In Silico Approach to Molecular Profiling of the Transition from Ovarian Epithelial Cells to Low-Grade Serous Ovarian Tumors for Targeted Therapeutic Insights

This paper aims to elucidate the differentially coexpressed genes, their potential mechanisms, and possible drug targets in low-grade invasive serous ovarian carcinoma (LGSC) in terms of the biologic continuity of normal, borderline, and malignant LGSC. We performed a bioinformatics analysis, integrating datasets generated using the GPL570 platform from different studies from the GEO database to identify changes in this transition, gene expression, drug targets, and their relationships with tumor microenvironmental characteristics. In the transition from ovarian epithelial cells to the serous borderline, the FGFR3 gene in the "Estrogen Response Late" pathway, the ITGB2 gene in the "Cell Adhesion Molecule", the CD74 gene in the "Regulation of Cell Migration", and the IGF1 gene in the "Xenobiotic Metabolism" pathway were upregulated in the transition from borderline to LGSC. The ERBB4 gene in "Proteoglycan in Cancer", the AR gene in "Pathways in Cancer" and "Estrogen Response Early" pathways, were upregulated in the transition from ovarian epithelial cells to LGSC. In addition, SPP1 and ITGB2 genes were correlated with macrophage infiltration in the LGSC group. This research provides a valuable framework for the development of personalized therapeutic approaches in the context of LGSC, with the aim of improving patient outcomes and quality of life. Furthermore, the main goal of the current study is a preliminary study designed to generate in silico inferences, and it is also important to note that subsequent in vitro and in vivo studies will be necessary to confirm the results before considering these results as fully reliable.

A double-robust test for high-dimensional gene coexpression networks conditioning on clinical information

It has been increasingly appealing to evaluate whether expression levels of two genes in a gene coexpression network are still dependent given samples' clinical information, in which the conditional independence test plays an essential role. For enhanced robustness regarding model assumptions, we propose a class of double-robust tests for evaluating the dependence of bivariate outcomes after controlling for known clinical information. Although the proposed test relies on the marginal density functions of bivariate outcomes given clinical information, the test remains valid as long as one of the density functions is correctly specified. Because of the closed-form variance formula, the proposed test procedure enjoys computational efficiency without requiring a resampling procedure or tuning parameters. We acknowledge the need to infer the conditional independence network with high-dimensional gene expressions, and further develop a procedure for multiple testing by controlling the false discovery rate. Numerical results show that our method accurately controls both the type-I error and false discovery rate, and it provides certain levels of robustness regarding model misspecification. We apply the method to a gastric cancer study with gene expression data to understand the associations between genes belonging to the transforming growth factor β signaling pathway given cancer-stage information.

HGCA2.0: An RNA-Seq Based Webtool for Gene Coexpression Analysis in Homo sapiens

Genes with similar expression patterns in a set of diverse samples may be considered coexpressed. Human Gene Coexpression Analysis 2.0 (HGCA2.0) is a webtool which studies the global coexpression landscape of human genes. The website is based on the hierarchical clustering of 55,431 Homo sapiens genes based on a large-scale coexpression analysis of 3500 GTEx bulk RNA-Seq samples of healthy individuals, which were selected as the best representative samples of each tissue type. HGCA2.0 presents subclades of coexpressed genes to a gene of interest, and performs various built-in gene term enrichment analyses on the coexpressed genes, including gene ontologies, biological pathways, protein families, and diseases, while also being unique in revealing enriched transcription factors driving coexpression. HGCA2.0 has been successful in identifying not only genes with ubiquitous expression patterns, but also tissue-specific genes. Benchmarking showed that HGCA2.0 belongs to the top performing coexpression webtools, as shown by STRING analysis. HGCA2.0 creates working hypotheses for the discovery of gene partners or common biological processes that can be experimentally validated. It offers a simple and intuitive website design and user interface, as well as an API endpoint.

The AP2/ERF transcription factor ORA59 regulates ethylene-induced phytoalexin synthesis through modulation of an acyltransferase gene expression

The gaseous ethylene (ET) and the oxylipin-derived jasmonic acid (JA) in plants jointly regulate an arsenal of pathogen responsive genes involved in defending against necrotrophic pathogens. The APETALA2 (AP2)/ETHYLENE RESPONSE FACTOR (ERF) transcription factor ORA59 is a major positive regulator of the ET/JA-mediated defense pathway in Arabidopsis thaliana. The Arabidopsis agmatine coumaroyltransferase (AtACT) catalyzes the formation of hydroxycinnamic acid amides (HCAAs) which are effective toxic antimicrobial substances known as phytoalexins and play an important role in plant defense response. However, induction and regulation of AtACT gene expression and HCAAs synthesis in plants remain less understood. Through gene coexpression network analysis, we identified a list of GCC-box cis-element containing genes that were coexpressed with ORA59 under diverse biotic stress conditions and might be potential downstream targets of this AP2/ERF-domain transcription factor. Particularly, ORA59 directly binds to AtACT gene promoter via the GCC-boxes and activates AtACT gene expression. The ET precursor 1-aminocyclopropane-1-carboxylic acid (ACC)-treatment significantly induces AtACT gene expression. Both ORA59 and members of the class II TGA transcription factors are indispensable for ACC-induced AtACT expression. Interestingly, the expression of AtACT is also subject to the signaling crosstalk of the salicylic acid- and ET/JA-mediated defense response pathways. In addition, we found that genes of the phenylpropanoid metabolism pathway were specifically induced by Botrytis cinerea. Taking together, these evidence suggest that the ET/JA signaling pathway activate the expression of AtACT to increase antimicrobial HCAAs production through the transcription factor ORA59 in response to the infection of necrotrophic plant pathogens.

Plant Foraging Strategies Driven by Distinct Genetic Modules: Cross-Ecosystem Transcriptomics Approach

Plants have evolved diverse strategies for foraging, e.g., mycorrhizae, modification of root system architecture, and secretion of phosphatase. Despite extensive molecular/physiological studies on individual strategies under laboratory/greenhouse conditions, there is little information about how plants orchestrate these strategies in the field. We hypothesized that individual strategies are independently driven by corresponding genetic modules in response to deficiency/unbalance in nutrients. Roots colonized by mycorrhizal fungi, leaves, and root-zone soils were collected from 251 maize plants grown across the United States Corn Belt and Japan, which provided a large gradient of soil characteristics/agricultural practice and thus gene expression for foraging. RNA was extracted from the roots, sequenced, and subjected to gene coexpression network analysis. Nineteen genetic modules were defined and functionally characterized, from which three genetic modules, mycorrhiza formation, phosphate starvation response (PSR), and root development, were selected as those directly involved in foraging. The mycorrhizal module consists of genes responsible for mycorrhiza formation and was upregulated by both phosphorus and nitrogen deficiencies. The PSR module that consists of genes encoding phosphate transporter, secreted acid phosphatase, and enzymes involved in internal-phosphate recycling was regulated independent of the mycorrhizal module and strongly upregulated by phosphorus deficiency relative to nitrogen. The root development module that consists of regulatory genes for root development and cellulose biogenesis was upregulated by phosphorus and nitrogen enrichment. The expression of this module was negatively correlated with that of the mycorrhizal module, suggesting that root development is intrinsically an opposite strategy of mycorrhizae. Our approach provides new insights into understanding plant foraging strategies in complex environments at the molecular level.

Network Biology Approaches to Achieve Precision Medicine in Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) is a chronic immune-mediated condition arising due to complex interactions between multiple genetic and environmental factors. Despite recent advances, the pathogenesis of the condition is not fully understood and patients still experience suboptimal clinical outcomes. Over the past few years, investigators are increasingly capturing multi-omics data from patient cohorts to better characterise the disease. However, reaching clinically translatable endpoints from these complex multi-omics datasets is an arduous task. Network biology, a branch of systems biology that utilises mathematical graph theory to represent, integrate and analyse biological data through networks, will be key to addressing this challenge. In this narrative review, we provide an overview of various types of network biology approaches that have been utilised in IBD including protein-protein interaction networks, metabolic networks, gene regulatory networks and gene co-expression networks. We also include examples of multi-layered networks that have combined various network types to gain deeper insights into IBD pathogenesis. Finally, we discuss the need to incorporate other data sources including metabolomic, histopathological, and high-quality clinical meta-data. Together with more robust network data integration and analysis frameworks, such efforts have the potential to realise the key goal of precision medicine in IBD.

Prognostic targets recognition of rectal adenocarcinoma based on transcriptomics

Colorectal cancer is currently the third most common cancer around the world. In this study, we chose a bioinformatics analysis method based on network analysis to dig out the pathological mechanism and key prognostic targets of rectal adenocarcinoma (READ).In this study, we downloaded the clinical information data and transcriptome data from the Cancer Genome Atlas database. Differentially expressed genes analysis was used to identify the differential expressed genes in READ. Community discovery algorithm analysis and Correlation analysis between gene modules and clinical data were performed to mine the key modules related to tumor proliferation, metastasis, and invasion. Genetic significance (GS) analysis and PageRank algorithm analysis were applied for find key genes in the key module. Finally, the importance of these genes was confirmed by survival analysis.Transcriptome datasets of 165 cancer tissue samples and 9 paracancerous tissue samples were selected. Gene coexpression networks were constructed, multilevel algorithm was used to divide the gene coexpression network into 11 modules. From GO enrichment analysis, module 11 significantly associated with clinical characteristic N, T, and event, mainly involved in 2 types of biological processes which were highly related to tumor metastasis, invasion, and tumor microenvironment regulation: cell development and differentiation; the development of vascular and nervous systems. Based on the results of survival analysis, 7 key genes were found negatively correlated to the survival rate of READ, such as MMP14, SDC2, LAMC1, ELN, ACTA2, ZNF532, and CYBRD1.Our study found that these key genes were predicted playing an important role in tumor invasion and metastasis, and being associated with the prognosis of READ. This may provide some new potential therapeutic targets and thoughts for the prognosis of READ.

Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19

Current coronavirus disease-2019 (COVID-19) pandemic has caused massive loss of lives. Clinical trials of vaccines and drugs are currently being conducted around the world; however, till now no effective drug is available for COVID-19. Identification of key genes and perturbed pathways in COVID-19 may uncover potential drug targets and biomarkers. We aimed to identify key gene modules and hub targets involved in COVID-19. We have analyzed SARS-CoV-2 infected peripheral blood mononuclear cell (PBMC) transcriptomic data through gene coexpression analysis. We identified 1520 and 1733 differentially expressed genes (DEGs) from the GSE152418 and CRA002390 PBMC datasets, respectively (FDR < 0.05). We found four key gene modules and hub gene signature based on module membership (MMhub) statistics and protein-protein interaction (PPI) networks (PPIhub). Functional annotation by enrichment analysis of the genes of these modules demonstrated immune and inflammatory response biological processes enriched by the DEGs. The pathway analysis revealed the hub genes were enriched with the IL-17 signaling pathway, cytokine-cytokine receptor interaction pathways. Then, we demonstrated the classification performance of hub genes (PLK1, AURKB, AURKA, CDK1, CDC20, KIF11, CCNB1, KIF2C, DTL and CDC6) with accuracy >0.90 suggesting the biomarker potential of the hub genes. The regulatory network analysis showed transcription factors and microRNAs that target these hub genes. Finally, drug-gene interactions analysis suggests amsacrine, BRD-K68548958, naproxol, palbociclib and teniposide as the top-scored repurposed drugs. The identified biomarkers and pathways might be therapeutic targets to the COVID-19.

Stratification of Estrogen Receptor-Negative Breast Cancer Patients by Integrating the Somatic Mutations and Transcriptomic Data

Patients with estrogen receptor-negative breast cancer generally have a worse prognosis than estrogen receptor-positive patients. Nevertheless, a significant proportion of the estrogen receptor-negative cases have favorable outcomes. Identifying patients with a good prognosis, however, remains difficult, as recent studies are quite limited. The identification of molecular biomarkers is needed to better stratify patients. The significantly mutated genes may be potentially used as biomarkers to identify the subtype and to predict outcomes. To identify the biomarkers of receptor-negative breast cancer among the significantly mutated genes, we developed a workflow to screen significantly mutated genes associated with the estrogen receptor in breast cancer by a gene coexpression module. The similarity matrix was calculated with distance correlation to obtain gene modules through a weighted gene coexpression network analysis. The modules highly associated with the estrogen receptor, called important modules, were enriched for breast cancer-related pathways or disease. To screen significantly mutated genes, a new gene list was obtained through the overlap of the important module genes and the significantly mutated genes. The genes on this list can be used as biomarkers to predict survival of estrogen receptor-negative breast cancer patients. Furthermore, we selected six hub significantly mutated genes in the gene list which were also able to separate these patients. Our method provides a new and alternative method for integrating somatic gene mutations and expression data for patient stratification of estrogen receptor-negative breast cancers.

Gene Coexpression Network Reveals Insights into the Origin and Evolution of a Theanine-Associated Regulatory Module in Non-Camellia and Camellia Species

Theanine (thea) is one of the most important plant-derived characteristic secondary metabolites and a major healthcare product because of its beneficial biological activities, such as being an antianxiety agent, promoting memory, and lowering blood pressure. Thea mostly accumulates in Camellia plants and is especially rich in Camellia sinensis (tea plant). Although some functional genes (e.g., TS, GOGAT, and GS) attributed to thea accumulation have been separately well explored in tea plants, the evolution of a regulatory module (highly interacting gene group) related to thea metabolism remains to be elaborated. Herein, a thea-associated regulatory module (TARM) was mined by using a comprehensive analysis of a weighted gene coexpression network in Camellia and non-Camellia species. Comparative genomic analysis of 84 green plant species revealed that TARM originated from the ancestor of green plants (algae) and that TARM genes were recruited from different evolutionary nodes with the most gene duplication events at the early stage. Among the TARM genes, two core transcription factors of NAC080 and LBD38 were deduced, which may play a crucial role in regulating the biosynthesis of thea. Our findings provide the first insights into the origin and evolution of TARM and indicate a promising paradigm for identifying vital regulatory genes involved in thea metabolism.

C3orf70 Is Involved in Neural and Neurobehavioral Development

Neurogenesis is the process by which undifferentiated progenitor cells develop into mature and functional neurons. Defects in neurogenesis are associated with neurodevelopmental and neuropsychiatric disorders; therefore, elucidating the molecular mechanisms underlying neurogenesis can advance our understanding of the pathophysiology of these disorders and facilitate the discovery of novel therapeutic targets. In this study, we performed a comparative transcriptomic analysis to identify common targets of the proneural transcription factors Neurog1/2 and Ascl1 during neurogenesis of human and mouse stem cells. We successfully identified C3orf70 as a novel common target gene of Neurog1/2 and Ascl1 during neurogenesis. Using in situ hybridization, we demonstrated that c3orf70a and c3orf70b, two orthologs of C3orf70, were expressed in the midbrain and hindbrain of zebrafish larvae. We generated c3orf70 knockout zebrafish using CRISPR/Cas9 technology and demonstrated that loss of c3orf70 resulted in significantly decreased expression of the mature neuron markers elavl3 and eno2. We also found that expression of irx3b, a zebrafish ortholog of IRX3 and a midbrain/hindbrain marker, was significantly reduced in c3orf70 knockout zebrafish. Finally, we demonstrated that neurobehaviors related to circadian rhythm and altered light–dark conditions were significantly impaired in c3orf70 knockout zebrafish. These results suggest that C3orf70 is involved in neural and neurobehavioral development and that defects in C3orf70 may be associated with midbrain/hindbrain-related neurodevelopmental and neuropsychiatric disorders.

Multiscale network analysis reveals molecular mechanisms and key regulators of the tumor microenvironment in gastric cancer

Gastric cancer (GC) is the third leading cause of cancer deaths and the fourth most prevalent malignancy worldwide. The high incidence and mortality rates of gastric cancer result from multiple factors such as ineffective screening, diagnosis, and limited treatment options. In our study, we sought to systematically identify predictive molecular networks and key regulators to elucidate complex interacting signaling pathways in GC. We performed an integrative network analysis of the transcriptomic data in The Cancer Genome Atlas (TCGA) gastric cancer cohort and then comprehensively characterized the predictive subnetworks and key regulators by the matched genetic and epigenetic data. We identified 221 gene subnetworks (modules) in GC. The most prognostic subnetworks captured multiple aspects of the tumor microenvironment in GC involving interactions among stromal, epithelial and immune cells. We revealed the genetic and epigenetic underpinnings of those subnetworks and their key transcriptional regulators. We computationally predicted and experimentally validated specific mechanisms of anticancer effects of GKN2 in gastric cancer proliferation and invasion in vitro. The network models and the key regulators of the tumor microenvironment in GC identified here pave a way for developing novel therapeutic strategies for GC.

What's new?

Gene signatures have been identified for diagnosis and classification of gastric cancer (GC) as well as prediction of therapeutic response. However, key molecular mechanisms underlying prognosis remain to be revealed. Our study systematically identifies and characterizes predictive molecular networks and key regulators. The most prognostic subnetworks capture multiple aspects of the tumor microenvironment in GC involving interactions among stromal, epithelial, and immune cells. The authors computationally predicted and experimentally validated specific mechanisms of anti‐cancer effects of GKN2 in GC proliferation and invasion in vitro. These network models and key regulators pave the way for developing novel therapeutic strategies for GC.

Decoding the gene coexpression network underlying the ability of Gevuina avellana to live in diverse light conditions

Gevuina avellana (Proteaceae) is a typical tree from the South American temperate rainforest. Although this species mostly regenerates in shaded understories, it exhibits an exceptional ecological breadth, being able to live under a wide range of light conditions. Here we studied the genetic basis that underlies physiological acclimation of the photosynthetic responses of G. avellana under contrasting light conditions. We analyzed carbon assimilation and light energy used for photochemical processes in plants acclimated to contrasting light conditions. Also, we used a transcriptional profile of leaf primordia from G. avellana saplings growing under different light environments in their natural habitat, to identify the gene coexpression network underpinning photosynthetic performance and light-related processes. The photosynthetic parameters revealed optimal performance regardless of light conditions. Strikingly, the mechanism involved in dissipation of excess light energy showed no significant differences between high- and low-light-acclimated plants. The gene coexpression network defined a community structure consistent with the photochemical responses, including genes involved mainly in assembly and functioning of photosystems, photoprotection, and retrograde signaling. This ecophysiological genomics approach improves our understanding of the intraspecific variability that allows G. avellana to have optimal photochemical and photoprotective mechanisms in the diverse light habitats it encounters in nature.

Transcriptomic context of DRD1 is associated with prefrontal activity and behavior during working memory

Dopamine D₁ receptor (D₁R) signaling shapes prefrontal cortex (PFC) activity during working memory (WM). Previous reports found higher WM performance associated with alleles linked to greater expression of the gene coding for D₁Rs (DRD1). However, there is no evidence on the relationship between genetic modulation of DRD1 expression in PFC and patterns of prefrontal activity during WM. Furthermore, previous studies have not considered that D₁Rs are part of a coregulated molecular environment, which may contribute to D₁R-related prefrontal WM processing. Thus, we hypothesized a reciprocal link between a coregulated (i.e., coexpressed) molecular network including DRD1 and PFC activity. To explore this relationship, we used three independent postmortem prefrontal mRNA datasets (total n = 404) to characterize a coexpression network including DRD1 Then, we indexed network coexpression using a measure (polygenic coexpression index-DRD1-PCI) combining the effect of single nucleotide polymorphisms (SNPs) on coexpression. Finally, we associated the DRD1-PCI with WM performance and related brain activity in independent samples of healthy participants (total n = 371). We identified and replicated a coexpression network including DRD1, whose coexpression was correlated with DRD1-PCI. We also found that DRD1-PCI was associated with lower PFC activity and higher WM performance. Behavioral and imaging results were replicated in independent samples. These findings suggest that genetically predicted expression of DRD1 and of its coexpression partners stratifies healthy individuals in terms of WM performance and related prefrontal activity. They also highlight genes and SNPs potentially relevant to pharmacological trials aimed to test cognitive enhancers modulating DRD1 signaling.

Identification of target gene of venous thromboembolism in patients with lymphoma via microarray analysis

Patients with lymphoma are at high risk of developing venous thromboembolism (VTE). The purpose of the present study was to identify the target gene associated with VTE for patients with lymphoma. Microarray data was downloaded from the gene expression omnibus database (GSE17078), which comprised the control group, 27 normal blood outgrowth endothelial cell (BOEC) samples, and the case group, 3 BOEC samples of venous thrombosis with protein C deficiency. Differentially expressed genes (DEGs) were identified by the Limma package of R. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway analyses were performed via the database for annotation, visualization and integrated discovery. Differentially coexpressed pairs were identified by the DCGL package of R. The subsequent protein-protein interaction (PPI) networks and gene coexpression networks were constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins database, and were visualized by Cytoscape software. A total of 110 DEGs were obtained, including 73 upregulated and 37 downregulated genes. GO and KEGG pathway enrichment analyses identified 132 significant GO terms and 9 significant KEGG pathways. In total, 97 PPI pairs for PPI network and 309 differential coexpression pairs for the gene coexpression network were obtained. Additionally, the connective tissue growth factor (CTGF) gene was closely connected with other genes in the two networks. A total of 2 KEGG pathways were associated with VTE and CTGF may be the target gene of VTE in patients with lymphoma. The present study may identify the molecular mechanism of VTE, but additional clinical study is required to validate the results.

Modular transcriptional repertoire and MicroRNA target analyses characterize genomic dysregulation in the thymus of Down syndrome infants

Trisomy 21-driven transcriptional alterations in human thymus were characterized through gene coexpression network (GCN) and miRNA-target analyses. We used whole thymic tissue--obtained at heart surgery from Down syndrome (DS) and karyotipically normal subjects (CT)--and a network-based approach for GCN analysis that allows the identification of modular transcriptional repertoires (communities) and the interactions between all the system's constituents through community detection. Changes in the degree of connections observed for hierarchically important hubs/genes in CT and DS networks corresponded to community changes. Distinct communities of highly interconnected genes were topologically identified in these networks. The role of miRNAs in modulating the expression of highly connected genes in CT and DS was revealed through miRNA-target analysis. Trisomy 21 gene dysregulation in thymus may be depicted as the breakdown and altered reorganization of transcriptional modules. Leading networks acting in normal or disease states were identified. CT networks would depict the "canonical" way of thymus functioning. Conversely, DS networks represent a "non-canonical" way, i.e., thymic tissue adaptation under trisomy 21 genomic dysregulation. This adaptation is probably driven by epigenetic mechanisms acting at chromatin level and through the miRNA control of transcriptional programs involving the networks' high-hierarchy genes.

Evolutionary Conservation and Divergence of Gene Coexpression Networks in Gossypium (Cotton) Seeds

The cotton genus (Gossypium) provides a superior system for the study of diversification, genome evolution, polyploidization, and human-mediated selection. To gain insight into phenotypic diversification in cotton seeds, we conducted coexpression network analysis of developing seeds from diploid and allopolyploid cotton species and explored network properties. Key network modules and functional associations were identified related to seed oil content and seed weight. We compared species-specific networks to reveal topological changes, including rewired edges and differentially coexpressed genes, associated with speciation, polyploidy, and cotton domestication. Network comparisons among species indicate that topologies are altered in addition to gene expression profiles, indicating that changes in transcriptomic coexpression relationships play a role in the developmental architecture of cotton seed development. The global network topology of allopolyploids, especially for domesticated G. hirsutum, resembles the network of the A-genome diploid more than that of the D-genome parent, despite its D-like phenotype in oil content. Expression modifications associated with allopolyploidy include coexpression level dominance and transgressive expression, suggesting that the transcriptomic architecture in polyploids is to some extent a modular combination of that of its progenitor genomes. Among allopolyploids, intermodular relationships are more preserved between two different wild allopolyploid species than they are between wild and domesticated forms of a cultivated cotton, and regulatory connections of oil synthesis-related pathways are denser and more closely clustered in domesticated vs. wild G. hirsutum. These results demonstrate substantial modification of genic coexpression under domestication. Our work demonstrates how network inference informs our understanding of the transcriptomic architecture of phenotypic variation associated with temporal scales ranging from thousands (domestication) to millions (speciation) of years, and by polyploidy.