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.
A comprehensive, detailed review on where we stand on GWAS and cardiovascular disease and what to expect in the near future.
The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore ...host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.
As clinicians, we understand the development of atherosclerosis as a consequence of cholesterol deposition and inflammation in the arterial wall, both being triggered by traditional risk factors such ...as hypertension, hyperlipidaemia or diabetes mellitus. Another risk factor is genetic predisposition, as indicated by the predictive value of a positive family history. However, we had to wait until recently to appreciate the abundant contribution of genetic variation to the manifestation of atherosclerosis. Indeed, by now 164 chromosomal loci have been identified by genome-wide association studies (GWAS) to affect the risk of coronary artery disease. By design, practically all risk variants discovered by GWAS are frequently found in our population, resulting in the fact that principally every Western European individual carries between 130 and 190 risk alleles at the known, genome-wide significant loci (there are 0, 1, or 2 risk alleles per locus). One can assume that it is this widespread disposition that makes mankind susceptible to the detrimental effects of lifestyle factors, which likewise increase the risk of atherosclerosis. In this review, we summarize the recent genetic discoveries and attempt to group the multiple genetic risk variants in functional groups that may become actionable from a preventive or therapeutic perspective.
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.
Foodborne diseases (FBDs) are infections of the gastrointestinal tract caused by foodborne pathogens (FBPs) such as bacteria
and Shiga toxin-producing
(STEC) and several viruses, but also parasites ...and some fungi. Artificial intelligence (AI) and its sub-discipline machine learning (ML) are re-emerging and gaining an ever increasing popularity in the scientific community and industry, and could lead to actionable knowledge in diverse ranges of sectors including epidemiological investigations of FBD outbreaks and antimicrobial resistance (AMR). As genotyping using whole-genome sequencing (WGS) is becoming more accessible and affordable, it is increasingly used as a routine tool for the detection of pathogens, and has the potential to differentiate between outbreak strains that are closely related, identify virulence/resistance genes and provide improved understanding of transmission events within hours to days. In most cases, the computational pipeline of WGS data analysis can be divided into four (though, not necessarily consecutive) major steps:
genome assembly, genome characterization, comparative genomics, and inference of phylogeny or phylogenomics. In each step, ML could be used to increase the speed and potentially the accuracy (provided increasing amounts of high-quality input data) of identification of the source of ongoing outbreaks, leading to more efficient treatment and prevention of additional cases. In this review, we explore whether ML or any other form of AI algorithms have already been proposed for the respective tasks and compare those with mechanistic model-based approaches.
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 <20% of the heritability. Hypothesis: Here, we hypothesize that mitochondrial (MT)-SNVs might present one potential source of this “missing heritability”. Methods: We analyzed 265 MT-SNVs in ~500,000 UK Biobank individuals, exploring two different CAD definitions: a more stringent (myocardial infarction and/or revascularization; HARD = 20,405), and a more inclusive (angina and chronic ischemic heart disease; SOFT = 34,782). Results: In HARD cases, the most significant (p < 0.05) associations were for m.295C>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 < 5%), all potentially conferring increased CAD risk. Of those, m.11251A>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.
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.
Mitochondrial damage and augmented production of reactive oxygen species (ROS) may represent an intermediate step by which hypercholesterolemia exacerbates atherosclerotic lesion formation.
To test ...this hypothesis, in mice with severe but genetically reversible hypercholesterolemia (i.e. the so called Reversa mouse model), we performed time-resolved analyses of mitochondrial transcriptome in the aortic arch employing a systems-level network approach.
During hypercholesterolemia, we observed a massive down-regulation (>28%) of mitochondrial genes, specifically at the time of rapid atherosclerotic lesion expansion and foam cell formation, i.e. between 30 and 40 weeks of age. Both phenomena - down-regulation of mitochondrial genes and lesion expansion - were largely reversible by genetically lowering plasma cholesterol (by >80%, from 427 to 54 ± 31 mg/L) at 30 weeks. Co-expression network analysis revealed that both mitochondrial signature genes were highly connected in two modules, negatively correlating with lesion size and supported as causal for coronary artery disease (CAD) in humans, as expression-associated single nucleotide polymorphisms (eSNPs) representing their genes overlapped markedly with established disease risk loci. Within these modules, we identified the transcription factor estrogen related receptor (ERR)-α and its co-factors PGC1-α and -β, i.e. two members of the peroxisome proliferator-activated receptor γ co-activator 1 family of transcription regulators, as key regulatory genes. Together, these factors are known as major orchestrators of mitochondrial biogenesis and antioxidant responses.
Using a network approach, we demonstrate how hypercholesterolemia could hamper mitochondrial activity during atherosclerosis progression and pinpoint potential therapeutic targets to counteract these processes.
•Down-regulation of mitochondrial genes during hypercholesterolemia and lesion expansion.•Largely reversible by genetically switching mice to normocholesterolemia.•Mitochondrial genes highly connected in two modules, causal for coronary artery disease in humans.•ERR-alpha/PGC-1 module key regulators orchestrate mitochondrial biogenesis and antioxidant responses.
Genome-wide association studies have to date identified 159 significant and suggestive loci for coronary artery disease (CAD). We now report comprehensive bioinformatics analyses of sequence ...variation in these loci to predict candidate causal genes.
All annotated genes in the loci were evaluated with respect to protein-coding single-nucleotide polymorphism and gene expression parameters. The latter included expression quantitative trait loci, tissue specificity, and miRNA binding. High priority candidate genes were further identified based on literature searches and our experimental data. We conclude that the great majority of causal variations affecting CAD risk occur in noncoding regions, with 41% affecting gene expression robustly versus 6% leading to amino acid changes. Many of these genes differed from the traditionally annotated genes, which was usually based on proximity to the lead single-nucleotide polymorphism. Indeed, we obtained evidence that genetic variants at CAD loci affect 98 genes which had not been linked to CAD previously.
Our results substantially revise the list of likely candidates for CAD and suggest that genome-wide association studies efforts in other diseases may benefit from similar bioinformatics analyses.
Remodeling of the bone marrow microenvironment in chronic inflammation and in aging reduces hematopoietic stem cell (HSC) function. To assess the mechanisms of this functional decline of HSC and find ...strategies to counteract it, we established a model in which the Sfrp1 gene was deleted in Osterix+ osteolineage cells (OS1Δ/Δ mice). HSC from these mice showed severely diminished repopulating activity with associated DNA damage, enriched expression of the reactive oxygen species pathway and reduced single-cell proliferation. Interestingly, not only was the protein level of Catenin beta-1 (bcatenin) elevated, but so was its association with the phosphorylated co-activator p300 in the nucleus. Since these two proteins play a key role in promotion of differentiation and senescence, we inhibited in vivo phosphorylation of p300 through PP2A-PR72/130 by administration of IQ-1 in OS1Δ/Δ mice. This treatment not only reduced the b-catenin/phosphop300 association, but also decreased nuclear p300. More importantly, in vivo IQ-1 treatment fully restored HSC repopulating activity of the OS1Δ/Δ mice. Our findings show that the osteoprogenitor Sfrp1 is essential for maintaining HSC function. Furthermore, pharmacological downregulation of the nuclear b-catenin/phospho-p300 association is a new strategy to restore poor HSC function.
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