Visceral adipose tissue but not subcutaneous adipose tissue is associated with urine and serum metabolites

Obesity is a complex multifactorial phenotype that influences several metabolic pathways. Yet, few studies have examined the relations of different body fat compartments to urinary and serum metabolites. Anthropometric phenotypes (visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), the ratio between VAT and SAT (VSR), body mass index (BMI), waist circumference (WC)) and urinary and serum metabolite concentrations measured by nuclear magnetic resonance spectroscopy were measured in a population-based sample of 228 healthy adults. Multivariable linear and logistic regression models, corrected for multiple testing using the false discovery rate, were used to associate anthropometric phenotypes with metabolites. We adjusted for potential confounding variables: age, sex, smoking, physical activity, menopausal status, estimated glomerular filtration rate (eGFR), urinary glucose, and fasting status. In a fully adjusted logistic regression model dichotomized for the absence or presence of quantifiable metabolite amounts, VAT, BMI and WC were inversely related to urinary choline (ß = -0.18, p = 2.73*10−3), glycolic acid (ß = -0.20, 0.02), and guanidinoacetic acid (ß = -0.12, p = 0.04), and positively related to ethanolamine (ß = 0.18, p = 0.02) and dimethylamine (ß = 0.32, p = 0.02). BMI and WC were additionally inversely related to urinary glutamine and lactic acid. Moreover, WC was inversely associated with the detection of serine. VAT, but none of the other anthropometric parameters, was related to serum essential amino acids, such as valine, isoleucine, and phenylalanine among men. Compared to other adiposity measures, VAT demonstrated the strongest and most significant relations to urinary and serum metabolites. The distinct relations of VAT, SAT, VSR, BMI, and WC to metabolites emphasize the importance of accurately differentiating between body fat compartments when evaluating the potential role of metabolic regulation in the development of obesity-related diseases, such as insulin resistance, type 2 diabetes, and cardiovascular disease

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

PROCOS: Computational analysis of protein–protein complexes

One of the main challenges in protein-protein docking is a meaningful evaluation of the many putative solutions. Here we present a program (PROCOS) that calculates a probability-like measure to be native for a given complex. In contrast to scores often used for analyzing complex structures, the calculated probabilities offer the advantage of providing a fixed range of expected values. This will allow, in principle, the comparison of models corresponding to different targets that were solved with the same algorithm. Judgments are based on distributions of properties derived from a large database of native and false complexes. For complex analysis PROCOS uses these property distributions of native and false complexes together with a support vector machine (SVM). PROCOS was compared to the established scoring schemes of ZRANK and DFIRE. Employing a set of experimentally solved native complexes, high probability values above 50% were obtained for 90% of these structures. Next, the performance of PROCOS was tested on the 40 binary targets of the Dockground decoy set, on 14 targets of the RosettaDock decoy set and on 9 targets that participated in the CAPRI scoring evaluation. Again the advantage of using a probability-based scoring system becomes apparent and a reasonable number of near native complexes was found within the top ranked complexes. In conclusion, a novel fully automated method is presented that allows the reliable evaluation of protein-protein complexes

Current Experimental, Bioinformatic and Statistical Methods used in NMR Based Metabolomics

The aim of this contribution is to familiarize the reader with experimental, bioinformatic and statistical strategies currently used in the field of solution NMR based metabolomics. Special emphasis is given to methods that have worked well in our hands. Methods covered include sample preparation, acquisition and processing of NMR spectra, and identification and quantification of metabolites. Further consideration is given to data normalization and scaling, unsupervised and supervised statistical data analysis, the biomedical interpretation of results, and the centralized community-wide storage and retrieval of NMR data

Data Normalization of (1)H NMR Metabolite Fingerprinting Data Sets in the Presence of Unbalanced Metabolite Regulation

Data normalization is an essential step in NMR-based metabolomics. Conducted properly, it improves data quality and removes unwanted biases. The choice of the appropriate normalization method is critical and depends on the inherent properties of the data set in question. In particular, the presence of unbalanced metabolic regulation, where the different specimens and cohorts under investigation do not contain approximately equal shares of up- and down-regulated features, may strongly influence data normalization. Here, we demonstrate the suitability of the Shapiro–Wilk test to detect such unbalanced regulation. Next, employing a Latin-square design consisting of eight metabolites spiked into a urine specimen at eight different known concentrations, we show that commonly used normalization and scaling methods fail to retrieve true metabolite concentrations in the presence of increasing amounts of glucose added to simulate unbalanced regulation. However, by learning the normalization parameters on a subset of nonregulated features only, Linear Baseline Normalization, Probabilistic Quotient Normalization, and Variance Stabilization Normalization were found to account well for different dilutions of the samples without distorting the true spike-in levels even in the presence of marked unbalanced metabolic regulation. Finally, the methods described were applied successfully to a real world example of unbalanced regulation, namely, a set of plasma specimens collected from patients with and without acute kidney injury after cardiac surgery with cardiopulmonary bypass use

Analysis of human urine reveals metabolic changes related to the development of acute kidney injury following cardiac surgery

Acute kidney injury (AKI) is a frequent complication of sepsis, major surgery or nephrotoxic medication use. It is associated with high morbidity and mortality. In an effort to identify novel biomarkers capable of predicting the development of AKI after cardiac surgery with cardiopulmonary bypass use, urine specimens were collected before and at 4 and 24 h after surgery from 106 patients and analyzed by means of nuclear magnetic resonance spectroscopy. Postoperative AKI of stage 1–3 as defined by the Acute Kidney Injury Network (AKIN) developed in 34 cases. Employing Quantile Normalization and support vector machine based classification, spectra of the 24-hour postoperative urine specimens were found to predict AKI across all stages with an average accuracy of 76.0 % and a corresponding area under the receiver operating characteristic curve of 0.83. Considering only AKIN-stage 2 and 3 patients, prediction accuracy increased to 81.7 % and 100 %, respectively. Among the small set of predictive biomarkers identified was carnitine, the urinary concentration of which was elevated significantly in AKI-free patients only, and tranexamic acid, which is routinely applied as an antifibrinolytic agent at the end of surgery, and whose renal excretion was delayed in AKI patients. The study underscores the power of NMR and bioinformatics in identifying novel biomarkers of disease and in gaining new insights into pathomechanisms

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

Epithelioid hemangioendotheliomas of the liver and lung in children and adolescents.

Epithelioid hemangioendothelioma (EHE) is a rare, vascular sarcoma. Visceral forms arise in the liver/ lungs. We review the clinical and molecular phenotype of pediatric visceral EHE based on the case of a 9-year-old male child with EHE of the liver/lungs. His tumor expressed the EHE-specific fusion oncogene WWTR1-CAMTA1. Molecular characterization revealed a low somatic mutation rate and activated interferon signaling, angiogenesis regulation, and blood vessel remodeling. After polychemotherapy and resection of lung tumors, residual disease remained stable on oral lenalidomide. Literature review identified another 24 children with EHE of the liver/lungs. Most presented with multifocal, systemic disease. Only those who underwent complete resection achieved complete remission. Four children experienced rapid progression and died. In six children, disease remained stable for years without therapy. Two patients died from progressive EHE 21 and 24 years after first diagnosis. Natural evolution of pediatric visceral EHE is variable, and long-term prognosis remains unclear

Identification of Plasma Metabolites Prognostic of Acute Kidney Injury after Cardiac Surgery with Cardiopulmonary Bypass

Acute kidney injury (AKI) is a frequent complication after cardiopulmonary bypass, but early detection of postoperative AKI remains challenging. Protein biomarkers predict AKI excellently in homogeneous cohorts but are less reliable in patients suffering from various comorbidities. We employed nuclear magnetic resonance spectroscopy in a prospective study of 85 adult cardiac surgery patients to identify metabolites prognostic of AKI in plasma specimens collected 24 h after surgery. Postoperative AKI of stages 1–3, as defined by the Acute Kidney Injury Network (AKIN), developed in 33 cases. A random forests classifier trained on the NMR spectra prognosticated AKI across all stages, with an average accuracy of 80 ± 0.9% and an area under the receiver operating characteristic curve of 0.87 ± 0.01. Prognostications were based, on average, on 24 ± 2.8 spectral features. Among the set of discriminative ions and molecules identified were Mg2+, lactate, and the glucuronide conjugate of propofol. Using creatinine, Mg2+, and lactate levels to derive an AKIN index score, we found AKIN 1 disease to be largely indistinguishable from AKIN 0, in concordance with the rather mild nature of AKIN 1 disease