Biology subjects

Halu, A.

Publications and source records attributed to Halu, A..

8 recordsLinked to original sources

bioRxiv2022

Integrating pharmacogenomics and cheminformatics with diverse disease phenotypes for cell type-guided drug discovery

Large-scale pharmacogenomic resources, such as the Connectivity Map (CMap), have greatly assisted computational drug discovery. However, despite their widespread use, CMap-based methods have thus far been agnostic to the biological activity of drugs, as well as to the genomic effects of drugs in multiple disease contexts. Here, we present a network-based statistical approach, Pathopticon, that uses CMap to build cell type-specific gene-drug perturbation networks and integrates these networks with cheminformatic data and diverse disease phenotypes for a nested prioritization of cell lines and drugs. Pathopticon demonstrates a better prediction performance than solely cheminformatic measures and state-of-the-art methods that rely exclusively on pharmacogenomic data. Top predictions made by Pathopticon have high chemical structural diversity, suggesting their potential for building compound libraries. In a proof-of-concept application on vein graft disease, we validate the predicted drugs in vitro and demonstrate that Pathopticon helps pinpoint the shared intermediate phenotypes targeted by each prediction. Overall, our analytical framework integrating pharmacogenomics and cheminformatics provides a feasible blueprint for a cell typespecific drug discovery and repositioning platform with broad implications for the efficiency and success of drug development.

systems biology↗

bioRxiv2022

Detecting and dissecting signaling crosstalk via the multilayer network integration of signaling and regulatory interactions

The versatility of cellular response arises from the communication, or crosstalk, of signaling pathways in a complex network of signaling and transcriptional regulatory interactions. Understanding the various mechanisms underlying crosstalk on a global scale requires untargeted computational approaches. We present a network-based statistical approach, MuXTalk, that uses high-dimensional edges called multilinks to model the unique ways in which signaling and regulatory interactions can interface. We demonstrate that the signaling-regulatory interface is located primarily in the intermediary region between signaling pathways where crosstalk occurs, and that multilinks can differentiate between distinct signaling-transcriptional mechanisms. Using statistically over-represented multilinks as proxies of crosstalk, we predict crosstalk among 60 signaling pathways, expanding currently available crosstalk databases by more than five-fold. MuXTalk surpasses existing methods in terms of prediction performance, identifies additions to manual curation efforts, and pinpoints potential mediators of crosstalk for each prediction. Moreover, it accommodates the inherent context-dependence of crosstalk, allowing future applications to cell type- and disease-specific crosstalk.

systems biology↗

bioRxiv2022

Vitamin D constrains inflammation by modulating the expression of key genes on Chr17q12-21.1

Vitamin D possesses immunomodulatory functions and vitamin D deficiency has been associated with the rise in chronic inflammatory diseases, including asthma (1). Vitamin D supplementation studies do not provide insight into the molecular genetic mechanisms of vitamin D mediated immunoregulation. Here we provide evidence for vitamin D regulation of two human chromosomal loci, Chr17q12-21.1 and Chr17q21.2, reliably associated with autoimmune and chronic inflammatory diseases (2-4). We demonstrate increased vitamin D receptor (VDR) expression in mouse lung CD4+ Th2 cells, differential expression of Chr17q12-21.1 and Chr17q21.2 genes in Th2 cells based on vitamin D status and identify the IL-2/Stat5 pathway as a target of vitamin D signaling. Vitamin D deficiency caused severe lung inflammation after allergen challenge in mice that was prevented by long term prenatal vitamin D supplementation. Mechanistically, vitamin D induced the expression of the Ikzf3 encoded protein Aiolos to suppress IL-2-signaling and ameliorate cytokine production in Th2 cells. These translational findings demonstrate mechanisms for the immune protective effect of vitamin D in allergic lung inflammation with a strong molecular genetic link to the regulation of both Chr17q12-21.1 and Chr17q21.2 genes and suggest further functional studies and interventional strategies for long-term prevention of asthma and other autoimmune disorders. One Sentence SummaryVitamin D regulates two human chromosomal loci, Chr17q12-21.1 and Chr17q21.2, that are associated with autoimmune and chronic inflammatory diseases.

systems biology↗

bioRxiv2020

Conserved and Divergent Modulation of Calcification in Atherosclerosis and Aortic Valve Disease by Tissue Extracellular Vesicles

BackgroundFewer than 50% of patients develop calcification of both atherosclerotic plaques and aortic valves, implying differential pathogenesis. While circulating extracellular vesicles (EVs) act as biomarkers of cardiovascular diseases, tissue-entrapped EVs associate with early mineralization, but their contents, function, and contributions to disease remain unknown. ResultsGlobal proteomics of human carotid artery endarterectomies and calcified aortic valves from a total of 27 donors/patients revealed significant over-representation of proteins with vesicle-associated pathways/ontologies common to both diseases. We exploited enzymatic digestion, serial (ultra)centrifugation and OptiPrep density-gradient separation to isolate EV populations from diseased arteries and valves. Mass spectrometry found 22 EV marker proteins to be highly enriched in the four least-dense OptiPrep fractions while extracellular matrix proteins predominated in denser fractions, as confirmed by CD63 immunogold electron microscopy and nanoparticle tracking analysis. Proteomics and miRNA-sequencing of OptiPrep-enriched tissue EVs quantified 1,104 proteins and 123 miR cargoes linked to 5,182 target genes. Pathway networks of proteins and miR targets common to artery and valve tissue EVs revealed a shared regulation of Rho GTPase and MAPK intracellular signaling cascades. 179 proteins and 5 miRs were significantly altered between artery and valve EVs; multi-omics integration determined that EVs differentially modulated cellular contraction and p53-mediated transcriptional regulation in diseased vascular vs. valvular tissue. ConclusionsOur findings delineate a strategy to isolate, purify, and study protein and RNA cargoes from EVs entrapped in fibrocalcific tissues. Multi-omics and network approaches implicated tissue-resident EVs in human cardiovascular disease.

systems biology↗

bioRxiv2019

Robustness and lethality in multilayer biological molecular networks

Robustness is a prominent feature of most biological systems. In a cell, the structure of the interactions between genes, proteins, and metabolites has a crucial role in maintaining the cells functionality and viability in presence of external perturbations and noise. Despite advances in characterizing the robustness of biological systems, most of the current efforts have been focused on studying homogeneous molecular networks in isolation, such as protein-protein or gene regulatory networks, neglecting the interactions among different molecular substrates. Here we propose a comprehensive framework for understanding how the interactions between genes, proteins and metabolites contribute to the determinants of robustness in a heterogeneous biological network. We integrate heterogeneous sources of data to construct a multilayer interaction network composed of a gene regulatory layer, and protein-protein interaction layer and a metabolic layer. We design a simulated perturbation process to characterize the contribution of each gene to the overall systems robustness, defined as its influence over the global network. We find that highly influential genes are enriched in essential and cancer genes, confirming the central role of these genes in critical cellular processes. Further, we determine that the metabolic layer is more vulnerable to perturbations involving genes associated to metabolic diseases. By comparing the robustness of the network to multiple randomized network models, we find that the real network is comparably or more robust than expected in the random realizations. Finally, we analytically derive the expected robustness of multilayer biological networks starting from the degree distributions within or between layers. These results provide new insights into the non-trivial dynamics occurring in the cell after a genetic perturbation is applied, confirming the importance of including the coupling between different layers of interaction in models of complex biological systems.

systems biology↗

bioRxiv2017

Controllability in an islet specific regulatory network identifies the transcriptional factor NFATC4, which regulates Type 2 Diabetes associated genes

Probing the dynamic control features of biological networks represents a new frontier in capturing the dysregulated pathways in complex diseases. Here, using patient samples obtained from a pancreatic islet transplantation program, we constructed a tissue-specific gene regulatory network and used the control centrality (Cc) concept to identify the high control centrality (HiCc) pathways, which might serve as key pathobiological pathways for Type 2 Diabetes (T2D). We found that HiCc pathway genes were significantly enriched with modest GWAS p-values in the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) study. We identified variants regulating gene expression (expression quantitative loci, eQTL) of HiCc pathway genes in islet samples. These eQTL genes showed higher levels of differential expression compared to non-eQTL genes in low, medium and high glucose concentrations in rat islets. Among genes with highly significant eQTL evidence, NFATC4 belonged to four HiCc pathways. We asked if the expressions of T2D-associated candidate genes from GWAS and literature are regulated by Nfatc4 in rat islets. Extensive in vitro silencing of Nfatc4 in rat islet cells displayed reduced expression of 16, and increased expression of 4 putative downstream T2D genes. Overall, our approach uncovers the mechanistic connection of NFATC4 with downstream targets including a previously unknown one, TCF7L2, and establishes the HiCc pathways relationship to T2D.

systems biology↗

bioRxiv2017

A systems approach to refine disease taxonomy by integrating phenotypic and molecular networks

The International Classification of Diseases (ICD) relies on clinical features and lags behind the current understanding of the molecular specificity of disease pathobiology, necessitating approaches that incorporate growing biomedical data for classifying diseases to meet the needs of precision medicine. Our analysis revealed that the heterogeneous molecular diversity of disease chapters and the blurred boundary between disease categories in ICD should be further investigated. Here, we propose a new classification of diseases (NCD) by developing an algorithm that predicts the additional categories of a disease by integrating multiple networks consisting of disease phenotypes and their molecular profiles. With statistical validations from phenotype-genotype associations and interactome networks, we demonstrate that NCD improves disease specificity owing to its overlapping categories and polyhierarchical structure. Furthermore, NCD captures the molecular diversity of diseases and defines clearer boundaries in terms of both phenotypic similarity and molecular associations, establishing a rational strategy to reform disease taxonomy.

bioinformatics↗

bioRxiv2017

The multiplex network of human diseases

Untangling the complex interplay between phenotype and genotype is crucial to the effective characterization and subtyping of diseases. Here we build and analyze the multiplex network of 779 human diseases, which consists of a genotype-based layer and a phenotype-based layer. We show that diseases with common genetic constituents tend to share symptoms, and uncover how phenotype information helps boost genotype information. Moreover, we offer a flexible classification of diseases that considers their molecular underpinnings alongside their clinical manifestations. We detect cohesive groups of diseases that have high intra-group similarity at both the molecular and the phenotypic level. Inspecting these disease classes, we demonstrate the underlying pathways that connect diseases mechanistically. We observe monogenic disorders grouped together with complex diseases for which they increase the risk factor. We propose potentially new disease associations that arise as a unique feature of the information flow within and across the two layers.

bioinformatics↗