Proteomic analysis of 92 circulating proteins and their effects in cardiometabolic diseases.

BACKGROUND: Human plasma contains a wide variety of circulating proteins. These proteins can be important clinical biomarkers in disease and also possible drug targets. Large scale genomics studies of circulating proteins can identify genetic variants that lead to relative protein abundance. METHODS: We conducted a meta-analysis on genome-wide association studies of autosomal chromosomes in 22,997 individuals of primarily European ancestry across 12 cohorts to identify protein quantitative trait loci (pQTL) for 92 cardiometabolic associated plasma proteins. RESULTS: We identified 503 (337 cis and 166 trans) conditionally independent pQTLs, including several novel variants not reported in the literature. We conducted a sex-stratified analysis and found that 118 (23.5%) of pQTLs demonstrated heterogeneity between sexes. The direction of effect was preserved but there were differences in effect size and significance. Additionally, we annotate trans-pQTLs with nearest genes and report plausible biological relationships. Using Mendelian randomization, we identified causal associations for 18 proteins across 19 phenotypes, of which 10 have additional genetic colocalization evidence. We highlight proteins associated with a constellation of cardiometabolic traits including angiopoietin-related protein 7 (ANGPTL7) and Semaphorin 3F (SEMA3F). CONCLUSION: Through large-scale analysis of protein quantitative trait loci, we provide a comprehensive overview of common variants associated with plasma proteins. We highlight possible biological relationships which may serve as a basis for further investigation into possible causal roles in cardiometabolic diseases.

Deep molecular phenotypes link complex disorders and physiological insult to CpG methylation.

Epigenetic regulation of cellular function provides a mechanism for rapid organismal adaptation to changes in health, lifestyle and environment. Associations of cytosine-guanine di-nucleotide (CpG) methylation with clinical endpoints that overlap with metabolic phenotypes suggest a regulatory role for these CpG sites in the body's response to disease or environmental stress. We previously identified 20 CpG sites in an epigenome-wide association study (EWAS) with metabolomics that were also associated in recent EWASs with diabetes-, obesity-, and smoking-related endpoints. To elucidate the molecular pathways that connect these potentially regulatory CpG sites to the associated disease or lifestyle factors, we conducted a multi-omics association study including 2474 mass-spectrometry-based metabolites in plasma, urine and saliva, 225 NMR-based lipid and metabolite measures in blood, 1124 blood-circulating proteins using aptamer technology, 113 plasma protein N-glycans and 60 IgG-glyans, using 359 samples from the multi-ethnic Qatar Metabolomics Study on Diabetes (QMDiab). We report 138 multi-omics associations at these CpG sites, including diabetes biomarkers at the diabetes-associated TXNIP locus, and smoking-specific metabolites and proteins at multiple smoking-associated loci, including AHRR. Mendelian randomization suggests a causal effect of metabolite levels on methylation of obesity-associated CpG sites, i.e. of glycerophospholipid PC(O-36: 5), glycine and a very low-density lipoprotein (VLDL-A) on the methylation of the obesity-associated CpG loci DHCR24, MYO5C and CPT1A, respectively. Taken together, our study suggests that multi-omics-associated CpG methylation can provide functional read-outs for the underlying regulatory response mechanisms to disease or environmental insults.

Metabolic and proteomic signatures of hypoglycaemia in type 2 diabetes.

AIMS: To determine the biochemical changes that underlie hypoglycaemia in a healthy control group and in people with type 2 diabetes (T2D). MATERIALS AND METHODS: We report a hypoglycaemic clamp study in seven healthy controls and 10 people with T2D. Blood was withdrawn at four time points: at baseline after an overnight fast; after clamping to euglycaemia at 5 mmol/L; after clamping to hypoglycaemia at 2.8 mmol/L; and 24 hours later, after overnight fast. Deep molecular phenotyping using non-targeted metabolomics and the SomaLogic aptamer-based proteomics platform was performed on collected samples. RESULTS: A total of 955 metabolites and 1125 proteins were identified, with significant alterations in >90 molecules. A number of metabolites significantly increased during hypoglycaemia, but only cortisol, adenosine-3',5'-cyclic monophosphate (cyclic AMP), and pregnenolone sulphate, were independent of insulin. By contrast, identified protein changes were triggered by hypoglycaemia rather than insulin. The T2D group had significantly higher levels of fatty acids including 10-nonadecenoate, linolenate and dihomo-linoleate during hypoglycaemia compared with the control group. Molecules contributing to cardiovascular complications such as fatty-acid-binding protein-3 and pregnenolone sulphate were altered in the participants with T2D during hypoglycaemia. Almost all molecules returned to baseline at 24 hours. CONCLUSIONS: The present study provides a comprehensive description of molecular events that are triggered by insulin-induced hypoglycaemia. We identified deregulated pathways in T2D that may play a role in the pathophysiology of hypoglycaemia-induced cardiovascular complications.

Measurement of 1,5-anhydroglucitol in blood and saliva: from non-targeted metabolomics to biochemical assay.

BACKGROUND: Diabetes testing using saliva, rather than blood and urine, could facilitate diabetes screening in public spaces. We previously identified 1,5-anhydro-D-glucitol (1,5-AG) in saliva as a diabetes biomarker. The Glycomark™ assay kit is FDA approved for 1,5-AG measurement in blood. Here we evaluated its applicability for 1,5-AG quantification in saliva. METHODS: Using pooled saliva samples, we validated Glycomark™ assay use with a RX Daytona(+) clinical chemistry analyser. We then used this set-up to analyse 82 paired blood and saliva samples from a diabetes case-control study, for which broad mass spectrometry-based characterization of the blood and saliva metabolome was also available. Osmolality was measured to account for potential variability in saliva samples. RESULTS: The technical variability of the read-outs for the pooled saliva samples (CV = 2.05 %) was comparable to that obtained with manufacturer-provided blood surrogate quality controls (CV = 1.38-1.8 %). We found a high correlation between Glycomark assay and mass spectrometry measurements of serum 1,5-AG (r(2) = 0.902), showing reproducibility of the non-targeted metabolomics results. The significant correlation between the osmolality measurements performed at two independent platforms with the time interval of 2 years (r(2) = 0.887), also indicates the sample integrity. The assay read-out for saliva was not correlated with the mass spectrometry-based 1,5-AG saliva measurements. Comparison with the full saliva metabolome revealed a high correlation of the saliva assay read-outs with galactose. CONCLUSIONS: Glycomark™ assay read-outs for saliva were stable and replicable. However, the signal was dominated by galactose, which is biochemically similar to 1,5-AG and absent in blood. Adapting the 1,5-AG kit for saliva analysis will require enzymatic depletion of galactose. This should be feasible, since the assay already includes a similar step for glucose depletion from blood samples.

Metabolic switch during adipogenesis: From branched chain amino acid catabolism to lipid synthesis.

Fat cell metabolism has an impact on body homeostasis and its proper function. Nevertheless, the knowledge about simultaneous metabolic processes, which occur during adipogenesis and in mature adipocytes, is limited. Identification of key metabolic events associated with fat cell metabolism could be beneficial in the field of novel drug development, drug repurposing, as well as for the discovery of patterns predicting obesity risk. The main objective of our work was to provide comprehensive characterization of metabolic processes occurring during adipogenesis and in mature adipocytes. In order to globally determine crucial metabolic pathways involved in fat cell metabolism, metabolomics and transcriptomics approaches were applied. We observed significantly regulated metabolites correlating with significantly regulated genes at different stages of adipogenesis. We identified the synthesis of phosphatidylcholines, the metabolism of even and odd chain fatty acids, as well as the catabolism of branched chain amino acids (BCAA; leucine, isoleucine and valine) as key regulated pathways. Our further analysis led to identification of an enzymatic switch comprising the enzymes Hmgcs2 (3-hydroxy-3-methylglutaryl-CoA synthase) and Auh (AU RNA binding protein/enoyl-CoA hydratase) which connects leucine degradation with cholesterol synthesis. In addition, propionyl-CoA, a product of isoleucine degradation, was identified as a putative substrate for odd chain fatty acid synthesis. The uncovered crosstalks between BCAA and lipid metabolism during adipogenesis might contribute to the understanding of molecular mechanisms of obesity and have potential implications in obesity prediction.

Metabolomics of dates (Phoenix dactylifera) reveals a highly dynamic ripening process accounting for major variation in fruit composition.

BACKGROUND: Dates are tropical fruits with appreciable nutritional value. Previous attempts at global metabolic characterization of the date metabolome were constrained by small sample size and limited geographical sampling. In this study, two independent large cohorts of mature dates exhibiting substantial diversity in origin, varieties and fruit processing conditions were measured by metabolomics techniques in order to identify major determinants of the fruit metabolome. RESULTS: Multivariate analysis revealed a first principal component (PC1) significantly associated with the dates' countries of production. The availability of a smaller dataset featuring immature dates from different development stages served to build a model of the ripening process in dates, which helped reveal a strong ripening signature in PC1. Analysis revealed enrichment in the dry type of dates amongst fruits with early ripening profiles at one end of PC1 as oppose to an overrepresentation of the soft type of dates with late ripening profiles at the other end of PC1. Dry dates are typical to the North African region whilst soft dates are more popular in the Gulf region, which partly explains the observed association between PC1 and geography. Analysis of the loading values, expressing metabolite correlation levels with PC1, revealed enrichment patterns of a comprehensive range of metabolite classes along PC1. Three distinct metabolic phases corresponding to known stages of date ripening were observed: An early phase enriched in regulatory hormones, amines and polyamines, energy production, tannins, sucrose and anti-oxidant activity, a second phase with on-going phenylpropanoid secondary metabolism, gene expression and phospholipid metabolism and a late phase with marked sugar dehydration activity and degradation reactions leading to increased volatile synthesis. CONCLUSIONS: These data indicate the importance of date ripening as a main driver of variation in the date metabolome responsible for their diverse nutritional and economical values. The biochemistry of the ripening process in dates is consistent with other fruits but natural dryness may prevent degenerative senescence in dates following ripening. Based on the finding that mature dates present varying extents of ripening, our survey of the date metabolome essentially revealed snapshots of interchanging metabolic states during ripening empowering an in-depth characterization of underlying biology.

Metabolic signatures differentiate ovarian from colon cancer cell lines.

BACKGROUND: In this era of precision medicine, the deep and comprehensive characterization of tumor phenotypes will lead to therapeutic strategies beyond classical factors such as primary sites or anatomical staging. Recently, "-omics" approached have enlightened our knowledge of tumor biology. Such approaches have been extensively implemented in order to provide biomarkers for monitoring of the disease as well as to improve readouts of therapeutic impact. The application of metabolomics to the study of cancer is especially beneficial, since it reflects the biochemical consequences of many cancer type-specific pathophysiological processes. Here, we characterize metabolic profiles of colon and ovarian cancer cell lines to provide broader insight into differentiating metabolic processes for prospective drug development and clinical screening. METHODS: We applied non-targeted metabolomics-based mass spectroscopy combined with ultrahigh-performance liquid chromatography and gas chromatography for the metabolic phenotyping of four cancer cell lines: two from colon cancer (HCT15, HCT116) and two from ovarian cancer (OVCAR3, SKOV3). We used the MetaP server for statistical data analysis. RESULTS: A total of 225 metabolites were detected in all four cell lines; 67 of these molecules significantly discriminated colon cancer from ovarian cancer cells. Metabolic signatures revealed in our study suggest elevated tricarboxylic acid cycle and lipid metabolism in ovarian cancer cell lines, as well as increased ß-oxidation and urea cycle metabolism in colon cancer cell lines. CONCLUSIONS: Our study provides a panel of distinct metabolic fingerprints between colon and ovarian cancer cell lines. These may serve as potential drug targets, and now can be evaluated further in primary cells, biofluids, and tissue samples for biomarker purposes.

Metabolomics of Ramadan fasting: an opportunity for the controlled study of physiological responses to food intake.

High-throughput screening techniques that analyze the metabolic endpoints of biological processes can identify the contributions of genetic predisposition and environmental factors to the development of common diseases. Studies applying controlled physiological challenges can reveal dysregulation in metabolic responses that may be predictive for or associated with these diseases. However, large-scale epidemiological studies with well controlled physiological challenge conditions, such as extended fasting periods and defined food intake, pose logistic challenges. Culturally and religiously motivated behavioral patterns of life style changes provide a natural setting that can be used to enroll a large number of study volunteers. Here we report a proof of principle study conducted within a Muslim community, showing that a metabolomics study during the Holy Month of Ramadan can provide a unique opportunity to explore the pre-prandial and postprandial response of human metabolism to nutritional challenges. Up to five blood samples were obtained from eleven healthy male volunteers, taken directly before and two hours after consumption of a controlled meal in the evening on days 7 and 26 of Ramadan, and after an over-night fast several weeks after Ramadan. The observed increases in glucose, insulin and lactate levels at the postprandial time point confirm the expected physiological response to food intake. Targeted metabolomics further revealed significant and physiologically plausible responses to food intake by an increase in bile acid and amino acid levels and a decrease in long-chain acyl-carnitine and polyamine levels. A decrease in the concentrations of a number of phospholipids between samples taken on days 7 and 26 of Ramadan shows that the long-term response to extended fasting may differ from the response to short-term fasting. The present study design is scalable to larger populations and may be extended to the study of the metabolic response in defined patient groups such as individuals with type 2 diabetes.

Metabolomics in cell culture--a strategy to study crucial metabolic pathways in cancer development and the response to treatment.

Metabolomics is a comprehensive tool for monitoring processes within biological systems. Thus, metabolomics may be widely applied to the determination of diagnostic biomarkers for certain diseases or treatment outcomes. There is significant potential for metabolomics to be implemented in cancer research because cancer may modify metabolic pathways in the whole organism. However, not all biological questions can be answered solely by the examination of small molecule composition in biofluids; in particular, the study of cellular processes or preclinical drug testing requires ex vivo models. The major objective of this review was to summarise the current achievement in the field of metabolomics in cancer cell culture-focusing on the metabolic pathways regulated in different cancer cell lines-and progress that has been made in the area of drug screening and development by the implementation of metabolomics in cell lines.

A genome-wide association study of mass spectrometry proteomics using the Seer Proteograph platform.

Genome-wide association studies (GWAS) with proteomics are essential tools for drug discovery. To date, most studies have used affinity proteomics platforms, which have limited discovery to protein panels covered by the available affinity binders. Furthermore, it is not clear to which extent protein epitope changing variants interfere with the detection of protein quantitative trait loci (pQTLs). Mass spectrometry-based (MS) proteomics can overcome some of these limitations. Here we report a GWAS using the MS-based Seer Proteograph™ platform with blood samples from a discovery cohort of 1,260 American participants and a replication in 325 individuals from Asia, with diverse ethnic backgrounds. We analysed 1,980 proteins quantified in at least 80% of the samples, out of 5,753 proteins quantified across the discovery cohort. We identified 252 and replicated 90 pQTLs, where 30 of the replicated pQTLs have not been reported before. We further investigated 200 of the strongest associated cis-pQTLs previously identified using the SOMAscan and the Olink platforms and found that up to one third of the affinity proteomics pQTLs may be affected by epitope effects, while another third were confirmed by MS proteomics to be consistent with the hypothesis that genetic variants induce changes in protein expression. The present study demonstrates the complementarity of the different proteomics approaches and reports pQTLs not accessible to affinity proteomics, suggesting that many more pQTLs remain to be discovered using MS-based platforms.

Nanoparticle enrichment mass-spectrometry proteomics identifies protein-altering variants for precise pQTL mapping.

Proteogenomics studies generate hypotheses on protein function and provide genetic evidence for drug target prioritization. Most previous work has been conducted using affinity-based proteomics approaches. These technologies face challenges, such as uncertainty regarding target identity, non-specific binding, and handling of variants that affect epitope affinity binding. Mass spectrometry-based proteomics can overcome some of these challenges. Here we report a pQTL study using the Proteograph™ Product Suite workflow (Seer, Inc.) where we quantify over 18,000 unique peptides from nearly 3000 proteins in more than 320 blood samples from a multi-ethnic cohort in a bottom-up, peptide-centric, mass spectrometry-based proteomics approach. We identify 184 protein-altering variants in 137 genes that are significantly associated with their corresponding variant peptides, confirming target specificity of co-associated affinity binders, identifying putatively causal cis-encoded proteins and providing experimental evidence for their presence in blood, including proteins that may be inaccessible to affinity-based proteomics.

piTracer - Automatic reconstruction of molecular cascades for the identification of synergistic drug targets.

Cancer cells frequently undergo metabolic reprogramming as a mechanism of resistance against chemotherapeutic drugs. Metabolomic profiling provides a direct readout of metabolic changes and can thus be used to identify these tumor escape mechanisms. Here, we introduce piTracer, a computational tool that uses multi-scale molecular networks to identify potential combination therapies from pre- and post-treatment metabolomics data. We first demonstrate piTracer’s core ability to reconstruct cellular cascades by inspecting well-characterized molecular pathways and previously studied associations between genetic variants and metabolite levels. We then apply a new gene ranking algorithm on differential metabolomic profiles from human breast cancer cells after glutaminase inhibition. Four of the automatically identified gene targets were experimentally tested by simultaneous inhibition of the respective targets and glutaminase. Of these combination treatments, two were be confirmed to induce synthetic lethality in the cell line. In summary, piTracer integrates the molecular monitoring of escape mechanisms into comprehensive pathway networks to accelerate drug target identification. The tool is open source and can be accessed at https://github.com/krumsieklab/pitracer .

Characterization of exercise-induced hemolysis in endurance horses.

Exercise-induced hemolysis occurs as the result of intense physical exercise and is caused by metabolic and mechanical factors including repeated muscle contractions leading to capillary vessels compression, vasoconstriction of internal organs and foot strike among others. We hypothesized that exercise-induced hemolysis occurred in endurance racehorses and its severity was associated with the intensity of exercise. To provide further insight into the hemolysis of endurance horses, the aim of the study was to deployed a strategy for small molecules (metabolites) profiling, beyond standard molecular methods. The study included 47 Arabian endurance horses competing for either 80, 100, or 120 km distances. Blood plasma was collected before and after the competition and analyzed macroscopically, by ELISA and non-targeted metabolomics with liquid chromatography-mass spectrometry. A significant increase in all hemolysis parameters was observed after the race, and an association was found between the measured parameters, average speed, and distance completed. Levels of hemolysis markers were highest in horses eliminated for metabolic reasons in comparison to finishers and horses eliminated for lameness (gait abnormality), which may suggest a connection between the intensity of exercise, metabolic challenges, and hemolysis. Utilization of omics methods alongside conventional methods revealed a broader insight into the exercise-induced hemolysis process by displaying, apart from commonly measured hemoglobin and haptoglobin, levels of hemoglobin degradation metabolites. Obtained results emphasized the importance of respecting horse limitations in regard to speed and distance which, if underestimated, may lead to severe damages.

Progesterone induces meiosis through two obligate co-receptors with PLA2 activity.

The steroid hormone progesterone (P4) regulates multiple aspects of reproductive and metabolic physiology. Classical P4 signaling operates through nuclear receptors that regulate transcription. In addition, P4 signals through membrane P4 receptors (mPRs) in a rapid nongenomic modality. Despite the established physiological importance of P4 nongenomic signaling, its detailed signal transduction remains elusive. Here, using Xenopus oocyte maturation as a well-established physiological readout of nongenomic P4 signaling, we identify the lipid hydrolase ABHD2 (α/ß hydrolase domain-containing protein 2) as an essential mPRß co-receptor to trigger meiosis. We show using functional assays coupled to unbiased and targeted cell-based lipidomics that ABHD2 possesses a phospholipase A2 (PLA2) activity that requires both P4 and mPRß. This PLA2 activity bifurcates P4 signaling by inducing mPRß clathrin-dependent endocytosis and producing lipid messengers that are G-protein coupled receptors agonists. Therefore, P4 drives meiosis by inducing the ABHD2 PLA2 activity that requires both mPRß and ABHD2 as obligate co-receptors.

Retinal nerve fibre layer thinning and corneal nerve loss in patients with Bardet-Biedl syndrome.

BACKGROUND: Bardet-Biedl syndrome (BBS) is an autosomal recessive, genetically heterogeneous, pleiotropic disorder caused by variants in genes involved in the function of the primary cilium. We have harnessed genomics to identify BBS and ophthalmic technologies to describe novel features of BBS. CASE PRESENTATION: A patient with an unclear diagnosis of syndromic type 2 diabetes mellitus, another affected sibling and unaffected siblings and parents were sequenced using DNA extracted from saliva samples. Corneal confocal microscopy (CCM) and retinal spectral domain optical coherence tomography (SD-OCT) were used to identify novel ophthalmic features in these patients. The two affected individuals had a homozygous variant in C8orf37 (p.Trp185*). SD-OCT and CCM demonstrated a marked and patchy reduction in the retinal nerve fiber layer thickness and loss of corneal nerve fibers, respectively. CONCLUSION: This report highlights the use of ophthalmic imaging to identify novel retinal and corneal abnormalities that extend the phenotype of BBS in a patient with syndromic type 2 diabetes.

Advancing Cancer Treatment by Targeting Glutamine Metabolism-A Roadmap.

Tumor growth and metastasis strongly depend on adapted cell metabolism. Cancer cells adjust their metabolic program to their specific energy needs and in response to an often challenging tumor microenvironment. Glutamine metabolism is one of the metabolic pathways that can be successfully targeted in cancer treatment. The dependence of many hematological and solid tumors on glutamine is associated with mitochondrial glutaminase (GLS) activity that enables channeling of glutamine into the tricarboxylic acid (TCA) cycle, generation of ATP and NADPH, and regulation of glutathione homeostasis and reactive oxygen species (ROS). Small molecules that target glutamine metabolism through inhibition of GLS therefore simultaneously limit energy availability and increase oxidative stress. However, some cancers can reprogram their metabolism to evade this metabolic trap. Therefore, the effectiveness of treatment strategies that rely solely on glutamine inhibition is limited. In this review, we discuss the metabolic and molecular pathways that are linked to dysregulated glutamine metabolism in multiple cancer types. We further summarize and review current clinical trials of glutaminolysis inhibition in cancer patients. Finally, we put into perspective strategies that deploy a combined treatment targeting glutamine metabolism along with other molecular or metabolic pathways and discuss their potential for clinical applications.

Defining the landscape of metabolic dysregulations in cancer metastasis.

Metastasis is the primary cause of cancer related deaths due to the limited number of efficient druggable targets. Signatures of dysregulated cancer metabolism could serve as a roadmap for the determination of new treatment strategies. However, the metabolic signatures of metastatic cells remain vastly elusive. Our aim was to determine metabolic dysregulations associated with high metastatic potential in breast cancer cell lines. We have selected 5 triple negative breast cancer (TNBC) cell lines including three with high metastatic potential (HMP) (MDA-MB-231, MDA-MB-436, MDA-MB-468) and two with low metastatic potential (LMP) (BT549, HCC1143). The normal epithelial breast cell line (hTERT-HME1) was also investigated. The untargeted metabolic profiling of cells and growth media was conducted and total of 479 metabolites were quantified. First we characterized metabolic features differentiating TNBC cell lines from normal cells as well as identified cell line specific metabolic fingerprints. Next, we determined 92 metabolites in cells and 22 in growth medium that display significant differences between LMP and HMP. The HMP cell lines had elevated level of molecules involved in glycolysis, TCA cycle and lipid metabolism. We identified metabolic advantages of cell lines with HMP beyond enhanced glycolysis by pinpointing the role of branched chain amino acids (BCAA) catabolism as well as molecules supporting coagulation and platelet activation as important contributors to the metastatic cascade. The landscape of metabolic dysregulations, characterized in our study, could serve as a roadmap for the identification of treatment strategies targeting cancer cells with enhanced metastatic potential.

Matching Drug Metabolites from Non-Targeted Metabolomics to Self-Reported Medication in the Qatar Biobank Study.

Modern metabolomics platforms are able to identify many drug-related metabolites in blood samples. Applied to population-based biobank studies, the detection of drug metabolites can then be used as a proxy for medication use or serve as a validation tool for questionnaire-based health assessments. However, it is not clear how well detection of drug metabolites in blood samples matches information on self-reported medication provided by study participants. Here, we curate free-text responses to a drug-usage questionnaire from 6000 participants of the Qatar Biobank (QBB) using standardized WHO Anatomical Therapeutic Chemical (ATC) Classification System codes and compare the occurrence of these ATC terms to the detection of drug-related metabolites in matching blood plasma samples from 2807 QBB participants for which we collected non-targeted metabolomics data. We found that the detection of 22 drug-related metabolites significantly associated with the self-reported use of the corresponding medication. Good agreement of self-reported medication with non-targeted metabolomics was observed, with self-reported drugs and their metabolites being detected in a same blood sample in 79.4% of the cases. On the other hand, only 29.5% of detected drug metabolites matched to self-reported medication. Possible explanations for differences include under-reporting of over-the-counter medications from the study participants, such as paracetamol, misannotation of low abundance metabolites, such as metformin, and inability of the current methods to detect them. Taken together, our study provides a broad real-world view of what to expect from large non-targeted metabolomics measurements in population-based biobank studies and indicates areas where further improvements can be made.

Ratios of Acetaminophen Metabolites Identify New Loci of Pharmacogenetic Relevance in a Genome-Wide Association Study.

Genome-wide association studies (GWAS) with non-targeted metabolomics have identified many genetic loci of biomedical interest. However, metabolites with a high degree of missingness, such as drug metabolites and xenobiotics, are often excluded from such studies due to a lack of statistical power and higher uncertainty in their quantification. Here we propose ratios between related drug metabolites as GWAS phenotypes that can drastically increase power to detect genetic associations between pairs of biochemically related molecules. As a proof-of-concept we conducted a GWAS with 520 individuals from the Qatar Biobank for who at least five of the nine available acetaminophen metabolites have been detected. We identified compelling evidence for genetic variance in acetaminophen glucuronidation and methylation by UGT2A15 and COMT, respectively. Based on the metabolite ratio association profiles of these two loci we hypothesized the chemical structure of one of their products or substrates as being 3-methoxyacetaminophen, which we then confirmed experimentally. Taken together, our study suggests a novel approach to analyze metabolites with a high degree of missingness in a GWAS setting with ratios, and it also demonstrates how pharmacological pathways can be mapped out using non-targeted metabolomics measurements in large population-based studies.

Metabolic and proteomic signatures of type 2 diabetes subtypes in an Arab population.

Type 2 diabetes (T2D) has a heterogeneous etiology influencing its progression, treatment, and complications. A data driven cluster analysis in European individuals with T2D previously identified four subtypes: severe insulin deficient (SIDD), severe insulin resistant (SIRD), mild obesity-related (MOD), and mild age-related (MARD) diabetes. Here, the clustering approach was applied to individuals with T2D from the Qatar Biobank and validated in an independent set. Cluster-specific signatures of circulating metabolites and proteins were established, revealing subtype-specific molecular mechanisms, including activation of the complement system with features of autoimmune diabetes and reduced 1,5-anhydroglucitol in SIDD, impaired insulin signaling in SIRD, and elevated leptin and fatty acid binding protein levels in MOD. The MARD cluster was the healthiest with metabolomic and proteomic profiles most similar to the controls. We have translated the T2D subtypes to an Arab population and identified distinct molecular signatures to further our understanding of the etiology of these subtypes.