Gene Co-Expression Network Analysis for Identifying Modules and Functionally Enriched Pathways in Type 1 Diabetes

A gene network-driven approach to infer novel pathogenicity-associated genes: application to Pseudomonas aeruginosa PAO1

IMPORTANCE

We present here a new systems-level approach to decipher genetic factors and biological pathways associated with virulence and/or antibiotic treatment of bacterial pathogens. The power of this approach was demonstrated by application to a well-studied pathogen Pseudomonas aeruginosa PAO1. Our gene co-expression network-based approach unraveled known and unknown genes and their networks associated with pathogenicity in P. aeruginosa PAO1. The systems-level investigation of P. aeruginosa PAO1 helped identify putative pathogenicity and resistance-associated genetic factors that could not otherwise be detected by conventional approaches of differential gene expression analysis. The network-based analysis uncovered modules that harbor genes not previously reported by several original studies on P. aeruginosa virulence and resistance. These could potentially act as molecular determinants of P. aeruginosa PAO1 pathogenicity and responses to antibiotics.

The proteome and phosphoproteome of circulating extracellular vesicle-enriched preparations are associated with characteristic clinical features in type 1 diabetes

Introduction

There are no validated clinical or laboratory biomarkers to identify and differentiate endotypes of type 1 diabetes (T1D) or the risk of progression to chronic complications. Extracellular vesicles (EVs) have been studied as biomarkers in several different disease states but have not been well studied in T1D.

Methods

As the initial step towards circulating biomarker identification in T1D, this pilot study aimed to provide an initial characterization of the proteomic and phosphoproteomic landscape of circulating EV-enriched preparations in participants with established T1D (N=10) and healthy normal volunteers (Controls) (N=7) (NCT03379792) carefully matched by age, race/ethnicity, sex, and BMI. EV-enriched preparations were obtained using EVtrap® technology. Proteins were identified and quantified by LC-MS analysis. Differential abundance and coexpression network (WGCNA), and pathway enrichment analyses were implemented.

Results

The detected proteins and phosphoproteins were enriched (75%) in exosomal proteins cataloged in the ExoCarta database. A total of 181 proteins and 8 phosphoproteins were differentially abundant in participants with T1D compared to controls, including some well-known EVproteins (i.e., CD63, RAB14, BSG, LAMP2, and EZR). Enrichment analyses of differentially abundant proteins and phosphoproteins of EV-enriched preparations identified associations with neutrophil, platelet, and immune response functions, as well as prion protein aggregation. Downregulated proteins were involved in MHC class II signaling and the regulation of monocyte differentiation. Potential key roles in T1D for C1q, plasminogen, IL6ST, CD40, HLA-DQB1, HLA-DRB1, CD74, NUCB1, and SAP, are highlighted. Remarkably, WGCNA uncovered two protein modules significantly associated with pancreas size, which may be implicated in the pathogenesis of T1D. Similarly, these modules showed significant enrichment for membrane compartments, processes associated with inflammation and the immune response, and regulation of viral processes, among others.

Discussion

This study demonstrates the potential of proteomic and phosphoproteomic signatures of EV-enriched preparations to provide insight into the pathobiology of T1D. The WGCNA analysis could be a powerful tool to discriminate signatures associated with different pathobiological components of the disease.

Identification of key regulatory genes and their working mechanisms in type 1 diabetes

BACKGROUND

Type 1 diabetes (T1D) is an autoimmune disease characterized by the destruction of beta cells in pancreatic islets. Identification of the key genes involved in T1D progression and their mechanisms of action may contribute to a better understanding of T1D.

METHODS

The microarray profile of T1D-related gene expression was searched using the Gene Expression Omnibus (GEO) database. Then, the expression data of two messenger RNAs (mRNAs) were integrated for Weighted Gene Co-Expression Network Analysis (WGCNA) to generate candidate genes related to T1D. In parallel, T1D microRNA (miRNA) data were analyzed to screen for possible regulatory miRNAs and their target genes. An miRNA-mRNA regulatory network was then established to predict the key regulatory genes and their mechanisms.

RESULTS

A total of 24 modules (i.e., clusters/communities) were selected using WGCNA analysis, in which three modules were significantly associated with T1D. Further correlation analysis of the gene module revealed 926 differentially expressed genes (DEGs), of which 327 genes were correlated with T1D. Analysis of the miRNA microarray showed that 13 miRNAs had significant expression differences in T1D. An miRNA-mRNA network was established based on the prediction of miRNA target genes and the combined analysis of mRNA, in which the target genes of two miRNAs were found in T1D correlated genes.

CONCLUSION

An miRNA-mRNA network for T1D was established, based on which 2 miRNAs and 12 mRNAs were screened, suggesting that they may play key regulatory roles in the initiation and development of T1D.

Construction and analysis of gene co-expression network in the pathogenic fungus Ustilago maydis

Introduction

Biological systems respond to environmental disturbances and a wide range of compounds through complex gene interaction networks. The enormous growth of experimental information obtained using large-scale genomic techniques such as microarrays and RNA sequencing led to the construction of a wide variety of gene co-expression networks in recent years. These networks allow the discovery of clusters of co-expressed genes that potentially work in the same process linking them to biological processes often of interest to industrial, medicinal, and academic research.

Methods

In this study, we built the gene co-expression network of Ustilago maydis from the gene expression data of 168 samples belonging to 19 series, which correspond to the GPL3681 platform deposited in the NCBI using WGCNA software. This network was analyzed to identify clusters of co-expressed genes, gene hubs and Gene Ontology terms. Additionally, we identified relevant modules through a hypergeometric approach based on a predicted set of transcription factors and virulence genes.

Results and Discussion

We identified 13 modules in the gene co-expression network of U. maydis. The TFs enriched in the modules of interest belong to the superfamilies of Nucleic acid-binding proteins, Winged helix DNA-binding, and Zn2/Cys6 DNA-binding. On the other hand, the modules enriched with virulence genes were classified into diseases related to corn smut, Invasive candidiasis, among others. Finally, a large number of hypothetical, a large number of hypothetical genes were identified as highly co-expressed with virulence genes, making them possible experimental targets.

In-depth systems biological evaluation of bovine alveolar macrophages suggests novel insights into molecular mechanisms underlying Mycobacterium bovis infection

OBJECTIVE

Bovine tuberculosis (bTB) is a chronic respiratory infectious disease of domestic livestock caused by intracellular Mycobacterium bovis infection, which causes ~$3 billion in annual losses to global agriculture. Providing novel tools for bTB managements requires a comprehensive understanding of the molecular regulatory mechanisms underlying the M. bovis infection. Nevertheless, a combination of different bioinformatics and systems biology methods was used in this study in order to clearly understand the molecular regulatory mechanisms of bTB, especially the immunomodulatory mechanisms of M. bovis infection.

METHODS

RNA-seq data were retrieved and processed from 78 (39 non-infected control vs. 39 M. bovis-infected samples) bovine alveolar macrophages (bAMs). Next, weighted gene co-expression network analysis (WGCNA) was performed to identify the co-expression modules in non-infected control bAMs as reference set. The WGCNA module preservation approach was then used to identify non-preserved modules between non-infected controls and M. bovis-infected samples (test set). Additionally, functional enrichment analysis was used to investigate the biological behavior of the non-preserved modules and to identify bTB-specific non-preserved modules. Co-expressed hub genes were identified based on module membership (MM) criteria of WGCNA in the non-preserved modules and then integrated with protein-protein interaction (PPI) networks to identify co-expressed hub genes/transcription factors (TFs) with the highest maximal clique centrality (MCC) score (hub-central genes).

RESULTS

As result, WGCNA analysis led to the identification of 21 modules in the non-infected control bAMs (reference set), among which the topological properties of 14 modules were altered in the M. bovis-infected bAMs (test set). Interestingly, 7 of the 14 non-preserved modules were directly related to the molecular mechanisms underlying the host immune response, immunosuppressive mechanisms of M. bovis, and bTB development. Moreover, among the co-expressed hub genes and TFs of the bTB-specific non-preserved modules, 260 genes/TFs had double centrality in both co-expression and PPI networks and played a crucial role in bAMs-M. bovis interactions. Some of these hub-central genes/TFs, including PSMC4, SRC, BCL2L1, VPS11, MDM2, IRF1, CDKN1A, NLRP3, TLR2, MMP9, ZAP70, LCK, TNF, CCL4, MMP1, CTLA4, ITK, IL6, IL1A, IL1B, CCL20, CD3E, NFKB1, EDN1, STAT1, TIMP1, PTGS2, TNFAIP3, BIRC3, MAPK8, VEGFA, VPS18, ICAM1, TBK1, CTSS, IL10, ACAA1, VPS33B, and HIF1A, had potential targets for inducing immunomodulatory mechanisms by M. bovis to evade the host defense response.

CONCLUSION

The present study provides an in-depth insight into the molecular regulatory mechanisms behind M. bovis infection through biological investigation of the candidate non-preserved modules directly related to bTB development. Furthermore, several hub-central genes/TFs were identified that were significant in determining the fate of M. bovis infection and could be promising targets for developing novel anti-bTB therapies and diagnosis strategies.

Drug repositioning in non-small cell lung cancer (NSCLC) using gene co-expression and drug–gene interaction networks analysis

Lung cancer is the most common cancer in men and women. This cancer is divided into two main types, namely non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Around 85 to 90 percent of lung cancers are NSCLC. Repositioning potent candidate drugs in NSCLC treatment is one of the important topics in cancer studies. Drug repositioning (DR) or drug repurposing is a method for identifying new therapeutic uses of existing drugs. The current study applies a computational drug repositioning method to identify candidate drugs to treat NSCLC patients. To this end, at first, the transcriptomics profile of NSCLC and healthy (control) samples was obtained from the GEO database with the accession number GSE21933. Then, the gene co-expression network was reconstructed for NSCLC samples using the WGCNA, and two significant purple and magenta gene modules were extracted. Next, a list of transcription factor genes that regulate purple and magenta modules' genes was extracted from the TRRUST V2.0 online database, and the TF–TG (transcription factors–target genes) network was drawn. Afterward, a list of drugs targeting TF–TG genes was obtained from the DGIdb V4.0 database, and two drug–gene interaction networks, including drug-TG and drug-TF, were drawn. After analyzing gene co-expression TF–TG, and drug–gene interaction networks, 16 drugs were selected as potent candidates for NSCLC treatment. Out of 16 selected drugs, nine drugs, namely Methotrexate, Olanzapine, Haloperidol, Fluorouracil, Nifedipine, Paclitaxel, Verapamil, Dexamethasone, and Docetaxel, were chosen from the drug-TG sub-network. In addition, nine drugs, including Cisplatin, Daunorubicin, Dexamethasone, Methotrexate, Hydrocortisone, Doxorubicin, Azacitidine, Vorinostat, and Doxorubicin Hydrochloride, were selected from the drug-TF sub-network. Methotrexate and Dexamethasone are common in drug-TG and drug-TF sub-networks. In conclusion, this study proposed 16 drugs as potent candidates for NSCLC treatment through analyzing gene co-expression, TF–TG, and drug–gene interaction networks.

Identification of Key Genes Associated With Early Calf-Hood Nutrition in Subcutaneous and Visceral Adipose Tissues by Co-Expression Analysis

Background

Substantive evidence has confirmed that nutrition state is associated with health risk and the onset of pubertal and metabolic profile. Due to heterogeneity, adipose tissues in different anatomical positions tend to show various metabolic mechanisms for nutrition. To date, the complicated molecular mechanisms of early calf-hood nutrition on bovine adipose tissue are still largely unknown. This study aimed to identify key genes and functionally enriched pathways associated with early calf-hood nutrition in visceral and subcutaneous adipose tissue.

Results

The RNA-seq data of visceral and subcutaneous adipose tissues of calves feeding on low and high dietary nutrition for more than 100 days were downloaded and analyzed by weighted gene co-expression network analysis (WGCNA). Two modules that positively associated with a low plane of nutrition diet and two modules with a high plane of nutrition diet were identified in the subcutaneous adipose tissue. The blue and yellow modules, most closely associated with low and high nutrition, were selected for the functional enrichment analysis and exploration of hub genes. The results showed that genes in the blue module were significantly enriched in pathways that related to fat metabolism, reproduction, and cell communication. Genes in the yellow module were enriched in pathways related to fat metabolism, reproduction, cell proliferation, and senescence. Meanwhile, the blue and brown modules in visceral adipose tissue were most closely associated with low and high nutrition, respectively. Notably, genes of the blue module were significantly enriched in pathways related to substance metabolism, and genes in the brown module were significantly enriched in energy metabolism and disease pathways. Finally, key genes in subcutaneous adipose tissue for low nutrition (PLCG1, GNA11, and ANXA5) and high nutrition (BUB1B, ASPM, RRM2, PBK, NCAPG, and MKI67), and visceral adipose tissue for low nutrition (RPS5, RPL4, RPL14, and RPLP0) and high nutrition (SDHA and AKT1) were obtained and verified.

Conclusion

The study applied WGCNA to identify hub genes and functionally enriched pathways in subcutaneous and visceral adipose tissue and provided a basis for studying the effect of early calf-hood nutrition on the two adipose tissue types.

Identifying key genes in milk fat metabolism by weighted gene co-expression network analysis

Milk fat is the most important and energy-rich substance in milk, and its content and composition are important reference elements in the evaluation of milk quality. However, the current identification of valuable candidate genes affecting milk fat is limited. IlluminaPE150 was used to sequence bovine mammary epithelial cells (BMECs) with high and low milk fat rates (MFP), the weighted gene co-expression network (WGCNA) was used to analyze mRNA expression profile data in this study. As a result, a total of 10,310 genes were used to construct WGCNA, and the genes were classified into 18 modules. Among them, violet (r = 0.74), yellow (r = 0.75) and darkolivegreen (r =  − 0.79) modules were significantly associated with MFP, and 39, 181, 75 hub genes were identified, respectively. Combining enrichment analysis and differential genes (DEs), we screened five key candidate DEs related to lipid metabolism, namely PI4K2A, SLC16A1, ATP8A2, VEGFD and ID1, respectively. Relative to the small intestine, liver, kidney, heart, ovary and uterus, the gene expression of PI4K2A is the highest in mammary gland, and is significantly enriched in GO terms and pathways related to milk fat metabolism, such as monocarboxylic acid transport, phospholipid transport, phosphatidylinositol signaling system, inositol phosphate metabolism and MAPK signaling pathway. This study uses WGCNA to form an overall view of MFP, providing a theoretical basis for identifying potential pathways and hub genes that may be involved in milk fat synthesis.

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

Background

The recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches.

Methods

RNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules.

Results

Based on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19's main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis.

Conclusion

This study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.

Precision Medicine in Patients with Differential Diabetic Phenotypes: Novel Opportunities from Network Medicine

INTRODUCTION

Diabetes mellitus (DM) comprises differential clinical phenotypes ranging from rare monogenic to common polygenic forms, such as type 1 (T1DM), type 2 (T2DM), and gestational diabetes, which are associated with cardiovascular complications. Also, the high- -risk prediabetic state is rising worldwide, suggesting the urgent need for early personalized strategies to prevent and treat a hyperglycemic state.

OBJECTIVE

We aim to discuss the advantages and challenges of Network Medicine approaches in clarifying disease-specific molecular pathways, which may open novel ways for repurposing approved drugs to reach diabetes precision medicine and personalized therapy.

CONCLUSION

The interactome or protein-protein interactions (PPIs) is a useful tool to identify subtle molecular differences between precise diabetic phenotypes and predict putative novel drugs. Despite being previously unappreciated as T2DM determinants, the growth factor receptor-bound protein 14 (GRB14), calmodulin 2 (CALM2), and protein kinase C-alpha (PRKCA) might have a relevant role in disease pathogenesis. Besides, in silico platforms have suggested that diflunisal, nabumetone, niflumic acid, and valdecoxib may be suitable for the treatment of T1DM; phenoxybenzamine and idazoxan for the treatment of T2DM by improving insulin secretion; and hydroxychloroquine reduce the risk of coronary heart disease (CHD) by counteracting inflammation. Network medicine has the potential to improve precision medicine in diabetes care and enhance personalized therapy. However, only randomized clinical trials will confirm the clinical utility of network- oriented biomarkers and drugs in the management of DM.

Dynamic changes in immune gene co-expression networks predict development of type 1 diabetes

Significant progress has been made in elucidating genetic risk factors influencing Type 1 diabetes (T1D); however, features other than genetic variants that initiate and/or accelerate islet autoimmunity that lead to the development of clinical T1D remain largely unknown. We hypothesized that genetic and environmental risk factors can both contribute to T1D through dynamic alterations of molecular interactions in physiologic networks. To test this hypothesis, we utilized longitudinal blood transcriptomic profiles in The Environmental Determinants of Diabetes in the Young (TEDDY) study to generate gene co-expression networks. In network modules that contain immune response genes associated with T1D, we observed highly dynamic differences in module connectivity in the 600 days (~ 2 years) preceding clinical diagnosis of T1D. Our results suggest that gene co-expression is highly plastic and that connectivity differences in T1D-associated immune system genes influence the timing and development of clinical disease.

Regulatory network of miRNA, lncRNA, transcription factor and target immune response genes in bovine mastitis

Pre- and post-transcriptional modifications of gene expression are emerging as foci of disease studies, with some studies revealing the importance of non-coding transcripts, like long non-coding RNAs (lncRNAs) and microRNAs (miRNAs). We hypothesize that transcription factors (TFs), lncRNAs and miRNAs modulate immune response in bovine mastitis and could potentially serve as disease biomarkers and/or drug targets. With computational analyses, we identified candidate genes potentially regulated by miRNAs and lncRNAs base pair complementation and thermodynamic stability of binding regions. Remarkably, we found six miRNAs, two being bta-miR-223 and bta-miR-24-3p, to bind to several targets. LncRNAs NONBTAT027932.1 and XR_003029725.1, were identified to target several genes. Functional and pathway analyses revealed lipopolysaccharide-mediated signaling pathway, regulation of chemokine (C-X-C motif) ligand 2 production and regulation of IL-23 production among others. The overarching interactome deserves further in vitro/in vivo explication for specific molecular regulatory mechanisms during bovine mastitis immune response and could lay the foundation for development of disease markers and therapeutic intervention.

Integrated Network Analysis to Identify Key Modules and Potential Hub Genes Involved in Bovine Respiratory Disease: A Systems Biology Approach

Background: Bovine respiratory disease (BRD) is the most common disease in the beef and dairy cattle industry. BRD is a multifactorial disease resulting from the interaction between environmental stressors and infectious agents. However, the molecular mechanisms underlying BRD are not fully understood yet. Therefore, this study aimed to use a systems biology approach to systematically evaluate this disorder to better understand the molecular mechanisms responsible for BRD. Methods: Previously published RNA-seq data from whole blood of 18 healthy and 25 BRD samples were downloaded from the Gene Expression Omnibus (GEO) and then analyzed. Next, two distinct methods of weighted gene coexpression network analysis (WGCNA), i.e., module-trait relationships (MTRs) and module preservation (MP) analysis were used to identify significant highly correlated modules with clinical traits of BRD and non-preserved modules between healthy and BRD samples, respectively. After identifying respective modules by the two mentioned methods of WGCNA, functional enrichment analysis was performed to extract the modules that are biologically related to BRD. Gene coexpression networks based on the hub genes from the candidate modules were then integrated with protein-protein interaction (PPI) networks to identify hub-hub genes and potential transcription factors (TFs). Results: Four significant highly correlated modules with clinical traits of BRD as well as 29 non-preserved modules were identified by MTRs and MP methods, respectively. Among them, two significant highly correlated modules (identified by MTRs) and six nonpreserved modules (identified by MP) were biologically associated with immune response, pulmonary inflammation, and pathogenesis of BRD. After aggregation of gene coexpression networks based on the hub genes with PPI networks, a total of 307 hub-hub genes were identified in the eight candidate modules. Interestingly, most of these hub-hub genes were reported to play an important role in the immune response and BRD pathogenesis. Among the eight candidate modules, the turquoise (identified by MTRs) and purple (identified by MP) modules were highly biologically enriched in BRD. Moreover, STAT1, STAT2, STAT3, IRF7, and IRF9 TFs were suggested to play an important role in the immune system during BRD by regulating the coexpressed genes of these modules. Additionally, a gene set containing several hub-hub genes was identified in the eight candidate modules, such as TLR2, TLR4, IL10, SOCS3, GZMB, ANXA1, ANXA5, PTEN, SGK1, IFI6, ISG15, MX1, MX2, OAS2, IFIH1, DDX58, DHX58, RSAD2, IFI44, IFI44L, EIF2AK2, ISG20, IFIT5, IFITM3, OAS1Y, HERC5, and PRF1, which are potentially critical during infection with agents of bovine respiratory disease complex (BRDC). Conclusion: This study not only helps us to better understand the molecular mechanisms responsible for BRD but also suggested eight candidate modules along with several promising hub-hub genes as diagnosis biomarkers and therapeutic targets for BRD.

Combining Genome and Gene Co-expression Network Analyses for the Identification of Genes Potentially Regulating Salt Tolerance in Rice

Salinity stress tolerance is a complex polygenic trait involving multi-molecular pathways. This study aims to demonstrate an effective transcriptomic approach for identifying genes regulating salt tolerance in rice. The chromosome segment substitution lines (CSSLs) of "Khao Dawk Mali 105 (KDML105)" rice containing various regions of DH212 between markers RM1003 and RM3362 displayed differential salt tolerance at the booting stage. CSSL16 and its nearly isogenic parent, KDML105, were used for transcriptome analysis. Differentially expressed genes in the leaves of seedlings, flag leaves, and second leaves of CSSL16 and KDML105 under normal and salt stress conditions were subjected to analyses based on gene co-expression network (GCN), on two-state co-expression with clustering coefficient (CC), and on weighted gene co-expression network (WGCN). GCN identified 57 genes, while 30 and 59 genes were identified using CC and WGCN, respectively. With the three methods, some of the identified genes overlapped, bringing the maximum number of predicted salt tolerance genes to 92. Among the 92 genes, nine genes, OsNodulin, OsBTBZ1, OsPSB28, OsERD, OsSub34, peroxidase precursor genes, and three expressed protein genes, displayed SNPs between CSSL16 and KDML105. The nine genes were differentially expressed in CSSL16 and KDML105 under normal and salt stress conditions. OsBTBZ1 and OsERD were identified by the three methods. These results suggest that the transcriptomic approach described here effectively identified the genes regulating salt tolerance in rice and support the identification of appropriate QTL for salt tolerance improvement.

Construction and Analysis of Coexpression Network to Understand Biological Responses in Chickens Infected by Eimeria tenella

Coccidiosis, caused by various Eimeria species, is a major parasitic disease in chickens. Our understanding of how chickens respond to coccidian infections is highly limited at both the molecular and cellular levels. In this study, coexpression modules were identified by weighted gene coexpression network analysis in chickens infected with Eimeria tenella. A total of 15 correlation modules were identified using 5,175 genes with 24 chicken samples, 12 with primary and 12 with secondary E. tenella infection. The analysis of the interactions between these modules showed a high degree of scale independence. Gene Ontology and Kyoto Encyclopedia of Gene and Genomes enrichment analyses revealed that genes in these functional modules were involved in a broad categories of functions, such as immune response, amino acid metabolism, cellular responses to lipids, sterol biosynthetic processes, and RNA transport. Two modules viz yellow and magenta were identified significantly associating with infection status. Preservation analysis showed that most of the modules identified in E. tenella infections were highly or moderately preserved in chickens infected with either Eimeria acervulina or Eimeria maxima. These analyses outline a biological responses landscape for chickens infected by E. tenella, and also indicates that infections with these three Eimeria species elicit similar biological responses in chickens at the system level. These findings provide new clues and ideas for investigating the relationship between parasites and host, and the control of parasitic diseases.

In silico-driven analysis of the Glossina morsitans morsitans antennae transcriptome in response to repellent or attractant compounds

BACKGROUND

High-throughput sequencing generates large volumes of biological data that must be interpreted to make meaningful inference on the biological function. Problems arise due to the large number of characteristics p (dimensions) that describe each record [n] in the database. Feature selection using a subset of variables extracted from the large datasets is one of the approaches towards solving this problem.

METHODOLOGY

In this study we analyzed the transcriptome of Glossina morsitans morsitans (Tsetsefly) antennae after exposure to either a repellant (δ-nonalactone) or an attractant (ε-nonalactone). We identified 308 genes that were upregulated or downregulated due to exposure to a repellant (δ-nonalactone) or an attractant (ε-nonalactone) respectively. Weighted gene coexpression network analysis was used to cluster the genes into 12 modules and filter unconnected genes. Discretized and association rule mining was used to find association between genes thereby predicting the putative function of unannotated genes.

RESULTS AND DISCUSSION

Among the significantly expressed chemosensory genes (FDR < 0.05) in response to Ɛ-nonalactone were gustatory receptors (GrIA and Gr28b), ionotrophic receptors (Ir41a and Ir75a), odorant binding proteins (Obp99b, Obp99d, Obp59a and Obp28a) and the odorant receptor (Or67d). Several non-chemosensory genes with no assigned function in the NCBI database were co-expressed with the chemosensory genes. Exposure to a repellent (δ-nonalactone) did not show any significant change between the treatment and control samples. We generated a coexpression network with 276 edges and 130 nodes. Genes CAH3, Ahcy, Ir64a, Or67c, Ir8a and Or67a had node degree values above 11 and therefore could be regarded as the top hub genes in the network. Association rule mining showed a relation between various genes based on their appearance in the same itemsets as consequent and antecedent.

Linking Coregulated Gene Modules with Polycyclic Aromatic Hydrocarbon-Related Cancer Risk in the 3D Human Bronchial Epithelium

Exposure to polycyclic aromatic hydrocarbons (PAHs) often occurs as complex chemical mixtures, which are linked to numerous adverse health outcomes in humans, with cancer as the greatest concern. The cancer risk associated with PAH exposures is commonly evaluated using the relative potency factor (RPF) approach, which estimates PAH mixture carcinogenic potential based on the sum of relative potency estimates of individual PAHs, compared to benzo[a]pyrene (BAP), a reference carcinogen. The present study evaluates molecular mechanisms related to PAH cancer risk through integration of transcriptomic and bioinformatic approaches in a 3D human bronchial epithelial cell model. Genes with significant differential expression from human bronchial epithelium exposed to PAHs were analyzed using a weighted gene coexpression network analysis (WGCNA) two-tiered approach: first to identify gene sets comodulated to RPF and second to link genes to a more comprehensive list of regulatory values, including inhalation-specific risk values. Over 3000 genes associated with processes of cell cycle regulation, inflammation, DNA damage, and cell adhesion processes were found to be comodulated with increasing RPF with pathways for cell cycle S phase and cytoskeleton actin identified as the most significantly enriched biological networks correlated to RPF. In addition, comodulated genes were linked to additional cancer-relevant risk values, including inhalation unit risks, oral cancer slope factors, and cancer hazard classifications from the World Health Organization's International Agency for Research on Cancer (IARC). These gene sets represent potential biomarkers that could be used to evaluate cancer risk associated with PAH mixtures. Among the values tested, RPF values and IARC categorizations shared the most similar responses in positively and negatively correlated gene modules. Together, we demonstrated a novel manner of integrating gene sets with chemical toxicity equivalence estimates through WGCNA to understand potential mechanisms.

Transcriptome Dynamics Underlying Chlamydospore Formation in Trichoderma virens GV29-8

Trichoderma spp. are widely used biocontrol agents which are antagonistic to a variety of plant pathogens. Chlamydospores are a type of propagules produced by many fungi that have thick walls and are highly resistant to adverse environmental conditions. Chlamydospore preparations of Trichoderma spp. can withstand various storage conditions, have a longer shelf life than conidial preparations and have better application potential. However, large-scale production of chlamydospores has proven difficult. To understand the molecular mechanisms governing chlamydospore formation (CF) in Trichoderma fungi, we performed a comprehensive analysis of transcriptome dynamics during CF across 8 different developmental time points, which were divided into 4 stages according to PCA analysis: the mycelium growth stage (S1), early and middle stage of CF (S2), flourishing stage of CF (S3), and late stage of CF and mycelia initial autolysis (S4). 2864, 3206, and 3630 DEGs were screened from S2 vs S1, S3 vs S2, and S4 vs S3, respectively. We then identified the pathways and genes that play important roles in each stage of CF by GO, KEGG, STC and WGCNA analysis. The results showed that DEGs in the S2 vs S1 were mainly enriched in organonitrogen compound metabolism, those in S3 vs S2 were mainly involved in secondary metabolite, cell cycle, and N-glycan biosynthesis, and DEGs in S4 vs S3 were mainly involved in lipid, glycogen, and chitin metabolic processes. We speculated that mycelial assimilation and absorption of exogenous nitrogen in the early growth stage (S1), resulted in subsequent nitrogen deficiency (S2). At the same time, secondary metabolites and active oxygen free radicals released during mycelial growth produced an adverse growth environment. The resulting nitrogen-deficient and toxin enriched medium may stimulate cell differentiation by initiating cell cycle regulation to induce morphological transformation of mycelia into chlamydospores. High expression of genes relating to glycogen, lipid, mannan, and chitin synthetic metabolic pathways during the flourishing (S3) and late stages (S4) of CF may be conducive to energy storage and cell wall construction in chlamydospores. For further verifying the functions of the amino sugar and nucleotide sugar metabolism (tre00520) pathway in the CF of T. virens GV29-8 strain, the chitin synthase gene (TRIVIDRAFT_90152), one key gene of the pathway, was deleted and resulted in the dysplasia of mycelia and an incapability to form normal chlamydospores, which illustrated the pathway affecting the CF of T. virens GV29-8 strain. Our results provide a new perspective for understanding the genetics of biochemical pathways involved in CF of Trichoderma spp.

Identification of dysregulated pathways through <i>SLC30A8</i> protein interaction in type 1 diabetes mellitus

<abstract><sec> <title>Objective</title> <p>The aim of the current study was to explore the gene enrichment and dysregulated pathways on the basis of interaction network analysis of <italic>SLC30A8</italic> in type 1 diabetes mellitus (T1DM). <italic>SLC30A8</italic> polymorphism could be characterized as a beneficial tool to identify the interacting gene in developing T1DM.</p> </sec><sec> <title>Materials and methods</title> <p><italic>SLC30A8</italic> interacting protein interaction network was obtained by String Interaction network Version 11.0. Ten proteins were identified interacting with <italic>SLC30A8</italic> and were analysed by protein-protein interaction and enrichment network analysis along with Functional Enrichment analysis tool (FunRich 3.1.3) to map the gene data sets. In entire analysis, FunRich database was used as background against all annotated gene/protein list. Protein-protein interaction (PPI) and enrichment network analysis of the selected protein: <italic>SLC30A8</italic> gene along with gene mapping and pathway enrichment were performed using FunRich 3.1.3 and String Interaction network Version 11.0.</p> </sec><sec> <title>Results</title> <p>Biological pathway grouping displayed enriched proteins in TRAIL signalling pathway (<italic>p</italic> &lt; 0.001). <italic>PTPRN, GAD2</italic> and <italic>TCF7L2</italic> were enriched in TRAIL Signalling pathway when <italic>INS</italic> was made focused gene and directly interacting with <italic>SLC30A8</italic>.</p> </sec><sec> <title>Conclusions</title> <p>TRAIL signalling pathways were enriched in T1DM. Therefore, <italic>SLC30A8</italic> along with <italic>PTPRN, GAD2</italic> and <italic>TCF7L2</italic> involved in TRAIL pathway must be further explored to understand their in vivo role in T1DM.</p> </sec></abstract>

Weighted Gene Co-expression Network Analysis of Key Biomarkers Associated With Bronchopulmonary Dysplasia

Bronchopulmonary dysplasia (BPD) is a complex disorder resulting from interactions between genes and the environment. The accurate molecular etiology of BPD remains largely unclear. This study aimed to identify key BPD-associated genes and pathways functionally enriched using weighted gene co-expression network analysis (WGCNA). We analyzed microarray data of 62 pre-term patients with BPD and 38 pre-term patients without BPD from Gene Expression Omnibus (GEO). WGCNA was used to construct a gene expression network, and genes were classified into definite modules. In addition, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of BPD-related hub genes were performed. Firstly, we constructed a weighted gene co-expression network, and genes were divided into 10 modules. Among the modules, the yellow module was related to BPD progression and severity and included the following hub genes: MMP25, MMP9, SIRPA, CKAP4, SLCO4C1, and SLC2A3; and the red module included some co-expression molecules that displayed a continuous decline in expression with BPD progression and included the following hub genes: LEF1, ITK, CD6, RASGRP1, IL7R, SKAP1, CD3E, and ICOS. GO and KEGG analyses showed that high expression of inflammatory response-related genes and low expression of T cell receptor activation-related genes are significantly correlated with BPD progression. The present WGCNA-based study thus provides an overall perspective of BPD and lays the foundation for identifying potential pathways and hub genes that contribute to the development of BPD.

Hub Genes Identification in a Murine Model of Allergic Rhinitis Based on Bioinformatics Analysis

This study aimed to identify allergic rhinitis (AR)-related hub genes and functionally enriched pathways in a murine model. Dataset GSE52804 (including three normal controls and three AR mice) was downloaded from Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein-protein interaction (PPI) analyses of DEGs were performed to identify the hub genes in AR. The DEGs were classified into different modules by using the weighted gene co-expression network analysis (WGCNA). Moreover, to verify the potential hub genes, nasal mucosa tissues were obtained from murine AR models (n = 5) and controls (n = 5), and qRT-PCR and Western blot were performed. In this study, a total of 634 DEGs were identified. They were significantly enriched in 14 GO terms, such as integral component of membrane, plasma membrane, and G-protein-coupled receptor signaling pathway. Meanwhile, there were eight terms of KEGG pathways significantly enriched, such as Olfactory transduction, Cytokine-cytokine receptor interaction, and TNF signaling pathway. The top 10 hub genes (Rtp1, Rps27a, Penk, Cxcl2, Gng8, Gng3, Cxcl1, Cxcr2, Ccl9, and Anxa1) were identified by the PPI network. DEGs were classified into seven modules by WGCNA. According to qRT-PCR validation of the five genes of interest (Rtp1, Rps27a, Penk, Cxcl2, and Anxa1), the expression level of Rtp1 mRNA was significantly decreased in the AR group compared with the control group, while there are enhanced Rps27a, Penk, Cxcl2, and Anxa1 mRNA expressions in the AR mice group compared with the control group. Western blot was also performed to further explore the expression of Anxa1 in the protein level, and the results showed a similar expression trend.

Computational Analysis of the Global Effects of Ly6E in the Immune Response to Coronavirus Infection Using Gene Networks

Gene networks have arisen as a promising tool in the comprehensive modeling and analysis of complex diseases. Particularly in viral infections, the understanding of the host-pathogen mechanisms, and the immune response to these, is considered a major goal for the rational design of appropriate therapies. For this reason, the use of gene networks may well encourage therapy-associated research in the context of the coronavirus pandemic, orchestrating experimental scrutiny and reducing costs. In this work, gene co-expression networks were reconstructed from RNA-Seq expression data with the aim of analyzing the time-resolved effects of gene Ly6E in the immune response against the coronavirus responsible for murine hepatitis (MHV). Through the integration of differential expression analyses and reconstructed networks exploration, significant differences in the immune response to virus were observed in Ly6E Δ H S C compared to wild type animals. Results show that Ly6E ablation at hematopoietic stem cells (HSCs) leads to a progressive impaired immune response in both liver and spleen. Specifically, depletion of the normal leukocyte mediated immunity and chemokine signaling is observed in the liver of Ly6E Δ H S C mice. On the other hand, the immune response in the spleen, which seemed to be mediated by an intense chromatin activity in the normal situation, is replaced by ECM remodeling in Ly6E Δ H S C mice. These findings, which require further experimental characterization, could be extrapolated to other coronaviruses and motivate the efforts towards novel antiviral approaches.

Systems biology approach identifies functional modules and regulatory hubs related to secondary metabolites accumulation after transition from autotrophic to heterotrophic growth condition in microalgae

Heterotrophic growth mode is among the most promising strategies put forth to overcome the low biomass and secondary metabolites productivity challenge. To shedding light on the underlying molecular mechanisms, transcriptome meta-analysis was integrated with weighted gene co-expression network analysis (WGCNA), connectivity analysis, functional enrichment, and hubs identification. Meta-analysis and Functional enrichment analysis demonstrated that most of the biological processes are up-regulated at heterotrophic growth condition, which leads to change of genetic architectures and phenotypic outcomes. WGNCA analysis of meta-genes also resulted four significant functional modules across logarithmic (LG), transition (TR), and production peak (PR) phases. The expression pattern and connectivity characteristics of the brown module as a non-preserved module vary across LG, TR, and PR phases. Functional analysis identified Carotenoid biosynthesis, Fatty acid metabolism and Methane metabolism as enriched pathways in the non-preserved module. Our integrated approach was applied here, identified some hubs, such as a serine hydroxymethyltransferase (SHMT1), which is the best candidate for development of metabolites accumulating strains in microalgae. Current study provided a new insight into underlying metabolite accumulation mechanisms and opens new avenue for the future applied studies in the microalgae field.

Identification of key molecular targets that correlate with breast cancer through bioinformatic methods

BACKGROUND

The present study aimed to identify key molecular targets of breast cancer for targeted treatment and to improve the survival rate.

METHODS

Overlapped difference expression genes in three datasets were identified in a weighted gene co-expression network analysis (WGCNA) module and MetaDE.ES analysis. Combined with the prognosis information [time, death, status and relative survival (RS)] in GSE42568, single-factor Cox regression analysis was used to screen the genes that were significantly related to the prognosis in the target gene set.

RESULTS

In total, 13 optimal gene combinations with a significantly correlated prognosis were obtained, including SSPN, NELL2, AGTR1, NRIP3, IKZF2, NAT1, CXCL12, NPY1R, PRAME, PPP1R1B, CRISP3, NMU and GSTP1. In addition, there was a significant correlation between the samples given by the prognostic prediction system and the validation dataset (GSE20685 and TCGA), with p values of 0.0299 in GSE20685 and 1.461 × 10^-5 in TCGA, and an area under the receiver operating characteristic of 0.942 and 0.923, respectively. RS-related differentially expressed genes between high- and low-risk groups were significantly related to biological processes such as cell period and the hormone stimulation response, and were also significantly involved in KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways such as cell period, the peroxisome proliferator-activated receptor signaling pathway and the cancer pathway.

CONCLUSIONS

By predicting the survival risk of breast cancer patients based on the 13 optimal genes, high-risk patients would be detected early. Accordingly, this would help in the formulation of an appropriate treatment plan for patients.

Identification of Modules With Similar Gene Regulation and Metabolic Functions Based on Co-expression Data

Biological systems respond to environmental perturbations and to a large diversity of compounds through gene interactions, and these genetic factors comprise complex networks. In particular, a wide variety of gene co-expression networks have been constructed in recent years thanks to the dramatic increase of experimental information obtained with techniques, such as microarrays and RNA sequencing. These networks allow the identification of groups of co-expressed genes that can function in the same process and, in turn, these networks may be related to biological functions of industrial, medical and academic interest. In this study, gene co-expression networks for 17 bacterial organisms from the COLOMBOS database were analyzed via weighted gene co-expression network analysis and clustered into modules of genes with similar expression patterns for each species. These networks were analyzed to determine relevant modules through a hypergeometric approach based on a set of transcription factors and enzymes for each genome. The richest modules were characterized using PFAM families and KEGG metabolic maps. Additionally, we conducted a Gene Ontology analysis for enrichment of biological functions. Finally, we identified modules that shared similarity through all the studied organisms by using comparative genomics.

Identifying Key Genes and Functionally Enriched Pathways in Sjögren's Syndrome by Weighted Gene Co-Expression Network Analysis

Purpose: Sjögren’s syndrome (SS) is an autoimmune disease characterized by dry mouth and eyes. To date, the exact molecular mechanisms of its etiology are still largely unknown. The aim of this study was to identify SS related key genes and functionally enriched pathways using the weighted gene co-expression network analysis (WGCNA).

Materials and Methods: We downloaded the microarray data of 190 SS patients and 32 controls from Gene Expression Omnibus (GEO). Gene network was constructed and genes were classified into different modules using WGCNA. In addition, for the hub genes in the most related module to SS, gene ontology analysis was applied. The expression profile and diagnostic capacity (ROC curve) of interested hub genes were verified using a dataset from the GEO. Moreover, gene set enrichment analysis (GSEA) was also performed.

Results: A total of 1483 differentially expressed genes were filtered. Weighted gene coexpression network was constructed and genes were classified into 17 modules. Among them, the turquoise module was most closely associated with SS, which contained 278 genes. These genes were significantly enriched in 10 Gene Ontology terms, such as response to virus, immune response, defense response, response to cytokine stimulus, and the inflammatory response. A total of 19 hub genes (GBP1, PARP9, EPSTI1, LOC400759, STAT1, STAT2, IFIH1, EIF2AK2, TDRD7, IFI44, PARP12, FLJ20035, PARP14, ISGF3G, XAF1, RSAD2,LY6E, IFI44L, and DDX58) were identified. The expression levels of the five interested genes including EIF2AK2, GBP1, PARP12, PARP14, and TDRD7 were also confirmed. ROC curve analysis determined that the above five genes’ expression can distinguish SS from controls (the area under the curve is all greater than 0.7). GSEA suggests that the SS samples with highly expressed EIF2AK2 or TDRD7 genes are correlated with inflammatory response, interferon α response, and interferon γ response.

Conclusion: The present study applied WGCNA to generate a holistic view of SS and provide a basis for the identification of potential pathways and hub genes that may be involved in the development of SS.

A Novel Genes Signature Associated with the Progression of Polycystic Ovary Syndrome

To identify genes involving in the pathogenesis of polycystic ovary syndrome (PCOS). In this study, the comprehensive analysis of GSE8157 was downloaded. Overlapping genes of differentially expressed genes (DEGs) were identified, and enrichment analysis for these genes was performed. A modular network of differentially expressed genes was constructed by weighted gene co-expression network analyses (WGCNA), and a total of 322 differentially expressed genes in 5 stable modules were screened. The correlations of genes of the stable modules in BioGRID 3.4, STRING 10.5, HPRD9 databases were screened, and the interaction network of 104 DEGs was constructed. In addition, some genes and the key words were searched in CTD. A total of 596 differentially expressed genes were screened, including 379 genes that were up-regulated in case group and down-regulated in control group and treat group, and 217 genes that were down-regulated in case group and up-regulated in control group and treat group. The differentially expressed genes were enriched in PPAR signaling pathway, Neuroactive ligand-receptor interaction, cAMP signaling pathway, of which pathways were involved in the cancer development. Finally, 7 important target genes were identified, such as APOC3 was interacted with pioglitazone, ADCY2 involved in cAMP signaling pathway, and the genes (C3AR1, HRH2, GRIA1, MLNR and TAAR2) involved in neuroactive ligand-receptor interaction. In addition, the important target genes were significantly differential expression. These results implied that the 7 important target genes were played an important role in the development and progression of PCOS. Our study implied that genes had played a key role in the development and progression of PCOS, the results showed that microarray can be use as a method for the discovery of new biomarkers and therapeutic targets for PCOS.

Novel Genomic Biomarker Candidates for Cervical Cancer As Identified by Differential Co-Expression Network Analysis

Cervical cancer is the second most common malignancy and the third reason for mortality among women in developing countries. Although infection by the oncogenic human papilloma viruses is a major cause, genomic contributors are still largely unknown. Network analyses, compared with candidate gene studies, offer greater promise to map the interactions among genomic loci contributing to cervical cancer risk. We report here a differential co-expression network analysis in five gene expression datasets (GSE7803, GSE9750, GSE39001, GSE52903, and GSE63514, from the Gene Expression Omnibus) in patients with cervical cancer and healthy controls. Kaplan-Meier Survival and principle component analyses were employed to evaluate prognostic and diagnostic performances of biomarker candidates, respectively. As a result, seven distinct co-expressed gene modules were identified. Among these, five modules (with sizes of 9-45 genes) presented high prognostic and diagnostic capabilities with hazard ratios of 2.28-11.3, and diagnostic odds ratios of 85.2-548.8. Moreover, these modules were associated with several key biological processes such as cell cycle regulation, keratinization, neutrophil degranulation, and the phospholipase D signaling pathway. In addition, transcription factors ETS1 and GATA2 were noted as common regulatory elements. These genomic biomarker candidates identified by differential co-expression network analysis offer new prospects for translational cancer research, not to mention personalized medicine to forecast cervical cancer susceptibility and prognosis. Looking into the future, we also suggest that the search for a molecular basis of common complex diseases should be complemented by differential co-expression analyses to obtain a systems-level understanding of disease phenotype variability.

Downregulation of genes outside the deleted region in individuals with 22q11.2 deletion syndrome

The 22q11.2 deletion syndrome (22q11.2DS) is caused by recurrent hemizygous deletions of chromosome 22q11.2. The phenotype of the syndrome is complex and varies widely among individuals. Little is known about the role of the different genes located in 22q11.2, and we hypothesized that genetic risk factors lying elsewhere in the genome might contribute to the phenotype. Here, we present the whole-genome gene expression data of 11 patients with approximately 3 Mb deletions. Apart from the hemizygous genes mapped to the 22q11.2 region, the TUBA8 and GNAZ genes, neighboring the deleted interval but in normal copy number, showed altered expression. When genes mapped to other chromosomes were considered in the gene expression analysis, a genome-wide dysregulation was observed, with increased or decreased expression levels. The enriched pathways of these genes were related to immune response, a deficiency that is frequently observed in 22q11.2DS patients. We also used the hypothesis-free weighted gene co-expression network analysis (WGCNA), which revealed the co-expression gene network modules with clear connection to mechanisms associated with 22q11.2DS such as immune response and schizophrenia. These findings, combined with the traditional gene expression profile, can be used for the identification of potential pathways and genes not previously considered to be related to the 22q11.2 deletion syndrome.

Identification of dynamic molecular networks in peripheral blood mononuclear cells in type 1 diabetes mellitus

BACKGROUND

Type 1 diabetes mellitus (T1DM) is an autoimmune disease caused by the immune destruction of islet β cells. Gene expression in peripheral blood mononuclear cells (PBMCs) could offer new disease and treatment markers in T1DM. The objective of this study was to explore the coexpression and dynamic molecular networks in PBMCs of T1DM patients.

METHODS

Dataset GSE9006 contains PBMC samples of healthy volunteers, newly diagnosed T1DM patients, T1DM patients after insulin treatment, and newly diagnosed type 2 diabetes mellitus (T2DM) patients. Weighted correlation network analysis (WGCNA) was used to generate coexpression networks in T1DM and T2DM. Functional pathways in highly correlated modules of T1DM were enriched by gene set enrichment analysis (GSEA). We next filtered the differentially expressed genes (DEGs) and revealed their dynamic expression profiles in T1DM with or without insulin treatment. Furthermore, dynamic clusters and dynamic protein-protein interaction networks were identified. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was developed in dynamic clusters.

RESULTS

WGCNA disclosed 12 distinct gene modules, and distinguished between correlated networks in T1DM and T2DM. Two modules were closely associated with T1DM. GSEA showed that the immune response and response to cytokines were enriched in the T1DM highly correlated module. Next, we screened 44 DEGs in newly diagnosed T1DM compared with healthy donors, and 71 DEGs in 1-month and 97 DEGs in 4-month insulin treatment groups compared with newly diagnosed T1DM. Dynamic expression profiles of DEGs indicated the potential targets for T1DM treatment. Moreover, four molecular dynamic clusters were analyzed in newly diagnosed and insulin-treated T1DM. Functional annotation showed that these clusters were mainly enriched in the IL-17 signaling pathway, nuclear factor-ϰB signaling pathway, and tumor necrosis factor signaling pathway.

CONCLUSION

The results indicate potential drug targets or clinical efficacy markers, as well as demonstrating the underlying molecular mechanisms of T1DM treatment.

Microarray analysis of long non-coding RNA and mRNA expression profiles in diabetic cardiomyopathy using human induced pluripotent stem cell-derived cardiomyocytes

AIM

This study aims to investigate the altered expression signature of long non-coding RNAs, mRNAs and deregulated pathways related to diabetic cardiomyopathy disease pathogenesis.

METHOD

We utilize the previously established in vitro diabetic cardiomyopathy model of human induced pluripotent stem cell-derived human cardiomyocytes to perform long non-coding RNA and mRNA expression analysis on glucose (11 mM), endothelin-1 (10 nM) and cortisol (1 µM) stimulated human induced pluripotent stem cell-derived human cardiomyocytes to interrogate diabetic cardiomyopathy associated RNA expression profile.

RESULT

Out of 20,730 mRNAs and 40,173 long non-coding RNAs being screened, 2046 long non-coding RNAs and 1582 mRNAs were differentially regulated (fold change > 2, p < 0.05) between diabetic cardiomyopathy and control group, of which more than half were intergenic and antisense long non-coding RNAs. Most of the coding transcripts were associated with processes like inflammation, structural reorganization, metabolism, smooth muscle contraction, focal adhesion and repair contributing towards the development of diabetic cardiomyopathy. The subgroup analysis further revealed 411 long non-coding RNAs being co-expressed with neighbouring genes. However, our coding-non-coding co-expression analysis showed an overall 48,155 co-expression network connections. In addition to that, the long non-coding RNAs with highest network connections were profoundly enriched for focal adhesion, cell-matrix adhesion and muscle contraction.

CONCLUSION

These results provide comprehensive data about the pathways and regulatory mechanisms associated with diabetic cardiomyopathy and indicate that long non-coding RNAs may play a crucial role in diabetic cardiomyopathy.

Comparison of gene co-networks analysis provide a systems view of rice (Oryza sativa L.) response to Tilletia horrida infection

The biotrophic soil-borne fungus Tilletia horrida causes rice kernel smut, an important disease affecting the production of rice male sterile lines in most hybrid rice growing regions of the world. There are no successful ways of controlling this disease and there has been little study of mechanisms of resistance to T. horrida. Based on transcriptional data of different infection time points, we found 23, 782 and 23, 718 differentially expressed genes (fragments per kilobase of transcript sequence per million, FPKM >1) in Jiangcheng 3A (resistant to T. horrida) and 9311A (susceptible to T. horrida), respectively. In order to illuminate the differential responses of the two rice male sterile lines to T. horrida, we identified gene co-expression modules using the method of weighted gene co-expression network analysis (WGCNA) and compared the different biological functions of gene co-expression networks in key modules at different infection time points. The results indicated that gene co-expression networks in the two rice genotypes were different and that genes contained in some modules of the two groups may play important roles in resistance to T. horrida, such as DTH8 and OsHop/Sti1a. Furthermore, these results provide a global view of the responses of two different phenotypes to T. horrida, and assist our understanding of the regulation of expression changes after T. horrida infection.

Key gene co-expression modules and functional pathways involved in the pathogenesis of Graves' disease

Graves' disease (GD) is a common autoimmune thyroid disease characterized by positive thyroid stimulating hormone receptor antibody. To better understand its molecular pathogenesis, we adopted the weighted gene co-expression network analysis to reveal co-expression modules of key genes involved in the pathogenesis of GD, protein-protein interaction network analysis to identify the hub genes related to GD development and functional analyses to explore their possible functions. Our results showed that 1) a total of 2667 differentially expressed genes in our microarray study and 16 different gene co-expression modules were associated with GD, and 2) the most significant module was associated with the percentage of macrophages, T follicular helper cells and CD4⁺ memory T cells and mainly enriched in immune regulation and immune response. Overall, our study reveals several key gene co-expression modules and functional pathways involved in GD, which provides some novel insights into the pathogenesis of GD.

Weighted Gene Co-expression Network Analysis of Endometriosis and Identification of Functional Modules Associated With Its Main Hallmarks

Although many genes have been identified using high throughput technologies in endometriosis (ES), only a small number of individual genes have been analyzed functionally. This is due to the complexity of the disease that has different stages and is affected by various genetic and environmental factors. Many genes are upregulated or downregulated at each stage of the disease, thus making it difficult to identify key genes. In addition, little is known about the differences between the different stages of the disease. We assumed that the study of the identified genes in ES at a system-level can help to better understand the molecular mechanism of the disease at different stages of the development. We used publicly available microarray data containing archived endometrial samples from women with minimal/mild endometriosis (MMES), mild/severe endometriosis (MSES) and without endometriosis. Using weighted gene co-expression analysis (WGCNA), functional modules were derived from normal endometrium (NEM) as the reference sample. Subsequently, we tested whether the topology or connectivity pattern of the modules was preserved in MMES and/or MSES. Common and specific hub genes were identified in non-preserved modules. Accordingly, hub genes were detected in the non-preserved modules at each stage. We identified sixteen co-expression modules. Of the 16 modules, nine were non-preserved in both MMES and MSES whereas five were preserved in NEM, MMES, and MSES. Importantly, two non-preserved modules were found in either MMES or MSES, highlighting differences between the two stages of the disease. Analyzing the hub genes in the non-preserved modules showed that they mostly lost or gained their centrality in NEM after developing the disease into MMES and MSES. The same scenario was observed, when the severeness of the disease switched from MMES to MSES. Interestingly, the expression analysis of the new selected gene candidates including CC2D2A, AEBP1, HOXB6, IER3, and STX18 as well as IGF-1, CYP11A1 and MMP-2 could validate such shifts between different stages. The overrepresented gene ontology (GO) terms were enriched in specific modules, such as genetic disposition, estrogen dependence, progesterone resistance and inflammation, which are known as endometriosis hallmarks. Some modules uncovered novel co-expressed gene clusters that were not previously discovered.

System network analysis of genomics and transcriptomics data identified type 1 diabetes-associated pathway and genes

Genome-wide association studies (GWASs) have discovered >50 risk loci for type 1 diabetes (T1D). However, those variations only have modest effects on the genetic risk of T1D. In recent years, accumulated studies have suggested that gene-gene interactions might explain part of the missing heritability. The purpose of our research was to identify potential and novel risk genes for T1D by systematically considering the gene-gene interactions through network analyses. We carried out a novel system network analysis of summary GWAS statistics jointly with transcriptomic gene expression data to identify some of the missing heritability for T1D using weighted gene co-expression network analysis (WGCNA). Using WGCNA, seven modules for 1852 nominally significant (P ≤ 0.05) GWAS genes were identified by analyzing microarray data for gene expression profile. One module (tagged as green module) showed significant association (P ≤ 0.05) between the module eigengenes and the trait. This module also displayed a high correlation (r = 0.45, P ≤ 0.05) between module membership (MM) and gene significant (GS), which indicated that the green module of co-expressed genes is of significant biological importance for T1D status. By further describing the module content and topology, the green module revealed a significant enrichment in the "regulation of immune response" (GO:0050776), which is a crucially important pathway in T1D development. Our findings demonstrated a module and several core genes that act as essential components in the etiology of T1D possibly via the regulation of immune response, which may enhance our fundamental knowledge of the underlying molecular mechanisms for T1D.

Comparison of gene co-networks reveals the molecular mechanisms of the rice (Oryza sativa L.) response to Rhizoctonia solani AG1 IA infection

Rhizoctonia solani causes rice sheath blight, an important disease affecting the growth of rice (Oryza sativa L.). Attempts to control the disease have met with little success. Based on transcriptional profiling, we previously identified more than 11,947 common differentially expressed genes (TPM > 10) between the rice genotypes TeQing and Lemont. In the current study, we extended these findings by focusing on an analysis of gene co-expression in response to R. solani AG1 IA and identified gene modules within the networks through weighted gene co-expression network analysis (WGCNA). We compared the different genes assigned to each module and the biological interpretations of gene co-expression networks at early and later modules in the two rice genotypes to reveal differential responses to AG1 IA. Our results show that different changes occurred in the two rice genotypes and that the modules in the two groups contain a number of candidate genes possibly involved in pathogenesis, such as the VQ protein. Furthermore, these gene co-expression networks provide comprehensive transcriptional information regarding gene expression in rice in response to AG1 IA. The co-expression networks derived from our data offer ideas for follow-up experimentation that will help advance our understanding of the translational regulation of rice gene expression changes in response to AG1 IA.

Co-Expression Network Analysis Identifies miRNA⁻mRNA Networks Potentially Regulating Milk Traits and Blood Metabolites

MicroRNAs (miRNA) regulate mRNA networks to coordinate cellular functions. In this study, we constructed gene co-expression networks to detect miRNA modules (clusters of miRNAs with similar expression patterns) and miRNA–mRNA pairs associated with blood (triacylglyceride and nonesterified fatty acids) and milk (milk yield, fat, protein, and lactose) components and milk fatty acid traits following dietary supplementation of cows’ diets with 5% linseed oil (LSO) (n = 6 cows) or 5% safflower oil (SFO) (n = 6 cows) for 28 days. Using miRNA transcriptome data from mammary tissues of cows for co-expression network analysis, we identified three consensus modules: blue, brown, and turquoise, composed of 70, 34, and 86 miRNA members, respectively. The hub miRNAs (miRNAs with the most connections with other miRNAs) were miR-30d, miR-484 and miR-16b for blue, brown, and turquoise modules, respectively. Cell cycle arrest, and p53 signaling and transforming growth factor–beta (TGF-β) signaling pathways were the common gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched for target genes of the three modules. Protein percent (p = 0.03) correlated with the turquoise module in LSO treatment while protein yield (p = 0.003) and milk yield (p = 7 × 10⁻⁰⁴) correlated with the turquoise model, protein and milk yields and lactose percent (p < 0.05) correlated with the blue module and fat percent (p = 0.04) correlated with the brown module in SFO treatment. Several fatty acids correlated (p < 0.05) with the blue (CLA:9,11) and brown (C4:0, C12:0, C22:0, C18:1n9c and CLA:10,12) modules in LSO treatment and with the turquoise (C14:0, C18:3n3 and CLA:9,11), blue (C14:0 and C23:0) and brown (C6:0, C16:0, C22:0, C22:6n3 and CLA:10,12) modules in SFO treatment. Correlation of miRNA and mRNA data from the same animals identified the following miRNA–mRNA pairs: miR-183/RHBDD2 (p = 0.003), miR-484/EIF1AD (p = 0.011) and miR-130a/SBSPON (p = 0.004) with lowest p-values for the blue, brown, and turquoise modules, respectively. Milk yield, protein yield, and protein percentage correlated (p < 0.05) with 28, 31 and 5 miRNA–mRNA pairs, respectively. Our results suggest that, the blue, brown, and turquoise modules miRNAs, hub miRNAs, miRNA–mRNA networks, cell cycle arrest GO term, p53 signaling and TGF-β signaling pathways have considerable influence on milk and blood phenotypes following dietary supplementation of dairy cows’ diets with 5% LSO or 5% SFO.

Identification of potential functional genes in papillary thyroid cancer by co-expression network analysis

Interactions between multiple genes are involved in the development of complex diseases. However, there are few analyses of gene interactions associated with papillary thyroid cancer (PTC). Weighted gene co-expression network analysis (WGCNA) is a novel and powerful method that detects gene interactions according to their co-expression similarities. In the present study, WGCNA was performed in order to identify functional genes associated with PTC using R package. First, differential gene expression analysis was conducted in order to identify the differentially expressed genes (DEGs) between PTC and normal samples. Subsequently, co-expression networks of the DEGs were constructed for the two sample groups, respectively. The two networks were compared in order to identify a poorly preserved module. Concentrating on the significant module, validation analysis was performed to confirm the identified genes and combined functional enrichment analysis was conducted in order to identify more functional associations of these genes with PTC. As a result, 1062 DEGs were identified for network construction. A brown module containing 118 highly related genes was selected as it exhibited the lowest module preservation. After validation analysis, 61 genes in the module were confirmed to be associated with PTC. Following the enrichment analysis, two PTC-related pathways were identified: Wnt signal pathway and transcriptional misregulation in cancer. LRP4, KLK7, PRICKLE1, ETV4 and ETV5 were predicted to be candidate genes regulating the pathogenesis of PTC. These results provide novel insights into the etiology of PTC and the identification of potential functional genes.

Transcriptome profiling of whisker follicles in methamphetamine self-administered rats

Methamphetamine (MA) is a highly addictive psychostimulant that disturbs the central nervous system; therefore, diagnosis of MA addiction is important in clinical and forensic toxicology. In this study, a MA self-administration rat model was used to illustrate the gene expression profiling of the rewarding effect caused by MA. RNA-sequencing was performed to examine changes in gene expression in rat whisker follicles collected before self-administration, after MA self-administration, and after withdrawal sessions. We identified six distinct groups of genes, with statistically significant expression patterns. By constructing the functional association network of these genes and performing the subsequent topological analysis, we identified 43 genes, which have the potential to regulate MA reward and addiction. The gene pathways were then analysed using the Reactome and Knowledgebase for Addiction-Related Gene database, and it was found that genes and pathways associated with Alzheimer's disease and the heparan sulfate biosynthesis were enriched in MA self-administration rats. The findings suggest that changes of the genes identified in rat whisker follicles may be useful indicators of the rewarding effect of MA. Further studies are needed to provide a comprehensive understanding of MA addiction.

Key genes and functional coexpression modules involved in the pathogenesis of systemic lupus erythematosus

We performed a systematic review of genome-wide gene expression datasets to identify key genes and functional modules involved in the pathogenesis of systemic lupus erythematosus (SLE) at a systems level. Genome-wide gene expression datasets involving SLE patients were searched in Gene Expression Omnibus and ArrayExpress databases. Robust rank aggregation (RRA) analysis was used to integrate those public datasets and identify key genes associated with SLE. The weighted gene coexpression network analysis (WGCNA) was adapted to identify functional modules involved in SLE pathogenesis, and the gene ontology enrichment analysis was utilized to explore their functions. The aberrant expressions of several randomly selected key genes were further validated in SLE patients through quantitative real-time polymerase chain reaction. Fifteen genome-wide gene expression datasets were finally included, which involved a total of 1,778 SLE patients and 408 healthy controls. A large number of significantly upregulated or downregulated genes were identified through RRA analysis, and some of those genes were novel SLE gene signatures and their molecular roles in etiology of SLE remained vague. WGCNA further successfully identified six main functional modules involved in the pathogenesis of SLE. The most important functional module involved in SLE included 182 genes and mainly enriched in biological processes, including defense response to virus, interferon signaling pathway, and cytokine-mediated signaling pathway. This study identifies a number of key genes and functional coexpression modules involved in SLE, which provides deepening insights into the molecular mechanism of SLE at a systems level and also provides some promising therapeutic targets.

mRNA-miRNA bipartite network reconstruction to predict prognostic module biomarkers in colorectal cancer stage differentiation

Biomarker detection is one of the most important and challenging problems in cancer studies. Recently, non-coding RNA based biomarkers such as miRNA expression levels have been used for early diagnosis of many cancer types. In this study, a systems biology approach was used to detect novel miRNA based biomarkers for CRC diagnosis in early stages. The mRNA expression data from three CRC stages (Low-grade Intraepithelial Neoplasia (LIN), High-grade Intraepithelial Neoplasia (HIN) and Adenocarcinoma) were used to reconstruct co-expression networks. The networks were clustered to extract co-expression modules and detected low preserved modules among CRC stages. Then, the experimentally validated mRNA-miRNA interaction data were applied to reconstruct three mRNA-miRNA bipartite networks. Twenty miRNAs with the highest degree (hub miRNAs) were selected in each bipartite network to reconstruct three bipartite subnetworks for further analysis. The analysis of these hub miRNAs in the bipartite subnetworks revealed 30 distinct important miRNAs as prognostic markers in CRC stages. There are two novel CRC related miRNAs (hsa-miR-190a-3p and hsa-miR-1277-5p) in these 30 hub miRNAs that have not been previously reported in CRC. Furthermore, a drug-gene interaction network was reconstructed to detect potential candidate drugs for CRC treatment. Our analysis shows that the hub miRNAs in the mRNA-miRNA bipartite network are very essential in CRC progression and should be investigated precisely in future studies. In addition, there are many important target genes in the results that may be critical in CRC progression and can be analyzed as therapeutic targets in future research.