Network-based modeling of drug effects on disease module in systemic sclerosis

Cellular senescence-Related genes define the immune microenvironment and molecular characteristics in severe asthma patients

Recent studies have shown that cellular senescence is involved in the pathogenesis of severe asthma (SA). The objective of this study was to investigate the role of cellular senescence-related genes (CSGs) in the pathogenesis of SA. Here, 54 differentially expressed CSGs were identified in SA patients compared to healthy control individuals. Among the 54 differentially expressed CSGs, 3 CSGs (ETS2, ETS1 and AURKA) were screened using the LASSO regression analysis and logistic regression analysis to establish the CSG-based prediction model to predict severe asthma. Moreover, we found that the protein expression levels of ETS2, ETS1 and AURKA were increased in the severe asthma mouse model. Then, two distinct senescence subtypes of SA with distinct immune microenvironments and molecular biological characteristics were identified. Cluster 1 was characterized by increased infiltration of immature dendritic cells, regulatory T cells, and other cells. Cluster 2 was characterized by increased infiltration levels of eosinophils, neutrophils, and other cells. The molecular biological characteristics of Cluster 1 included aerobic respiration and oxidative phosphorylation, whereas the molecular biological characteristics of Cluster 2 included activation of the immune response and immune receptor activity. Then, we established an Random Forest model to predict the senescence subtypes of SA to guide treatment. Finally, potential drugs were searched for each senescence subgroup of SA patients via the Connectivity Map database. A peroxisome proliferator-activated receptor agonist may be a potential therapeutic drug for patients in Cluster 1, whereas a tachykinin antagonist may be a potential therapeutic drug for patients in Cluster 2. In summary, CSGs are likely involved in the pathogenesis of SA, which may lead to new therapeutic options for SA patients.

Computational screen for sex-specific drug effects in a cardiac fibroblast signaling network model

Heart disease is the leading cause of death in both men and women. Cardiac fibrosis is the uncontrolled accumulation of extracellular matrix proteins, which can exacerbate the progression of heart failure, and there are currently no drugs approved specifically to target matrix accumulation in the heart. Computational signaling network models (SNMs) can be used to facilitate discovery of novel drug targets. However, the vast majority of SNMs are not sex-specific and/or are developed and validated using data skewed towards male in vitro and in vivo samples. Biological sex is an important consideration in cardiovascular health and drug development. In this study, we integrate a cardiac fibroblast SNM with estrogen signaling pathways to create sex-specific SNMs. The sex-specific SNMs demonstrated high validation accuracy compared to in vitro experimental studies in the literature while also elucidating how estrogen signaling can modulate the effect of fibrotic cytokines via multi-pathway interactions. Further, perturbation analysis and drug screening uncovered several drug compounds predicted to generate divergent fibrotic responses in male vs. female conditions, which warrant further study in the pursuit of sex-specific treatment recommendations for cardiac fibrosis. Future model development and validation will require more generation of sex-specific data to further enhance modeling capabilities for clinically relevant sex-specific predictions of cardiac fibrosis and treatment.

Making inroads to precision medicine for the treatment of autoimmune diseases: Harnessing genomic studies to better diagnose and treat complex disorders

Precision Medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle. Autoimmune diseases are those in which the body's natural defense system loses discriminating power between its own cells and foreign cells, causing the body to mistakenly attack healthy tissues. These conditions are very heterogeneous in their presentation and therefore difficult to diagnose and treat. Achieving precision medicine in autoimmune diseases has been challenging due to the complex etiologies of these conditions, involving an interplay between genetic, epigenetic, and environmental factors. However, recent technological and computational advances in molecular profiling have helped identify patient subtypes and molecular pathways which can be used to improve diagnostics and therapeutics. This review discusses the current understanding of the disease mechanisms, heterogeneity, and pathogenic autoantigens in autoimmune diseases gained from genomic and transcriptomic studies and highlights how these findings can be applied to better understand disease heterogeneity in the context of disease diagnostics and therapeutics.

Computational Screen for Sex-Specific Drug Effects in a Cardiac Fibroblast Network Model

Heart disease is the leading cause of death in both men and women. Cardiac fibrosis is the uncontrolled accumulation of extracellular matrix proteins which can exacerbate the progression of heart failure, and there are currently no drugs approved specifically to target matrix accumulation in the heart. Computational signaling network models (SNMs) can be used to facilitate discovery of novel drug targets. However, the vast majority of SNMs are not sex-specific and/or are developed and validated using data skewed towards male in vitro and in vivo samples. Biological sex is an important consideration in cardiovascular health and drug development. In this study, we integrate a previously constructed cardiac fibroblast SNM with estrogen signaling pathways to create sex-specific SNMs. The sex-specific SNMs maintained previously high validation when compared to in vitro experimental studies in the literature. A sex-specific perturbation analysis and drug screen uncovered several potential pathways that warrant further study in the pursuit of sex-specific treatment recommendations for cardiac fibrosis.

An Integrative Network Approach to Identify Common Genes for the Therapeutics in Tuberculosis and Its Overlapping Non-Communicable Diseases

Tuberculosis (TB) is the leading cause of death from a single infectious agent. The estimated total global TB deaths in 2019 were 1.4 million. The decline in TB incidence rate is very slow, while the burden of noncommunicable diseases (NCDs) is exponentially increasing in low- and middle-income countries, where the prevention and treatment of TB disease remains a great burden, and there is enough empirical evidence (scientific evidence) to justify a greater research emphasis on the syndemic interaction between TB and NCDs. The current study was proposed to build a disease-gene network based on overlapping TB with NCDs (overlapping means genes involved in TB and other/s NCDs), such as Parkinson’s disease, cardiovascular disease, diabetes mellitus, rheumatoid arthritis, and lung cancer. We compared the TB-associated genes with genes of its overlapping NCDs to determine the gene-disease relationship. Next, we constructed the gene interaction network of disease-genes by integrating curated and experimentally validated interactions in humans and find the 13 highly clustered modules in the network, which contains a total of 86 hub genes that are commonly associated with TB and its overlapping NCDs, which are largely involved in the Inflammatory response, cellular response to cytokine stimulus, response to cytokine, cytokine-mediated signaling pathway, defense response, response to stress and immune system process. Moreover, the identified hub genes and their respective drugs were exploited to build a bipartite network that assists in deciphering the drug-target interaction, highlighting the influential roles of these drugs on apparently unrelated targets and pathways. Targeting these hub proteins by using drugs combination or drug repurposing approaches will improve the clinical conditions in comorbidity, enhance the potency of a few drugs, and give a synergistic effect with better outcomes. Thus, understanding the Mycobacterium tuberculosis (Mtb) infection and associated NCDs is a high priority to contain its short and long-term effects on human health. Our network-based analysis opens a new horizon for more personalized treatment, drug-repurposing opportunities, investigates new targets, multidrug treatment, and can uncover several side effects of unrelated drugs for TB and its overlapping NCDs.

GPCRs Are Optimal Regulators of Complex Biological Systems and Orchestrate the Interface between Health and Disease

GPCRs arguably represent the most effective current therapeutic targets for a plethora of diseases. GPCRs also possess a pivotal role in the regulation of the physiological balance between healthy and pathological conditions; thus, their importance in systems biology cannot be underestimated. The molecular diversity of GPCR signaling systems is likely to be closely associated with disease-associated changes in organismal tissue complexity and compartmentalization, thus enabling a nuanced GPCR-based capacity to interdict multiple disease pathomechanisms at a systemic level. GPCRs have been long considered as controllers of communication between tissues and cells. This communication involves the ligand-mediated control of cell surface receptors that then direct their stimuli to impact cell physiology. Given the tremendous success of GPCRs as therapeutic targets, considerable focus has been placed on the ability of these therapeutics to modulate diseases by acting at cell surface receptors. In the past decade, however, attention has focused upon how stable multiprotein GPCR superstructures, termed receptorsomes, both at the cell surface membrane and in the intracellular domain dictate and condition long-term GPCR activities associated with the regulation of protein expression patterns, cellular stress responses and DNA integrity management. The ability of these receptorsomes (often in the absence of typical cell surface ligands) to control complex cellular activities implicates them as key controllers of the functional balance between health and disease. A greater understanding of this function of GPCRs is likely to significantly augment our ability to further employ these proteins in a multitude of diseases.

Centrality of drug targets in protein networks

Background

In the pharmaceutical industry, competing for few validated drug targets there is a drive to identify new ways of therapeutic intervention. Here, we attempted to define guidelines to evaluate a target’s ‘fitness’ based on its node characteristics within annotated protein functional networks to complement contingent therapeutic hypotheses.

Results

We observed that targets of approved, selective small molecule drugs exhibit high node centrality within protein networks relative to a broader set of investigational targets spanning various development stages. Targets of approved drugs also exhibit higher centrality than other proteins within their respective functional class. These findings expand on previous reports of drug targets’ network centrality by suggesting some centrality metrics such as low topological coefficient as inherent characteristics of a ‘good’ target, relative to other exploratory targets and regardless of its functional class. These centrality metrics could thus be indicators of an individual protein’s ‘fitness’ as potential drug target. Correlations between protein nodes’ network centrality and number of associated publications underscored the possibility of knowledge bias as an inherent limitation to such predictions.

Conclusions

Despite some entanglement with knowledge bias, like structure-oriented ‘druggability’ assessments of new protein targets, centrality metrics could assist early pharmaceutical discovery teams in evaluating potential targets with limited experimental proof of concept and help allocate resources for an effective drug discovery pipeline.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04342-x.

The association between hypertension and nonalcoholic fatty liver disease (NAFLD): literature evidence and systems biology analysis

Nonalcoholic fatty liver disease (NAFLD) has become a major public health issue as its progression increases risks of multisystem morbidity and mortality. Recent evidence indicates a more complex relationship between hypertension and NAFLD than previously thought. In this study, a comprehensive literature search was used to gather information supporting the comorbidity phenomenon of hypertension and NAFLD. Then, systems biology approach was applied to identify the potential genes and mechanisms simultaneously associated with hypertension and NAFLD. With the help of protein-protein interaction network-based algorithm, we found that the distance between hypertension and NAFLD was much less than random ones. Sixty-four shared genes of hypertension and NAFLD modules were identified as core genes. Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment analysis indicated that some inflammatory, metabolic and endocrine signals were related to the potential biological functions of core genes. More importantly, drugs used to treat cardiovascular diseases, hypertension, hyperlipidemia, inflammatory diseases and depression could be potential therapeutics against hypertension-NAFLD co-occurrence. After analyzing public OMICs data, ALDH1A1 was identified as a potential therapeutic target, without being affected by reverse causality. These findings give a clue for the potential mechanisms of comorbidity of hypertension and NAFLD and highlight the multiple target-therapeutic strategy of NAFLD for future clinical research.