Reverse tracking from drug-induced transcriptomes through multilayer molecular networks reveals hidden drug targets.

Identifying molecular targets of a drug is an essential process for drug discovery and development. The recent in-silico approaches are usually based on the structure information of chemicals and proteins. However, 3D structure information is hard to obtain and machine-learning methods using 2D structure suffer from data imbalance problem. Here, we present a reverse tracking method from genes to target proteins using drug-perturbed gene transcriptional profiles and multilayer molecular networks. We scored how well the protein explains gene expression changes perturbed by a drug. We validated the protein scores of our method in predicting known targets of drugs. Our method performs better than other methods using the gene transcriptional profiles and shows the ability to suggest the molecular mechanism of drugs. Furthermore, our method has the potential to predict targets for objects that do not have rigid structural information, such as coronavirus.

Genomic regulatory network of human olfactory neuroepithelial cells infected with novel coronavirus (SARS-COV-2).

Objective To explore the genomic changes of human olfactory neuroepithelial cells after the novel coronavirus (SARS-COV-2) infecting the human body, and establish a protein-protein interaction (PPI) network of differentially expressed genes (DEGs), in order to understand the impact of SARS-COV-2 infection on human olfactory neuroepithelial cells, and provide reference for the prevention and treatment of new coronavirus pneumonia. Methods The public dataset GSE151973 was analyzed by NetworkAnalyst. DEGs were selected by conducting Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathway analysis. PPI network, DEGs-microRNA regulatory network, transcription factor-DEGs regulatory network, environmental chemicals-DEGs regulatory network, and drug-DEGs regulatory network were created and visualized by using Cytoscape 3.7.2. Results After SAR-COV-2 invading human olfactory neuroepithelial cells, part of the gene expression profile was significantly up-regulated or down-regulated. A total of 568 DEGs were found, including 550 up-regulated genes (96.8%) and 18 down-regulated genes (3.2%). DEGs were mainly involved in biological processes such as endothelial development and angiogenesis of the olfactory epithelium, and the expression of molecular functions such as the binding of the N-terminal myristylation domain. PPI network suggested that RTP1 and RTP2 were core proteins. MAZ was the most influential transcription factor. Hsa-mir-26b-5p had the most obvious interaction with DEGs regulation. Environmental chemical valproic acid and drug ethanol had the most influence on the regulation of DEG. Conclusion The gene expression of olfactory neuroepithelial cells is significantly up-regulated or down-regulated after infection with SAR-COV-2. SARS-CoV-2 may inhibit the proliferation and differentiation of muscle satellite cells by inhibiting the function of PAX7. RTP1 and RTP2 may resist SARS-CoV-2 by promoting the ability of olfactory receptors to coat the membrane and enhancing the ability of olfactory receptors to respond to odorant ligands. MAZ may regulate DEGs by affecting cell growth and proliferation. Micro RNA, environmental chemicals and drugs also play an important role in the anti-SAR-COV-2 infection process of human olfactory neuroepithelial cells.Copyright © 2022 Editorial Department of Journal of Shanghai Second Medical University. All rights reserved.

Genomic regulatory network of human olfactory neuroepithelial cells infected with novel coronavirus (SARS-COV-2)

Objective To explore the genomic changes of human olfactory neuroepithelial cells after the novel coronavirus (SARS-COV-2) infecting the human body, and establish a protein-protein interaction (PPI) network of differentially expressed genes (DEGs), in order to understand the impact of SARS-COV-2 infection on human olfactory neuroepithelial cells, and provide reference for the prevention and treatment of new coronavirus pneumonia. Methods The public dataset GSE151973 was analyzed by NetworkAnalyst. DEGs were selected by conducting Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathway analysis. PPI network, DEGs-microRNA regulatory network, transcription factor-DEGs regulatory network, environmental chemicals-DEGs regulatory network, and drug-DEGs regulatory network were created and visualized by using Cytoscape 3.7.2. Results After SAR-COV-2 invading human olfactory neuroepithelial cells, part of the gene expression profile was significantly up-regulated or down-regulated. A total of 568 DEGs were found, including 550 up-regulated genes (96.8%) and 18 down-regulated genes (3.2%). DEGs were mainly involved in biological processes such as endothelial development and angiogenesis of the olfactory epithelium, and the expression of molecular functions such as the binding of the N-terminal myristylation domain. PPI network suggested that RTP1 and RTP2 were core proteins. MAZ was the most influential transcription factor. Hsa-mir-26b-5p had the most obvious interaction with DEGs regulation. Environmental chemical valproic acid and drug ethanol had the most influence on the regulation of DEG. Conclusion The gene expression of olfactory neuroepithelial cells is significantly up-regulated or down-regulated after infection with SAR-COV-2. SARS-CoV-2 may inhibit the proliferation and differentiation of muscle satellite cells by inhibiting the function of PAX7. RTP1 and RTP2 may resist SARS-CoV-2 by promoting the ability of olfactory receptors to coat the membrane and enhancing the ability of olfactory receptors to respond to odorant ligands. MAZ may regulate DEGs by affecting cell growth and proliferation. Micro RNA, environmental chemicals and drugs also play an important role in the anti-SAR-COV-2 infection process of human olfactory neuroepithelial cells.

Boolean modeling reveals that cyclic attractors in macrophage polarization serve as reservoirs of states to balance external perturbations from the tumor microenvironment.

Cyclic attractors generated from Boolean models may explain the adaptability of a cell in response to a dynamical complex tumor microenvironment. In contrast to this idea, we postulate that cyclic attractors in certain cases could be a systemic mechanism to face the perturbations coming from the environment. To justify our conjecture, we present a dynamic analysis of a highly curated transcriptional regulatory network of macrophages constrained into a cancer microenvironment. We observed that when M1-associated transcription factors (STAT1 or NF-κB) are perturbed and the microenvironment balances to a hyper-inflammation condition, cycle attractors activate genes whose signals counteract this effect implicated in tissue damage. The same behavior happens when the M2-associated transcription factors are disturbed (STAT3 or STAT6); cycle attractors will prevent a hyper-regulation scenario implicated in providing a suitable environment for tumor growth. Therefore, here we propose that cyclic macrophage phenotypes can serve as a reservoir for balancing the phenotypes when a specific phenotype-based transcription factor is perturbed in the regulatory network of macrophages. We consider that cyclic attractors should not be simply ignored, but it is necessary to carefully evaluate their biological importance. In this work, we suggest one conjecture: the cyclic attractors can serve as a reservoir to balance the inflammatory/regulatory response of the network under external perturbations.

Predicting gene regulatory networks from multi-omics to link genetic risk variants and neuroimmunology to Alzheimer's disease phenotypes

Background: Genome-wide association studies have found many genetic risk variants associated with Alzheimer's disease (AD). However, how these risk variants affect deeper phenotypes such as disease progression and immune response remains elusive. Also, our understanding of cellular and molecular mechanisms from disease SNPs to various phenotypes is still limited. To address these problems, we performed an integrative multi-omics analysis of genotype, transcriptomics, and epigenomics for revealing gene regulatory mechanisms from disease variants to AD phenotypes. Method(s): First, given population gene expression data of a cohort, we construct and cluster its gene co-expression network to identify modules for various AD phenotypes. Next, we predict transcription factors (TFs) regulating co-expressed genes and SNPs interrupting TF binding sites on regulatory elements. Finally, we construct a gene regulatory network (GRN) linking SNPs, interrupted TFs, and regulatory elements to target genes and modules for AD phenotypes. This network provides systematic insights into gene regulatory mechanisms from SNPs to phenotypes. We looked at our GRNs relating to genes from shared AD-Covid pathways (e.g. NFKB Pathway) and used machine learning to prioritize those genes for predicting Covid-19 severity. Result(s): Our analysis predicted cross-region-conserved and region-specific GRNs in 3 regions Hippocampus, Dorsolateral Prefrontal Cortex (DLPFC), Lateral Temporal Lobe (LTL). For instance, SNPs rs13404184 and rs61068452 disrupt SPI1 binding and regulation of INPP5D in Hippocampus and LTL. While rs4802200 disrupts E2F7 regulation of KCNN4 (belongs to AD LTL module), rs117863556 interrupts REST regulation of GAB2 in DLPFC. Further, we used Covid-19 as a proxy for immune dysregulation to identify possible regulatory mechanisms for AD neuroimmunology. Decision Curve Analysis suggest our AD-Covid genes along with linked SNPs (that outperform known genes) can be potential novel biomarkers for neuroimmunology. Finally, our results are open-source available as a comprehensive AD functional genomic map, providing deeper mechanistic understanding of the interplay among multi-omics, regions, gene functions, phenotypes. Conclusion(s): Our pipeline predicts how non-coding risk SNPs may be associated with changes in regulation and subsequent expression of genes associated with different phenotypes and pathways in AD. Moreover, we flagged 51 potential AD-neuroinflammatory risk genes, which may be early biomarkers as neuroinflammation may begin decades before clinical onset. Copyright © 2022 the Alzheimer's Association.

Single-Cell Gene Regulatory Network Analysis Reveals Potential Mechanisms of Action of Antimalarials Against SARS-CoV-2

The efficiency of antimalarials, chloroquine (CQ) and hydroxychloroquine (HCQ), in the prevention and treatment of coronavirus disease 2019 (COVID-19) is under intense debate. The mechanisms of action of antimalarials against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have not been fully elucidated. Here, we applied a network-based comparative analysis, implemented in our machine learning workflow-scTenifoldNet, to scRNA-seq data from COVID-19 patientswith different levels of severity. We found that genes of the Malaria pathway expressed in macrophages are significantly differentially regulated between patientswith moderate and severe symptoms. Our findings help reveal the mechanisms of action of CQ and HCQ during SARS-CoV-2 infection, providing new evidence to support the use of these antimalarial drugs in the treatment of COVID-19, especially for patients who are mildly affected or in the early stage of the infection.

Using bioinformatics and systems biology to discover common pathogenetic processes between sarcoidosis and COVID-19.

The coronavirus disease (COVID-19) pandemic caused by SARS-CoV-2 is ongoing. Individuals with sarcoidosis tend to develop severe COVID-19; however, the underlying pathological mechanisms remain elusive. To determine common transcriptional signatures and pathways between sarcoidosis and COVID-19, we investigated the whole-genome transcriptome of peripheral blood mononuclear cells (PBMCs) from patients with COVID-19 and sarcoidosis and conducted bioinformatic analysis, including gene ontology and pathway enrichment, protein-protein interaction (PPI) network, and gene regulatory network (GRN) construction. We identified 33 abnormally expressed genes that were common between COVID-19 and sarcoidosis. Functional enrichment analysis showed that these differentially expressed genes were associated with cytokine production involved in the immune response and T cell cytokine production. We identified several hub genes from the PPI network encoded by the common genes. These hub genes have high diagnostic potential for COVID-19 and sarcoidosis and can be potential biomarkers. Moreover, GRN analysis identified important microRNAs and transcription factors that regulate the common genes. This study provides a novel characterization of the transcriptional signatures and biological processes commonly dysregulated in sarcoidosis and COVID-19 and identified several critical regulators and biomarkers. This study highlights a potential pathological association between COVID-19 and sarcoidosis, establishing a theoretical basis for future clinical trials.

DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-seq data.

Single-cell RNA sequencing has enabled to capture the gene activities at single-cell resolution, thus allowing reconstruction of cell-type-specific gene regulatory networks (GRNs). The available algorithms for reconstructing GRNs are commonly designed for bulk RNA-seq data, and few of them are applicable to analyze scRNA-seq data by dealing with the dropout events and cellular heterogeneity. In this paper, we represent the joint gene expression distribution of a gene pair as an image and propose a novel supervised deep neural network called DeepDRIM which utilizes the image of the target TF-gene pair and the ones of the potential neighbors to reconstruct GRN from scRNA-seq data. Due to the consideration of TF-gene pair's neighborhood context, DeepDRIM can effectively eliminate the false positives caused by transitive gene-gene interactions. We compared DeepDRIM with nine GRN reconstruction algorithms designed for either bulk or single-cell RNA-seq data. It achieves evidently better performance for the scRNA-seq data collected from eight cell lines. The simulated data show that DeepDRIM is robust to the dropout rate, the cell number and the size of the training data. We further applied DeepDRIM to the scRNA-seq gene expression of B cells from the bronchoalveolar lavage fluid of the patients with mild and severe coronavirus disease 2019. We focused on the cell-type-specific GRN alteration and observed targets of TFs that were differentially expressed between the two statuses to be enriched in lysosome, apoptosis, response to decreased oxygen level and microtubule, which had been proved to be associated with coronavirus infection.

FunCoup 5: Functional Association Networks in All Domains of Life, Supporting Directed Links and Tissue-Specificity.

FunCoup (https://funcoup.sbc.su.se) is one of the most comprehensive functional association networks of genes/proteins available. Functional associations are inferred by integrating different types of evidence using a redundancy-weighted naïve Bayesian approach, combined with orthology transfer. FunCoup's high coverage comes from using eleven different types of evidence, and extensive transfer of information between species. Since the latest update of the database, the availability of source data has improved drastically, and user expectations on a tool for functional associations have grown. To meet these requirements, we have made a new release of FunCoup with updated source data and improved functionality. FunCoup 5 now includes 22 species from all domains of life, and the source data for evidences, gold standards, and genomes have been updated to the latest available versions. In this new release, directed regulatory links inferred from transcription factor binding can be visualized in the network viewer for the human interactome. Another new feature is the possibility to filter by genes expressed in a certain tissue in the network viewer. FunCoup 5 further includes the SARS-CoV-2 proteome, allowing users to visualize and analyze interactions between SARS-CoV-2 and human proteins in order to better understand COVID-19. This new release of FunCoup constitutes a major advance for the users, with updated sources, new species and improved functionality for analysis of the networks.