Untargeted Metabolome Quantitative Trait Locus Mapping Associates Variation in Urine Glycerate to Mutant Glycerate Kinase

A large-scale genome-lipid association map guides lipid identification

Despite the crucial roles of lipids in metabolism, we are still at the early stages of comprehensively annotating lipid species and their genetic basis. Mass spectrometry-based discovery lipidomics offers the potential to globally survey lipids and their relative abundances in various biological samples. To discover the genetics of lipid features obtained through high-resolution liquid chromatography-tandem mass spectrometry, we analysed liver and plasma from 384 diversity outbred mice, and quantified 3,283 molecular features. These features were mapped to 5,622 lipid quantitative trait loci and compiled into a public web resource termed LipidGenie. The data are cross-referenced to the human genome and offer a bridge between genetic associations in humans and mice. Harnessing this resource, we used genome-lipid association data as an additional aid to identify a number of lipids, for example gangliosides through their association with B4galnt1, and found evidence for a group of sex-specific phosphatidylcholines through their shared locus. Finally, LipidGenie's ability to query either mass or gene-centric terms suggests acyl-chain-specific functions for proteins of the ABHD family.

pJRES Binning Algorithm (JBA): a new method to facilitate the recovery of metabolic information from pJRES 1H NMR spectra

Motivation

Data processing is a key bottleneck for ¹H NMR-based metabolic profiling of complex biological mixtures, such as biofluids. These spectra typically contain several thousands of signals, corresponding to possibly few hundreds of metabolites. A number of binning-based methods have been proposed to reduce the dimensionality of 1 D ¹H NMR datasets, including statistical recoupling of variables (SRV). Here, we introduce a new binning method, named JBA (“pJRES Binning Algorithm”), which aims to extend the applicability of SRV to pJRES spectra.

Results

The performance of JBA is comprehensively evaluated using 617 plasma ¹H NMR spectra from the FGENTCARD cohort. The results presented here show that JBA exhibits higher sensitivity than SRV to detect peaks from low-abundance metabolites. In addition, JBA allows a more efficient removal of spectral variables corresponding to pure electronic noise, and this has a positive impact on multivariate model building

Availability and implementation

The algorithm is implemented using the MWASTools R/Bioconductor package.

Supplementary information

Supplementary data are available at Bioinformatics online.

Analysis of NMR Metabolomics Data

In this chapter, we summarize data preprocessing and data analysis strategies used for analysis of NMR data for metabolomics studies. Metabolomics consists of the analysis of the low molecular weight compounds in cells, tissues, or biological fluids, and has been used to reveal biomarkers for early disease detection and diagnosis, to monitor interventions, and to provide information on pathway perturbations to inform mechanisms and identifying targets. Metabolic profiling (also termed metabotyping) involves the analysis of hundreds to thousands of molecules using mainly state-of-the-art mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy technologies. While NMR is less sensitive than mass spectrometry, NMR does provide a wealth of complex and information rich metabolite data. NMR data together with the use of conventional statistics, modeling methods, and bioinformatics tools reveals biomarker and mechanistic information. A typical NMR spectrum, with up to 64k data points, of a complex biological fluid or an extract of cells and tissues consists of thousands of sharp signals that are mainly derived from small molecules. In addition, a number of advanced NMR spectroscopic methods are available for extracting information on high molecular weight compounds such as lipids or lipoproteins. There are numerous data preprocessing, data reduction, and analysis methods developed and evolving in the field of NMR metabolomics. Our goal is to provide an extensive summary of NMR data preprocessing and analysis strategies by providing examples and open source and commercially available analysis software and bioinformatics tools.

Mild inborn errors of metabolism in commonly used inbred mouse strains

Inbred mouse strains are a cornerstone of translational research but paradoxically many strains carry mild inborn errors of metabolism. For example, α-aminoadipic acidemia and branched-chain ketoacid dehydrogenase deficiency are known in C57BL/6J mice. Using RNA sequencing, we now reveal the causal variants in Dhtkd1 and Bckdhb, and the molecular mechanism underlying these metabolic defects. C57BL/6J mice have decreased Dhtkd1 mRNA expression due to a solitary long terminal repeat (LTR) in intron 4 of Dhtkd1. This LTR harbors an alternate splice donor site leading to a partial splicing defect and as a consequence decreased total and functional Dhtkd1 mRNA, decreased DHTKD1 protein and α-aminoadipic acidemia. Similarly, C57BL/6J mice have decreased Bckdhb mRNA expression due to an LTR retrotransposon in intron 1 of Bckdhb. This transposable element encodes an alternative exon 1 causing aberrant splicing, decreased total and functional Bckdhb mRNA and decreased BCKDHB protein. Using a targeted metabolomics screen, we also reveal elevated plasma C5-carnitine in 129 substrains. This biochemical phenotype resembles isovaleric acidemia and is caused by an exonic splice mutation in Ivd leading to partial skipping of exon 10 and IVD protein deficiency. In summary, this study identifies three causal variants underlying mild inborn errors of metabolism in commonly used inbred mouse strains.

Systems Genetics of Hepatic Metabolome Reveals Octopamine as a Target for Non-Alcoholic Fatty Liver Disease Treatment

Non-alcoholic fatty liver disease (NAFLD) is often associated with obesity and type 2 diabetes. To disentangle etiological relationships between these conditions and identify genetically-determined metabolites involved in NAFLD processes, we mapped 1H nuclear magnetic resonance (NMR) metabolomic and disease-related phenotypes in a mouse F2 cross derived from strains showing resistance (BALB/c) and increased susceptibility (129S6) to these diseases. Quantitative trait locus (QTL) analysis based on single nucleotide polymorphism (SNP) genotypes identified diet responsive QTLs in F2 mice fed control or high fat diet (HFD). In HFD fed F2 mice we mapped on chromosome 18 a QTL regulating liver micro- and macrovesicular steatosis and inflammation, independently from glucose intolerance and adiposity, which was linked to chromosome 4. Linkage analysis of liver metabolomic profiling data identified a QTL for octopamine, which co-localised with the QTL for liver histopathology in the cross. Functional relationship between these two QTLs was validated in vivo in mice chronically treated with octopamine, which exhibited reduction in liver histopathology and metabolic benefits, underlining its role as a mechanistic biomarker of fatty liver with potential therapeutic applications.

Advances in Computational Analysis of Metabolomic NMR Data

In this chapter we discuss some of the more recent developments in preprocessing and statistical analysis of NMR spectra in metabolomics. Bayesian methods for analyzing NMR spectra are summarized and we describe one particular approach, BATMAN, in more detail. We consider techniques based on statistical associations, such as correlation spectroscopy (e.g. STOCSY and recent variants), as well as approaches that model the associations as a network and how these change under different biological conditions. The link between metabolism and genotype is explored by looking at metabolic GWAS and related techniques. Finally, we describe the relevance and current status of data standards for NMR metabolomics.

A pilot study of the effect of human breast milk on urinary metabolome analysis in infants

BACKGROUND

This study aimed to examine the nutritional effect of breast feeding on healthy term infants by using urinary metabolome analysis.

METHODS

Urine samples were collected from 19 and 14 infants at 1 and 6 months, respectively. Infants were separated into two groups: the breast-fed group receiving <540 mL/week of their intake from formula (n=13 at 1 month; n=9 at 6 months); and the formula-fed group receiving no breast milk (BM) (n=6 at 1 month; n=5 at 6 months). Urinary metabolome analysis was performed using capillary electrophoresis-time-of-flight mass spectrometry (CE-TOF/MS).

RESULTS

A total of 29 metabolites were detected by CE-TOF/MS metabolome analysis in all samples. Urinary excretion of choline metabolites (choline base solution, N,N-dimethylglycine, sarcosine, and betaine) at 1 month were significantly (p<0.05) higher in breast-fed infants than in formula-fed infants. However, choline metabolites were not significantly different between the groups at 6 months. Urinary excretion of lactic acid in breast-fed infants at 1 and 6 months was significantly lower than that in formula-fed infants. Urinary l(-)-threonine and l-carnosine excretion at 1 month was significantly lower in breast-fed infants than in formula-fed infants, but it was not significantly different between the groups at 6 months.

CONCLUSIONS

The type of feeding in early infancy affects choline metabolism, as well as lactate, threonine, and carnosine levels, in healthy term infants. Urinary metabolome analysis by the CE-TOF/MS method is useful for assessing nutritional metabolism in infants.

Adrenal cortex expression quantitative trait loci in a German Holstein × Charolais cross

Background

The importance of the adrenal gland in regard to lactation and reproduction in cattle has been recognized early. Caused by interest in animal welfare and the impact of stress on economically important traits in farm animals the adrenal gland and its function within the stress response is of increasing interest. However, the molecular mechanisms and pathways involved in stress-related effects on economically important traits in farm animals are not fully understood. Gene expression is an important mechanism underlying complex traits, and genetic variants affecting the transcript abundance are thought to influence the manifestation of an expressed phenotype. We therefore investigated the genetic background of adrenocortical gene expression by applying an adaptive linear rank test to identify genome-wide expression quantitative trait loci (eQTL) for adrenal cortex transcripts in cattle.

Results

A total of 10,986 adrenal cortex transcripts and 37,204 single nucleotide polymorphisms (SNPs) were analysed in 145 F2 cows of a Charolais × German Holstein cross. We identified 505 SNPs that were associated with the abundance of 129 transcripts, comprising 482 cis effects and 17 trans effects. These SNPs were located on all chromosomes but X, 16, 24 and 28. Associated genes are mainly involved in molecular and cellular functions comprising free radical scavenging, cellular compromise, cell morphology and lipid metabolism, including genes such as CYP27A1 and LHCGR that have been shown to affect economically important traits in cattle.

Conclusions

In this study we showed that adrenocortical eQTL affect the expression of genes known to contribute to the phenotypic manifestation in cattle. Furthermore, some of the identified genes and related molecular pathways were previously shown to contribute to the phenotypic variation of behaviour, temperament and growth at the onset of puberty in the same population investigated here. We conclude that eQTL analysis appears to be a useful approach providing insight into the molecular and genetic background of complex traits in cattle and will help to understand molecular networks involved.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-016-0442-x) contains supplementary material, which is available to authorized users.

From integrative genomics to systems genetics in the rat to link genotypes to phenotypes

Complementary to traditional gene mapping approaches used to identify the hereditary components of complex diseases, integrative genomics and systems genetics have emerged as powerful strategies to decipher the key genetic drivers of molecular pathways that underlie disease. Broadly speaking, integrative genomics aims to link cellular-level traits (such as mRNA expression) to the genome to identify their genetic determinants. With the characterization of several cellular-level traits within the same system, the integrative genomics approach evolved into a more comprehensive study design, called systems genetics, which aims to unravel the complex biological networks and pathways involved in disease, and in turn map their genetic control points. The first fully integrated systems genetics study was carried out in rats, and the results, which revealed conserved trans-acting genetic regulation of a pro-inflammatory network relevant to type 1 diabetes, were translated to humans. Many studies using different organisms subsequently stemmed from this example. The aim of this Review is to describe the most recent advances in the fields of integrative genomics and systems genetics applied in the rat, with a focus on studies of complex diseases ranging from inflammatory to cardiometabolic disorders. We aim to provide the genetics community with a comprehensive insight into how the systems genetics approach came to life, starting from the first integrative genomics strategies [such as expression quantitative trait loci (eQTLs) mapping] and concluding with the most sophisticated gene network-based analyses in multiple systems and disease states. Although not limited to studies that have been directly translated to humans, we will focus particularly on the successful investigations in the rat that have led to primary discoveries of genes and pathways relevant to human disease.

Topological analysis of metabolic networks integrating co-segregating transcriptomes and metabolomes in type 2 diabetic rat congenic series

Background

The genetic regulation of metabolic phenotypes (i.e., metabotypes) in type 2 diabetes mellitus occurs through complex organ-specific cellular mechanisms and networks contributing to impaired insulin secretion and insulin resistance. Genome-wide gene expression profiling systems can dissect the genetic contributions to metabolome and transcriptome regulations. The integrative analysis of multiple gene expression traits and metabolic phenotypes (i.e., metabotypes) together with their underlying genetic regulation remains a challenge. Here, we introduce a systems genetics approach based on the topological analysis of a combined molecular network made of genes and metabolites identified through expression and metabotype quantitative trait locus mapping (i.e., eQTL and mQTL) to prioritise biological characterisation of candidate genes and traits.

Methods

We used systematic metabotyping by ¹H NMR spectroscopy and genome-wide gene expression in white adipose tissue to map molecular phenotypes to genomic blocks associated with obesity and insulin secretion in a series of rat congenic strains derived from spontaneously diabetic Goto-Kakizaki (GK) and normoglycemic Brown-Norway (BN) rats. We implemented a network biology strategy approach to visualize the shortest paths between metabolites and genes significantly associated with each genomic block.

Results

Despite strong genomic similarities (95–99 %) among congenics, each strain exhibited specific patterns of gene expression and metabotypes, reflecting the metabolic consequences of series of linked genetic polymorphisms in the congenic intervals. We subsequently used the congenic panel to map quantitative trait loci underlying specific mQTLs and genome-wide eQTLs. Variation in key metabolites like glucose, succinate, lactate, or 3-hydroxybutyrate and second messenger precursors like inositol was associated with several independent genomic intervals, indicating functional redundancy in these regions. To navigate through the complexity of these association networks we mapped candidate genes and metabolites onto metabolic pathways and implemented a shortest path strategy to highlight potential mechanistic links between metabolites and transcripts at colocalized mQTLs and eQTLs. Minimizing the shortest path length drove prioritization of biological validations by gene silencing.

Conclusions

These results underline the importance of network-based integration of multilevel systems genetics datasets to improve understanding of the genetic architecture of metabotype and transcriptomic regulation and to characterize novel functional roles for genes determining tissue-specific metabolism.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-016-0352-6) contains supplementary material, which is available to authorized users.

mQTL.NMR: an integrated suite for genetic mapping of quantitative variations of (1)H NMR-based metabolic profiles

High-throughput (1)H nuclear magnetic resonance (NMR) is an increasingly popular robust approach for qualitative and quantitative metabolic profiling, which can be used in conjunction with genomic techniques to discover novel genetic associations through metabotype quantitative trait locus (mQTL) mapping. There is therefore a crucial necessity to develop specialized tools for an accurate detection and unbiased interpretability of the genetically determined metabolic signals. Here we introduce and implement a combined chemoinformatic approach for objective and systematic analysis of untargeted (1)H NMR-based metabolic profiles in quantitative genetic contexts. The R/Bioconductor mQTL.NMR package was designed to (i) perform a series of preprocessing steps restoring spectral dependency in collinear NMR data sets to reduce the multiple testing burden, (ii) carry out robust and accurate mQTL mapping in human cohorts as well as in rodent models, (iii) statistically enhance structural assignment of genetically determined metabolites, and (iv) illustrate results with a series of visualization tools. Built-in flexibility and implementation in the powerful R/Bioconductor framework allow key preprocessing steps such as peak alignment, normalization, or dimensionality reduction to be tailored to specific problems. The mQTL.NMR package is freely available with its source code through the Comprehensive R/Bioconductor repository and its own website ( http://www.ican-institute.org/tools/ ). It represents a significant advance to facilitate untargeted metabolomic data processing and quantitative analysis and their genetic mapping.

[Nuclear magnetic resonance based metabolic phenotyping for patient evaluations in operating rooms and intensive care units]

Metabolic phenotyping consists in the identification of subtle and coordinated metabolic variations associated with various pathophysiological stimuli. Different analytical methods, such as nuclear magnetic resonance, allow the simultaneous quantification of a large number of metabolites. Statistical analyses of these spectra thus lead to the discrimination between samples and the identification of a metabolic phenotype corresponding to the effect under study. This approach allows the extraction of candidate biomarkers and the recovery of perturbed metabolic networks, driving to the generation of biochemical hypotheses (pathophysiological mechanisms, diagnostic tests, therapeutic targets…). Metabolic phenotyping could be useful in anaesthesiology and intensive care medicine for the evaluation, monitoring or diagnosis of life-threatening situations, to optimise patient managements. This review introduces the physical and statistical fundamentals of NMR-based metabolic phenotyping, describes the work already achieved by this approach in anaesthesiology and intensive care medicine. Finally, potential areas of interest are discussed for the perioperative and intensive management of patients, from newborns to adults.

Metabolic phenotyping and systems biology approaches to understanding metabolic syndrome and fatty liver disease

Metabolic syndrome, a cluster of risk factors for type 2 diabetes mellitus and cardiovascular disease, is becoming an increasing global health concern. Insulin resistance is often associated with metabolic syndrome and also typical hepatic manifestations such as nonalcoholic fatty liver disease. Profiling of metabolic products (metabolic phenotyping or metabotyping) has provided new insights into metabolic syndrome and nonalcoholic fatty liver disease. Data from nuclear magnetic resonance spectroscopy and mass spectrometry combined with statistical modeling and top-down systems biology have allowed us to analyze and interpret metabolic signatures in terms of metabolic pathways and protein interaction networks and to identify the genomic and metagenomic determinants of metabolism. For example, metabolic phenotyping has shown that relationships between host cells and the microbiome affect development of the metabolic syndrome and fatty liver disease. We review recent developments in metabolic phenotyping and systems biology technologies and how these methodologies have provided insights into the mechanisms of metabolic syndrome and nonalcoholic fatty liver disease. We discuss emerging areas of research in this field and outline our vision for how metabolic phenotyping could be used to study metabolic syndrome and fatty liver disease.

Effect of bisphenol A on rat metabolic profiling studied by using capillary electrophoresis time-of-flight mass spectrometry

Bisphenol A (BPA), a chemical widely used in the manufacture of polycarbonate plastics, has raised considerable concern in recent decades because of its hormone-like properties. Whether BPA exposure is a health risk remains controversial in many countries. A metabolomics study based on capillary electrophoresis time-of-flight mass spectrometry (CE-TOF/MS) was performed to study the urine metabolic profiles of Sprague-Dawley rats fed with four dose levels of BPA (0, 1, 10, and 100 μg/kg body weight) for 45 days. Multivariate pattern recognition directly reflected the metabolic perturbations caused by BPA. On the basis of univariate analysis, 42 metabolites including amino acids, polyamines, nucleosides, organic acids, carbohydrates, pterins, polyphenols, and sugar phosphates were found as the most significantly differential metabolites. The marked perturbations were related with valine, leucine and isoleucine biosynthesis, D-glutamine and D-glutamate metabolism, etc. Significant alterations of neurotransmitters (glutamate, gamma-aminobutyric acid, and noradrenaline) and neurotransmitter-related metabolites (tyrosine, histamine, valine, and taurine) suggested that the toxic effects of small-dose BPA (below 50 mg/kg/day) may contribute to its interactions with the neuromediating system. Our study demonstrated that metabolomics may offer more specific insights into the molecular changes underlying the physiological effects of BPA.

Systems mapping of metabolic genes through control theory

The formation of any complex phenotype involves a web of metabolic pathways in which one chemical is transformed through the catalysis of enzymes into another. Traditional approaches for mapping quantitative trait loci (QTLs) are based on a direct association analysis between DNA marker genotypes and end-point phenotypes, neglecting the mechanistic processes of how a phenotype is formed biochemically. Here, we propose a new dynamic framework for mapping metabolic QTLs (mQTLs) responsible for phenotypic formation. By treating metabolic pathways as a biological system, robust differential equations have proven to be a powerful means of studying and predicting the dynamic behavior of biochemical reactions that cause a high-order phenotype. The new framework integrates these differential equations into a statistical mixture model for QTL mapping. Since the mathematical parameters that define the emergent properties of the metabolic system can be estimated and tested for different mQTL genotypes, the framework allows the dynamic pattern of genetic effects to be quantified on metabolic capacity and efficacy across a time-space scale. Based on a recent study of glycolysis in Saccharomyces cerevisiae, we design and perform a series of simulation studies to investigate the statistical properties of the framework and validate its usefulness and utilization in practice. This framework can be generalized to mapping QTLs for any other dynamic systems and may stimulate pharmacogenetic research toward personalized drug and treatment intervention.

Statistical spectroscopic tools for biomarker discovery and systems medicine

Metabolic profiling based on comparative, statistical analysis of NMR spectroscopic and mass spectrometric data from complex biological samples has contributed to increased understanding of the role of small molecules in affecting and indicating biological processes. To enable this research, the development of statistical spectroscopy has been marked by early beginnings in applying pattern recognition to nuclear magnetic resonance data and the introduction of statistical total correlation spectroscopy (STOCSY) as a tool for biomarker identification in the past decade. Extensions of statistical spectroscopy now compose a family of related tools used for compound identification, data preprocessing, and metabolic pathway analysis. In this Perspective, we review the theory and current state of research in statistical spectroscopy and discuss the growing applications of these tools to medicine and systems biology. We also provide perspectives on how recent institutional initiatives are providing new platforms for the development and application of statistical spectroscopy tools and driving the development of integrated "systems medicine" approaches in which clinical decision making is supported by statistical and computational analysis of metabolic, phenotypic, and physiological data.

Genetic determinants of metabolism in health and disease: from biochemical genetics to genome-wide associations

Increasingly sophisticated measurement technologies have allowed the fields of metabolomics and genomics to identify, in parallel, risk factors of disease; predict drug metabolism; and study metabolic and genetic diversity in large human populations. Yet the complementarity of these fields and the utility of studying genes and metabolites together is belied by the frequent separate, parallel applications of genomic and metabolomic analysis. Early attempts at identifying co-variation and interaction between genetic variants and downstream metabolic changes, including metabolic profiling of human Mendelian diseases and quantitative trait locus mapping of individual metabolite concentrations, have recently been extended by new experimental designs that search for a large number of gene-metabolite associations. These approaches, including metabolomic quantitiative trait locus mapping and metabolomic genome-wide association studies, involve the concurrent collection of both genomic and metabolomic data and a subsequent search for statistical associations between genetic polymorphisms and metabolite concentrations across a broad range of genes and metabolites. These new data-fusion techniques will have important consequences in functional genomics, microbial metagenomics and disease modeling, the early results and implications of which are reviewed.