Weighted Gene Co-expression Network Analysis Modules Research Articles

Integrative analyses of bulk and single-cell transcriptomics reveals the infiltration and crosstalk of cancer-associated fibroblasts as a novel predictor for prognosis and microenvironment remodeling in intrahepatic cholangiocarcinoma

BackgroundIntrahepatic cholangiocarcinoma (ICC) is a highly malignant neoplasm and characterized by desmoplastic matrix. The heterogeneity and crosstalk of tumor microenvironment remain incompletely understood.MethodsTo address this gap, we performed Weighted Gene Co-expression Network Analysis (WGCNA) to identify and construct a cancer associated fibroblasts (CAFs) infiltration biomarker. We also depicted the intercellular communication network and important receptor-ligand complexes using the single-cell transcriptomics analysis of tumor and Adjacent normal tissue.ResultsThrough the intersection of TCGA DEGs and WGCNA module genes, 784 differential genes related to CAFs infiltration were obtained. After a series of regression analyses, the CAFs score was generated by integrating the expressions of EVA1A, APBA2, LRRTM4, GOLGA8M, BPIFB2, and their corresponding coefficients. In the TCGA-CHOL, GSE89748, and 107,943 cohorts, the high CAFs score group showed unfavorable survival prognosis (p < 0.001, p = 0.0074, p = 0.028, respectively). Additionally, a series of drugs have been predicted to be more sensitive to the high-risk group (p < 0.05). Subsequent to dimension reduction and clustering, thirteen clusters were identified to construct the single-cell atlas. Cell-cell interaction analysis unveiled significant enhancement of signal transduction in tumor tissues, particularly from fibroblasts to malignant cells via diverse pathways. Moreover, SCENIC analysis indicated that HOXA5, WT1, and LHX2 are fibroblast specific motifs.ConclusionsThis study reveals the key role of fibroblasts - oncocytes interaction in the remodeling of the immunosuppressive microenvironment in intrahepatic cholangiocarcinoma. Subsequently, it may trigger cascade activation of downstream signaling pathways such as PI3K-AKT and Notch in tumor, thus initiating tumorigenesis. Targeted drugs aimed at disrupting fibroblasts-tumor cell interaction, along with associated enrichment pathways, show potential in mitigating the immunosuppressive microenvironment that facilitates tumor progression.

CWGCNA: an R package to perform causal inference from the WGCNA framework.

WGCNA (weighted gene co-expression network analysis) is a very useful tool for identifying co-expressed gene modules and detecting their correlations to phenotypic traits. Here, we explored more possibilities about it and developed the R package CWGCNA (causal WGCNA), which works from the traditional WGCNA pipeline but mines more information. It couples a mediation model with WGCNA, so the causal relationships among WGCNA modules, module features, and phenotypes can be found, demonstrating whether the module change causes the phenotype change or vice versa. After that, when annotating the module gene set functions, it uses a novel network-based method, considering the modules' topological structures and capturing their influence on the gene set functions. In addition to conducting these biological explorations, CWGCNA also contains a machine learning section to perform clustering and classification on multi-omics data, given the increasing popularity of this data type. Some basic functions, such as differential feature identification, are also available in our package. Its effectiveness is proved by the performance on three single or multi-omics datasets, showing better performance than existing methods. CWGCNA is available at: https://github.com/yuabrahamliu/CWGCNA.

Identifying of immune-associated genes for assessing the obesity-associated risk to the offspring in maternal obesity: Abioinformatics and machine learning.

Perinatal exposure to maternal obesity predisposes offspring to develop obesity later in life. Immune dysregulation in the hypothalamus, the brain center governing energy homeostasis, is pivotal in obesity development. This study aimed to identify key candidate genes associated with the risk of offspring obesity in maternal obesity. We obtained obesity-related datasets from the Gene Expression Omnibus (GEO) database. GSE135830 comprises gene expression data from the hypothalamus of mouse offspring in a maternal obesity model induced by a high-fat diet model (maternal high-fat diet (mHFD) group and maternal chow (mChow) group), while GSE127056 consists of hypothalamus microarray data from young adult mice with obesity (high-fat diet (HFD) and Chow groups). We identified differentially expressed genes (DEGs) and module genes using Limma and weighted gene co-expression network analysis (WGCNA), conducted functional enrichment analysis, and employed a machine learning algorithm (least absolute shrinkage and selection operator (LASSO) regression) to pinpoint candidate hub genes for diagnosing obesity-associated risk in offspring of maternal obesity. We constructed a nomogram receiver operating characteristic (ROC) curve to evaluate the diagnostic value. Additionally, we analyzed immune cell infiltration to investigate immune cell dysregulation in maternal obesity. Furthermore, we verified the expression of the candidate hub genes both invivo and invitro. The GSE135830 dataset revealed 2868 DEGs between the mHFD offspring and the mChow group and 2627 WGCNA module genes related to maternal obesity. The overlap of DEGs and module genes in the offspring with maternal obesity in GSE135830 primarily enriched in neurodevelopment and immune regulation. In the GSE127056 dataset, 133 DEGs were identified in the hypothalamus of HFD-induced adult obese individuals. A total of 13 genes intersected between the GSE127056 adult obesity DEGs and the GSE135830 maternal obesity module genes that were primarily enriched in neurodevelopment and the immune response. Following machine learning, two candidate hub genes were chosen for nomogram construction. Diagnostic value evaluation by ROC analysis determined Sytl4 and Kncn2 as hub genes for maternal obesity in the offspring. A gene regulatory network with transcription factor-miRNA interactions was established. Dysregulated immune cells were observed in the hypothalamus of offspring with maternal obesity. Expression of Sytl4 and Kncn2 was validated in a mouse model of hypothalamic inflammation and a palmitic acid-stimulated microglial inflammation model. Two candidate hub genes (Sytl4 and Kcnc2) were identified and a nomogram was developed to predict obesity risk in offspring with maternal obesity. These findings offer potential diagnostic candidate genes for identifying obesity-associated risks in the offspring of obese mothers.

Identifying functional subtypes of IgA nephropathy based on three machine learning algorithms and WGCNA

BackgroundIgA nephropathy (IgAN) is one of the most common primary glomerulonephritis, which is a significant cause of renal failure. At present, the classification of IgAN is often limited to pathology, and its molecular mechanism has not been established. Therefore we aim to identify subtypes of IgAN at the molecular level and explore the heterogeneity of subtypes in terms of immune cell infiltration, functional level.MethodsTwo microarray datasets (GSE116626 and GSE115857) were downloaded from GEO. Differential expression genes (DEGs) for IgAN were screened with limma. Three unsupervised clustering algorithms (hclust, PAM, and ConsensusClusterPlus) were combined to develop a single-sample subtype random forest classifier (SSRC). Functional subtypes of IgAN were defined based on functional analysis and current IgAN findings. Then the correlation between IgAN subtypes and clinical features such as eGFR and proteinuria was evaluated by using Pearson method. Subsequently, subtype heterogeneity was verified by subtype-specific modules identification based on weighted gene co-expression network analysis(WGCNA) and immune cell infiltration analysis based on CIBERSORT algorithm.ResultsWe identified 102 DEGs as marker genes for IgAN and three functional subtypes namely: viral-hormonal, bacterial-immune and mixed type. We screened seventeen genes specific to viral hormonal type (ATF3, JUN and FOS etc.), and seven genes specific to bacterial immune type (LIF, C19orf51 and SLPI etc.). The subtype-specific genes showed significantly high correlation with proteinuria and eGFR. The WGCNA modules were in keeping with functions of the IgAN subtypes where the MEcyan module was specific to the viral-hormonal type and the MElightgreen module was specific to the bacterial-immune type. The results of immune cell infiltration revealed subtype-specific cell heterogeneity which included significant differences in T follicular helper cells, resting NK cells between viral-hormone type and control group; significant differences in eosinophils, monocytes, macrophages, mast cells and other cells between bacterial-immune type and control.ConclusionIn this study, we identified three functional subtypes of IgAN for the first time and specific expressed genes for each subtype. Then we constructed a subtype classifier and classify IgAN patients into specific subtypes, which may be benefit for the precise treatment of IgAN patients in future.

Transcriptome Analysis Reveals Coexpression Networks and Hub Genes Involved in Papillae Development in Lilium auratum.

Lilium is a genus of important ornamental plants with many colouring pattern variations. Lilium auratum is the parent of Oriental hybrid lilies. A typical feature of L. auratum is the presence of red-orange special raised spots named papillae on the interior tepals. Unlike the usual raised spots, the papillae are slightly rounded or connected into sheets and usually have hairy tips. To elucidate the potential genes regulating papillae development in L. auratum, we performed high-throughput sequencing of its tepals at different stages. Genes involved in the flavonoid biosynthesis pathway were significantly enriched during the colouration of the papillae, and CHS, F3H, F3'H, FLS, DFR, ANS, and UFGT were significantly upregulated. To identify the key genes involved in the papillae development of L. auratum, we performed weighted gene coexpression network analysis (WGCNA) and further analysed four modules. In total, 51, 24, 1, and 6 hub genes were identified in four WGCNA modules, MEbrown, MEyellow, MEpurple, and MEred, respectively. Then, the coexpression networks were constructed, and important genes involved in trichome development and coexpressed with anthocyanin biosynthesis genes, such as TT8, TTG1, and GEM, were identified. These results indicated that the papillae are essentially trichomes that accumulate anthocyanins. Finally, we randomly selected 12 hub genes for qRT-PCR analysis to verify the accuracy of our RNA-Seq analysis. Our results provide new insights into the papillae development in L. auratum flowers.

Molecular characterization of allergic constitution based on network pharmacology and multi-omics analysis methods.

The objective of this study was to identify critical pathways associated with allergic constitution. Shared genes among allergic rhinitis (AR), asthma (AA), and atopic dermatitis (AD) were extracted from the GWAS catalog. RNA-seq data of AR, AA, and AD from gene expression omnibus (GEO) database were preprocessed and subjected to differential gene expression analysis. The differentially expressed genes (DEGs) were merged using the Robust Rank Aggregation (RRA) algorithm. Weighted gene co-expression network analysis (WGCNA) was performed to identify modules associated with allergies. Components of Guominkang (GMK) were obtained from 6 databases and activate components were identified by SwissADME website. Utilizing the SwissTarget Prediction, PharmMapper, SymMap, and HERB, the targets of GMK were predicted and subsequently validated using gene chip data from our team previous study. Differentially expressed proteins (DEPs) related to the allergic constitution were also extracted based on a previous study. Pathway enrichment analysis was performed using KOBAS-i on the GWAS, RRA, WGCNA modules, DEPs, and GMK targets. P values from multi-omics datasets were combined by meta-analysis, and Bonferroni correction was applied. The significant pathways were further validated using Gene Set Enrichment Analysis (GSEA) with intervention data of GMK. The GWAS results yielded 172 genes. Four datasets AR1, AA1, AD1, and AD2 were acquired from GSE75011, GSE125916, and GSE184237. The RRA algorithm identified 19 upregulated and 20 downregulated genes. WGCNA identified 5 significant modules, with the blue and turquoise modules displaying a moderate correlation with allergies. By performing network pharmacology analysis, we identified 127 active ingredients of GMK and predicted 618 targets. Validation using gene chip data confirmed 107 GMK targets. Single-omics pathway analysis was conducted using KOBAS-i, and 39 significant pathways were identified across multiple omics datasets. GSEA analysis using GMK intervention data identified 11 of 39 significant pathways as the final key pathways associated with the allergic constitution. Through multi-omics integrated pathway analysis, we identified 11 critical pathways of allergic constitution, including TH1 and TH2 cell differentiation, TLR cascade, and TH17 cell differentiation. Identifying these pathways suggests that the observed alterations at the pathway level may play significant roles in the molecular characteristics of the allergic constitution.

Identification of potential biomarkers in the peripheral blood of neonates with bronchopulmonary dysplasia using WGCNA and machine learning algorithms.

Bronchopulmonary dysplasia (BPD) is often seen as a pulmonary complication of extreme preterm birth, resulting in persistent respiratory symptoms and diminished lung function. Unfortunately, current diagnostic and treatment options for this condition are insufficient. Hence, this study aimed to identify potential biomarkers in the peripheral blood of neonates affected by BPD. The Gene Expression Omnibus provided the expression dataset GSE32472 for BPD. Initially, using this database, we identified differentially expressed genes (DEGs) in GSE32472. Subsequently, we conducted gene set enrichment analysis on the DEGs and employed weighted gene co-expression network analysis (WGCNA) to screen the most relevant modules for BPD. We then mapped the DEGs to the WGCNA module genes, resulting in a gene intersection. We conducted detailed functional enrichment analyses on these overlapping genes. To identify hub genes, we used 3 machine learning algorithms, including SVM-RFE, LASSO, and Random Forest. We constructed a diagnostic nomogram model for predicting BPD based on the hub genes. Additionally, we carried out transcription factor analysis to predict the regulatory mechanisms and identify drugs associated with these biomarkers. We used differential analysis to obtain 470 DEGs and conducted WGCNA analysis to identify 1351 significant genes. The intersection of these 2 approaches yielded 273 common genes. Using machine learning algorithms, we identified CYYR1, GALNT14, and OLAH as potential biomarkers for BPD. Moreover, we predicted flunisolide, budesonide, and beclomethasone as potential anti-BPD drugs. The genes CYYR1, GALNT14, and OLAH have the potential to serve as diagnostic biomarkers for BPD. This may prove beneficial in clinical diagnosis and prevention of BPD.

Graph Neural Network Model for Prediction of Non-Small Cell Lung Cancer Lymph Node Metastasis Using Protein-Protein Interaction Network and 18F-FDG PET/CT Radiomics.

The image texture features obtained from 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images of non-small cell lung cancer (NSCLC) have revealed tumor heterogeneity. A combination of genomic data and radiomics may improve the prediction of tumor prognosis. This study aimed to predict NSCLC metastasis using a graph neural network (GNN) obtained by combining a protein-protein interaction (PPI) network based on gene expression data and image texture features. 18F-FDG PET/CT images and RNA sequencing data of 93 patients with NSCLC were acquired from The Cancer Imaging Archive. Image texture features were extracted from 18F-FDG PET/CT images and area under the curve receiver operating characteristic curve (AUC) of each image feature was calculated. Weighted gene co-expression network analysis (WGCNA) was used to construct gene modules, followed by functional enrichment analysis and identification of differentially expressed genes. The PPI of each gene module and genes belonging to metastasis-related processes were converted via a graph attention network. Images and genomic features were concatenated. The GNN model using PPI modules from WGCNA and metastasis-related functions combined with image texture features was evaluated quantitatively. Fifty-five image texture features were extracted from 18F-FDG PET/CT, and radiomic features were selected based on AUC (n = 10). Eighty-six gene modules were clustered by WGCNA. Genes (n = 19) enriched in the metastasis-related pathways were filtered using DEG analysis. The accuracy of the PPI network, derived from WGCNA modules and metastasis-related genes, improved from 0.4795 to 0.5830 (p < 2.75 × 10-12). Integrating PPI of four metastasis-related genes with 18F-FDG PET/CT image features in a GNN model elevated its accuracy over a without image feature model to 0.8545 (95% CI = 0.8401-0.8689, p-value < 0.02). This model demonstrated significant enhancement compared to the model using PPI and 18F-FDG PET/CT derived from WGCNA (p-value < 0.02), underscoring the critical role of metastasis-related genes in prediction model. The enhanced predictive capability of the lymph node metastasis prediction GNN model for NSCLC, achieved through the integration of comprehensive image features with genomic data, demonstrates promise for clinical implementation.

Distinct Patterns of Gene Expression Changes in the Colon and Striatum of Young Mice Overexpressing Alpha-Synuclein Support Parkinson's Disease as a Multi-System Process.

Evidence supports a role for the gut-brain axis in Parkinson's disease (PD). Mice overexpressing human wild type α- synuclein (Thy1-haSyn) exhibit slow colonic transit prior to motor deficits, mirroring prodromal constipation in PD. Identifying molecular changes in the gut could provide both biomarkers for early diagnosis and gut-targeted therapies to prevent progression. To identify early molecular changes in the gut-brain axis in Thy1-haSyn mice through gene expression profiling. Gene expression profiling was performed on gut (colon) and brain (striatal) tissue from Thy1-haSyn and wild-type (WT) mice aged 1 and 3 months using 3' RNA sequencing. Analysis included differential expression, gene set enrichment and weighted gene co-expression network analysis (WGCNA). At one month, differential expression (Thy1-haSyn vs. WT) of mitochondrial genes and pathways related to PD was discordant between gut and brain, with negative enrichment in brain (enriched in WT) but positive enrichment in gut. Linear regression of WGCNA modules showed partial independence of gut and brain gene expression changes. Thy1-haSyn-associated WGCNA modules in the gut were enriched for PD risk genes and PD-relevant pathways including inflammation, autophagy, and oxidative stress. Changes in gene expression were modest at 3 months. Overexpression of haSyn acutely disrupts gene expression in the colon. While changes in colon gene expression are highly related to known PD-relevant mechanisms, they are distinct from brain changes, and in some cases, opposite in direction. These findings are in line with the emerging view of PD as a multi-system disease.

PyWGCNA: a Python package for weighted gene co-expression network analysis.

Weighted gene co-expression network analysis (WGCNA) is frequently used to identify modules of genes that are co-expressed across many RNA-seq samples. However, the current R implementation is slow, is not designed to compare modules between multiple WGCNA networks, and its results can be hard to interpret as well as to visualize. We introduce the PyWGCNA Python package, which is designed to identify co-expression modules from large RNA-seq datasets. PyWGCNA has a faster implementation than the R version of WGCNA and several additional downstream analysis modules for functional enrichment analysis using GO, KEGG, and REACTOME, inter-module analysis of protein-protein interactions, as well as comparison of multiple co-expression modules to each other and/or external lists of genes such as marker genes from single cell. We apply PyWGCNA to two distinct datasets of brain bulk RNA-seq from MODEL-AD to identify modules associated with the genotypes. We compare the resulting modules to each other to find shared co-expression signatures in the form of modules with significant overlap across the datasets. The PyWGCNA library for Python 3 is available on PyPi at pypi.org/project/PyWGCNA and on GitHub at github.com/mortazavilab/PyWGCNA. The data underlying this article are available in GitHub at github.com/mortazavilab/PyWGCNA/tutorials/5xFAD_paper.

Identification of molecular mechanisms causing skin lesions of cutaneous leishmaniasis using weighted gene coexpression network analysis (WGCNA)

Leishmaniasis is an infectious disease, caused by a protozoan parasite. Its most common form is cutaneous leishmaniasis, which leaves scars on exposed body parts from bites by infected female phlebotomine sandflies. Approximately 50% of cases of cutaneous leishmaniasis fail to respond to standard treatments, creating slow-healing wounds which cause permanent scars on the skin. We performed a joint bioinformatics analysis to identify differentially expressed genes (DEGs) in healthy skin biopsies and Leishmania cutaneous wounds. DEGs and WGCNA modules were analyzed based on the Gene Ontology function, and the Cytoscape software. Among almost 16,600 genes that had significant expression changes on the skin surrounding Leishmania wounds, WGCNA determined that one of the modules, with 456 genes, has the strongest correlation with the size of the wounds. Functional enrichment analysis indicated that this module includes three gene groups with significant expression changes. These produce tissue-damaging cytokines or disrupt the production and activation of collagen, fibrin proteins, and the extracellular matrix, causing skin wounds or preventing them from healing. The hub genes of these groups are OAS1, SERPINH1, and FBLN1 respectively. This information can provide new ways to deal with unwanted and harmful effects of cutaneous leishmaniasis.

Novel functional atrial fibrillation risk genes and pathways identified from coexpression analyses in human left atria

Genomewide association studies have associated >100 genetic loci with atrial fibrillation (AF), but establishing causal genes contributing to AF remains challenging. The purpose of this study was to determine candidate novel causal genes and mechanistic pathways associated with AF risk loci by incorporating gene expression and coexpression analyses and to provide a resource for functional studies and targeting of AF-associated genes. Cis-expression quantitative trait loci were identified for candidate genes near AF risk variants in human left atrial tissues. Coexpression partners were identified for each candidate gene. Weighted gene coexpression network analysis (WGCNA) identified modules and modules with overrepresentation of candidate AF genes. Ingenuity pathway analysis (IPA) was applied to the coexpression partners of each candidate gene. IPA and gene set over representation analysis were applied to each WGCNA module. One hundred sixty-six AF-risk single nucleotide polymorphisms were located in 135 loci. Eighty-one novel genes not previously annotated as putative AF risk genes were identified. IPA identified mitochondrial dysfunction, oxidative stress, epithelial adherens junction signaling, and sirtuin signaling as the most frequent significant pathways. WGCNA characterized 64 modules (candidate AF genes overrepresented in 8), represented by cell injury, death, stress, developmental, metabolic/mitochondrial, transcription/translation, and immune activation/inflammation regulatory pathways. Candidate gene coexpression analyses suggest significant roles for cellular stress and remodeling in AF, supporting a dual risk model for AF: Genetic susceptibility to AF may not manifest until later in life, when cellular stressors overwhelm adaptive responses. These analyses also provide a novel resource to guide functional studies on potential causal AF genes.

Screening of novel biomarkers for breast cancer based on WGCNA and multiple machine learning algorithms.

Breast cancer (BC) ranks first in incidence among women, with approximately 2 million new cases per year. Therefore, it is essential to investigate emerging targets for BC patients' diagnosis and prognosis. We analyzed gene expression data from 99 normal and 1,081 BC tissues in The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were identified using "limma" R package, and relevant modules were chosen through Weighted Gene Coexpression Network Analysis (WGCNA). Intersection genes were obtained by matching DEGs to WGCNA module genes. Functional enrichment studies were performed on these genes using Gene Ontology (GO), Disease Ontology (DO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Biomarkers were screened via Protein-Protein Interaction (PPI) networks and multiple machine-learning algorithms. The Gene Expression Profiling Interactive Analysis (GEPIA), The University of ALabama at Birmingham CANcer (UALCAN), and Human Protein Atlas (HPA) databases were employed to examine mRNA and protein expression of eight biomarkers. Kaplan-Meier mapper tool assessed their prognostic capabilities. Key biomarkers were analyzed via single-cell sequencing, and their relationship with immune infiltration was examined using Tumor Immune Estimation Resource (TIMER) database and "xCell" R package. Lastly, drug prediction was conducted based on the identified biomarkers. We identified 1,673 DEGs and 542 important genes through differential analysis and WGCNA, respectively. Intersection analysis revealed 76 genes, which play significant roles in immune-related viral infection and IL-17 signaling pathways. DIX domain containing 1 (DIXDC1), Dual specificity phosphatase 6 (DUSP6), Pyruvate dehydrogenase kinase 4 (PDK4), C-X-C motif chemokine ligand 12 (CXCL12), Interferon regulatory factor 7 (IRF7), Integrin subunit alpha 7 (ITGA7), NIMA related kinase 2 (NEK2), and Nuclear receptor subfamily 3 group C member 1 (NR3C1) were selected as BC biomarkers using machine-learning algorithms. NEK2 was the most critical gene for diagnosis. Prospective drugs targeting NEK2 include etoposide and lukasunone. Our study identified DIXDC1, DUSP6, PDK4, CXCL12, IRF7, ITGA7, NEK2, and NR3C1 as potential diagnostic biomarkers for BC, with NEK2 having the highest potential to aid in diagnosis and prognosis in clinical settings.

Transcriptome and co-expression network analysis reveals the molecular mechanism of inosine monophosphate-specific deposition in chicken muscle.

The inosine monophosphate (IMP) content in chicken meat is closely related to muscle quality and is an important factor affecting meat flavor. However, the molecular regulatory mechanisms underlying the IMP-specific deposition in muscle remain unclear. This study performed transcriptome analysis of muscle tissues from different parts, feeding methods, sexes, and breeds of 180-day-old Jingyuan chickens, combined with differential expression and weighted gene co-expression network analysis (WGCNA), to identify the functional genes that regulate IMP deposition. Out of the four comparison groups, 1,775, 409, 102, and 60 differentially expressed genes (DEGs) were identified, of which PDHA2, ACSS2, PGAM1, GAPDH, PGM1, GPI, and TPI1 may be involved in the anabolic process of muscle IMP in the form of energy metabolism or amino acid metabolism. WGCNA identified 11 biofunctional modules associated with IMP deposition. The brown, midnight blue, red, and yellow modules were strongly correlated with IMP and cooking loss (p < 0.05). Functional enrichment analysis showed that glycolysis/gluconeogenesis, arginine and proline metabolism, and pyruvate metabolism, regulated by PYCR1, SMOX, and ACSS2, were necessary for muscle IMP-specific deposition. In addition, combined analyses of DEGs and four WGCNA modules identified TGIF1 and THBS1 as potential candidate genes affecting IMP deposition in muscle. This study explored the functional genes that regulate muscle development and IMP synthesis from multiple perspectives, providing an important theoretical basis for improving the meat quality and molecular breeding of Jingyuan chickens.

Module representatives for refining gene co-expression modules

This paper concerns the identification of gene co-expression modules in transcriptomics data, i.e. collections of genes which are highly co-expressed and potentially linked to a biological mechanism. Weighted gene co-expression network analysis (WGCNA) is a widely used method for module detection based on the computation of eigengenes, the weights of the first principal component for the module gene expression matrix. This eigengene has been used as a centroid in a k-means algorithm to improve module memberships. In this paper, we present four new module representatives: the eigengene subspace, flag mean, flag median and module expression vector. The eigengene subspace, flag mean and flag median are subspace module representatives which capture more variance of the gene expression within a module. The module expression vector is a weighted centroid of the module which leverages the structure of the module gene co-expression network. We use these module representatives in Linde–Buzo–Gray clustering algorithms to refine WGCNA module membership. We evaluate these methodologies on two transcriptomics data sets. We find that most of our module refinement techniques improve upon the WGCNA modules by two statistics: (1) module classification between phenotype and (2) module biological significance according to Gene Ontology terms.

Transcriptome and weighted gene co-expression network analysis of jujube (Ziziphus jujuba Mill.) fruit reveal putative genes involved in proanthocyanin biosynthesis and regulation

Proanthocyanidin (PA) is an important bioactive compound with multiple physiological benefits in jujube (Ziziphus jujube Mill.). However, the molecular mechanisms underlying PA biosynthesis in jujube fruit have not been investigated. Here, the profiling of PA, (+)-catechin and (–)-epicatechin and transcriptome sequencing of three jujube cultivars from Xinjiang Uyghur Autonomous Region of China at five developmental stages were analyzed. The levels of total PAs and catechin exhibited a decreased trend over jujube ripening, and epicatechin content of two jujube cultivars increased first and then declined. Transcriptome analysis revealed that the differentially expressed genes (DEGs) were mainly enriched in ribosome, glycolysis/gluconeogenesis, fructose and mannose metabolism. 17 DEGs encoding PAL, CHS, CHI, CHS, F3’H, LAR, ANR, C4Hs, 4CLs, FLSs, DFRs and UFGTs involved in PA biosynthesis were relatively abundant. The highly transcribed LAR gene may greatly contribute to epicatechin accumulation. A weighted gene co-expression network analysis (WGCNA) was performed, and a network module including 1620 genes highly correlated with content of Pas and catechin was established. We identified 58 genes including 9 structural genes and 49 regulatory genes related to PA biosynthesis and regulation in the WGCNA module. Sixteen genes encoding 9 families of transcriptional factors (i.e., MYB, bHLH, ERF, bZIP, NAC, SBP, MIKC, HB, WRKY) were considered as hub genes. The results of qRT-PCR analysis validating 10 genes were well consistent with the transcriptome data. These findings provide valuable knowledge to facilitate its genetic studies and molecular breeding.

EGCG identified as an autophagy inducer for rosacea therapy.

Background: Rosacea is a common facial skin inflammatory disease featured by hyperactivation of mTORC1 signaling in the epidermis. Due to unclear pathogenesis, the effective treatment options for rosacea remain limited. Methods: Weighted gene co-expression network analysis (WGCNA) analyzed the relationship between epidermis autophagy and mTOR pathways in rosacea, and further demonstrated it through immunofluorescence and qPCR analysis. A potential therapeutic agent for rosacea was predicted based on the key genes of the WGCNA module. In vivo and in vitro experiments were conducted to verify its therapeutic role. Drug-target prediction (TargetNet, Swiss, and Tcmsp) and molecular docking offered potential pharmacological targets. Results: WGCNA showed that epidermis autophagy was related to the activation of mTOR pathways in rosacea. Next, autophagy was downregulated in the epidermis of rosacea, which was regulated by mTOR. In addition, the in vivo experiment demonstrated that autophagy induction could be an effective treatment strategy for rosacea. Subsequently, based on the key genes of the WGCNA module, epigallocatechin-3-gallate (EGCG) was predicted as a potential therapeutic agent for rosacea. Furthermore, the therapeutic role of EGCG on rosacea was confirmed in vivo and in vitro. Finally, drug-target prediction and molecular docking revealed that AKT1/MAPK1/MMP9 could be the pharmacological targets of EGCG in rosacea. Conclusion: Collectively, our findings revealed the vital role of autophagy in rosacea and identified that EGCG, as a therapeutic agent for rosacea, attenuated rosacea-like inflammation via inducing autophagy in keratinocytes.

Chlormequat chloride treatment inhibits grapevine stem growth via the VviRAP2.12 - VviEXPA7 regulatory module

Treatment with chlormequat chloride (CCC) can lead to dwarfism, which has been studied in many plants. However, the underlying molecular mechanisms have been less studied. Herein, the phenotypic changes and gene expression levels of grapevine in response to CCC were investigated. Results showed that the height of grape plants became significantly shorter after CCC treatment, especially between the first two stems above the roots. Transcriptome sequencing was then performed and analyzed, 120 differentially expressed genes (DEGs) were detected in the stems after CCC treatment compared to the control. The gene ontology (GO) enrichment results showed that processes such as cellulose synthase (UDP-forming) activity, regulation of the auxin-mediated signaling pathway, and the cellulose biosynthetic process were common. In addition, weighted gene co-expression network analysis (WGCNA) identified the strong correlation between the MEbrown module and stem development. VviEXAP7 was selected from the genes belonging to key GO pathways and WGCNA modules. The quantitative real-time PCR (qRT-PCR) results showed that VviEXPA7 was significantly downregulated because of the CCC treatment. Additionally, analysis of VviEXPA7 promoter activity showed that VviEXPA7 activated GUS gene expression, and was induced by CCC, abscisic acid (ABA), and other hormones. VviEXPA7 was used as a target gene for transcription factor prediction, and after employing the PlantTFDB and JASPAR databases, VviRAP2.12 was identified as a transcription factor that might regulate VviEXPA7. qRT-PCR revealed that VviRAP2.12 was significantly upregulated after CCC treatment. Finally, the interaction between VviEXPA7 and VviRAP2.12 was verified by the dual-luciferase assay. Thus, the current study shows that CCC decreased the expression of the cell growth-related gene VviEXPA7 by promoting the expression of VviRAP2.12, which resulted in dwarfed plants. This study generated new information that will increase our understanding of the mechanism of action of CCC and provides a scientific basis for simplified cultivation of grapes.

Identification and validation of oxidative stress and immune-related hub genes in Alzheimer’s disease through bioinformatics analysis

Alzheimer’s disease (AD) is the leading cause of dementia in aged population. Oxidative stress and neuroinflammation play important roles in the pathogenesis of AD. Investigation of hub genes for the development of potential therapeutic targets and candidate biomarkers is warranted. The differentially expressed genes (DEGs) in AD were screened in GSE48350 dataset. The differentially expressed oxidative stress genes (DEOSGs) were analyzed by intersection of DEGs and oxidative stress-related genes. The immune-related DEOSGs and hub genes were identified by weighted gene co-expression network analysis (WGCNA) and protein–protein interaction (PPI) analysis, respectively. Enrichment analysis was performed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. The diagnostic value of hub genes was assessed by receiver operating characteristic analysis and validated in GSE1297. The mRNA expression of diagnostic genes was determined by qRT-PCR analysis. Finally, we constructed the drug, transcription factors (TFs), and microRNA network of the diagnostic genes. A total of 1160 DEGs (259 up-regulated and 901 down-regulated) were screened in GSE48350. Among them 111 DEOSGs were identified in AD. Thereafter, we identified significant difference of infiltrated immune cells (effector memory CD8 T cell, activated B cell, memory B cell, natural killer cell, CD56 bright natural killer cell, natural killer T cell, plasmacytoid dendritic cell, and neutrophil) between AD and control samples. 27 gene modules were obtained through WGCNA and turquoise module was the most relevant module. We obtained 66 immune-related DEOSGs by intersecting turquoise module with the DEOSGs and identified 15 hub genes through PPI analysis. Among them, 9 hub genes (CCK, CNR1, GAD1, GAP43, NEFL, NPY, PENK, SST, and TAC1) were identified with good diagnostic values and verified in GSE1297. qRT-PCR analysis revealed the downregulation of SST, NPY, GAP43, CCK, and PENK and upregulation of NEFL in AD. Finally, we identified 76 therapeutic agents, 152 miRNAs targets, and 91 TFs regulatory networks. Our study identified 9 key genes associated with oxidative stress and immune reaction in AD pathogenesis. The findings may help to provide promising candidate biomarkers and therapeutic targets for AD.

Bulk and single cell transcriptomic analysis of different dementia types in human hippocampus

AbstractBackgroundAlzheimer’s disease (AD) is characterized by cognitive deterioration and extracellular amyloid beta deposits and neurofibrillary tangles in the brain. These may occur in tandem with markers of Lewy body (LB) and vascular dementia (VaD). Studying bulk and single‐cell expression profiles for these disorders may identify factors shared between and unique to AD and other dementias.MethodBulk RNA‐sequencing was performed using 210 human postmortem hippocampal region samples obtained from brains donated to the BU Alzheimer Disease Research Center. After quality checks (QC), 191 samples classified as AD+LB (n=43), AD+VaD (n=54), AD alone (n=73), and controls (n=17) were included in the study. Analysis of expression differences between each of these AD groups and controls was performed using limma software with models including covariates for age, sex, RNA quality, plate, and two surrogate variables to adjust for unknown variation. Weighted gene co‐expression network analysis (WGCNA) was used to identify clusters of co‐expressed genes. Additionally, high quality single‐nuclei RNA sequencing data (snRNA‐seq) was obtained from 8 of the 210 hippocampal samples with an average of 6000 cells detected per sample and 1800 genes per cell after QC. This snRNA‐seq data was integrated with the bulk data using Scissor to identify cell populations positively and negatively associated with AD.Result481 genes were differentially expressed between AD cases and controls. Upregulated genes in AD subjects were enriched for cilia function and morphological development and downregulated genes in AD subjects were enriched for mitochondrial energy production. A WGCNA module strongly associated with ciliary function was found to be overrepresented in AD (and especially AD+VaD) samples compared to controls. Multiple brain cell types were detected from our snRNA‐seq data. Of these, a subpopulation of ependymal cells related to the ciliary WGCNA module was positively associated with AD status.ConclusionOur results suggest that different sets of co‐expressed genes in hippocampus distinguish AD brains with and without pathological hallmarks related to other forms of dementia, and brains without AD pathology. This study also provides preliminary evidence for the involvement of ependymal cell populations in AD