RESUMO
In-depth multiomic phenotyping provides molecular insights into complex physiological processes and their pathologies. Here, we report on integrating 18 diverse deep molecular phenotyping (omics-) technologies applied to urine, blood, and saliva samples from 391 participants of the multiethnic diabetes Qatar Metabolomics Study of Diabetes (QMDiab). Using 6,304 quantitative molecular traits with 1,221,345 genetic variants, methylation at 470,837 DNA CpG sites, and gene expression of 57,000 transcripts, we determine (1) within-platform partial correlations, (2) between-platform mutual best correlations, and (3) genome-, epigenome-, transcriptome-, and phenome-wide associations. Combined into a molecular network of > 34,000 statistically significant trait-trait links in biofluids, our study portrays "The Molecular Human". We describe the variances explained by each omics in the phenotypes (age, sex, BMI, and diabetes state), platform complementarity, and the inherent correlation structures of multiomics data. Further, we construct multi-molecular network of diabetes subtypes. Finally, we generated an open-access web interface to "The Molecular Human" ( http://comics.metabolomix.com ), providing interactive data exploration and hypotheses generation possibilities.
Assuntos
Fenótipo , Humanos , Masculino , Feminino , Metabolômica/métodos , Diabetes Mellitus/genética , Diabetes Mellitus/metabolismo , Metilação de DNA , Transcriptoma , Pessoa de Meia-Idade , Estudo de Associação Genômica Ampla , Catar/epidemiologia , Epigenoma , Adulto , Ilhas de CpG/genética , Diabetes Mellitus Tipo 2/genética , Diabetes Mellitus Tipo 2/metabolismo , MultiômicaRESUMO
BACKGROUND: Immunoglobulin (Ig) glycosylation modulates the immune response and plays a critical role in ageing and diseases. Studies have mainly focused on IgG glycosylation, and little is known about the genetics and epidemiology of IgA glycosylation. METHODS: We generated, using a novel liquid chromatography-mass spectrometry method, the first large-scale IgA glycomics dataset in serum from 2423 twins, encompassing 71 N- and O-glycan species. RESULTS: We showed that, despite the lack of a direct genetic template, glycosylation is highly heritable, and that glycopeptide structures are sex-specific, and undergo substantial changes with ageing. We observe extensive correlations between the IgA and IgG glycomes, and, exploiting the twin design, show that they are predominantly influenced by shared genetic factors. A genome-wide association study identified eight loci associated with both the IgA and IgG glycomes (ST6GAL1, ELL2, B4GALT1, ABCF2, TMEM121, SLC38A10, SMARCB1, and MGAT3) and two novel loci specifically modulating IgA O-glycosylation (C1GALT1 and ST3GAL1). Validation of our findings in an independent cohort of 320 individuals from Qatar showed that the underlying genetic architecture is conserved across ancestries. CONCLUSIONS: Our study delineates the genetic landscape of IgA glycosylation and provides novel potential functional links with the aetiology of complex immune diseases, including genetic factors involved in IgA nephropathy risk.
Assuntos
Estudo de Associação Genômica Ampla , Glicômica , Imunoglobulina A , Humanos , Imunoglobulina A/sangue , Imunoglobulina A/genética , Glicosilação , Feminino , Masculino , Polissacarídeos/metabolismo , Adulto , Imunoglobulina G/sangue , Pessoa de Meia-Idade , IdosoRESUMO
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.
RESUMO
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.
Assuntos
Proteogenômica , Proteômica , Humanos , Proteômica/métodos , Espectrometria de Massas/métodos , Proteínas/análise , Peptídeos/análise , Proteogenômica/métodos , Proteínas MutantesRESUMO
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.
Assuntos
Síndrome de Bardet-Biedl , Diabetes Mellitus Tipo 2 , Humanos , Síndrome de Bardet-Biedl/complicações , Síndrome de Bardet-Biedl/genética , Síndrome de Bardet-Biedl/diagnóstico , Diabetes Mellitus Tipo 2/complicações , Diabetes Mellitus Tipo 2/genética , Retina , Fenótipo , Fibras Nervosas , Mutação , Proteínas/genéticaRESUMO
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.
Assuntos
Diabetes Mellitus Tipo 1 , Diabetes Mellitus Tipo 2 , Humanos , Diabetes Mellitus Tipo 2/genética , Diabetes Mellitus Tipo 2/metabolismo , Proteômica , Árabes , InsulinaRESUMO
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.
RESUMO
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.
RESUMO
Kit-based assays, such as AbsoluteIDQTM p150, are widely used in large cohort studies and provide a standardized method to quantify blood concentrations of phosphatidylcholines (PCs). Many disease-relevant associations of PCs were reported using this method. However, their interpretation is hampered by lack of functionally-relevant information on the detailed fatty acid side-chain compositions as only the total number of carbon atoms and double bonds is identified by the kit. To enable more substantiated interpretations, we characterized these PC sums using the side-chain resolving LipidyzerTM platform, analyzing 223 samples in parallel to the AbsoluteIDQTM. Combining these datasets, we estimated the quantitative composition of PC sums and subsequently tested their replication in an independent cohort. We identified major constituents of 28 PC sums, revealing also various unexpected compositions. As an example, PC 16:0_22:5 accounted for more than 50% of the PC sum with in total 38 carbon atoms and 5 double bonds (PC aa 38:5). For 13 PC sums, we found relatively high abundances of odd-chain fatty acids. In conclusion, our study provides insights in PC compositions in human plasma, facilitating interpretation of existing epidemiological data sets and potentially enabling imputation of PC compositions for future meta-analyses of lipidomics data.
RESUMO
This article provides detailed information on the phenotypes and the metabolic profiles of 196 date fruits from 123 unique date fruit varieties. These date fruits are extensively diverse in their country of origin, variety and post harvesting conditions. We used a non-targeted mass-spectrometry based metabolomics approach to metabolically characterize date fruits, and measured 427 metabolites from a wide range of metabolic pathways. The metabolomics data for all the date fruit samples are available at the NIH Common Fund's Data Repository and Coordinating Center (supported by NIH grant, U01-DK097430) website, http://www.metabolomicsworkbench.org), under Metabolomics Workbench StudyID: ST000867. The data are directly accessible at http://www.metabolomicsworkbench.org/data/DRCCMetadata.php?Mode=Study&StudyID=ST000867&StudyType=MS&ResultType=1.
RESUMO
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.