C3HeB/FeJ Mice mimic many aspects of gene expression and pathobiological features of human hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) remains a deadly cancer, underscoring the need for relevant preclinical models. Male C3HeB/FeJ mice model spontaneous HCC with some hepatocarcinogenesis susceptibility loci corresponding to syntenic regions of human chromosomes altered in HCC. We tested other properties of C3HeB/FeJ tumors for similarity to human HCC. C3HeB/FeJ tumors were grossly visible at 4 months of age, with prevalence and size increasing until about 11 months of age. Histologic features shared with human HCC include hepatosteatosis, tumor progression from dysplasia to poorly differentiated, vascular invasion, and trabecular, oncocytic, vacuolar, and clear cell variants. More tumor cells displayed cytoplasmic APE1 staining versus normal liver. Ultrasound effectively detected and monitored tumors, with 85.7% sensitivity. Over 5000 genes were differentially expressed based on the GSE62232 and GSE63898 human HCC datasets. Of these, 158 and 198 genes, respectively, were also differentially expressed in C3HeB/FeJ. Common cancer pathways, cell cycle, p53 signaling and other molecular aspects, were shared between human and mouse differentially expressed genes. We established eigengenes that distinguish HCC from normal liver in the C3HeB/FeJ model and a subset of human HCC. These features extend the relevance and improve the utility of the C3HeB/FeJ line for HCC studies.

Preservation affinity in consensus modules among stages of HIV-1 progression.

BACKGROUND: Analysis of gene expression data provides valuable insights into disease mechanism. Investigating relationship among co-expression modules of different stages is a meaningful tool to understand the way in which a disease progresses. Identifying topological preservation of modular structure also contributes to that understanding. METHODS: HIV-1 disease provides a well-documented progression pattern through three stages of infection: acute, chronic and non-progressor. In this article, we have developed a novel framework to describe the relationship among the consensus (or shared) co-expression modules for each pair of HIV-1 infection stages. The consensus modules are identified to assess the preservation of network properties. We have investigated the preservation patterns of co-expression networks during HIV-1 disease progression through an eigengene-based approach. RESULTS: We discovered that the expression patterns of consensus modules have a strong preservation during the transitions of three infection stages. In particular, it is noticed that between acute and non-progressor stages the preservation is slightly more than the other pair of stages. Moreover, we have constructed eigengene networks for the identified consensus modules and observed the preservation structure among them. Some consensus modules are marked as preserved in two pairs of stages and are analyzed further to form a higher order meta-network consisting of a group of preserved modules. Additionally, we observed that module membership (MM) values of genes within a module are consistent with the preservation characteristics. The MM values of genes within a pair of preserved modules show strong correlation patterns across two infection stages. CONCLUSIONS: We have performed an extensive analysis to discover preservation pattern of co-expression network constructed from microarray gene expression data of three different HIV-1 progression stages. The preservation pattern is investigated through identification of consensus modules in each pair of infection stages. It is observed that the preservation of the expression pattern of consensus modules remains more prominent during the transition of infection from acute stage to non-progressor stage. Additionally, we observed that the module membership values of genes are coherent with preserved modules across the HIV-1 progression stages.

Differential eigengene network analysis reveals benzo[a]pyrene and 2,3,7,8-tetrachlorodibenzo-p-dioxin consensus regulatory network in human liver cell line HepG2.

Aryl hydrocarbon receptor (AHR) is one of the main mediators of the toxic effects of benzo[a]pyrene (BaP) and 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). However, a vast number of BaP- and TCDD-affected genes may suggest a more complex transcriptional regulatory network driving common adverse effects of these two chemicals. Unlike TCDD, BaP is rapidly metabolized in the liver, yielding products with a questionable ability to bind and activate AHR. In this study, we used transcriptomics data from the BaP- and TCCD-exposed human liver cell line HepG2, and performed differential eigengene network analysis to understand the correlation among genes and to untangle the common regulatory mechanism in the action of BaP and TCDD. The genes were grouped into 11 meta-modules with an overall preservation of 0.72 and were also segregated into three consensus time clusters: 12, 24, and 48 h. The analysis showed that the consensus genes in each time cluster were either directly regulated by the AHR or the AHR-TF interactions. Some TFs form a direct physical interaction with AHR such as ESR1, FOXA1, and E2F1, whereas others, including CTCF, RXRA, FOXO1, CEBPA, CEBPB, and TP53 show an indirect interaction with AHR. The analysis of biological processes (BPs) identified unique and common BPs in BaP and TCDD samples, with DNA damage response detected in all three time points. In summary, we identified a consensus transcriptional regulatory network common for BaP and TCDD consisting of direct AHR targets and AHR-TF targets. This analysis sheds new light on the common mechanism of action of a genotoxic (BaP) and non-genotoxic (TCDD) chemical in liver cells.

Identification of disease-related genes and construction of a gene co-expression database in non-alcoholic fatty liver disease.

Background: The mechanism of NAFLD progression remains incompletely understood. Current gene-centric analysis methods lack reproducibility in transcriptomic studies. Methods: A compendium of NAFLD tissue transcriptome datasets was analyzed. Gene co-expression modules were identified in the RNA-seq dataset GSE135251. Module genes were analyzed in the R gProfiler package for functional annotation. Module stability was assessed by sampling. Module reproducibility was analyzed by the ModulePreservation function in the WGCNA package. Analysis of variance (ANOVA) and Student's t-test was used to identify differential modules. The receiver operating characteristic (ROC) curve was used to illustrate the classification performance of modules. Connectivity Map was used to mine potential drugs for NAFLD treatment. Results: Sixteen gene co-expression modules were identified in NAFLD. These modules were associated with multiple functions such as nucleus, translation, transcription factors, vesicle, immune response, mitochondrion, collagen, and sterol biosynthesis. These modules were stable and reproducible in the other 10 datasets. Two modules were positively associated with steatosis and fibrosis and were differentially expressed between non-alcoholic steatohepatitis (NASH) and non-alcoholic fatty liver (NAFL). Three modules can efficiently separate control and NAFL. Four modules can separate NAFL and NASH. Two endoplasmic reticulum related modules were both upregulated in NAFL and NASH compared to normal control. Proportions of fibroblasts and M1 macrophages are positively correlated with fibrosis. Two hub genes Aebp1 and Fdft1 may play important roles in fibrosis and steatosis. m6A genes were strongly correlated with the expression of modules. Eight candidate drugs for NAFLD treatment were proposed. Finally, an easy-to-use NAFLD gene co-expression database was developed (available at https://nafld.shinyapps.io/shiny/). Conclusion: Two gene modules show good performance in stratifying NAFLD patients. The modules and hub genes may provide targets for disease treatment.

Co-expression network of heat-response transcripts: A glimpse into how splicing factors impact rice basal thermotolerance.

To identify novel solutions to improve rice yield under rising temperatures, molecular components of thermotolerance must be better understood. Alternative splicing (AS) is a major post-transcriptional mechanism impacting plant tolerance against stresses, including heat stress (HS). AS is largely regulated by splicing factors (SFs) and recent studies have shown their involvement in temperature response. However, little is known about the splicing networks between SFs and AS transcripts in the HS response. To expand this knowledge, we constructed a co-expression network based on a publicly available RNA-seq dataset that explored rice basal thermotolerance over a time-course. Our analyses suggest that the HS-dependent control of the abundance of specific transcripts coding for SFs might explain the widespread, coordinated, complex, and delicate AS regulation of critical genes during a plant's inherent response to extreme temperatures. AS changes in these critical genes might affect many aspects of plant biology, from organellar functions to cell death, providing relevant regulatory candidates for future functional studies of basal thermotolerance.

Integrative analysis of drug response and clinical outcome in acute myeloid leukemia.

Acute myeloid leukemia (AML) is a cancer of myeloid-lineage cells with limited therapeutic options. We previously combined ex vivo drug sensitivity with genomic, transcriptomic, and clinical annotations for a large cohort of AML patients, which facilitated discovery of functional genomic correlates. Here, we present a dataset that has been harmonized with our initial report to yield a cumulative cohort of 805 patients (942 specimens). We show strong cross-cohort concordance and identify features of drug response. Further, deconvoluting transcriptomic data shows that drug sensitivity is governed broadly by AML cell differentiation state, sometimes conditionally affecting other correlates of response. Finally, modeling of clinical outcome reveals a single gene, PEAR1, to be among the strongest predictors of patient survival, especially for young patients. Collectively, this report expands a large functional genomic resource, offers avenues for mechanistic exploration and drug development, and reveals tools for predicting outcome in AML.

Gene Co-expression Network Analysis.

Gene co-expression analysis is a data analysis technique that helps identify groups of genes with similar expression patterns across several different conditions. By means of these techniques, different groups have been able to assign putative metabolic pathways and functions to understudied genes and to identify novel metabolic regulation networks for different metabolites. Some groups have even used network comparative studies to understand the evolution of these networks from green algae to land plants. In this chapter, we will go over the basic definitions required to understand network topology and gene module identification. Additionally, we offer the reader a walk-through a standard analysis pipeline as implemented in the package WGCNA that takes as input raw fastq files and obtains co-expressed gene clusters and representative gene expression patterns from each module for downstream applications.

Modular network inference between miRNA-mRNA expression profiles using weighted co-expression network analysis.

Connecting transcriptional and post-transcriptional regulatory networks solves an important puzzle in the elucidation of gene regulatory mechanisms. To decipher the complexity of these connections, we build co-expression network modules for mRNA as well as miRNA expression profiles of breast cancer data. We construct gene and miRNA co-expression modules using the weighted gene co-expression network analysis (WGCNA) method and establish the significance of these modules (Genes/miRNAs) for cancer phenotype. This work also infers an interaction network between the genes of the turquoise module from mRNA expression data and hubs of the turquoise module from miRNA expression data. A pathway enrichment analysis using a miRsystem web tool for miRNA hubs and some of their targets, reveal their enrichment in several important pathways associated with the progression of cancer.

Identification of key immune regulatory genes in HIV-1 progression.

Human immunodeficiency virus (HIV) infection causes acquired immunodeficiency syndrome (AIDS), one of the most devastating diseases affecting humankind. Here, we have proposed a framework to examine the differences among microarray gene expression data of uninfected and three different HIV-1 infection stages using module preservation statistics. We leverage the advantage of gene co-expression networks (GCN) constructed for each infection stages to detect the topological and structural changes of a group of differentially expressed genes. We examine the relationship among a set of co-expression modules by constructing a module eigengene network considering the overall similarity/dissimilarity among the genes within the modules. We have utilized different module preservation statistics with two composite statistics: "Zsummary" and "MedianRank" to examine the changes in co-expression patterns between modules. We have found several interesting results on the preservation characteristics of gene modules across different stages. Some genes are identified to be preserved in a pair of stages while altering their characteristics across other stages. We further validated the obtained results using permutation test and classification techniques. The biological significances of the obtained modules have also been examined using gene ontology and pathway-based analysis. Additionally, we have identified a set of key immune regulatory hub genes in the associated protein-protein interaction networks (PPINs) of the differentially expressed (DE) genes, which interacts with HIV-1 proteins and are likely to act as potential biomarkers in HIV-1 progression.

Unravelling the Role of miR-20b-5p, CCNB1, HMGA2 and E2F7 in Development and Progression of Non-Small Cell Lung Cancer (NSCLC).

Lung cancer is a prime cause of worldwide cancer deaths, with non-small cell lung cancer (NSCLC) as a frequent subtype. Surgical resection, chemotherapy are the currently used treatment methods. Delayed detection, poor prognosis, tumor heterogeneity, and chemoresistance make them relatively ineffective. Genomic medicine is a budding aspect of cancer therapeutics, where miRNAs are impressively involved. miRNAs are short ncRNAs that bind to 3'UTR of target mRNA, causing its degradation or translational repression to regulate gene expression. This study aims to identify important miRNA-mRNA-TF interactions in NSCLC using bioinformatics analysis. GEO datasets containing mRNA expression data of NSCLC were used to determine differentially expressed genes (DEGs), and identification of hub genes-BIRC5, CCNB1, KIF11, KIF20A, and KIF4A (all functionally enriched in cell cycle). The FFL network involved, comprised of miR-20b-5p, CCNB1, HMGA2, and E2F7. KM survival analysis determines that these components may be effective prognostic biomarkers and would be a new contemplation in NSCLC therapeutics as they target cell cycle and immunosurveillance mechanisms via HMGA2 and E2F7. They provide survival advantage and evasion of host immune response (via downregulation of cytokines-IL6, IL1R1 and upregulation of chemokines-CXCL13, CXCL14) to NSCLC. The study has provided innovative targets, but further validation is needed to confirm the proposed mechanism.

Network-based identification of signature genes KLF6 and SPOCK1 associated with oral submucous fibrosis.

The molecular mechanism of oral submucous fibrosis (OSF) is yet to be fully elucidated. The identification of reliable signature genes to screen patients with a high risk of OSF and to provide oral cancer surveillance is therefore required. The present study produced a filtering criterion based on network characteristics and principal component analysis, and identified the genes that were involved in OSF prognosis. Two gene expression datasets were analyzed using meta-analysis, the results of which revealed 1,176 biologically significant genes. A co-expression network was subsequently constructed and weighted gene modules were detected. The pathway and functional enrichment analyses of the present study allowed for the identification of modules 1 and 2, and their respective genes, SPARC (osteonectin), cwcv and kazal like domain proteoglycan 1 (SPOCK1) and kruppel like factor 6 (KLF6), which were involved in the occurrence of OSF. The results revealed that both genes had a prominent role in epithelial to mesenchymal transition during OSF progression. The genes identified in the present study require further exploration and validation within clinical settings to determine their roles in OSF.

Major Depressive Disorder Is Associated With Differential Expression of Innate Immune and Neutrophil-Related Gene Networks in Peripheral Blood: A Quantitative Review of Whole-Genome Transcriptional Data From Case-Control Studies.

BACKGROUND: Whole-genome transcription has been measured in peripheral blood samples as a candidate biomarker of inflammation associated with major depressive disorder. METHODS: We searched for all case-control studies on major depressive disorder that reported microarray or RNA sequencing measurements on whole blood or peripheral blood mononuclear cells. Primary datasets were reanalyzed, when openly accessible, to estimate case-control differences and to evaluate the functional roles of differentially expressed gene lists by technically harmonized methods. RESULTS: We found 10 eligible studies (N = 1754 depressed cases and N = 1145 healthy controls). Fifty-two genes were called significant by 2 of the primary studies (published overlap list). After harmonization of analysis across 8 accessible datasets (n = 1706 cases, n = 1098 controls), 272 genes were coincidentally listed in the top 3% most differentially expressed genes in 2 or more studies of whole blood or peripheral blood mononuclear cells with concordant direction of effect (harmonized overlap list). By meta-analysis of standardized mean difference across 4 studies of whole-blood samples (n = 1567 cases, n = 954 controls), 343 genes were found with false discovery rate <5% (standardized mean difference meta-analysis list). These 3 lists intersected significantly. Genes abnormally expressed in major depressive disorder were enriched for innate immune-related functions, coded for nonrandom protein-protein interaction networks, and coexpressed in the normative transcriptome module specialized for innate immune and neutrophil functions. CONCLUSIONS: Quantitative review of existing case-control data provided robust evidence for abnormal expression of gene networks important for the regulation and implementation of innate immune response. Further development of white blood cell transcriptional biomarkers for inflamed depression seems warranted.

Plant Networks as Traits and Hypotheses: Moving Beyond Description.

Biology relies on the central thesis that the genes in an organism encode molecular mechanisms that combine with stimuli and raw materials from the environment to create a final phenotypic expression representative of the genomic programming. While conceptually simple, the genotype-to-phenotype linkage in a eukaryotic organism relies on the interactions of thousands of genes and an environment with a potentially unknowable level of complexity. Modern biology has moved to the use of networks in systems biology to try to simplify this complexity to decode how an organism's genome works. Previously, biological networks were basic ways to organize, simplify, and analyze data. However, recent advances are allowing networks to move beyond description and become phenotypes or hypotheses in their own right. This review discusses these efforts, like mapping responses across biological scales, including relationships among cellular entities, and the direct use of networks as traits or hypotheses.

Super-Enhancer-Associated Hub Genes In Chronic Myeloid Leukemia Identified Using Weighted Gene Co-Expression Network Analysis.

PURPOSE: Super-enhancer (SE)-associated oncogenes extensively potentiate the uncontrolled proliferation capacity of cancer cells. In this study, we aimed to identify the SE-associated hub genes associated with the clinical characteristics of chronic myeloid leukemia (CML). METHODS: Eigengenes from CML clinical modules were determined using weighted gene co-expression network analysis (WGCNA). Overlapping genes between eigengenes and SE-associated genes were used to construct protein-protein interaction (PPI) networks and annotate for pathway enrichment analysis. Expression patterns of the top-ranked SE-associated hub genes were further determined in CML patients and healthy controls via real-time PCR. After treatment of K562 cells with the BRD4 inhibitor, JQ1, for 24 hrs, mRNA and protein levels of SE-associated hub genes were evaluated using real-time PCR and Western blotting, respectively. H3K27ac, H3K4me1 and BRD4 ChIP-seq signal peaks were used to predict and identify SEs visualized by the Integrative Genomics Viewer. RESULTS: The yellow module was significantly related to the status and pathological phase of CML. SE-associated hub candidate genes were mainly enriched in the cell cycle pathway. Based on the PPI networks of hub genes and the top rank of degree, five SE-associated genes were identified: specifically, BUB1, CENPO, KIF2C, ORC1, and RRM2. Elevated expression of these five genes was not only related to CML status and phase but also positively regulated by SE and suppressed by the BRD4 inhibitor, JQ1, in K562 cells. Strong signal peaks of H3K27ac, H3K4me1 and BRD4 ChIP-seq of the five genes were additionally observed close to the predicted SE regions. CONCLUSION: This is the first study to characterize SE-associated genes linked to clinical characteristics of CML via weighted gene co-expression network analysis. Our results support a novel mechanism involving aberrant expression of hub SE-associated genes in CML patients and K562 cells, and these genes will be potential new therapeutic targets for human leukemia.

Network analysis reveals seasonal variation of co-occurrence correlations between Cyanobacteria and other bacterioplankton.

Association network approaches have recently been proposed as a means for exploring the associations between bacterial communities. In the present study, high-throughput sequencing was employed to investigate the seasonal variations in the composition of bacterioplankton communities in six eutrophic urban lakes of Nanjing City, China. Over 150,000 16S rRNA sequences were derived from 52 water samples, and correlation-based network analyses were conducted. Our results demonstrated that the architecture of the co-occurrence networks varied in different seasons. Cyanobacteria played various roles in the ecological networks during different seasons. Co-occurrence patterns revealed that members of Cyanobacteria shared a very similar niche and they had weak positive correlations with other phyla in summer. To explore the effect of environmental factors on species-species co-occurrence networks and to determine the most influential environmental factors, the original positive network was simplified by module partitioning and by calculating module eigengenes. Module eigengene analysis indicated that temperature only affected some Cyanobacteria; the rest were mainly affected by nitrogen associated factors throughout the year. Cyanobacteria were dominant in summer which may result from strong co-occurrence patterns and suitable living conditions. Overall, this study has improved our understanding of the roles of Cyanobacteria and other bacterioplankton in ecological networks.

Network-based approach to identify prognostic biomarkers for estrogen receptor-positive breast cancer treatment with tamoxifen.

This study aims to identify effective gene networks and prognostic biomarkers associated with estrogen receptor positive (ER+) breast cancer using human mRNA studies. Weighted gene coexpression network analysis was performed with a complex ER+ breast cancer transcriptome to investigate the function of networks and key genes in the prognosis of breast cancer. We found a significant correlation of an expression module with distant metastasis-free survival (HR = 2.25; 95% CI .21.03-4.88 in discovery set; HR = 1.78; 95% CI = 1.07-2.93 in validation set). This module contained genes enriched in the biological process of the M phase. From this module, we further identified and validated 5 hub genes (CDK1, DLGAP5, MELK, NUSAP1, and RRM2), the expression levels of which were strongly associated with poor survival. Highly expressed MELK indicated poor survival in luminal A and luminal B breast cancer molecular subtypes. This gene was also found to be associated with tamoxifen resistance. Results indicated that a network-based approach may facilitate the discovery of biomarkers for the prognosis of ER+ breast cancer and may also be used as a basis for establishing personalized therapies. Nevertheless, before the application of this approach in clinical settings, in vivo and in vitro experiments and multi-center randomized controlled clinical trials are still needed.