Integrating Genes Affecting Coronary Artery Disease in Functional Networks by Multi-OMICs Approach

Coronary artery disease (CAD) and myocardial infarction (MI) remain among the leading causes of mortality worldwide, urgently demanding a better understanding of disease etiology, and more efficient therapeutic strategies. Genetic predisposition as well as the environment and lifestyle are thought to contribute to disease risk. It is likely that non-linear and complex interactions occur between these multiple factors, involving simultaneous pathological changes in diverse cell types, tissues, and organs, at multiple molecular levels. Recent technological advances have exponentially expanded the breadth of available -omics data, from genome, epigenome, transcriptome, proteome, metabolome to even the microbiome. Integration of multiple layers of information across several -omics domains, i.e., the so-called multi-omics approach, currently holds the promise as a path toward precision medicine. Indeed, a more meaningful interpretation of genotype-phenotype relationships and the development of successful therapeutics tailored to individual patients are urgently needed. In this review, we will summarize recent findings and applications of integrative multi-omics in elucidating the etiology of CAD/MI; with a special focus on established disease susceptibility loci sequentially identified in genome-wide association studies (GWAS) over the last 10 years. Moreover, in addition to the autosomal genome, we will also consider the genetic variation in our “second genome”—the mitochondrial genome. Finally, we will summarize the current challenges in the field and point to future research directions required in order to successfully and effectively apply these approaches for precision medicine

Examining the Association between Mitochondrial Genome Variation and Coronary Artery Disease

Funding Information: Funding: This research was funded by the Latvian Council of Science, project, “Using Machine Learning To Model The Complex Interplay Between Diet, Genetic Factors and Mitochondria in Coronary Artery Disease”, project No. lzp-2020/2-0338. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Large-scale genome-wide association studies have identified hundreds of single-nucleotide variants (SNVs) significantly associated with coronary artery disease (CAD). However, collectively, these explain T (control region) and m.12612A>G (ND5), found more frequently in cases (OR = 1.05), potentially related to reduced cardiorespiratory fitness in response to exercise, as well as for m.12372G>A (ND5) and m.11467A>G (ND4), present more frequently in controls (OR = 0.97), previously associated with lower ROS production rate. In SOFT cases, four MT-SNVs survived multiple testing corrections (at FDR G (ND4) and m.15452C>A (CYB) have previously shown significant associations with body height. In line with this, we observed that CAD cases were slightly less physically active, and their average body height was ~2.00 cm lower compared to controls; both traits are known to be related to increased CAD risk. Gene-based tests identified CO2 associated with HARD/SOFT CAD, whereas ND3 and CYB associated with SOFT cases (p < 0.05), dysfunction of which has been related to MT oxidative stress, obesity/T2D (CO2), BMI (ND3), and angina/exercise intolerance (CYB). Finally, we observed that macro-haplogroup I was significantly (p < 0.05) more frequent in HARD cases vs. controls (3.35% vs. 3.08%), potentially associated with response to exercise. Conclusions: We found only spurious associations between MT genome variation and HARD/SOFT CAD and conclude that more MT-SNV data in even larger study cohorts may be needed to conclusively determine the role of MT DNA in CAD.publishersversionPeer reviewe

The impact of genome-wide association studies on the pathophysiology and therapy of cardiovascular disease

Funding Information: This work was supported by grants from the Fondation Leducq (CADgenomics: Understanding CAD Genes, 12CVD02), the German Federal Ministry of Education and Research (BMBF) within the framework of the e: Med research and funding concept (e:AtheroSysMed, grant 01ZX1313A-2014) and the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement no. HEALTH-F2-2013-601456 (CVgenes-at-target). Further grants were received from the DFG as part of the Sonderforschungsbereich CRC 1123 (B2). T.K. was supported by a DZHK Rotation Grant. Figures and contain modified image material available at Servier Medical Art under a Creative Commons Attribution 3.0 Unported License. Publisher Copyright: © 2016 The Authors. Published under the terms of the CC BY 4.0 license Copyright: Copyright 2016 Elsevier B.V., All rights reserved.Cardiovascular diseases are leading causes for death worldwide. Genetic disposition jointly with traditional risk factors precipitates their manifestation. Whereas the implications of a positive family history for individual risk have been known for a long time, only in the past few years have genome-wide association studies (GWAS) shed light on the underlying genetic variations. Here, we review these studies designed to increase our understanding of the pathophysiology of cardiovascular diseases, particularly coronary artery disease and myocardial infarction. We focus on the newly established pathways to exemplify the translation from the identification of risk-related genetic variants to new preventive and therapeutic strategies for cardiovascular disease.publishersversionPeer reviewe

Long-Term Immunological Memory of SARS-CoV-2 Is Present in Patients with Primary Antibody Deficiencies for up to a Year after Vaccination

Funding Information: This research was funded by the Latvian Council of Science project Nr.lzp-2020/1–0269. Publisher Copyright: © 2023 by the authors.Some studies have found increased coronavirus disease-19 (COVID-19)-related morbidity and mortality in patients with primary antibody deficiencies. Immunization against COVID-19 may, therefore, be particularly important in these patients. However, the durability of the immune response remains unclear in such patients. In this study, we evaluated the cellular and humoral response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigens in a cross-sectional study of 32 patients with primary antibody deficiency (n = 17 with common variable immunodeficiency (CVID) and n = 15 with selective IgA deficiency) and 15 healthy controls. Serological and cellular responses were determined using enzyme-linked immunosorbent assay and interferon-gamma release assays. The subsets of B and T lymphocytes were measured using flow cytometry. Of the 32 patients, 28 had completed the vaccination regimen with a median time after vaccination of 173 days (IQR = 142): 27 patients showed a positive spike-peptide-specific antibody response, and 26 patients showed a positive spike-peptide-specific T-cell response. The median level of antibody response in CVID patients (5.47 ratio (IQR = 4.08)) was lower compared to healthy controls (9.43 ratio (IQR = 2.13)). No difference in anti-spike T-cell response was found between the groups. The results of this study indicate that markers of the sustained SARS-CoV-2 spike-specific immune response are detectable several months after vaccination in patients with primary antibody deficiencies comparable to controls.publishersversionpublishersversionPeer reviewe

Could Artificial Intelligence/Machine Learning and Inclusion of Diet-Gut Microbiome Interactions Improve Disease Risk Prediction? Case Study : Coronary Artery Disease

Funding Information: This research was funded by the Latvian Council of Science within the project Gut microbiome composition and diversity among health and lifestyle induced dietary regimen, project No. lzp-2018/2-0266. Publisher Copyright: Copyright © 2022 Vilne, Ķibilds, Siksna, Lazda, Valciņa and Krūmiņa.Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and the main leading cause of morbidity and mortality worldwide, posing a huge socio-economic burden to the society and health systems. Therefore, timely and precise identification of people at high risk of CAD is urgently required. Most current CAD risk prediction approaches are based on a small number of traditional risk factors (age, sex, diabetes, LDL and HDL cholesterol, smoking, systolic blood pressure) and are incompletely predictive across all patient groups, as CAD is a multi-factorial disease with complex etiology, considered to be driven by both genetic, as well as numerous environmental/lifestyle factors. Diet is one of the modifiable factors for improving lifestyle and disease prevention. However, the current rise in obesity, type 2 diabetes (T2D) and CVD/CAD indicates that the “one-size-fits-all” approach may not be efficient, due to significant variation in inter-individual responses. Recently, the gut microbiome has emerged as a potential and previously under-explored contributor to these variations. Hence, efficient integration of dietary and gut microbiome information alongside with genetic variations and clinical data holds a great promise to improve CAD risk prediction. Nevertheless, the highly complex nature of meals combined with the huge inter-individual variability of the gut microbiome poses several Big Data analytics challenges in modeling diet-gut microbiota interactions and integrating these within CAD risk prediction approaches for the development of personalized decision support systems (DSS). In this regard, the recent re-emergence of Artificial Intelligence (AI) / Machine Learning (ML) is opening intriguing perspectives, as these approaches are able to capture large and complex matrices of data, incorporating their interactions and identifying both linear and non-linear relationships. In this Mini-Review, we consider (1) the most used AI/ML approaches and their different use cases for CAD risk prediction (2) modeling of the content, choice and impact of dietary factors on CAD risk; (3) classification of individuals by their gut microbiome composition into CAD cases vs. controls and (4) modeling of the diet-gut microbiome interactions and their impact on CAD risk. Finally, we provide an outlook for putting it all together for improved CAD risk predictions.publishersversionPeer reviewe

Polygenic Risk Score Predicts Modified Risk in BRCA1 Pathogenic Variant c.4035del and c.5266dup Carriers in Breast Cancer Patients

Funding Information: This research has been developed with financing from the European Social Fund and Latvian state budget within the project no. 8.2.2.0/20/I/004 “Support for involving doctoral students in scientific research and studies” at Rīga Stradiņš University. Publisher Copyright: © 2023 by the authors.The aim of this study was to assess the power of the polygenic risk score (PRS) in estimating the overall genetic risk of women carrying germline BRCA1 pathogenic variants (PVs) c.4035del or c.5266dup to develop breast (BC) or ovarian cancer (OC) due to additional genetic variations. In this study, PRSs previously developed from two joint models using summary statistics of age-at-onset (BayesW model) and case–control data (BayesRR-RC model) from a genome-wide association analysis (GWAS) were applied to 406 germline BRCA1 PV (c.4035del or c.5266dup) carriers affected by BC or OC, compared with unaffected individuals. A binomial logistic regression model was used to assess the association of PRS with BC or OC development risk. We observed that the best-fitting BayesW PRS model effectively predicted the individual’s BC risk (OR = 1.37; 95% CI = 1.03–1.81, p = 0.02905 with AUC = 0.759). However, none of the applied PRS models was a good predictor of OC risk. The best-fitted PRS model (BayesW) contributed to assessing the risk of developing BC for germline BRCA1 PV (c.4035del or c.5266dup) carriers and may facilitate more precise and timely patient stratification and decision-making to improve the current BC treatment or even prevention strategies.Peer reviewe

Dental Research Data Availability and Quality According to the FAIR Principles

Funding Information: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by MikroTik-RSU to S.E. Uribe (toward implementing the RSU data repository and the FAIR data management principles). S.E. Uribe also acknowledges financial support from the European Union’s Horizon 2020 Research and Innovation Programme (grant 857287). Publisher Copyright: © International Association for Dental Research and American Association for Dental, Oral, and Craniofacial Research 2022.According to the FAIR principles, data produced by scientific research should be findable, accessible, interoperable, and reusable—for instance, to be used in machine learning algorithms. However, to date, there is no estimate of the quantity or quality of dental research data evaluated via the FAIR principles. We aimed to determine the availability of open data in dental research and to assess compliance with the FAIR principles (or FAIRness) of shared dental research data. We downloaded all available articles published in PubMed-indexed dental journals from 2016 to 2021 as open access from Europe PubMed Central. In addition, we took a random sample of 500 dental articles that were not open access through Europe PubMed Central. We assessed data sharing in the articles and compliance of shared data to the FAIR principles programmatically. Results showed that of 7,509 investigated articles, 112 (1.5%) shared data. The average (SD) level of compliance with the FAIR metrics was 32.6% (31.9%). The average for each metric was as follows: findability, 3.4 (2.7) of 7; accessibility, 1.0 (1.0) of 3; interoperability, 1.1 (1.2) of 4; and reusability, 2.4 (2.6) of 10. No considerable changes in data sharing or quality of shared data occurred over the years. Our findings indicated that dental researchers rarely shared data, and when they did share, the FAIR quality was suboptimal. Machine learning algorithms could understand 1% of available dental research data. These undermine the reproducibility of dental research and hinder gaining the knowledge that can be gleaned from machine learning algorithms and applications.publishersversionPeer reviewe

Genetic Basis of Early Onset Atrial Fibrillation in Patients without Risk Factors

Funding Information: This research was funded by the Latvian Council of Science, project, “The role of clonal hemato-poiesis of indeterminate potential as a potential driver of cardiovascular diseases and its associ-ation with clinical outcome”, project No. lzp-2021/1-0293. Publisher Copyright: © 2023 by the authors.Background: Atrial fibrillation (AF) is the most common arrhythmia and typically occurs in elderly patients with other cardiovascular and extracardiac diseases. However, up to 15% of AF develops without any related risk factors. Recently, the role of genetic factors has been highlighted in this particular form of AF. Aims: The aims of this study were to determine the prevalence of pathogenic variants in early-onset AF in patients without known disease-related risk factors and to identify any structural cardiac abnormalities in these patients. Materials and Methods: We conducted exome sequencing and interpretation in 54 risk factor-free early-onset AF patients and further validated our findings in a similar AF patient cohort from the UK Biobank. Results: Pathogenic/likely pathogenic variants were found in 13/54 (24%) patients. The variants were identified in cardiomyopathy-related and not arrhythmia-related genes. The majority of the identified variants were TTN gene truncating variants (TTNtvs) (9/13 (69%) patients). We also observed two TTNtvs founder variants in the analysed population—c.13696C>T p.(Gln4566Ter) and c.82240C>T p.(Arg27414Ter). Pathogenic/likely pathogenic variants were found in 9/107 (8%) individuals from an independent similar AF patient cohort from the UK Biobank. In correspondence with our Latvian patients, only variants in cardiomyopathy-associated genes were identified. In five (38%) of the thirteen Latvian patients with pathogenic/likely pathogenic variants, dilation of one or both ventricles was identified on a follow-up cardiac magnetic resonance scan. Conclusions: We observed a high prevalence of pathogenic/likely pathogenic variants in cardiomyopathy-associated genes in patients with risk factor-free early-onset AF. Moreover, our follow-up imaging data indicate that these types of patients are at risk of developing ventricular dilation. Furthermore, we identified two TTNtvs founder variants in our Latvian study population.publishersversionPeer reviewe

Serum microRNA-1233 is a specific biomarker for diagnosing acute pulmonary embolism

Publisher Copyright: © 2016 The Author(s). Copyright: Copyright 2017 Elsevier B.V., All rights reserved.Background: Circulating microRNAs (miRNAs) emerge as novel biomarkers in cardiovascular diseases. Diagnosing acute pulmonary embolism (PE) remains challenging due to a diverse clinical presentation and the lack of specific biomarkers. Here we evaluate serum miRNAs as potential biomarkers in acute PE. Methods: We enrolled 30 patients with acute, CT (computed tomography)-angiographically confirmed central PE and collected serum samples on the day of emergency room admission (1st day) and from 22 of these patients 9 months thereafter. For comparison, we examined serum samples from patients with acute non ST-segment elevation myocardial infarction (NSTEMI, n = 30) and healthy individuals (n = 12). Results: We randomly selected 16 out of 30 PE patients and screened sera from the acute (1st day) and chronic stages (9 months) for 754 miRNAs using microarrays and found 37 miRNAs to be differentially regulated. Across all miRNAs, miRNA-1233 displayed the highest fold change (FC) from acute to chronic stage (log2FC 11.5, p < 0.004). We validated miRNA-1233 by real-time quantitative polymerase chain reaction (RT-qPCR). In acute PE (1st day) we found elevated levels of miRNA-1233 in comparison to NSTEMI (log2FC 5.7, p < 0.0001) and healthy controls (log2FC 7.7, p < 0.0001). miRNA-1233 differentiated acute PE from NSTEMI patients and healthy individuals with 90 and 90 % sensitivity, and 100 and 92 % specificity [area under the curve (AUC) 0.95, p < 0.001 and 0.91, p < 0.001], respectively. Conclusions: This is the first report that identifies a miRNA that allows distinguishing acute PE from acute NSTEMI and healthy individuals with high specificity and sensitivity.publishersversionPeer reviewe

Multiomics tools for improved atherosclerotic cardiovascular disease management

Multiomics studies offer accurate preventive and therapeutic strategies for atherosclerotic cardiovascular disease (ASCVD) beyond traditional risk factors. By using artificial intelligence (AI) and machine learning (ML) approaches, it is possible to integrate multiple ‘omics and clinical data sets into tools that can be utilized for the development of personalized diagnostic and therapeutic approaches. However, currently multiple challenges in data quality, integration, and privacy still need to be addressed. In this opinion, we emphasize that joined efforts, exemplified by the AtheroNET COST Action, have a pivotal role in overcoming the challenges to advance multiomics approaches in ASCVD research, with the aim to foster more precise and effective patient care