Metabolomics and biomarker discovery: NMR spectral data of urine and hepatotoxicity by carbon tetrachloride, acetaminophen, and d-galactosamine in rats

The primary objective of this study was to discover biomarkers which are correlated with hepatotoxicity induced by chemicals using ¹H NMR spectral data of urine. A procedure of nuclear magnetic resonance (NMR) urinalysis using pattern recognition was proposed for early screening of the hepatotoxicity of CCl₄, acetaminophen (AAP), and d-galactosamine (GalN) in rats. The hepatotoxic compounds were expected to induce necrosis in hepatocytes. This was confirmed through blood biochemistry and histopathology. CCl₄ (1 ml/kg, po) or GalN (0.8 g/kg, ip) was single administered to Sprague–Dawley (S–D) rats and urine was collected every 24 h. Animals were sacrificed 24 h or 48 h post-dosing. AAP (2 g/kg, po) was administered for 2 days and then the animals were sacrificed 24 h after the last treatment. NMR spectroscopy revealed evidently different clustering between control groups and hepatotoxicant treatment groups in global metabolic profilings as indicated by partial least square (PLS)-discrimination analysis (DA). In targeted profilings, endogenous metabolites of allantoin, citrate, taurine, 2-oxoglutarate, acetate, lactate, phenylacetyl glycine, succinate, phenylacetate, 1-methylnicotinamide, hippurate, and benzoate were selected as putative biomarkers for hepatoxicity by CCl₄, AAP, and GalN. Comparison of our rat ¹H NMR PLS-DA data with histopathological changes suggests that ¹H NMR urinalysis can be used to predict hepatotoxicity induced by CCl₄, AAP, and GalN.     

Metabolomics of biomarker discovery in ovarian cancer: a systematic review of the current literature

Introduction

Metabolomics is the emerging member of “omics” sciences advancing the understanding, diagnosis and treatment of many cancers, including ovarian cancer (OC).

Objectives

To systematically identify the metabolomic abnormalities in OC detection, and the dominant metabolic pathways associated with the observed alterations.

Methods

An electronic literature search was performed, up to and including January 15th 2016, for studies evaluating the metabolomic profile of patients with OC compared to controls. QUADOMICS tool was used to assess the quality of the twenty-three studies included in this systematic review.

Results

Biological samples utilized for metabolomic analysis include: serum/plasma (n = 13), urine (n = 4), cyst fluid (n = 3), tissue (n = 2) and ascitic fluid (n = 1). Metabolites related to cellular respiration, carbohydrate, lipid, protein and nucleotide metabolism were significantly altered in OC. Increased levels of tricarboxylic acid cycle intermediates and altered metabolites of the glycolytic pathway pointed to perturbations in cellular respiration. Alterations in lipid metabolism included enhanced fatty acid oxidation, abnormal levels of glycerolipids, sphingolipids and free fatty acids with common elevations of palmitate, oleate, and myristate. Increased levels of glutamine, glycine, cysteine and threonine were commonly reported while enhanced degradations of tryptophan, histidine and phenylalanine were found. N-acetylaspartate, a brain amino acid, was found elevated in primary and metastatic OC tissue and ovarian cyst fluid. Further, elevated levels of ketone bodies including 3-hydroxybutyrate were commonly reported. Increased levels of nucleotide metabolites and tocopherols were consistent through out the studies.

Conclusion

Metabolomics presents significant new opportunities for diagnostic biomarker development, elucidating previously unknown mechanisms of OC pathogenesis.

     

Ridge regression for longitudinal biomarker data

Technological advances facilitating the acquisition of large arrays of biomarker data have led to new opportunities to understand and characterize disease progression over time. This creates an analytical challenge, however, due to the large numbers of potentially informative markers, the high degrees of correlation among them, and the time-dependent trajectories of association. We propose a mixed ridge estimator, which integrates ridge regression into the mixed effects modeling framework in order to account for both the correlation induced by repeatedly measuring an outcome on each individual over time, as well as the potentially high degree of correlation among possible predictor variables. An expectation-maximization algorithm is described to account for unknown variance and covariance parameters. Model performance is demonstrated through a simulation study and an application of the mixed ridge approach to data arising from a study of cardiometabolic biomarker responses to evoked inflammation induced by experimental low-dose endotoxemia.     

Metabolomics and the challenges ahead

Eight years have passed since the proposal that the characterizationof mutations with silent phenotypes should be carried out bya global view of metabolism [1]. Since then, the field of metabolomicshas flourished both in the development of new     

Bayesian biomarker identification based on marker-expression proteomics data

We are studying variable selection in multiple regression models in which molecular markers and/or gene-expression measurements as well as intensity measurements from protein spectra serve as predictors for the outcome variable (i.e., trait or disease state). Finding genetic biomarkers and searching genetic–epidemiological factors can be formulated as a statistical problem of variable selection, in which, from a large set of candidates, a small number of trait-associated predictors are identified. We illustrate our approach by analyzing the data available for chronic fatigue syndrome (CFS). CFS is a complex disease from several aspects, e.g., it is difficult to diagnose and difficult to quantify. To identify biomarkers we used microarray data and SELDI-TOF-based proteomics data. We also analyzed genetic marker information for a large number of SNPs for an overlapping set of individuals. The objectives of the analyses were to identify markers specific to fatigue that are also possibly exclusive to CFS. The use of such models can be motivated, for example, by the search for new biomarkers for the diagnosis and prognosis of cancer and measures of response to therapy. Generally, for this we use Bayesian hierarchical modeling and Markov Chain Monte Carlo computation.     

A repeated measures approach to pooled and calibrated biomarker data

Participant-level meta-analysis across multiple studies increases the sample size for pooled analyses, thereby improving precision in effect estimates and enabling subgroup analyses. For analyses involving biomarker measurements as an exposure of interest, investigators must first calibrate the data to address measurement variability arising from usage of different laboratories and/or assays. In practice, the calibration process involves reassaying a random subset of biospecimens from each study at a central laboratory and fitting models that relate the study-specific “local” and central laboratory measurements. Previous work in this area treats the calibration process from the perspective of measurement error techniques and imputes the estimated central laboratory value among individuals with only a local laboratory measurement. In this work, we propose a repeated measures method to calibrate biomarker measurements pooled from multiple studies with study-specific calibration subsets. We account for correlation between measurements made on the same person and between measurements made at the same laboratory. We demonstrate that the repeated measures approach provides valid inference, and compare it to existing calibration approaches grounded in measurement error techniques in an example describing the association between circulating vitamin D and stroke.     

Metabolomics across the globe

This article highlights some of the larger and more recent metabolomics activities which are funded and organised at local (mostly national) level. While being just a snap-shot, and far from exhaustive, the details clearly illustrate the extent to which metabolomics has already become established and integrated in both basic fundamental and more applied research covering a wide range of organisms. Many national (service) centres for metabolomics already exist and additional ones are envisaged.     

Opportunities and challenges for biomarker discovery using electronic health record data

Electronic health records (EHRs) have become increasingly relied upon as a source for biomedical research. One important research application of EHRs is the identification of biomarkers associated with specific patient states, especially within complex conditions. However, using EHRs for biomarker identification can be challenging because the EHR was not designed with research as the primary focus. Despite this challenge, the EHR offers huge potential for biomarker discovery research to transform our understanding of disease etiology and treatment and generate biological insights informing precision medicine initiatives. This review paper provides an in-depth analysis of how EHR data is currently used for phenotyping and identifying molecular biomarkers, current challenges and limitations, and strategies we can take to mitigate challenges going forward.     

"Omics" data and levels of evidence for biomarker discovery

With the development of new technologies for assaying biological activity on a global basis in experimental samples, various new "-omics" signatures have been developed to predict disease progression. Such signatures hold the potential to alter the nature of clinical management of human disease. In this article, we describe some necessary statistical considerations needed to take these signatures from the discovery phase to a clinically useful assay. Much of the work discussed is in the area of cancer.     

Difference detection in LC-MS data for protein biomarker discovery

MOTIVATION: There is a pressing need for improved proteomic screening methods allowing for earlier diagnosis of disease, systematic monitoring of physiological responses and the uncovering of fundamental mechanisms of drug action. The combined platform of LC-MS (Liquid-Chromatography-Mass-Spectrometry) has shown promise in moving toward a solution in these areas. In this paper we present a technique for discovering differences in protein signal between two classes of samples of LC-MS serum proteomic data without use of tandem mass spectrometry, gels or labeling. This method works on data from a lower-precision MS instrument, the type routinely used by and available to the community at large today. We test our technique on a controlled (spike-in) but realistic (serum biomarker discovery) experiment which is therefore verifiable. We also develop a new method for helping to assess the difficulty of a given spike-in problem. Lastly, we show that the problem of class prediction, sometimes mistaken as a solution to biomarker discovery, is actually a much simpler problem. RESULTS: Using precision-recall curves with experimentally extracted ground truth, we show that (1) our technique has good performance using seven replicates from each class, (2) performance degrades with decreasing number of replicates, (3) the signal that we are teasing out is not trivially available (i.e. the differences are not so large that the task is easy). Lastly, we easily obtain perfect classification results for data in which the problem of extracting differences does not produce absolutely perfect results. This emphasizes the different nature of the two problems and also their relative difficulties. AVAILABILITY: Our data are publicly available as a benchmark for further studies of this nature at http://www.cs.toronto.edu/~jenn/LCMS     

Metabolomics technology and bioinformatics

Metabolomics is the global analysis of all or a large number of cellular metabolites. Like other functional genomics research, metabolomics generates large amounts of data. Handling, processing and analysis of this data is a clear challenge and requires specialized mathematical, statistical and bioinformatics tools. Metabolomics needs for bioinformatics span through data and information management, raw analytical data processing, metabolomics standards and ontology, statistical analysis and data mining, data integration and mathematical modelling of metabolic networks within a framework of systems biology. The major approaches in metabolomics, along with the modern analytical tools used for data generation, are reviewed in the context of these specific bioinformatics needs.     

Regularized ROC method for disease classification and biomarker selection with microarray data

MOTIVATION: An important application of microarrays is to discover genomic biomarkers, among tens of thousands of genes assayed, for disease classification. Thus there is a need for developing statistical methods that can efficiently use such high-throughput genomic data, select biomarkers with discriminant power and construct classification rules. The ROC (receiver operator characteristic) technique has been widely used in disease classification with low-dimensional biomarkers because (1) it does not assume a parametric form of the class probability as required for example in the logistic regression method; (2) it accommodates case-control designs and (3) it allows treating false positives and false negatives differently. However, due to computational difficulties, the ROC-based classification has not been used with microarray data. Moreover, the standard ROC technique does not incorporate built-in biomarker selection. RESULTS: We propose a novel method for biomarker selection and classification using the ROC technique for microarray data. The proposed method uses a sigmoid approximation to the area under the ROC curve as the objective function for classification and the threshold gradient descent regularization method for estimation and biomarker selection. Tuning parameter selection based on the V-fold cross validation and predictive performance evaluation are also investigated. The proposed approach is demonstrated with a simulation study, the Colon data and the Estrogen data. The proposed approach yields parsimonious models with excellent classification performance.     

A robust biomarker discovery pipeline for high-performance mass spectrometry data

A high-throughput software pipeline for analyzing high-performance mass spectral data sets has been developed to facilitate rapid and accurate biomarker determination. The software exploits the mass precision and resolution of high-performance instrumentation, bypasses peak-finding steps, and instead uses discrete m/z data points to identify putative biomarkers. The technique is insensitive to peak shape, and works on overlapping and non-Gaussian peaks which can confound peak-finding algorithms. Methods are presented to assess data set quality and the suitability of groups of m/z values that map to peaks as potential biomarkers. The algorithm is demonstrated with serum mass spectra from patients with and without ovarian cancer. Biomarker candidates are identified and ranked by their ability to discriminate between cancer and noncancer conditions. Their discriminating power is tested by classifying unknowns using a simple distance calculation, and a sensitivity of 95.6% and a specificity of 97.1% are obtained. In contrast, the sensitivity of the ovarian cancer blood marker CA125 is approximately 50% for stage I/II and approximately 80% for stage III/IV cancers. While the generalizability of these markers is currently unknown, we have demonstrated the ability of our analytical package to extract biomarker candidates from high-performance mass spectral data.