Stress Conditions Modulate the Chromatin Interactions Network in Arabidopsis

Stresses have been known to cause various responses like cellular physiology, gene regulation, and genome remodeling in the organism to cope and survive. Here, we assessed the impact of stress conditions on the chromatin-interactome network of Arabidopsis thaliana. We identified thousands of chromatin interactions in native as well as in salicylic acid treatment and high temperature conditions in a genome-wide fashion. Our analysis revealed the definite pattern of chromatin interactions and stress conditions could modulate the dynamics of chromatin interactions. We found the heterochromatic region of the genome actively involved in the chromatin interactions. We further observed that the establishment or loss of interactions in response to stress does not result in the global change in the expression profile of interacting genes; however, interacting regions (genes) containing motifs for known TFs showed either lower expression or no difference than non-interacting genes. The present study also revealed that interactions preferred among the same epigenetic state (ES) suggest interactions clustered the same ES together in the 3D space of the nucleus. Our analysis showed that stress conditions affect the dynamics of chromatin interactions among the chromatin loci and these interaction networks govern the folding principle of chromatin by bringing together similar epigenetic marks.

A Multilayer Interactome Network Constructed in a Forest Poplar Population Mediates the Pleiotropic Control of Complex Traits

The effects of genes on physiological and biochemical processes are interrelated and interdependent; it is common for genes to express pleiotropic control of complex traits. However, the study of gene expression and participating pathways in vivo at the whole-genome level is challenging. Here, we develop a coupled regulatory interaction differential equation to assess overall and independent genetic effects on trait growth. Based on evolutionary game theory and developmental modularity theory, we constructed multilayer, omnigenic networks of bidirectional, weighted, and positive or negative epistatic interactions using a forest poplar tree mapping population, which were organized into metagalactic, intergalactic, and local interstellar networks that describe layers of structure between modules, submodules, and individual single nucleotide polymorphisms, respectively. These multilayer interactomes enable the exploration of complex interactions between genes, and the analysis of not only differential expression of quantitative trait loci but also previously uncharacterized determinant SNPs, which are negatively regulated by other SNPs, based on the deconstruction of genetic effects to their component parts. Our research framework provides a tool to comprehend the pleiotropic control of complex traits and explores the inherent directional connections between genes in the structure of omnigenic networks.

EXPRESS: Metabolic Endophenotype Associated with Right Ventricular Glucose Uptake in Pulmonary Hypertension

Alterations in metabolism and bioenergetics are hypothesized in the mechanisms leading to pulmonary vascular remodeling and heart failure in pulmonary hypertension (PH). To test this, we performed metabolomic analyses on 30 PH individuals and 12 controls. Furthermore, using 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography (FDG-PET), we dichotomized PH patients into metabolic phenotypes of high and low right ventricle (RV) glucose uptake and followed them longitudinally. In support of metabolic alterations in PH and its progression, the high RV glucose group had higher RVSP (p < 0.001), worse RV function as measured by RV fractional area change and peak global longitudinal strain (both p < 0.05) and may be associated with poorer outcomes (33% death or transplantation in the high glucose RV uptake group compared to 7% in the low RV glucose uptake group at 5 years follow up, log-ranked p = 0.07). Pathway enrichment analysis identified key metabolic pathways including fructose catabolism, arginine-nitric oxide metabolism, tricarboxylic acid (TCA) cycle, and ketones metabolism. Integrative human protein-protein interactome network analysis of metabolomic and transcriptomic data identified key pathobiological pathways: arginine biosynthesis, TCA cycle, purine metabolism, hypoxia-inducible factor 1 and apelin signaling. These findings identify a PH metabolomic endophenotype, and for the first time link this to disease severity and outcomes.

Interactome, Network, and Enrichment Motif Analysis Reveals Possible Linkage Between Human Mental Disorders, Emotions, and Coronavirus: In-Silico Insight

The pandemic of COVID-19 has caused a global crisis. Today, everybody focuses on COVID-19 infection prevention, preparation, and discussion of physical health effects issues. It is important to understand, however, that a few will face life-threatening negative effects on physical health, but that all people will face the negative impact of the pandemic on mental health. COVID-19 hospitals are established in different locations to address the physical health implications of the pandemic. However, it is necessary to understand the effects of infections on mental health more effectively to prevent the negative consequences of infection. Here, we try to find out how the infection could affect mental health. We identify motifs in SARS-CoV-2 that are predicted to interact with human transcription factors (TF). Those TFs regulating behavior and mental health. Our results show that SARS-CoV-2 infection may lead to overactivation or inhibition of critical genes already known to affect behavior and mental health. This study is still limited to in silico limits so, clinical investigation needs to be addressed to assess our hypothesis.

Identification of disease treatment mechanisms through the multiscale interactome

Most diseases disrupt multiple proteins, and drugs treat such diseases by restoring the functions of the disrupted proteins. How drugs restore these functions, however, is often unknown as a drug’s therapeutic effects are not limited to the proteins that the drug directly targets. Here, we develop the multiscale interactome, a powerful approach to explain disease treatment. We integrate disease-perturbed proteins, drug targets, and biological functions into a multiscale interactome network. We then develop a random walk-based method that captures how drug effects propagate through a hierarchy of biological functions and physical protein-protein interactions. On three key pharmacological tasks, the multiscale interactome predicts drug-disease treatment, identifies proteins and biological functions related to treatment, and predicts genes that alter a treatment’s efficacy and adverse reactions. Our results indicate that physical interactions between proteins alone cannot explain treatment since many drugs treat diseases by affecting the biological functions disrupted by the disease rather than directly targeting disease proteins or their regulators. We provide a general framework for explaining treatment, even when drugs seem unrelated to the diseases they are recommended for.

Towards a global investigation of transcriptomic signatures through co-expression networks and pathway knowledge for the identification of disease mechanisms

In this work, we attempt to address a key question in the joint analysis of transcriptomic data: can we correlate the patterns we observe in transcriptomic datasets to known molecular interactions and pathway knowledge to broaden our understanding of disease pathophysiology? We present a systematic approach that sheds light on the patterns observed in hundreds of transcriptomic datasets from over sixty indications by using pathways and molecular interactions as a template. Our analysis employs transcriptomic datasets to construct dozens of disease specific co-expression networks, alongside a human interactome network of protein-protein interactions described in the literature. Leveraging the interoperability between these two network templates, we explore patterns both common and particular to these diseases on three different levels. Firstly, at the node-level, we identify the most and least common proteins in these diseases and evaluate their consistency against the interactome as a proxy for their prevalence in the scientific literature. Secondly, we overlay both network templates to analyze common correlations and interactions across diseases at the edge-level. Thirdly, we explore the similarity between patterns observed at the disease level and pathway knowledge to identify pathway signatures associated with specific diseases and indication areas. Finally, we present a case scenario in the context of schizophrenia, where we show how our approach can be used to investigate disease pathophysiology.

In silico Analyses of Immune System Protein Interactome Network, Single-Cell RNA Sequencing of Human Tissues, and Artificial Neural Networks Reveal Potential Therapeutic Targets for Drug Repurposing Against COVID-19

Background: There is pressing urgency to identify therapeutic targets and drugs that allow treating COVID-19 patients effectively.Methods: We performed in silico analyses of immune system protein interactome network, single-cell RNA sequencing of human tissues, and artificial neural networks to reveal potential therapeutic targets for drug repurposing against COVID-19.Results: We screened 1,584 high-confidence immune system proteins in ACE2 and TMPRSS2 co-expressing cells, finding 25 potential therapeutic targets significantly overexpressed in nasal goblet secretory cells, lung type II pneumocytes, and ileal absorptive enterocytes of patients with several immunopathologies. Then, we performed fully connected deep neural networks to find the best multitask classification model to predict the activity of 10,672 drugs, obtaining several approved drugs, compounds under investigation, and experimental compounds with the highest area under the receiver operating characteristics.Conclusion: After being effectively analyzed in clinical trials, these drugs can be considered for treatment of severe COVID-19 patients. Scripts can be downloaded at https://github.com/muntisa/immuno-drug-repurposing-COVID-19.

Gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery

In this study, we integrate the outcomes of co-expression network analysis with the human interactome network to predict novel putative disease genes and modules. We first apply the SWItch Miner (SWIM) methodology, which predicts important (switch) genes within the co-expression network that regulate disease state transitions, then map them to the human protein–protein interaction network (PPI, or interactome) to predict novel disease–disease relationships (i.e., a SWIM-informed diseasome). Although the relevance of switch genes to an observed phenotype has been recently assessed, their performance at the system or network level constitutes a new, potentially fascinating territory yet to be explored. Quantifying the interplay between switch genes and human diseases in the interactome network, we found that switch genes associated with specific disorders are closer to each other than to other nodes in the network, and tend to form localized connected subnetworks. These subnetworks overlap between similar diseases and are situated in different neighborhoods for pathologically distinct phenotypes, consistent with the well-known topological proximity property of disease genes. These findings allow us to demonstrate how SWIM-based correlation network analysis can serve as a useful tool for efficient screening of potentially new disease gene associations. When integrated with an interactome-based network analysis, it not only identifies novel candidate disease genes, but also may offer testable hypotheses by which to elucidate the molecular underpinnings of human disease and reveal commonalities between seemingly unrelated diseases.