Matthias S. Klein

State-of-the art data normalization methods improve NMR-based metabolomic analysis

Extracting biomedical information from large metabolomic datasets by multivariate data analysis is of considerable complexity. Common challenges include among others screening for differentially produced metabolites, estimation of fold changes, and sample classification. Prior to these analysis steps, it is important to minimize contributions from unwanted biases and experimental variance. This is the goal of data preprocessing. In this work, different data normalization methods were compared systematically employing two different datasets generated by means of nuclear magnetic resonance (NMR) spectroscopy. To this end, two different types of normalization methods were used, one aiming to remove unwanted sample-to-sample variation while the other adjusts the variance of the different metabolites by variable scaling and variance stabilization methods. The impact of all methods tested on sample classification was evaluated on urinary NMR fingerprints obtained from healthy volunteers and patients suffering from autosomal polycystic kidney disease (ADPKD). Performance in terms of screening for differentially produced metabolites was investigated on a dataset following a Latin-square design, where varied amounts of 8 different metabolites were spiked into a human urine matrix while keeping the total spike-in amount constant. In addition, specific tests were conducted to systematically investigate the influence of the different preprocessing methods on the structure of the analyzed data. In conclusion, preprocessing methods originally developed for DNA microarray analysis, in particular, Quantile and Cubic-Spline Normalization, performed best in reducing bias, accurately detecting fold changes, and classifying samples.

Urinary Metabolite Quantification Employing 2D NMR Spectroscopy

Two-dimensional (2D) nuclear magnetic resonance (NMR) spectroscopy is a fairly novel method for the quantification of metabolites in biological fluids and tissue extracts. We show in this contribution that, compared to 1D 1H spectra, superior quantification of human urinary metabolites is obtained from 2D 1H-13C heteronuclear single-quantum correlation (HSQC) spectra measured at natural abundance. This was accomplished by the generation of separate calibration curves for the different 2D HSQC signals of each metabolite. Lower limits of detection were in the low to mid micromolar range and were generally the lower the greater the number of methyl groups contained in an analyte. The quantitative 2D NMR data obtained for a selected set of 17 urinary metabolites were compared to those obtained independently by means of gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry of amino acids and hippurate as well as enzymatic and colorimetric measurements of creatinine. As a typical application, 2D-NMR was used for the investigation of urine from patients with inborn errors of metabolism.

Nuclear magnetic resonance and mass spectrometry-based milk metabolomics in dairy cows during early and late lactation

Milk production in dairy cows has dramatically increased over the past few decades. The selection for higher milk yield affects the partitioning of available nutrients, with more energy being allocated to milk synthesis and less to physiological processes essential to fertility and fitness. In this study, the abundance of numerous milk metabolites in early and late lactation was systematically investigated, with an emphasis on metabolites related to energy metabolism. The aim of the study was the identification and correlation of milk constituents to the metabolic status of the cows. To investigate the influence of lactation stage on physiological and metabolic variables, 2 breeds of different productivity were selected for investigation by high-resolution nuclear magnetic resonance spectroscopy and gas chromatography-mass spectrometry. We could reliably quantify 44 different milk metabolites. The results show that biomarkers such as acetone and beta-hydroxybutyrate are clearly correlated to the metabolic status of the individual cows during early lactation. Based on these data, the selection of cows that cope well with the metabolic stress of early lactation should become an option.

NMR metabolomic analysis of dairy cows reveals milk glycerophosphocholine to phosphocholine ratio as prognostic biomarker for risk of ketosis.

Ketosis is a common metabolic disease in dairy cows. Diagnostic markers for ketosis such as acetone and beta-hydroxybutyric acid (BHBA) are known, but disease prediction remains an unsolved challenge. Milk is a steadily available biofluid and routinely collected on a daily basis. This high availability makes milk superior to blood or urine samples for diagnostic purposes. In this contribution, we show that high milk glycerophosphocholine (GPC) levels and high ratios of GPC to phosphocholine (PC) allow for the reliable selection of healthy and metabolically stable cows for breeding purposes. Throughout lactation, high GPC values are connected with a low ketosis incidence. During the first month of lactation, molar GPC/PC ratios equal or greater than 2.5 indicate a very low risk for developing ketosis. This threshold was validated for different breeds (Holstein-Friesian, Brown Swiss, and Simmental Fleckvieh) and for animals in different lactations, with observed odds ratios between 1.5 and 2.38. In contrast to acetone and BHBA, these measures are independent of the acute disease status. A possible explanation for the predictive effect is that GPC and PC are measures for the ability to break down phospholipids as a fatty acid source to meet the enhanced energy requirements of early lactation.

Detection of autosomal dominant polycystic kidney disease by NMR spectroscopic fingerprinting of urine

Autosomal dominant polycystic kidney disease (ADPKD) is a frequent cause of kidney failure; however, urinary biomarkers for the disease are lacking. In a step towards identifying such markers, we used multidimensional-multinuclear nuclear magnetic resonance (NMR) spectroscopy with support vector machine-based classification and analyzed urine specimens of 54 patients with ADPKD and slightly reduced estimated glomerular filtration rates. Within this cohort, 35 received medication for arterial hypertension and 19 did not. The results were compared with NMR profiles of 46 healthy volunteers, 10 ADPKD patients on hemodialysis with residual renal function, 16 kidney transplant patients, and 52 type 2 diabetic patients with chronic kidney disease. Based on the average of 51 out of 701 NMR features, we could reliably discriminate ADPKD patients with moderately advanced disease from ADPKD patients with end-stage renal disease, patients with chronic kidney disease of other etiologies, and healthy probands with an accuracy of >80%. Of the 35 patients with ADPKD receiving medication for hypertension, most showed increased excretion of proteins and also methanol. In contrast, elevated urinary methanol was not found in any of the control and other patient groups. Thus, we found that NMR fingerprinting of urine differentiates ADPKD from several other kidney diseases and individuals with normal kidney function. The diagnostic and prognostic potential of these profiles requires further evaluation.

Metabolomics and Type 2 Diabetes: Translating Basic Research into Clinical Application

Type 2 diabetes (T2D) and its comorbidities have reached epidemic proportions, with more than half a billion cases expected by 2030. Metabolomics is a fairly new approach for studying metabolic changes connected to disease development and progression and for finding predictive biomarkers to enable early interventions, which are most effective against T2D and its comorbidities. In metabolomics, the abundance of a comprehensive set of small biomolecules (metabolites) is measured, thus giving insight into disease-related metabolic alterations. This review shall give an overview of basic metabolomics methods and will highlight current metabolomics research successes in the prediction and diagnosis of T2D. We summarized key metabolites changing in response to T2D. Despite large variations in predictive biomarkers, many studies have replicated elevated plasma levels of branched-chain amino acids and their derivatives, aromatic amino acids and α-hydroxybutyrate ahead of T2D manifestation. In contrast, glycine levels and lysophosphatidylcholine C18:2 are depressed in both predictive studies and with overt disease. The use of metabolomics for predicting T2D comorbidities is gaining momentum, as are our approaches for translating basic metabolomics research into clinical applications. As a result, metabolomics has the potential to enable informed decision-making in the realm of personalized medicine.

Performance Evaluation of Algorithms for the Classification of Metabolic 1H NMR Fingerprints

Nontargeted metabolite fingerprinting is increasingly applied to biomedical classification. The choice of classification algorithm may have a considerable impact on outcome. In this study, employing nested cross-validation for assessing predictive performance, six binary classification algorithms in combination with different strategies for data-driven feature selection were systematically compared on five data sets of urine, serum, plasma, and milk one-dimensional fingerprints obtained by proton nuclear magnetic resonance (NMR) spectroscopy. Support Vector Machines and Random Forests combined with t-score-based feature filtering performed well on most data sets, whereas the performance of the other tested methods varied between data sets.

Early changes in the liver-soluble proteome from mice fed a nonalcoholic steatohepatitis inducing diet

Despite the increasing incidence of nonalcoholic steatohepatitis (NASH) with the rise in lifestyle‐related diseases such as the metabolic syndrome, little is known about the changes in the liver proteome that precede the onset of inflammation and fibrosis. Here, we investigated early changes in the liver‐soluble proteome of female C57BL/6N mice fed an NASH‐inducing diet by 2D‐DIGE and nano‐HPLC‐MS/MS. In parallel, histology and measurements of hepatic content of triglycerides, cholesterol and intermediates of the methionine cycle were performed. Hepatic steatosis manifested itself after 2 days of feeding, albeit significant changes in the liver‐soluble proteome were not evident before day 10 in the absence of inflammatory or fibrotic signs. Proteomic alterations affected mainly energy and amino acid metabolism, detoxification processes, urea cycle, and the one‐carbon/S‐adenosylmethionine pathways. Additionally, intermediates of relevant affected pathways were quantified from liver tissue, confirming the findings from the proteomic analysis.

Metabolomic Modeling To Monitor Host Responsiveness to Gut Microbiota Manipulation in the BTBRT+tf/j Mouse

The microbiota, the entirety of microorganisms residing in the gut, is increasingly recognized as an environmental factor in the maintenance of health and the development of disease. The objective of this analysis was to model in vivo interactions between gut microbiota and both serum and liver metabolites. Different genotypic models (C57BL/6 and BTBR(T+tf/j) mice) were studied in combination with significant dietary manipulations (chow vs ketogenic diets) to perturb the gut microbiota. Diet rather than genotype was the primary driver of microbial changes, with the ketogenic diet diminishing total bacterial levels. Fecal but not cecal microbiota profiles were associated with the serum and liver metabolomes. Modeling metabolome-microbiota interactions showed fecal Clostridium leptum to have the greatest impact on host metabolism, significantly correlating with 10 circulating metabolites, including 5 metabolites that did not correlate with any other microbes. C. leptum correlated negatively with serum ketones and positively with glucose and glutamine. Interestingly, microbial groups most strongly correlated with host metabolism were those modulating gut barrier function, the primary site of microbe-host interactions. These results show very robust relationships and provide a basis for future work wherein the compositional and functional associations of the microbiome can be modeled in the context of the metabolome.

Changes in the hepatic mitochondrial and membrane proteome in mice fed a non-alcoholic steatohepatitis inducing diet

Non-alcoholic steatohepatitis (NASH) accounts for a large proportion of cryptic cirrhosis in the Western societies. Nevertheless, we lack a deeper understanding of the underlying pathomolecular processes, particularly those preceding hepatic inflammation and fibrosis. In order to gain novel insights into early NASH-development from the first appearance of proteomic alterations to the onset of hepatic inflammation and fibrosis, we conducted a time-course analysis of proteomic changes in liver mitochondria and membrane-enriched fractions of female C57Bl/6N mice fed either a mere steatosis or NASH inducing diet. This data was complemented by quantitative measurements of hepatic glycerol-containing lipids, cholesterol and intermediates of the methionine cycle. Aside from energy metabolism and stress response proteins, enzymes of the urea cycle and methionine metabolism were found regulated. Alterations in the methionine cycle occur early in disease progression preceding molecular signs of inflammation. Proteins that hold particular promise in the early distinction between benign steatosis and NASH are methyl-transferase Mettl7b, the glycoprotein basigin and the microsomal glutathione-transferase.