Reducing acetylated tau is neuroprotective in brain injury.

Traumatic brain injury (TBI) is the largest non-genetic, non-aging related risk factor for Alzheimer's disease (AD). We report here that TBI induces tau acetylation (ac-tau) at sites acetylated also in human AD brain. This is mediated by S-nitrosylated-GAPDH, which simultaneously inactivates Sirtuin1 deacetylase and activates p300/CBP acetyltransferase, increasing neuronal ac-tau. Subsequent tau mislocalization causes neurodegeneration and neurobehavioral impairment, and ac-tau accumulates in the blood. Blocking GAPDH S-nitrosylation, inhibiting p300/CBP, or stimulating Sirtuin1 all protect mice from neurodegeneration, neurobehavioral impairment, and blood and brain accumulation of ac-tau after TBI. Ac-tau is thus a therapeutic target and potential blood biomarker of TBI that may represent pathologic convergence between TBI and AD. Increased ac-tau in human AD brain is further augmented in AD patients with history of TBI, and patients receiving the p300/CBP inhibitors salsalate or diflunisal exhibit decreased incidence of AD and clinically diagnosed TBI.

Quantitative trait locus (xQTL) approaches identify risk genes and drug targets from human non-coding genomes.

Advances and reduction of costs in various sequencing technologies allow for a closer look at variations present in the non-coding regions of the human genome. Correlating non-coding variants with large-scale multi-omic data holds the promise not only of a better understanding of likely causal connections between non-coding DNA and expression of traits but also identifying potential disease-modifying medicines. Genome-phenome association studies have created large datasets of DNA variants that are associated with multiple traits or diseases, such as Alzheimer's disease; yet, the functional consequences of variants, in particular of non-coding variants, remain largely unknown. Recent advances in functional genomics and computational approaches have led to the identification of potential roles of DNA variants, such as various quantitative trait locus (xQTL) techniques. Multi-omic assays and analytic approaches toward xQTL have identified links between genetic loci and human transcriptomic, epigenomic, proteomic and metabolomic data. In this review, we first discuss the recent development of xQTL from multi-omic findings. We then highlight multimodal analysis of xQTL and genetic data for identification of risk genes and drug targets using Alzheimer's disease as an example. We finally discuss challenges and future research directions (e.g. artificial intelligence) for annotation of non-coding variants in complex diseases.

Multimodal single-cell/nucleus RNA sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer's disease.

Because disease-associated microglia (DAM) and disease-associated astrocytes (DAA) are involved in the pathophysiology of Alzheimer's disease (AD), we systematically identified molecular networks between DAM and DAA to uncover novel therapeutic targets for AD. Specifically, we develop a network-based methodology that leverages single-cell/nucleus RNA sequencing data from both transgenic mouse models and AD patient brains, as well as drug-target network, metabolite-enzyme associations, the human protein-protein interactome, and large-scale longitudinal patient data. Through this approach, we find both common and unique gene network regulators between DAM (i.e., PAK1, MAPK14, and CSF1R) and DAA (i.e., NFKB1, FOS, and JUN) that are significantly enriched by neuro-inflammatory pathways and well-known genetic variants (i.e., BIN1). We identify shared immune pathways between DAM and DAA, including Th17 cell differentiation and chemokine signaling. Last, integrative metabolite-enzyme network analyses suggest that fatty acids and amino acids may trigger molecular alterations in DAM and DAA. Combining network-based prediction and retrospective case-control observations with 7.2 million individuals, we identify that usage of fluticasone (an approved glucocorticoid receptor agonist) is significantly associated with a reduced incidence of AD (hazard ratio [HR] = 0.86, 95% confidence interval [CI] 0.83-0.89, P < 1.0 × 10-8). Propensity score-stratified cohort studies reveal that usage of mometasone (a stronger glucocorticoid receptor agonist) is significantly associated with a decreased risk of AD (HR = 0.74, 95% CI 0.68-0.81, P < 1.0 × 10-8) compared to fluticasone after adjusting age, gender, and disease comorbidities. In summary, we present a network-based, multimodal methodology for single-cell/nucleus genomics-informed drug discovery and have identified fluticasone and mometasone as potential treatments in AD.

Peripheral sTREM2-Related Inflammatory Activity Alterations in Early-Stage Alzheimer's Disease.

Alzheimer's disease (AD) has been linked to multiple immune system-related genetic variants. Triggering receptor expressed on myeloid cells 2 (TREM2) genetic variants are risk factors for AD and other neurodegenerative diseases. In addition, soluble TREM2 (sTREM2) isoform is elevated in cerebrospinal fluid in the early stages of AD and is associated with slower cognitive decline in a disease stage-dependent manner. Multiple studies have reported an altered peripheral immune response in AD. However, less is known about the relationship between peripheral sTREM2 and an altered peripheral immune response in AD. The objective of this study was to explore the relationship between human plasma sTREM2 and inflammatory activity in AD. The hypothesis of this exploratory study was that sTREM2-related inflammatory activity differs by AD stage. We observed different patterns of inflammatory activity across AD stages that implicate early-stage alterations in peripheral sTREM2-related inflammatory activity in AD. Notably, fractalkine showed a significant relationship with sTREM2 across different analyses in the control groups that was lost in later AD-related stages with high levels in mild cognitive impairment. Although multiple other inflammatory factors either differed significantly between groups or were significantly correlated with sTREM2 within specific groups, three inflammatory factors (fibroblast growth factor-2, GM-CSF, and IL-1ß) are notable because they exhibited both lower levels in AD, compared with mild cognitive impairment, and a change in the relationship with sTREM2. This evidence provides important support to the hypothesis that sTREM2-related inflammatory activity alterations are AD stage specific and provides critical information for therapeutic strategies focused on the immune response.

Microglial immunometabolism endophenotypes contribute to sex difference in Alzheimer's disease.

INTRODUCTION: The molecular mechanisms that contribute to sex differences, in particular female predominance, in Alzheimer's disease (AD) prevalence, symptomology, and pathology, are incompletely understood. METHODS: To address this problem, we investigated cellular metabolism and immune responses ("immunometabolism endophenotype") across AD individuals as a function of sex with diverse clinical diagnosis of cognitive status at death (cogdx), Braak staging, and Consortium to Establish a Registry for AD (CERAD) scores using human cortex metabolomics and transcriptomics data from the Religious Orders Study / Memory and Aging Project (ROSMAP) cohort. RESULTS: We identified sex-specific metabolites, immune and metabolic genes, and pathways associated with the AD diagnosis and progression. We identified female-specific elevation in glycerophosphorylcholine and N-acetylglutamate, which are AD inflammatory metabolites involved in interleukin (IL)-17 signaling, C-type lectin receptor, interferon signaling, and Toll-like receptor pathways. We pinpointed distinct microglia-specific immunometabolism endophenotypes (i.e., lipid- and amino acid-specific IL-10 and IL-17 signaling pathways) between female and male AD subjects. In addition, female AD subjects showed evidence of diminished excitatory neuron and microglia communications via glutamate-mediated immunometabolism. DISCUSSION: Our results point to new understanding of the molecular basis for female predominance in AD, and warrant future independent validations with ethnically diverse patient cohorts to establish a likely causal relationship of microglial immunometabolism in the sex differences in AD. HIGHLIGHTS: Sex-specific immune metabolites, gene networks and pathways, are associated with Alzheimer's disease pathogenesis and disease progression. Female AD subjects exhibit microglial immunometabolism endophenotypes characterized by decreased glutamate metabolism and elevated interleukin-10 pathway activity. Female AD subjects showed a shift in glutamate-mediated cell-cell communications between excitatory neurons to microglia and astrocyte.

MBOAT7-driven lysophosphatidylinositol acylation in adipocytes contributes to systemic glucose homeostasis.

We previously demonstrated that antisense oligonucleotide-mediated knockdown of Mboat7, the gene encoding membrane bound O-acyltransferase 7, in the liver and adipose tissue of mice promoted high fat diet-induced hepatic steatosis, hyperinsulinemia, and systemic insulin resistance. Thereafter, other groups showed that hepatocyte-specific genetic deletion of Mboat7 promoted striking fatty liver and NAFLD progression in mice but does not alter insulin sensitivity, suggesting the potential for cell autonomous roles. Here, we show that MBOAT7 function in adipocytes contributes to diet-induced metabolic disturbances including hyperinsulinemia and systemic insulin resistance. We generated Mboat7 floxed mice and created hepatocyte- and adipocyte-specific Mboat7 knockout mice using Cre-recombinase mice under the control of the albumin and adiponectin promoter, respectively. Here, we show that MBOAT7 function in adipocytes contributes to diet-induced metabolic disturbances including hyperinsulinemia and systemic insulin resistance. The expression of Mboat7 in white adipose tissue closely correlates with diet-induced obesity across a panel of ∼100 inbred strains of mice fed a high fat/high sucrose diet. Moreover, we found that adipocyte-specific genetic deletion of Mboat7 is sufficient to promote hyperinsulinemia, systemic insulin resistance, and mild fatty liver. Unlike in the liver, where Mboat7 plays a relatively minor role in maintaining arachidonic acid-containing PI pools, Mboat7 is the major source of arachidonic acid-containing PI pools in adipose tissue. Our data demonstrate that MBOAT7 is a critical regulator of adipose tissue PI homeostasis, and adipocyte MBOAT7-driven PI biosynthesis is closely linked to hyperinsulinemia and insulin resistance in mice.

A network medicine approach to investigation and population-based validation of disease manifestations and drug repurposing for COVID-19.

The global coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to unprecedented social and economic consequences. The risk of morbidity and mortality due to COVID-19 increases dramatically in the presence of coexisting medical conditions, while the underlying mechanisms remain unclear. Furthermore, there are no approved therapies for COVID-19. This study aims to identify SARS-CoV-2 pathogenesis, disease manifestations, and COVID-19 therapies using network medicine methodologies along with clinical and multi-omics observations. We incorporate SARS-CoV-2 virus-host protein-protein interactions, transcriptomics, and proteomics into the human interactome. Network proximity measurement revealed underlying pathogenesis for broad COVID-19-associated disease manifestations. Analyses of single-cell RNA sequencing data show that co-expression of ACE2 and TMPRSS2 is elevated in absorptive enterocytes from the inflamed ileal tissues of Crohn disease patients compared to uninflamed tissues, revealing shared pathobiology between COVID-19 and inflammatory bowel disease. Integrative analyses of metabolomics and transcriptomics (bulk and single-cell) data from asthma patients indicate that COVID-19 shares an intermediate inflammatory molecular profile with asthma (including IRAK3 and ADRB2). To prioritize potential treatments, we combined network-based prediction and a propensity score (PS) matching observational study of 26,779 individuals from a COVID-19 registry. We identified that melatonin usage (odds ratio [OR] = 0.72, 95% CI 0.56-0.91) is significantly associated with a 28% reduced likelihood of a positive laboratory test result for SARS-CoV-2 confirmed by reverse transcription-polymerase chain reaction assay. Using a PS matching user active comparator design, we determined that melatonin usage was associated with a reduced likelihood of SARS-CoV-2 positive test result compared to use of angiotensin II receptor blockers (OR = 0.70, 95% CI 0.54-0.92) or angiotensin-converting enzyme inhibitors (OR = 0.69, 95% CI 0.52-0.90). Importantly, melatonin usage (OR = 0.48, 95% CI 0.31-0.75) is associated with a 52% reduced likelihood of a positive laboratory test result for SARS-CoV-2 in African Americans after adjusting for age, sex, race, smoking history, and various disease comorbidities using PS matching. In summary, this study presents an integrative network medicine platform for predicting disease manifestations associated with COVID-19 and identifying melatonin for potential prevention and treatment of COVID-19.

Single-cell network biology characterizes cell type gene regulation for drug repurposing and phenotype prediction in Alzheimer's disease.

Dysregulation of gene expression in Alzheimer's disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, AD-induced regulatory changes across brain cell types remains uncharted. To address this, we integrated single-cell multi-omics datasets to predict the gene regulatory networks of four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes, in control and AD brains. Importantly, we analyzed and compared the structural and topological features of networks across cell types and examined changes in AD. Our analysis shows that hub TFs are largely common across cell types and AD-related changes are relatively more prominent in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are also highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes. The further disease-module-drug association analysis suggests cell-type candidate drugs and their potential target genes. Finally, our network-based machine learning analysis systematically prioritized cell type risk genes likely involved in AD. Our strategy is validated using an independent dataset which showed that top ranked genes can predict clinical phenotypes (e.g., cognitive impairment) of AD with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals cell-type gene dysregulation in AD.

Population-based discovery and Mendelian randomization analysis identify telmisartan as a candidate medicine for Alzheimer's disease in African Americans.

INTRODUCTION: African Americans (AAs) and European Americans (EAs) differ in Alzheimer's disease (AD) prevalence, risk factors, and symptomatic presentation and AAs are less likely to enroll in AD clinical trials. METHODS: We conducted race-conscious pharmacoepidemiologic studies of 5.62 million older individuals (age ≥60) to investigate the association of telmisartan exposure and AD outcome using Cox analysis, Kaplan-Meier analysis, and log-rank test. We performed Mendelian randomization (MR) analysis of large ethnically diverse genetic data to test likely causal relationships between telmisartan's target and AD. RESULTS: We identified that moderate/high telmisartan exposure was significantly associated with a reduced incidence of AD in the AAs compared to low/no telmisartan exposure (hazard ratio [HR] = 0.77, 95% CI: 0.65-0.91, p-value = 0.0022), but not in the non-Hispanic EAs (HR = 0.97, 95% CI: 0.89-1.05, p-value = 0.4110). Sensitivity and sex-/age-stratified patient subgroup analyses identified that telmisartan's medication possession ratio (MPR) and average hypertension daily dosage were significantly associated with a stronger reduction in the incidence of both AD and dementia in AAs. Using MR analysis from large genome-wide association studies (GWAS) (over 2 million individuals) across AD, hypertension, and diabetes, we further identified AA-specific beneficial effects of telmisartan for AD. DISCUSSION: Randomized controlled trials with ethnically diverse patient cohorts are warranted to establish causality and therapeutic outcomes of telmisartan and AD. HIGHLIGHTS: Telmisartan is associated with lower risk of Alzheimer's disease (AD) in African Americans (AAs). Telmisartan is the only angiotensin II receptor blockers having PPAR-γ agonistic properties with beneficial anti-diabetic and renal function effects, which mitigate AD risk in AAs. Mendelian randomization (MR) analysis demonstrates the specificity of telmisartan's protective mechanism to AAs.

KRAS Activating Signaling Triggers Arteriovenous Malformations.

The underlying genetic causes and altered signaling pathways of brain arteriovenous malformations remain unknown. A study published in The New England Journal of Medicine reported that KRAS somatic mutations (p.Gly12Val/Asp) were identified in brain arteriovenous malformations of human subjects and endothelial cell-enriched cultures, which might specifically activate the MAPK (mitogen-activated protein kinase)-ERK (extracellular signal-regulated kinase) signaling pathway in brain endothelial cells.

Pharmacogenomics meets precision cardio-oncology: is there synergistic potential?

An individual's inherited genetic makeup and acquired genomic variants may account for a significant portion of observable variability in therapy efficacy and toxicity. Pharmacogenomics (PGx) is the concept that treatments can be modified to account for these differences to increase chances of therapeutic efficacy while minimizing risk of adverse effects. This is particularly applicable to oncology in which treatment may be multimodal. Each tumor type has a unique genomic signature that lends to inclusion of targeted therapy but may be associated with cumulative toxicity, such as cardiotoxicity, and can impact quality of life. A greater understanding of therapeutic agents impacted by PGx and subsequent implementation has the potential to improve outcomes and reduce risk of drug-induced adverse effects.

Pharmacogenomics for immunotherapy and immune-related cardiotoxicity.

Immune checkpoint blockade (ICB) has become a standard of care in a subset of solid tumors. Although cancer survivorship has extended, rates of durable response of ICB remain poor; furthermore, cardiac adverse effects are emerging, which impact several mechanical aspects of the heart. Cardio-oncology programs implement a clinical assessment to curtail cardiovascular disease progression but are limited to the current clinical parameters used in cardiology. Pharmacogenomics provides the potential to unveil heritable and somatic genetic variations for guiding precision immunotherapy treatment to reduce the risk of immune-related cardiotoxicity. A better understanding of pharmacogenomics will optimize the current treatment selection and dosing of immunotherapy. Here, we summarize the recent pharmacogenomics studies in immunotherapy responsiveness and its related cardiotoxicity and highlight how patient genetics and epigenetics can facilitate researchers and clinicians in designing new approaches for precision immunotherapy. We highlight and discuss how single-cell technologies, human-induced pluripotent stem cells and systems pharmacogenomics accelerate future studies of precision cardio-oncology.

Conformational Dynamics and Allosteric Regulation Landscapes of Germline PTEN Mutations Associated with Autism Compared to Those Associated with Cancer.

Individuals with germline PTEN tumor-suppressor variants have PTEN hamartoma tumor syndrome (PHTS). Clinically, PHTS has variable presentations; there are distinct subsets of PHTS-affected individuals, such as those diagnosed with autism spectrum disorder (ASD) or cancer. It remains unclear why mutations in one gene can lead to such seemingly disparate phenotypes. Therefore, we sought to determine whether it is possible to predict a given PHTS-affected individual's a priori risk of ASD, cancer, or the co-occurrence of both phenotypes. By integrating network proximity analysis performed on the human interactome, molecular simulations, and residue-interaction networks, we demonstrate the role of conformational dynamics in the structural communication and long-range allosteric regulation of germline PTEN variants associated with ASD or cancer. We show that the PTEN interactome shares significant overlap with the ASD and cancer interactomes, providing network-based evidence that PTEN is a crucial player in the biology of both disorders. Importantly, this finding suggests that a germline PTEN variant might perturb the ASD or cancer networks differently, thus favoring one disease outcome at any one time. Furthermore, protein-dynamic structural-network analysis reveals small-world structural communication mediated by highly conserved functional residues and potential allosteric regulation of PTEN. We identified a salient structural-communication pathway that extends across the inter-domain interface for cancer-only mutations. In contrast, the structural-communication pathway is predominantly restricted to the phosphatase domain for ASD-only mutations. Our integrative approach supports the prediction and potential modulation of the relevant conformational states that influence structural communication and long-range perturbations associated with mutational effects that lead to PTEN-ASD or PTEN-cancer phenotypes.

A network-based deep learning methodology for stratification of tumor mutations.

MOTIVATION: Tumor stratification has a wide range of biomedical and clinical applications, including diagnosis, prognosis and personalized treatment. However, cancer is always driven by the combination of mutated genes, which are highly heterogeneous across patients. Accurately subdividing the tumors into subtypes is challenging. RESULTS: We developed a network-embedding based stratification (NES) methodology to identify clinically relevant patient subtypes from large-scale patients' somatic mutation profiles. The central hypothesis of NES is that two tumors would be classified into the same subtypes if their somatic mutated genes located in the similar network regions of the human interactome. We encoded the genes on the human protein-protein interactome with a network embedding approach and constructed the patients' vectors by integrating the somatic mutation profiles of 7344 tumor exomes across 15 cancer types. We firstly adopted the lightGBM classification algorithm to train the patients' vectors. The AUC value is around 0.89 in the prediction of the patient's cancer type and around 0.78 in the prediction of the tumor stage within a specific cancer type. The high classification accuracy suggests that network embedding-based patients' features are reliable for dividing the patients. We conclude that we can cluster patients with a specific cancer type into several subtypes by using an unsupervised clustering algorithm to learn the patients' vectors. Among the 15 cancer types, the new patient clusters (subtypes) identified by the NES are significantly correlated with patient survival across 12 cancer types. In summary, this study offers a powerful network-based deep learning methodology for personalized cancer medicine. AVAILABILITY AND IMPLEMENTATION: Source code and data can be downloaded from https://github.com/ChengF-Lab/NES. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Personal Mutanomes Meet Modern Oncology Drug Discovery and Precision Health.

Recent remarkable advances in genome sequencing have enabled detailed maps of identified and interpreted genomic variation, dubbed "mutanomes." The availability of thousands of exome/genome sequencing data has prompted the emergence of new challenges in the identification of novel druggable targets and therapeutic strategies. Typically, mutanomes are viewed as one- or two-dimensional. The three-dimensional protein structural view of personal mutanomes sheds light on the functional consequences of clinically actionable mutations revealed in tumor diagnosis and followed up in personalized treatments, in a mutanome-informed manner. In this review, we describe the protein structural landscape of personal mutanomes and provide expert opinions on rational strategies for more streamlined oncological drug discovery and molecularly targeted therapies for each individual and each tumor. We provide the structural mechanism of orthosteric versus allosteric drugs at the atom-level via targeting specific somatic alterations for combating drug resistance and the "undruggable" challenges in solid and hematologic neoplasias. We discuss computational biophysics strategies for innovative mutanome-informed cancer immunotherapies and combination immunotherapies. Finally, we highlight a personal mutanome infrastructure for the emerging development of personalized cancer medicine using a breast cancer case study.

Cardiac risk stratification in cancer patients: A longitudinal patient-patient network analysis.

BACKGROUND: Cardiovascular disease is a leading cause of death in general population and the second leading cause of mortality and morbidity in cancer survivors after recurrent malignancy in the United States. The growing awareness of cancer therapy-related cardiac dysfunction (CTRCD) has led to an emerging field of cardio-oncology; yet, there is limited knowledge on how to predict which patients will experience adverse cardiac outcomes. We aimed to perform unbiased cardiac risk stratification for cancer patients using our large-scale, institutional electronic medical records. METHODS AND FINDINGS: We built a large longitudinal (up to 22 years' follow-up from March 1997 to January 2019) cardio-oncology cohort having 4,632 cancer patients in Cleveland Clinic with 5 diagnosed cardiac outcomes: atrial fibrillation, coronary artery disease, heart failure, myocardial infarction, and stroke. The entire population includes 84% white Americans and 11% black Americans, and 59% females versus 41% males, with median age of 63 (interquartile range [IQR]: 54 to 71) years old. We utilized a topology-based K-means clustering approach for unbiased patient-patient network analyses of data from general demographics, echocardiogram (over 25,000), lab testing, and cardiac factors (cardiac). We performed hazard ratio (HR) and Kaplan-Meier analyses to identify clinically actionable variables. All confounding factors were adjusted by Cox regression models. We performed random-split and time-split training-test validation for our model. We identified 4 clinically relevant subgroups that are significantly correlated with incidence of cardiac outcomes and mortality. Among the 4 subgroups, subgroup I (n = 625) has the highest risk of de novo CTRCD (28%) with an HR of 3.05 (95% confidence interval (CI) 2.51 to 3.72). Patients in subgroup IV (n = 1,250) had the worst survival probability (HR 4.32, 95% CI 3.82 to 4.88). From longitudinal patient-patient network analyses, the patients in subgroup I had a higher percentage of de novo CTRCD and a worse mortality within 5 years after the initiation of cancer therapies compared to long-time exposure (6 to 20 years). Using clinical variable network analyses, we identified that serum levels of NT-proB-type Natriuretic Peptide (NT-proBNP) and Troponin T are significantly correlated with patient's mortality (NT-proBNP > 900 pg/mL versus NT-proBNP = 0 to 125 pg/mL, HR = 2.95, 95% CI 2.28 to 3.82, p < 0.001; Troponin T > 0.05 µg/L versus Troponin T ≤ 0.01 µg/L, HR = 2.08, 95% CI 1.83 to 2.34, p < 0.001). Study limitations include lack of independent cardio-oncology cohorts from different healthcare systems to evaluate the generalizability of the models. Meanwhile, the confounding factors, such as multiple medication usages, may influence the findings. CONCLUSIONS: In this study, we demonstrated that the patient-patient network clustering methodology is clinically intuitive, and it allows more rapid identification of cancer survivors that are at greater risk of cardiac dysfunction. We believed that this study holds great promise for identifying novel cardiac risk subgroups and clinically actionable variables for the development of precision cardio-oncology.

Network-based prediction of drug-target interactions using an arbitrary-order proximity embedded deep forest.

MOTIVATION: Systematic identification of molecular targets among known drugs plays an essential role in drug repurposing and understanding of their unexpected side effects. Computational approaches for prediction of drug-target interactions (DTIs) are highly desired in comparison to traditional experimental assays. Furthermore, recent advances of multiomics technologies and systems biology approaches have generated large-scale heterogeneous, biological networks, which offer unexpected opportunities for network-based identification of new molecular targets among known drugs. RESULTS: In this study, we present a network-based computational framework, termed AOPEDF, an arbitrary-order proximity embedded deep forest approach, for prediction of DTIs. AOPEDF learns a low-dimensional vector representation of features that preserve arbitrary-order proximity from a highly integrated, heterogeneous biological network connecting drugs, targets (proteins) and diseases. In total, we construct a heterogeneous network by uniquely integrating 15 networks covering chemical, genomic, phenotypic and network profiles among drugs, proteins/targets and diseases. Then, we build a cascade deep forest classifier to infer new DTIs. Via systematic performance evaluation, AOPEDF achieves high accuracy in identifying molecular targets among known drugs on two external validation sets collected from DrugCentral [area under the receiver operating characteristic curve (AUROC) = 0.868] and ChEMBL (AUROC = 0.768) databases, outperforming several state-of-the-art methods. In a case study, we showcase that multiple molecular targets predicted by AOPEDF are associated with mechanism-of-action of substance abuse disorder for several marketed drugs (such as aripiprazole, risperidone and haloperidol). AVAILABILITY AND IMPLEMENTATION: Source code and data can be downloaded from https://github.com/ChengF-Lab/AOPEDF.

iDrug: Integration of drug repositioning and drug-target prediction via cross-network embedding.

Computational drug repositioning and drug-target prediction have become essential tasks in the early stage of drug discovery. In previous studies, these two tasks have often been considered separately. However, the entities studied in these two tasks (i.e., drugs, targets, and diseases) are inherently related. On one hand, drugs interact with targets in cells to modulate target activities, which in turn alter biological pathways to promote healthy functions and to treat diseases. On the other hand, both drug repositioning and drug-target prediction involve the same drug feature space, which naturally connects these two problems and the two domains (diseases and targets). By using the wisdom of the crowds, it is possible to transfer knowledge from one of the domains to the other. The existence of relationships among drug-target-disease motivates us to jointly consider drug repositioning and drug-target prediction in drug discovery. In this paper, we present a novel approach called iDrug, which seamlessly integrates drug repositioning and drug-target prediction into one coherent model via cross-network embedding. In particular, we provide a principled way to transfer knowledge from these two domains and to enhance prediction performance for both tasks. Using real-world datasets, we demonstrate that iDrug achieves superior performance on both learning tasks compared to several state-of-the-art approaches. Our code and datasets are available at: https://github.com/Case-esaC/iDrug.

Individualized genetic network analysis reveals new therapeutic vulnerabilities in 6,700 cancer genomes.

Tumor-specific genomic alterations allow systematic identification of genetic interactions that promote tumorigenesis and tumor vulnerabilities, offering novel strategies for development of targeted therapies for individual patients. We develop an Individualized Network-based Co-Mutation (INCM) methodology by inspecting over 2.5 million nonsynonymous somatic mutations derived from 6,789 tumor exomes across 14 cancer types from The Cancer Genome Atlas. Our INCM analysis reveals a higher genetic interaction burden on the significantly mutated genes, experimentally validated cancer genes, chromosome regulatory factors, and DNA damage repair genes, as compared to human pan-cancer essential genes identified by CRISPR-Cas9 screenings on 324 cancer cell lines. We find that genes involved in the cancer type-specific genetic subnetworks identified by INCM are significantly enriched in established cancer pathways, and the INCM-inferred putative genetic interactions are correlated with patient survival. By analyzing drug pharmacogenomics profiles from the Genomics of Drug Sensitivity in Cancer database, we show that the network-predicted putative genetic interactions (e.g., BRCA2-TP53) are significantly correlated with sensitivity/resistance of multiple therapeutic agents. We experimentally validated that afatinib has the strongest cytotoxic activity on BT474 (IC50 = 55.5 nM, BRCA2 and TP53 co-mutant) compared to MCF7 (IC50 = 7.7 µM, both BRCA2 and TP53 wild type) and MDA-MB-231 (IC50 = 7.9 µM, BRCA2 wild type but TP53 mutant). Finally, drug-target network analysis reveals several potential druggable genetic interactions by targeting tumor vulnerabilities. This study offers a powerful network-based methodology for identification of candidate therapeutic pathways that target tumor vulnerabilities and prioritization of potential pharmacogenomics biomarkers for development of personalized cancer medicine.

Epilepsy subtype-specific copy number burden observed in a genome-wide study of 17 458 subjects.

Cytogenic testing is routinely applied in most neurological centres for severe paediatric epilepsies. However, which characteristics of copy number variants (CNVs) confer most epilepsy risk and which epilepsy subtypes carry the most CNV burden, have not been explored on a genome-wide scale. Here, we present the largest CNV investigation in epilepsy to date with 10 712 European epilepsy cases and 6746 ancestry-matched controls. Patients with genetic generalized epilepsy, lesional focal epilepsy, non-acquired focal epilepsy, and developmental and epileptic encephalopathy were included. All samples were processed with the same technology and analysis pipeline. All investigated epilepsy types, including lesional focal epilepsy patients, showed an increase in CNV burden in at least one tested category compared to controls. However, we observed striking differences in CNV burden across epilepsy types and investigated CNV categories. Genetic generalized epilepsy patients have the highest CNV burden in all categories tested, followed by developmental and epileptic encephalopathy patients. Both epilepsy types also show association for deletions covering genes intolerant for truncating variants. Genome-wide CNV breakpoint association showed not only significant loci for genetic generalized and developmental and epileptic encephalopathy patients but also for lesional focal epilepsy patients. With a 34-fold risk for developing genetic generalized epilepsy, we show for the first time that the established epilepsy-associated 15q13.3 deletion represents the strongest risk CNV for genetic generalized epilepsy across the whole genome. Using the human interactome, we examined the largest connected component of the genes overlapped by CNVs in the four epilepsy types. We observed that genetic generalized epilepsy and non-acquired focal epilepsy formed disease modules. In summary, we show that in all common epilepsy types, 1.5-3% of patients carry epilepsy-associated CNVs. The characteristics of risk CNVs vary tremendously across and within epilepsy types. Thus, we advocate genome-wide genomic testing to identify all disease-associated types of CNVs.