Enhanced construction of gene regulatory networks using hub gene information

Integrated Analysis of Hub Genes and MicroRNAs in Human Placental Tissues from In Vitro Fertilization-Embryo Transfer

OBJECTIVE

Supraphysiological hormone exposure, in vitro culture and embryo transfer throughout the in vitro fertilization-embryo transfer (IVF-ET) procedures may affect placental development. The present study aimed to identify differences in genomic expression profiles between IVF-ET and naturally conceived placentals and to use this as a basis for understanding the underlying effects of IVF-ET on placental function.

METHODS

Full-term human placental tissues were subjected to next-generation sequencing to determine differentially expressed miRNAs (DEmiRs) and genes (DEGs) between uncomplicated IVF-ET assisted and naturally conceived pregnancies. Gene ontology (GO) enrichment analysis and transcription factor enrichment analysis were used for DEmiRs. MiRNA-mRNA interaction and protein-protein interaction (PPI) networks were constructed. In addition, hub genes were obtained by using the STRING database and Cytoscape. DEGs were analyzed using GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Differentially expressed miRNAs were validated through qRT-PCR.

RESULTS

Compared against natural pregnancies, 12 DEmiRs and 258 DEGs were identified in IVF-ET placental tissues. In a validation cohort, it was confirmed that hsa-miR-204-5p, hsa-miR-1269a, and hsa-miR-941 were downregulation, while hsa-miR-4286, hsa-miR-31-5p and hsa-miR-125b-5p were upregulation in IVF-ET placentas. Functional analysis suggested that these differentially expressed genes were significantly enriched in angiogenesis, pregnancy, PI3K-Akt and Ras signaling pathways. The miRNA-mRNA regulatory network revealed the contribution of 10 miRNAs and 109 mRNAs while EGFR was the most highly connected gene among ten hub genes in the PPI network.

CONCLUSION

Even in uncomplicated IVF-ET pregnancies, differences exist in the placental transcriptome relative to natural pregnancies. Many of the differentially expressed genes in IVF-ET are involved in essential placental functions, and moreover, they provide a ready resource of molecular markers to assess the association between placental function and safety in IVF-ET offspring.

A developmental stage-specific network approach for studying dynamic co-regulation of transcription factors and microRNAs during craniofacial development

Craniofacial development is regulated through dynamic and complex mechanisms that involve various signaling cascades and gene regulations. Disruption of such regulations can result in craniofacial birth defects. Here, we propose the first developmental stage-specific network approach by integrating two crucial regulators, transcription factors (TFs) and microRNAs (miRNAs), to study their co-regulation during craniofacial development. Specifically, we used TFs, miRNAs and non-TF genes to form feed-forward loops (FFLs) using genomic data covering mouse embryonic days E10.5 to E14.5. We identified key novel regulators (TFs Foxm1, Hif1a, Zbtb16, Myog, Myod1 and Tcf7, and miRNAs miR-340-5p and miR-129-5p) and target genes (Col1a1, Sgms2 and Slc8a3) expression of which changed in a developmental stage-dependent manner. We found that the Wnt-FoxO-Hippo pathway (from E10.5 to E11.5), tissue remodeling (from E12.5 to E13.5) and miR-129-5p-mediated Col1a1 regulation (from E10.5 to E14.5) might play crucial roles in craniofacial development. Enrichment analyses further suggested their functions. Our experiments validated the regulatory roles of miR-340-5p and Foxm1 in the Wnt-FoxO-Hippo subnetwork, as well as the role of miR-129-5p in the miR-129-5p-Col1a1 subnetwork. Thus, our study helps understand the comprehensive regulatory mechanisms for craniofacial development.

Identification and Validation of Key miRNAs and a microRNA-mRNA Regulatory Network Associated with Ulcerative Colitis

Ulcerative colitis (UC) is a chronic, nonspecific, intestinal inflammatory disease that involves various genes and pathways in its pathogenesis. The current study revealed the key miRNAs and potential target gene regulatory network as a model for predicting the molecular mechanism of UC. This may provide novel insights for unraveling the pathogenesis of UC. Differentially expressed miRNAs (DEMIs) and mRNAs (differentially expressed genes [DEGs]) between UC patients and normal controls were screened using the Gene Expression Omnibus database. DEMI target genes were predicted using the miRDB, miRWalk, starBase, TarBase, and TargetScan databases, and an miRNA-mRNA network was established using DEGs that altered in opposition to DEMIs. We verified the expression of key DEMIs in a rodent UC model. The miRNA-mRNA network contained 31 DEMIs and 199 DEGs, which showed enrichment in inflammatory bowel disease. We selected 2 key miRNAs and 4 hub genes. In addition, we identified six DEMIs and genes from the preliminary validation analysis in model tissues. In the pathophysiological process of UC, various genes and proteins display expression differences and complex interactions with each other. These findings provide new insights into the potential key mechanisms associated with UC development.

Comparison of single and module-based methods for modeling gene regulatory networks

MOTIVATION

Gene regulatory networks describe the regulatory relationships among genes, and developing methods for reverse engineering these networks is an ongoing challenge in computational biology. The majority of the initially proposed methods for gene regulatory network discovery create a network of genes and then mine it in order to uncover previously unknown regulatory processes. More recent approaches have focused on inferring modules of co-regulated genes, linking these modules with regulatory genes and then mining them to discover new molecular biology.

RESULTS

In this work we analyze module-based network approaches to build gene regulatory networks, and compare their performance to single gene network approaches. In the process, we propose a novel approach to estimate gene regulatory networks drawing from the module-based methods. We show that generating modules of co-expressed genes which are predicted by a sparse set of regulators using a variational Bayes method, and then building a bipartite graph on the generated modules using sparse regression, yields more informative networks than previous single and module-based network approaches as measured by: (i) the rate of enriched gene sets, (ii) a network topology assessment, (iii) ChIP-Seq evidence and (iv) the KnowEnG Knowledge Network collection of previously characterized gene-gene interactions.

AVAILABILITY AND IMPLEMENTATION

The code is written in R and can be downloaded from https://github.com/mikelhernaez/linker.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Integrated Bioinformatic Analysis Identifies Networks and Promising Biomarkers for Hepatitis B Virus-Related Hepatocellular Carcinoma

Chronic infection with hepatitis B virus (HBV) has long been recognized as a dominant hazard factor for hepatocellular carcinoma (HCC) and accounts for at least half of HCC instances globally. However, the underlying molecular mechanism of HBV-linked HCC has not been completely elucidated. Here, three microarray datasets, totally containing 170 tumoral samples and 181 adjacent normal tissues from the liver of patients suffering from HBV-related HCC assembled from the Gene Expression Omnibus (GEO) database, were subjected to integrated analysis of differentially expressed genes (DEGs). Subsequently, the analysis of function and pathway enrichment as well as the protein-protein interaction network (PPI) was performed. The ten hub genes screened out from the PPI network were further subjected to expression profile and survival analysis. Overall, 329 DEGs (67 upregulated and 262 downregulated) were identified. Ten DEGs with the highest degree of connectivity included cyclin-dependent kinase 1 (CDK1), cyclin B1 (CCNB1), cyclin B2 (CCNB2), PDZ-binding kinase (PBK), abnormal spindle microtubule assembly (ASPM), nuclear division cycle 80 (NDC80), aurora kinase A (AURKA), targeting protein for xenopus kinesin-like protein 2 (TPX2), kinesin family member 2C (KIF2C), and centromere protein F (CENPF). Kaplan-Meier analysis unveiled that overexpression levels of KIF2C and TPX2 were relevant to both the poor overall survival and relapse-free survival. In summary, the hub genes validated in the present study may provide promising targets for the diagnosis, prognosis, and therapy of HBV-associated HCC. Additionally, our work uncovers various crucial biological components (e.g., extracellular exosome) and signaling pathways that participate in the progression of HCC induced by HBV, serving comprehensive knowledge of the mechanisms regarding HBV-related HCC.

Gaussian and Mixed Graphical Models as (multi-)omics data analysis tools

Gaussian Graphical Models (GGMs) are tools to infer dependencies between biological variables. Popular applications are the reconstruction of gene, protein, and metabolite association networks. GGMs are an exploratory research tool that can be useful to discover interesting relations between genes (functional clusters) or to identify therapeutically interesting genes, but do not necessarily infer a network in the mechanistic sense. Although GGMs are well investigated from a theoretical and applied perspective, important extensions are not well known within the biological community. GGMs assume, for instance, multivariate normal distributed data. If this assumption is violated Mixed Graphical Models (MGMs) can be the better choice. In this review, we provide the theoretical foundations of GGMs, present extensions such as MGMs or multi-class GGMs, and illustrate how those methods can provide insight in biological mechanisms. We summarize several applications and present user-friendly estimation software. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

The biomarkers of key miRNAs and target genes associated with acute myocardial infarction

Background

Acute myocardial infarction (AMI) is considered one of the most prominent causes of death from cardiovascular disease worldwide. Knowledge of the molecular mechanisms underlying AMI remains limited. Accurate biomarkers are needed to predict the risk of AMI and would be beneficial for managing the incidence rate. The gold standard for the diagnosis of AMI, the cardiac troponin T (cTnT) assay, requires serial testing, and the timing of measurement with respect to symptoms affects the results. As attractive candidate diagnostic biomarkers in AMI, circulating microRNAs (miRNAs) are easily detectable, generally stable and tissue specific.

Methods

The Gene Expression Omnibus (GEO) database was used to compare miRNA expression between AMI and control samples, and the interactions between miRNAs and mRNAs were analysed for expression and function. Furthermore, a protein-protein interaction (PPI) network was constructed. The miRNAs identified in the bioinformatic analysis were verified by RT-qPCR in an H9C2 cell line. The miRNAs in plasma samples from patients with AMI (n = 11) and healthy controls (n = 11) were used to construct receiver operating characteristic (ROC) curves to evaluate the clinical prognostic value of the identified miRNAs.

Results

We identified eight novel miRNAs as potential candidate diagnostic biomarkers for patients with AMI. In addition, the predicted target genes provide insight into the molecular mechanisms underlying AMI.

A Cytosolic Protein Kinase STY46 in Arabidopsis thaliana is Involved in Plant Growth and Abiotic Stress Response

Starch provides plants with carbon and energy during stressful periods; however, relatively few regulators of starch metabolism under stress-induced carbon starvation have been discovered. We studied a protein kinase Ser/Thr/Tyr (STY) 46, identified by gene co-expression network analysis as a potential regulator of the starch starvation response in Arabidopsis thaliana. We showed that STY46 was induced by (1) abscisic acid and prolonged darkness, (2) by abiotic stressors, including salinity and osmotic stress, and (3) by conditions associated with carbon starvation. Characterization of STY46 T-DNA knockout mutants indicated that there was functional redundancy among the STY gene family, as these genotypes did not show strong phenotypes. However, Arabidopsis with high levels of STY46 transcripts (OE-25) grew faster at the early seedling stage, had higher photosynthetic rates, and more carbon was stored as protein in the seeds under control conditions. Further, OE-25 source leaf accumulated more sugars under 100 mM NaCl stress, and salinity also accelerated root growth, which is consistent with an adaptive response. Salt-stressed OE-25 partitioned ¹⁴C towards sugars and amino acids, and away from starch and protein in source leaves. Together, these findings suggested that STY46 may be part of the salinity stress response pathway that utilizes starch during early plant growth.

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.

Transcriptional Networks of Microglia in Alzheimer's Disease and Insights into Pathogenesis

Microglia, the main immune cells of the central nervous system, are increasingly implicated in Alzheimer's disease (AD). Manifold transcriptomic studies in the brain have not only highlighted microglia's role in AD pathogenesis, but also mapped crucial pathological processes and identified new therapeutic targets. An important component of many of these transcriptomic studies is the investigation of gene expression networks in AD brain, which has provided important new insights into how coordinated gene regulatory programs in microglia (and other cell types) underlie AD pathogenesis. Given the rapid technological advancements in transcriptional profiling, spanning from microarrays to single-cell RNA sequencing (scRNA-seq), tools used for mapping gene expression networks have evolved to keep pace with the unique features of each transcriptomic platform. In this article, we review the trajectory of transcriptomic network analyses in AD from brain to microglia, highlighting the corresponding methodological developments. Lastly, we discuss examples of how transcriptional network analysis provides new insights into AD mechanisms and pathogenesis.

Co-regulatory Network of Oncosuppressor miRNAs and Transcription Factors for Pathology of Human Hepatic Cancer Stem Cells (HCSC)

Hepatic cancer stem cells (HCSCs) are considered as main players for the hepatocellular carcinoma (HCC) initiation, metastasis, drug resistance and recurrence. There is a growing evidence supporting the down-regulated miRNAs in HCSCs as key suppressors for the stemness traits, but still more details are vague about how these miRNAs modulate the HCC development. To uncover some of these miRNA regulatory aspects in HCSC, we compiled 15 down-regulated miRNA and their validated and predicted up-regulated targets in HCSC. The targets were enriched for several cancer cell stemness hallmarks and CSC pre-metastatic niche, which support these miRNAs role in suppression of HCSCs neoplastic transformation. Further, we constructed miRNA-Transcription factor (TF) regulatory networks, which provided new insights on the role of the proposed miRNA-TF co-regulation in the cancer stemness axis and its cross talk with the surrounding microenvironment. Our analysis revealed HCSC important hubs as candidate regulators for targeting hepatic cancer stemness such as, miR-148a, miR-214, E2F family, MYC and SLC7A5. Finally, we proposed a possible model for miRNA and TF co-regulation of HCSC signaling pathways. Our study identified an HCSC signature and set bridges between the reported results to give guide for future validation of HCC therapeutic strategies avoiding drug resistance.

Improving GRN re-construction by mining hidden regulatory signals

Inferring gene regulatory networks (GRNs) from gene expression data is an important but challenging issue in systems biology. Here, the authors propose a dictionary learning-based approach that aims to infer GRNs by globally mining regulatory signals, known or latent. Gene expression is often regulated by various regulatory factors, some of which are observed and some of which are latent. The authors assume that all regulators are unknown for a target gene and the expression of the target gene can be mapped into a regulatory space spanned by all the regulators. Specifically, the authors modify the dictionary learning model, k-SVD, according to the sparse property of GRNs for mining the regulatory signals. The recovered regulatory signals are then used as a pool of regulatory factors to calculate a confidence score for a given transcription factor regulating a target gene. The capability of recovering hidden regulatory signals was verified on simulated data. Comparative experiments for GRN inference between the proposed algorithm (OURM) and some state-of-the-art algorithms, e.g. GENIE3 and ARACNE, on real-world data sets show the superior performance of OURM in inferring GRNs: higher area under the receiver operating characteristic curves and area under the precision-recall curves.

GeNeCK: a web server for gene network construction and visualization

Background

Reverse engineering approaches to infer gene regulatory networks using computational methods are of great importance to annotate gene functionality and identify hub genes. Although various statistical algorithms have been proposed, development of computational tools to integrate results from different methods and user-friendly online tools is still lagging.

Results

We developed a web server that efficiently constructs gene networks from expression data. It allows the user to use ten different network construction methods (such as partial correlation-, likelihood-, Bayesian- and mutual information-based methods) and integrates the resulting networks from multiple methods. Hub gene information, if available, can be incorporated to enhance performance.

Conclusions

GeNeCK is an efficient and easy-to-use web application for gene regulatory network construction. It can be accessed at http://lce.biohpc.swmed.edu/geneck.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2560-0) contains supplementary material, which is available to authorized users.