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Background: Mutations in genes encoding lipoprotein lipase (LPL) or its regulators can cause severe hypertriglyceridemia (HTG). Thus far, the effect of genetic HTG on the lipid profile has been mainly determined via conventional techniques. Objective: To show detailed differences in the (apo)lipoprotein profile of patients with genetic HTG by combining LC-MS and NMR techniques. Methods: Fasted serum from 7 patients with genetic HTG and 10 normolipidemic controls was used to measure the concentration of a spectrum of apolipoproteins by LC-MS, and to estimate the concentration and size of lipoprotein subclasses and class-specific lipid composition using NMR spectroscopy. Results: Patients with genetic HTG compared to normolipidemic controls had higher levels of apoB48 (fold change [FC] 11.3, P<0.001), apoC-I (FC 1.5, P<0.001), apoC-II (FC 4.3, P=0.007), apoC-III (FC 3.4, P<0.001), and apoE (FC 4.3, P<0.001), without altered apoB100. In addition, patients with genetic HTG had higher concentrations of TG-rich lipoproteins (i.e., chylomicrons and very low-density lipoproteins [VLDL]; FC 3.0, P<0.001), but lower LDL (FC 0.4, P=0.001), of which medium and small-sized LDL particles appeared even absent. While the correlation coefficient between NMR and enzymatic analysis in normolipidemic controls was high, it was considerably reduced in patients with genetic HTG. Conclusion: The lipoprotein profile of patients with genetic HTG is predominated with large lipoproteins (i.e., chylomicrons, VLDL), explaining high levels of apoC-I, apoC-II, apoC-III and apoE, whereas small atherogenic LDL particles are absent. The presence of chylomicrons in patients with HTG weakens the accuracy of the NMR-based model as it was designed for normolipidemic fasted individuals.

In recent years, preclinical studies have illustrated the potential role of intestinal bacterial composition in the risk of stroke and post-stroke infections. The results of these studies suggest that bacteria capable of producing volatile metabolites, including trimethylamine-N-oxide (TMAO) and butyrate, play opposing, yet important roles in the cascade of events leading to stroke. However, no large-scale studies have been undertaken to determine the abundance of these bacterial communities in stroke patients and to assess the impact of disrupted compositions of the intestinal microbiota on patient outcomes. In this prospective case–control study, rectal swabs from 349 ischemic and hemorrhagic stroke patients (median age, 71 years; IQR: 67–75) were collected within 24 h of hospital admission. Samples were subjected to 16S rRNA amplicon sequencing and subsequently compared with samples obtained from 51 outpatient age- and sex-matched controls (median age, 72 years; IQR, 62–80) with similar cardiovascular risk profiles but without active signs of stroke. Plasma protein biomarkers were analyzed using a combination of nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography–mass spectrometry (LC-MS). Alpha and beta diversity analyses revealed higher disruption of intestinal communities during ischemic and hemorrhagic stroke compared with non-stroke matched control subjects. Additionally, we observed an enrichment of bacteria implicated in TMAO production and a loss of butyrate-producing bacteria. Stroke patients displayed two-fold lower plasma levels of TMAO than controls (median 1.97 vs 4.03 μM, Wilcoxon p < 0.0001). Finally, lower abundance of butyrate-producing bacteria within 24 h of hospital admission was an independent predictor of enhanced risk of post-stroke infection (odds ratio 0.77, p = 0.005), but not of mortality or functional patient outcome. In conclusion, aberrations in trimethylamine- and butyrate-producing gut bacteria are associated

Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus. This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the "big picture" of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years. A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic pro

Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus. This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the "big picture" of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years. A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic pro

Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus. This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the 'big picture' of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years. A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic proces

Metabolomics examines the small molecules involved in cellular metabolism. Approximately 50% of total phenotypic differences in metabolite levels is due to genetic variance, but heritability estimates differ across metabolite classes. We perform a review of all genome-wide association and (exome-) sequencing studies published between November 2008 and October 2018, and identify >800 class-specific metabolite loci associated with metabolite levels. In a twin-family cohort (N = 5117), these metabolite loci are leveraged to simultaneously estimate total heritability (h2 total), and the proportion of heritability captured by known metabolite loci (h2 Metabolite-hits) for 309 lipids and 52 organic acids. Our study reveals significant differences in h2 Metabolite-hits among different classes of lipids and organic acids. Furthermore, phosphatidylcholines with a high degree of unsaturation have higher h2 Metabolite-hits estimates than phosphatidylcholines with low degrees of unsaturation. This study highlights the importance of common genetic variants for metabolite levels, and elucidates the genetic architecture of metabolite classes.

The original version of the Supplementary Information associated with this Article included an incorrect Supplementary Data 1 file, in which additional delimiters were included in the first column for a number of rows, resulting in column shifts for some of these rows. The HTML has been updated to include a corrected version of Supplementary Data 1; the original incorrect version of Supplementary Data 1 can be found as Supplementary Information associated with this Correction. In addition, the original version of this Article contained an error in the author affiliations. An affiliation of Abdel Abdellaoui with Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands was inadvertently omitted. This has now been corrected in both the PDF and HTML versions of the Article.

Predicting longer-term mortality risk requires collection of clinical data, which is often cumbersome. Therefore, we use a well-standardized metabolomics platform to identify metabolic predictors of long-term mortality in the circulation of 44,168 individuals (age at baseline 18–109), of whom 5512 died during follow-up. We apply a stepwise (forward-backward) procedure based on meta-analysis results and identify 14 circulating biomarkers independently associating with all-cause mortality. Overall, these associations are similar in men and women and across different age strata. We subsequently show that the prediction accuracy of 5- and 10-year mortality based on a model containing the identified biomarkers and sex (C-statistic = 0.837 and 0.830, respectively) is better than that of a model containing conventional risk factors for mortality (C-statistic = 0.772 and 0.790, respectively). The use of the identified metabolic profile as a predictor of mortality or surrogate endpoint in clinical studies needs further investigation.

Introduction: Identifying circulating metabolites that are associated with cognition and dementia may improve our understanding of the pathogenesis of dementia and provide crucial readouts for preventive and therapeutic interventions. Methods: We studied 299 metabolites in relation to cognition (general cognitive ability) in two discovery cohorts (N total = 5658). Metabolites significantly associated with cognition after adjusting for multiple testing were replicated in four independent cohorts (N total = 6652), and the associations with dementia and Alzheimer's disease (N = 25,872) and lifestyle factors (N = 5168) were examined. Results: We discovered and replicated 15 metabolites associated with cognition including subfractions of high-density lipoprotein, docosahexaenoic acid, ornithine, glutamine, and glycoprotein acetyls. These associations were independent of classical risk factors including high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, glucose, and apolipoprotein E (APOE) genotypes. Six of the cognition-associated metabolites were related to the risk of dementia and lifestyle factors. Discussion: Circulating metabolites were consistently associated with cognition, dementia, and lifestyle factors, opening new avenues for prevention of cognitive decline and dementia. (C) 2018 The Authors. Published by Elsevier Inc. on behalf of the Alzheimer's Association.

Introduction: Identifying circulating metabolites that are associated with cognition and dementia may improve our understanding of the pathogenesis of dementia and provide crucial readouts for preventive and therapeutic interventions. Methods: We studied 299 metabolites in relation to cognition (general cognitive ability) in two discovery cohorts (N total = 5658). Metabolites significantly associated with cognition after adjusting for multiple testing were replicated in four independent cohorts (N total = 6652), and the associations with dementia and Alzheimer's disease (N = 25,872) and lifestyle factors (N = 5168) were examined. Results: We discovered and replicated 15 metabolites associated with cognition including subfractions of high-density lipoprotein, docosahexaenoic acid, ornithine, glutamine, and glycoprotein acetyls. These associations were independent of classical risk factors including high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, glucose, and apolipoprotein E (APOE) genotypes. Six of the cognition-associated metabolites were related to the risk of dementia and lifestyle factors. Discussion: Circulating metabolites were consistently associated with cognition, dementia, and lifestyle factors, opening new avenues for prevention of cognitive decline and dementia. (C) 2018 The Authors. Published by Elsevier Inc. on behalf of the Alzheimer's Association.