Proof of concept for quantitative urine NMR metabolomics pipeline for large-scale epidemiology and genetics

Abstract Background Quantitative molecular data from urine are rare in epidemiology and genetics. NMR spectroscopy could provide these data in high throughput, and it has already been applied in epidemiological settings to analyse urine samples. However, quantitative protocols for large-scale applications are not available. Methods We describe in detail how to prepare urine samples and perform NMR experiments to obtain quantitative metabolic information. Semi-automated quantitative line shape fitting analyses were set up for 43 metabolites and applied to data from various analytical test samples and from 1004 individuals from a population-based epidemiological cohort. Novel analyses on how urine metabolites associate with quantitative serum NMR metabolomics data (61 metabolic measures; n = 995) were performed. In addition, confirmatory genome-wide analyses of urine metabolites were conducted (n = 578). The fully automated quantitative regression-based spectral analysis is demonstrated for creatinine and glucose (n = 4548). Results Intra-assay metabolite variations were mostly <5%, indicating high robustness and accuracy of urine NMR spectroscopy methodology per se. Intra-individual metabolite variations were large, ranging from 6% to 194%. However, population-based inter-individual metabolite variations were even larger (from 14% to 1655%), providing a sound base for epidemiological applications. Metabolic associations between urine and serum were found to be clearly weaker than those within serum and within urine, indicating that urinary metabolomics data provide independent metabolic information. Two previous genome-wide hits for formate and 2-hydroxyisobutyrate were replicated at genome-wide significance. Conclusion Quantitative urine metabolomics data suggest broad novelty for systems epidemiology. A roadmap for an open access methodology is provided.

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Proof of concept for quantitative urine NMR metabolomics pipeline for large-scale epidemiology and genetics

10.1101/288993 ◽

2018 ◽

Author(s):

Tuulia Tynkkynen ◽

Qin Wang ◽

Jussi Ekholm ◽

Olga Anufrieva ◽

Pauli Ohukainen ◽

...

Keyword(s):

Nmr Spectroscopy ◽

Large Scale ◽

Molecular Data ◽

Population Based ◽

Urine Samples ◽

Metabolomics Data ◽

Urine Metabolites ◽

Nmr Metabolomics ◽

Genome Wide ◽

Metabolic Information

AbstractBackgroundQuantitative molecular data from urine are rare in epidemiology and genetics. NMR spectroscopy could provide these data in high-throughput, and it has already been applied in epidemiological settings to analyse urine samples. However, quantitative protocols for large-scale applications are not available.MethodsWe describe in detail how to prepare urine samples and perform NMR experiments to obtain quantitative metabolic information. Semi-automated quantitative lineshape fitting analyses were set up for 43 metabolites and applied to data from various analytical test samples and from 1,004 individuals from a population-based epidemiological cohort. Novel analyses on how urine metabolites associate with quantitative serum NMR metabolomics data (61 metabolic measures; n=995) were performed. In addition, confirmatory genome-wide analyses of urine metabolites were conducted (n=578). The fully automated quantitative regression-based spectral analysis is demonstrated for creatinine and glucose (n= 4,548).ResultsIntra-assay metabolite variations were mostly <5% indicating high robustness and accuracy of the urine NMR spectroscopy methodology per se. Intra-individual metabolite variations were large, ranging from 6% to 194%. However, population-based inter-individual metabolite variations were even larger (from 14% to 1655%), providing a sound base for epidemiological applications. Metabolic associations between urine and serum were found clearly weaker than those within serum and within urine, indicating that urinary metabolomics data provide independent metabolic information. Two previous genome-wide hits for formate and 2-hydroxyisobutyrate were replicated at genome-wide significance.ConclusionsQuantitative urine metabolomics data suggest broad novelty for systems epidemiology. A roadmap for an open access methodology is provided.

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Combined effects of host genetics and diet on human gut microbiota and incident disease in a single population cohort

10.1101/2020.09.12.20193045 ◽

2020 ◽

Author(s):

Youwen Qin ◽

Aki S Havulinna ◽

Yang Liu ◽

Pekka Jousilahti ◽

Scott C Ritchie ◽

...

Keyword(s):

Gut Microbiota ◽

Microbial Communities ◽

Large Scale ◽

Genome Wide Association Study ◽

Dietary Habits ◽

Population Based ◽

Genome Wide ◽

Scale Population ◽

Important Health ◽

Disease Analysis

Co-evolution between humans and the microbial communities colonizing them has resulted in an intimate assembly of thousands of microbial species mutualistically living on and in their body and impacting multiple aspects of host physiology and health. Several studies examining whether human genetic variation can affect gut microbiota suggest a complex combination of environmental and host factors. Here, we leverage a single large-scale population-based cohort of 5,959 genotyped individuals with matched gut microbial shotgun metagenomes, dietary information and health records up to 16 years post-sampling, to characterize human genetic variations associated with microbial abundances, and predict possible causal links with various diseases using Mendelian randomization (MR). Genome-wide association study (GWAS) identified 583 independent SNP-taxon associations at genome-wide significance (p<5.0×10-8), which included notable strong associations with LCT (p=5.02×10-35), ABO (p=1.1×10-12), and MED13L (p=1.84×10-12). A combination of genetics and dietary habits was shown to strongly shape the abundances of certain key bacterial members of the gut microbiota, and explain their genetic association. Genetic effects from the LCT locus on Bifidobacterium and three other associated taxa significantly differed according to dairy intake. Variation in mucin-degrading Faecalicatena lactaris abundances were associated with ABO, highlighting a preferential utilization of secreted A/B/AB-antigens as energy source in the gut, irrespectively of fibre intake. Enterococcus faecalis levels showed a robust association with a variant in MED13L, with putative links to colorectal cancer. Finally, we identified putative causal relationships between gut microbes and complex diseases using MR, with a predicted effect of Morganella on major depressive disorder that was consistent with observational incident disease analysis. Overall, we present striking examples of the intricate relationship between humans and their gut microbial communities, and highlight important health implications.

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Metabolomics: population epidemiology and concordance in Australian children aged 11–12 years and their parents

BMJ Open ◽

10.1136/bmjopen-2017-020900 ◽

2019 ◽

Vol 9 (Suppl 3) ◽

pp. 106-117 ◽

Cited By ~ 12

Author(s):

Susan Ellul ◽

Melissa Wake ◽

Susan A Clifford ◽

Katherine Lange ◽

Peter Würtz ◽

...

Keyword(s):

Fatty Acids ◽

Energy Homeostasis ◽

Population Based ◽

Lipoprotein Subclass ◽

Cross Sectional ◽

Metabolomics Data ◽

Nmr Metabolomics ◽

Markers Of Inflammation ◽

Age And Sex ◽

Parent Child

ObjectivesNuclear magnetic resonance (NMR) metabolomics is high throughput and cost-effective, with the potential to improve the understanding of disease and risk. We examine the circulating metabolic profile by quantitative NMR metabolomics of a sample of Australian 11–12 year olds children and their parents, describe differences by age and sex, and explore the correlation of metabolites in parent–child dyads.DesignThe population-based cross-sectional Child Health CheckPoint study nested within the Longitudinal Study of Australian Children.SettingBlood samples collected from CheckPoint participants at assessment centres in seven Australian cities and eight regional towns; February 2015–March 2016.Participants1180 children and 1325 parents provided a blood sample and had metabolomics data available. This included 1133 parent–child dyads (518 mother–daughter, 469 mother–son, 68 father–daughter and 78 father–son).Outcome measures228 metabolic measures were obtained for each participant. We focused on 74 biomarkers including amino acid species, lipoprotein subclass measures, lipids, fatty acids, measures related to fatty acid saturation, and composite markers of inflammation and energy homeostasis.ResultsWe identified differences in the concentration of specific metabolites between childhood and adulthood and in metabolic profiles in children and adults by sex. In general, metabolite concentrations were higher in adults than children and sex differences were larger in adults than in children. Positive correlations were observed for the majority of metabolites including isoleucine (CC 0.33, 95% CI 0.27 to 0.38), total cholesterol (CC 0.30, 95% CI 0.24 to 0.35) and omega 6 fatty acids (CC 0.28, 95% CI 0.23 to 0.34) in parent–child comparisons.ConclusionsWe describe the serum metabolite profiles from mid-childhood and adulthood in a population-based sample, together with a parent–child concordance. Differences in profiles by age and sex were observed. These data will be informative for investigation of the childhood origins of adult non-communicable diseases and for comparative studies in other populations.

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Direct Implementation of Intestinal Permeability Test in NMR Metabolomics for Simultaneous Biomarker Discovery—A Feasibility Study in a Preterm Piglet Model

Metabolites ◽

10.3390/metabo10010022 ◽

2020 ◽

Vol 10 (1) ◽

pp. 22 ◽

Cited By ~ 1

Author(s):

Masoumeh Alinaghi ◽

Duc Ninh Nguyen ◽

Per Torp Sangild ◽

Hanne Christine Bertram

Keyword(s):

Intestinal Permeability ◽

1H Nmr ◽

Biomarker Discovery ◽

Urine Samples ◽

Gastrointestinal Disorders ◽

Urinary Recovery ◽

Metabolomics Data ◽

Enzymatic Assays ◽

Nmr Metabolomics ◽

Plasma And Urine Samples

Measurement of intestinal permeability (IP) is often used in the examination of inflammatory gastrointestinal disorders. IP can be assessed by measurement of urinary recovery of ingested non-metabolizable lactulose (L) and mannitol (M). The present study aimed to examine how measurements of IP can be integrated in a NMR-based metabolomics approach for a simultaneous quantification of L/M ratio and biomarker exploration. For this purpose, plasma and urine samples were collected from five-day-old preterm piglets (n = 20) with gastrointestinal disorders (subjected to intra-amniotic lipopolysaccharide (LPS, 1 mg/fetus)) after they had been administrated a 5% lactulose and 5% mannitol solution (15 mL/kg). The collected plasma and urine samples were analyzed by 1H NMR-based metabolomics. Urine L/M ratio measured by 1H NMR spectroscopy showed high correlation with the standard measurement of the urinary recoveries by enzymatic assays (r = 0.93, p < 0.05). Partial least squares (PLS) regressions and correlation analyses between L/M ratio and NMR metabolomics data revealed that L/M ratio was positively correlated with plasma lactate, acetate and succinate levels and negatively correlated with urinary hippuric acid and glycine. In conclusion, the present study demonstrated that NMR metabolomics enables simultaneous IP testing and discovery of biomarkers associated with an impaired intestinal permeability.

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Urine Metabolomics by 1H-NMR Spectroscopy Indicates Associations between Serum 3,5-T2 Concentrations and Intermediary Metabolism in Euthyroid Humans

European Thyroid Journal ◽

10.1159/000381308 ◽

2015 ◽

Vol 4 (Suppl. 1) ◽

pp. 92-100 ◽

Cited By ~ 22

Author(s):

Maik Pietzner ◽

Georg Homuth ◽

Kathrin Budde ◽

Ina Lehmphul ◽

Uwe Völker ◽

...

Keyword(s):

Lipid Metabolism ◽

Nmr Spectroscopy ◽

1H Nmr ◽

1H Nmr Spectroscopy ◽

Population Based ◽

Metabolic Effects ◽

Linear Regression Models ◽

Urine Metabolites ◽

Glucose And Lipid Metabolism ◽

Design And Methods

Context: 3,5-Diiodo-L-thyronine (3,5-T2) is a thyroid hormone metabolite which exhibited versatile effects in rodent models, including the prevention of insulin resistance or hepatic steatosis typically forced by a high-fat diet. With respect to euthyroid humans, we recently observed a putative link between serum 3,5-T2 and glucose but not lipid metabolism. Objective: The aim of the present study was to widely screen the urine metabolome for associations with serum 3,5-T2 concentrations in healthy individuals. Study Design and Methods: Urine metabolites of 715 euthyroid participants of the population-based Study of Health in Pomerania (SHIP-TREND) were analyzed by 1H-NMR spectroscopy. Multinomial logistic and multivariate linear regression models were used to detect associations between urine metabolites and serum 3,5-T2 concentrations. Results: Serum 3,5-T2 concentrations were positively associated with urinary levels of trigonelline, pyroglutamate, acetone and hippurate. In detail, the odds for intermediate or suppressed serum 3,5-T2 concentrations doubled owing to a 1-standard deviation (SD) decrease in urine trigonelline levels, or increased by 29-50% in relation to a 1-SD decrease in urine pyroglutamate, acetone and hippurate levels. Conclusion: Our findings in humans confirmed the metabolic effects of circulating 3,5-T2 on glucose and lipid metabolism, oxidative stress and enhanced drug metabolism as postulated before based on interventional pharmacological studies in rodents. Of note, 3,5-T2 exhibited a unique urinary metabolic profile distinct from previously published results for the classical thyroid hormones.

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Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis

Metabolites ◽

10.3390/metabo11050285 ◽

2021 ◽

Vol 11 (5) ◽

pp. 285

Author(s):

Kristina E. Haslauer ◽

Philippe Schmitt-Kopplin ◽

Silke S. Heinzmann

Keyword(s):

Nmr Spectroscopy ◽

Data Processing ◽

Large Scale ◽

Linear Regression Analysis ◽

Untargeted Metabolomics ◽

Metabolomics Data ◽

Non Linear ◽

Data Processing Tool ◽

Processing Optimization ◽

Spectral Libraries

Nuclear magnetic resonance (NMR) spectroscopy is well-established to address questions in large-scale untargeted metabolomics. Although several approaches in data processing and analysis are available, significant issues remain. NMR spectroscopy of urine generates information-rich but complex spectra in which signals often overlap. Furthermore, slight changes in pH and salt concentrations cause peak shifting, which introduces, in combination with baseline irregularities, un-informative noise in statistical analysis. Within this work, a straight-forward data processing tool addresses these problems by applying a non-linear curve fitting model based on Voigt function line shape and integration of the underlying peak areas. This method allows a rapid untargeted analysis of urine metabolomics datasets without relying on time-consuming 2D-spectra based deconvolution or information from spectral libraries. The approach is validated with spiking experiments and tested on a human urine 1H dataset compared to conventionally used methods and aims to facilitate metabolomics data analysis.

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Direct Estimation of HDL-Mediated Cholesterol Efflux Capacity from Serum

Clinical Chemistry ◽

10.1373/clinchem.2018.299222 ◽

2019 ◽

Vol 65 (8) ◽

pp. 1042-1050 ◽

Cited By ~ 3

Author(s):

Sanna Kuusisto ◽

Michael V Holmes ◽

Pauli Ohukainen ◽

Antti J Kangas ◽

Mari Karsikas ◽

...

Keyword(s):

Nmr Spectroscopy ◽

Cholesterol Efflux ◽

Large Scale ◽

Cost Effective ◽

Population Based ◽

Protective Role ◽

New Method ◽

Cholesterol Efflux Capacity ◽

Cell Assays

Abstract BACKGROUND HDL-mediated cholesterol efflux capacity (HDL-CEC) is a functional attribute that may have a protective role in atherogenesis. However, the estimation of HDL-CEC is based on in vitro cell assays that are laborious and hamper large-scale phenotyping. METHODS Here, we present a cost-effective high-throughput nuclear magnetic resonance (NMR) spectroscopy method to estimate HDL-CEC directly from serum. We applied the new method in a population-based study of 7603 individuals including 574 who developed incident coronary heart disease (CHD) during 15 years of follow-up, making this the largest quantitative study for HDL-CEC. RESULTS As estimated by NMR-spectroscopy, a 1-SD higher HDL-CEC was associated with a lower risk of incident CHD (hazards ratio, 0.86; 95%CI, 0.79–0.93, adjusted for traditional risk factors and HDL-C). These findings are consistent with published associations based on in vitro cell assays. CONCLUSIONS These corroborative large-scale findings provide further support for a potential protective role of HDL-CEC in CHD and substantiate this new method and its future applications.

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BIGwas: Single-command quality control and association testing for multi-cohort and biobank-scale GWAS/PheWAS data

GigaScience ◽

10.1093/gigascience/giab047 ◽

2021 ◽

Vol 10 (6) ◽

Cited By ~ 1

Author(s):

Jan Christian Kässens ◽

Lars Wienbrandt ◽

David Ellinghaus

Keyword(s):

Quality Control ◽

High Performance ◽

Large Scale ◽

Association Studies ◽

Population Based ◽

Genome Wide Association Studies ◽

Association Testing ◽

Genome Wide ◽

Software Execution ◽

Automated Quality Control

Abstract Background Genome-wide association studies (GWAS) and phenome-wide association studies (PheWAS) involving 1 million GWAS samples from dozens of population-based biobanks present a considerable computational challenge and are carried out by large scientific groups under great expenditure of time and personnel. Automating these processes requires highly efficient and scalable methods and software, but so far there is no workflow solution to easily process 1 million GWAS samples. Results Here we present BIGwas, a portable, fully automated quality control and association testing pipeline for large-scale binary and quantitative trait GWAS data provided by biobank resources. By using Nextflow workflow and Singularity software container technology, BIGwas performs resource-efficient and reproducible analyses on a local computer or any high-performance compute (HPC) system with just 1 command, with no need to manually install a software execution environment or various software packages. For a single-command GWAS analysis with 974,818 individuals and 92 million genetic markers, BIGwas takes ∼16 days on a small HPC system with only 7 compute nodes to perform a complete GWAS QC and association analysis protocol. Our dynamic parallelization approach enables shorter runtimes for large HPCs. Conclusions Researchers without extensive bioinformatics knowledge and with few computer resources can use BIGwas to perform multi-cohort GWAS with 1 million GWAS samples and, if desired, use it to build their own (genome-wide) PheWAS resource. BIGwas is freely available for download from http://github.com/ikmb/gwas-qc and http://github.com/ikmb/gwas-assoc.

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Direct estimation of HDL-mediated cholesterol efflux capacity from serum

10.1101/396929 ◽

2018 ◽

Author(s):

Sanna Kuusisto ◽

Michael V. Holmes ◽

Pauli Ohukainen ◽

Antti J. Kangas ◽

Mari Karsikas ◽

...

Keyword(s):

Nmr Spectroscopy ◽

Cholesterol Efflux ◽

Large Scale ◽

Density Lipoprotein ◽

Population Based ◽

Protective Role ◽

New Method ◽

Cholesterol Efflux Capacity ◽

Cell Assays

AbstractHigh-density lipoprotein mediated cholesterol efflux capacity (HDL-CEC) is a functional attribute that may have a protective role in atherogenesis. However, the estimation of HDL-CEC is based on in vitro cell assays that are laborious and hamper large-scale phenotyping. Here, we present a cost-effective high-throughput nuclear magnetic resonance (NMR) spectroscopy method to estimate HDL-CEC directly from serum. We applied the new method in a population-based study of 7,603 individuals including 574 who developed incident coronary heart disease (CHD) during 15 years of follow-up, making this the largest quantitative study for HDL-CEC. As estimated by NMR-spectroscopy, a 1-SD higher HDL-CEC was associated with a lower risk of incident CHD (hazards ratio 0.86; 95%CI 0.79-0.93, adjusted for traditional risk factors and HDL-C). These findings are consistent with published associations based on in vitro cell assays. These corroborative large-scale findings provide further support for a potential protective role of HDL-CEC in CHD, and substantiate this new method and its future applications.

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A large-scale genomic investigation of susceptibility to infection and its association with mental disorders in the Danish population

Translational Psychiatry ◽

10.1038/s41398-019-0622-3 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 5

Author(s):

Ron Nudel ◽

Yunpeng Wang ◽

Vivek Appadurai ◽

Andrew J. Schork ◽

Alfonso Buil ◽

...

Keyword(s):

Mental Disorders ◽

Genetic Correlation ◽

Psychiatric Diagnosis ◽

Large Scale ◽

Population Based ◽

Genome Wide Association ◽

Danish Population ◽

Polygenic Risk ◽

Susceptibility To Infection ◽

Genome Wide

Abstract Infections and mental disorders are two of the major global disease burdens. While correlations between mental disorders and infections have been reported, the possible genetic links between them have not been assessed in large-scale studies. Moreover, the genetic basis of susceptibility to infection is largely unknown, as large-scale genome-wide association studies of susceptibility to infection have been lacking. We utilized a large Danish population-based sample (N = 65,534) linked to nationwide population-based registers to investigate the genetic architecture of susceptibility to infection (heritability estimation, polygenic risk analysis, and a genome-wide association study (GWAS)) and examined its association with mental disorders (comorbidity analysis and genetic correlation). We found strong links between having at least one psychiatric diagnosis and the occurrence of infection (P = 2.16 × 10−208, OR = 1.72). The SNP heritability of susceptibility to infection ranged from ~2 to ~7% in samples of differing psychiatric diagnosis statuses (suggesting the environment as a major contributor to susceptibility), and polygenic risk scores moderately but significantly explained infection status in an independent sample. We observed a genetic correlation of 0.496 (P = 2.17 × 10−17) between a diagnosis of infection and a psychiatric diagnosis. While our GWAS did not identify genome-wide significant associations, we found 90 suggestive (P ≤ 10−5) associations for susceptibility to infection. Our findings suggest a genetic component in susceptibility to infection and indicate that the occurrence of infections in individuals with mental illness may be in part genetically driven.

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