Integration of Genome-Wide SNP Data and Gene-Expression Profiles Reveals Six Novel Loci and Regulatory Mechanisms for Amino Acids and Acylcarnitines in Whole Blood

Whole Genome Association Study of the Plasma Metabolome Identifies Metabolites Linked to Cardiometabolic Disease in Black Individuals

Integrating genetic information with metabolomics has provided new insights into genes affecting human metabolism. However, gene-metabolite integration has been primarily studied in individuals of European Ancestry, limiting the opportunity to leverage genomic diversity for discovery. In addition, these analyses have principally involved known metabolites, with the majority of the profiled peaks left unannotated. Here, we perform a whole genome association study of 2,291 metabolite peaks (known and unknown features) in 2,466 Black individuals from the Jackson Heart Study. We identify 519 locus-metabolite associations for 427 metabolite peaks and validate our findings in two multi-ethnic cohorts. A significant proportion of these associations are in ancestry specific alleles including findings in APOE, TTR and CD36. We leverage tandem mass spectrometry to annotate unknown metabolites, providing new insight into hereditary diseases including transthyretin amyloidosis and sickle cell disease. Our integrative omics approach leverages genomic diversity to provide novel insights into diverse cardiometabolic diseases.

Whole Blood Metabolite Profiles Reflect Changes in Energy Metabolism in Heart Failure

A variety of atherosclerosis and cardiovascular disease (ASCVD) phenotypes are tightly linked to changes in the cardiac energy metabolism that can lead to a loss of metabolic flexibility and to unfavorable clinical outcomes. We conducted an association analysis of 31 ASCVD phenotypes and 97 whole blood amino acids, acylcarnitines and derived ratios in the LIFE-Adult (n = 9646) and LIFE-Heart (n = 5860) studies, respectively. In addition to hundreds of significant associations, a total of 62 associations of six phenotypes were found in both studies. Positive associations of various amino acids and a range of acylcarnitines with decreasing cardiovascular health indicate disruptions in mitochondrial, as well as peroxisomal fatty acid oxidation. We complemented our metabolite association analyses with whole blood and peripheral blood mononuclear cell (PBMC) gene-expression analyses of fatty acid oxidation and ketone-body metabolism related genes. This revealed several differential expressions for the heart failure biomarker N-terminal prohormone of brain natriuretic peptide (NT-proBNP) in peripheral blood mononuclear cell (PBMC) gene expression. Finally, we constructed and compared three prediction models of significant stenosis in the LIFE-Heart study using (1) traditional risk factors only, (2) the metabolite panel only and (3) a combined model. Area under the receiver operating characteristic curve (AUC) comparison of these three models shows an improved prediction accuracy for the combined metabolite and classical risk factor model (AUC = 0.78, 95%-CI: 0.76–0.80). In conclusion, we improved our understanding of metabolic implications of ASCVD phenotypes by observing associations with metabolite concentrations and gene expression of the mitochondrial and peroxisomal fatty acid oxidation. Additionally, we demonstrated the predictive potential of the metabolite profile to improve classification of patients with significant stenosis.

Verification of immunology-related genetic associations in BPD supports ABCA3 and five other genes

BACKGROUND

Inflammatory processes are key drivers of bronchopulmonary dysplasia (BPD), a chronic lung disease in preterm infants. In a large sample, we verify previously reported associations of genetic variants of immunology-related genes with BPD.

METHODS

Preterm infants with a gestational age ≤32 weeks from PROGRESS and the German Neonatal Network (GNN) were included. Through a consensus case/control definition, 278 BPD cases and 670 controls were identified. We identified 49 immunity-related genes and 55 single-nucleotide polymorphisms (SNPs) previously associated with BPD through a comprehensive literature survey. Additionally, a quantitative genetic association analysis regarding oxygen supplements, mechanical ventilation, and continuous positive air pressure (CPAP) was performed.

RESULTS

Five candidate SNPs were nominally associated with BPD-related phenotypes with effect directions not conflicting the original studies: rs11265269-CRP, rs1427793-NUAK1, rs2229569-SELL, rs1883617-VNN2, and rs4148913-CHST3. Four of these genes are involved in cell adhesion. Extending our analysis to all well-imputed SNPs of all candidate genes, the strongest association was rs45538638-ABCA3 with CPAP (p = 4.9 × 10^-7, FDR = 0.004), an ABC transporter involved in surfactant formation.

CONCLUSIONS

Most of the previously reported associations could not be replicated. We found additional support for SNPs in CRP, NUAK1, SELL, VNN2, and ABCA3. Larger studies and meta-analyses are required to corroborate these findings.

IMPACT

Larger cohort for improved statistical power to detect genetic associations with bronchopulmonary dysplasia (BPD). Most of the previously reported genetic associations with BPD could not be replicated in this larger study. Among investigated immunological relevant candidate genes, additional support was found for variants in genes CRP, NUAK1, SELL, VNN2, and CHST3, four of them related to cell adhesion. rs45538638 is a novel candidate SNP in reported candidate gene ABC-transporter ABCA3. Results help to prioritize molecular candidate pathomechanisms in follow-up studies.

Biomarkers related to fatty acid oxidative capacity are predictive for continued weight loss in cachectic cancer patients

BACKGROUND

Cachexia is characterized by a negative protein and energy balance leading to loss of adipose tissue and muscle mass. Cancer cachexia negatively impacts treatment tolerability and prognosis. Supportive interventions should be initiated as early as possible. Biomarkers for early prediction of continuing weight loss during the course of disease are currently lacking.

METHODS

In this pilot, observational, cross-sectional, case-control study, cachectic cancer patients undergoing systemic first-line cancer treatment were matched 2:1 with healthy controls according to age, gender and body mass index. Alterations in amino acid and energy metabolism, as indicated by acylcarnitine levels, were analysed using mass spectrometry in plasma samples (PS) and dried blood specimen (DBS). Welch's two-sample t-test was used for comparative analysis of metabolites between cancer patients and healthy matched controls and to identify the metabolomic profiles related to weight loss across different time points. A linear regression model was applied to correlate weight loss and single metabolites as predictor variables. Finally, metabolite pathway enrichment analyses were performed.

RESULTS

Eighteen cases (14 male and 4 female) and 36 paired controls were enrolled. There was a good correlation between baseline PS and DBS of healthy controls for the levels of most amino acids but not for acylcarnitine. Amino acid levels related to cancer metabolism were significantly altered in cancer patients compared with controls in both DBS and PS for arginine, citrulline, histidine and ornithine and in DBS only for asparagine, glutamine, methylhistidine, methionine, ornithine, serine, threonine and leucine/isoleucine. Metabolite enrichment analysis in PS of cancer patients revealed histidine metabolism activation (P = 0.0025). Baseline acylcarnitine analysis in DBS was indicative for alterations of the mitochondrial carnitine shuttle, related to β-oxidation: The ratio palmitoylcarnitine/acylcarnitine (Q2) and the ratio palmitoylcarnitine + octadecenoylcarnitine/acylcarnitine (Q3) were predictive for early weight loss (P < 0.0001) and weight loss during follow-up. Activation of tryptophan metabolism (P = 0.035) in DBS and PS and activation of serine/glycine metabolism (P = 0.017) in PS were also related to early weight loss and across successive time points.

CONCLUSIONS

We found alterations in amino acid levels most likely attributable to cancer metabolism itself in cancer patients compared with controls. Baseline DBS represent a valuable analyte to study energy metabolism related to cancer cachexia. Acylcarnitine patterns (Q2, Q3) predicted further weight loss in cachectic cancer patients undergoing systemic therapy, and pathway analyses indicated involvement of the serine/glycine and the tryptophan pathway in this condition. Validation in larger cohorts is warranted.

Untargeted Metabolome- and Transcriptome-Wide Association Study Suggests Causal Genes Modulating Metabolite Concentrations in Urine

Gene products can affect the concentrations of small molecules (aka “metabolites”), and conversely, some metabolites can modulate the concentrations of gene transcripts. While many specific instances of this interplay have been revealed, a global approach to systematically uncover human gene-metabolite interactions is still lacking. We performed a metabolome- and transcriptome-wide association study to identify genes influencing the human metabolome using untargeted metabolome features, extracted from ¹H nuclear magnetic resonance spectroscopy (NMR) of urine samples, and gene expression levels, quantified from RNA-Seq of lymphoblastoid cell lines (LCL) from 555 healthy individuals. We identified 20 study-wide significant associations corresponding to 15 genes, of which 5 associations (with 2 genes) were confirmed with follow-up NMR data. Using metabomatching, we identified the metabolites corresponding to metabolome features associated with the genes, namely, N-acetylated compounds with ALMS1 and trimethylamine (TMA) with HPS1. Finally, Mendelian randomization analysis supported a potential causal link between the expression of genes in both the ALMS1- and HPS1-loci and their associated metabolite concentrations. In the case of HPS1, we additionally observed that TMA concentration likely exhibits a reverse causal effect on HPS1 expression levels, indicating a negative feedback loop. Our study highlights how the integration of metabolomics, gene expression, and genetic data can pinpoint causal genes modulating metabolite concentrations.

Applications of Multi-omics Approaches for Exploring the Molecular Mechanism of Ovarian Carcinogenesis

Ovarian cancer ranks as the fifth most common cause of cancer-related death in females. The molecular mechanisms of ovarian carcinogenesis need to be explored in order to identify effective clinical therapies for ovarian cancer. Recently, multi-omics approaches have been applied to determine the mechanisms of ovarian oncogenesis at genomics (DNA), transcriptomics (RNA), proteomics (proteins), and metabolomics (metabolites) levels. Multi-omics approaches can identify some diagnostic and prognostic biomarkers and therapeutic targets for ovarian cancer, and these molecular signatures are beneficial for clarifying the development and progression of ovarian cancer. Moreover, the discovery of molecular signatures and targeted therapy strategies could noticeably improve the prognosis of ovarian cancer patients.

Integration of metabolomics, genomics, and immune phenotypes reveals the causal roles of metabolites in disease

BACKGROUND

Recent studies highlight the role of metabolites in immune diseases, but it remains unknown how much of this effect is driven by genetic and non-genetic host factors.

RESULT

We systematically investigate circulating metabolites in a cohort of 500 healthy subjects (500FG) in whom immune function and activity are deeply measured and whose genetics are profiled. Our data reveal that several major metabolic pathways, including the alanine/glutamate pathway and the arachidonic acid pathway, have a strong impact on cytokine production in response to ex vivo stimulation. We also examine the genetic regulation of metabolites associated with immune phenotypes through genome-wide association analysis and identify 29 significant loci, including eight novel independent loci. Of these, one locus (rs174584-FADS2) associated with arachidonic acid metabolism is causally associated with Crohn's disease, suggesting it is a potential therapeutic target.

CONCLUSION

This study provides a comprehensive map of the integration between the blood metabolome and immune phenotypes, reveals novel genetic factors that regulate blood metabolite concentrations, and proposes an integrative approach for identifying new disease treatment targets.

A metabolomic approach to identify the link between sports activity and atheroprotection

AIMS

Physical activity (PA) is a mainstay of cardiovascular prevention. This study aimed to identify metabolic mediators of PA that protect against the development of atherosclerosis.

METHODS AND RESULTS

A total of 2160 participants in the LIFE heart study were analysed with data on PA and vascular phenotyping. In a targeted metabolomic approach, 61 metabolites (amino acids and acylcarnitines) were measured using liquid chromatography-tandem mass spectrometry. We investigated the interactions between PA, metabolites and markers of atherosclerosis in order to uncover possible mediation effects. Intended sports activity, but no daily PA, was associated with a lower degree of atherosclerosis, odds ratio (OR) for total atherosclerotic burden of 0.76 (95% confidence interval 0.62-0.94), carotid artery plaque OR 0.79 (0.66-0.96), and peripheral artery disease OR 0.74 (0.56-0.98). Twelve amino acids, free carnitine, five acylcarnitines were associated with sports activity. Of these, eight metabolites were also associated with the degree of atherosclerosis. In the mediation analyses, a cluster of amino acids (arginine, glutamine, pipecolic acid, taurine) were considered as possible mediators of atheroprotection. In contrast, a group of members of the carnitine metabolism (free carnitine, acetyl carnitine, octadecenoyl carnitine) were associated with inactivity and higher atherosclerotic burden.

CONCLUSION

Our metabolomic approach, which is integrated into a mediation model, provides transformative insights into the complex metabolic processes involved in atheroprotection. Metabolites with antioxidant and endothelial active properties are believed to be possible mediators of atheroprotection. The metabolomic mediation approach can support the understanding of complex diseases in order to identify targets for prevention and therapy.

Genetic test for the prescription of diets in support of physical activity

Owing to the fields of nutrigenetics and nutrigenomics today we can think of devising approaches to optimize health, delay onset of diseases and reduce its severity according to our genetic blue print. However this requires a deep understanding of nutritional impact on expression of genes that may result in a specific phenotype. The extensive research and observational studies during last two decades reporting interactions between genes, diet and physical activity suggest a cross talk between various genetic and environmental factors and lifestyle interventions. Although considerable efforts have been made in unraveling the mechanisms of gene-diet interactions the scientific evidences behind developing commercial genetic tests for providing personalized nutrition recommendations are still scarce. In this scenario the current mini-review aims to provide useful insights into salient feature of nutrition based genetic research and its commercial application and the ethical issue and concerns related to its outcome.

Metabolism and Toxicity of Emodin: Genome-Wide Association Studies Reveal Hepatocyte Nuclear Factor 4α Regulates UGT2B7 and Emodin Glucuronidation

Emodin is the main toxic component in Chinese medicinal herbs such as rhubarb. Our previous studies demonstrated that genetic polymorphisms of UDP-glucuronosyltransferase 2B7 (UGT2B7) had an effect on the glucuronidation and detoxification of emodin. This study aimed to reveal the transcriptional regulation mechanism of UGT2B7 on emodin glucuronidation and its effect on toxicity. Emodin glucuronic activity and genome and transcriptome data were obtained from 36 clinical human kidney tissues. The genome-wide association studies (GWAS) identified that four single nucleotide polymorphisms (SNPs) (rs6093966, rs2868094, rs2071197, and rs6073433), which were located on the hepatocyte nuclear factor 4α (HNF4A) gene, were significantly associated with the emodin glucuronidation (p < 0.05). Notably, rs2071197 was significantly associated with the gene expression of HNF4A and UGT2B7 and the glucuronidation of emodin. The gene expression of HNF4A showed a high correlation with UGT2B7 (R² = 0.721, p = 5.83 × 10^-11). The luciferase activity was increased 7.68-fold in 293T cells and 2.03-fold in HepG2 cells, confirming a significant transcriptional activation of UGT2B7 promoter by HNF4A. The knockdown of HNF4A in HepG2 cells (36.6%) led to a significant decrease of UGT2B7 (19.8%) and higher cytotoxicity (p < 0.05). The overexpression of HNF4A in HepG2 cells (31.2%) led to a significant increase of UGT2B7 (24.4%) and improved cell viability (p < 0.05). Besides, HNF4A and UGT2B7 were both decreased in HepG2 cells and rats after treatment with emodin. In conclusion, emodin used long term or in high doses could inhibit the expression of HNF4A, thereby reducing the expression of UGT2B7 and causing hepatotoxicity.

Integrating untargeted metabolomics, genetically informed causal inference, and pathway enrichment to define the obesity metabolome

BACKGROUND

Obesity and its associated diseases are major health problems characterized by extensive metabolic disturbances. Understanding the causal connections between these phenotypes and variation in metabolite levels can uncover relevant biology and inform novel intervention strategies. Recent studies have combined metabolite profiling with genetic instrumental variable (IV) analysis (Mendelian randomization) to infer the direction of causality between metabolites and obesity, but often omitted a large portion of untargeted profiling data consisting of unknown, unidentified metabolite signals.

METHODS

We expanded upon previous research by identifying body mass index (BMI)-associated metabolites in multiple untargeted metabolomics datasets, and then performing bidirectional IV analysis to classify metabolites based on their inferred causal relationships with BMI. Meta-analysis and pathway analysis of both known and unknown metabolites across datasets were enabled by our recently developed bioinformatics suite, PAIRUP-MS.

RESULTS

We identified ten known metabolites that are more likely to be causes (e.g., alpha-hydroxybutyrate) or effects (e.g., valine) of BMI, or may have more complex bidirectional cause-effect relationships with BMI (e.g., glycine). Importantly, we also identified about five times more unknown than known metabolites in each of these three categories. Pathway analysis incorporating both known and unknown metabolites prioritized 40 enriched (p < 0.05) metabolite sets for the cause versus effect groups, providing further support that these two metabolite groups are linked to obesity via distinct biological mechanisms.

CONCLUSIONS

These findings demonstrate the potential utility of our approach to uncover causal connections with obesity from untargeted metabolomics datasets. Combining genetically informed causal inference with the ability to map unknown metabolites across datasets provides a path to jointly analyze many untargeted datasets with obesity or other phenotypes. This approach, applied to larger datasets with genotype and untargeted metabolite data, should generate sufficient power for robust discovery and replication of causal biological connections between metabolites and various human diseases.

Slc22a5 haploinsufficiency does not aggravate the phenotype of the long-chain acyl-CoA dehydrogenase KO mouse

Secondary carnitine deficiency is commonly observed in inherited metabolic diseases characterised by the accumulation of acylcarnitines such as mitochondrial fatty acid oxidation (FAO) disorders. It is currently unclear if carnitine deficiency and/or acylcarnitine accumulation play a role in the pathophysiology of FAO disorders. The long-chain acyl-CoA dehydrogenase (LCAD) KO mouse is a model for long-chain FAO disorders and is characterised by decreased levels of tissue and plasma free carnitine. Tissue levels of carnitine are controlled by SLC22A5, the plasmalemmal carnitine transporter. Here, we have further decreased carnitine availability in the LCAD KO mouse through a genetic intervention by introducing one defective Slc22a5 allele (jvs). Slc22a5 haploinsufficiency decreased free carnitine levels in liver, kidney, and heart of LCAD KO animals. The resulting decrease in the tissue long-chain acylcarnitines levels had a similar magnitude as the decrease in free carnitine. Levels of cardiac deoxycarnitine, a carnitine biosynthesis intermediate, were elevated due to Slc22a5 haploinsufficiency in LCAD KO mice. A similar increase in heart and muscle deoxycarnitine was observed in an independent experiment using Slc22a5^jvs/jvs mice. Cardiac hypertrophy, fasting-induced hypoglycemia and increased liver weight, the major phenotypes of the LCAD KO mouse, were not affected by Slc22a5 haploinsufficiency. This may suggest that secondary carnitine deficiency does not play a major role in the pathophysiology of these phenotypes. Similarly, our data do not support a major role for toxicity of long-chain acylcarnitines in the phenotype of the LCAD KO mouse.

Relation of Whole Blood Amino Acid and Acylcarnitine Metabolome to Age, Sex, BMI, Puberty, and Metabolic Markers in Children and Adolescents

Background: Changes in the metabolic fingerprint of blood during child growth and development are a largely under-investigated area of research. The examination of such aspects requires a cohort of healthy children and adolescents who have been subjected to deep phenotyping, including collection of biospecimens for metabolomic analysis. The present study considered whether amino acid (AA) and acylcarnitine (AC) concentrations are associated with age, sex, body mass index (BMI), and puberty during childhood and adolescence. It also investigated whether there are associations between amino acids (AAs) and acylcarnitines (ACs) and laboratory parameters of glucose and lipid metabolism, as well as liver, kidney, and thyroid parameters. Methods: A total of 3989 dried whole blood samples collected from 2191 healthy participants, aged 3 months to 18 years, from the LIFE Child cohort (Leipzig, Germany) were analyzed using liquid chromatography tandem mass spectrometry to detect levels of 23 AAs, 6 ACs, and free carnitine (C0). Age- and sex-related percentiles were estimated for each metabolite. In addition, correlations between laboratory parameters and levels of the selected AAs and ACs were calculated using hierarchical models. Results: Four different age-dependent profile types were identified for AAs and ACs. Investigating the association with puberty, we mainly identified peak metabolite levels at Tanner stages 2 to 3 in girls and stages 3 to 5 in boys. Significant correlations were observed between BMI standard deviation score (BMI-SDS) and certain metabolites, among them, branched-chain (leucine/isoleucine, valine) and aromatic (phenylalanine, tyrosine) amino acids. Most of the metabolites correlated significantly with absolute concentrations of glucose, glycated hemoglobin (HbA1c), triglycerides, cystatin C (CysC), and creatinine. After age adjustment, significant correlations were observed between most metabolites and CysC, as well as HbA1c. Conclusions: During childhood, several AA and AC levels are related to age, sex, BMI, and puberty. Moreover, our data verified known associations but also revealed new correlations between AAs/ACs and specific key markers of metabolic function.

HLA Class II Allele Analyses Implicate Common Genetic Components in Type 1 and Non-Insulin-Treated Type 2 Diabetes

CONTEXT

Common genetic susceptibility may underlie the frequently observed co-occurrence of type 1 and type 2 diabetes in families. Given the role of HLA class II genes in the pathophysiology of type 1 diabetes, the aim of the present study was to test the association of high density imputed human leukocyte antigen (HLA) genotypes with type 2 diabetes.

OBJECTIVES AND DESIGN

Three cohorts (Ntotal = 10 413) from Leipzig, Germany were included in this study: LIFE-Adult (N = 4649), LIFE-Heart (N = 4815) and the Sorbs (N = 949) cohort. Detailed metabolic phenotyping and genome-wide single nucleotide polymorphism (SNP) data were available for all subjects. Using 1000 Genome imputation data, HLA genotypes were imputed on 4-digit level and association tests for type 2 diabetes, and related metabolic traits were conducted.

RESULTS

In a meta-analysis including all 3 cohorts, the absence of HLA-DRB5 was associated with increased risk of type 2 diabetes (P = 0.001). In contrast, HLA-DQB*06:02 and HLA-DQA*01:02 had a protective effect on type 2 diabetes (P = 0.005 and 0.003, respectively). Both alleles are part of the well-established type 1 diabetes protective haplotype DRB1*15:01~DQA1*01:02~DQB1*06:02, which was also associated with reduced risk of type 2 diabetes (OR 0.84; P = 0.005). On the contrary, the DRB1*07:01~DQA1*02:01~DQB1*03:03 was identified as a risk haplotype in non-insulin-treated diabetes (OR 1.37; P = 0.002).

CONCLUSIONS

Genetic variation in the HLA class II locus exerts risk and protective effects on non-insulin-treated type 2 diabetes. Our data suggest that the genetic architecture of type 1 diabetes and type 2 diabetes might share common components on the HLA class II locus.

Powerful and efficient SNP-set association tests across multiple phenotypes using GWAS summary data

MOTIVATION

Many GWAS conducted in the past decade have identified tens of thousands of disease related variants, which in total explained only part of the heritability for most traits. There remain many more genetics variants with small effect sizes to be discovered. This has motivated the development of sequencing studies with larger sample sizes and increased resolution of genotyped variants, e.g., the ongoing NHLBI Trans-Omics for Precision Medicine (TOPMed) whole genome sequencing project. An alternative approach is the development of novel and more powerful statistical methods. The current dominating approach in the field of GWAS analysis is the "single trait single variant" association test, despite the fact that most GWAS are conducted in deeply-phenotyped cohorts with many correlated traits measured. In this paper, we aim to develop rigorous methods that integrate multiple correlated traits and multiple variants to improve the power to detect novel variants. In recognition of the difficulty of accessing raw genotype and phenotype data due to privacy and logistic concerns, we develop methods that are applicable to publicly available GWAS summary data.

RESULTS

We build rigorous statistical models for GWAS summary statistics to motivate novel multi-trait SNP-set association tests, including variance component test, burden test and their adaptive test, and develop efficient numerical algorithms to quickly compute their analytical P-values. We implement the proposed methods in an open source R package. We conduct thorough simulation studies to verify the proposed methods rigorously control type I errors at the genome-wide significance level, and further demonstrate their utility via comprehensive analysis of GWAS summary data for multiple lipids traits and glycemic traits. We identified many novel loci that were not detected by the individual trait based GWAS analysis.

AVAILABILITY AND IMPLEMENTATION

We have implemented the proposed methods in an R package freely available at http://www.github.com/baolinwu/MSKAT.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Clinical and lifestyle related factors influencing whole blood metabolite levels - A comparative analysis of three large cohorts

Objective

Human blood metabolites are influenced by a number of lifestyle and environmental factors. Identification of these factors and the proper quantification of their relevance provides insights into human biological and metabolic disease processes, is key for standardized translation of metabolite biomarkers into clinical applications, and is a prerequisite for comparability of data between studies. However, so far only limited data exist from large and well-phenotyped human cohorts and current methods for analysis do not fully account for the characteristics of these data. The primary aim of this study was to identify, quantify and compare the impact of a comprehensive set of clinical and lifestyle related factors on metabolite levels in three large human cohorts. To achieve this goal, we improve current methodology by developing a principled analysis approach, which could be translated to other cohorts and metabolite panels.

Methods

63 Metabolites (amino acids, acylcarnitines) were quantified by liquid chromatography tandem mass spectrometry in three cohorts (total N = 16,222). Supported by a simulation study evaluating various analytical approaches, we developed an analysis pipeline including preprocessing, identification, and quantification of factors affecting metabolite levels. We comprehensively identified uni- and multivariable metabolite associations considering 29 environmental and clinical factors and performed metabolic pathway enrichment and network analyses.

Results

Inverse normal transformation of batch corrected and outlier removed metabolite levels accompanied by linear regression analysis proved to be the best suited method to deal with the metabolite data. Association analyses revealed numerous uni- and multivariable significant associations. 15 of the analyzed 29 factors explained >1% of variance for at least one of the metabolites. Strongest factors are application of steroid hormones, reticulocytes, waist-to-hip ratio, sex, haematocrit, and age. Effect sizes of factors are comparable across studies.

Conclusions

We introduced a principled approach for the analysis of MS data allowing identification, and quantification of effects of clinical and lifestyle factors with metabolite levels. We detected a number of known and novel associations broadening our understanding of the regulation of the human metabolome. The large heterogeneity observed between cohorts could almost completely be explained by differences in the distribution of influencing factors emphasizing the necessity of a proper confounder analysis when interpreting metabolite associations.

•

Amino-acids and acylcarnitines analyzed in three studies with >16,000 individuals.

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Develop a generic and adaptable bioinformatics workflow.

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Analysis of the impact of 29 clinical and life-style factors on blood metabolites.

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Analysis of network between factors and metabolites.

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Comparison of results between studies.

A collective diabetes cross in combination with a computational framework to dissect the genetics of human obesity and Type 2 diabetes

To explore the genetic determinants of obesity and Type 2 diabetes (T2D), the German Center for Diabetes Research (DZD) conducted crossbreedings of the obese and diabetes-prone New Zealand Obese mouse strain with four different lean strains (B6, DBA, C3H, 129P2) that vary in their susceptibility to develop T2D. Genome-wide linkage analyses localized more than 290 quantitative trait loci (QTL) for obesity, 190 QTL for diabetes-related traits and 100 QTL for plasma metabolites in the outcross populations. A computational framework was developed that allowed to refine critical regions and to nominate a small number of candidate genes by integrating reciprocal haplotype mapping and transcriptome data. The efficiency of the complex procedure was demonstrated for one obesity QTL. The genomic interval of 35 Mb with 502 annotated candidate genes was narrowed down to six candidates. Accordingly, congenic mice retained the obesity phenotype owing to an interval that contains three of the six candidate genes. Among these the phospholipase PLA2G4A exhibited an elevated expression in adipose tissue of obese human subjects and is therefore a critical regulator of the obesity locus. Together, our broad and complex approach demonstrates that combined- and comparative-cross analysis exhibits improved mapping resolution and represents a valid tool for the identification of disease genes.

Function, Detection and Alteration of Acylcarnitine Metabolism in Hepatocellular Carcinoma

Acylcarnitines play an essential role in regulating the balance of intracellular sugar and lipid metabolism. They serve as carriers to transport activated long-chain fatty acids into mitochondria for β-oxidation as a major source of energy for cell activities. The liver is the most important organ for endogenous carnitine synthesis and metabolism. Hepatocellular carcinoma (HCC), a primary malignancy of the live with poor prognosis, may strongly influence the level of acylcarnitines. In this paper, the function, detection and alteration of acylcarnitine metabolism in HCC were briefly reviewed. An overview was provided to introduce the metabolic roles of acylcarnitines involved in fatty acid β-oxidation. Then different analytical platforms and methodologies were also briefly summarised. The relationship between HCC and acylcarnitine metabolism was described. Many of the studies reported that short, medium and long-chain acylcarnitines were altered in HCC patients. These findings presented current evidence in support of acylcarnitines as new candidate biomarkers for studies on the pathogenesis and development of HCC. Finally we discussed the challenges and perspectives of exploiting acylcarnitine metabolism and its related metabolic pathways as a target for HCC diagnosis and prognosis.

Genome-wide meta-analysis identifies novel determinants of circulating serum progranulin

Progranulin is a secreted protein with important functions in processes including immune and inflammatory response, metabolism and embryonic development. The present study aimed at identification of genetic factors determining progranulin concentrations. We conducted a genome-wide association meta-analysis for serum progranulin in three independent cohorts from Europe: Sorbs (N = 848) and KORA (N = 1628) from Germany and PPP-Botnia (N = 335) from Finland (total N = 2811). Single nucleotide polymorphisms (SNPs) associated with progranulin levels were replicated in two additional German cohorts: LIFE-Heart Study (Leipzig; N = 967) and Metabolic Syndrome Berlin Potsdam (Berlin cohort; N = 833). We measured mRNA expression of genes in peripheral blood mononuclear cells (PBMC) by micro-arrays and performed mRNA expression quantitative trait and expression-progranulin association studies to functionally substantiate identified loci. Finally, we conducted siRNA silencing experiments in vitro to validate potential candidate genes within the associated loci. Heritability of circulating progranulin levels was estimated at 31.8% and 26.1% in the Sorbs and LIFE-Heart cohort, respectively. SNPs at three loci reached study-wide significance (rs660240 in CELSR2-PSRC1-MYBPHL-SORT1, rs4747197 in CDH23-PSAP and rs5848 in GRN) explaining 19.4%/15.0% of the variance and 61%/57% of total heritability in the Sorbs/LIFE-Heart Study. The strongest evidence for association was at rs660240 (P = 5.75 × 10-50), which was also associated with mRNA expression of PSRC1 in PBMC (P = 1.51 × 10-21). Psrc1 knockdown in murine preadipocytes led to a consecutive 30% reduction in progranulin secretion. In conclusion, the present meta-GWAS combined with mRNA expression identified three loci associated with progranulin and supports the role of PSRC1 in the regulation of progranulin secretion.

Exploring the genetic basis of gene transcript abundance and metabolite levels in loblolly pine (Pinus taeda L.) using association mapping and network construction

BACKGROUND

Identifying genetic variations that shape important complex traits is fundamental to the genetic improvement of important forest tree species, such as loblolly pine (Pinus taeda L.), which is one of the most commonly planted forest tree species in the southern U.S. Gene transcripts and metabolites are important regulatory intermediates that link genetic variations to higher-order complex traits such as wood development and drought response. A few prior studies have associated intermediate phenotypes including mRNA expression and metabolite levels with a limited number of molecular markers, but the identification of genetic variations that regulate intermediate phenotypes needs further investigation.

RESULTS

We identified 1841 single nucleotide polymorphisms (SNPs) associated with 191 gene expression mRNA phenotypes and 524 SNPs associated with 53 metabolite level phenotypes using 2.8 million exome-derived SNPs. The identified SNPs reside in genes with a wide variety of functions. We further integrated the identified SNPs and the associated expressed genes and metabolites into networks. We described the SNP-SNP interactions that significantly impacted the gene transcript abundance and metabolite level in the networks. Key loci and genes in the wood development and drought response networks were identified and analyzed.

CONCLUSIONS

This work provides new candidate genes for research on the genetic basis of gene expression and metabolism linked to wood development and drought response in loblolly pine and highlights the efficiency of using association-mapping-based networks to discover candidate genes with important roles in complex biological processes.

A variant at 9q34.11 is associated with HLA-DQB1*06:02 negative essential hypersomnia

Essential hypersomnia (EHS) is a lifelong disorder characterized by excessive daytime sleepiness without cataplexy. EHS is associated with human leukocyte antigen (HLA)-DQB1*06:02, similar to narcolepsy with cataplexy (narcolepsy). Previous studies suggest that DQB1*06:02-positive and -negative EHS are different in terms of their clinical features and follow different pathological pathways. DQB1*06:02-positive EHS and narcolepsy share the same susceptibility genes. In the present study, we report a genome-wide association study with replication for DQB1*06:02-negative EHS (408 patients and 2247 healthy controls, all Japanese). One single-nucleotide polymorphism, rs10988217, which is located 15-kb upstream of carnitine O-acetyltransferase (CRAT), was significantly associated with DQB1*06:02-negative EHS (P = 7.5 × 10^-9, odds ratio = 2.63). The risk allele of the disease-associated SNP was correlated with higher expression levels of CRAT in various tissues and cell types, including brain tissue. In addition, the risk allele was associated with levels of succinylcarnitine (P = 1.4 × 10^-18) in human blood. The leading SNP in this region was the same in associations with both DQB1*06:02-negative EHS and succinylcarnitine levels. The results suggest that DQB1*06:02-negative EHS may be associated with an underlying dysfunction in energy metabolic pathways.

Systematic Mendelian randomization framework elucidates hundreds of CpG sites which may mediate the influence of genetic variants on disease

We have undertaken a systematic Mendelian randomization (MR) study using methylation quantitative trait loci (meQTL) as genetic instruments to assess the relationship between genetic variation, DNA methylation and 139 complex traits. Using two-sample MR, we identified 1148 associations across 61 traits where genetic variants were associated with both proximal DNA methylation (i.e. cis-meQTL) and complex trait variation (P < 1.39 × 10-08). Joint likelihood mapping provided evidence that the genetic variant which influenced DNA methylation levels for 348 of these associations across 47 traits was also responsible for variation in complex traits. These associations showed a high rate of replication in the BIOS QTL and UK Biobank datasets for 14 selected traits, as 101 of the attempted 128 associations survived multiple testing corrections (P < 3.91 × 10-04). Integrating expression quantitative trait loci (eQTL) data suggested that genetic variants responsible for 306 of the 348 refined meQTL associations also influence gene expression, which indicates a coordinated system of effects that are consistent with causality. CpG sites were enriched for histone mark peaks in tissue types relevant to their associated trait and implicated genes were enriched across relevant biological pathways. Though we are unable to distinguish mediation from horizontal pleiotropy in these analyses, our findings should prove valuable in prioritizing candidate loci where DNA methylation may influence traits and help develop mechanistic insight into the aetiology of complex disease.

Genome-Wide Association Studies of Metabolites in Patients with CKD Identify Multiple Loci and Illuminate Tubular Transport Mechanisms

Background The kidneys have a central role in the generation, turnover, transport, and excretion of metabolites, and these functions can be altered in CKD. Genetic studies of metabolite concentrations can identify proteins performing these functions.Methods We conducted genome-wide association studies and aggregate rare variant tests of the concentrations of 139 serum metabolites and 41 urine metabolites, as well as their pairwise ratios and fractional excretions in up to 1168 patients with CKD.Results After correction for multiple testing, genome-wide significant associations were detected for 25 serum metabolites, two urine metabolites, and 259 serum and 14 urinary metabolite ratios. These included associations already known from population-based studies. Additional findings included an association for the uremic toxin putrescine and variants upstream of an enzyme catalyzing the oxidative deamination of polyamines (AOC1, P-min=2.4×10^-12), a relatively high carrier frequency (2%) for rare deleterious missense variants in ACADM that are collectively associated with serum ratios of medium-chain acylcarnitines (P-burden=6.6×10^-16), and associations of a common variant in SLC7A9 with several ratios of lysine to neutral amino acids in urine, including the lysine/glutamine ratio (P=2.2×10^-23). The associations of this SLC7A9 variant with ratios of lysine to specific neutral amino acids were much stronger than the association with lysine concentration alone. This finding is consistent with SLC7A9 functioning as an exchanger of urinary cationic amino acids against specific intracellular neutral amino acids at the apical membrane of proximal tubular cells.Conclusions Metabolomic indices of specific kidney functions in genetic studies may provide insight into human renal physiology.

Sphingolipid Metabolic Pathway Impacts Thiazide Diuretics Blood Pressure Response: Insights From Genomics, Metabolomics, and Lipidomics

BACKGROUND

Although hydrochlorothiazide (HCTZ) is a well-established first-line antihypertensive in the United States, <50% of HCTZ treated patients achieve blood pressure (BP) control. Thus, identifying biomarkers that could predict the BP response to HCTZ is critically important. In this study, we utilized metabolomics, genomics, and lipidomics to identify novel pathways and biomarkers associated with HCTZ BP response.

METHODS AND RESULTS

First, we conducted a pathway analysis for 13 metabolites we recently identified to be significantly associated with HCTZ BP response. From this analysis, we found the sphingolipid metabolic pathway as the most significant pathway (P=5.8E-05). Testing 78 variants, within 14 genes involved in the sphingolipid metabolic canonical pathway, with the BP response to HCTZ identified variant rs6078905, within the SPTLC3 gene, as a novel biomarker significantly associated with the BP response to HCTZ in whites (n=228). We found that rs6078905 C-allele carriers had a better BP response to HCTZ versus noncarriers (∆SBP/∆DBP: -11.4/-6.9 versus -6.8/-3.5 mm Hg; ∆SBP P=6.7E-04; ∆DBP P=4.8E-04). Additionally, in blacks (n=148), we found genetic signals in the SPTLC3 genomic region significantly associated with the BP response to HCTZ (P<0.05). Last, we observed that rs6078905 significantly affects the baseline level of 4 sphingomyelins (N24:2, N24:3, N16:1, and N22:1; false discovery rate <0.05), from which N24:2 sphingomyelin has a significant correlation with both HCTZ DBP-response (r=-0.42; P=7E-03) and SBP-response (r=-0.36; P=2E-02).

CONCLUSIONS

This study provides insight into potential pharmacometabolomic and genetic mechanisms underlying HCTZ BP response and suggests that SPTLC3 is a potential determinant of the BP response to HCTZ.

CLINICAL TRIAL REGISTRATION

URL: http://www.clinicaltrials.gov. Unique identifier: NCT00246519.

Transcriptome-wide association study revealed two novel genes associated with nonobstructive azoospermia in a Chinese population

OBJECTIVE

To investigate the associations between genetically cis-regulated gene expression levels and nonobstructive azoospermia (NOA) susceptibility.

DESIGN

Transcriptome-wide association study (TWAS).

SETTING

Medical university.

INTERVENTIONS

None.

MAIN OUTCOME MEASURE(S)

The cis-hg² values for each gene were estimated with GCTA software. The effect sizes of cis-single-nucleotide polymorphisms (SNPs) on gene expression were measured using GEMMA software. Gene expression levels were entered into our existing NOA GWAS cohort using GEMMA software. The TWAS P-values were calculated using logistic regression models.

RESULT(S)

Expression levels of 1,296 cis-heritable genes were entered into our existing NOA GWAS data. The TWAS results identified two novel genes as statistically significantly associated with NOA susceptibility: PILRA and ZNF676. In addition, 6p21.32, previously reported in NOA GWAS, was further validated to be a susceptible region to NOA risk.

CONCLUSION(S)

Analysis with TWAS provides fruitful targets for follow-up functional studies.

Association between lipoprotein(a) level and type 2 diabetes: no evidence for a causal role of lipoprotein(a) and insulin

AIMS

Inverse relationships have been described between the largely genetically determined levels of serum/plasma lipoprotein(a) [Lp(a)], type 2 diabetes (T2D) and fasting insulin. Here, we aimed to evaluate the nature of these relationships with respect to causality.

METHODS

We tested whether we could replicate the recent negative findings on causality between Lp(a) and T2D by employing the Mendelian randomization (MR) approach using cross-sectional data from three independent cohorts, Berlin Aging Study II (BASE-II; n = 2012), LIFE-Adult (n = 3281) and LIFE-Heart (n = 2816). Next, we explored another frequently discussed hypothesis in this context: Increasing insulin levels during the course of T2D disease development inhibits hepatic Lp(a) synthesis and thereby might explain the inverse Lp(a)-T2D association. We used two fasting insulin-associated variants, rs780094 and rs10195252, as instrumental variables in MR analysis of n = 4937 individuals from BASE-II and LIFE-Adult. We further investigated causality of the association between fasting insulin and Lp(a) by combined MR analysis of 12 additional SNPs in LIFE-Adult.

RESULTS

While an Lp(a)-T2D association was observed in the combined analysis (meta-effect of OR [95% CI] = 0.91 [0.87-0.96] per quintile, p = 1.3x10^-4), we found no evidence of causality in the Lp(a)-T2D association (p = 0.29, fixed effect model) when using the variant rs10455872 as the instrumental variable in the MR analyses. Likewise, no evidence of a causal effect of insulin on Lp(a) levels was found.

CONCLUSIONS

While these results await confirmation in larger cohorts, the nature of the inverse Lp(a)-T2D association remains to be elucidated.

Mendelian Randomization Analysis Identifies CpG Sites as Putative Mediators for Genetic Influences on Cardiovascular Disease Risk

The extent to which genetic influences on cardiovascular disease risk are mediated by changes in DNA methylation levels has not been systematically explored. We developed an analytical framework that integrates genetic fine mapping and Mendelian randomization with epigenome-wide association studies to evaluate the causal relationships between methylation levels and 14 cardiovascular disease traits. We identified ten genetic loci known to influence proximal DNA methylation which were also associated with cardiovascular traits after multiple-testing correction. Bivariate fine mapping provided evidence that the individual variants responsible for the observed effects on cardiovascular traits at the ADCY3 and ADIPOQ loci were potentially mediated through changes in DNA methylation, although we highlight that we are unable to reliably separate causality from horizontal pleiotropy. Estimates of causal effects were replicated with results from large-scale consortia. Genetic variants and CpG sites identified in this study were enriched for histone mark peaks in relevant tissue types and gene promoter regions. Integrating our results with expression quantitative trait loci data, we provide evidence that variation at these regulatory regions is likely to also influence gene expression levels at these loci.

Gene-Diet Interaction and Precision Nutrition in Obesity

The rapid rise of obesity during the past decades has coincided with a profound shift of our living environment, including unhealthy dietary patterns, a sedentary lifestyle, and physical inactivity. Genetic predisposition to obesity may have interacted with such an obesogenic environment in determining the obesity epidemic. Growing studies have found that changes in adiposity and metabolic response to low-calorie weight loss diets might be modified by genetic variants related to obesity, metabolic status and preference to nutrients. This review summarized data from recent studies of gene-diet interactions, and discussed integration of research of metabolomics and gut microbiome, as well as potential application of the findings in precision nutrition.