Weighted Gene Co-expression Analysis Research Articles

Exploring novel independent prognostic biomarkers for hepatocellular carcinoma based on TCGA and GEO databases.

Hepatocellular carcinoma (HCC) has become the fifth most common cancer globally, with the second-highest mortality rate and poor survival outcomes. In our research, we aimed to use The Cancer Genome Atlas and gene expression omnibus databases to identify potential genetic biomarkers to predict and improve the survival rate of HCC patients. In GSE60502, GSE76427, and GSE84402, we performed differential expression analysis to obtain differentially expressed genes (DEGs). In the The Cancer Genome Atlas database, the FPKM expression profile was subjected to weighted gene co-expression analysis to obtain modules closely related to HCC. We received common genes by intersecting the genes in the module with the differential genes. Then, we fused the common genes' expression profiles, survival time, and survival status for univariate, Least Absolute Shrinkage and Selection Operator, and multivariate COX regression analysis to obtain prognostic genes. Predictive genes were performed in K-M survival analysis and combined with clinical data for independent predictive analysis. After differential expression analysis, GSE60502 obtained 1107 DEGs, GSE76427 obtained 424 DEGs, and GSE84402 obtained 1668 DEGs. Through weighted gene co-expression analysis analysis, we can see that the blue and brown modules were closely associated with HCC. After single and multivariate COX regression analysis, we found that suppressor of cytokine signaling 2 (SOCS2) and SERPINF2 were independent prognostic genes for HCC. After survival analysis, HCC patients with high expression of SOCS2 and SERPINF2 had a longer survival time. These 2 genes in normal liver tissues were higher than in HCC at the transcriptional level. SOCS2 and SERPINF2 were new independent prognostic genes of HCC. So, they may provide new treatment methods and measures for diagnosing HCC.

Identification of HIBCH as a Fatty Acid Metabolism-Related Biomarker in Aortic Valve Calcification Using Bioinformatics

Objective. To identify fatty acid metabolism-related biomarkers of aortic valve calcification (AVC) using bioinformatics and to research the role of immune cell infiltration for AVC. Methods. The AVC dataset was retrieved from the Gene Expression Omnibus database. R package is used for differential expression genes analysis and weighted gene coexpression analysis. The differentially coexpressed genes were identified by the Venn diagram, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of differentially coexpressed genes. Functions closely related to AVC were identified by GO and KEGG enrichment analyses of differentially coexpressed genes. Genes related to fatty acid metabolism were retrieved from the Molecular Signatures Database (MSigDB) database. After removing duplicate genes, least absolute shrinkage and selection operator (LASSO) regression analysis, support vector machine recursive feature elimination (SVM-RFE), and random forest were applied to recognize biomarkers related to fatty acid metabolism in AVC. The CIBERSORT tool was used to analyze infiltration of immune cells in normal and AVC samples. Correlations between biomarkers and immune cells were calculated. Finally, HIBCH-related pathway was predicted by single-gene gene set enrichment analysis (GSEA). Results. 2416 differentially expressed genes and one coexpression module were identified. A total of 1473 differentially coexpressed genes were acquired. GO and KEGG enrichment analyses demonstrated that differentially coexpressed genes were closely related to fatty acid metabolism. LASSO regression analysis, SVM-REF, and random forest revealed that 3-hydroxyisobutyryl-CoA hydrolase (HIBCH) was a biomarker of fatty acid metabolism-related genes in AVC. Significant high levels of memory B cells were found in AVC than normal samples, while activated natural killer (NK) cells were significantly low in AVC than normal samples. A significantly positive relevance was observed between HIBCH and activated NK cells, regulatory T cells, monocytes, naïve B cells, activated dendritic cells, resting memory CD4 T cells, resting NK cells, and CD8 T cells. A significantly negative relevance was observed between HIBCH and activated memory CD4 T cells, memory B cells, neutrophils, gamma delta T cells, M0 macrophages, and plasma cells. The single-gene GSEA results suggest that HIBCH may work through the inhibition of multiple immune-related pathways. Conclusion. HIBCH is closely relevant to immune cell infiltration in AVC and could be applied as a diagnostic marker for AVC.

A Novel Beta-Glucosidase Gene for Plant Type Was Identified by Genome-Wide Association Study and Gene Co-Expression Analysis in Widespread Bermudagrass.

Bermudagrass (Cynodon spp.) is one of the most widely distributed warm-season grasses globally. The growth habits and plant type of bermudagrass are strongly associated with the applied purpose of the landscape, livestock, and eco-remediation. Therefore, persistent efforts are made to investigate the genetic basis of plant type and growth habits of bermudagrass. Here, we dissect the genetic diversity of 91 wild bermudagrass resources by genome-wide association studies (GWAS) combined with weighted gene co-expression analysis (WGCNA). This work is based on the RNA-seq data and the genome of African bermudagrass (Cynodon transvaalensis Burtt Davy). Sixteen reliable single-nucleotide polymorphisms (SNPs) in transcribed regions were identified to be associated with the plant height and IAA content in diverse bermudagrass by GWAS. The integration of the results from WGCNA indicates that beta-glucosidase 31 (CdBGLU31) is a candidate gene underlying a G/A SNP signal. Furthermore, both qRT-PCR and correlation coefficient analyses indicate that CdBGLU31 might play a comprehensive role in plant height and IAA biosynthesis and signal. In addition, we observe lower plant height in Arabidopsis bglu11 mutants (homologs of CdBGLU31). It uncovers the breeding selection history of different plant types from diverse bermudagrass and provides new insights into the molecular function of CdBGLU31 both in plant types and in IAA biosynthetic pathways.

The exploration of the potential mechanism of oxymatrine-mediated antipruritic effect based on network pharmacology and weighted gene co-expression network analysis.

The treatment of chronic itch is considered to be a challenge for its non-histamine dependence and the search for alternative medicine is still striving. The pathology of the chronic itch is closely related to immune system regulation and inflammatory response. Oxymatrine (OMT) is a traditional Chinese medicine ingredient extracted from the roots of Sophora flavescens Aiton with significant antitumor, analgesic, and anti-inflammatory effects. However, the underlying mechanism of OMT on chronic itch is obscure, which limits clinical application. Hence, this study is aimed to clarify the pruritus alleviation mechanism of OMT by combining network pharmacology analysis, weighted gene co-expression analysis (WGCNA), and molecular docking. We screened 125 common targets of OMT regulating inflammation and pruritus with pharmacology technology, the GO enrichment function analysis and KEGG signaling pathway analysis to demonstrate the close relation to the signaling pathways regulating inflammation such as MAPK signaling pathway and PI3K-AKT signaling pathway. We adopted the most relevant templates for pruritus diseases, combined with network pharmacology to preliminarily screen out 3 OMT functions and regulatory targets, exerting a good connection and correlation with the target at the screened disease targets. Further experiments were conducted to explore the potential mechanism of OMT using the LPS-induced RAW264.7 cell inflammation model. The results showed that pretreatment with different concentrations of OMT (25 μM, 50 μM, and 100 μM) for 24 h, inhibited expression of IL-6, iNOS TLR4 and TGFR-1 as well as apoptosis of Raw264.7 cells induced by LPS. Moreover, OMT effectively inhibited LPS-induced MAPK pathway activation and the expression of related sites MAP2K1, MAPK8 MAP2K4, and MAPKAP-K2 in RAW 264.7 cells. The OMT also reduced the phosphorylation of p-38, associated with site in the activation of MAPK signaling pathway. These results could contribute to a better understanding of the mechanisms underlying how OMT alleviates inflammation to treat chronic pruritic diseases and provide a potential drug for the treatment of chronic itch.

Transcriptome analysis of Homo sapiens and Mus musculus reveals mechanisms of CD8+ T cell exhaustion caused by different factors.

T cell exhaustion is a state of T cell dysfunction during chronic infection and cancer. Antibody-targeting immune checkpoint inhibitors to reverse T cell exhaustion is a promising approach for cancer immunotherapy. However, molecular mechanisms of T cell exhaustion remain incompletely understood. Here, we performed a transcriptome analysis by integrating seven exhaustion datasets caused by multiple diseases in both humans and mice. In this study, an overlap of 21 upregulated and 37 downregulated genes was identified in human and mouse exhausted CD8+ T cells. These genes were significantly enriched in exhaustion response-related pathways, such as signal transduction, immune system processes, and regulation of cytokine production. Gene expression network analysis revealed that the well-documented exhaustion genes were defined as hub genes in upregulated genes. In addition, a weighted gene co-expression analysis identified 175 overlapping genes that were significantly correlated with the exhaustion trait in both humans and mice. This study found that overlapping six genes were significantly upregulated and highly related to T cell exhaustion. Finally, we revealed that CD200R1 and ADGRG1, less described previously in exhaustion, contributed to T cell exhaustion. Overall, our findings reveal the mechanisms of T cell exhaustion and provide an important reference to the immunology community.

Identification of Metastasis-Associated Genes in Cutaneous Squamous Cell Carcinoma Based on Bioinformatics Analysis and Experimental Validation.

Cutaneous squamous cell carcinoma (cSCC) is a global malignant tumor with a high degree of malignancy. Once metastasis occurs, it will lead to poor prognosis and even death. This study attempts to find out the central genes closely related to cSCC metastasis, so as to clarify the molecular regulatory mechanism of cSCC metastasis and open up new ideas for clinical treatment. Firstly, cSCC data set GSE98767 was used to establish a tumor metastasis model via clustering analysis. The key module and hub genes associated with cSCC metastasis were analyzed by weighted gene co-expression analysis (WGCNA). Next, the prognostic functions of hub genes were identified by functional and pathway enrichment analysis, pan-cancer analysis, and receiver operating characteristic-area under the curve (ROC-AUC) validation. Finally, the key genes were verified by clinical sample detection and biological in vitro test. A total of 19 hub genes related to cSCC metastasis were identified. They were highly expressed in cSCC metastatic tissues and were mainly enriched in cellular material and energy metabolism pathways. Overall survival (OS) and disease-free survival (DFS) results from pan-cancer analysis showed that eight and six highly expressed genes, respectively, with PAPSS2 and SCG5 had highly reliable ROC-AUC validation values and were poor prognostic factors. Clinical and biological tests also confirmed the upregulation of PAPSS2 and SCG5 in cSCC. Deletion of PAPSS2 and SCG5 resulted in decreased viability, migration, and invasion of A-431 cells. PAPSS2 and SCG5 may be important factors for cSCC metastasis, and they are involved in the regulation of cSCC cell viability, migration, and invasion.

Co-expression network analysis of human tau-transgenic mice reveals protein modules associated with tau-induced pathologies

SummaryAbnormally accumulated tau protein aggregates are one of the hallmarks of neurodegenerative diseases, including Alzheimer’s disease (AD). In order to investigate proteomic alteration driven by tau aggregates, we implemented quantitative proteomics to analyze disease model mice expressing human MAPTP301S transgene (hTau-Tg) and quantified more than 9,000 proteins in total. We applied the weighted gene co-expression analysis (WGCNA) algorithm to the datasets and explored protein co-expression modules that were associated with the accumulation of tau aggregates and were preserved in proteomes of AD brains. This led us to identify four modules with functions related to neuroinflammatory responses, mitochondrial energy production processes (including the tricarboxylic acid cycle and oxidative phosphorylation), cholesterol biosynthesis, and postsynaptic density. Furthermore, a phosphoproteomics study uncovered phosphorylation sites that were highly correlated with these modules. Our datasets represent resources for understanding the molecular basis of tau-induced neurodegeneration, including AD.

Transcriptomic profiling identifies host-derived biomarker panels for assessing cerebral malaria

Cerebral malaria (CM) is a rare but fatal form of severe malaria sequelae. Host-derived biomarkers have the potential to assist in the early assessment, diagnosis, and prognosis of CM. However, previously reported host-derived CM biomarkers have limited clinical utility despite their ability to discriminate between CM and non-CM conditions, either due to their specificity to other non-malaria conditions or consist of many biomarkers for test implementation. Here, we describe the identification of host-derived transcriptomic signatures that address these limitations using whole-blood transcriptomic profiling, weighted gene co-expression analysis, and random forest classification models. We report three panels; PADI4 + ANGPT2, SLC2A3 + ANGPT2, and SLC2A3 + CA4, that distinguished CM from other malaria manifestations with high accuracy and are poor discriminators of non-malaria conditions that share similar blood-based signatures with severe malaria. Also, these transcripts showed elevated expression in the blood during CM. Our results provide preliminary evidence to support the potential of these biomarker panels in assisting early assessment, diagnosis, and prognosis of CM. Further validations are needed in large and diverse malaria-infected cohorts to establish the clinical utility of these biomarker panels.

CEBPD modulates the airway smooth muscle transcriptomic response to glucocorticoids

BackgroundCCAAT/Enhancer Binding Protein D (CEBPD), a pleiotropic glucocorticoid-responsive transcription factor, modulates inflammatory responses. Of relevance to asthma, expression of CEBPD in airway smooth muscle (ASM) increases with glucocorticoid exposure. We sought to characterize CEBPD-mediated transcriptomic responses to glucocorticoid exposure in ASM by measuring changes observed after knockdown of CEBPD and its impact on asthma-related ASM function.MethodsPrimary ASM cells derived from four donors were transfected with CEBPD or non-targeting (NT) siRNA and exposed to vehicle control, budesonide (100 nM, 18 h), TNFα (10 ng/ml, 18 h), or both budesonide and TNFα. Subsequently, RNA-Seq was used to measure gene expression levels, and pairwise differential expression results were obtained for exposures versus vehicle and knockdown versus control conditions. Weighted gene co-expression analysis was performed to identify groups of genes with similar expression patterns across the various experimental conditions (i.e., CEBPD knockdown status, exposures).ResultsCEBPD knockdown altered expression of 3037 genes under at least one exposure (q-value < 0.05). Co-expression analysis identified sets of 197, 152 and 290 genes that were correlated with CEBPD knockdown status, TNFα exposure status, and both, respectively. JAK-STAT signaling pathway genes, including IL6R and SOCS3, were among those influenced by both TNFα and CEBPD knockdown. Immunoblot assays revealed that budesonide-induced IL-6R protein expression and augmented IL-6-induced STAT3 phosphorylation levels were attenuated by CEBPD knockdown in ASM.ConclusionsCEBPD modulates glucocorticoid responses in ASM, in part via modulation of IL-6 receptor signaling.

Identification of underlying mechanisms and hub gene-miRNA networks of the genomic subgroups in preeclampsia development.

Preeclampsia is a hypertensive disorder of pregnancy that can lead to multiorgan complications in the mother and fetus. Our study aims to uncover the underlying mechanisms and hub genes between genomic subgroups of preeclampsia.A total of 180 preeclampsia cases from 4 gene profiles were classified into 3 subgroups. Weighted gene coexpression analysis was performed to uncover the genomic characteristics associated with different clinical features. Functional annotation was executed within the significant modules and hub genes were predicted using Cytoscape software. Subsequently, miRNet analysis was performed to identify potential miRNA–mRNA networks.Three key subgroup-specific modules were identified. Patients in subgroup II were found to develop more severe preeclampsia symptoms. Subgroup II, characterized by classical markers, was considered representative of typical preeclampsia patients. Subgroup I was considered as an early stage of preeclampsia with normal-like gene expression patterns. Moreover, subgroup III was a proinflammatory subgroup, which presented immune-related genomic characteristics. Subsequently, miR-34a-5p and miR-106a-5p were found to be correlated with all 3 significant gene modules.This study revealed the transcriptome classification of preeclampsia cases with unique gene expression patterns. Potential hub genes and miRNAs may facilitate the identification of therapeutic targets for preeclampsia in future.

Integrated transcriptome and small RNA sequencing in revealing miRNA-mediated regulatory network of floral bud break in Prunus mume.

MicroRNAs is one class of small non-coding RNAs that play important roles in plant growth and development. Though miRNAs and their target genes have been widely studied in many plant species, their functional roles in floral bud break and dormancy release in woody perennials is still unclear. In this study, we applied transcriptome and small RNA sequencing together to systematically explore the transcriptional and post-transcriptional regulation of floral bud break in P. mume. Through expression profiling, we identified a few candidate genes and miRNAs during different developmental stage transitions. In total, we characterized 1,553 DEGs associated with endodormancy release and 2,084 DEGs associated with bud flush. Additionally, we identified 48 known miRNAs and 53 novel miRNAs targeting genes enriched in biological processes such as floral organ morphogenesis and hormone signaling transudation. We further validated the regulatory relationship between differentially expressed miRNAs and their target genes combining computational prediction, degradome sequencing, and expression pattern analysis. Finally, we integrated weighted gene co-expression analysis and constructed miRNA-mRNA regulatory networks mediating floral bud flushing competency. In general, our study revealed the miRNA-mediated networks in modulating floral bud break in P. mume. The findings will contribute to the comprehensive understanding of miRNA-mediated regulatory mechanism governing floral bud break and dormancy cycling in wood perennials.

Transcriptome Classification Reveals Molecular Subgroups in Idiopathic Pulmonary Fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a disease of progressive lung fibrosis with a high mortality rate. This study aimed to uncover the underlying molecular features for different types of IPF. IPF microarray datasets were retrieved from GEO databases. Weighted gene co-expression analysis (WGCNA) was used and identified subgroup-specific WGCNA modules. Infiltration-level immune cells in different subgroups of microenvironments were analyzed with CIBERSORT algorithms. The result is we classified 173 IPF cases into two subgroups based on gene expression profiles, which were retrieved from the GEO databases. The SGRQ score and age were significantly higher in C2 than in C1. Using WGCNA, five subgroup-specific modules were identified. M4 was mainly enriched by MAPK signaling, which was mainly expressed in C2; M1, M2, and M3 were mainly enriched by metabolic pathways and Chemokine signaling, and the pathway of M5 was phagosome inflammation; M1, M2, M3, and M5 were mainly expressed in C1. Utilizing the CIBERSORT, we showed that the number of M1 macrophage cells, CD8 T cells, regulatory T cells (Tregs), and Plasma cells was significantly different between C1 and C2. We found the molecular subgroups of IPF revealed that cases from different subgroups may have their unique patterns and provide novel information to understand the mechanisms of IPF itself.

Identification of Immune-Related Genes in Patients with Acute Myocardial Infarction Using Machine Learning Methods.

ObjectiveThis study aimed to analyze immune-related genes and immune cell components in the peripheral blood of patients with acute myocardial infarction (AMI).MethodsSix datasets were obtained from the GEO repository comprising 88 healthy samples and 215 AMI samples. We performed the weighted gene co-expression analysis (WGCNA) and five machine learning (ML) methods to identify immune-related genes and construct diagnostic models. CIBERSORT algorithm was adopted for the assessment of the degree of immune infiltration. Finally, RT-PCR, immunofluorescence double and immunohistochemistry were conducted to analyze the expression level of the identification of featured immune-related genes and localization relationship in heart tissue of AMI mouse model.ResultsA total of 496 immune-related DEGs were obtained between AMI and normal samples. WGCNA finally determined the co-expression modules that showed the most significantly positively associated with AMI (r=0.41; P<0.001). Among the five ML models, XGBoost had the highest AUC (0.849) and accuracy (0.812) to discriminate patients with AMI from normal in the validation sets. Furthermore, we found that the proportion of chemokine receptor (CCR), macrophages, neutrophils, and Treg cells in the AMI groups was significantly higher than that in the normal groups. In vitro RT-PCR verification revealed that SOCS3, MMP9, and AQP9 expression increased significantly in the AMI mouse model. Among the 22 immune cells, AQP9, MMP9, and SOCS3 displayed the strongest positive correlation with neutrophils. In MI-mice, MPO stained strongly along the lateral cardiomyocytes, whereas it was weaker in sham mice. Combined immunofluorescence was observed in same parts of the cytoplasm of cardiomyocytes in myocardial infarction area, indicating co-localization of MPO with MMP9 and SOCS3 in these areas, respectively.ConclusionImmune-related genes and immune cells are intimately related to AMI. Constructing different ML models based on these biomarkers could be a valuable approach to diagnosing AMI in clinical practice.

Molecular Subgroup Classification in Alzheimer's Disease by Transcriptomic Profiles.

Alzheimer's disease (AD) is a progressive cognitive disorder that occurs worldwide, and the lack of disease-modifying targets and pathways is a pressing issue. This study aimed to provide new targets and pathways by performing molecular subgroup classification. After normalizing the collected data, the subgroup number was confirmed with consensus clustering. Comparisons of clinical features among subgroups were conducted to clarify the clinical traits of each subgroup. Subgroup-specific genes were identified to perform weighted gene coexpression analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were carried out. Next, gene set enrichment analysis (GSEA) was performed. Protein-protein interaction networks were built to screen core genes and in each subgroup to perform Spearman correlation analysis with clinical traits. Sequencing profiles of 1068 AD samples collected from 2 datasets were classified into 3 subgroups. Clinical comparisons revealed that patients in subgroup III tended to be younger, while their pathological grades were the most severe. WGCNA detected four gene modules, and the turquoise module, where the dopaminergic synapse pathway was enriched, was related to subgroup I. The neurotrophin signaling pathway and TGF-beta signaling pathway were robustly enriched in the blue and brown modules, respectively, in subgroup III. Moreover, 3 hub genes in subgroup I were negatively correlated with the sum of neurofibrillary tangle (Nft) density. Conversely, hub genes in subgroups II and III exhibited positive correlations with the sum of Nft density. These results provide new pathways and targets for AD treatment.

Integration of gene co-expression analysis and multi-class SVM specifies the functional players involved in determining the fate of HTLV-1 infection toward the development of cancer (ATLL) or neurological disorder (HAM/TSP).

Human T-cell Leukemia Virus type-1 (HTLV-1) is an oncovirus that may cause two main life-threatening diseases including a cancer type named Adult T-cell Leukemia/Lymphoma (ATLL) and a neurological and immune disturbance known as HTLV-1 Associated Myelopathy/Tropical Spastic Paraparesis (HAM/TSP). However, a large number of the infected subjects remain as asymptomatic carriers (ACs). There is no comprehensive study that determines which dysregulated genes differentiate the pathogenesis routes toward ATLL or HAM/TSP. Therefore, two main algorithms including weighted gene co-expression analysis (WGCNA) and multi-class support vector machines (SVM) were utilized to find major gene players in each condition. WGCNA was used to find the highly co-regulated genes and multi-class SVM was employed to identify the most important classifier genes. The identified modules from WGCNA were validated in the external datasets. Furthermore, to find specific modules for ATLL and HAM/TSP, the non-preserved modules in another condition were found. In the next step, a model was constructed by multi-class SVM. The results revealed 467, 3249, and 716 classifiers for ACs, ATLL, and HAM/TSP, respectively. Eventually, the common genes between the WGCNA results and classifier genes resulted from multi-class SVM that also determined as differentially expressed genes, were identified. Through these step-wise analyses, PAIP1, BCAS2, COPS2, CTNNB1, FASLG, GTPBP1, HNRNPA1, RBBP6, TOP1, SLC9A1, JMY, PABPC3, and PBX1 were found as the possible critical genes involved in the progression of ATLL. Moreover, FBXO9, ZNF526, ERCC8, WDR5, and XRCC3 were identified as the conceivable major involved genes in the development of HAM/TSP. These genes can be proposed as specific biomarker candidates and therapeutic targets for each disease.

Identification of Potential Prognosticators for Sepsis through Expression Analysis of Transcriptomic Data from Sepsis Survivors and Nonsurvivors.

Infection can be severely complicated by a dysregulated, whole-body inflammatory response known as sepsis. While previous research showed that genetic predisposition is linked to outcome differences, current patient characterization fails to determine which septic patients have greater tendencies to develop into severe sepsis or go into septic shock. As such, the identification of prognostic biomarkers may assist in identifying these high-risk patients and help improve the clinical management of the disease. In this study, we aimed to identify molecular patterns involved in sepsis. We also aimed to identify essential genes associated with the disease's survival which could serve as potential prognosticators for the disease. We used weighted gene co-expression analysis (WGCNA) to analyze GSE63042, an RNA expression dataset from 129 patients with systemic inflammatory response syndrome or sepsis, including 78 sepsis survivors and 28 sepsis nonsurvivors. This analysis included identifying gene modules that differentiate sepsis survivors from nonsurvivors and qualitatively assessing differentially expressed genes. We then used STRING's protein-protein interaction and gene ontology analysis to determine the functional and pathway relationships of the genes in the top modules. Lastly, we assessed the prognosticator abilities of the hub genes using ROC analysis. We found four diverse co-expression gene modules significantly associated with sepsis survival. Our differential gene expression analysis, combined with protein-protein interaction and gene ontology analysis, revealed that the hub genes of these modules - TAF10, SNAPIN, PSME2, PSMB9, JUNB, and CEBPD - may serve as candidate markers for sepsis prognosis. These markers were significantly downregulated in sepsis nonsurvivors compared with sepsis survivors. Weighted gene co-expression analysis, gene ontology enrichment analysis, and proteinprotein network interaction analysis of transcriptomic data from sepsis survivors and nonsurvivors revealed TAF10, SNAPIN, PSME2, PSMB9, JUNB, and CEBPD as potential biomarkers for sepsis prognosis. These genes are associated with functions related to proper immune response, and their downregulation in sepsis nonsurvivors suggests eventual immune exhaustion in late sepsis. Further analyses, however, are necessary to validate their roles in sepsis progression and patient survival.

Weighted Gene Co-Expression Analysis Network-Based Analysis on the Candidate Pathways and Hub Genes in Eggplant Bacterial Wilt-Resistance: A Plant Research Study.

Solanum melongena L. (eggplant) bacterial wilt is a severe soil borne disease. Here, this study aimed to explore the regulation mechanism of eggplant bacterial wilt-resistance by transcriptomics with weighted gene co-expression analysis network (WGCNA). The different expression genes (DEGs) of roots and stems were divided into 21 modules. The module of interest (root: indianred4, stem: coral3) with the highest correlation with the target traits was selected to elucidate resistance genes and pathways. The selected module of roots and stems co-enriched the pathways of MAPK signalling pathway, plant pathogen interaction, and glutathione metabolism. Each top 30 hub genes of the roots and stems co-enriched a large number of receptor kinase genes. A total of 14 interesting resistance-related genes were selected and verified with quantitative polymerase chain reaction (qPCR). The qPCR results were consistent with those of WGCNA. The hub gene of EGP00814 (namely SmRPP13L4) was further functionally verified; SmRPP13L4 positively regulated the resistance of eggplant to bacterial wilt by qPCR and virus-induced gene silencing (VIGS). Our study provides a reference for the interaction between eggplants and bacterial wilt and the breeding of broad-spectrum and specific eggplant varieties that are bacterial wilt-resistant.

Genomic modules and intramodular network concordance in susceptible and resilient male mice across models of stress

The multifactorial etiology of stress-related disorders necessitates a constant interrogation of the molecular convergences in preclinical models of stress that use disparate paradigms as stressors spanning from environmental challenges to genetic predisposition to hormonal signaling. Using RNA-sequencing, we investigated the genomic signatures in the ventral hippocampus common to mouse models of stress. Chronic oral corticosterone (CORT) induced increased anxiety- and depression-like behavior in wild-type male mice and male mice heterozygous for the gene coding for brain-derived neurotrophic factor Val66Met, a variant associated with genetic susceptibility to stress. In a separate set of male mice, chronic social defeat stress (CSDS) led to a susceptible or a resilient population, whose proportion was dependent on housing conditions, namely standard housing or enriched environment. Rank-rank-hypergeometric overlap (RRHO), a threshold-free approach that ranks genes by their p value and effect size direction, was used to identify genes from a continuous gradient of significancy that were concordant across groups. In mice treated with CORT and in standard-housed susceptible mice, differentially expressed genes (DEGs) were concordant for gene networks involved in neurotransmission, cytoskeleton function, and vascularization. Weighted gene co-expression analysis generated 54 gene hub modules and revealed two modules in which both CORT and CSDS-induced enrichment in DEGs, whose function was concordant with the RRHO predictions, and correlated with behavioral resilience or susceptibility. These data showed transcriptional concordance across models in which the stress coping depends upon hormonal, environmental, or genetic factors revealing common genomic drivers that embody the multifaceted nature of stress-related disorders.

Comparative analysis of physiological, agronomic and transcriptional responses to drought stress in wheat local varieties from Mongolia and Northern China

Drought is one of the major abiotic stresses that threaten wheat production worldwide, especially in the Mongolian Plateau and adjacent regions. This study aims to find local wheat varieties with high yields and drought resistance at various developmental stages based on agronomic traits and drought resistance indices analysis and explore the underlining molecular mechanisms by transcriptome analysis. Our results revealed that drought stress started at the seedling stage has a greater impact on crop yields. Four types of drought responses were found among the tested varieties. Type 1 and type 2 show low tolerance to drought stress despite high or low yield in control condition, type 3 exhibits high yield under control condition but dropped significantly after drought, and type 4 displays relatively high and stable yields under control and drought conditions. Transcriptome analysis performed with the representative varieties of the four types revealed GO terms and KEGG pathways enriched among drought-triggered differential expressed genes (DEGs). A network containing 18 modules was constructed using weighted gene co-expression analysis (WGCNA). Ten modules were significantly correlated to yield by module-trait correlation, and 3 modules showed Darkhan 144 specific gene expression patterns. C2H2 zinc finger factor-recognized motifs were identified from the promoters of genes in these modules. qRT-PCR confirmed several key DEGs with specific expression patterns and physiological measurements validated the relatively low oxidative damage and high antioxidant capacity in the drought tolerant variety Dankhan 144. These findings provide an important basis for local agriculture and breeding of drought-tolerant high yield wheat varieties.

Microglia-specific ApoE knock-out does not alter Alzheimer's disease plaque pathogenesis or gene expression.

Previous studies suggest that microglial-expressed Apolipoprotein E (ApoE) is necessary to shift microglia into a neurodegenerative transcriptional state in Alzheimer's disease (AD) mouse models. On the other hand, elimination of microglia shifts amyloid beta (Aβ) accumulation from parenchymal plaques to cerebral amyloid angiopathy (CAA), mimicking the effects of global APOE*4 knock-in. Here, we specifically knock-out microglial-expressed ApoE while keeping astrocytic-expressed ApoE intact. When microglial-specific ApoE is knocked-out of a 5xFAD mouse model of AD, we found a ~35% increase in average Aβ plaque size, but no changes in plaque load, microglial number, microglial clustering around Aβ plaques, nor the formation of CAA. Immunostaining revealed ApoE protein present in plaque-associated microglia in 5xFAD mice with microglial-specific ApoE knockout, suggesting that microglia can take up ApoE from other cellular sources. Mice with Apoe knocked-out of microglia had lower synaptic protein levels than control mice, indicating that microglial-expressed ApoE may have a role in synapse maintenance. Surprisingly, microglial-specific ApoE knock-out resulted in few differentially expressed genes in both 5xFAD and control mice; however, some rescue of 5xFAD associated neuronal networks may occur with microglial-specific ApoE knock-out as shown by weighted gene co-expression analysis. Altogether, our data indicates that microglial-expressed ApoE may not be necessary for plaque formation or for the microglial transcriptional shift into a Disease Associated Microglia state that is associated with reactivity to plaques but may be necessary for plaque homeostasis in disease and synaptic maintenance under normal conditions.