Gene-gene Network Research Articles

ECD-CDGI: An efficient energy-constrained diffusion model for cancer driver gene identification.

The identification of cancer driver genes (CDGs) poses challenges due to the intricate interdependencies among genes and the influence of measurement errors and noise. We propose a novel energy-constrained diffusion (ECD)-based model for identifying CDGs, termed ECD-CDGI. This model is the first to design an ECD-Attention encoder by combining the ECD technique with an attention mechanism. ECD-Attention encoder excels at generating robust gene representations that reveal the complex interdependencies among genes while reducing the impact of data noise. We concatenate topological embedding extracted from gene-gene networks through graph transformers to these gene representations. We conduct extensive experiments across three testing scenarios. Extensive experiments show that the ECD-CDGI model possesses the ability to not only be proficient in identifying known CDGs but also efficiently uncover unknown potential CDGs. Furthermore, compared to the GNN-based approach, the ECD-CDGI model exhibits fewer constraints by existing gene-gene networks, thereby enhancing its capability to identify CDGs. Additionally, ECD-CDGI is open-source and freely available. We have also launched the model as a complimentary online tool specifically crafted to expedite research efforts focused on CDGs identification.

Gene expression analysis reveals GRIN1, SYT1, and SYN2 as significant therapeutic targets and drug repurposing reveals lorazepam and lorediplon as potent inhibitors to manage Alzheimer’s disease

Alzheimer’s disease (AD) is a slowly progressive neurodegenerative disease and a leading cause of dementia. We aim to identify key genes for the development of therapeutic targets and biomarkers for potential treatments for AD. Meta-analysis was performed on six microarray datasets and identified the differentially expressed genes between healthy and Alzheimer’s disease samples. Thereafter, we filtered out the common genes which were present in at least four microarray datasets for downstream analysis. We have constructed a gene-gene network for the common genes and identified six hub genes. Furthermore, we investigated the regulatory mechanisms of these hub genes by analysing their interaction with miRNAs and transcription factors. The gene ontology analysis results highlighted the enriched terms significantly associated with hub genes. Through an extensive literature survey, we found that three of the hub genes including GRIN1, SYN2, and SYT1 were critically involved in disease development. To leverage existing drugs for potential repurposing, we predicted drug-gene interaction using the drug-gene interaction database, and performed molecular docking studies. The docking results revealed that the drug compounds had strong interactions and favorable binding with selected hub genes. Lorazepam exhibits a binding energy of −7.3 kcal/mol with GRIN1, Lorediplon exhibits binding energies of −7.7 kcal/mol and −6.3 kcal/mol with the SYT1, and SYN2 respectively. In addition, 100 ns molecular dynamics simulations were carried out for the top complexes and apo protein as well. Furthermore, the MM-PBSA free energy calculations also revealed that these complexes are stable and had favorable energies. According to our study, the identified hub gene could serve as a biomarker as well as a therapeutic target for AD, and the proposed repurposed drug molecules appear to have promising efficacy in treating the disease. Communicated by Ramaswamy H. Sarma

Identification and validation of differentially expressed genes for targeted therapy in NSCLC using integrated bioinformatics analysis.

Despite the high prevalence of lung cancer, with a five-year survival rate of only 23%, the underlying molecular mechanisms of non-small cell lung cancer (NSCLC) remain unknown. There is a great need to identify reliable candidate biomarker genes for early diagnosis and targeted therapeutic strategies to prevent cancer progression. In this study, four datasets obtained from the Gene Expression Omnibus were evaluated for NSCLC- associated differentially expressed genes (DEGs) using bioinformatics analysis. About 10 common significant DEGs were shortlisted based on their p-value and FDR (DOCK4, ID2, SASH1, NPR1, GJA4, TBX2, CD24, HBEGF, GATA3, and DDR1). The expression of significant genes was validated using experimental data obtained from TCGA and the Human Protein Atlas database. The human proteomic data for post- translational modifications was used to interpret the mutations in these genes. Validation of DEGs revealed a significant difference in the expression of hub genes in normal and tumor tissues. Mutation analysis revealed 22.69%, 48.95%, and 47.21% sequence predicted disordered regions of DOCK4, GJA4, and HBEGF, respectively. The gene-gene and drug-gene network analysis revealed important interactions between genes and chemicals suggesting they could act as probable drug targets. The system-level network showed important interactions between these genes, and the drug interaction network showed that these genes are affected by several types of chemicals that could serve as potential drug targets. The study demonstrates the importance of systemic genetics in identifying potential drug- targeted therapies for NSCLC. The integrative system- level approach should contribute to a better understanding of disease etiology and may accelerate drug discovery for many cancer types.

MultiNEP: a multi-omics network enhancement framework for prioritizing disease genes and metabolites simultaneously.

Many studies have successfully used network information to prioritize candidate omics profiles associated with diseases. The metabolome, as the link between genotypes and phenotypes, has accumulated growing attention. Using a "multi-omics" network constructed with a gene-gene network, a metabolite-metabolite network, and a gene-metabolite network to simultaneously prioritize candidate disease-associated metabolites and gene expressions could further utilize gene-metabolite interactions that are not used when prioritizing them separately. However, the number of metabolites is usually 100 times fewer than that of genes. Without accounting for this imbalance issue, we cannot effectively use gene-metabolite interactions when simultaneously prioritizing disease-associated metabolites and genes. Here, we developed a Multi-omics Network Enhancement Prioritization (MultiNEP) framework with a weighting scheme to reweight contributions of different sub-networks in a multi-omics network to effectively prioritize candidate disease-associated metabolites and genes simultaneously. In simulation studies, MultiNEP outperforms competing methods that do not address network imbalances and identifies more true signal genes and metabolites simultaneously when we down-weight relative contributions of the gene-gene network and up-weight that of the metabolite-metabolite network to the gene-metabolite network. Applications to two human cancer cohorts show that MultiNEP prioritizes more cancer-related genes by effectively using both within- and between-omics interactions after handling network imbalance. The developed MultiNEP framework is implemented in an R package and available at: https://github.com/Karenxzr/MultiNep.

High expression of cytoplasmic FOXO3 protein associated with poor prognosis of rectal cancer patients: A study from Swedish clinical trial of preoperative radiotherapy to big database analysis

IntroductionAccumulating evidence has implicated a pivotal role for FOXO3, FOXM1 and SIRT6 in cancer progression. The majority of researches focused on the functions of these proteins in drug resistance, but their relationships with radiotherapy (RT) response remain unclear. In this study, we examined protein expression of FOXO3, FOXM1 and SIRT6 and their clinical significance in a Swedish rectal cancer trial of preoperative RT. MethodsExpression of FOXO3, FOXM1 and SIRT6 protein was examined by immunohistochemistry in patient samples. Genetic analysis of FOXO3, FOXM1 and SIRT6 were performed by cBioportal and MEXPRESS database. Gene-gene network analysis was conducted using GeneMANIA. Functional enrichment analysis was performed based on LinkedOmics and Metascape online software. ResultsFOXO3 and FOXM1were mainly expressed in the cytoplasm in both normal and tumour tissues, and SIRT6 in both the cytoplasm and nucleus in normal and tumour tissues. FOXO3 and FOXM1 expression increased from normal mucosa to primary cancer (P < 0.001), while SIRT6 expression decreased from normal mucosa to primary cancer (P < 0.001). High FOXO3 expression correlated with late TNM stage (P = 0.040), distant metastasis (P = 0.032) and independently with disease free survival (DFS) in the RT patients (HR = 7.948; P = 0.049; 95% CI = 1.002–63.032) but not in non-RT patients (P > 0.05). Genetic analysis indicated that DNA methylation status contributed to FOXO3 overexpression. Functional enrichment analysis demonstrated that FOXO3 was closely related to metabolism-related signalling pathway which in turn associated with cancer radioresistance. Moreover, there were strong gene–gene interactions between FOXO3 and metabolism-related signalling. ConclusionsOur findings suggest that FOXO3 may be a prognostic factor in rectal cancer patients with RT.

MiRNA-gene network embedding for predicting cancer driver genes.

The development and progression of cancer arise due to the accumulation of mutations in driver genes. Correctly identifying the driver genes that lead to cancer development can significantly assist the drug design, cancer diagnosis and treatment. Most computer methods detect cancer drivers based on gene-gene networks by assuming that driver genes tend to work together, form protein complexes and enrich pathways. However, they ignore that microribonucleic acid (RNAs; miRNAs) regulate the expressions of their targeted genes and are related to human diseases. In this work, we propose a graph convolution network (GCN) approach called GM-GCN to identify the cancer driver genes based on a gene-miRNA network. First, we constructed a gene-miRNA network, where the nodes are miRNAs and their targeted genes. The edges connecting miRNA and genes indicate the regulatory relationship between miRNAs and genes. We prepared initial attributes for miRNA and genes according to their biological properties and used a GCN model to learn the gene feature representations in the network by aggregating the features of their neighboring miRNA nodes. And then, the learned features were passed through a 1D convolution module for feature dimensionality change. We employed the learned and original gene features to optimize model parameters. Finally, the gene features learned from the network and the initial input gene features were fed into a logistic regression model to predict whether a gene is a driver gene. We applied our model and state-of-the-art methods to predict cancer drivers for pan-cancer and individual cancer types. Experimental results show that our model performs well in terms of the area under the receiver operating characteristic curve and the area under the precision-recall curve compared to state-of-the-art methods that work on gene networks. The GM-GCN is freely available via https://github.com/weiba/GM-GCN.

Characterization of the microenvironment of diabetic foot ulcers and potential drug identification based on scRNA-seq.

Diabetes foot ulcers (DFUs) are a type of foot infection, ulcer, and/or deep tissue destruction caused by neuropathy and vascular disease in the distal extremities of diabetic patients. Its pathogenesis and its microenvironment are not entirely understood. Initially, the GSE165816 data set from the GEO database was utilized for single cell analysis to reveal the microenvironment and functional status of DFUs. The GSE199939 RNA-seq data set was utilized for external validation. On the basis of the logistic regression machine learning algorithm (OCLR), pseudo time series analysis, dryness index analysis, and drug target gene analysis were then performed. By constructing drug-gene and gene-gene networks, we can locate the most recent DFUs treatments. Finally, immunofluorescence technology was used to detect the cell-related markers of the DFUs microenvironment, and qPCR was used to detect the expression of drug targets in DFUs. Firstly, we used the Cell Maker database to obtain information about human cells and related gene markers, and manually reviewed a total of 45 kinds of cells and maker information that may appear in the DFUs microenvironment, which were divided into 17 cell clusters after annotation. Subsequently, we counted the proportions of DM and DFUs in different types of cells, and the results showed that the proportions of macrophages, white blood cells, and monocytes were higher in patients with DFUs, while the proportions of pluripotent stem cells and stromal cells were higher in patients with DM. The Pseudo-time series analysis of cells in DFUs showed that the differentiation pathways of immune cells, mesenchymal cells and stem cells were similar in the three states, while the other cells were distributed in different stages. At the level of a single cell, the scores of both multipotential stem cells and hematopoietic stem cells were significantly lower in DFU healing and non-healing than in DM. Additionally, the highly expressed genes in DFU were chosen as drug targets. We identified seven potential target genes and discovered twenty drugs with high significance. Finally, the colocalization relationship between CD19, ITGAM, and HLA-DR expression in monocytes and macrophages of DFU skin tissue and healthy subjects was analyzed by laser confocal microscopy with the immunofluorescence triple labeling method. The results showed that the expressions of CD19, ITGAM, and HLA-DR in the skin of DFUs were significantly higher than those in the skin of healthy subjects, and the co-localization relationship was significant in DFUs. This study can serve as a resource for the treatment of DFUs.

Physiological and transcriptome analyses to infer regulatory networks in flowering transition of&lt;i&gt; Rosa rugosa&lt;/i&gt;

<italic>Rosa rugosa</italic> is a famous Chinese traditional flowering species with high economic value. Flowering transition is an important process in plant growth and development. Although the flowering transition process has made great progress in some plants such as model plants, flowering transition in <italic>R. rugosa</italic> has not been rigorously characterized to establish a mechanism. In this study, the changes of buds during flowering transition in <italic>R. rugosa</italic> ‘Duoji Huangmei’ were analyzed through transcriptomic sequencing combined with morphological and physiological index determinations. Results showed that with the morphology changes of buds, both sugar and starch contents showed a similar up-down pattern while phytohormones contents displayed various trends, which implied that sugar, starch and phytohormones might play diverse roles during flowering transition in <italic>R. rugosa</italic>. Moreover, a total of 4363 differentially expressed genes (DEGs) were identified at three developmental stages. Among them, 74 DEGs were involved in metabolism, transport, and signal transduction of sugar, starch, and phytohormones, as well as photoperiod and vernalization response. We proposed that these DEGs were not regulated independently but interacted with each other to construct a gene-gene network to regulate flowering transition of <italic>R. rugosa</italic>, and the regulatory network from vegetative growth stage (S1) to flowering transition stage (S2) was more complicated. These results further enriched the study of flowering transition in Rosa and lay an important foundation for breeding new varieties with desired floral traits.

Transcriptional responses of human intestinal epithelial HT-29 cells to spore-displayed p40 derived from Lacticaseibacillus rhamnosus GG

BackgroundsThe aims of this study were to construct spore-displayed p40, a Lacticaseibacillus rhamnosus GG-derived soluble protein, using spore surface display technology and to evaluate transcriptional responses in human intestinal epithelial cells.Resultsp40 was displayed on the surface of Bacillus subtilis spores using spore coat protein CotG as an anchor protein. Effects of spore-displayed p40 (CotG-p40) on gene expression of intestinal epithelial cell line HT-29 were evaluated by transcriptome analysis using RNA-sequencing. As a result of differentially expressed gene (DEG) analysis, 81 genes were up-regulated and 82 genes were down-regulated in CotG-p40 stimulated cells than in unstimulated cells. Gene ontology enrichment analysis showed that CotG-p40 affected biological processes such as developmental process, metabolic process, cell surface receptor linked signaling pathway, and retinoic acid metabolic process. Gene-gene network analysis suggested that 10 DEGs (EREG, FOXF1, GLI2, PTGS2, SPP1, MMP19, TNFRSF1B, PTGER4, CLDN18, and ALDH1A3) activated by CotG-p40 were associated with probiotic action.ConclusionsThis study demonstrates the regulatory effects of CotG-p40 on proliferation and homeostasis of HT-29 cells. This study provided comprehensive insights into the transcriptional response of human intestinal epithelial cells stimulated by CotG-p40.

HSSG: Identification of Cancer Subtypes Based on Heterogeneity Score of A Single Gene.

Cancer is a highly heterogeneous disease, which leads to the fact that even the same cancer can be further classified into different subtypes according to its pathology. With the multi-omics data widely used in cancer subtypes identification, effective feature selection is essential for accurately identifying cancer subtypes. However, the feature selection in the existing cancer subtypes identification methods has the problem that the most helpful features cannot be selected from a biomolecular perspective, and the relationship between the selected features cannot be reflected. To solve this problem, we propose a method for feature selection to identify cancer subtypes based on the heterogeneity score of a single gene: HSSG. In the proposed method, the sample-similarity network of a single gene is constructed, and pseudo-F statistics calculates the heterogeneity score for cancer subtypes identification of each gene. Finally, we construct gene-gene networks using genes with higher heterogeneity scores and mine essential genes from the networks. From the seven TCGA data sets for three experiments, including cancer subtypes identification in single-omics data, the performance in feature selection of multi-omics data, and the effectiveness and stability of the selected features, HSSG achieves good performance in all. This indicates that HSSG can effectively select features for subtypes identification.

Network analysis of synovial RNA sequencing identifies gene-gene interactions predictive of response in rheumatoid arthritis

BackgroundTo determine whether gene-gene interaction network analysis of RNA sequencing (RNA-Seq) of synovial biopsies in early rheumatoid arthritis (RA) can inform our understanding of RA pathogenesis and yield improved treatment response prediction models.MethodsWe utilized four well curated pathway repositories obtaining 10,537 experimentally evaluated gene-gene interactions. We extracted specific gene-gene interaction networks in synovial RNA-Seq to characterize histologically defined pathotypes in early RA and leverage these synovial specific gene-gene networks to predict response to methotrexate-based disease-modifying anti-rheumatic drug (DMARD) therapy in the Pathobiology of Early Arthritis Cohort (PEAC). Differential interactions identified within each network were statistically evaluated through robust linear regression models. Ability to predict response to DMARD treatment was evaluated by receiver operating characteristic (ROC) curve analysis.ResultsAnalysis comparing different histological pathotypes showed a coherent molecular signature matching the histological changes and highlighting novel pathotype-specific gene interactions and mechanisms. Analysis of responders vs non-responders revealed higher expression of apoptosis regulating gene-gene interactions in patients with good response to conventional synthetic DMARD. Detailed analysis of interactions between pairs of network-linked genes identified the SOCS2/STAT2 ratio as predictive of treatment success, improving ROC area under curve (AUC) from 0.62 to 0.78. We identified a key role for angiogenesis, observing significant statistical interactions between NOS3 (eNOS) and both CAMK1 and eNOS activator AKT3 when comparing responders and non-responders. The ratio of CAMKD2/NOS3 enhanced a prediction model of response improving ROC AUC from 0.63 to 0.73.ConclusionsWe demonstrate a novel, powerful method which harnesses gene interaction networks for leveraging biologically relevant gene-gene interactions leading to improved models for predicting treatment response.

Peritoneal M2 macrophage-derived extracellular vesicles as natural multitarget nanotherapeutics to attenuate cytokine storms after severe infections.

Cytokine storms are a primary cause of multiple organ damage and death after severe infections, such as SARS-CoV-2. However, current single cytokine-targeted strategies display limited therapeutic efficacy. Here, we report that peritoneal M2 macrophage-derived extracellular vesicles (M2-EVs) are multitarget nanotherapeutics that can be used to resolve cytokine storms. In detail, primary peritoneal M2 macrophages exhibited superior anti-inflammatory potential than immobilized cell lines. Systemically administered M2-EVs entered major organs and were taken up by phagocytes (e.g., macrophages). M2-EV treatment effectively reduced excessive cytokine (e.g., TNF-α and IL-6) release in vitro and in vivo, thereby attenuating oxidative stress and multiple organ (lung, liver, spleen and kidney) damage in endotoxin-induced cytokine storms. Moreover, M2-EVs simultaneously inhibited multiple key proinflammatory pathways (e.g., NF-κB, JAK-STAT and p38 MAPK) by regulating complex miRNA-gene and gene-gene networks, and this effect was collectively mediated by many functional cargos (miRNAs and proteins) in EVs. In addition to the direct anti-inflammatory role, human peritoneal M2-EVs expressed angiotensin-converting enzyme 2 (ACE2), a receptor of SARS-CoV-2 spike protein, and thus could serve as nanodecoys to prevent SARS-CoV-2 pseudovirus infection in vitro. As cell-derived nanomaterials, the therapeutic index of M2-EVs can be further improved by genetic/chemical modification or loading with specific drugs. This study highlights that peritoneal M2-EVs are promising multifunctional nanotherapeutics to attenuate infectious disease-related cytokine storms.

Polymorphisms in immune-mediator genes and the risk of dengue virus infection: Lights from a systematic revaluation by Bayesian approaches

Dengue fever is a clinical manifestation of dengue virus (DENV) infection well defined by the intense host immune response with the development of high fever, anorexia, headache and muscle pain. Several immune mediators are involved in the pathophysiology of DENV infection, in which polymorphisms in immune molecule genes contribute with the susceptibility and severity of the infection. Several meta-analyses are available with significant findings in the association between genetic variants in immune-mediator genes and dengue, though the results may be false positive. Hence, to solve this issue, we have performed a systematic revaluation with Bayesian approaches to verify the false positive rate in these results. A systematic search was performed for meta-analytic studies on the aforementioned issue. The calculations of false positive report probability (FPRP) and the Bayesian false-discovery probability (BFDP) at the prior probability of 10-3 and 10-6 have been performed. To verify the methodological quality of the studies included, the evaluation by the Venice criteria was applied. In addition, gene-gene and protein-protein networks were designed. As results, seven meta-analyses on genetic variants in several immune-inflammatory mediator genes and DENV infection comprise the results. Only the polymorphisms in the TNF, MICB, PLCE1, VDR, CD32 and HLA-A genes were considered as noteworthy. There was a heterogeneity profile for the results on Venice criteria indicating variability in the methodological quality. The gene-gene and protein-protein networks showed these immune mediators as relevant players in the disease. We suggest these polymorphisms as potential biomarkers for the pathogenesis and immune response against DENV.

Shared Nearest Neighbors Approach and Interactive Browser for Network Analysis of a Comprehensive Non-Small-Cell Lung Cancer Data Set.

Advances in biological measurement technologies are enabling large-scale studies of patient cohorts across multiple omics platforms. Holistic analysis of these data can generate actionable insights for translational research and necessitate new approaches for data integration and mining. We present a novel approach for integrating data across platforms on the basis of the shared nearest neighbors algorithm and use it to create a network of multiplatform data from the immunogenomic profiling of non-small-cell lung cancer project. Benchmarking demonstrates that the shared nearest neighbors-based network approach outperforms a traditional gene-gene network in capturing established interactions while providing new ones on the basis of the interplay between measurements from different platforms. When used to examine patient characteristics of interest, our approach provided signatures associated with and new leads related to recurrence and TP53 oncogenotype. The network developed offers an unprecedented, holistic view into immunogenomic profiling of non-small-cell lung cancer, which can be explored through the accompanying interactive browser that we built.

A Bayesian framework to integrate multi-level genome-scale data for Autism risk gene prioritization

BackgroundAutism spectrum disorder (ASD) is a group of complex neurodevelopment disorders with a strong genetic basis. Large scale sequencing studies have identified over one hundred ASD risk genes. Nevertheless, the vast majority of ASD risk genes remain to be discovered, as it is estimated that more than 1000 genes are likely to be involved in ASD risk. Prioritization of risk genes is an effective strategy to increase the power of identifying novel risk genes in genetics studies of ASD. As ASD risk genes are likely to exhibit distinct properties from multiple angles, we reason that integrating multiple levels of genomic data is a powerful approach to pinpoint genuine ASD risk genes.ResultsWe present BNScore, a Bayesian model selection framework to probabilistically prioritize ASD risk genes through explicitly integrating evidence from sequencing-identified ASD genes, biological annotations, and gene functional network. We demonstrate the validity of our approach and its improved performance over existing methods by examining the resulting top candidate ASD risk genes against sets of high-confidence benchmark genes and large-scale ASD genome-wide association studies. We assess the tissue-, cell type- and development stage-specific expression properties of top prioritized genes, and find strong expression specificity in brain tissues, striatal medium spiny neurons, and fetal developmental stages.ConclusionsIn summary, we show that by integrating sequencing findings, functional annotation profiles, and gene-gene functional network, our proposed BNScore provides competitive performance compared to current state-of-the-art methods in prioritizing ASD genes. Our method offers a general and flexible strategy to risk gene prioritization that can potentially be applied to other complex traits as well.

The structure balance of gene-gene networks beyond pairwise interactions.

Despite its high and direct impact on nearly all biological processes, the underlying structure of gene-gene interaction networks is investigated so far according to pair connections. To address this, we explore the gene interaction networks of the yeast Saccharomyces cerevisiae beyond pairwise interaction using the structural balance theory (SBT). Specifically, we ask whether essential and nonessential gene interaction networks are structurally balanced. We study triadic interactions in the weighted signed undirected gene networks and observe that balanced and unbalanced triads are over and underrepresented in both networks, thus beautifully in line with the strong notion of balance. Moreover, we note that the energy distribution of triads is significantly different in both essential and nonessential networks compared to the shuffled networks. Yet, this difference is greater in the essential network regarding the frequency as well as the energy of triads. Additionally, results demonstrate that triads in the essential gene network are more interconnected through sharing common links, while in the nonessential network they tend to be isolated. Last but not least, we investigate the contribution of all-length signed walks and its impact on the degree of balance. Our findings reveal that interestingly when considering longer cycles, not only, both essential and nonessential gene networks are more balanced compared to their corresponding shuffled networks, but also, the nonessential gene network is more balanced compared to the essential network.

Structural, molecular hybridization and network based identification of miR-373-3p and miR-520e-3p as regulators of NR4A2 human gene involved in neurodegeneration

MicroRNAs (miRNAs) are short non-coding RNAs with a 22 nucleotide sequence length and docks to the 3′UTR/5’UTR of the gene to regulate their mRNA translation to play a vital role in neurodegenerative diseases. The Nuclear Receptor gene (NR4A2), a transcription factor, and a steroid-thyroid hormone retinoid receptor is involved in neural development, memory formation, dopaminergic neurotransmission, and cellular protection from inflammatory damage. Therefore, recognizing the miRNAs is essential to efficiently target the 3′UTR/5’UTR of the NR4A2 gene and regulate neurodegeneration. Highly stabilized top miRNA-mRNA hybridized structures, their homologs, and identification of the best structures based on their least free energy were evaluated using in silico techniques. The miR-gene, gene-gene network analysis, miR-disease association, and transcription factor binding sites were also investigated. Results suggest top 166 miRNAs targeting the NR4A2 mRNA, but with a total of 10 miRNAs bindings with 100% seed sequence identity (both at 3’ and 5’UTR) at the same position on the NR4A2 mRNA region. The miR-373-3p and miR-520e-3p are considered the best candidate miRNAs hybridizing with high efficiency at both 3’ and 5’UTR of NR4A2 mRNA. This could be due to the most significant seed sequence length complementary, supplementary pairing, and absence of non-canonical base pairs. Furthermore, the miR-gene network, target gene-gene interaction analysis, and miR-disease association provide an understanding of the molecular, cellular, and biological processes involved in various pathways regulated by four transcription factors (PPARG, ZNF740, NRF1, and RREB1). Therefore, miR-373-3p, 520e-3p, and four transcription factors can regulate the NR4A2 gene involved in the neurodegenerative process.

Interpretable network-guided epistasis detection.

BackgroundDetecting epistatic interactions at the gene level is essential to understanding the biological mechanisms of complex diseases. Unfortunately, genome-wide interaction association studies involve many statistical challenges that make such detection hard. We propose a multi-step protocol for epistasis detection along the edges of a gene-gene co-function network. Such an approach reduces the number of tests performed and provides interpretable interactions while keeping type I error controlled. Yet, mapping gene interactions into testable single-nucleotide polymorphism (SNP)-interaction hypotheses, as well as computing gene pair association scores from SNP pair ones, is not trivial.ResultsHere we compare 3 SNP-gene mappings (positional overlap, expression quantitative trait loci, and proximity in 3D structure) and use the adaptive truncated product method to compute gene pair scores. This method is non-parametric, does not require a known null distribution, and is fast to compute. We apply multiple variants of this protocol to a genome-wide association study dataset on inflammatory bowel disease. Different configurations produced different results, highlighting that various mechanisms are implicated in inflammatory bowel disease, while at the same time, results overlapped with known disease characteristics. Importantly, the proposed pipeline also differs from a conventional approach where no network is used, showing the potential for additional discoveries when prior biological knowledge is incorporated into epistasis detection.

Space-log: a novel approach to inferring gene-gene net-works using SPACE model with log penalty.

Gene expression data have been used to infer gene-gene networks (GGN) where an edge between two genes implies the conditional dependence of these two genes given all the other genes. Such gene-gene networks are of-ten referred to as gene regulatory networks since it may reveal expression regulation. Most of existing methods for identifying GGN employ penalized regression with L1(lasso), L2(ridge), or elastic net penalty, which spans the range of L1to L2penalty. However, for high dimensional gene expression data, a penalty that spans the range of L0and L1penalty, such as the log penalty, is often needed for variable selection consistency. Thus, we develop a novel method that em-ploys log penalty within the framework of an earlier network identification method space (Sparse PArtial Correlation Estimation), and implement it into a R package space-log. We show that the space-log is computationally efficient (source code implemented in C), and has good performance comparing with other methods, particularly for networks with hubs. Space-log is open source and available at GitHub,https://github.com/wuqian77/SpaceLog.

Changes in gene-gene interactions associated with cancer onset and progression are largely independent of changes in gene expression

SummaryRecent findings indicate that changes underlying cancer onset and progression are not only attributable to changes in DNA structure and expression of individual genes but to changes in interactions among these genes as well. We examined co-expression changes in gene-network structure occurring during the onset and progression of nine different cancer types. Network complexity is generally reduced in the transition from normal precursor tissues to corresponding primary tumors. Cross-tissue cancer network similarity generally increases in early-stage cancers followed by a subsequent loss in cross-tissue cancer similarity as tumors reacquire cancer-specific network complexity. Gene-gene connections remaining stable through cancer development are enriched for “housekeeping” gene functions, whereas newly acquired interactions are associated with established cancer-promoting functions. Surprisingly, >90% of changes in gene-gene network interactions in cancers are not associated with changes in the expression of network genes relative to normal precursor tissues.