A genomic and evolutionary approach reveals non-genetic drug resistance in malaria

Background: Drug resistance remains a major public health challenge for malaria treatment and eradication. Individual loci associated with drug resistance to many antimalarials have been identified, but their epistasis with other resistance mechanisms has not yet been elucidated. Results: We previously described two mutations in the cytoplasmic prolyl-tRNA synthetase (cPRS) gene that confer resistance to halofuginone. We describe here the evolutionary trajectory of halofuginone resistance of two independent drug resistance selections in Plasmodium falciparum. Using this novel methodology, we discover an unexpected non-genetic drug resistance mechanism that P. falciparum utilizes before genetic modification of the cPRS. P. falciparum first upregulates its proline amino acid homeostasis in response to halofuginone pressure. We show that this non-genetic adaptation to halofuginone is not likely mediated by differential RNA expression and precedes mutation or amplification of the cPRS gene. By tracking the evolution of the two drug resistance selections with whole genome sequencing, we further demonstrate that the cPRS locus accounts for the majority of genetic adaptation to halofuginone in P. falciparum. We further validate that copy-number variations at the cPRS locus also contribute to halofuginone resistance. Conclusions: We provide a three-step model for multi-locus evolution of halofuginone drug resistance in P. falciparum. Informed by genomic approaches, our results provide the first comprehensive view of the evolutionary trajectory malaria parasites take to achieve drug resistance. Our understanding of the multiple genetic and non-genetic mechanisms of drug resistance informs how we will design and pair future anti-malarials for clinical use. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0511-2) contains supplementary material, which is available to authorized users

Prediagnostic plasma metabolomics and the risk of amyotrophic lateral sclerosis

Objective: To identify prediagnostic plasma metabolomic biomarkers associated with amyotrophic lateral sclerosis (ALS). Methods: We conducted a global metabolomic study using a nested case-control study design within 5 prospective cohorts and identified 275 individuals who developed ALS during follow-up. We profiled plasma metabolites using liquid chromatography–mass spectrometry and identified 404 known metabolites. We used conditional logistic regression to evaluate the associations between metabolites and ALS risk. Further, we used machine learning analyses to determine whether the prediagnostic metabolomic profile could discriminate ALS cases from controls. Results: A total of 31 out of 404 identified metabolites were associated with ALS risk (p < 0.05). We observed inverse associations (n = 27) with plasma levels of diacylglycerides and triacylglycerides, urate, purine nucleosides, and some organic acids and derivatives, while we found positive associations for a cholesteryl ester, 2 phosphatidylcholines, and a sphingomyelin. The number of significant associations increased to 67 (63 inverse) in analyses restricted to cases with blood samples collected within 5 years of onset. None of these associations remained significant after multiple comparison adjustment. Further, we were not able to reliably distinguish individuals who became cases from controls based on their metabolomic profile using partial least squares discriminant analysis, elastic net regression, random forest, support vector machine, or weighted correlation network analyses. Conclusions: Although the metabolomic profile in blood samples collected years before ALS diagnosis did not reliably separate presymptomatic ALS cases from controls, our results suggest that ALS is preceded by a broad, but poorly defined, metabolic dysregulation years before the disease onset

Plasma lipidome and risk of atrial fibrillation: results from the PREDIMED trial

The potential role of the lipidome in atrial fibrillation (AF) development is still widely unknown. We aimed to assess the association between lipidome profiles of the Prevención con Dieta Mediterránea (PREDIMED) trial participants and incidence of AF. We conducted a nested case-control study (512 incident centrally adjudicated AF cases and 735 controls matched by age, sex, and center). Baseline plasma lipids were profiled using a Nexera X2 U-HPLC system coupled to an Exactive Plus orbitrap mass spectrometer. We estimated the association between 216 individual lipids and AF using multivariable conditional logistic regression and adjusted the p values for multiple testing. We also examined the joint association of lipid clusters with AF incidence. Hitherto, we estimated the lipidomics network, used machine learning to select important network-clusters and AF-predictive lipid patterns, and summarized the joint association of these lipid patterns weighted scores. Finally, we addressed the possible interaction by the randomized dietary intervention.Forty-one individual lipids were associated with AF at the nominal level (p < 0.05), but no longer after adjustment for multiple-testing. However, the network-based score identified with a robust data-driven lipid network showed a multivariable-adjusted ORper+1SD of 1.32 (95% confidence interval: 1.16-1.51; p < 0.001). The score included PC plasmalogens and PE plasmalogens, palmitoyl-EA, cholesterol, CE 16:0, PC 36:4;O, and TG 53:3. No interaction with the dietary intervention was found. A multilipid score, primarily made up of plasmalogens, was associated with an increased risk of AF. Future studies are needed to get further insights into the lipidome role on AF.Current Controlled Trials number, ISRCTN35739639

Plasma lipidome and risk of atrial fibrillation: results from the PREDIMED trial

The potential role of the lipidome in atrial fibrillation (AF) development is still widely unknown. We aimed to assess the association between lipidome profiles of the Prevenci\uf3n con Dieta Mediterr\ue1nea (PREDIMED) trial participants and incidence of AF. We conducted a nested case–control study (512 incident centrally adjudicated AF cases and 735 controls matched by age, sex, and center). Baseline plasma lipids were profiled using a Nexera X2 U-HPLC system coupled to an Exactive Plus orbitrap mass spectrometer. We estimated the association between 216 individual lipids and AF using multivariable conditional logistic regression and adjusted the p values for multiple testing. We also examined the joint association of lipid clusters with AF incidence. Hitherto, we estimated the lipidomics network, used machine learning to select important network-clusters and AF-predictive lipid patterns, and summarized the joint association of these lipid patterns weighted scores. Finally, we addressed the possible interaction by the randomized dietary intervention. Forty-one individual lipids were associated with AF at the nominal level (p &lt; 0.05), but no longer after adjustment for multiple-testing. However, the network-based score identified with a robust data-driven lipid network showed a multivariable-adjusted ORper+1SD of 1.32 (95% confidence interval: 1.16–1.51; p &lt; 0.001). The score included PC plasmalogens and PE plasmalogens, palmitoyl-EA, cholesterol, CE 16:0, PC 36:4;O, and TG 53:3. No interaction with the dietary intervention was found. A multilipid score, primarily made up of plasmalogens, was associated with an increased risk of AF. Future studies are needed to get further insights into the lipidome role on AF. Current Controlled Trials number, ISRCTN35739639

Plasma Metabolites Associated with Coffee Consumption: A Metabolomic Approach within the PREDIMED Study

Few studies have examined the association of a wide range of metabolites with total and subtypes of coffee consumption. The aim of this study was to investigate associations of plasma metabolites with total, caffeinated, and decaffeinated coffee consumption. We also assessed the ability of metabolites to discriminate between coffee consumption categories. This is a cross-sectional analysis of 1664 participants from the PREDIMED study. Metabolites were semiquantitatively profiled using a multiplatform approach. Consumption of total coffee, caffeinated coffee and decaffeinated coffee was assessed by using a validated food frequency questionnaire. We assessed associations between 387 metabolite levels with total, caffeinated, or decaffeinated coffee consumption (≥50 mL coffee/day) using elastic net regression analysis. Ten-fold cross-validation analyses were used to estimate the discriminative accuracy of metabolites for total and subtypes of coffee. We identified different sets of metabolites associated with total coffee, caffeinated and decaffeinated coffee consumption. These metabolites consisted of lipid species (e.g., sphingomyelin, phosphatidylethanolamine, and phosphatidylcholine) or were derived from glycolysis (alpha-glycerophosphate) and polyphenol metabolism (hippurate). Other metabolites included caffeine, 5-acetylamino-6-amino-3-methyluracil, cotinine, kynurenic acid, glycocholate, lactate, and allantoin. The area under the curve (AUC) was 0.60 (95% CI 0.56–0.64), 0.78 (95% CI 0.75–0.81) and 0.52 (95% CI 0.49–0.55), in the multimetabolite model, for total, caffeinated, and decaffeinated coffee consumption, respectively. Our comprehensive metabolic analysis did not result in a new, reliable potential set of metabolites for coffee consumption

Type 2 Diabetes Variants Disrupt Function of SLC16A11 through Two Distinct Mechanisms

Type 2 diabetes (T2D) affects Latinos at twice the rate seen in populations of European descent. We recently identified a risk haplotype spanning SLC16A11 that explains ∼20% of the increased T2D prevalence in Mexico. Here, through genetic fine-mapping, we define a set of tightly linked variants likely to contain the causal allele(s). We show that variants on the T2D-associated haplotype have two distinct effects: (1) decreasing SLC16A11 expression in liver and (2) disrupting a key interaction with basigin, thereby reducing cell-surface localization. Both independent mechanisms reduce SLC16A11 function and suggest SLC16A11 is the causal gene at this locus. To gain insight into how SLC16A11 disruption impacts T2D risk, we demonstrate that SLC16A11 is a proton-coupled monocarboxylate transporter and that genetic perturbation of SLC16A11 induces changes in fatty acid and lipid metabolism that are associated with increased T2D risk. Our findings suggest that increasing SLC16A11 function could be therapeutically beneficial for T2D. Video Abstract [Figure presented] Keywords: type 2 diabetes (T2D); genetics; disease mechanism; SLC16A11; MCT11; solute carrier (SLC); monocarboxylates; fatty acid metabolism; lipid metabolism; precision medicin

Intrapersonal Stability of Plasma Metabolomic Profiles over 10 Years among Women

In epidemiological studies, samples are often collected long before disease onset or outcome assessment. Understanding the long-term stability of biomarkers measured in these samples is crucial. We estimated within-person stability over 10 years of metabolites and metabolite features (n = 5938) in the Nurses&rsquo; Health Study (NHS): the primary dataset included 1880 women with 1184 repeated samples donated 10 years apart while the secondary dataset included 1456 women with 488 repeated samples donated 10 years apart. We quantified plasma metabolomics using two liquid chromatography mass spectrometry platforms (lipids and polar metabolites) at the Broad Institute (Cambridge, MA, USA). Intra-class correlations (ICC) were used to estimate long-term (10 years) within-person stability of metabolites and were calculated as the proportion of the total variability (within-person + between-person) attributable to between-person variability. Within-person variability was estimated among participants who donated two blood samples approximately 10 years apart while between-person variability was estimated among all participants. In the primary dataset, the median ICC was 0.43 (1st quartile (Q1): 0.36; 3rd quartile (Q3): 0.50) among known metabolites and 0.41 (Q1: 0.34; Q3: 0.48) among unknown metabolite features. The three most stable metabolites were N6,N6-dimethyllysine (ICC = 0.82), dimethylguanidino valerate (ICC = 0.72), and N-acetylornithine (ICC = 0.72). The three least stable metabolites were palmitoylethanolamide (ICC = 0.05), ectoine (ICC = 0.09), and trimethylamine-N-oxide (ICC = 0.16). Results in the secondary dataset were similar (Spearman correlation = 0.87) to corresponding results in the primary dataset. Within-person stability over 10 years is reasonable for lipid, lipid-related, and polar metabolites, and varies by metabolite class. Additional studies are required to estimate within-person stability over 10 years of other metabolites groups

Chromatin regulator SMARCAL1 modulates cellular lipid metabolism

Abstract Biallelic mutations of the chromatin regulator SMARCAL1 cause Schimke Immunoosseous Dysplasia (SIOD), characterized by severe growth defects and premature mortality. Atherosclerosis and hyperlipidemia are common among SIOD patients, yet their onset and progression are poorly understood. Using an integrative approach involving proteomics, mouse models, and population genetics, we investigated SMARCAL1’s role. We found that SmarcAL1 interacts with angiopoietin-like 3 (Angptl3), a key regulator of lipoprotein metabolism. In vitro and in vivo analyses demonstrate SmarcAL1’s vital role in maintaining cellular lipid homeostasis. The observed translocation of SmarcAL1 to cytoplasmic peroxisomes suggests a potential regulatory role in lipid metabolism through gene expression. SmarcAL1 gene inactivation reduces the expression of key genes in cellular lipid catabolism. Population genetics investigations highlight significant associations between SMARCAL1 genetic variations and body mass index, along with lipid-related traits. This study underscores SMARCAL1’s pivotal role in cellular lipid metabolism, likely contributing to the observed lipid phenotypes in SIOD patients

Metabolomic profiling of left ventricular diastolic dysfunction in women with or at risk for HIV infection: The women’s interagency HIV study

Background-—People living with HIV have an increased risk of left ventricular diastolic dysfunction (LVDD) and heart failure. HIVassociated LVDD may reflect both cardiomyocyte and systemic metabolic derangements, but the underlying pathways remain unclear. Methods and Results-—To explore such pathways, we conducted a pilot study in the Bronx and Brooklyn sites of the WIHS (Women’s Interagency HIV Study) who participated in concurrent, but separate, metabolomics and echocardiographic ancillary studies. Liquid chromatography tandem mass spectrometry–based metabolomic profiling was performed on plasma samples from 125 HIV-infected (43 with LVDD) and 35 HIV-uninfected women (9 with LVDD). Partial least squares discriminant analysis identified polar metabolites and lipids in the glycerophospholipid-metabolism and fatty-acid-oxidation pathways associated with LVDD. After multivariable adjustment, LVDD was significantly associated with higher concentrations of diacylglycerol 30:0 (odds ratio [OR], 1.60, 95% CI [1.01–2.55]); triacylglycerols 46:0 (OR 1.60 [1.04–2.48]), 48:0 (OR 1.63 [1.04–2.54]), 48:1 (OR 1.62 [1.01–2.60]), and 50:0 (OR 1.61 [1.02–2.53]); acylcarnitine C7 (OR 1.88 [1.21–2.92]), C9 (OR 1.99 [1.27–3.13]), and C16 (OR 1.80 [1.13–2.87]); as well as lower concentrations of phosphocholine (OR 0.59 [0.38–0.91]). There was no evidence of effect modification of these relationships by HIV status. Conclusions-—In this pilot study, women with or at risk of HIV with LVDD showed alterations in plasma metabolites in the glycerophospholipid-metabolism and fatty-acid-oxidation pathways. Although these findings require replication, they suggest that improved understanding of metabolic perturbations and their potential modification could offer new approaches to prevent cardiac dysfunction in this high-risk group.United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Heart Lung & Blood Institute (NHLBI) K01HL129892 R01HL140976 R01 HL132794 R01HL083760 R01HL09 5140 K01HL137557 K24 HL135413 R01 HL126543 United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Institute of Allergy & Infectious Diseases (NIAID) United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD) United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Cancer Institute (NCI) United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Institute on Drug Abuse (NIDA) United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Institute of Mental Health (NIMH) United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Institute of Dental & Craniofacial Research (NIDCR) United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Institute on Alcohol Abuse & Alcoholism (NIAAA) United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Institute on Deafness & Other Communication Disorders (NIDCD) United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH Office of Research on Women's Health (ORWH) United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Center for Advancing Translational Sciences (NCATS) UL1-TR000004 United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Center for Advancing Translational Sciences (NCATS) UL1TR000454 UNC CFAR P30-AI-050410 UAB CFAR P30-AI-027767 U01-AI-103401 U01-AI-103408 U01-AI-035004 U01AI-031834 U01-AI-034993 U01-AI-034994 U01-AI-103397 U01-AI103390 U01-AI-034989 U01-AI-042590 U01-HD-03263