Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts

Lipid metabolism transcriptomics of murine microglia in Alzheimer's disease and neuroinflammation

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by the accumulation of amyloid-β (Aβ) plaques followed by intracellular neurofibrillary tangles (NFTs) composed of hyperphosphorylated tau. An unrestrained immune response by microglia, the resident cells of the central nervous system (CNS), leads to neuroinflammation which can amplify AD pathology. AD pathology is also driven by metabolic dysfunction with strong correlations between dementia and metabolic disorders such as diabetes, hypercholesterolemia, and hypertriglyceridemia. Since elevated cholesterol and triglyceride levels appear to be a major risk factor for developing AD, we investigated the lipid metabolism transcriptome in an AD versus non-AD state using RNA-sequencing (RNA-seq) and microarray datasets from N9 cells and murine microglia. We identified 52 differentially expressed genes (DEG) linked to lipid metabolism in LPS-stimulated N9 microglia versus unstimulated control cells using RNA-seq, 86 lipid metabolism DEG in 5XFAD versus wild-type mice by microarray, with 16 DEG common between both datasets. Functional enrichment and network analyses identified several biological processes and molecular functions, such as cholesterol homeostasis, insulin signaling, and triglyceride metabolism. Furthermore, therapeutic drugs targeting lipid metabolism DEG found in our study were identified. Focusing on drugs that target genes associated with lipid metabolism and neuroinflammation could provide new targets for AD drug development.

Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery

Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug-target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI's expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI's growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts.

MSDRP: a deep learning model based on multisource data for predicting drug response

MOTIVATION

Cancer heterogeneity drastically affects cancer therapeutic outcomes. Predicting drug response in vitro is expected to help formulate personalized therapy regimens. In recent years, several computational models based on machine learning and deep learning have been proposed to predict drug response in vitro. However, most of these methods capture drug features based on a single drug description (e.g. drug structure), without considering the relationships between drugs and biological entities (e.g. target, diseases, and side effects). Moreover, most of these methods collect features separately for drugs and cell lines but fail to consider the pairwise interactions between drugs and cell lines.

RESULTS

In this paper, we propose a deep learning framework, named MSDRP for drug response prediction. MSDRP uses an interaction module to capture interactions between drugs and cell lines, and integrates multiple associations/interactions between drugs and biological entities through similarity network fusion algorithms, outperforming some state-of-the-art models in all performance measures for all experiments. The experimental results of de novo test and independent test demonstrate the excellent performance of our model for new drugs. Furthermore, several case studies illustrate the rationality for using feature vectors derived from drug similarity matrices from multisource data to represent drugs and the interpretability of our model.

AVAILABILITY AND IMPLEMENTATION

The codes of MSDRP are available at https://github.com/xyzhang-10/MSDRP.

Discovery of 95 PTSD loci provides insight into genetic architecture and neurobiology of trauma and stress-related disorders

Posttraumatic stress disorder (PTSD) genetics are characterized by lower discoverability than most other psychiatric disorders. The contribution to biological understanding from previous genetic studies has thus been limited. We performed a multi-ancestry meta-analysis of genome-wide association studies across 1,222,882 individuals of European ancestry (137,136 cases) and 58,051 admixed individuals with African and Native American ancestry (13,624 cases). We identified 95 genome-wide significant loci (80 novel). Convergent multi-omic approaches identified 43 potential causal genes, broadly classified as neurotransmitter and ion channel synaptic modulators (e.g., GRIA1, GRM8, CACNA1E ), developmental, axon guidance, and transcription factors (e.g., FOXP2, EFNA5, DCC ), synaptic structure and function genes (e.g., PCLO, NCAM1, PDE4B ), and endocrine or immune regulators (e.g., ESR1, TRAF3, TANK ). Additional top genes influence stress, immune, fear, and threat-related processes, previously hypothesized to underlie PTSD neurobiology. These findings strengthen our understanding of neurobiological systems relevant to PTSD pathophysiology, while also opening new areas for investigation.

Drug Repurposing Analysis for Colorectal Cancer through Network Medicine Framework: Novel Candidate Drugs and Small Molecules

This study aimed to reveal the drug-repurposing candidates for colorectal cancer (CRC) via drug-repurposing methods and network biology approaches. A novel, differentially co-expressed, highly interconnected, and co-regulated prognostic gene module was identified for CRC. Based on the gene module, polyethylene glycol (PEG), gallic acid, pyrazole, cordycepin, phenothiazine, pantoprazole, cysteamine, indisulam, valinomycin, trametinib, BRD-K81473043, AZD8055, dovitinib, BRD-A17065207, and tyrphostin AG1478 presented as drugs and small molecule candidates previously studied in the CRC. Lornoxicam, suxamethonium, oprelvekin, sirukumab, levetiracetam, sulpiride, NVP-TAE684, AS605240, 480743.cdx, HDAC6 inhibitor ISOX, BRD-K03829970, and L-6307 are proposed as novel drugs and small molecule candidates for CRC.

Comparison of Tumour-Specific Phenotypes in Human Primary and Expandable Pancreatic Cancer Cell Lines

There is an ongoing need for patient-specific chemotherapy for pancreatic cancer. Tumour cells isolated from human tissues can be used to predict patients' response to chemotherapy. However, the isolation and maintenance of pancreatic cancer cells is challenging because these cells become highly vulnerable after losing the tumour microenvironment. Therefore, we investigated whether the cells retained their original characteristics after lentiviral transfection and expansion. Three human primary pancreatic cancer cell lines were lentivirally transduced to create expandable (Ex) cells which were then compared with primary (Pri) cells. No obvious differences in the morphology or epithelial-mesenchymal transition (EMT) were observed between the primary and expandable cell lines. The two expandable cell lines showed higher proliferation rates in the 2D and 3D models. All three expandable cell lines showed attenuated migratory ability. Differences in gene expression between primary and expandable cell lines were then compared using RNA-Seq data. Potential target drugs were predicted by differentially expressed genes (DEGs), and differentially expressed pathways (DEPs) related to tumour-specific characteristics such as proliferation, migration, EMT, drug resistance, and reactive oxygen species (ROS) were investigated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. We found that the two expandable cell lines expressed similar chemosensitivity and redox-regulatory capability to gemcitabine and oxaliplatin in the 2D model as compared to their counterparts. In conclusion, we successfully generated expandable primary pancreatic cancer cell lines using lentiviral transduction. These expandable cells not only retain some tumour-specific biological traits of primary cells but also show an ongoing proliferative capacity, thereby yielding sufficient material for drug response assays, which may provide a patient-specific platform for chemotherapy drug screening.

Integration of Omics Data and Network Models to Unveil Negative Aspects of SARS-CoV-2, from Pathogenic Mechanisms to Drug Repurposing

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the COVID-19 health emergency, affecting and killing millions of people worldwide. Following SARS-CoV-2 infection, COVID-19 patients show a spectrum of symptoms ranging from asymptomatic to very severe manifestations. In particular, bronchial and pulmonary cells, involved at the initial stage, trigger a hyper-inflammation phase, damaging a wide range of organs, including the heart, brain, liver, intestine and kidney. Due to the urgent need for solutions to limit the virus' spread, most efforts were initially devoted to mapping outbreak trajectories and variant emergence, as well as to the rapid search for effective therapeutic strategies. Samples collected from hospitalized or dead COVID-19 patients from the early stages of pandemic have been analyzed over time, and to date they still represent an invaluable source of information to shed light on the molecular mechanisms underlying the organ/tissue damage, the knowledge of which could offer new opportunities for diagnostics and therapeutic designs. For these purposes, in combination with clinical data, omics profiles and network models play a key role providing a holistic view of the pathways, processes and functions most affected by viral infection. In fact, in addition to epidemiological purposes, networks are being increasingly adopted for the integration of multiomics data, and recently their use has expanded to the identification of drug targets or the repositioning of existing drugs. These topics will be covered here by exploring the landscape of SARS-CoV-2 survey-based studies using systems biology approaches derived from omics data, paying particular attention to those that have considered samples of human origin.

Genetic pathways regulating the longitudinal acquisition of cocaine self-administration in a panel of inbred and recombinant inbred mice

To identify addiction genes, we evaluate intravenous self-administration of cocaine or saline in 84 inbred and recombinant inbred mouse strains over 10 days. We integrate the behavior data with brain RNA-seq data from 41 strains. The self-administration of cocaine and that of saline are genetically distinct. We maximize power to map loci for cocaine intake by using a linear mixed model to account for this longitudinal phenotype while correcting for population structure. A total of 15 unique significant loci are identified in the genome-wide association study. A transcriptome-wide association study highlights the Trpv2 ion channel as a key locus for cocaine self-administration as well as identifying 17 additional genes, including Arhgef26, Slc18b1, and Slco5a1. We find numerous instances where alternate splice site selection or RNA editing altered transcript abundance. Our work emphasizes the importance of Trpv2, an ionotropic cannabinoid receptor, for the response to cocaine.

Identification of immune infiltration-related biomarkers in carotid atherosclerotic plaques

Atherosclerosis is a chronic lipid-driven inflammatory response of the innate and adaptive immune systems, and it is responsible for several cardiovascular ischemic events. The present study aimed to determine immune infiltration-related biomarkers in carotid atherosclerotic plaques (CAPs). Gene expression profiles of CAPs were extracted from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between the CAPs and control groups were screened by the "limma" package in R software. Immune cell infiltration between the CAPs and control groups was evaluated by the single sample gene set enrichment analysis. Key infiltrating immune cells in the CAPs group were screened by the Wilcoxon test and least absolute shrinkage and selection operator regression. The weighted gene co-expression network analysis was used to identify immune cell-related genes. Hub genes were identified by the protein-protein interaction (PPI) network. Receiver operating characteristic curve analysis was performed to assess the gene's ability to differentiate between the CAPs and control groups. Finally, we constructed a miRNA-gene-transcription factor network of hub genes by using the ENCODE database. Eleven different types of immune infiltration-related cells were identified between the CAPs and control groups. A total of 1,586 differentially expressed immunity-related genes were obtained through intersection between DEGs and immune-related genes. Twenty hub genes were screened through the PPI network. Eventually, 7 genes (BTK, LYN, PTPN11, CD163, CD4, ITGAL, and ITGB7) were identified as the hub genes of CAPs, and these genes may serve as the estimable drug targets for patients with CAPs.

Tumor cell p38 inhibition to overcome immunotherapy resistance

Patients with tumors that do not respond to immune-checkpoint inhibition often harbor a non-T cell-inflamed tumor microenvironment, characterized by the absence of IFN-γ-associated CD8+ T cell and dendritic cell activation. Understanding the molecular mechanisms underlying immune exclusion in non-responding patients may enable the development of novel combination therapies. p38 MAPK is a known regulator of dendritic and myeloid cells however a tumor-intrinsic immunomodulatory role has not been previously described. Here we identify tumor cell p38 signaling as a therapeutic target to potentiate anti-tumor immunity and overcome resistance to immune-checkpoint inhibitors (ICI). Molecular analysis of tumor tissues from patients with human papillomavirus-negative head and neck squamous carcinoma reveals a p38-centered network enriched in non-T cell-inflamed tumors. Pan-cancer single-cell RNA analysis suggests that p38 activation may be an immune-exclusion mechanism across multiple tumor types. P38 knockdown in cancer cell lines increases T cell migration, and p38 inhibition plus ICI in preclinical models shows greater efficacy compared to monotherapies. In a clinical trial of patients refractory to PD1/L1 therapy, pexmetinib, a p38 inhibitor, plus nivolumab demonstrated deep and durable clinical responses. Targeting of p38 with anti-PD1 has the potential to induce the T cell-inflamed phenotype and overcome immunotherapy resistance.

Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response

The spatial organization of the tumor microenvironment has a profound impact on biology and therapy response. Here, we perform an integrative single-cell and spatial transcriptomic analysis on HPV-negative oral squamous cell carcinoma (OSCC) to comprehensively characterize malignant cells in tumor core (TC) and leading edge (LE) transcriptional architectures. We show that the TC and LE are characterized by unique transcriptional profiles, neighboring cellular compositions, and ligand-receptor interactions. We demonstrate that the gene expression profile associated with the LE is conserved across different cancers while the TC is tissue specific, highlighting common mechanisms underlying tumor progression and invasion. Additionally, we find our LE gene signature is associated with worse clinical outcomes while TC gene signature is associated with improved prognosis across multiple cancer types. Finally, using an in silico modeling approach, we describe spatially-regulated patterns of cell development in OSCC that are predictably associated with drug response. Our work provides pan-cancer insights into TC and LE biology and interactive spatial atlases ( http://www.pboselab.ca/spatial_OSCC/ ; http://www.pboselab.ca/dynamo_OSCC/ ) that can be foundational for developing novel targeted therapies.

myCMIE: My cancer molecular information exchange

Molecular profiling reports (MPRs) are critical for determining treatment options for cancer patients. They include several pages of information on genomic findings, drugs, and trial options that are challenging to synthesize for effectively and expeditiously informing on treatment. Xu and Kowalski present a web application, myCMIE, that synthesizes MPR content to define a patient-centric, information system in which molecular profiles are exchanged between a query case(s) and public resources or user-input case series for context-informed treatment and conjecture with therapeutic implication. myCMIE offers an interactive build of coordinately connected digital-twin communities to expand our understanding of treatment context with multiple visuals to stimulate discussions among diverse stakeholders in care.

The identification of key genes and pathways in glioblastoma by bioinformatics analysis

GBM is the most common and aggressive type of brain tumor. It is classified as a grade IV tumor by the WHO, the highest grade. Prognosis is generally poor, with most patients surviving only about a year. Only 5% of patients survive longer than 5 years. Understanding the molecular mechanisms that drive GBM progression is critical for developing better diagnostic and treatment strategies. Identifying key genes involved in GBM pathogenesis is essential to fully understand the disease and develop targeted therapies. In this study two datasets, GSE108474 and GSE50161, were obtained from the Gene Expression Omnibus (GEO) to compare gene expression between GBM and normal samples. Differentially expressed genes (DEGs) were identified and analyzed. To construct a protein-protein interaction (PPI) network of the commonly up-regulated and down-regulated genes, the STRING 11.5 and Cytoscape 3.9.1 were utilized. Key genes were identified through this network analysis. The GEPIA database was used to confirm the expression levels of these key genes and their association with survival. Functional and pathway enrichment analyses on the DEGs were conducted using the Enrichr server. In total, 698 DEGs were identified, consisting of 377 up-regulated genes and 318 down-regulated genes. Within the PPI network, 11 key up-regulated genes and 13 key down-regulated genes associated with GBM were identified. NOTCH1, TOP2A, CD44, PTPRC, CDK4, HNRNPU, and PDGFRA were found to be important targets for potential drug design against GBM. Additionally, functional enrichment analysis revealed the significant impact of Epstein-Barr virus (EBV), Cell Cycle, and P53 signaling pathways on GBM.

Identification of lysosome-related genes in connection with prognosis and immune cell infiltration for drug candidates in head and neck cancer

Lysosome dysfunction has been shown to play an important role in cancer progression. However, few research studies have reported the role of lysosomes in head and neck squamous cell carcinoma (HNSCC) progression. Lysosome-related genes (LRGs) were collected from the Molecular Signatures Database. Differentially expressed lysosome-related genes (DELRGs) were identified from the TCGA-HNSCC dataset. The least absolute shrinkage and selection operator and multivariate Cox regression analysis were used to identify the prognostic genes. The prognostic values and expression of hub DELRGs were further validated by GEO datasets. Estimation of STromal and Immune cells in MAlignant Tumors using Expression data and the single-sample gene set enrichment analysis were applied to evaluate the correlation between cathepsin G (CTSG) and immune infiltrates. Twenty-two DELRGs were identified. Among them, CTSG was an independent prognostic biomarker for HNSCC patients. Gene set enrichment analysis indicated that the potential mechanism of CTSG in regulating HNSCC was associated with the immune- and inflammation-related pathways. CTSG expression was highly correlated with immune cell infiltration. Finally, two potential compounds (CH and MAN) targeting CTSG protein were identified, and their reliability was validated through molecular docking analysis. CTSG was associated with immune infiltration and had prognostic value in HNSCC patients, which may be a potential biomarker for predicting the outcome of immunotherapy.

Genomic landscape in Saudi patients with hepatocellular carcinoma using whole-genome sequencing: a pilot study

Background and aims

Hepatocellular carcinoma (HCC) is the third most prevalent cancer in Saudi Arabia. HCC poses a significant clinical challenge due to the presence of resistance among certain patients to the standard therapeutic agent sorafenib. This study aims to unravel the genomic characteristics of HCC patients in Saudi Arabia, investigate the genetic makeup of tumors in both sorafenib-sensitive and sorafenib-resistant patients, and analyze the functional implications of genomic abnormalities observed in these individuals. The resistance displayed by some HCC patients toward sorafenib underscores the need for alternative treatment approaches to effectively combat this formidable disease burden.

Methods

Whole-genome sequencing (WGS) was performed on 16 HCC samples and targeted sequencing was performed on seven additional tumors. We identified and validated somatic and germline genetic aberrations. Employing a prize-collecting Steiner tree algorithm, we identified important altered genetic modules and potential biomarkers for each patient. Furthermore, we analyzed non-synonymous germline and somatic mutations, specifically in patients who underwent sorafenib treatment.

Results

Out of the 13 patients who received sorafenib, three exhibited sorafenib sensitivity, while the others showed resistance to the drug. Notably, 3 out of 16 individuals carried cancer-predisposing mutations. Additionally, 8 out of 16 patients displayed non-synonymous somatic alterations in genes associated with cancer. In the targeted-sequencing samples, rare non-synonymous variants were observed across all seven cases. The study also revealed the presence of specific somatic aberrations, including TP53, PIK3CA, APOB, CTNNB1, DPYD, LRP1B, MYC, and NFE2L2, which were identified in two patients. Among the 42 genes linked to sorafenib treatment, 4 out of 10 resistant patients carried somatic non-synonymous variants. Furthermore, when analyzing the 5,000 genes most relevant to the 42 genes, 7 out of 10 resistant individuals exhibited rare non-synonymous germline variants. Interestingly, none of the three sorafenib-sensitive patients displayed any concerning variants in those genes.

Conclusion

Our findings indicate that most of the HCC patients possess cancer-related genetic variants, and the altered pathways in these patients exhibit similarities. Notably, resistant patients exhibit a higher frequency of aberrations in sorafenib-related genes than do sensitive patients. Specifically, 4 out of 10 resistant individuals demonstrated 13 somatic mutations, whereas none of the three sensitive patients exhibited any. Similarly, 7 out of 10 resistant patients possessed 30 germline mutations, while none were observed in the sensitive group (two-sided Fisher’s exact test; somatic: p=0.50, germline: 0.07). These results contribute to our understanding of the genetic landscape of HCC and highlight potential therapeutic targets that could aid in overcoming treatment resistance.

MultiDS-MDA: Integrating multiple data sources into heterogeneous network for predicting novel metabolite-drug associations

Metabolic processes in the human body play an important role in maintaining normal life activities, and the abnormal concentration of metabolites is closely related to the occurrence and development of diseases. The use of drugs is considered to have a major impact on metabolism, and drug metabolites can contribute to efficacy, drug toxicity and drug-drug interaction. However, our understanding of metabolite-drug associations is far from complete, and individual data source tends to be incomplete and noisy. Therefore, the integration of various types of data sources for inferring reliable metabolite-drug associations is urgently needed. In this study, we proposed a computational framework, MultiDS-MDA, for identifying metabolite-drug associations by integrating multiple data sources, including chemical structure information of metabolites and drugs, the relationships of metabolite-gene, metabolite-disease, drug-gene and drug-disease, the data of gene ontology (GO) and disease ontology (DO) and known metabolite-drug connections. The performance of MultiDS-MDA was evaluated by 5-fold cross-validation, which achieved an area under the ROC curve (AUROC) of 0.911 and an area under the precision-recall curve (AUPRC) of 0.907. Additionally, MultiDS-MDA showed outstanding performance compared with similar approaches. Case studies for three metabolites (cholesterol, thromboxane B2 and coenzyme Q10) and three drugs (simvastatin, pravastatin and morphine) also demonstrated the reliability and efficiency of MultiDS-MDA, and it is anticipated that MultiDS-MDA will serve as a powerful tool for future exploration of metabolite-drug interactions and contribute to drug development and drug combination.

Identification of drug repurposing candidates for the treatment of anxiety: A genetic approach

Anxiety disorders are a group of prevalent and heritable neuropsychiatric diseases. We previously conducted a genome-wide association study (GWAS) which identified genomic loci associated with anxiety; however, the biological consequences underlying the genetic associations are largely unknown. Integrating GWAS and functional genomic data may improve our understanding of the genetic effects on intermediate molecular phenotypes such as gene expression. This can provide an opportunity for the discovery of drug targets for anxiety via drug repurposing. We used the GWAS summary statistics to determine putative causal genes for anxiety using MAGMA and colocalization analyses. A transcriptome-wide association study was conducted to identify genes with differential genetically regulated levels of gene expression in human brain tissue. The genes were integrated with a large drug-gene expression database (Connectivity Map), discovering compounds that are predicted to "normalise" anxiety-associated expression changes. The study identified 64 putative causal genes associated with anxiety (35 genes upregulated; 29 genes downregulated). Drug mechanisms adrenergic receptor agonists, sigma receptor agonists, and glutamate receptor agonists gene targets were enriched in anxiety-associated genetic signal and exhibited an opposing effect on the anxiety-associated gene expression signature. The significance of the project demonstrated genetic links for novel drug candidates to potentially advance anxiety therapeutics.

Donafenib and GSK-J4 Synergistically Induce Ferroptosis in Liver Cancer by Upregulating HMOX1 Expression

Hepatocellular carcinoma (HCC) is one of the most lethal cancers worldwide. Donafenib is a multi-receptor tyrosine kinase inhibitor approved for the treatment of patients with advanced HCC, but its clinical effect is very limited. Here, through integrated screening of a small-molecule inhibitor library and a druggable CRISPR library, that GSK-J4 is synthetically lethal with donafenib in liver cancer is shown. This synergistic lethality is validated in multiple HCC models, including xenograft, orthotopically induced HCC, patient-derived xenograft, and organoid models. Furthermore, co-treatment with donafenib and GSK-J4 resulted in cell death mainly via ferroptosis. Mechanistically, through integrated RNA sequencing (RNA-seq) and assay for transposase-accessible chromatin with high throughput sequencing (ATAC-seq) analyses, that donafenib and GSK-J4 synergistically promoted the expression of HMOX1 and increased the intracellular Fe2+ level is found, eventually leading to ferroptosis. Additionally, through cleavage under targets & tagmentation followed by sequencing (CUT&Tag-seq), it is found that the enhancer regions upstream of HMOX1 promoter significantly increased under donafenib and GSK-J4 co-treatment. A chromosome conformation capture assay confirmed that the increased expression of HMOX1 is caused by the significantly enhanced interaction between the promoter and upstream enhancer under dual-drug combination. Taken together, this study elucidates a new synergistic lethal interaction in liver cancer.

Identification of signature genes and drug candidates for primary plasma cell leukemia: An integrated system biology approach

BACKGROUND

Plasma cell leukemia (PCL) is one of the rare cancer which is characterized by the uncontrolled proliferation of plasma cells in peripheral blood and bone marrow. The aggressive behavior of the disease and high mortality rate among PCL patients makes it a thirst area to be explored.

METHODS

The dataset for PCL was obtained from the GEO database and was analyzed using GEO2R for differentially expressed genes. Further, the functional enrichment analysis was carried out for DEGs using DAVID. The protein-protein interactions (PPI) for DEGs were obtained using STRING 11.5 and were analyzed in Cytoscape 3.7.2. to obtain the key hub genes. These key hub genes were investigated for their interaction with suitable drug candidates using DGIdb, DrugMAP, and Schrodinger's version 2022-1.

RESULTS

Out of the total of 104 DEGs, 39 genes were up-regulated whereas 65 genes were down-regulated. A total of 11 biological processes, 2 cellular components, and 5 molecular functions were enriched along with the 7 KEGG pathways for the DEGs. Further, a total of 11 hub genes were obtained from the PPI of DEGs of which TP53, MAPK1, SOCS1, MBD3, and YES1 were the key hub genes. Oxaliplatin, mitoxantrone, and ponatinib were found to have the highest binding affinity towards the p53, MAPK1, and YES1 proteins respectively.

CONCLUSION

TP53, MAPK1, SOCS1, MBD3, and YES1 are the signature hub genes that might be responsible for the aggressive prognosis of PCL leading to poor survival rate. However, p53, MAPK1, and YES1 can be targeted with oxaliplatin, mitoxantrone, and ponatinib.

PhosphoDisco: A Toolkit for Co-regulated Phosphorylation Module Discovery in Phosphoproteomic Data

Kinases are key players in cancer-relevant pathways and are the targets of many successful precision cancer therapies. Phosphoproteomics is a powerful approach to study kinase activity and has been used increasingly for the characterization of tumor samples leading to the identification of novel chemotherapeutic targets and biomarkers. Finding co-regulated phosphorylation sites which represent potential kinase-substrate sets or members of the same signaling pathway allows us to harness these data to identify clinically relevant and targetable alterations in signaling cascades. Unfortunately, studies have found that databases of co-regulated phosphorylation sites are only experimentally supported in a small number of substrate sets. To address the inherent challenge of defining co-regulated phosphorylation modules relevant to a given dataset, we developed PhosphoDisco, a toolkit for determining co-regulated phosphorylation modules. We applied this approach to tandem mass spectrometry based phosphoproteomic data for breast and non-small cell lung cancer and identified canonical as well as putative new phosphorylation site modules. Our analysis identified several interesting modules in each cohort. Among these was a new cell cycle checkpoint module enriched in basal breast cancer samples and a module of PRKC isozymes putatively co-regulated by CDK12 in lung cancer. We demonstrate that modules defined by PhosphoDisco can be used to further personalized cancer treatment strategies by establishing active signaling pathways in a given patient tumor or set of tumors, and in providing new ways to classify tumors based on signaling activity.

Transcriptome-Wide Structural Equation Modeling of 13 Major Psychiatric Disorders for Cross-Disorder Risk and Drug Repurposing

Importance

Psychiatric disorders display high levels of comorbidity and genetic overlap, necessitating multivariate approaches for parsing convergent and divergent psychiatric risk pathways. Identifying gene expression patterns underlying cross-disorder risk also stands to propel drug discovery and repurposing in the face of rising levels of polypharmacy.

Objective

To identify gene expression patterns underlying genetic convergence and divergence across psychiatric disorders along with existing pharmacological interventions that target these genes.

Design, Setting, and Participants

This genomic study applied a multivariate transcriptomic method, transcriptome-wide structural equation modeling (T-SEM), to investigate gene expression patterns associated with 5 genomic factors indexing shared risk across 13 major psychiatric disorders. Follow-up tests, including overlap with gene sets for other outcomes and phenome-wide association studies, were conducted to better characterize T-SEM results. The Broad Institute Connectivity Map Drug Repurposing Database and Drug-Gene Interaction Database public databases of drug-gene pairs were used to identify drugs that could be repurposed to target genes found to be associated with cross-disorder risk. Data were collected from database inception up to February 20, 2023.

Main Outcomes and Measures

Gene expression patterns associated with genomic factors or disorder-specific risk and existing drugs that target these genes.

Results

In total, T-SEM identified 466 genes whose expression was significantly associated (z ≥ 5.02) with genomic factors and 36 genes with disorder-specific effects. Most associated genes were found for a thought disorders factor, defined by bipolar disorder and schizophrenia. Several existing pharmacological interventions were identified that could be repurposed to target genes whose expression was associated with the thought disorders factor or a transdiagnostic p factor defined by all 13 disorders.

Conclusions and Relevance

The findings from this study shed light on patterns of gene expression associated with genetic overlap and uniqueness across psychiatric disorders. Future versions of the multivariate drug repurposing framework outlined here have the potential to identify novel pharmacological interventions for increasingly common, comorbid psychiatric presentations.

Identification of the Key Immune Cells and Genes for the Diagnostics and Therapeutics of Meningioma

BACKGROUND

Dysregulation of immune infiltration critically contributes to the tumorigenesis and progression of meningiomas. However, the landscape of immune microenvironment and key genes correlated with immune cell infiltration remains unclear.

METHODS

Four Gene Expression Omnibus data sets were included. CIBERSORT algorithm was utilized to analyze the immune cell infiltration in samples. Wilcoxon test, Random Forest algorithm, and Least Absolute Shrinkage and Selection Operator regression were adopted in identifying significantly different infiltrating immune cells and differentially expressed genes (DEGs). Functional enrichment analysis was performed by Kyoto Encyclopedia of Genes and Genomes and Gene Ontology. The correlation between genes and immune cells was evaluated via Spearman's correlation analysis. Receiver Operator Characteristic curve analysis evaluated the markers' diagnostic effectiveness. The mRNA-miRNA and Drug-Gene-Immune cell interaction networks were constructed to identify potential diagnostic and therapeutic targets.

RESULTS

Plasma cells, M1 macrophages, M2 macrophages, neutrophils, eosinophils, and activated NK cells were the significantly different infiltrating immune cells in meningioma. A total of 951 DEGs, associated with synaptic function and structure, ion transport regulation, brain function, and immune-related pathways, were identified. Among 11 hub DEGs, RYR2 and TTR were correlated with plasma cells; SNCG was associated with NK cells; ADCY1 exhibited excellent diagnostic effectiveness; and ADCY1, BMX, KCNA5, SLCO4A1, and TTR could be considered as therapeutic targets.

CONCLUSIONS

ADCY1 can be identified as a diagnostic marker; ADCY1, BMX, KCNA5, SLCO4A1, and TTR are potential therapeutic targets, and their associations with macrophages, neutrophils, NK cells, and plasma cells might impact the tumorigenesis of meningiomas.

New "drugs and targets" in the GWAS era of bipolar disorder

OBJECTIVE

Due to the phenotypic heterogeneity and etiological complexity of bipolar disorder (BD), many patients do not respond well to the current medications, and developing novel effective treatment is necessary. Whether any BD genome-wide association study (GWAS) risk genes were targets of existing drugs or novel drugs that can be repurposed in the clinical treatment of BD is a hot topic in the GWAS era of BD.

METHODS

A list of 425 protein-coding BD risk genes was distilled through the BD GWAS, and 4479 protein-coding druggable targets were retrieved from the druggable genome. The overlapped genes/targets were subjected to further analyses in DrugBank, Pharos, and DGIdb datasets in terms of their FDA status, mechanism of action and primary indication, to identify their potential for repurposing.

RESULTS

We identified 58 BD GWAS risk genes grouped as the druggable targets, and several genes were given higher priority. These BD risk genes were targets of antipsychotics, antidepressants, antiepileptics, calcium channel antagonists, as well as anxiolytics and analgesics, either existing clinically-approved drugs for BD or the drugs than can be repurposed for treatment of BD in the future. Those genes were also likely relevant to BD pathophysiology, as many of them encode ion channel, ion transporter or neurotransmitter receptor, or the mice manipulating those genes are likely to mimic the phenotypes manifest in BD patients.

CONCLUSIONS

This study identifies several targets that may facilitate the discovery of novel treatments in BD, and implies the value of conducting GWAS into clinical translation.

A Functional Pipeline of Genome-Wide Association Data Leads to Midostaurin as a Repurposed Drug for Alzheimer's Disease

Genome-wide association studies (GWAS) constitute a powerful tool to identify the different biochemical pathways associated with disease. This knowledge can be used to prioritize drugs targeting these routes, paving the road to clinical application. Here, we describe DAGGER (Drug Repositioning by Analysis of GWAS and Gene Expression in R), a straightforward pipeline to find currently approved drugs with repurposing potential. As a proof of concept, we analyzed a meta-GWAS of 1.6 × 107 single-nucleotide polymorphisms performed on Alzheimer's disease (AD). Our pipeline uses the Genotype-Tissue Expression (GTEx) and Drug Gene Interaction (DGI) databases for a rational prioritization of 22 druggable targets. Next, we performed a two-stage in vivo functional assay. We used a C. elegans humanized model over-expressing the Aβ1-42 peptide. We assayed the five top-scoring candidate drugs, finding midostaurin, a multitarget protein kinase inhibitor, to be a protective drug. Next, 3xTg AD transgenic mice were used for a final evaluation of midostaurin's effect. Behavioral testing after three weeks of 20 mg/kg intraperitoneal treatment revealed a significant improvement in behavior, including locomotion, anxiety-like behavior, and new-place recognition. Altogether, we consider that our pipeline might be a useful tool for drug repurposing in complex diseases.

Profiling neuronal methylome and hydroxymethylome of opioid use disorder in the human orbitofrontal cortex

Opioid use disorder (OUD) is influenced by genetic and environmental factors. While recent research suggests epigenetic disturbances in OUD, this is mostly limited to DNA methylation (5mC). DNA hydroxymethylation (5hmC) has been widely understudied. We conducted a multi-omics profiling of OUD in a male cohort, integrating neuronal-specific 5mC and 5hmC as well as gene expression profiles from human postmortem orbitofrontal cortex (OUD = 12; non-OUD = 26). Single locus methylomic analysis and co-methylation analysis showed a higher number of OUD-associated genes and gene networks for 5hmC compared to 5mC; these were enriched for GPCR, Wnt, neurogenesis, and opioid signaling. 5hmC marks also showed a higher correlation with gene expression patterns and enriched for GWAS of psychiatric traits. Drug interaction analysis revealed interactions with opioid-related drugs, some used as OUD treatments. Our multi-omics findings suggest an important role of 5hmC and reveal loci epigenetically dysregulated in OFC neurons of individuals with OUD.

Genetic analysis of potential biomarkers and therapeutic targets associated with ferroptosis from bronchopulmonary dysplasia

Ferroptosis is a recently identified form of cell death that is distinct from the conventional modes such as necrosis, apoptosis, and autophagy. Its role in bronchopulmonary dysplasia (BPD) remains inadequately understood. To address this gap, we obtained BPD-related RNA-seq data and ferroptosis-related genes (FRGs) from the GEO database and FerrDb, respectively. A total of 171 BPD-related differentially expressed ferroptosis-related genes (DE-FRGs) linked to the regulation of autophagy and immune response were identified. Least absolute shrinkage and selection operator and SVM-RFE algorithms identified 23 and 14 genes, respectively, as marker genes. The intersection of these 2 sets yielded 9 genes (ALOX12B, NR1D1, LGMN, IFNA21, MEG3, AKR1C1, CA9, ABCC5, and GALNT14) with acceptable diagnostic capacity. The results of the functional enrichment analysis indicated that these identified marker genes may be involved in the pathogenesis of BPD through the regulation of immune response, cell cycle, and BPD-related pathways. Additionally, we identified 29 drugs that target 5 of the marker genes, which could have potential therapeutic implications. The ceRNA network we constructed revealed a complex regulatory network based on the marker genes, further highlighting their potential roles in BPD. Our findings offer diagnostic potential and insight into the mechanism underlying BPD. Further research is needed to assess its clinical utility.

A Transcriptome-Wide Analysis of Psoriasis: Identifying the Potential Causal Genes and Drug Candidates

Psoriasis is a chronic inflammatory skin disease characterized by cutaneous eruptions and pruritus. Because the genetic backgrounds of psoriasis are only partially revealed, an integrative and rigorous study is necessary. We conducted a transcriptome-wide association study (TWAS) with the new Genotype-Tissue Expression version 8 reference panels, including some tissue and multi-tissue panels that were not used previously. We performed tissue-specific heritability analyses on genome-wide association study data to prioritize the tissue panels for TWAS analysis. TWAS and colocalization (COLOC) analyses were performed with eight tissues from the single-tissue panels and the multi-tissue panels of context-specific genetics (CONTENT) to increase tissue specificity and statistical power. From TWAS, we identified the significant associations of 101 genes in the single-tissue panels and 64 genes in the multi-tissue panels, of which 26 genes were replicated in the COLOC. Functional annotation and network analyses identified that the genes were associated with psoriasis and/or immune responses. We also suggested drug candidates that interact with jointly significant genes through a conditional and joint analysis. Together, our findings may contribute to revealing the underlying genetic mechanisms and provide new insights into treatments for psoriasis.

Application of multiple machine learning approaches to determine key pyroptosis molecules in type 2 diabetes mellitus

Objective

Pyroptosis, a lytic and inflammatory programmed cell death, has been implicated in type 2 diabetes mellitus (T2DM) and its complications. Nonetheless, it remains elusive exactly which pyroptosis molecule exerts an essential role in T2DM, and this study aims to solve such issue.

Methods

Transcriptional profiling datasets of T2DM, i.e., GSE20966, GSE95849, and GSE26168, were acquired. Four machine learning models, namely, random forest, support vector machine, extreme gradient boosting, and generalized linear modeling, were built based on pyroptosis genes. A nomogram of key pyroptosis genes was also generated, and the clinical value was appraised via calibration curves and decision curve analysis. Immune infiltration was inferred utilizing CIBERSORT. Drug-druggable target relationships were acquired from the Drug Gene Interaction Database. Through WGCNA, key pyroptosis-relevant genes were selected.

Results

Most pyroptosis genes exhibited upregulation in T2DM relative to controls, indicating the activity of pyroptosis in T2DM. The SVM model composed of BAK1, CHMP2B, NLRP6, PLCG1, and TIRAP exhibited the best performance in T2DM diagnosis, with AUC = 1. The nomogram can predict the risk of T2DM for clinical practice. NK cells resting exhibited a lower abundance in T2DM versus normal specimens, with a higher abundance of neutrophils. NLRP6 was positively linked with neutrophils. Drugs (keracyanin, 9,10-phenanthrenequinone, diclofenac, phosphomethylphosphonic acid adenosyl ester, acetaminophen, cefixime, aspirin, ustekinumab) potentially targeted the key pyroptosis genes. Additionally, CHMP2B-relevant genes were determined.

Conclusion

Altogether, this work proposes the key pyroptosis genes in T2DM, which might become possible molecules for the management and treatment of T2DM and its complications.

Aberrations in ion channels interacting with lipid metabolism and epithelial-mesenchymal transition in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is the most prevalent malignant gastrointestinal tumor. Ion channels contribute to tumor growth and progression through interactions with their neighboring molecules including lipids. The dysregulation of membrane ion channels and lipid metabolism may contribute to the epithelial-mesenchymal transition (EMT), leading to metastatic progression. Herein, transcriptome profiles of patients with ESCC were analyzed by performing differential gene expression and weighted gene co-expression network analysis to identify the altered ion channels, lipid metabolism- and EMT-related genes in ESCC. A total of 1,081 differentially expressed genes, including 113 ion channels, 487 lipid metabolism-related, and 537 EMT-related genes, were identified in patients with ESCC. Thereafter, EMT scores were correlated with altered co-expressed genes. The altered co-expressed genes indicated a correlation with EMT signatures. Interactions among 22 ion channels with 3 hub lipid metabolism- and 13 hub EMT-related proteins were determined using protein-protein interaction networks. A pathway map was generated to depict deregulated signaling pathways including insulin resistance and the estrogen receptor-Ca²⁺ signaling pathway in ESCC. The relationship between potential ion channels and 5-year survival rates in ESCC was determined using Kaplan-Meier plots and Cox proportional hazard regression analysis. Inositol 1,4,5-trisphosphate receptor type 3 (ITPR3) was found to be associated with poor prognosis of patients with ESCC. Additionally, drugs interacting with potential ion channels, including GJA1 and ITPR3, were identified. Understanding alterations in ion channels with lipid metabolism and EMT in ESCC pathophysiology would most likely provide potential targets for the better treatment of patients with ESCC.

The dcGO Domain-Centric Ontology Database in 2023: New Website and Extended Annotations for Protein Structural Domains

Protein structural domains have been less studied than full-length proteins in terms of ontology annotations. The dcGO database has filled this gap by providing mappings from protein domains to ontologies. The dcGO update in 2023 extends annotations for protein domains of multiple definitions (SCOP, Pfam, and InterPro) with commonly used ontologies that are categorised into functions, phenotypes, diseases, drugs, pathways, regulators, and hallmarks. This update adds new dimensions to the utility of both ontology and protein domain resources. A newly designed website at http://www.protdomainonto.pro/dcGO offers a more centralised and user-friendly way to access the dcGO database, with enhanced faceted search returning term- and domain-specific information pages. Users can navigate both ontology terms and annotated domains through improved ontology hierarchy browsing. A newly added facility enables domain-based ontology enrichment analysis.

Characterization of ferroptosis driver gene signature in head and neck squamous cell carcinoma (HNSC)

BACKGROUND

Head and neck squamous cell carcinoma (HNSC), a prevalent malignant tumor with a low survival rate, is often accompanied by ferroptosis, which is a recently-described type ofprogrammed cell death. Investigating the significance of ferroptosis driver genes in HNSC, this study aimed to assess their diagnostic and prognostic values, as well as their impact on treatment and tumor immune function. The results of this investigation provide novel insight into using ferroptosis-related genes as molecular biomarkers as well as precise chemotherapeutic targets for the therapy of HNSC.

METHODOLOGY

A detailed in silico and in vitro experiment-based methodology was adopted to achieve the goals.

RESULTS

A total of 233 ferroptosis driver genes were downloaded from the FerrDB database. After comprehensively analyzing these 233 ferroptosis driver genes by various TCGA databases, RNA-sequencing (RNA-seq), and Reverse Transcription Quantitative Real-Time Polymerase Chain Reaction (RT-qPCR) techniques, TP53 (tumor protein 53), PTEN (Phosphatase and TENsin homolog deleted on chromosome 10), KRAS (Ki-ras2 Kirsten rat sarcoma viral oncogene homolog), and HRAS (Harvey Rat sarcoma virus) were identified as differentially expressed hub genes. Interestingly, these hub genes were found to have significant (P < 0.05) variations in their mRNA and protein expressions and effects on overall survival of the HNSC patients. Moreover, targeted bisulfite-sequencing (bisulfite-seq) analysis revealed that promoter hypomethylation pattern was associated with up-regulation of hub genes (TP53, PTEN, KRAS, and HRAS). In addition to this, hub genes were involved in diverse oncogenic pathways.

CONCLUSION

Since HNSC pathogenesis is a complex process, using ferroptosis driver hub genes (TP53, PTEN, KRAS, and HRAS) as a diagnostic and prognostic tool, and therapeutically targeting those genes through appropriate drugs could bring a milestone change in the drug discovery and management and survival in HNSC.

In vitro analysis of PI3K pathway activation genes for exploring novel biomarkers and therapeutic targets in clear cell renal carcinoma

OBJECTIVES

The regulation of various cellular functions such as growth, proliferation, metabolism, and angiogenesis, is dependent on the PI3K pathway. Recent evidence has indicated that kidney renal clear cell carcinoma (KIRC) can be triggered by the deregulation of this pathway. The objective of this research was to investigate 25 genes associated with activation of the PI3K pathway in KIRC and control samples to identify four hub genes that might serve as novel molecular biomarkers and therapeutic targets for treating KIRC.

METHODS

Multi-omics in silico and in vitro analysis was employed to find hub genes related to the PI3K pathway that may be biomarkers and therapeutic targets for KIRC.

RESULTS

Using STRING software, a protein-protein interaction (PPI) network of 25 PI3K pathway-related genes was developed. Based on the degree scoring method, the top four hub genes were identified using Cytoscape's Cytohubba plug-in. TCGA datasets, KIRC (786-O and A-498), and normal (HK2) cells were used to validate the expression of hub genes. Additionally, further bioinformatic analyses were performed to investigate the mechanisms by which hub genes are involved in the development of KIRC. Out of a total of 25 PI3K pathway-related genes, we developed and validated a diagnostic and prognostic model based on the up-regulation of TP53 (tumor protein 53) and CCND1 (Cyclin D1) and the down-regulation of PTEN (Phosphatase and TENsin homolog deleted on chromosome 10), and GSK3B (Glycogen synthase kinase-3 beta) hub genes. The hub genes included in our model may be a novel therapeutic target for KIRC treatment. Additionally, associations between hub genes and infiltration of immune cells can enhance comprehension of immunotherapy for KIRC.

CONCLUSION

We have created a new diagnostic and prognostic model for KIRC patients that uses PI3K pathway-related hub genes (TP53, PTEN, CCND1, and GSK3B). Nevertheless, further experimental studies are required to ascertain the efficacy of our model.

Identification of novel gene signatures and immune cell infiltration in intervertebral disc degeneration using bioinformatics analysis

Background: Intervertebral disc degeneration (IDD) is the leading cause of lower back pain, and an overall understanding of the molecular mechanisms related to IDD is still lacking. The purpose of this study was to explore gene signatures and immune cell infiltration related to IDD via bioinformatics analysis. Methods: A total of five expression profiles of mRNA and non-coding RNA were downloaded from the Gene Expression Omnibus (GEO) database. The potentially involved lncRNA/circRNA-miRNA-mRNA networks and protein-protein interaction networks were constructed by miRNet, circBank, STRING, and the Cytoscape database. Gene ontology, Kyoto Encyclopaedia of Genes and Genomes Analysis, Gene Set Enrichment Analysis, Gene Set Variation Analysis, Immune Infiltration Analysis, and Drug-Gene Interaction were used to analyse the top 20 hub genes. RT-qPCR was conducted to confirm the 12 differential expressions of genes both in the nucleus pulposus and annulus fibrosus tissues Results: There were 346 differentially expressed mRNAs, 12 differentially expressed miRNAs, 883 differentially expressed lncRNAs, and 916 differentially expressed circRNAs in the GEO database. Functional and enrichment analyses revealed hub genes associated with platelet activation, immune responses, focal adhesion, and PI3K-Akt signalling. The apoptotic pathway, the reactive oxygen species pathway, and oxidative phosphorylation play an essential role in IDD. Immune infiltration analysis demonstrated that the Treg cells had significant infiltration, and three levels of immune cells, including dendritic cells, Th2 cells, and tumour-infiltrating lymphocytes, were inhibited in IDD. Drug-gene interaction analysis showed that COL1A1 and COL1A2 were targeted by collagenase clostridium histolyticum, ocriplasmin, and PDGFRA was targeted by 66 drugs or molecular compounds. Finally, 24 cases of IDD tissues and 12 cases of normal disc tissues were collected, and the results of RT-qPCR were consistent with the bioinformatics results. Conclusion: Our data indicated that the 20 hub genes and immune cell infiltration were involved in the pathological process of IDD. In addition, the PDGFRA and two potential drugs were found to be significant in IDD development.

omicSynth: an Open Multi-omic Community Resource for Identifying Druggable Targets across Neurodegenerative Diseases

Treatments for neurodegenerative disorders remain rare, although recent FDA approvals, such as Lecanemab and Aducanumab for Alzheimer's Disease, highlight the importance of the underlying biological mechanisms in driving discovery and creating disease modifying therapies. The global population is aging, driving an urgent need for therapeutics that stop disease progression and eliminate symptoms. In this study, we create an open framework and resource for evidence-based identification of therapeutic targets for neurodegenerative disease. We use Summary-data-based Mendelian Randomization to identify genetic targets for drug discovery and repurposing. In parallel, we provide mechanistic insights into disease processes and potential network-level consequences of gene-based therapeutics. We identify 116 Alzheimer's disease, 3 amyotrophic lateral sclerosis, 5 Lewy body dementia, 46 Parkinson's disease, and 9 Progressive supranuclear palsy target genes passing multiple test corrections (pSMR_multi < 2.95×10-6 and pHEIDI > 0.01). We created a therapeutic scheme to classify our identified target genes into strata based on druggability and approved therapeutics - classifying 41 novel targets, 3 known targets, and 115 difficult targets (of these 69.8% are expressed in the disease relevant cell type from single nucleus experiments). Our novel class of genes provides a springboard for new opportunities in drug discovery, development and repurposing in the pre-competitive space. In addition, looking at drug-gene interaction networks, we identify previous trials that may require further follow-up such as Riluzole in AD. We also provide a user-friendly web platform to help users explore potential therapeutic targets for neurodegenerative diseases, decreasing activation energy for the community [https://nih-card-ndd-smr-home-syboky.streamlit.app/].

Multi-layered genetic approaches to identify approved drug targets

Drugs targeting genes linked to disease via evidence from human genetics have increased odds of approval. Approaches to prioritize such genes include genome-wide association studies (GWASs), rare variant burden tests in exome sequencing studies (Exome), or integration of a GWAS with expression/protein quantitative trait loci (eQTL/pQTL-GWAS). Here, we compare gene-prioritization approaches on 30 clinically relevant traits and benchmark their ability to recover drug targets. Across traits, prioritized genes were enriched for drug targets with odds ratios (ORs) of 2.17, 2.04, 1.81, and 1.31 for the GWAS, eQTL-GWAS, Exome, and pQTL-GWAS methods, respectively. Adjusting for differences in testable genes and sample sizes, GWAS outperforms e/pQTL-GWAS, but not the Exome approach. Furthermore, performance increased through gene network diffusion, although the node degree, being the best predictor (OR = 8.7), revealed strong bias in literature-curated networks. In conclusion, we systematically assessed strategies to prioritize drug target genes, highlighting the promises and pitfalls of current approaches.

Exploration and Validation of Potential Biomarkers and Therapeutic Targets in Ferroptosis of Asthma

Purpose

Asthma is a chronic inflammatory airway disease involving multiple mechanisms, of which ferroptosis is a form of programmed cell death. Recent studies have shown that ferroptosis may play a crucial role in the pathogenesis of asthma, but no specific ferroptosis gene has been found in asthma, and the exact mechanism is still unclear. The present study aimed to screen ferroptosis genes associated with asthma and find therapeutic targets, in order to contribute a new clue for the diagnosis and therapy of asthma.

Methods

Ferroptosis-related differentially expressed genes (FR-DEGs) in asthma were selected by the GSE41861, GSE43696 and ferroptosis datasets. Next, the FR-DEGs were subjected by GO and KEGG enrichment, and the mRNA-miRNA network was constructed. Then, GSEA and GSVA enrichment analysis and Immune infiltration analysis were performed, followed by targeted drug prediction. Finally, the expression of FR-DEGs was confirmed using GSE63142 dataset and RT-PCR assay.

Results

We found 13 FR-DEGs by the GSE41861, GSE43696 and ferroptosis database. Functional enrichment analysis revealed that the 13 FR-DEGs were enriched in oxidative stress, immune response, ferroptosis, lysosome, necrosis, apoptosis etc. Moreover, our results revealed the mRNA-miRNA network of the FR-DEGs and identified candidate drugs. Also, immune infiltration revealed that ELAVL1, CREB5, CBR1 and NR1D2 are associated with the immune cells and may be potential targets in asthma. Finally, 10 FR-DEGs were validated by the GSE63142 database. It was verified that 7 FR-DEGs were differentially expressed by collecting asthma patients and healthy controls.

Conclusion

This study ultimately identified 7 FR-DEGs for the diagnosis and therapy of asthma. These 7 FR-DEGs contribute to oxidative stress and immune responses. This study provides potential therapeutic targets and biomarkers for asthma patients, shedding further light on the pathogenesis of asthma as well as providing new insights into the treatment of asthma.

Comprehensive Analysis for Anti-Cancer Target-Indication Prioritization of Placental Growth Factor Inhibitor (PGF) by Use of Omics and Patient Survival Data

The expression of the placental growth factor (PGF) in cancer cells and the tumor microenvironment can contribute to the induction of angiogenesis, supporting cancer cell metabolism by ensuring an adequate blood supply. Angiogenesis is a key component of cancer metabolism as it facilitates the delivery of nutrients and oxygen to rapidly growing tumor cells. PGF is recognized as a novel target for anti-cancer treatment due to its ability to overcome resistance to existing angiogenesis inhibitors and its impact on the tumor microenvironment. We aimed to integrate bioinformatics evidence using various data sources and analytic tools for target-indication identification of the PGF target and prioritize the indication across various cancer types as an initial step of drug development. The data analysis included PGF gene function, molecular pathway, protein interaction, gene expression and mutation across cancer type, survival prognosis and tumor immune infiltration association with PGF. The overall evaluation was conducted given the totality of evidence, to target the PGF gene to treat the cancer where the PGF level was highly expressed in a certain tumor type with poor survival prognosis as well as possibly associated with poor tumor infiltration level. PGF showed a significant impact on overall survival in several cancers through univariate or multivariate survival analysis. The cancers considered as target diseases for PGF inhibitors, due to their potential effects on PGF, are adrenocortical carcinoma, kidney cancers, liver hepatocellular carcinoma, stomach adenocarcinoma, and uveal melanoma.

OpenXGR: a web-server update for genomic summary data interpretation

How to effectively convert genomic summary data into downstream knowledge discovery represents a major challenge in human genomics research. To address this challenge, we have developed efficient and effective approaches and tools. Extending our previously established software tools, we here introduce OpenXGR (http://www.openxgr.com), a newly designed web server that offers almost real-time enrichment and subnetwork analyses for a user-input list of genes, SNPs or genomic regions. It achieves so through leveraging ontologies, networks, and functional genomic datasets (such as promoter capture Hi-C, e/pQTL and enhancer-gene maps for linking SNPs or genomic regions to candidate genes). Six analysers are provided, each doing specific interpretations tailored to genomic summary data at various levels. Three enrichment analysers are designed to identify ontology terms enriched for input genes, as well as genes linked from input SNPs or genomic regions. Three subnetwork analysers allow users to identify gene subnetworks from input gene-, SNP- or genomic region-level summary data. With a step-by-step user manual, OpenXGR provides a user-friendly and all-in-one platform for interpreting summary data on the human genome, enabling more integrated and effective knowledge discovery.

MOKPE: drug-target interaction prediction via manifold optimization based kernel preserving embedding

BACKGROUND

In many applications of bioinformatics, data stem from distinct heterogeneous sources. One of the well-known examples is the identification of drug-target interactions (DTIs), which is of significant importance in drug discovery. In this paper, we propose a novel framework, manifold optimization based kernel preserving embedding (MOKPE), to efficiently solve the problem of modeling heterogeneous data. Our model projects heterogeneous drug and target data into a unified embedding space by preserving drug-target interactions and drug-drug, target-target similarities simultaneously.

RESULTS

We performed ten replications of ten-fold cross validation on four different drug-target interaction network data sets for predicting DTIs for previously unseen drugs. The classification evaluation metrics showed better or comparable performance compared to previous similarity-based state-of-the-art methods. We also evaluated MOKPE on predicting unknown DTIs of a given network. Our implementation of the proposed algorithm in R together with the scripts that replicate the reported experiments is publicly available at https://github.com/ocbinatli/mokpe .

Identification of the biological processes, immune cell landscape, and hub genes shared by acute anaphylaxis and ST-segment elevation myocardial infarction

Background: Patients with anaphylaxis are at risk for ST-segment elevation myocardial infarction (STEMI). However, the pathological links between anaphylaxis and STEMI remain unclear. Here, we aimed to explore shared biological processes, immune effector cells, and hub genes of anaphylaxis and STEMI. Methods: Gene expression data for anaphylactic (GSE69063) and STEMI (GSE60993) patients with corresponding healthy controls were pooled from the Gene Expression Omnibus database. Differential expression analysis, enrichment analysis, and CIBERSORT were used to reveal transcriptomic signatures and immune infiltration profiles of anaphylaxis and STEMI, respectively. Based on common differentially expressed genes (DEGs), Gene Ontology analysis, cytoHubba algorithms, and correlation analyses were performed to identify biological processes, hub genes, and hub gene-related immune cells shared by anaphylaxis and STEMI. The robustness of hub genes was assessed in external anaphylactic (GSE47655) and STEMI (GSE61144) datasets. Furthermore, a murine model of anaphylaxis complicated STEMI was established to verify hub gene expressions. The logistic regression analysis was used to evaluate the diagnostic efficiency of hub genes. Results: 265 anaphylaxis-related DEGs were identified, which were associated with immune-inflammatory responses. 237 STEMI-related DEGs were screened, which were involved in innate immune response and myeloid leukocyte activation. M0 macrophages and dendritic cells were markedly higher in both anaphylactic and STEMI samples compared with healthy controls, while CD4⁺ naïve T cells and CD8⁺ T cells were significantly lower. Enrichment analysis of 33 common DEGs illustrated shared biological processes of anaphylaxis and STEMI, including cytokine-mediated signaling pathway, response to reactive oxygen species, and positive regulation of defense response. Six hub genes were identified, and their expression levels were positively correlated with M0 macrophage abundance and negatively correlated with CD4⁺ naïve T cell abundance. In external anaphylactic and STEMI samples, five hub genes (IL1R2, FOS, MMP9, DUSP1, CLEC4D) were confirmed to be markedly upregulated. Moreover, experimentally induced anaphylactic mice developed impaired heart function featuring STEMI and significantly increased expression of the five hub genes. DUSP1 and CLEC4D were screened as blood diagnostic biomarkers of anaphylaxis and STEMI based on the logistic regression analysis. Conclusion: Anaphylaxis and STEMI share the biological processes of inflammation and defense responses. Macrophages, dendritic cells, CD8⁺ T cells, and CD4⁺ naïve T cells constitute an immune cell population that acts in both anaphylaxis and STEMI. Hub genes (DUSP1 and CLEC4D) identified here provide candidate genes for diagnosis, prognosis, and therapeutic targeting of STEMI in anaphylactic patients.

Identification and comprehensive analysis of ferroptosis-related genes as potential biomarkers for the diagnosis and treatment of proliferative diabetic retinopathy by bioinformatics methods

Diabetic retinopathy (DR) is the most common retinal vascular disease. Proliferative DR (PDR) is the aggressive stage of DR with angiogenesis as a pathological hallmark, which is the main cause of blindness. There is growing evidence that ferroptosis plays a vital role in diabetics as well as its complications including DR. However, the potential functions and mechanisms of ferroptosis have not been completely elucidated in PDR. The ferroptosis-related differentially expressed genes (FRDEGs) were identified in GSE60436 and GSE94019. Then we constructed a protein-protein interaction (PPI) network and screened ferroptosis-related hub genes (FRHGs). The GO functional annotation and the KEGG pathway enrichment analyses of FRHGs were performed. The miRNet and miRTarbase databases were applied to construct the ferroptosis-related mRNA-miRNA-lncRNA network, and the Drug-Gene Interaction Database (DGIdb) was used for predicting potential therapeutic drugs. Finally, we identified 21 upregulated and 9 downregulated FRDEGs, among which 10 key target genes (P53, TXN, PTEN, SLC2A1, HMOX1, PRKAA1, ATG7, HIF1A, TGFBR1, and IL1B) were recognized with enriched functions, mainly relating to responses to oxidative stress and hypoxia in biological processes of PDR. HIF-1, FoxO and MAPK signalling may be the main pathways that influence ferroptosis in PDR. Moreover, a mRNA-miRNA-lncRNA network was constructed based on the 10 FRHGs and their co-expressed miRNAs. Finally, potential drugs targeting 10 FRHGs for PDR were predicted. Results of the receiver operator characteristic (ROC) curve indicated, with high predictive accuracy in two testing datasets (AUC>0.8), that ATG7, TGFB1, TP53, HMOX1 and ILB1 had the potential to be biomarkers of PDR.

Comparing methods for drug-gene interaction prediction on the biomedical literature knowledge graph: performance versus explainability

This paper applies different link prediction methods on a knowledge graph generated from biomedical literature, with the aim to compare their ability to identify unknown drug-gene interactions and explain their predictions. Identifying novel drug-target interactions is a crucial step in drug discovery and repurposing. One approach to this problem is to predict missing links between drug and gene nodes, in a graph that contains relevant biomedical knowledge. Such a knowledge graph can be extracted from biomedical literature, using text mining tools. In this work, we compare state-of-the-art graph embedding approaches and contextual path analysis on the interaction prediction task. The comparison reveals a trade-off between predictive accuracy and explainability of predictions. Focusing on explainability, we train a decision tree on model predictions and show how it can aid the understanding of the prediction process. We further test the methods on a drug repurposing task and validate the predicted interactions against external databases, with very encouraging results.

Integrated bioinformatics and wet-lab analysis revealed cell adhesion prominent genes CDC42, TAGLN and GSN as prognostic biomarkers in colonic-polyp lesions

Colorectal cancers are derived from intestinal polyps. Normally, alterations in cell adhesion genes expression cause deviation from the normal cell cycle, leading to cancer development, progression, and invasion. The present study aimed to investigate the elusive expression pattern of CDC42, TAGLN, and GSN genes in patients with high and low-risk polyp samples, and also colorectal cancer patients and their adjacent normal tissues. In upcoming study, 40 biopsy samples from Taleghani Hospital (Tehran, Iran) were collected, consisting of 20 colon polyps and 20 paired adjacent normal tissues. The expression of the nominated genes CDC42, TAGLN, and GSN was analyzed using quantitative polymerase chain reaction (Q-PCR) and relative quantification was determined using the 2^-ΔΔCt method. ROC curve analysis was performed to compare high-risk and low-risk polyps for the investigated genes. The expression of adhesion molecule genes was also evaluated using TCGA data and the correlation between adhesion molecule gene expression and immunophenotype was analyzed. The role of mi-RNAs and lncRNAs in overexpression of adhesion molecule genes was studied. Lastly, GO and KEGG were performed to identify pathways related to adhesion molecule genes expression in healthy, normal adjacent, and COAD tissues. The results showed that the expression patterns of these genes were significantly elevated in high-risk adenomas compared to low-risk polyps and normal tissues and were associated with various clinicopathological characteristics. The estimated AUC for CDC42, TAGLN, and GSN were 0.87, 0.77, and 0.80, respectively. The study also analyzed COAD cancer patient data and found that the selected gene expression in cancer patients was significantly reduced compared to high-risk polyps and healthy tissues. Survival analysis showed that while the expression level of the GSN gene had no significant relationship with survival rate, the expression of CDC42 and TAGLN genes did have a meaningful relationship, but with opposite effects, suggesting the potential use of these genes as diagnostic or prognostic markers for colorectal cancer. The present study's findings suggest that the expression pattern of CDC42, TAGLN, and GSN genes was significantly increased during the transformation of normal tissue to polyp lesions, indicating their potential as prognostic biomarkers for colorectal polyp development. Further results provide valuable insights into the potential use of these genes as diagnostic or prognostic markers for colorectal cancer. However, further studies are necessary to validate these findings in larger cohorts and to explore the underlying mechanisms of these genes in the development and progression of colorectal cancer.

Transcriptomics-driven drug repositioning for the treatment of diabetic foot ulcer

Diabetic foot ulcers (DFUs) are a common complication of diabetes and can lead to severe disability and even amputation. Despite advances in treatment, there is currently no cure for DFUs and available drugs for treatment are limited. This study aimed to identify new candidate drugs and repurpose existing drugs to treat DFUs based on transcriptomics analysis. A total of 31 differentially expressed genes (DEGs) were identified and used to prioritize the biological risk genes for DFUs. Further investigation using the database DGIdb revealed 12 druggable target genes among 50 biological DFU risk genes, corresponding to 31 drugs. Interestingly, we highlighted that two drugs (urokinase and lidocaine) are under clinical investigation for DFU and 29 drugs are potential candidates to be repurposed for DFU therapy. The top 5 potential biomarkers for DFU from our findings are IL6ST, CXCL9, IL1R1, CXCR2, and IL10. This study highlights IL1R1 as a highly promising biomarker for DFU due to its high systemic score in functional annotations, that can be targeted with an existing drug, Anakinra. Our study proposed that the integration of transcriptomic and bioinformatic-based approaches has the potential to drive drug repurposing for DFUs. Further research will further examine the mechanisms by which targeting IL1R1 can be used to treat DFU.

Comprehensive analysis of endoplasmic reticulum stress-related mechanisms in type 2 diabetes mellitus

BACKGROUND

The endoplasmic reticulum (ER) is closely related to a wide range of cellular functions and is a key component to maintain and restore metabolic health. Type 2 diabetes mellitus (T2DM) is a serious threat to human health, but the ER stress (ERS)-related mechanisms in T2DM have not been fully elucidated.

AIM

To identify potential ERS-related mechanisms and crucial biomarkers in T2DM.

METHODS

We conducted gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) in myoblast and myotube form GSE166502, and obtained the differentially expressed genes (DEGs). After intersecting with ERS-related genes, we obtained ERS-related DEGs. Finally, functional analyses, immune infiltration, and several networks were established.

RESULTS

Through GSEA and GSVA, we identified several metabolic and immune-related pathways. We obtained 227 ERS-related DEGs and constructed several important networks that help to understand the mechanisms and treatment of T2DM. Finally, memory CD4⁺ T cells accounted for the largest proportion of immune cells.

CONCLUSION

This study revealed ERS-related mechanisms in T2DM, which might contribute to new ideas and insights into the mechanisms and treatment of T2DM.

Network pharmacology, a promising approach to reveal the pharmacology mechanism of Chinese medicine formula

ETHNOPHARMACOLOGICAL RELEVANCE

Network pharmacology is a new discipline based on systems biology theory, biological system network analysis, and multi-target drug molecule design specific signal node selection. The mechanism of action of TCM formula has the characteristics of multiple targets and levels. The mechanism is similar to the integrity, systematization and comprehensiveness of network pharmacology, so network pharmacology is suitable for the study of the pharmacological mechanism of Chinese medicine compounds.

AIM OF THE STUDY

The paper summarizes the present application status and existing problems of network pharmacology in the field of Chinese medicine formula, and formulates the research ideas, up-to-date key technology and application method and strategy of network pharmacology. Its purpose is to provide guidance and reference for using network pharmacology to reveal the modern scientific connotation of Chinese medicine.

MATERIALS AND METHODS

Literatures in this review were searched in PubMed, China National Knowledge Infrastructure (CNKI), Web of Science, ScienceDirect and Google Scholar using the keywords "traditional Chinese medicine", "Chinese herb medicine" and "network pharmacology". The literature cited in this review dates from 2002 to 2022.

RESULTS

Using network pharmacology methods to predict the basis and mechanism of pharmacodynamic substances of traditional Chinese medicines has become a trend.

CONCLUSION

Network pharmacology is a promising approach to reveal the pharmacology mechanism of Chinese medicine formula.

Molecular crosstalk between COVID-19 and Alzheimer's disease using microarray and RNA-seq datasets: A system biology approach

Objective

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). The clinical and epidemiological analysis reported the association between SARS-CoV-2 and neurological diseases. Among neurological diseases, Alzheimer's disease (AD) has developed as a crucial comorbidity of SARS-CoV-2. This study aimed to understand the common transcriptional signatures between SARS-CoV-2 and AD.

Materials and methods

System biology approaches were used to compare the datasets of AD and COVID-19 to identify the genetic association. For this, we have integrated three human whole transcriptomic datasets for COVID-19 and five microarray datasets for AD. We have identified differentially expressed genes for all the datasets and constructed a protein-protein interaction (PPI) network. Hub genes were identified from the PPI network, and hub genes-associated regulatory molecules (transcription factors and miRNAs) were identified for further validation.

Results

A total of 9,500 differentially expressed genes (DEGs) were identified for AD and 7,000 DEGs for COVID-19. Gene ontology analysis resulted in 37 molecular functions, 79 cellular components, and 129 biological processes were found to be commonly enriched in AD and COVID-19. We identified 26 hub genes which includes AKT1, ALB, BDNF, CD4, CDH1, DLG4, EGF, EGFR, FN1, GAPDH, INS, ITGB1, ACTB, SRC, TP53, CDC42, RUNX2, HSPA8, PSMD2, GFAP, VAMP2, MAPK8, CAV1, GNB1, RBX1, and ITGA2B. Specific miRNA targets associated with Alzheimer's disease and COVID-19 were identified through miRNA target prediction. In addition, we found hub genes-transcription factor and hub genes-drugs interaction. We also performed pathway analysis for the hub genes and found that several cell signaling pathways are enriched, such as PI3K-AKT, Neurotrophin, Rap1, Ras, and JAK-STAT.

Conclusion

Our results suggest that the identified hub genes could be diagnostic biomarkers and potential therapeutic drug targets for COVID-19 patients with AD comorbidity.

Network pharmacology and bioinformatics analysis identifies potential therapeutic targets of Naringenin against COVID-19/LUSC

Background

Coronavirus disease 2019 (COVID-19) is a highly contagious respiratory disease that has posed a serious threat to people's daily lives and caused an unprecedented challenge to public health and people's health worldwide. Lung squamous cell carcinoma (LUSC) is a common type of lung malignancy with a highly aggressive nature and poor prognosis. Patients with LUSC could be at risk for COVID-19, We conducted this study to examine the potential for naringenin to develop into an ideal medicine and investigate the underlying action mechanisms of naringenin in COVID-19 and LUSC due to the anti-viral, anti-tumor, and anti-inflammatory activities of naringenin.

Methods

LUSC related genes were obtained from TCGA, PharmGKB, TTD,GeneCards and NCBI, and then the transcriptome data for COVID-19 was downloaded from GEO, DisGeNET, CTD, DrugBank, PubChem, TTD, NCBI Gene, OMIM. The drug targets of Naringenin were revealed through CTD, BATMAN, TCMIP, SymMap, Chemical Association Networks, SwissTargetPrediction, PharmMapper, ECTM, and DGIdb. The genes related to susceptibility to COVID-19 in LUSC patients were obtained through differential analysis. The interaction of COVID-19/LUSC related genes was evaluated and demonstrated using STRING to develop a a COX risk regression model to screen and evaluate the association of genes with clinical characteristics. To investigate the related functional and pathway analysis of the common targets of COVID-19/LUSC and Naringenin, KEGG and GO enrichment analysis were employed to perform the functional analysis of the target genes. Finally, The Hub Gene was screened and visualized using Cytoscape, and molecular docking between the drug and the target was performed using Autodock.

Results

We discovered numerous COVID-19/LUSC target genes and examined their prognostic value in LUSC patients utilizing a variety of bioinformatics and network pharmacology methods. Furthermore, a risk score model with strong predictive performance was developed based on these target genes to assess the prognosis of LUSC patients with COVID-19. We intersected the therapeutic target genes of naringenin with the LUSC, COVID-19-related targets, and identified 354 common targets, which could be used as potential target genes for naringenin to treat COVID-19/LUSC. The treatment of COVID-19/LUSC with naringenin may involve oxidative stress, anti-inflammatory, antiviral, antiviral, apoptosis, immunological, and multiple pathways containing PI3K-Akt, HIF-1, and VEGF, according to the results of the GO and KEGG enrichment analysis of these 354 common targets. By constructing a PPI network, we ascertained AKT1, TP53, SRC, MAPK1, MAPK3, and HSP90AA1 as possible hub targets of naringenin for the treatment of COVID-19/LUSC. Last but not least, molecular docking investigations showed that naringenin has strong binding activity in COVID-19/LUSC.

Conclusion

We revealed for the first time the pharmacological targets and potential molecular processes of naringenin for the treatment of COVID-19/LUSC. However, these results need to be confirmed by additional research and validation in real LUSC patients with COVID-19.

Bioinformatic analyses reveal the prognostic significance and potential role of ankyrin 3 (ANK3) in kidney renal clear cell carcinoma

Kidney renal clear cell carcinoma (KIRC) is one of the most aggressive cancer type of the urinary system. Metastatic KIRC patients have poor prognosis and limited therapeutic options. Ankyrin 3 (ANK3) is a scaffold protein that plays important roles in maintaining physiological function of the kidney and its alteration is implicated in many cancers. In this study, we investigated differential expression of ANK3 in KIRC using GEPIA2, UALCAN, and HPA databases. Survival analysis was performed by GEPIA2, Kaplan-Meier plotter, and OSkirc databases. Genetic alterations of ANK3 in KIRC were assessed using cBioPortal database. Interaction network and functional enrichment analyses of ANK3-correlated genes in KIRC were performed using GeneMANIA and Shiny GO, respectively. Finally, the TIMER2.0 database was used to assess correlation between ANK3 expression and immune infiltration in KIRC. We found that ANK3 expression was significantly decreased in KIRC compared to normal tissues. The KIRC patients with low ANK3 expression had poorer survival outcomes than those with high ANK3 expression. ANK3 mutations were found in 2.4% of KIRC patients and were frequently co-mutated with several genes with a prognostic significance. ANK3-correlated genes were significantly enriched in various biological processes, mainly involved in peroxisome proliferator-activated receptor (PPAR) signaling pathway, in which positive correlations of ANK3 with PPARA and PPARG expressions were confirmed. Expression of ANK3 in KIRC was significantly correlated with infiltration level of B cell, CD8+ T cell, macrophage, and neutrophil. These findings suggested that ANK3 could serve as a prognostic biomarker and promising therapeutic target for KIRC.

A machine learning method for the identification and characterization of novel COVID-19 drug targets

In addition to vaccines, the World Health Organization sees novel medications as an urgent matter to fight the ongoing COVID-19 pandemic. One possible strategy is to identify target proteins, for which a perturbation by an existing compound is likely to benefit COVID-19 patients. In order to contribute to this effort, we present GuiltyTargets-COVID-19 ( https://guiltytargets-covid.eu/ ), a machine learning supported web tool to identify novel candidate drug targets. Using six bulk and three single cell RNA-Seq datasets, together with a lung tissue specific protein-protein interaction network, we demonstrate that GuiltyTargets-COVID-19 is capable of (i) prioritizing meaningful target candidates and assessing their druggability, (ii) unraveling their linkage to known disease mechanisms, (iii) mapping ligands from the ChEMBL database to the identified targets, and (iv) pointing out potential side effects in the case that the mapped ligands correspond to approved drugs. Our example analyses identified 4 potential drug targets from the datasets: AKT3 from both the bulk and single cell RNA-Seq data as well as AKT2, MLKL, and MAPK11 in the single cell experiments. Altogether, we believe that our web tool will facilitate future target identification and drug development for COVID-19, notably in a cell type and tissue specific manner.