Hub Genes In Module Research Articles

Unraveling verticillium wilt resistance: insight from the integration of transcriptome and metabolome in wild eggplant.

Verticillium wilt, caused by Verticillium dahliae, is a soil-borne disease affecting eggplant. Wild eggplant, recognized as an excellent disease-resistant resource against verticillium wilt, plays a pivotal role in grafting and breeding for disease resistance. However, the underlying resistance mechanisms of wild eggplant remain poorly understood. This study compared two wild eggplant varieties, LC-2 (high resistance) and LC-7 (sensitive) at the phenotypic, transcriptomic, and metabolomic levels to determine the molecular basis of their resistance to verticillium wilt. These two varieties exhibit substantial phenotypic differences in petal color, leaf spines, and fruit traits. Following inoculation with V. dahliae, LC-2 demonstrated significantly higher activities of polyphenol oxidase, superoxide dismutase, peroxidase, phenylalanine ammonia lyase, β-1,3 glucanase, and chitinase than did LC-7. RNA sequencing revealed 4,017 differentially expressed genes (DEGs), with a significant portion implicated in processes associated with disease resistance and growth. These processes encompassed defense responses, cell wall biogenesis, developmental processes, and biosynthesis of spermidine, cinnamic acid, and cutin. A gene co-expression analysis identified 13 transcription factors as hub genes in modules related to plant defense response. Some genes exhibited distinct expression patterns between LC-2 and LC-7, suggesting their crucial roles in responding to infection. Further, metabolome analysis identified 549 differentially accumulated metabolites (DAMs) between LC-2 and LC-7, primarily consisting of compounds such as flavonoids, phenolic acids, lipids, and other metabolites. Integrated transcriptome and metabolome analyses revealed the association of 35 gene-metabolite pairs in modules related to the plant defense response, highlighting the interconnected processes underlying the plant defense response. These findings characterize the molecular basis of LC-2 resistance to verticillium wilt and thus have potential value for future breeding of wilt-resistant eggplant varieties.

Systems Genetics Analyses Reveals Key Genes Related to Behavioral Traits in the Striatum of CFW Mice.

The striatum plays a central role in directing many complex behaviors ranging from motor control to action choice and reward learning. In our study, we used 55 male CFW mice with rapid decay linkage disequilibrium to systematically mine the striatum-related behavioral functional genes by analyzing their striatal transcriptomes and 79 measured behavioral phenotypic data. By constructing a gene coexpression network, we clustered the genes into 13 modules, with most of them being positively correlated with motor traits. Based on functional annotations as well as Fisher's exact and hypergeometric distribution tests, brown and magenta modules were identified as core modules. They were significantly enriched for striatal-related functional genes. Subsequent Mendelian randomization analysis verified the causal relationship between the core modules and dyskinesia. Through the intramodular gene connectivity analysis, Adcy5 and Kcnma1 were identified as brown and magenta module hub genes, respectively. Knock outs of both Adcy5 and Kcnma1 lead to motor dysfunction in mice, and KCNMA1 acts as a risk gene for schizophrenia and smoking addiction in humans. We also evaluated the cellular composition of each module and identified oligodendrocytes in the striatum to have a positive role in motor regulation.

Differential Gene Analysis of Langerhans Cell Histiocytosis and the Significance of MMP1-Targeted Drug Repositioning.

Langerhans cell histiocytosis (LCH) is a rare condition predominantly affecting young children. Activation of the MAPK pathway has offered key new insights into the pathogenesis of LCH; however, the precise mechanisms underlying its occurrence and development are still far from being completely elucidated. There is still a relapse/reactivation rate in patients with multisystem LCH. Therefore, this study aimed to investigate other potential LCH pathophysiologies and prospective therapeutic targets. The gene expression omnibus (GEO) database was used to retrieve gene expression profiles of LCH (GSE16395). Three distinct types of analyses were performed after identifying the common differentially expressed genes (DEGs) in LCH: hub gene identification, functional annotation, module construction, drug repositioning, and expression analysis via immunohistochemistry (IHC). We identified 417 common DEGs and 50 central hub genes. This functional study highlighted the significance of keratinization, skin development, and inflammation. In addition, we predicted new drug candidates (RS2 drugs targeting matrix metalloprotease1, MMP1) that could be used for LCH treatment. Finally, gene-miRNA and gene-TF networks and immune cell infiltration were analyzed for MMP1-related genes. MMP1 expression levels in LCH tissues were validated by IHC. Our study identified the central communal genes and novel drug candidates. These shared pathways and hub genes offer new perspectives on future mechanisms of action and therapeutic targets.

Bovine lactoferrin inhibits inflammatory response and apoptosis in lipopolysaccharide-induced acute lung injury by targeting the PPAR-γ pathway.

Lactoferrin (LF) is an iron-binding multifunctional cationic glycoprotein. Previous studies have demonstrated that LF may be a potential drug for treating acute lung injury (ALI) and acute respiratory distress syndrome (ARDS). In this study, we explored the anti-inflammatory effect and mechanism of bovine lactoferrin (bLF) in ALI using the RNA sequencing (RNA-seq) technology and transcriptome analysis. Based on the differentially expressed genes (DEGs) obtained from RNA-seq of the Lung from mouse model, the bioinformatics workflow was implemented using the BGISEQ-500 platform. The protein-protein interaction (PPI) network was obtained using STRING, and the hub gene was screened using Cytoscape. To verify the results of transcriptome analysis, the effects of bLF on Lipopolysaccharide (LPS)-induced BEAS-2B cells and its anti-reactive oxygen species (ROS), anti-inflammatory, and antiapoptotic effects were studied via Cell Counting Kit-8 (CCK-8) test, active oxygen detection test, ELISA, and western blot assay. Transcriptome analysis revealed that two hub gene modules of DEGs were screened via PPI analysis using the STRING and MCODE plug-ins of Cytoscape. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that these core modules are enriched in the PPAR (peroxisome proliferator-activated receptor) and AMPK (AMP-activated protein kinase) signaling pathways. Through cell experiments, our study shows that bLF can inhibit ROS, inflammatory reaction, and LPS-induced BEAS-2B cell apoptosis, which are significantly antagonized by the PPAR-γ inhibitor GW9662. This study has suggested that the PPAR-γ pathway is the critical target of bLF in anti-inflammatory reactions and apoptosis of ALI, which provides a direction for further research.

Identification of Differentially Expressed Genes and Protein-Protein Interaction in Patients With COVID-19 and Diabetes Peripheral Neuropathy: A Bioinformatics and System Biology Approach.

The coronavirus disease 2019 (COVID-19) pandemic has had a significant impact globally, resulting in a higher death toll and persistent health issues for survivors, particularly those with pre-existing medical conditions. Numerous studies have demonstrated a strong correlation between catastrophic COVID-19 results and diabetes. To gain deeper insights, we analysed the transcriptome dataset from COVID-19 and diabetic peripheral neuropathic patients. Using the R programming language, differentially expressed genes (DEGs) were identified and classified based on up and down regulations. The overlaps of DEGs were then explored between these groups. Functional annotation of those common DEGs was performed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Bio-Planet, Reactome, and Wiki pathways. A protein-protein interaction (PPI) network was created with bioinformatics tools to understand molecular interactions. Through topological analysis of the PPI network, we determined hub gene modules and explored gene regulatory networks (GRN). Furthermore, the study extended to suggesting potential drug molecules for the identified mutual DEG based on the comprehensive analysis. These approaches may contribute to understanding the molecular intricacies of COVID-19 in diabetic peripheral neuropathy patients through insights into potential therapeutic interventions.

Unfurling the functional association between long intergenic noncoding RNAs (lincRNAs) and HPV16-related cervical cancer pathogenesis through weighted gene co-expression network analysis of differentially expressed lincRNAs and coding genes.

Long intergenic noncoding RNAs (lincRNAs) do not overlap annotated coding genes and are located in intergenic regions, as opposed to antisense and sense-intronic lncRNAs, located in genic regions. LincRNAs influence gene expression profiles and are thereby key to disease pathogenesis. In this study, we assessed the association between lincRNAs and HPV16-positive cervical cancer (CaCx) pathogenesis using weighted gene co-expression network analysis (WGCNA) with coding genes, comparing differentially expressed lincRNA and coding genes (DElincGs and DEcGs, respectively) in HPV16-positive patients with CaCx (n = 44) with those in HPV-negative healthy individuals (n = 34). Our analysis revealed five DElincG modules, co-expressing and correlating with DEcGs. We validated a substantial number of such module-specific correlations in the HPV16-positive cancer TCGA-CESC dataset. Four such modules, displayed significant correlations with patient traits, such as HPV16 physical status, lymph node involvement and overall survival (OS), highlighting a collaborative effect of all genes within specific modules on traits. Using the DAVID bioinformatics knowledgebase, we identified the underlying biological processes associated with these modules as cancer development and progression-associated pathways. Next, we identified the top 10 DElincGs with the highest connectivity within each functional module. Focusing on the prognostic module hub genes, downregulated CTD-2619J13.13 expression was associated with poor patient OS. This lincRNA gene interacted with 25 coding genes of its module and was associated with such biological processes as keratinization loss and keratinocyte differentiation, reflecting severe disease phenotypes. This study has translational relevance in fighting various cancers with high mortality rates in underdeveloped countries.

KIF26B and CREB3L1 Derived from Immunoscore Could Inhibit the Progression of Ovarian Cancer.

Ovarian cancer (OV) is characteristic of high incidence rate and fatality rate in the malignant tumors of female reproductive system. Researches on pathogenesis and therapeutic targets for OV need to be continued. This study mainly analyzed the immune-related pathogenesis and discovered the key immunotherapy targets for OV. WGCNA was used for excavating hub gene modules and hub genes related to the immunity of OV. Enrichment analysis was aimed to analyze the related pathways of hub gene modules. Biological experiments were used for exploring the effect of hub genes on SKOV3 cells. We identified two hub gene modules related to the immunoscore of OV and found that these genes in the modules were related to the extracellular matrix and viral infections. At the same time, we also discovered six hub genes related to the immunity of OV. Among them, KIF26B and CREB3L1 can affect the proliferation, migration, and invasion of SKOV3 cells by the Wnt/β-catenin pathway. The local infection or inflammation of ovarian may affect the immunity of OV. KIF26B and CREB3L1 are expected to be potential targets for the immunotherapy of OV.

Identification of potential therapeutic targets for COVID-19 through a structural-based similarity approach between SARS-CoV-2 and its human host proteins.

Background: The COVID-19 pandemic caused by SARS-CoV-2 has led to millions of deaths worldwide, and vaccination efficacy has been decreasing with each lineage, necessitating the need for alternative antiviral therapies. Predicting host-virus protein-protein interactions (HV-PPIs) is essential for identifying potential host-targeting drug targets against SARS-CoV-2 infection. Objective: This study aims to identify therapeutic target proteins in humans that could act as virus-host-targeting drug targets against SARS-CoV-2 and study their interaction against antiviral inhibitors. Methods: A structure-based similarity approach was used to predict human proteins similar to SARS-CoV-2 ("hCoV-2"), followed by identifying PPIs between hCoV-2 and its target human proteins. Overlapping genes were identified between the protein-coding genes of the target and COVID-19-infected patient's mRNA expression data. Pathway and Gene Ontology (GO) term analyses, the construction of PPI networks, and the detection of hub gene modules were performed. Structure-based virtual screening with antiviral compounds was performed to identify potential hits against target gene-encoded protein. Results: This study predicted 19,051 unique target human proteins that interact with hCoV-2, and compared to the microarray dataset, 1,120 target and infected group differentially expressed genes (TIG-DEGs) were identified. The significant pathway and GO enrichment analyses revealed the involvement of these genes in several biological processes and molecular functions. PPI network analysis identified a significant hub gene with maximum neighboring partners. Virtual screening analysis identified three potential antiviral compounds against the target gene-encoded protein. Conclusion: This study provides potential targets for host-targeting drug development against SARS-CoV-2 infection, and further experimental validation of the target protein is required for pharmaceutical intervention.

Identification of novel potential homologous repair deficiency-associated genes in pancreatic adenocarcinoma via WGCNA coexpression network analysis and machine learning

ABSTRACT Homologous repair deficiency (HRD) impedes double-strand break repair, which is a common driver of carcinogenesis. Positive HRD status can be used as theranostic markers of response to platinum- and PARP inhibitor-based chemotherapies. Here, we aimed to fully investigate the therapeutic and prognostic potential of HRD in pancreatic adenocarcinoma (PAAD) and identify effective biomarkers related to HRD using comprehensive bioinformatics analysis. The HRD score was defined as the unweighted sum of the LOH, TAI, and LST scores, and it was obtained based on the previous literature. To characterize PAAD immune infiltration subtypes, the “ConsensusClusterPlus” package in R was used to conduct unsupervised clustering. A WGCNA was conducted to elucidate the gene coexpression modules and hub genes in the HRD-related gene module of PAAD. The functional enrichment study was performed using Metascape. LASSO analysis was performed using the “glmnet” package in R, while the random forest algorithm was realized using the “randomForest” package in R. The prognostic variables were evaluated using univariate Cox analysis. The prognostic risk model was built using the LASSO approach. ROC curve and KM survival analyses were performed to assess the prognostic potential of the risk model. The half-maximal inhibitory concentration (IC50) of the PARP inhibitors was estimated using the “pRRophetic” package in R and the Genomics of Drug Sensitivity in Cancer database. The “rms” package in R was used to create the nomogram. A high HRD score indicated a poor prognosis and an advanced clinical process in PAAD patients. PAAD tumors with high HRD levels revealed significant T helper lymphocyte depletion, upregulated levels of cancer stem cells, and increased sensitivity to rucaparib, Olaparib, and veliparib. Using WGCNA, 11 coexpression modules were obtained. The red module and 122 hub genes were identified as the most correlated with HRD in PAAD. Functional enrichment analysis revealed that the 122 hub genes were mainly concentrated in cell cycle pathways. One novel HRD-related gene signature consisting of CKS1B, HJURP, and TPX2 were screened via LASSO analysis and a random forest algorithm, and they were validated using independent validation sets. No direct association between HRD and CKS1B, HJURP, or TPX2 has not been reported in the literature so far. Thus, these findings indicated that CKS1B, HJURP, and TPX2 have potential as diagnostic and prognostic biomarkers for PAAD. We constructed a novel HRD-related prognostic model that provides new insights into PAAD prognosis and immunotherapy. Based on bioinformatics analysis, we comprehensively explored the therapeutic and prognostic potential of HRD in PAAD. One novel HRD-related gene signature consisting of CKS1B, HJURP, and TPX2 were identified through the combination of WGCNA, LASSO analysis and a random forest algorithm. A novel HRD-related risk model that can predict clinical prognosis and immunotherapeutic response in PAAD patients was constructed.

Comparative transcriptome analysis reveals candidate genes for cold stress response and early flowering in pineapple

Pineapple originates from tropical regions in South America and is therefore significantly impacted by cold stress. Periodic cold events in the equatorial regions where pineapple is grown may induce early flowering, also known as precocious flowering, resulting in monetary losses due to small fruit size and the need to make multiple passes for harvesting a single field. Currently, pineapple is one of the most important tropical fruits in the world in terms of consumption, and production losses caused by weather can have major impacts on worldwide exportation potential and economics. To further our understanding of and identify mechanisms for low-temperature tolerance in pineapple, and to identify the relationship between low-temperature stress and flowering time, we report here a transcriptomic analysis of two pineapple genotypes in response to low-temperature stress. Using meristem tissue collected from precocious flowering-susceptible MD2 and precocious flowering-tolerant Dole-17, we performed pairwise comparisons and weighted gene co-expression network analysis (WGCNA) to identify cold stress, genotype, and floral organ development-specific modules. Dole-17 had a greater increase in expression of genes that confer cold tolerance. The results suggested that low temperature stress in Dole-17 plants induces transcriptional changes to adapt and maintain homeostasis. Comparative transcriptomic analysis revealed differences in cuticular wax biosynthesis, carbohydrate accumulation, and vernalization-related gene expression between genotypes. Cold stress induced changes in ethylene and abscisic acid-mediated pathways differentially between genotypes, suggesting that MD2 may be more susceptible to hormone-mediated early flowering. The differentially expressed genes and module hub genes identified in this study are potential candidates for engineering cold tolerance in pineapple to develop new varieties capable of maintaining normal reproduction cycles under cold stress. In addition, a total of 461 core genes involved in the development of reproductive tissues in pineapple were also identified in this study. This research provides an important genomic resource for understanding molecular networks underlying cold stress response and how cold stress affects flowering time in pineapple.

Identification of hub genes and gene modules associated with Behçet's disease by weighted gene co-expression network analysis of neutrophil transcriptome.

Behçet's disease (BD) is a chronic inflammatory condition with recurrent skin lesions, uveitis, and oral and genital ulcers. Neutrophils are important in the pathogenesis of BD, but their molecular mechanisms are unclear. We performed weighted gene co-expression network analysis on the transcriptome of neutrophils from 10 BD patients and 10 healthy controls to identify hub genes and gene modules associated with BD. We found eight co-expression modules with different biological functions. The turquoise module was involved in response to hydrogen peroxide and reactive oxygen species, the blue module was involved in response to external stimulus and inflammatory response, and the brown module was involved in the type I interferon signalling pathway. We further identified hub genes and transcription factors in each module by using module membership and gene significance. Our results reveal novel gene modules and hub genes that are associated with neutrophil activation and dysfunction in BD, which could serve as potential biomarkers and therapeutic targets for this disease.

The early life immune dynamics and cellular drivers at single-cell resolution in lamb forestomachs and abomasum

BackgroundFour-chambered stomach including the forestomachs (rumen, reticulum, and omasum) and abomasum allows ruminants convert plant fiber into high-quality animal products. The early development of this four-chambered stomach is crucial for the health and well-being of young ruminants, especially the immune development. However, the dynamics of immune development are poorly understood.ResultsWe investigated the early gene expression patterns across the four-chambered stomach in Hu sheep, at 5, 10, 15, and 25 days of age. We found that forestomachs share similar gene expression patterns, all four stomachs underwent widespread activation of both innate and adaptive immune responses from d 5 to 25, whereas the metabolic function were significantly downregulated with age. We constructed a cell landscape of the four-chambered stomach using single-cell sequencing. Integrating transcriptomic and single-cell transcriptomic analyses revealed that the immune-associated module hub genes were highly expressed in T cells, monocytes and macrophages, as well as the defense-associated module hub genes were highly expressed in endothelial cells in the four-stomach tissues. Moreover, the non-immune cells such as epithelial cells play key roles in immune maturation. Cell communication analysis predicted that in addition to immune cells, non-immune cells recruit immune cells through macrophage migration inhibitory factor signaling in the forestomachs.ConclusionsOur results demonstrate that the immune and defense responses of four stomachs are quickly developing with age in lamb's early life. We also identified the gene expression patterns and functional cells associated with immune development. Additionally, we identified some key receptors and signaling involved in immune regulation. These results help to understand the early life immune development at single-cell resolution, which has implications to develop nutritional manipulation and health management strategies based on specific targets including key receptors and signaling pathways.

Identification of hub genes and potential inhibitory compounds in the process of liver transplantation through transcriptome sequencing

Liver transplantation (LT) is the best choice for patients with end-stage liver diseases. In order to better understand pathophysiological alterations in LT, we aimed to identify potential hub genes and inhibitory compounds involved in the LT process. Four pairs of peripheral blood mononuclear cell (PBMC) samples of the LT recipients before and after surgery were collected and taken for transcriptome sequencing. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed for the screened differentially expressed genes (DEGs) between pre- and post-operation groups. Common DEGs were obtained from GO and KEGG enriched pathways, followed by protein-protein interaction (PPI) network construction, hub gene identification, module analysis, and structure-based virtual screening process (SBVS). Compared to the pre-operation stage, 4745 genes were down-regulated and 798 up-regulated after LT. GO analysis showed that the DEGs were enriched in ribosome-related translation regulation, and KEGG analysis indicated that infection and immune-related pathways and diseases were largely enriched. A large number of down-regulated DEGs were not only associated with ribosome-related pathways but also with the alterations of epigenetic modifications, in particular ubiquitination. Moreover, through the PPI network of 29 common genes from GO and KEGG-enriched pathways, 7 hub genes were identified, including PTEN, MYC, EIF2S1, EIF4EBP1, HSP90AB1, TP53, and HSPA8, which were mainly involved in the PI3K-AKT signaling pathway. SBVS of the seed molecule PTEN (PDB code: 1D5R) predicted top hits compounds that may serve as potential inhibitors of PTEN, of which the compound ZINC4235331 had the lowest binding affinity of -10 kcal/mol. The significance of screened hub genes and potential inhibitors involved in the process of LT provides novel therapeutic strategies for improving the outcomes of LT recipients during surgery.

BIRC5 Inhibition Is Associated with Pyroptotic Cell Death via Caspase3-GSDME Pathway in Lung Adenocarcinoma Cells.

Lung adenocarcinoma (LUAD) is a prevalent type of thoracic cancer with a poor prognosis and high mortality rate. However, the exact pathogenesis of this cancer is still not fully understood. One potential factor that can contribute to the development of lung adenocarcinoma is DNA methylation, which can cause changes in chromosome structure and potentially lead to the formation of tumors. The baculoviral IAP repeat containing the 5 (BIRC5) gene encodes the Survivin protein, which is a multifunctional gene involved in cell proliferation, migration, and invasion of tumor cells. This gene is elevated in various solid tumors, but its specific role and mechanism in lung adenocarcinoma are not well-known. To identify the potential biomarkers associated with lung adenocarcinoma, we screened the methylation-regulated differentially expressed genes (MeDEGs) of LUAD via bioinformatics analysis. Gene ontology (GO) process and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were applied to investigate the biological function and pathway of MeDEGs. A protein-protein interaction (PPI) network was employed to explore the key module and screen hub genes. We screened out eight hub genes whose products are aberrantly expressed, and whose DNA methylation modification level is significantly changed in lung adenocarcinoma. BIRC5 is a bona fide marker which was remarkably up-regulated in tumor tissues. Flow cytometry analysis, lactate dehydrogenase release (LDH) assay and Micro-PET imaging were performed in A549 cells and a mouse xenograft tumor to explore the function of BIRC5 in cell death of lung adenocarcinoma. We found that BIRC5 was up-regulated and related to a high mortality rate in lung adenocarcinoma patients. Mechanically, the knockdown of BIRC5 inhibited the proliferation of A549 cells and induced pyroptosis via caspase3/GSDME signaling. Our findings have unraveled that BIRC5 holds promise as a novel biomarker and therapeutic target for lung adenocarcinoma. Additionally, we have discovered a novel pathway in which BIRC5 inhibition can induce pyroptosis through the caspase3-GSDME pathway in lung adenocarcinoma cells.

Arbuscular mycorrhizal fungi improve selenium uptake by modulating root transcriptome of rice (Oryza sativa L.).

Although selenium (Se) is an essential trace element in humans, the intake of Se from food is still generally inadequate throughout the world. Inoculation with arbuscular mycorrhizal fungi (AMF) improves the uptake of Se in rice (Oryza sativa L.). However, the mechanism by which AMF improves the uptake of Se in rice at the transcriptome level is unknown. Only a few studies have evaluated the effects of uptake of other elements in rice under the combined effects of Se and AMF. In this study, Se combined with the AMF Funneliformis mosseae (Fm) increased the biomass and Se concentration of rice plants, altered the pattern of ionomics of the rice roots and shoots, and reduced the antagonistic uptake of Se with nickel, molybdenum, phosphorus, and copper compared with the treatment of Se alone, indicating that Fm can enhance the effect of fertilizers rich in Se. Furthermore, a weighted gene co-expression network analysis (WGCNA) showed that the hub genes in modules significantly associated with the genes that contained Se and were related to protein phosphorylation, protein serine/threonine kinase activity, membrane translocation, and metal ion binding, suggesting that the uptake of Se by the rice roots may be associated with these genes when Fm and Se act in concert. This study provides a reference for the further exploration of genes related to Se uptake in rice under Fm treatment.

Bioinformatical analysis of the key differentially expressed genes for screening potential biomarkers in Wilms tumor

Wilms tumor (WT) is the most common pediatric renal malignant tumor in the world. Overall, the prognosis of Wilms tumor is very good. However, the prognosis of patients with anaplastic tumor histology or disease relapse is still poor, and their recurrence rate, metastasis rate and mortality are significantly increased compared with others. Currently, the combination of histopathological examination and molecular biology is essential to predict prognosis and guide the treatment. However, the molecular mechanism has not been well studied. Genetic profiling may be helpful in some way. Hence, we sought to identify novel promising biomarkers of WT by integrating bioinformatics analysis and to identify genes associated with the pathogenesis of WT. In the presented study, the NCBI Gene Expression Omnibus was used to download two datasets of gene expression profiles related to WT patients for the purpose of detecting overlapped differentially expressed genes (DEGs). The DEGs were then uploaded to DAVID database for enrichment analysis. In addition, the functional interactions between proteins were evaluated by simulating the protein–protein interaction (PPI) network of DEGs. The impact of selected hub genes on survival in WT patients was analyzed by using the online tool R2: Genomics Analysis and Visualization Platform. The correlation between gene expression and the degree of immune infiltration was assessed by the Estimation of Stromal and Immune cells in Malignant Tumor tissues using the Expression (ESTIMATE) algorithm and the single sample GSEA. Top 12 genes were identified for further study after constructing a PPI network and screening hub gene modules. Kinesin family member 2C (KIF2C) was identified as the most significant gene predicting the overall survival of WT patients. The expression of KIF2C in WT was further verified by quantitative real-time polymerase chain reaction and immunohistochemistry. Furthermore, we found that KIF2C was significantly correlated with immune cell infiltration in WT. Our present study demonstrated that altered expression of KIF2C may be involved in WT and serve as a potential prognostic biomarker for WT patients.

Identification of prognostic biomarkers of breast cancer based on the immune-related gene module

Breast cancer (BC) is highly malignant and its mortality rate remains high. The development of immunotherapy has gradually improved the prognosis and survival rate of patients. Therefore, identifying molecular markers concerned with BC immunity is of great importance for the treatment of this disease. The Cancer Genome Atlas-breast invasive carcinoma (TCGA-BRCA) was utilized as the training set while the BC expression dataset from the gene expression omnibus database was taken as the validation set here. Weighted gene co-expression network analysis combined with Pearson analysis and Tumor immune estimation resource (TIMER) was used to obtain immune cell-related hub gene module. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on this module. Then, receiver operating characteristic curves combining Kaplan–Meier was used to evaluate the effectiveness of the model. Feature genes were screened and the independence of risk score was evaluated by univariate and multivariate Cox analyses. Differences in immune characteristics were analyzed via single-sample gene set enrichment analysis and CIBERSORT, and differences in gene mutation frequency were assessed via GenVisR analysis. Finally, the expression levels of prognostic feature genes in BC cells were validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR). In this study, cell immune-related gene modules in TCGA-BRCA were successfully excavated, and a five-gene (TNFRSF14, NFKBIA, DLG3, IRF2, and CYP27A1) prognostic model was established. The prognostic model could effectively forecast the prognosis and survival rate of BC patients. The result showed that human leukocyte antigen-related proteins and macrophage M2 scores were remarkably highly expressed in the high-risk group, whereas CD8+ T cells, natural killer cells, M1, and other anti-tumor cells were lowly expressed. The model could be used as an independent prognostic factor to predict the prognosis of BC patients. The results of qRT-PCR validation were consistent with the results in the database, that is, except DLG3, the other four feature genes were lowly expressed in BC. The five-gene model established in this study can predict the prognostic and immune mode of BC patients effectively, which is anticipated to become a feasible molecular target for BC therapy.

Nicotinamide mononucleotide (NMN) alleviates the poly(I:C)-induced inflammatory response in human primary cell cultures

NMN is the direct precursor of nicotinamide adenine dinucleotide (NAD+) and is considered as a key factor for increasing NAD+ levels and mitochondrial activity in cells. In this study, based on transcriptome analysis, we showed that NMN alleviates the poly(I:C)-induced inflammatory response in cultures of two types of human primary cells, human pulmonary microvascular endothelial cells (HPMECs) and human coronary artery endothelial cells (HCAECs). Major inflammatory mediators, including IL6 and PARP family members, were grouped into coexpressed gene modules and significantly downregulated under NMN exposure in poly(I:C)-activated conditions in both cell types. The Bayesian network analysis of module hub genes predicted common genes, including eukaryotic translation initiation factor 4B (EIF4B), and distinct genes, such as platelet-derived growth factor binding molecules, in HCAECs, which potentially regulate the identified inflammation modules. These results suggest a robust regulatory mechanism by which NMN alleviates inflammatory pathway activation, which may open up the possibility of a new role for NMN replenishment in the treatment of chronic or acute inflammation.

Exploring hub pyroptosis-related genes, molecular subtypes, and potential drugs in ankylosing spondylitis by comprehensive bioinformatics analysis and molecular docking

BackgroundAnkylosing spondylitis (AS) is a chronic inflammatory autoimmune disease, and the diagnosis and treatment of AS have been limited because its pathogenesis is still unclear. Pyroptosis is a proinflammatory type of cell death that plays an important role in the immune system. However, the relationship between pyroptosis genes and AS has never been elucidated.MethodsGSE73754, GSE25101, and GSE221786 datasets were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed pyroptosis-related genes (DE-PRGs) were identified by R software. Machine learning and PPI networks were used to screen key genes to construct a diagnostic model of AS. AS patients were clustered into different pyroptosis subtypes according to DE-PRGs using consensus cluster analysis and validated using principal component analysis (PCA). WGCNA was used for screening hub gene modules between two subtypes. Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used for enrichment analysis to elucidate underlying mechanisms. The ESTIMATE and CIBERSORT algorithms were used to reveal immune signatures. The connectivity map (CMAP) database was used to predict potential drugs for the treatment of AS. Molecular docking was used to calculate the binding affinity between potential drugs and the hub gene.ResultsSixteen DE-PRGs were detected in AS compared to healthy controls, and some of these genes showed a significant correlation with immune cells such as neutrophils, CD8 + T cells, and resting NK cells. Enrichment analysis showed that DE-PRGs were mainly related to pyroptosis, IL-1β, and TNF signaling pathways. The key genes (TNF, NLRC4, and GZMB) screened by machine learning and the protein–protein interaction (PPI) network were used to establish the diagnostic model of AS. ROC analysis showed that the diagnostic model had good diagnostic properties in GSE73754 (AUC: 0.881), GSE25101 (AUC: 0.797), and GSE221786 (AUC: 0.713). Using 16 DE-PRGs, AS patients were divided into C1 and C2 subtypes, and these two subtypes showed significant differences in immune infiltration. A key gene module was identified from the two subtypes using WGCNA, and enrichment analysis suggested that the module was mainly related to immune function. Three potential drugs, including ascorbic acid, RO 90–7501, and celastrol, were selected based on CMAP analysis. Cytoscape showed GZMB as the highest-scoring hub gene. Finally, molecular docking results showed that GZMB and ascorbic acid formed three hydrogen bonds, including ARG-41, LYS-40, and HIS-57 (affinity: -5.3 kcal/mol). GZMB and RO-90–7501 formed one hydrogen bond, including CYS-136 (affinity: -8.8 kcal/mol). GZMB and celastrol formed three hydrogen bonds, including TYR-94, HIS-57, and LYS-40 (affinity: -9.4 kcal/mol).ConclusionsOur research systematically analyzed the relationship between pyroptosis and AS. Pyroptosis may play an essential role in the immune microenvironment of AS. Our findings will contribute to a further understanding of the pathogenesis of AS.

Transcriptomic and co-expression network analyses on diverse wheat landraces identifies candidate master regulators of the response to early drought.

Over four billion people around the world rely on bread wheat (Triticum aestivum L.) as a major constituent of their diet. The changing climate, however, threatens the food security of these people, with periods of intense drought stress already causing widespread wheat yield losses. Much of the research into the wheat drought response has centred on the response to drought events later in development, during anthesis or grain filling. But as the timing of periods of drought stress become increasingly unpredictable, a more complete understanding of the response to drought during early development is also needed. Here, we utilized the YoGI landrace panel to identify 10,199 genes which were differentially expressed under early drought stress, before weighted gene co-expression network analysis (WGCNA) was used to construct a co-expression network and identify hub genes in modules particularly associated with the early drought response. Of these hub genes, two stood out as novel candidate master regulators of the early drought response - one as an activator (TaDHN4-D1; TraesCS5D02G379200) and the other as a repressor (uncharacterised gene; TraesCS3D02G361500). As well as appearing to coordinate the transcriptional early drought response, we propose that these hub genes may be able to regulate the physiological early drought response due to potential control over the expression of members of gene families well-known for their involvement in the drought response in many plant species, namely dehydrins and aquaporins, as well as other genes seemingly involved in key processes such as, stomatal opening, stomatal closing, stomatal morphogenesis and stress hormone signalling.