Identification of Characteristic Genes in Whole Blood of Intervertebral Disc Degeneration Patients by Weighted Gene Coexpression Network Analysis (WGCNA)

Intervertebral disc degeneration (IDD) is a major cause of lower back pain. However, to date, the molecular mechanism of the IDD remains unclear. Gene expression profiles and clinical traits were downloaded from the Gene Expression Omnibus (GEO) database. Firstly, weighted gene coexpression network analysis (WGCNA) was used to screen IDD-related genes. Moreover, least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine (SVM) algorithms were used to identify characteristic genes. Furthermore, we further investigated the immune landscape by the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm and the correlations between key characteristic genes and infiltrating immune cells. Finally, a competing endogenous RNA (ceRNA) network was established to show the regulatory mechanisms of characteristic genes. A total of 2458 genes were identified by WGCNA, and 48 of them were disordered. After overlapping the genes obtained by LASSO and SVM-RFE algorithms, genes including LINC01347, ASAP1-IT1, lnc-SEPT7L-1, B3GNT8, CHRNB3, CLEC4F, LOC102724000, SERINC2, and LOC102723649 were identified as characteristic genes of IDD. Moreover, differential analysis further identified ASAP1-IT1 and SERINC2 as key characteristic genes. Furthermore, we found that the expression of both ASAP1-IT1 and SERINC2 was related to the proportions of T cells gamma delta and Neutrophils. Finally, a ceRNA network was established to show the regulatory mechanisms of ASAP1-IT1 and SERINC2. In conclusion, the present study identified ASAP1-IT1 and SERINC2 as the key characteristic genes of IDD through integrative bioinformatic analyses, which may contribute to the diagnosis and treatment of IDD.

ceRNA Network and Functional Enrichment Analysis of Preeclampsia by Weighted Gene Coexpression Network Analysis

Background. Preeclampsia (PE) is a multisystemic syndrome which has short- and long-term risk to mothers and children and has pluralistic etiology. Objective. This study is aimed at constructing a competitive endogenous RNA (ceRNA) network for pathways most related to PE using a data mining strategy based on weighted gene coexpression network analysis (WGCNA). Methods. We focused on pathways involving hypoxia, angiogenesis, and epithelial mesenchymal transition according to the gene set variation analysis (GSVA) scores. The gene sets of these three pathways were enriched by gene set enrichment analysis (GSEA). WGCNA was used to study the underlying molecular mechanisms of the three pathways in the pathogenesis of PE by analyzing the relationship among pathways and genes. The soft threshold power (β) and topological overlap matrix allowed us to obtain 15 modules, among which the red module was chosen for the downstream analysis. We chose 10 hub genes that satisfied ∣ log 2 Fold Change ∣ > 2 and had a higher degree of connectivity within the module. These candidate genes were subsequently confirmed to have higher gene significance and module membership in the red module. Coexpression networks were established for the hub genes to unfold the connection between the genes in the red module and PE. Finally, ceRNA networks were constructed to further clarify the underlying molecular mechanism involved in the occurrence of PE. 56 circRNAs, 17 lncRNAs, and 20 miRNAs participated in the regulation of the hub genes. Coagulation factor II thrombin receptor (F2R) and lumican (LUM) were considered the most relevant genes, and ceRNA networks of them were constructed. Conclusion. The microarray data mining process based on bioinformatics methods constructed lncRNA and miRNA networks for ten hub genes that were closely related to PE and focused on ceRNAs of F2R and LUM finally. The results of our study may provide insight into the mechanisms underlying PE occurrence.

Integrated weighted gene coexpression network analysis identifies Frizzled 2 (FZD2) as a key gene in invasive malignant pleomorphic adenoma

Background Invasive malignant pleomorphic adenoma (IMPA) is a highly malignant neoplasm of the oral salivary glands with a poor prognosis and a considerable risk of recurrence. Many disease-causing genes of IMPA have been identified in recent decades (e.g., P53, PCNA and HMGA2), but many of these genes remain to be explored. Weighted gene coexpression network analysis (WGCNA) is a newly emerged algorithm that can cluster genes and form modules based on similar gene expression patterns. This study constructed a gene coexpression network of IMPA via WGCNA and then carried out multifaceted analysis to identify novel disease-causing genes. Methods RNA sequencing (RNA-seq) was performed for 10 pairs of IMPA and normal tissues to acquire the gene expression profiles. Differentially expressed genes (DEGs) were screened out with the cutoff criteria of |log2 Fold change (FC)|> 1 and adjusted p value < 0.05. Then, WGCNA was applied to systematically identify the hidden diagnostic hub genes of IMPA. Results In this research, a total of 1970 DEGs were screened out in IMPA tissues, including 1056 upregulated DEGs and 914 downregulated DEGs. Functional enrichment analysis was performed for identified DEGs and revealed an enrichment of tumor-associated GO terms and KEGG pathways. We used WGCNA to identify gene module most relevant with the histological grade of IMPA. The gene FZD2 was then recognized as the hub gene of the selected module with the highest module membership (MM) value and intramodule connectivity in protein–protein interaction (PPI) network. According to immunohistochemistry (IHC) staining, the expression level of FZD2 was higher in low-grade IMPA than in high-grade IMPA. Conclusion FZD2 shows an expression dynamic that is negatively correlated with the clinical malignancy of IMPA and it plays a central role in the transcription network of IMPA. Thus, FZD2 serves as a promising histological indicator for the precise prediction of IMPA histological stages.

Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data

Background Constructing gene coexpression networks is a powerful approach for analyzing high-throughput gene expression data towards module identification, gene function prediction, and disease-gene prioritization. While optimal workflows for constructing coexpression networks, including good choices for data pre-processing, normalization, and network transformation, have been developed for microarray-based expression data, such well-tested choices do not exist for RNA-seq data. Almost all studies that compare data processing and normalization methods for RNA-seq focus on the end goal of determining differential gene expression. Results Here, we present a comprehensive benchmarking and analysis of 36 different workflows, each with a unique set of normalization and network transformation methods, for constructing coexpression networks from RNA-seq datasets. We test these workflows on both large, homogenous datasets and small, heterogeneous datasets from various labs. We analyze the workflows in terms of aggregate performance, individual method choices, and the impact of multiple dataset experimental factors. Our results demonstrate that between-sample normalization has the biggest impact, with counts adjusted by size factors producing networks that most accurately recapitulate known tissue-naive and tissue-aware gene functional relationships. Conclusions Based on this work, we provide concrete recommendations on robust procedures for building an accurate coexpression network from an RNA-seq dataset. In addition, researchers can examine all the results in great detail at https://krishnanlab.github.io/RNAseq_coexpression to make appropriate choices for coexpression analysis based on the experimental factors of their RNA-seq dataset.

Weighted Gene Coexpression Network Analysis in Mouse Livers following Ischemia-Reperfusion and Extensive Hepatectomy

In mouse models, the recovery of liver volume is mainly mediated by the proliferation of hepatocytes after partial hepatectomy that is commonly accompanied with ischemia-reperfusion. The identification of differently expressed genes in liver following partial hepatectomy benefits the better understanding of the molecular mechanisms during liver regeneration (LR) with appliable clinical significance. Briefly, studying different gene expression patterns in liver tissues collected from the mice group that survived through extensive hepatectomy will be of huge critical importance in LR than those collected from the mice group that survived through appropriate hepatectomy. In this study, we performed the weighted gene coexpression network analysis (WGCNA) to address the central candidate genes and to construct the free-scale gene coexpression networks using the identified dynamic different expressive genes in liver specimens from the mice with 85% hepatectomy (20% for seven-day survial rate) and 50% hepatectomy (100% for seven-day survial rate under ischemia-reperfusion condition compared with the sham group control mice). The WGCNA combined with Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses pinpointed out the apparent distinguished importance of three gene expression modules: the blue module for apoptotic process, the turquoise module for lipid metabolism, and the green module for fatty acid metabolic process in LR following extensive hepatectomy. WGCNA analysis and protein-protein interaction (PPI) network construction highlighted FAM175B, OGT, and PDE3B were the potential three hub genes in the previously mentioned three modules. This work may help to provide new clues to the future fundamental study and treatment strategy for LR following liver injury and hepatectomy.

Constructing local cell-specific networks from single-cell data

Gene coexpression networks yield critical insights into biological processes, and single-cell RNA sequencing provides an opportunity to target inquiries at the cellular level. However, due to the sparsity and heterogeneity of transcript counts, it is challenging to construct accurate gene networks. We develop an approach, locCSN, that estimates cell-specific networks (CSNs) for each cell, preserving information about cellular heterogeneity that is lost with other approaches. LocCSN is based on a nonparametric investigation of the joint distribution of gene expression; hence it can readily detect nonlinear correlations, and it is more robust to distributional challenges. Although individual CSNs are estimated with considerable noise, average CSNs provide stable estimates of networks, which reveal gene communities better than traditional measures. Additionally, we propose downstream analysis methods using CSNs to utilize more fully the information contained within them. Repeated estimates of gene networks facilitate testing for differences in network structure between cell groups. Notably, with this approach, we can identify differential network genes, which typically do not differ in gene expression, but do differ in terms of the coexpression networks. These genes might help explain the etiology of disease. Finally, to further our understanding of autism spectrum disorder, we examine the evolution of gene networks in fetal brain cells and compare the CSNs of cells sampled from case and control subjects to reveal intriguing patterns in gene coexpression.

Cross-Kingdom Gene Coexpression Analysis Using a Stemphylium botryosum–Lens ervoides System Revealed Plasticity of Intercommunication Between the Pathogen Secretome and the Host Immune Systems

Necrotrophic pathogens are responsible for significant declines in crop yield and quality worldwide. During the infection process, a pathogen releases a series of secretory proteins to counteract the plant immune system, and this interaction of pathogen and host molecules determines whether the pathogen will successfully invade the host plant tissues. In this study, we adopted co-transcriptomic approaches to analyze the Lens ervoides–Stemphylium botryosum system, with a focus on 1,216 fungal genes coding for secretory proteins and 8,810 disease-responsive genes of the host 48, 96, and 144 h postinoculation, captured in two F9 recombinant inbred lines (RILs) displaying contrasting disease responses. By constructing in planta gene coexpression networks (GCNs) for S. botryosum, we found that the pathogen tended to co-upregulate genes regulating cell wall degradation enzymes, effectors, oxidoreductases, and peptidases to a much higher degree in the susceptible host LR-66-577 than in the resistant RIL LR-66-637, indicating that the promotion of these digestive enzymes and toxins increased S. botryosum virulence. Construction of cross-kingdom GCNs between pathogen and plant for the two RILs revealed that the co-upregulation of these fungal digestive enzymes and toxins simultaneously promoted a series of defense responses such as redox change, expression of membrane-related genes and serine/threonine kinase, and stress and disease responses in the susceptible RIL which was not observed in the resistant RIL, indicating that these activities exacerbated susceptibility to S. botryosum. [Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Transcriptomic Analysis Reveals Regulatory Networks for Osmotic Water Stress and Rewatering Response in the Leaves of Ginkgo biloba

To elucidate the transcriptomic regulation mechanisms that underlie the response of Ginkgo biloba to dehydration and rehydration, we used ginkgo saplings exposed to osmotically driven water stress and subsequent rewatering. When compared with a control group, 137, 1453, 1148, and 679 genes were differentially expressed in ginkgo leaves responding to 2, 6, 12, and 24 h of water deficit, and 796 and 1530 genes were differentially expressed responding to 24 and 48 h of rewatering. Upregulated genes participated in the biosynthesis of abscisic acid, eliminating reactive oxygen species (ROS), and biosynthesis of flavonoids and bilobalide, and downregulated genes were involved in water transport and cell wall enlargement in water stress-treated ginkgo leaves. Under rehydration conditions, the genes associated with water transport and cell wall enlargement were upregulated, and the genes that participated in eliminating ROS and the biosynthesis of flavonoids and bilobalide were downregulated in the leaves of G. biloba. Furthermore, the weighted gene coexpression networks were established and correlated with distinct water stress and rewatering time-point samples. Hub genes that act as key players in the networks were identified. Overall, these results indicate that the gene coexpression networks play essential roles in the transcriptional reconfiguration of ginkgo leaves in response to water stress and rewatering.

Identification of four hub genes in venous thromboembolism via weighted gene coexpression network analysis

Background The pathogenic mechanisms of venous thromboembolism (VT) remain to be defined. This study aimed to identify differentially expressed genes (DEGs) that could serve as potential therapeutic targets for VT. Methods Two human datasets (GSE19151 and GSE48000) were analyzed by the robust rank aggregation method. Gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analyses were conducted for the DEGs. To explore potential correlations between gene sets and clinical features and to identify hub genes, we utilized weighted gene coexpression network analysis (WGCNA) to build gene coexpression networks incorporating the DEGs. Then, the levels of the hub genes were analyzed in the GSE datasets. Based on the expression of the hub genes, the possible pathways were explored by gene set enrichment analysis and gene set variation analysis. Finally, the diagnostic value of the hub genes was assessed by receiver operating characteristic (ROC) analysis in the GEO database. Results In this study, we identified 54 upregulated and 10 downregulated genes that overlapped between normal and VT samples. After performing WGCNA, the magenta module was the module with the strongest negative correlation with the clinical characteristics. From the key module, FECH, GYPA, RPIA and XK were chosen for further validation. We found that these genes were upregulated in VT samples, and high expression levels were related to recurrent VT. Additionally, the four hub genes might be highly correlated with ribosomal and metabolic pathways. The ROC curves suggested a diagnostic value of the four genes for VT. Conclusions These results indicated that FECH, GYPA, RPIA and XK could be used as promising biomarkers for the prognosis and prediction of VT.

Weighted gene coexpression correlation network analysis reveals a potential molecular regulatory mechanism of anthocyanin accumulation under different storage temperatures in ‘Friar’ plum

Background Flesh is prone to accumulate more anthocyanin in postharvest ‘Friar’ plum (Prunus salicina Lindl.) fruit stored at an intermediate temperature. However, little is known about the molecular mechanism of anthocyanin accumulation regulated by storage temperature in postharvest plum fruit. Results To reveal the potential molecular regulation mechanism of anthocyanin accumulation in postharvest ‘Friar’ plum fruit stored at different temperatures (0 °C, 10 °C and 25 °C), the fruit quality, metabolite profile and transcriptome of its flesh were investigated. Compared to the plum fruit stored at 0 °C and 25 °C, the fruit stored at 10 °C showed lower fruit firmness after 14 days and reduced the soluble solids content after 21 days of storage. The metabolite analysis indicated that the fruit stored at 10 °C had higher contents of anthocyanins (pelargonidin-3-O-glucoside, cyanidin-3-O-glucoside, cyanidin-3-O-rutinoside and quercetin-3-O-rutinose), quercetin and sucrose in the flesh. According to the results of weighted gene coexpression correlation network analysis (WGCNA), the turquoise module was positively correlated with the content of anthocyanin components, and flavanone 3-hydroxylase (F3H) and chalcone synthase (CHS) were considered hub genes. Moreover, MYB family transcription factor APL (APL), MYB10 transcription factor (MYB10), ethylene-responsive transcription factor WIN1 (WIN1), basic leucine zipper 43-like (bZIP43) and transcription factor bHLH111-like isoform X2 (bHLH111) were closely related to these hub genes. Further qRT–PCR analysis verified that these transcription factors were specifically more highly expressed in plum flesh stored at 10 °C, and their expression profiles were significantly positively correlated with the structural genes of anthocyanin synthesis as well as the content of anthocyanin components. In addition, the sucrose biosynthesis-associated gene sucrose synthase (SS) was upregulated at 10 °C, which was also closely related to the anthocyanin content of plum fruit stored at 10 °C. Conclusions The present results suggest that the transcription factors APL, MYB10, WIN1, bZIP43 and bHLH111 may participate in the accumulation of anthocyanin in ‘Friar’ plum flesh during intermediate storage temperatures by regulating the expression of anthocyanin biosynthetic structural genes. In addition, the SS gene may play a role in anthocyanin accumulation in plum flesh by regulating sucrose biosynthesis.