

Follicular Helper ◽

Memory Cd4 T Cells

To assess the types of salivary gland (SG) T cells contributing to Sjögren’s syndrome (SS), we evaluated SG T cell subtypes for association with disease features and compared the SG CD4+ memory T cell transcriptomes of subjects with either primary SS (pSS) or non-SS sicca (nSS). SG biopsies were evaluated for proportions and absolute numbers of CD4+ and CD8+ T cells. SG memory CD4+ T cells were evaluated for gene expression by microarray. Differentially-expressed genes were identified, and gene set enrichment and pathways analyses were performed. CD4+CD45RA− T cells were increased in pSS compared to nSS subjects (33.2% vs. 22.2%, p < 0.0001), while CD8+CD45RA− T cells were decreased (38.5% vs. 46.0%, p = 0.0014). SG fibrosis positively correlated with numbers of memory T cells. Proportions of SG CD4+CD45RA− T cells correlated with focus score (r = 0.43, p < 0.0001), corneal damage (r = 0.43, p < 0.0001), and serum Ro antibodies (r = 0.40, p < 0.0001). Differentially-expressed genes in CD4+CD45RA− cells indicated a T follicular helper (Tfh) profile, increased homing and increased cellular interactions. Predicted upstream drivers of the Tfh signature included TCR, TNF, TGF-β1, IL-4, and IL-21. In conclusion, the proportions and numbers of SG memory CD4+ T cells associate with key SS features, consistent with a central role in disease pathogenesis.

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Single Cell Transcriptomics Implicate Novel Monocyte and T Cell Immune Dysregulation in Sarcoidosis

Frontiers in Immunology ◽

10.3389/fimmu.2020.567342 ◽

2020 ◽

Vol 11 ◽

Author(s):

Lori Garman ◽

Richard C. Pelikan ◽

Astrid Rasmussen ◽

Caleb A. Lareau ◽

Kathryn A. Savoy ◽

...

Keyword(s):

T Cells ◽

T Cell ◽

Single Cell ◽

Differentially Expressed Genes ◽

Immune Cells ◽

Immune Dysregulation ◽

Differentially Expressed ◽

Cell Type ◽

Cell Type Specific ◽

Classical Monocytes

Sarcoidosis is a systemic inflammatory disease characterized by infiltration of immune cells into granulomas. Previous gene expression studies using heterogeneous cell mixtures lack insight into cell-type-specific immune dysregulation. We performed the first single-cell RNA-sequencing study of sarcoidosis in peripheral immune cells in 48 patients and controls. Following unbiased clustering, differentially expressed genes were identified for 18 cell types and bioinformatically assessed for function and pathway enrichment. Our results reveal persistent activation of circulating classical monocytes with subsequent upregulation of trafficking molecules. Specifically, classical monocytes upregulated distinct markers of activation including adhesion molecules, pattern recognition receptors, and chemokine receptors, as well as enrichment of immunoregulatory pathways HMGB1, mTOR, and ephrin receptor signaling. Predictive modeling implicated TGFβ and mTOR signaling as drivers of persistent monocyte activation. Additionally, sarcoidosis T cell subsets displayed patterns of dysregulation. CD4 naïve T cells were enriched for markers of apoptosis and Th17/Treg differentiation, while effector T cells showed enrichment of anergy-related pathways. Differentially expressed genes in regulatory T cells suggested dysfunctional p53, cell death, and TNFR2 signaling. Using more sensitive technology and more precise units of measure, we identify cell-type specific, novel inflammatory and regulatory pathways. Based on our findings, we suggest a novel model involving four convergent arms of dysregulation: persistent hyperactivation of innate and adaptive immunity via classical monocytes and CD4 naïve T cells, regulatory T cell dysfunction, and effector T cell anergy. We further our understanding of the immunopathology of sarcoidosis and point to novel therapeutic targets.

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Bioinformatics Analysis of Differentially Expressed Genes Involved in Irritable Bowel Syndrome With Diarrhea

10.21203/rs.3.rs-513744/v1 ◽

2021 ◽

Author(s):

Yuan-Mei Lou ◽

Yan-Zhi Ge ◽

Wen Chen ◽

Lin Su ◽

Jia-Qi Zhang ◽

...

Keyword(s):

Irritable Bowel Syndrome ◽

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Bioinformatics Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Hub Genes ◽

Ppi Networks ◽

Black Module ◽

Irritable Bowel

Abstract Purpose: Irritable bowel syndrome with diarrhea (IBS-D) is a common functional gastrointestinal disorder around the world. However, the molecular mechanisms of IBS-D are still not well understood. This study was designed to identify key biomarkers and immune infiltration in the rectal mucosa of IBS-D by bioinformatics analysis. Methods: The gene expression profiles of GSE36701 were downloaded from the GEO database. The differentially expressed genes (DEGs) were identified and functional enrichment and pathway analyses were performed. Using STRING and Cytoscape, protein-protein interaction (PPI) networks were constructed and core genes were identified. Subsequently, 22 immune cell types of IBS-D tissues were explored by the Cell type Identification by Estimating Relative Subsets of RNA Transcripts. Finally, the co-expression network of DEGs was estimated by the weigh gene co-expression network analysis method to identify IBS-D-related modules and deeply hub genes. Results: 224 up-regulated and 171 down-regulated genes in IBS-D patients: Our analysis indicated that several DEGs might play crucial roles in IBS-D, such as CDC20, UBE2C, AURKA, CDC26, CKS1B and PSMB3. Later, we found that immune infiltrating cells such as T cells CD4 memory resting, M2 macrophages are crucial in IBS-D progression. In the end, a total of 9 co-expression gene modules were calculated and the black module was found to have the highest correlation. 15 hub genes were identified both in DEGs and the black module. Conclusions: This study identified molecular mechanisms and a series of candidate genes as well as significant pathways from the bioinformatics network, which may provide a diagnostic method and therapeutic targets for IBS-D.

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Identification of multiple sclerosis-related genes regulated by EBV-encoded microRNAs in B cells

10.21203/rs.3.rs-338909/v1 ◽

2021 ◽

Author(s):

Xinming Rang ◽

Yuan Liu ◽

Jingguo Wang ◽

Yifei Wang ◽

Chaohan Xu ◽

...

Keyword(s):

Multiple Sclerosis ◽

B Cells ◽

Differentially Expressed Genes ◽

Target Genes ◽

Differentially Expressed ◽

Systematic Analysis ◽

Barr Virus ◽

Ebv Infection ◽

Pathway Gene

Abstract Background: Multiple sclerosis (MS) is driven by the interaction between genetic susceptibility and environmental triggers, particularly to Epstein-Barr virus (EBV) infection. EBV-encoded microRNAs (miRNAs) are abundantly expressed in all stages of EBV infection and latency, which can target both viral and host cellular mRNAs, allowing EBV-infected B cells to evade the host immune response. However, it remains a big gap to understand the roles of EBV miRNAs and their target genes in MS pathogenesis.Methods: We investigated the correlation between MS-related viruses infection and MS risk quantitatively by systematic analysis. All MS-related genes in B cells were obtained by integrating MS susceptibility genes and differentially expressed genes from B cells. In comparison with differentially expressed genes from B cells after EBV infection in vitro, we confirmed EBV-regulated, MS-related genes. Subsequently, we obtained target EBV miRNAs which can regulate these genes from several online databases. By constructing pathway-pathway, pathway-gene and protein-protein interaction networks, we further screened out MS-related genes and risk pathways regulated by EBV miRNAs. Finally, we identified target EBV miRNAs may directly regulate MS-related genes through bioinformatic prediction and experimental validation.Results: EBV infection showed the strongest correlation with MS risk. A total of 873 MS-related genes and 52 risk pathways in B cells were obtained. We then identified 150 MS-related genes and 18 associated risk pathways that EBV was involved in. In addition, 42 human target genes regulated by 36 EBV miRNAs overlapped with EBV-regulated, MS-related genes. Finally, 15 target EBV miRNAs and their regulated, 6 MS-related genes (MALT1, BCL10, IFNGR2, STAT3, CDK6 and FOXP1) have been confirmed as crucial pathogenic molecules, which could promote the initiation and development of MS through NF-kappa B (MALT1 and BCL10) and PD-L1/PD-1 (IFNGR2 and STAT3) pathways. Surprisingly, ebv-miR-BHRF1-2-5p directly targeting MALT1 was confirmed by our experiments, and FOXP1 was identified as a target gene of ebv-miR-BART11.Conclusions: This work identified the target EBV miRNAs and their regulated, MS-related genes and risk pathways, which may provide a novel insight into discovering diagnostic biomarkers and therapeutic targets for MS.

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