Identification of hub genes and potential biomarkers of neutrophilic asthma: evidence from a bioinformatics analysis.

OBJECTIVE: Asthma is a chronic airway inflammatory disease caused by multiple genetic and environmental factors. This study mainly sought to provide potential therapeutic targets and biomarkers for neutrophilic asthma (NA). METHODS: Three gene expression profiling datasets were obtained from the Genome Expression Omnibus (GEO) database. GSE45111 and GSE41863 were used to identify hub genes and potential biomarkers, and GSE137268 was used for data verification. We verified the repeatability of intragroup data and identified differentially expressed genes (DEGs). Then, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the DEGs, and a protein-protein interaction (PPI) network was constructed to identify the hub genes. Finally, receiver operating characteristic (ROC) analysis was used to verify the ability of the hub genes to differentiate between NA and eosinophilic asthma (EA). RESULTS: In this study, we identified 411 DEGs by comprehensive analysis of NA/EA patients and NA/healthy controls (HCs). Ten hub genes (CXCR1, FCGR3B, CXCR2, SELL, S100A12, CSF3R, IL6R, JAK3, CD48, and GNG2) were identified from the PPI network. Finally, based on the ROC analysis, 7 genes showed good diagnostic value for discriminating NA from EA-CXCR1, FCGR3B, CXCR2, SELL, S100A12, CSF3R, and IL6R (AUC > 0.7). CONCLUSION: We identified 7 hub genes that can distinguish NA from EA. The IL-8-mediated signaling may be the primary pathway to determine the NA phenotype in asthma. CXCR1/2 and S100A12 may be the primary genes determining the NA phenotype. CXCR1/2 and S100A12 might be biomarkers and new therapeutic targets for NA.Supplemental data for this article is available online at at.

Hub Genes and Long Noncoding RNAs That Regulates It Associated with the Prognosis of Esophageal Squamous Cell Carcinoma Based on Bioinformatics Analysis.

Objective: Through bioinformatics analysis methods, the public databases GEO and TCGA were used to research mRNA and squamous cell carcinoma of the esophagus, construct a lncRNA-mRNA network, and screen hub genes and lncRNAs related to prognosis. Method: Download esophageal squamous cell carcinoma-related mRNA and lncRNA datasets GEO and TCGA public datasets, as well as clinical data, use bioinformatic tools to perform gene differential expression analysis on the datasets to obtain differentially expressing mRNA (DEmRNA) and lncRNA (DElncRNA), and plot volcano plots and cluster heatmaps. The differential intersection of differentially expressed DEmRNA and DElncRNA was extracted by Venn diagram and imported into CytoScape software, a regulatory network visualization software, to construct a lncRNA-mRNA network and use cytoHubba and MCODE plug-ins to screen hub genes and key lncRNAs. The DEmRNA in the network was imported into the Gene and Protein Interaction Retrieval Database (STRING), gene-encoded protein-protein interactions (PPI) network maps were created, and the genes in the PPI network maps were submitted to GO functional annotation and pathway enrichment analysis using Kyoto Encyclopedia of Gene Genomes (KEGG) (KEGG). The link between hub gene and prognosis was studied using the clinical data collected by TCGA. Result: Retrieve the datasets GSE23400 and GSE38129 from the GEO database and the esophageal squamous cell carcinoma-related mRNAs from TCGA databases and then obtain intersection. Differentially regulated genes revealed a correlation of 326 (up) with 191 (down) in terms of the differential intersection; for this study, we need to collect the GSE130078 dataset from GEO, as well as the lncRNAs from TCGA databases that are connected to esophageal squamous cell cancer. There were 184 differentially up- and downregulated genes in the differential intersection. A differential intersection network of the differential intersection lncRNA-mRNA network allowed us to identify the hub genes, including COL5A2 (COL3A1), COL1A1 (COL1A1), CTD-2171N6.1 (CTD-2171N6.1), and RP11-863P13.3 (RP11-863P13.3). The extracellular matrix, which is important in protein digestion and absorption, was shown to be the primary site of functional enrichment, as shown by GO/KEGG analysis. Squamous cell carcinoma of the mouth and throat is associated with a poor prognosis because of a change in the extracellular matrix structure caused by specific long noncoding RNA (lncRNA) regulatory upregulation. Conclusion: For the purpose of predicting the prognosis of cancer of the esophagus, researchers studied the esophageal squamous cell carcinoma-related hub genes and important noncoding RNAs (ncRNAs).

Bioinformatics Analysis of Hub Genes Involved in Alcohol-Related Hemifacial Microsomia Pathogenesis.

OBJECTIVE: Alcohol is a recognized teratogen, and alcohol exposure increases the risk for hemifacial microsomia (HFM) of the fetus during maternal pregnancy. The present study aimed to explore potential mechanisms and verify hub genes of HFM associated with alcohol by bioinformatics methods. METHODS: First, HFM and alcohol pathogenic genes were obtained. Thereafter, a protein-protein interactional (PPI) network was constructed. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses and molecular complex detection were performed by Metascape. Finally, we used the cytoHubba plugin to screen the hub genes. RESULTS: A total of 43 HFM genes and 50 optimal alcohol candidate genes were selected. The PPI networks for pathogenic genes contained 93 nodes and 503 edges. Functional enrichment analysis largely focused on tissue formation and development. Two modules were identified from the PPI network, and 10 hub genes were screened out. The genes most relevant to alcohol-induced HFM pathogenesis included CTNNB1, TP53, MYC, HDAC1, and SOX2. CONCLUSIONS: This study identified some significant hub genes, pathways, and modules of HFM related to alcohol by bioinformatics analyses. Our results suggest that the CTNNB1, TP53, MYC, HDAC1, and SOX B1 gene subfamilies may have played a major role in alcohol-induced HFM.

Identification of Hub Genes of Lung Adenocarcinoma Based on Weighted Gene Co-Expression Network in Chinese Population.

Purpose: Lung adenocarcinoma is one of the most common malignancies. Though some historic breakthroughs have been made in lung adenocarcinoma, its molecular mechanisms of development remain elusive. The aim of this study was to identify the potential genes associated with the lung adenocarcinoma progression and to provide new ideas for the prognosis evaluation of lung adenocarcinoma. Methods: The transcriptional profiles of ten pairs of snap-frozen tumor and adjacent normal lung tissues were obtained by performing RNA-seq. Weighted gene co-expression network analysis (WGCNA) was used to construct free-scale gene co-expression networks in order to explore the associations of gene sets with the clinical features and to investigate the functional enrichment analysis of co-expression genes. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and Gene Set Enrichment Analysis (GSEA) analyses were performed using clusterProfiler. The protein-protein network (PPI) was established using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and hub genes were identified using Cytohubba in Cytoscape. Transcription factor enrichment analysis was performed by the RcisTarget program in R language. Results: Based on RNA-seq data, 1,545 differentially expressed genes (DEGs) were found. Eight co-expression modules were identified among these DEGs. The blue module exhibited a strong correlation with LUAD, in which ADCY4, RXFP1, AVPR2, CALCRL, ADRB1, RAMP3, RAMP2 and VIPR1 were hub genes. A low expression level of RXFP1, AVPR2, ADRB1 and VIPR1 was detrimental to the survival of LUAD patients. Genes in the blue module enriched in 86 Gene Ontology terms and five KEGG pathways. We also found that transcription factors EGR3 and EXOSC3 were related to the biological function of the blue module. Overall, this study brings a new perspective to the understanding of LUAD and provides possible molecular biomarkers for prognosis evaluation of LUAD.

Dihydroartemisinin Attenuates Hypoxia-Induced Pulmonary Hypertension Through the ELAVL2/miR-503/PI3K/AKT Axis.

ABSTRACT: Dihydroartemisinin (DHA) is an active form of artemisinin extracted from the traditional Chinese medicine Artemisia annua , which is used to treat malaria. Previous studies have shown that DHA has a therapeutic effect on pulmonary hypertension (PH), but its specific mechanism has not been fully elucidated. In this study, a hypoxia-induced PH mouse model was established and DHA was administered as a therapeutic intervention. We measured hemodynamics and right ventricular hypertrophy and observed hematoxylin and eosin staining of lung tissue sections, proving the therapeutic effect of DHA on PH. Furthermore, cell counting kit-8 and 5-ethynyl-2'-deoxyuridine (EdU) cell proliferation assay kit were performed to examine cell proliferation of pulmonary artery smooth muscle cells cultured in hypoxia or in normoxia. Transwell migration chamber assay was performed to examine cell migration of the same cell model. Consistent with the therapeutic effect in vivo, DHA inhibited hypoxia-induced cell proliferation and migration. Through high-throughput sequencing of mouse lung tissue, we screened embryonic lethal abnormal vision-like 2 (ELAVL2) as a key RNA binding protein in PH. Mechanistically, DHA inhibited the proliferation and migration of pulmonary artery smooth muscle cells by promoting the expression of ELAVL2 and regulating the miR-503/PI3K/AKT pathway. The binding relationship between ELAVL2 and pre-miR-503 was verified by RNA binding protein immunoprecipitation assay. In conclusion, we first propose that DHA alleviates PH through the ELAVL2/miR-503/PI3K/AKT pathway, which may provide a basis for new therapeutic strategies of PH.

Sequence and tissue targeting specificity of ZFP36L2 reveals Elavl2 as a novel target with co-regulation potential.

Zinc finger protein 36 like 2 (ZFP36L2) is an RNA-binding protein that destabilizes transcripts containing adenine-uridine rich elements (AREs). The overlap between ZFP36L2 targets in different tissues is minimal, suggesting that ZFP36L2-targeting is highly tissue specific. We developed a novel Zfp36l2-lacking mouse model (L2-fKO) to identify factors governing this tissue specificity. We found 549 upregulated genes in the L2-fKO spleen by RNA-seq. These upregulated genes were enriched in ARE motifs in the 3'UTRs, which suggests that they are ZFP36L2 targets, however the precise sequence requirement for targeting was not evident from motif analysis alone. We therefore used gel-shift mobility assays on 12 novel putative targets and established that ZFP36L2 requires a 7-mer (UAUUUAU) motif to bind. We observed a statistically significant enrichment of 7-mer ARE motifs in upregulated genes and determined that ZFP36L2 targets are enriched for multiple 7-mer motifs. Elavl2 mRNA, which has three 7-mer (UAUUUAU) motifs, was also upregulated in L2-fKO spleens. Overexpression of ZFP36L2, but not a ZFP36L2(C176S) mutant, reduced Elavl2 mRNA expression, suggesting a direct negative effect. Additionally, a reporter assay demonstrated that the ZFP36L2 effect on Elavl2 decay is dependent on the Elavl2-3'UTR and requires the 7-mer AREs. Our data indicate that Elavl2 mRNA is a novel target of ZFP36L2, specific to the spleen. Likely, ZFP36L2 combined with other RNA binding proteins, such as ELAVL2, governs tissue specificity.

Identifying the hub gene and immune infiltration of Parkinson's disease using bioinformatical methods.

BACKGROUND: Parkinson's disease (PD) is a common neurodegenerative disorder that affects 1%-2% of the population over 60 years old. Immune response dysfunction in the brain contributes to the occurrence and development of PD. This study aimed to uncover the potential diagnostic genes for PD and characterize the immune cell infiltrates. METHODS: We downloaded the microarray data of patients with PD samples from the Gene Expression Omnibus (GEO) database. Weighted Gene Co-Expression Network Analysis (WGCNA) was used to identify the modules linked to PD in the GSE20163 dataset. Meanwhile, differentially expressed genes (DEGs) between the healthy control samples and PD samples were also identified. Then the PD-related genes were integrated based on the genes in the key module and DEGs. Functional enrichment analysis was used to explore the molecular mechanisms of these PD-related genes. Protein-protein interaction (PPI) network and least absolute shrinkage and selection operator (LASSO) analysis were used to further screen candidate genes for PD. Gene set enrichment analysis (GSEA) was applied to explore the biological functions of these candidate genes. The infiltration of immune cells was detected by single-sample gene set enrichment analysis (ssGSEA) algorithm in the GSE20163 dataset, and Pearson analysis was used to investigate the correlation of candidate genes with immune cells and immune checkpoint proteins. The expression of candidate genes in clinical samples was verified by qPCR. RESULTS: Altogether, we found a unique gene module related to PD, where 109 DEGs were identified in the GSE20163 dataset. Following these results, we screened 68 genes associated with PD. Gene Expression Omnibus (GEO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses suggested that these genes were markedly enriched in the pathway of synthesis and transport of neurons. Three candidate genes (SLC18A2, CALB1, and SYNGR3) were further identified in PD patients through PPI network and LASSO analysis. The receiver operating characteristic (ROC) curve indicated that the three candidate genes had a good performance in distinguishing the PD samples from healthy control samples. The proportions of the aDC, DC, NK CD56dim cells, and follicular helper T cells (TFH) were obviously different between the healthy control and PD samples. Moreover, CTLA4, LAG3, CEACAM1, and CD27 were highly expressed in the PD group. GSEA analysis for candidate genes revealed that they were all closely related to the neurogenic disease. Additionally, the three candidate genes were all strongly correlated with the above immune cells and immune checkpoint proteins. The qPCR results validated the expression differences of SLC18A2 and SYNGR3 in the clinical PD and control samples. CONCLUSION: The three candidate genes may be a useful tool for diagnosing PD patients. These findings provide a reference for exploring new therapeutic targets and strategies for PD treatment.

Glucose deprivation enhances resistance to paclitaxel via ELAVL2/4-mediated modification of glycolysis in ovarian cancer cells.

The dysregulation of glycolysis regardless of oxygen availability is one of the major characteristics of cancer cells. While the drug resistance of ovarian cancer cells has been extensively studied, the molecular mechanism of anticancer drug resistance under low-glucose conditions remains unknown. In this study, we investigated the pathway mediating drug resistance under low-glucose conditions by examining the relationship between embryonic lethal abnormal vision Drosophila homolog-like (ELAVL) protein and glycolysis-related enzymes. Ovarian cancer cells resistant to 2.5 nM paclitaxel were exposed to low-glucose media for 2 weeks, and the expression levels of ELAVL2, ELAVL4, glycolytic enzymes, and drug resistance-related proteins were elevated to levels comparable to those in cells resistant to 100 nM paclitaxel. Gene silencing of ELAVL2/4 using small interfering RNA prevented the upregulation of glycolysis-related enzymes, reduced lactate production, and sensitized 2.5 nM paclitaxel-resistant ovarian cancer cells to anticancer agents under hypoglycemic conditions. Furthermore, pharmacological inhibition of glycolytic enzymes with 2-deoxyglucose, a specific inhibitor of glycolysis, triggered caspase-dependent apoptosis, reduced lactate generation, and blocked the expression of drug resistance-related proteins under low-glucose conditions. These results suggest that the level of ELAVL2/4 is responsible for the development of chemoresistance through activation of the glycolysis pathway under glucose deprivation conditions.

Text Mining and Hub Gene Network Analysis of Endometriosis.

This study is aimed at systematically characterizing the endometriosis-associated genes based on text mining and at annotating the functions, pathways, and networks of endometriosis-associated hub genes. We extracted endometriosis-associated abstracts published between 1970 and 2020 from the PubMed database. A neural-named entity recognition and multitype normalization tool for biomedical text mining was used to recognize and normalize the genes and proteins embedded in the abstracts. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted to annotate the functions and pathways of recognized genes. Protein-protein interaction analysis was conducted on the genes significantly cooccurring with endometriosis to identify the endometriosis-associated hub genes. A total of 433 genes were recognized as endometriosis-associated genes (P < 0.05), and 154 pathways were significantly enriched (P < 0.05). A network of endometriosis-associated genes with 278 gene nodes and 987 interaction links was established. The 15 proteins that interacted with 20 or more other proteins were identified as the hub proteins of the endometriosis-associated protein network. This study provides novel insights into the hub genes that play key roles in the development of endometriosis and have implications for developing targeted interventions for endometriosis.

Bioinformatic Analysis Identified Hub Genes Associated with Heterocyclic Amines Induced Cytotoxicity of Peripheral Blood Mononuclear Cells.

Heterocyclic amines (HCAs) are a set of food contaminants that may exert a cytotoxic effect on human peripheral blood mononuclear cells (PBMC). However, the genetic mechanism underlying the cytotoxicity of HCAs on PBMC has not been investigated. In the study, bioinformatic analysis on gene dataset GSE19078 was performed. The results of weighted correlation network analysis and linear models for microarray and RNA-seq data analysis showed that four gene modules were relevant to 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) exposure while one gene module was correlated with 2-amino-3-methyl-3H-imidazo[4,5f]quinoline (IQ) exposure. Gene functional analysis showed that the five modules were annotated mainly with mRNA transcriptional regulation, mitochondrial function, RNA catabolic process, protein targeting, and immune function. Five genes, MIER1, NDUFA4, MLL3, CD53 and CSF3 were recognized as the feature genes for each hub gene network of the corresponding gene module, and the expression of feature genes was observed with a significant difference between the PhIP/IQ samples and the other samples. Our results provide novel genes and promising mechanisms for exploration on the genetic mechanism of HCAs on PBMC.

Weighted gene co-expression network analysis to identify key modules and hub genes associated with paucigranulocytic asthma.

BACKGROUND: Asthma is a heterogeneous disease that can be divided into four inflammatory phenotypes: eosinophilic asthma (EA), neutrophilic asthma (NA), mixed granulocytic asthma (MGA), and paucigranulocytic asthma (PGA). While research has mainly focused on EA and NA, the understanding of PGA is limited. In this study, we aimed to identify underlying mechanisms and hub genes of PGA. METHODS: Based on the dataset from Gene Expression Omnibus(GEO), weighted gene coexpression network analysis (WGCNA), differentially expressed genes (DEGs) analysis and protein-protein interaction (PPI) network analysis were conducted to construct a gene network and to identify key gene modules and hub genes. Functional enrichment analyses were performed to investigate the biological process, pathways and immune status of PGA. The hub genes were validated in a separate dataset. RESULTS: Compared to non-PGA, PGA had a different gene expression pattern, in which 449 genes were differentially expressed. One gene module significantly associated with PGA was identified. Intersection between the differentially expressed genes (DEGs) and the genes from the module that were most relevant to PGA were mainly enriched in inflammation and immune response regulation. The single sample Gene Set Enrichment Analysis (ssGSEA) suggested a decreased immune infiltration and function in PGA. Finally six hub genes of PGA were identified, including ADCY2, CXCL1, FPRL1, GPR109B, GPR109A and ADCY3, which were validated in a separate dataset of GSE137268. CONCLUSIONS: Our study characterized distinct gene expression patterns, biological processes and immune status of PGA and identified hub genes, which may improve the understanding of underlying mechanism and provide potential therapeutic targets for PGA.

Identifying 13 Hub Genes Associated with Progression and Prognosis of Hepatocellular Carcinoma with Weighted Gene Co-Expression Network Analysis.

Hepatocellular carcinoma (HCC) is a type of common cancer, often accompanied by tumor recurrence and metastasis after surgery with poor prognosis. Therefore, searching into potential biomarkers that can effectively predict the prognosis and progression of HCC is crucial. In this study, we identified 1,981 differentially expressed genes (DEGs) using mRNA expression profiles from the TCGA-LIHC dataset. Subsequently, weighted gene co-expression network analysis found that the turquoise module closely associated with the pathological grade and clinical stage of HCC was identified. Then, from the key genes in the turquoise module and protein-protein interaction network analysis, 13 hub genes significantly related to the prognosis of HCC were screened. Through co-expression and functional enrichment analyses, these 13 hub genes were found to play an important role in mitosis. Finally, we evaluated the relationship between these hub genes and overall survival and disease-free survival through survival analysis. The result indicated that HCC patients with high hub gene expression had a poorer prognosis than HCC patients with low expression. Receiver operating characteristic curves showed that each hub gene could predict the prognosis of HCC patients. In summary, a total of 13 hub genes were identified that play an important role in the progression of HCC, which can be used as potential biomarkers for HCC patients.

UBL5/Hub1: An Atypical Ubiquitin-Like Protein with a Typical Role as a Stress-Responsive Regulator.

Members of the ubiquitin-like protein family are known for their ability to modify substrates by covalent conjugation. The highly conserved ubiquitin relative UBL5/Hub1, however, is atypical because it lacks a carboxy-terminal di-glycine motif required for conjugation, and the whole E1-E2-E3 enzyme cascade is likely absent. Though the conjugation-mediated role of UBL5/Hub1 is controversial, it undoubtedly functions by interacting non-covalently with its partners. Several interactors of UBL5/Hub1 identified to date have suggested broad stress-responsive functions of the protein, for example, stress-induced control of pre-mRNA splicing, Fanconi anemia pathway of DNA damage repair, and mitochondrial unfolded protein response. While having an atypical mode of function, UBL5/Hub1 is still a stress protein that regulates feedback to various stimuli in a similar manner to other ubiquitin-like proteins. In this review, I discuss recent progress in understanding the functions of UBL5/Hub1 and the fundamental questions which remain to be answered.

RNA-binding protein ELAVL2 plays post-transcriptional roles in the regulation of spermatogonia proliferation and apoptosis.

OBJECTIVES: RNA-binding proteins (RBPs) play essential post-transcriptional roles in regulating spermatogonial stem cells (SSCs) maintenance and differentiation. We identified a conserved and SSCs-enriched RBP ELAVL2 from our single-cell sequencing data, but its function and mechanism in SSCs were unclear. MATERIALS AND METHODS: Expressions of ELAVL2 during human and mouse testis development were validated. Stable C18-4 and TCam-2 cell lines with overexpression and knockdown of ELAVL2 were established, which were applied to proliferation and apoptosis analysis. RNA immunoprecipitation and sequencing were used to identify ELAVL2 targets, and regulatory functions of ELAVL2 on target mRNAs were studied. Proteins interacting with ELAVL2 in human and mouse testes were identified using immunoprecipitation and mass spectrometric, which were validated by in vivo and in vitro experiments. RESULTS: ELAVL2 was testis-enriched and preferentially expressed in human and mouse SSCs. ELAVL2 was down-regulated in NOA patients. ELAVL2 promoted proliferation and inhibited apoptosis of C18-4 and TCam-2 cell lines via activating ERK and AKT pathways. ELAVL2 associated with mRNAs encoding essential regulators of SSCs proliferation and survival, and promoted their protein expression at post-transcriptional level. ELAVL2 interacted with DAZL in vivo and in vitro in both human and mouse testes. CONCLUSIONS: Taken together, these results indicate that ELAVL2 is a conserved SSCs-enriched RBP that down-regulated in NOA, which regulates spermatogonia proliferation and apoptosis by promoting protein expression of targets.

Elavl2 Regulates Retinal Function Via Modulating the Differentiation of Amacrine Cells Subtype.

Purpose: The neuronal ELAV-like proteins (nElavls; Elavl2, Elavl3, Elavl4) have been known to regulate neuronal differentiation, maintenance, and axonogenesis in the brain. However, the specific role of nElavls in retina remains unclear. Here, we attempted to identify the expression pattern of Elavl2 during retinogenesis and aimed to decipher the function of Elavl2 in the retina. Methods: We have used the Cre-loxP system to conditionally inactivate Elavl2 in order to examine its role in developing retina. Eyes were collected for histology, immunohistochemistry, and TUNEL analysis to identify the structure of retina, and examined by RNA sequencing to analyze the function and pathway enrichment of differentially expressed genes in transgenic mice. Moreover, the mechanism by which Elavl2 regulates the differentiation of amacrine cells (ACs) was explored by RNA immunoprecipitation assays. Finally, eyes were functionally assessed by whole-cell patch-clamp, electroretinography (ERG) and optomotor response. Results: Elavl2 was expressed in retinal progenitor cells and retinal ganglion cells (RGCs), ACs, and horizontal cells. Retina-specific ablation of Elavl2 led to the loss of ACs and the transcription factors involved in ACs differentiation were also downregulated. In addition, the spontaneous activities of RGCs were obviously increased in Elavl2-deficient mice. Meanwhile, the loss of ACs that induced by Elavl2 deficiency lead to a decrease in ERG responses and visual acuity. Conclusions: Elavl2 is an intrinsic factor that involved in the differentiation of ACs subtype during retinogenesis, and essential for maintaining the normal retinal function.

The Potential Genes Mediate the Pathogenicity of Allogeneic CD4+T Cell in aGVHD Mouse Model.

Acute graft-versus-host disease (aGVHD) remains a significant and severe complication of allogeneic hematopoietic stem cell transplantation (allo-HSCT). Due to the occurrence of aGVHD, allo-HSCT significantly increases the mortality rate compared with autologous hematopoietic stem cell transplantation (auto-HSCT). In this study, auto-HSCT and allo-HSCT aGVHD mouse models were built to detect the difference in CD4+ lymphocyte in different tissues based on ribonucleic acid sequencing (RNA-Seq) analysis. Clustering analysis, functional annotation, and pathway enrichment analysis were performed on differentially expressed genes (DEGs). The protein-protein interaction (PPI) network was used to find hub genes. CD4+T cells were activated by MLR and cytokine stimulation. Cells were sorted out by a flow cell sorter. The selected genes were verified by qRT-PCR, histology, and immunofluorescence staining. The GSE126518 GEO dataset was used to verify the hub genes. Enrichment analysis revealed four immune-related pathways that play an important role in aGVHD, including immunoregulatory interactions between a lymphoid and a nonlymphoid cell, chemokine receptors binding chemokines, cytokine and cytokine receptor interaction, and the chemokine signaling pathway. At the same time, with the PPI network, 11 novel hub genes that were most likely to participate in immunoregulation in aGVHD were identified, which were further validated by qRT-PCR and the GSE126518 dataset. Besides, the protein expression level of Cxcl7 was consistent with the sequencing results. In summary, this study revealed that immunoregulation-related DEGs and pathways played a vital role in the onset of aGVHD. These findings may provide some new clues for probing the pathogenesis and treatment of aGVHD.

HuB and HuD repress telomerase activity by dissociating HuR from TERC.

The ubiquitous RNA-binding protein HuR (ELAVL1) promotes telomerase activity by associating with the telomerase noncoding RNA TERC. However, the role of the neural-specific members HuB, HuC, and HuD (ELAVL2-4) in telomerase activity is unknown. Here, we report that HuB and HuD, but not HuC, repress telomerase activity in human neuroblastoma cells. By associating with AU-rich sequences in TERC, HuB and HuD repressed the assembly of the TERT-TERC core complex. Furthermore, HuB and HuD competed with HuR for binding to TERC and antagonized the function of HuR that was previously shown to enhance telomerase activity to promote cell growth. Our findings reveal a novel mechanism controlling telomerase activity in human neuroblastoma cells that involves a competition between HuR and the related, neural-specific proteins HuB and HuD.

Global analysis of RNA-protein interactions in TNF-α induced alternative splicing in metabolic disorders.

In this report, using the database of RNA-binding protein specificities (RBPDB) and our previously published RNA-seq data, we analyzed the interactions between RNA and RNA-binding proteins to decipher the role of alternative splicing in metabolic disorders induced by TNF-α. We identified 13 395 unique RNA-RBP interactions, including 385 unique RNA motifs and 35 RBPs, some of which (including MBNL-1 and 3, ZFP36, ZRANB2, and SNRPA) are transcriptionally regulated by TNF-α. In addition to some previously reported RBPs, such as RBMX and HuR/ELAVL1, we found a few novel RBPs, such as ZRANB2 and SNRPA, to be involved in the regulation of metabolic syndrome-associated genes that contain an enrichment of tetrameric RNA sequences (AUUU). Taken together, this study paves the way for novel RNA-protein interaction-based therapeutics for treating metabolic syndromes.

Investigation of hub genes and immune status in heart transplant rejection using endomyocardial biopsies.

T cell-mediated rejection (TCMR) and antibody-mediated rejection (ABMR) are severe post-transplantation complications for heart transplantation (HTx), whose molecular and immunological pathogenesis remains unclear. In the present study, the mRNA microarray data set GSE124897 containing 645 stable, 52 TCMR and 144 ABMR endomyocardial biopsies was obtained to screen for differentially expressed genes (DEGs) between rejected and stable HTx samples and to investigate immune cell infiltration. Functional enrichment analyses indicated roles of the DEGs primarily in immune-related mechanisms. Protein-protein interaction networks were then constructed, and ICAM1, CD44, HLA-A and HLA-B were identified as hub genes using the maximal clique centrality method. Immune cell infiltration analysis revealed differences in adaptive and innate immune cell populations between TCMR, ABMR and stable HTx samples. Additionally, hub gene expression levels significantly correlated with the degree and composition of immune cell infiltration in HTx rejection samples. Furthermore, drug-gene interactions were constructed, and 12 FDA-approved drugs were predicted to target hub genes. Finally, an external GSE2596 data set was used to validate the expression of the hub genes, and ROC curves indicated all four hub genes had promising diagnostic value for HTx rejection. This study provides a comprehensive perspective of molecular and immunological regulatory mechanisms underlying HTx rejection.

Genome-wide study of key genes and scoring system as potential noninvasive biomarkers for detection of suicide behavior in major depression disorder.

Although some progress has been made in the molecular biological detection of major depression disorder (MDD), its specificity and accuracy are still insufficient. This study is aimed to find hub genes, which could contribute to MDD related suicide and provide potential therapeutic targets for diagnosis and treatment. We downloaded RNA expression and clinical information from Gene Expression Omnibus (GEO) Dataset. Then, weighted gene co-expression network analysis (WGCNA) was applied to find core modules. Logistic regression was performed to identify the independent risk factors, and a scoring system was constructed based on these independent risk factors. As a result, a total of 16487 genes were selected to further conducted WGCNA analysis. We found that tan and green functional modules were exhibited high correlation with suicide behavior. 309 genes were identified in tan modules that were the strongest positively correlated with suicide behavior. Functional analysis in tan module indicated that activation of enzymes including nitric-oxide synthase and endoribonuclease, estrogen signaling pathway, glucagon signaling pathway, and legionellosis pathway were most enriched in MDD. Furthermore, we applied protein-protein interaction (PPI) analysis to select the hub genes and 10 genes were found in the core area of network. Then, we identified three-gene base independent risk signature by logistic regression model, including HSPA1A, RASEF, TBC1D8B. In conclusion, our study suggests that the tan module genes are closely related to suicide behaviors, which is mainly caused by multiple signaling pathway activation. The three-genes-based signature could provide a better efficacy to predict suicidal behavior in MDD patients.