In humans, the nc886 locus is a polymorphically imprinted metastable epiallele. Periconceptional conditions have an effect on the methylation status of nc886, and further, this methylation status is ...associated with health outcomes in later life, in line with the Developmental Origins of Health and Disease (DOHaD) hypothesis. Animal models would offer opportunities to study the associations between periconceptional conditions, nc886 methylation status and metabolic phenotypes further. Thus, we set out to investigate the methylation pattern of the nc886 locus in non-human mammals.
We obtained DNA methylation data from the data repository GEO for mammals, whose nc886 gene included all three major parts of nc886 and had sequency similarity of over 80% with the human nc886. Our final sample set consisted of DNA methylation data from humans, chimpanzees, bonobos, gorillas, orangutangs, baboons, macaques, vervets, marmosets and guinea pigs.
In human data sets the methylation pattern of nc886 locus followed the expected bimodal distribution, indicative of polymorphic imprinting. In great apes, we identified a unimodal DNA methylation pattern with 50% methylation level in all individuals and in all subspecies. In Old World monkeys, the between individual variation was greater and methylation on average was close to 60%. In guinea pigs the region around the nc886 homologue was non-methylated. Results obtained from the sequence comparison of the CTCF binding sites flanking the nc886 gene support the results on the DNA methylation data.
Our results indicate that unlike in humans, nc886 is not a polymorphically imprinted metastable epiallele in non-human primates or in guinea pigs, thus implying that animal models are not applicable for nc886 research. The obtained data suggests that the nc886 region may be classically imprinted in great apes, and potentially also in Old World monkeys, but not in guinea pigs.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Genetic architecture of plasma lipidome provides insights into regulation of lipid metabolism and related diseases. We applied an unsupervised machine learning method, PGMRA, to discover ...phenotype-genotype many-to-many relations between genotype and plasma lipidome (phenotype) in order to identify the genetic architecture of plasma lipidome profiled from 1,426 Finnish individuals aged 30-45 years. PGMRA involves biclustering genotype and lipidome data independently followed by their inter-domain integration based on hypergeometric tests of the number of shared individuals. Pathway enrichment analysis was performed on the SNP sets to identify their associated biological processes. We identified 93 statistically significant (hypergeometric p-value < 0.01) lipidome-genotype relations. Genotype biclusters in these 93 relations contained 5977 SNPs across 3164 genes. Twenty nine of the 93 relations contained genotype biclusters with more than 50% unique SNPs and participants, thus representing most distinct subgroups. We identified 30 significantly enriched biological processes among the SNPs involved in 21 of these 29 most distinct genotype-lipidome subgroups through which the identified genetic variants can influence and regulate plasma lipid related metabolism and profiles. This study identified 29 distinct genotype-lipidome subgroups in the studied Finnish population that may have distinct disease trajectories and therefore could be useful in precision medicine research.
Studies have shown that cardiovascular health (CVH) is related to depression. We aimed to identify gene networks jointly associated with depressive symptoms and cardiovascular health metrics using ...the whole blood transcriptome.
We analyzed human blood transcriptomic data to identify gene co-expression networks, termed gene modules, shared by Beck's depression inventory (BDI-II) scores and cardiovascular health (CVH) metrics as markers of depression and cardiovascular health, respectively. The BDI-II scores were derived from Beck's Depression Inventory, a 21-item self-report inventory that measures the characteristics and symptoms of depression. CVH metrics were defined according to the American Heart Association criteria using seven indices: smoking, diet, physical activity, body mass index (BMI), blood pressure, total cholesterol, and fasting glucose. Joint association of the modules, identified with weighted co-expression analysis, as well as the member genes of the modules with the markers of depression and CVH were tested with multivariate analysis of variance (MANOVA).
We identified a gene module with 256 genes that were significantly correlated with both the BDI-II score and CVH metrics. Based on the MANOVA test results adjusted for age and sex, the module was associated with both depression and CVH markers. The three most significant member genes in the module were
,
, and
. Genes in the module were enriched with biological pathways involved in brain diseases such as Alzheimer's, Parkinson's, and Huntington's.
The identified gene module and its members can provide new joint biomarkers for depression and CVH.
Osteoporosis and atherosclerosis are complex multifactorial diseases sharing common risk factors and pathophysiological mechanisms suggesting that these are comorbidities. Omics studies identifying ...joint molecular markers associated with these diseases are sparse.
Using liquid chromatography-tandem mass spectrometry, we quantified 437 molecular lipid species from the Young Finns Study cohort (aged 30–45 years and 57% women) and performed lipidome-wide multivariate analysis of variance (MANOVA) with early markers for both diseases. Carotid intima-media thickness for atherosclerosis measured with ultrasound and bone mineral density from distal radius and tibia for osteoporosis measured with peripheral quantitative computed tomography were used as early markers of the diseases.
MANOVA adjusted with age, sex and body mass index, identified eight statistically significant (adjusted p-value (padj) < 0.05) and 15 suggestively significant (padj < 0.25) molecular lipid species associated with the studied markers. Similar analysis adjusted additionally for smoking habit, physical activity and alcohol consumption identified four significant and six suggestively significant molecular lipid species. These most significant lipid classes/species jointly associated with the studied markers were glycerolipid/TAG(18:0/18:0/18:1), glycerophospholipid/PC(40:3), sphingolipid/Gb3(d18:1/22:0), and sphingolipid/Gb3(d18:1/24:0).
Our results support the osteoporosis-atherosclerosis comorbidity hypothesis and present potential new joint lipid biomarkers for these diseases.
•Lipidome-wide association study of early markers of osteoporosis and atherosclerosis•Four lipid species significantly associated with the early markers of both the diseases.•The results support the osteoporosis-atherosclerosis comorbidity hypothesis.
Investigation of the clinical potential of extensive phenotype data and machine learning (ML) in the prediction of mortality in acute coronary syndrome (ACS).
The value of ML and extensive clinical ...data was analyzed in a retrospective registry study of 9066 consecutive ACS patients (January 2007 to October 2017). Main outcome was six-month mortality. Prediction models were developed using two ML methods, logistic regression and extreme gradient boosting (xgboost). The models were fitted in training set of patients treated in 2007-2014 and 2017 (81%, n = 7344) and validated in a separate validation set of patients treated in 2015-2016 with full GRACE score data available for comparison of model accuracy (19%, n = 1722).
Overall, six-month mortality was 7.3% (n = 660). Several variables were found to be significantly associated with six-month mortality by both ML methods. The xgboost scored the best performance: AUC 0.890 (0.864-0.916). The AUC values for logistic regression and GRACE score were 0.867(0.837-0.897) and 0.822 (0.785-0.859), respectively. The AUC value of xgboost was better when compared to logistic regression (p = .012) and GRACE score (p < .00001).
The use of extensive phenotype data and novel machine learning improves prediction of mortality in ACS over traditional GRACE score.
KEY MESSAGES
The collection of extensive cardiovascular phenotype data from electronic health records as well as from data recorded by physicians can be used highly effectively in prediction of mortality after acute coronary syndrome.
Supervised machine learning methods such as logistic regression and extreme gradient boosting using extensive phenotype data significantly outperform conventional risk assessment by the current golden standard GRACE score.
Integration of electronic health records and the use of supervised machine learning methods can be easily applied in a single centre level to model the risk of mortality.
Smoking as a major risk factor for morbidity affects numerous regulatory systems of the human body including DNA methylation. Most of the previous studies with genome-wide methylation data are based ...on conventional association analysis and earliest threshold-based gene set analysis that lacks sensitivity to be able to reveal all the relevant effects of smoking. The aim of the present study was to investigate the impact of active smoking on DNA methylation at three biological levels: 5'-C-phosphate-G-3' (CpG) sites, genes and functionally related genes (gene sets). Gene set analysis was done with mGSZ, a modern threshold-free method previously developed by us that utilizes all the genes in the experiment and their differential methylation scores. Application of such method in DNA methylation study is novel. Epigenome-wide methylation levels were profiled from Young Finns Study (YFS) participants' whole blood from 2011 follow-up using Illumina Infinium HumanMethylation450 BeadChips. We identified three novel smoking related CpG sites and replicated 57 of the previously identified ones. We found that smoking is associated with hypomethylation in shore (genomic regions 0-2 kilobases from CpG island). We identified smoking related methylation changes in 13 gene sets with false discovery rate (FDR) ≤ 0.05, among which is olfactory receptor activity, the flagship novel finding of the present study. Overall, we extended the current knowledge by identifying: (i) three novel smoking related CpG sites, (ii) similar effects as aging on average methylation in shore, and (iii) a novel finding that olfactory receptor activity pathway responds to tobacco smoke and toxin exposure through epigenetic mechanisms.
Preterm birth (PTB) is associated with increased risk of type 2 diabetes and neurocognitive impairment later in life. We analyzed for the first time the associations of PTB with blood miRNA levels in ...adulthood. We also investigated the relationship of PTB associated miRNAs and adulthood phenotypes previously linked with premature birth. Blood MicroRNA profiling, genome-wide gene expression analysis, computer-based cognitive testing battery (CANTAB) and serum NMR metabolomics were performed for Young Finns Study subjects (aged 34-49 years, full-term n = 682, preterm n = 84). Preterm birth (vs. full-term) was associated with adulthood levels of hsa-miR-29b-3p in a fully adjusted regression model (p = 1.90 × 10
, FDR = 0.046). The levels of hsa-miR-29b-3p were down-regulated in subjects with PTB with appropriate birthweight for gestational age (p = 0.002, fold change FC = - 1.20) and specifically in PTB subjects with small birthweight for gestational age (p = 0.095, FC = - 1.39) in comparison to individuals born full term. Hsa-miR-29b-3p levels correlated with the expressions of its target-mRNAs BCL11A and CS and the gene set analysis results indicated a target-mRNA driven association between hsa-miR-29b-3p levels and Alzheimer's disease, Parkinson's disease, Insulin signaling and Regulation of Actin Cytoskeleton pathway expression. The level of hsa-miR-29b-3p was directly associated with visual processing and sustained attention in CANTAB test and inversely associated with serum levels of VLDL subclass component and triglyceride levels. In conlcusion, adult blood levels of hsa-miR-29b-3p were lower in subjects born preterm. Hsa-miR-29b-3p associated with cognitive function and may be linked with adulthood morbidities in subjects born preterm, possibly through regulation of gene sets related to neurodegenerative diseases and insulin signaling as well as VLDL and triglyceride metabolism.
We analyzed the associations between whole blood microRNA profiles and the indices of glucose metabolism and impaired fasting glucose and examined whether the discovered microRNAs correlate with the ...expression of their mRNA targets. MicroRNA and gene expression profiling were performed for the Young Finns Study participants (n = 871). Glucose, insulin, and glycated hemoglobin (HbA1c) levels were measured, the insulin resistance index (HOMA2-IR) was calculated, and the glycemic status (normoglycemic n = 534/impaired fasting glucose IFG n = 252/type 2 diabetes T2D n = 24) determined. Levels of hsa-miR-144-5p, -122-5p, -148a-3p, -589-5p, and hsa-let-7a-5p associated with glycemic status. hsa-miR-144-5p and -148a-3p associated with glucose levels, while hsa-miR-144-5p, -122-5p, -184, and -339-3p associated with insulin levels and HOMA2-IR, and hsa-miR-148a-3p, -15b-3p, -93-3p, -146b-5p, -221-3p, -18a-3p, -642a-5p, and -181-2-3p associated with HbA1c levels. The targets of hsa-miR-146b-5p that correlated with its levels were enriched in inflammatory pathways, and the targets of hsa-miR-221-3p were enriched in insulin signaling and T2D pathways. These pathways showed indications of co-regulation by HbA1c-associated miRNAs. There were significant differences in the microRNA profiles associated with glucose, insulin, or HOMA-IR compared to those associated with HbA1c. The HbA1c-associated miRNAs also correlated with the expression of target mRNAs in pathways important to the development of T2D.
Studies have shown that osteoporosis and atherosclerosis are comorbid conditions sharing common risk factors and pathophysiological mechanisms. Understanding these is crucial in order to develop ...shared methods for risk stratification, prevention, diagnosis and treatment. The aim of this study was to apply a system-level bioinformatics approach to lipidome-wide data in order to pinpoint the lipidomic architecture jointly associated with surrogate markers of these complex comorbid diseases.
The study was based on the Cardiovascular Risk in Young Finns Study cohort from the 2007 follow-up (n = 1494, aged 30–45 years, women: 57%). Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to analyse the serum lipidome, involving 437 molecular lipid species. The subclinical osteoporotic markers included indices of bone mineral density and content, measured using peripheral quantitative computer tomography from the distal and shaft sites of both the tibia and the radius. The subclinical atherosclerotic markers included carotid and bulbus intima media thickness measured with high-resolution ultrasound. Weighted co-expression network analysis was performed to identify networks of densely interconnected lipid species (i.e. lipid modules) associated with subclinical markers of both osteoporosis and atherosclerosis. The levels of lipid species (lipid profiles) of each of the lipid modules were summarized by the first principal component termed as module eigenlipid. Then, Pearson's correlation (r) was calculated between the module eigenlipids and the markers. Lipid modules that were significantly and jointly correlated with subclinical markers of both osteoporosis and atherosclerosis were considered to be related to the comorbidities. The hypothesis that the eigenlipids and profiles of the constituent lipid species in the modules have joint effects on the markers was tested with multivariate analysis of variance (MANOVA).
Among twelve studied molecular lipid modules, we identified one module with 105 lipid species significantly and jointly associated with both subclinical markers of both osteoporosis (r = 0.24, p-value = 2 × 10−20) and atherosclerosis (r = 0.16, p-value = 2 × 10−10). The majority of the lipid species in this module belonged to the glycerolipid (n = 60), glycerophospholipid (n = 13) and sphingolipid (n = 29) classes. The module was also enriched with ceramides (n = 20), confirming their significance in cardiovascular outcomes and suggesting their joint role in the comorbidities. The top three of the 37 statistically significant (adjusted p-value < 0.05) lipid species jointly associated with subclinical markers of both osteoporosis and atherosclerosis within the module were all triacylglycerols (TAGs) – TAG(18:0/18:0/18:1) with an adjusted p-value of 8.6 × 10−8, TAG(18:0/18:1/18:1) with an adjusted p-value of 3.7 × 10−6, and TAG(16:0/18:0/18:1) with an adjusted p-value of 8.5 × 10−6.
This study identified a novel lipid module associated with both surrogate markers of both subclinical osteoporosis and subclinical atherosclerosis. Alterations in the metabolism of the identified lipid module and, more specifically, the TAG related molecular lipids within the module may provide potential new biomarkers for testing the comorbidities, opening avenues for the emergence of dual-purpose prevention measures.
•Molecular lipid modules shared by subclinical markers of osteoporosis and atherosclerosis were investigated from lipidome.•A lipid module associated with subclinical markers of both osteoporosis and atherosclerosis was identified.•Multivariate analysis of variance of the module revealed 37 constituent lipid species to be jointly associated with the studied markers.•The top three of the 37 jointly associated lipid species were all triacylglycerols.
Different observations have suggested that patients with depression have a higher risk for a number of comorbidities and mortality. The underlying causes have not been fully understood yet.
The aim ...of our study was to investigate the association of a genetic depression risk score (GDRS) with mortality all-cause and cardiovascular (CV) and markers of depression (including intake of antidepressants and a history of depression) in the Ludwigshafen Risk and Cardiovascular Health (LURIC) study involving 3,316 patients who had been referred for coronary angiography.
The GDRS was calculated in 3,061 LURIC participants according to a previously published method and was found to be associated with all-cause (
= 0.016) and CV mortality (
= 0.0023). In Cox regression models adjusted for age, sex, body mass index, LDL-cholesterol, HDL-cholesterol, triglycerides, hypertension, smoking, and diabetes mellitus, the GDRS remained significantly associated with all-cause 1.18 (1.04-1.34,
= 0.013) and CV 1.31 (1.11-1.55,
= 0.001) mortality. The GDRS was not associated with the intake of antidepressants or a history of depression. However, this cohort of CV patients had not specifically been assessed for depression, leading to marked underreporting. We were unable to identify any specific biomarkers correlated with the GDRS in LURIC participants.
A genetic predisposition for depression estimated by a GDRS was independently associated with all-cause and CV mortality in our cohort of patients who had been referred for coronary angiography. No biomarker correlating with the GDRS could be identified.
PDF
