Dorrit Baunsgaard

Summary recommendations for standardization and reporting of metabolic analyses.

The Standard Metabolic Reporting Structures (SMRS) working group outlines its vision for an open,community-driven specification for the standardization and reporting of metabolic studies.The Standard Metabolic Reporting Structures (SMRS) working group outlines its vision for an open,community-driven specification for the standardization and reporting of metabolic studies.

Direct quantitative trait locus mapping of mammalian metabolic phenotypes in diabetic and normoglycemic rat models

Characterizing the relationships between genomic and phenotypic variation is essential to understanding disease etiology. Information-dense data sets derived from pathophysiological, proteomic and transcriptomic profiling have been applied to map quantitative trait loci (QTLs). Metabolic traits, already used in QTL studies in plants, are essential phenotypes in mammalian genetics to define disease biomarkers. Using a complex mammalian system, here we show chromosomal mapping of untargeted plasma metabolic fingerprints derived from NMR spectroscopic analysis in a cross between diabetic and control rats. We propose candidate metabolites for the most significant QTLs. Metabolite profiling in congenic strains provided evidence of QTL replication. Linkage to a gut microbial metabolite (benzoate) can be explained by deletion of a uridine diphosphate glucuronosyltransferase. Mapping metabotypic QTLs provides a practical approach to understanding genome-phenotype relationships in mammals and may uncover deeper biological complexity, as extended genome (microbiome) perturbations that affect disease processes through transgenomic effects may influence QTL detection.

Human metabolic profiles are stably controlled by genetic and environmental variation.

1H Nuclear Magnetic Resonance spectroscopy (1H NMR) is increasingly used to measure metabolite concentrations in sets of biological samples for top‐down systems biology and molecular epidemiology. For such purposes, knowledge of the sources of human variation in metabolite concentrations is valuable, but currently sparse. We conducted and analysed a study to create such a resource. In our unique design, identical and non‐identical twin pairs donated plasma and urine samples longitudinally. We acquired 1H NMR spectra on the samples, and statistically decomposed variation in metabolite concentration into familial (genetic and common‐environmental), individual‐environmental, and longitudinally unstable components. We estimate that stable variation, comprising familial and individual‐environmental factors, accounts on average for 60% (plasma) and 47% (urine) of biological variation in 1H NMR‐detectable metabolite concentrations. Clinically predictive metabolic variation is likely nested within this stable component, so our results have implications for the effective design of biomarker‐discovery studies. We provide a power‐calculation method which reveals that sample sizes of a few thousand should offer sufficient statistical precision to detect 1H NMR‐based biomarkers quantifying predisposition to disease.

Combination of 'omics' data to investigate the mechanism(s) of hydrazine-induced hepatotoxicity in rats and to identify potential biomarkers

To gain novel insight into the molecular mechanisms underlying hydrazine-induced hepatotoxicity, mRNAs, proteins and endogenous metabolites were identified that were altered in rats treated with hydrazine compared with untreated controls. These changes were resolved in a combined genomics, proteomics and metabonomics study. Sprague–Dawley rats were assigned to three treatment groups with 10 animals per group and given a single oral dose of vehicle, 30 or 90 mg kg−1 hydrazine, respectively. RNA was extracted from rat liver 48 h post-dosing and transcribed into cDNA. The abundance of mRNA was investigated on cDNA microarrays containing 699 rat-specific genes involved in toxic responses. In addition, proteins from rat liver samples (48 and 120/168 h post-dosing) were resolved by two-dimensional differential gel electrophoresis and proteins with changed expression levels after hydrazine treatment were identified by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry peptide mass fingerprinting. To elucidate how regulation was reflected in biochemical pathways, endogenous metabolites were measured in serum samples collected 48 h post-dosing by 600-MHz 1H-NMR. In summary, a single dose of hydrazine caused gene, protein and metabolite changes, which can be related to glucose metabolism, lipid metabolism and oxidative stress. These findings support known effects of hydrazine toxicity and provide potential new biomarkers of hydrazine-induced toxicity.

Metabonomics in Diabetes Research

Metabonomics has been defined as “quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification” and can provide information on disease processes, drug toxicity, and gene function. In this approach many samples of biological origin (biofluids such as urine or plasma) are analyzed using techniques that produce simultaneous detection. A variety of analytical metabolic profiling tools are used routinely, are also currently under development, and include proton nuclear magnetic resonance spectroscopy and mass spectrometry with a prior online separation step such as high-performance liquid chromatography, ultra-performance liquid chromatography, or gas chromatography. Data generated by these analytical techniques are often combined with multivariate data analysis, i.e., pattern recognition, for respectively generating and interpreting the metabolic profiles of the investigated samples. Metabonomics has gained great prominence in diabetes research within the last few years and has already been applied to understand the metabolism in a range of animal models and, more recently, attempts have been done to process complex metabolic data sets from clinical studies. A future hope for the metabonomic approach is the identification of biomarkers that are able to highlight individuals likely to suffer from diabetes and enable early diagnosis of the disease or the identification of those at risk. This review summarizes the technologies currently being used in metabonomics, as well as the studies reported related to diabetes prior to a description of the general objective of the research plan of the metabonomics part of the European Union project, Molecular Phenotyping to Accelerate Genomic Epidemiology.

A Comprehensive Evaluation of Nanoparticle Tracking Analysis (NanoSight) for Characterization of Proteinaceous Submicron Particles

Nanoparticle tracking analysis (NTA) has attracted great interest for application in the field of submicron particle characterization for biopharmaceuticals. It has the virtue of direct sample visualization and particle-by-particle tracking, but the complexity of method development has limited its routine applicability. We systematically evaluated data collection and processing parameters as well as sample handling methods using shake-stressed protein samples. The camera shutter and gain were identified as the key factors influencing NTA results. We also demonstrated that sample filtration was necessary for NTA analysis if there were high numbers of micron particles, whereas the choice of filter membrane was critical for data quality. Sample dilution into corresponding formulation buffer did not affect particle size distributions in our study. Finally, NTA analysis exhibited excellent repeatability in intraday comparison of multiple measurements on the same sample and interday comparison on different batches of samples. Shaking-induced protein aggregation could also be sensitively monitored by NTA. In conclusion, NTA analysis can be used as a robust stability-indicating method for the characterization of proteinaceous submicron particles and thereby complement other analytical methods, provided that consistent sample handling and parametric settings are established for the specific case study.

Metabolic Profiling in Maturity-Onset Diabetes of the Young (MODY) and Young Onset Type 2 Diabetes Fails to Detect Robust Urinary Biomarkers

It is important to identify patients with Maturity-onset diabetes of the young (MODY) as a molecular diagnosis determines both treatment and prognosis. Genetic testing is currently expensive and many patients are therefore not assessed and are misclassified as having either type 1 or type 2 diabetes. Biomarkers could facilitate the prioritisation of patients for genetic testing. We hypothesised that patients with different underlying genetic aetiologies for their diabetes could have distinct metabolic profiles which may uncover novel biomarkers. The aim of this study was to perform metabolic profiling in urine from patients with MODY due to mutations in the genes encoding glucokinase (GCK) or hepatocyte nuclear factor 1 alpha (HNF1A), type 2 diabetes (T2D) and normoglycaemic control subjects. Urinary metabolic profiling by Nuclear Magnetic Resonance (NMR) and ultra performance liquid chromatography hyphenated to Q-TOF mass spectrometry (UPLC-MS) was performed in a Discovery set of subjects with HNF1A-MODY (n = 14), GCK-MODY (n = 17), T2D (n = 14) and normoglycaemic controls (n = 34). Data were used to build a valid partial least squares discriminate analysis (PLS-DA) model where HNF1A-MODY subjects could be separated from the other diabetes subtypes. No single metabolite contributed significantly to the separation of the patient groups. However, betaine, valine, glycine and glucose were elevated in the urine of HNF1A-MODY subjects compared to the other subgroups. Direct measurements of urinary amino acids and betaine in an extended dataset did not support differences between patients groups. Elevated urinary glucose in HNF1A-MODY is consistent with the previously reported low renal threshold for glucose in this genetic subtype. In conclusion, we report the first metabolic profiling study in monogenic diabetes and show that, despite the distinct biochemical pathways affected, there are unlikely to be robust urinary biomarkers which distinguish monogenic subtypes from T2D. Our results have implications for studies investigating metabolic profiles in complex traits including T2D.