Mapping gene and gene pathways associated with coronary artery disease: a CARDIoGRAM exome and multi-ancestry UK biobank analysis

Predicting structure-targeted food bioactive compounds for middle-aged and elderly Asians with myocardial infarction: insights from genetic variations and bioinformatics-integrated deep learning analysis

Myocardial infarction (MI) is a significant global health issue. Despite the advances in genome-wide association studies, a complete genetic and molecular understanding of MI is elusive and needs to be fully explored. This study aimed to elucidate the genetic framework of MI and explore the potential health benefits of natural compounds (NCs). The genetic architecture of MI was explored using data from the Korean Genome and Epidemiology Study. We pinpointed crucial protein-coding genes related to MI by multi-marker analysis of genomic annotation for gene-based analysis. The bioinformatics-integrated deep neural analysis of NCs (BioDeepNat), a novel disease discovery application, was utilized to assess the influence of NCs on MI-related target proteins and validated with molecular docking analysis. The BioDeepNat application revealed significant NCs on MI-related target proteins, such as E-resveratrol, epicatechin 3-gallate, and kaempferol. The E3 region of RNF213 protein with a point mutation (Arg4810Lys) had different binding energies with NCs, such as ursolic acid and olean-12-en-28-oic acid, compared to the wild type. However, ginsenosides, eleutheroside, oleanolic acid, and hederagenin showed similar binding energies to wild and mutated types of RNF213 protein. The predicted NCs were primarily sourced from foods such as common grapes and teas. Aromatic hydrocarbons are frequently observed as the prevalent functional groups with high binding affinity for NCs in a molecular docking analysis. In conclusion, the proteins encoded by these genes identified by gene-based analysis interacted with several NCs with health promotion found in day-to-day foods, particularly E-resveratrol and kaempferol. This understanding offers promising directions for precision nutrition strategies in MI.

Evolutionary Action-Machine Learning Model Identifies Candidate Genes Associated With Early-Onset Coronary Artery Disease

Background Coronary artery disease is a primary cause of death around the world, with both genetic and environmental risk factors. Although genome-wide association studies have linked >100 unique loci to its genetic basis, these only explain a fraction of disease heritability. Methods and Results To find additional gene drivers of coronary artery disease, we applied machine learning to quantitative evolutionary information on the impact of coding variants in whole exomes from the Myocardial Infarction Genetics Consortium. Using ensemble-based supervised learning, the Evolutionary Action-Machine Learning framework ranked each gene's ability to classify case and control samples and identified 79 significant associations. These were connected to known risk loci; enriched in cardiovascular processes like lipid metabolism, blood clotting, and inflammation; and enriched for cardiovascular phenotypes in knockout mouse models. Among them, INPP5F and MST1R are examples of potentially novel coronary artery disease risk genes that modulate immune signaling in response to cardiac stress. Conclusions We concluded that machine learning on the functional impact of coding variants, based on a massive amount of evolutionary information, has the power to suggest novel coronary artery disease risk genes for mechanistic and therapeutic discoveries in cardiovascular biology, and should also apply in other complex polygenic diseases.

A Novel 3-Gene Signature for Identifying COVID-19 Patients Based on Bioinformatics and Machine Learning

Although many biomarkers associated with coronavirus disease 2019 (COVID-19) were found, a novel signature relevant to immune cells has not been developed. In this work, the “CIBERSORT” algorithm was used to assess the fraction of immune infiltrating cells in GSE152641 and GSE171110. Key modules associated with important immune cells were selected by the “WGCNA” package. The “GO” enrichment analysis was used to reveal the biological function associated with COVID-19. The “Boruta” algorithm was used to screen candidate genes, and the “LASSO” algorithm was used for collinearity reduction. A novel gene signature was developed based on multivariate logistic regression analysis. Subsequently, M0 macrophages (PRAUC = 0.948 in GSE152641 and PRAUC = 0.981 in GSE171110) and neutrophils (PRAUC = 0.892 in GSE152641 and PRAUC = 0.960 in GSE171110) were considered as important immune cells. Forty-three intersected genes from two modules were selected, which mainly participated in some immune-related activities. Finally, a three-gene signature comprising CLEC4D, DUSP13, and UNC5A that can accurately distinguish COVID-19 patients and healthy controls in three datasets was constructed. The ROCAUC was 0.974 in the training set, 0.946 in the internal test set, and 0.709 in the external test set. In conclusion, we constructed a three-gene signature to identify COVID-19, and CLEC4D, DUSP13, and UNC5A may be potential biomarkers for COVID-19 patients.

MOB3A Bypasses BRAF and RAS Oncogene-Induced Senescence by Engaging the Hippo Pathway

Oncogenic activation of the RTK-RAS-RAF-MEK-ERK pathway occurs in approximately 25% of all human cancers, yet activated RAS, BRAF, or MEK expression in primary cells leads to a prolonged and predominantly irreversible cell-cycle arrest termed oncogene-induced senescence (OIS). OIS acts as an intrinsic tumor suppressor mechanism, serving as a barrier to tumor progression. Screening a library of activated kinases and kinase-regulatory proteins we identified MOB3A, a Mps-one binder coactivator (MOB) protein family member, whose constitutive expression permits proliferation and suppresses senescence in response to oncogenic RAS and BRAF signals. MOB3A is one of seven human MOB genes, which are highly conserved from yeast to human and that function to activate the Hippo pathway kinases (MST/LATS) or NDR kinases through direct association. Here we show that within the MOB family of genes MOB3A and C are unique in their ability to allow primary cell proliferation in the face of sustained oncogene signaling. Unlike the canonical MOB1A/B proteins, MOB3A inhibits Hippo/MST/LATS signaling and constitutive MOB3A membrane localization phenocopies OIS bypass seen with elevated YAP expression. Moreover, inhibition of MOB3 family member expression results in decreased proliferation and tumor growth of cancer cell lines. Together these data identify MOB3A's role in bypass of oncogene induced senescence and its role as a Hippo pathway inhibitor.

IMPLICATIONS

These results suggest that MOB3 targeting to re-engage the Hippo pathway, or direct targeting of YAP/TAZ, may be viable therapeutic strategies potential for RAS-pathway driven tumours.