Systems Genetics as a Tool to Identify Master Genetic Regulators in Complex Disease

Trends of Artificial Intelligence (AI) Use in Drug Targets, Discovery and Development: Current Status and Future Perspectives

The applications of artificial intelligence (AI) in pharmaceutical sectors have advanced drug discovery and development methods. AI has been applied in virtual drug design, molecule synthesis, advanced research, various screening methods, and decision-making processes. In the fourth industrial revolution, when medical discoveries are happening swiftly, AI technology is essential to reduce the costs, effort, and time in the pharmaceutical industry. Further, it will aid "genome-based medicine" and "drug discovery." AI may prepare proactive databases according to diseases, disorders, and appropriate usage of drugs which will facilitate the required data for the process of drug development. The application of AI has improved clinical trials on patient selection in a population, stratification, and sample assessment such as biomarkers, effectiveness measures, dosage selection, and trial length. Various studies suggest AI could be perform better compared to conventional techniques in drug discovery. The present review focused on the positive impact of AI in drug discovery and development processes in the pharmaceutical industry and beneficial usage in health sectors as well.

The Role of HECT-Type E3 Ligase in the Development of Cardiac Disease

Despite advances in medicine, cardiac disease remains an increasing health problem associated with a high mortality rate. Maladaptive cardiac remodeling, such as cardiac hypertrophy and fibrosis, is a risk factor for heart failure; therefore, it is critical to identify new therapeutic targets. Failing heart is reported to be associated with hyper-ubiquitylation and impairment of the ubiquitin–proteasome system, indicating an importance of ubiquitylation in the development of cardiac disease. Ubiquitylation is a post-translational modification that plays a pivotal role in protein function and degradation. In 1995, homologous to E6AP C-terminus (HECT) type E3 ligases were discovered. E3 ligases are key enzymes in ubiquitylation and are classified into three families: really interesting new genes (RING), HECT, and RING-between-RINGs (RBRs). Moreover, 28 HECT-type E3 ligases have been identified in human beings. It is well conserved in evolution and is characterized by the direct attachment of ubiquitin to substrates. HECT-type E3 ligase is reported to be involved in a wide range of human diseases and health. The role of HECT-type E3 ligases in the development of cardiac diseases has been uncovered in the last decade. There are only a few review articles summarizing recent advancements regarding HECT-type E3 ligase in the field of cardiac disease. This study focused on cardiac remodeling and described the role of HECT-type E3 ligases in the development of cardiac disease. Moreover, this study revealed that the current knowledge could be exploited for the development of new clinical therapies.

A systems genetics approach to deciphering the effect of dosage variation on leaf morphology in Populus

Gene copy number variation is frequent in plant genomes of various species, but the impact of such gene dosage variation on morphological traits is poorly understood. We used a large population of Populus carrying genomically characterized insertions and deletions across the genome to systematically assay the effect of gene dosage variation on a suite of leaf morphology traits. A systems genetics approach was used to integrate insertion and deletion locations, leaf morphology phenotypes, gene expression, and transcriptional network data, to provide an overview of how gene dosage influences morphology. Dosage-sensitive genomic regions were identified that influenced individual or pleiotropic morphological traits. We also identified cis-expression quantitative trait loci (QTL) within these dosage QTL regions, a subset of which modulated trans-expression QTL as well. Integration of data types within a gene co-expression framework identified co-expressed gene modules that are dosage sensitive, enriched for dosage expression QTL, and associated with morphological traits. Functional description of these modules linked dosage-sensitive morphological variation to specific cellular processes, as well as candidate regulatory genes. Together, these results show that gene dosage variation can influence morphological variation through complex changes in gene expression, and suggest that frequently occurring gene dosage variation has the potential to likewise influence quantitative traits in nature.

ReQTL: identifying correlations between expressed SNVs and gene expression using RNA-sequencing data

MOTIVATION

By testing for associations between DNA genotypes and gene expression levels, expression quantitative trait locus (eQTL) analyses have been instrumental in understanding how thousands of single nucleotide variants (SNVs) may affect gene expression. As compared to DNA genotypes, RNA genetic variation represents a phenotypic trait that reflects the actual allele content of the studied system. RNA genetic variation at expressed SNV loci can be estimated using the proportion of alleles bearing the variant nucleotide (variant allele fraction, VAFRNA). VAFRNA is a continuous measure which allows for precise allele quantitation in loci where the RNA alleles do not scale with the genotype count. We describe a method to correlate VAFRNA with gene expression and assess its ability to identify genetically regulated expression solely from RNA-sequencing (RNA-seq) datasets.

RESULTS

We introduce ReQTL, an eQTL modification which substitutes the DNA allele count for the variant allele fraction at expressed SNV loci in the transcriptome (VAFRNA). We exemplify the method on sets of RNA-seq data from human tissues obtained though the Genotype-Tissue Expression (GTEx) project and demonstrate that ReQTL analyses are computationally feasible and can identify a subset of expressed eQTL loci.

AVAILABILITY AND IMPLEMENTATION

A toolkit to perform ReQTL analyses is available at https://github.com/HorvathLab/ReQTL.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

WWP2 regulates pathological cardiac fibrosis by modulating SMAD2 signaling

Cardiac fibrosis is a final common pathology in inherited and acquired heart diseases that causes cardiac electrical and pump failure. Here, we use systems genetics to identify a pro-fibrotic gene network in the diseased heart and show that this network is regulated by the E3 ubiquitin ligase WWP2, specifically by the WWP2-N terminal isoform. Importantly, the WWP2-regulated pro-fibrotic gene network is conserved across different cardiac diseases characterized by fibrosis: human and murine dilated cardiomyopathy and repaired tetralogy of Fallot. Transgenic mice lacking the N-terminal region of the WWP2 protein show improved cardiac function and reduced myocardial fibrosis in response to pressure overload or myocardial infarction. In primary cardiac fibroblasts, WWP2 positively regulates the expression of pro-fibrotic markers and extracellular matrix genes. TGFβ1 stimulation promotes nuclear translocation of the WWP2 isoforms containing the N-terminal region and their interaction with SMAD2. WWP2 mediates the TGFβ1-induced nucleocytoplasmic shuttling and transcriptional activity of SMAD2.

Systems genetics identifies a macrophage cholesterol network associated with physiological wound healing

Among other cells, macrophages regulate the inflammatory and reparative phases during wound healing but genetic determinants and detailed molecular pathways that modulate these processes are not fully elucidated. Here, we took advantage of normal variation in wound healing in 1,378 genetically outbred mice, and carried out macrophage RNA-sequencing profiling of mice with extreme wound healing phenotypes (i.e., slow and fast healers, n = 146 in total). The resulting macrophage coexpression networks were genetically mapped and led to the identification of a unique module under strong trans-acting genetic control by the Runx2 locus. This macrophage-mediated healing network was specifically enriched for cholesterol and fatty acid biosynthetic processes. Pharmacological blockage of fatty acid synthesis with cerulenin resulted in delayed wound healing in vivo, and increased macrophage infiltration in the wounded skin, suggesting the persistence of an unresolved inflammation. We show how naturally occurring sequence variation controls transcriptional networks in macrophages, which in turn regulate specific metabolic pathways that could be targeted in wound healing.

Genomic approaches for the elucidation of genes and gene networks underlying cardiovascular traits

Genome-wide association studies have shed light on the association between natural genetic variation and cardiovascular traits. However, linking a cardiovascular trait associated locus to a candidate gene or set of candidate genes for prioritization for follow-up mechanistic studies is all but straightforward. Genomic technologies based on next-generation sequencing technology nowadays offer multiple opportunities to dissect gene regulatory networks underlying genetic cardiovascular trait associations, thereby aiding in the identification of candidate genes at unprecedented scale. RNA sequencing in particular becomes a powerful tool when combined with genotyping to identify loci that modulate transcript abundance, known as expression quantitative trait loci (eQTL), or loci modulating transcript splicing known as splicing quantitative trait loci (sQTL). Additionally, the allele-specific resolution of RNA-sequencing technology enables estimation of allelic imbalance, a state where the two alleles of a gene are expressed at a ratio differing from the expected 1:1 ratio. When multiple high-throughput approaches are combined with deep phenotyping in a single study, a comprehensive elucidation of the relationship between genotype and phenotype comes into view, an approach known as systems genetics. In this review, we cover key applications of systems genetics in the broad cardiovascular field.

Identification of Ceruloplasmin as a Gene that Affects Susceptibility to Glomerulonephritis Through Macrophage Function

Crescentic glomerulonephritis (Crgn) is a complex disorder where macrophage activity and infiltration are significant effector causes. In previous linkage studies using the uniquely susceptible Wistar Kyoto (WKY) rat strain, we have identified multiple crescentic glomerulonephritis QTL (Crgn) and positionally cloned genes underlying Crgn1 and Crgn2, which accounted for 40% of total variance in glomerular inflammation. Here, we have generated a backcross (BC) population (n = 166) where Crgn1 and Crgn2 were genetically fixed and found significant linkage to glomerular crescents on chromosome 2 (Crgn8, LOD = 3.8). Fine mapping analysis by integration with genome-wide expression QTLs (eQTLs) from the same BC population identified ceruloplasmin (Cp) as a positional eQTL in macrophages but not in serum. Liquid chromatography-tandem mass spectrometry confirmed Cp as a protein QTL in rat macrophages. WKY macrophages overexpress Cp and its downregulation by RNA interference decreases markers of glomerular proinflammatory macrophage activation. Similarly, short incubation with Cp results in a strain-dependent macrophage polarization in the rat. These results suggest that genetically determined Cp levels can alter susceptibility to Crgn through macrophage function and propose a new role for Cp in early macrophage activation.

Towards precision medicine in Alzheimer's disease: deciphering genetic data to establish informative biomarkers

INTRODUCTION

Developing biomarker tools for identification of individuals at high-risk for late-onset Alzheimer's disease (LOAD) is important for prognosis and early treatment. This review focuses on genetic factors and their potential role for precision medicine in LOAD.

AREAS COVERED

APOEe4 is the strongest genetic risk factor for non-Mendelian LOAD, and the APOE-linkage disequilibrium (LD) region has produced the most significant association signal in multi-center genome-wide-association-studies (GWAS). Consideration of extended haplotypes in the APOE-LD region and specifically, non-coding variants in putative enhancer elements, such as the TOMM40-polyT, in-addition to the coding variants that comprise the APOE-genotypes, may be useful for predicting subjects at high-risk of developing LOAD and estimating age-of-onset of early disease-stage symptoms. A genetic-biomarker based on APOE-TOMM40-polyT haplotypes, and age is currently applied in a clinical trial for prevention/delay of LOAD onset. Additionally, we discuss LOAD-GWAS discoveries and the development of new genetic risk scores based on LOAD-GWAS findings other than the APOE-LD region.

EXPERT COMMENTARY

Deciphering the precise causal genetic-variants within LOAD-GWAS regions will advance the development of genetic-biomarkers to complement and refine the APOE-LD region based prediction model. Collectively, the genetic-biomarkers will be translational for early diagnosis and enrichment of clinical trials with subjects at high-risk.