Computational Systems Biology of Psoriasis: Are We Ready for the Age of Omics and Systems Biomarkers?

Computational biology and omics' systems sciences are greatly impacting research on common diseases such as cancer. By contrast, dermatology covering an array of skin diseases with high prevalence in society, has received relatively less attention from omics' and computational biosciences. We are focusing on psoriasis, a common and debilitating autoimmune disease involving skin and joints. Using computational systems biology and reconstruction, topological, modular, and a novel correlational analyses (based on fold changes) of biological and transcriptional regulatory networks, we analyzed and integrated data from a total of twelve studies from the Gene Expression Omnibus (sample size=534). Samples represented a comprehensive continuum from lesional and nonlesional skin, as well as bone marrow and dermal mesenchymal stem cells. We identified and propose here a JAK/STAT signaling pathway significant for psoriasis. Importantly, cytokines, interferon-stimulated genes, antimicrobial peptides, among other proteins, were involved in intrinsic parts of the proposed pathway. Several biomarker and therapeutic candidates such as SUB1 are discussed for future experimental studies. The integrative systems biology approach presented here illustrates a comprehensive perspective on the molecular basis of psoriasis. This also attests to the promise of systems biology research in skin diseases, with psoriasis as a systemic component. The present study reports, to the best of our knowledge, the largest set of microarray datasets on psoriasis, to offer new insights into the disease mechanisms with a proposal of a disease pathway. We call for greater computational systems biology research and analyses in dermatology and skin diseases in general

Integrative analysis of motor neuron and microglial transcriptomes from SOD1(G93A) mice models uncover potential drug treatments for ALS

Amyotrophic lateral sclerosis (ALS) is a fatal disease of motor neurons that mainly affects the motor cortex, brainstem, and spinal cord. Under disease conditions, microglia could possess two distinct profiles, M1 (toxic) and M2 (protective), with the M2 profile observed at disease onset. SOD1 (superoxide dismutase 1) gene mutations account for up to 20% of familial ALS cases. Comparative gene expression differences in M2-protective (early) stage SOD1(G93A) microglia and age-matched SOD1(G93A) motor neurons are poorly understood. We evaluated the differential gene expression profiles in SOD1(G93A) microglia and SOD1(G93A) motor neurons utilizing publicly available transcriptomics data and bioinformatics analyses, constructed biomolecular networks around them, and identified gene clusters as potential drug targets. Following a drug repositioning strategy, 5 small compounds (belinostat, auranofin, BRD-K78930611, AZD-8055, and COT-10b) were repositioned as potential ALS therapeutic candidates that mimic the protective state of microglia and reverse the toxic state of motor neurons. We anticipate that this study will provide new insights into the ALS pathophysiology linking the M2 state of microglia and drug repositioning

Novel Genomic Biomarker Candidates for Cervical Cancer As Identified by Differential Co-Expression Network Analysis

Cervical cancer is the second most common malignancy and the third reason for mortality among women in developing countries. Although infection by the oncogenic human papilloma viruses is a major cause, genomic contributors are still largely unknown. Network analyses, compared with candidate gene studies, offer greater promise to map the interactions among genomic loci contributing to cervical cancer risk. We report here a differential co-expression network analysis in five gene expression datasets (GSE7803, GSE9750, GSE39001, GSE52903, and GSE63514, from the Gene Expression Omnibus) in patients with cervical cancer and healthy controls. Kaplan-Meier Survival and principle component analyses were employed to evaluate prognostic and diagnostic performances of biomarker candidates, respectively. As a result, seven distinct co-expressed gene modules were identified. Among these, five modules (with sizes of 9-45 genes) presented high prognostic and diagnostic capabilities with hazard ratios of 2.28-11.3, and diagnostic odds ratios of 85.2-548.8. Moreover, these modules were associated with several key biological processes such as cell cycle regulation, keratinization, neutrophil degranulation, and the phospholipase D signaling pathway. In addition, transcription factors ETS1 and GATA2 were noted as common regulatory elements. These genomic biomarker candidates identified by differential co-expression network analysis offer new prospects for translational cancer research, not to mention personalized medicine to forecast cervical cancer susceptibility and prognosis. Looking into the future, we also suggest that the search for a molecular basis of common complex diseases should be complemented by differential co-expression analyses to obtain a systems-level understanding of disease phenotype variability

A pan-cancer atlas of differentially interacting hallmarks of cancer proteins

Cancer hallmark genes and proteins orchestrate and drive carcinogenesis to a large extent, therefore, it is important to study these features in different cancer types to understand the process of tumorigenesis and discover measurable indicators. We performed a pan-cancer analysis to map differentially interacting hallmarks of cancer proteins (DIHCP). The TCGA transcriptome data associated with 12 common cancers were analyzed and the differential interactome algorithm was applied to determine DIHCPs and DIHCP-centric modules (i.e., DIHCPs and their interacting partners) that exhibit significant changes in their interaction patterns between the tumor and control phenotypes. The diagnostic and prognostic capabilities of the identified modules were assessed to determine the ability of the modules to function as system biomarkers. In addition, the druggability of the prognostic and diagnostic DIHCPs was investigated. As a result, we found a total of 30 DIHCP-centric modules that showed high diagnostic or prognostic performance in any of the 12 cancer types. Furthermore, from the 16 DIHCP-centric modules examined, 29% of these were druggable. Our study presents candidate systems\" biomarkers that may be valuable for understanding the process of tumorigenesis and improving personalized treatment strategies for various cancers, with a focus on their ten hallmark characteristics

Triple Negative Breast Cancer: A Multi-Omics Network Discovery Strategy for Candidate Targets and Driving Pathways

Triple negative breast cancer (TNBC) represents approximately 15% of breast cancers and is characterized by lack of expression of both estrogen receptor (ER) and progesterone receptor (PR), together with absence of human epidermal growth factor 2 (HER2). TNBC has attracted considerable attention due to its aggressiveness such as large tumor size, high proliferation rate, and metastasis. The absence of clinically efficient molecular targets is of great concern in treatment of patients with TNBC. In light of the complexity of TNBC, we applied a systematic and integrative transcriptomics and interactomics approach utilizing transcriptional regulatory and protein-protein interaction networks to discover putative transcriptional control mechanisms of TNBC. To this end, we identified TNBC-driven molecular pathways such as the Janus kinase-signal transducers, and activators of transcription (JAK-STAT) and tumor necrosis factor (TNF) signaling pathways. The multi-omics molecular target and biomarker discovery approach presented here can offer ways forward on novel diagnostics and potentially help to design personalized therapeutics for TNBC in the future

Identification of Novel Components of Target-of-Rapamycin Signaling Pathway by Network-Based Multi-Omics Integrative Analysis

Target of rapamycin (TOR) is a major signaling pathway and regulator of cell growth. TOR serves as a hub of many signaling routes, and is implicated in the pathophysiology of numerous human diseases, including cancer, diabetes, and neurodegeneration. Therefore, elucidation of unknown components of TOR signaling that could serve as potential biomarkers and drug targets has a great clinical importance. In this study, our aim is to integrate transcriptomics, interactomics, and regulomics data in Saccharomyces cerevisiae using a network-based multiomics approach to enlighten previously unidentified, potential components of TOR signaling. We constructed the TOR-signaling protein interaction network, which was used as a template to search for TOR-mediated rapamycin and caffeine signaling paths. We scored the paths passing from at least one component of TOR Complex 1 or 2 (TORC1/TORC2) using the co-expression levels of the genes in the transcriptome data of the cells grown in the presence of rapamycin or caffeine. The resultant network revealed seven hitherto unannotated proteins, namely, Atg14p, Rim20p, Ret2p, Spt21p, Ylr257wp, Ymr295cp, and Ygr017wp, as potential components of TOR-mediated rapamycin and caffeine signaling in yeast. Among these proteins, we suggest further deciphering of the role of Ylr257wp will be particularly informative in the future because it was the only protein whose removal from the constructed network hindered the signal transduction to the TORC1 effector kinase Npr1p. In conclusion, this study underlines the value of network-based multiomics integrative data analysis in discovering previously unidentified components of the signaling networks by revealing potential components of TOR signaling for future experimental validation