Urine metabolomics analysis for adrenal incidentaloma activity detection and biomarker discovery

This study describes the development of a method suitable for the analysis of nineteen major urinary steroid metabolites in human urine. The analytes of interest were isolated from urine using solid phase extraction, subjected to enzymatic hydrolysis and again extracted applying solid phase extraction. After derivatization, methyloxime-trimethylsilyl ether derivatives of steroid hormones were identified by gas chromatography-mass spectrometry (GC/MS) and quantified by gas chromatography with flame ionization detector (GC/FID). The quantification method was validated for linearity, trueness, precision and selectivity. The limits of detection were between 6.2 and 7.2 ng/mL and limits of quantification were between 12.3 and 14.8 ng/mL. The established method was applied to analyze 28 urine samples from patients diagnosed with non-functioning adrenal incidentalomas (AIs) and 30 healthy subjects. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were employed to visualize the differences between metabolic profiles of patients and the control group and to determine possible markers of AIs activity. Both multivariate methods separated seven patients from the rest of the examined individuals. Five urinary metabolites including α-cortol, tetrahydrocorticosterone, tetrahydrocortisol, allo-tetrahydrocortisol and etiocholanolone were identified as potential biomarkers of pathological adrenal function. The altered metabolites reflected pathological metabolism mainly of cortisol and cortisone. This research proved that metabolomics is a suitable tool for disease research.     

Radiation metabolomics. 4. UPLC-ESI-QTOFMS-Based metabolomics for urinary biomarker discovery in gamma-irradiated rats

Radiation metabolomics has aided in the identification of a number of biomarkers in cells and mice by ultra-performance liquid chromatography-coupled time-of-flight mass spectrometry (UPLC-ESI-QTOFMS) and in rats by gas chromatography-coupled mass spectrometry (GCMS). These markers have been shown to be both dose- and time-dependent. Here UPLC-ESI-QTOFMS was used to analyze rat urine samples taken from 12 rats over 7 days; they were either sham-irradiated or γ-irradiated with 3 Gy after 4 days of metabolic cage acclimatization. Using multivariate data analysis, nine urinary biomarkers of γ radiation in rats were identified, including a novel mammalian metabolite, N-acetyltaurine. These upregulated urinary biomarkers were confirmed through tandem mass spectrometry and comparisons with authentic standards. They include thymidine, 2'-deoxyuridine, 2'deoxyxanthosine, N(1)-acetylspermidine, N-acetylglucosamine/galactosamine-6-sulfate, N-acetyltaurine, N-hexanoylglycine, taurine and, tentatively, isethionic acid. Of these metabolites, 2'-deoxyuridine and thymidine were previously identified in the rat by GCMS (observed as uridine and thymine) and in the mouse by UPLC-ESI-QTOFMS. 2'Deoxyxanthosine, taurine and N-hexanoylglycine were also seen in the mouse by UPLC-ESI-QTOFMS. These are now unequivocal cross-species biomarkers for ionizing radiation exposure. Downregulated biomarkers were shown to be related to food deprivation and starvation mechanisms. The UPLC-ESI-QTOFMS approach has aided in the advance for finding common biomarkers of ionizing radiation exposure.     

Proteomics and metabolomics in biomarker discovery for cardiovascular diseases: progress and potential

Introduction: The process of discovering novel biomarkers and potential therapeutic targets may be shortened using proteomic and metabolomic approaches.

Areas covered: Several complementary strategies, each one presenting different advantages and limitations, may be used with these novel approaches. In vitro studies show how cells involved in cardiovascular disease react, although the phenotype of cultured cells differs to that occurring in vivo. Tissue analysis either in human specimens or animal models may show the proteins that are expressed in the pathological process, although the presence of structural proteins may be confounding. To identify circulating biomarkers, analyzing the secretome of cultured atherosclerotic tissue, analysis of blood cells and/or plasma may be more straightforward. However, in the latter approach, high-abundant proteins may mask small molecules that could be potential biomarkers. The study of sub-proteomes such as high-density lipoproteins may be useful to circumvent this limitation. Regarding metabolomics, most studies have been performed in small populations, and we need to perform studies in large populations in order to discover robust biomarkers.

Expert commentary: It is necessary to involve the clinicians in these areas to improve the design of clinical studies, including larger populations, in order to obtain consistent novel biomarkers.     

Human saliva proteome analysis and disease biomarker discovery

Human saliva is an attractive body fluid for disease diagnosis and prognosis because saliva testing is simple, safe, low-cost and noninvasive. Comprehensive analysis and identification of the proteomic content in human whole and ductal saliva will not only contribute to the understanding of oral health and disease pathogenesis, but also form a foundation for the discovery of saliva protein biomarkers for human disease detection. In this article, we have summarized the proteomic technologies for comprehensive identification of proteins in human whole and ductal saliva. We have also discussed potential quantitative proteomic approaches to the discovery of saliva protein biomarkers for human oral and systemic diseases. With the fast development of mass spectrometry and proteomic technologies, we are enthusiastic that saliva protein biomarkers will be developed for clinical diagnosis and prognosis of human diseases in the future.     

Human saliva proteome analysis and disease biomarker discovery

Human saliva is an attractive body fluid for disease diagnosis and prognosis because saliva testing is simple, safe, low-cost and noninvasive. Comprehensive analysis and identification of the proteomic content in human whole and ductal saliva will not only contribute to the understanding of oral health and disease pathogenesis, but also form a foundation for the discovery of saliva protein biomarkers for human disease detection. In this article, we have summarized the proteomic technologies for comprehensive identification of proteins in human whole and ductal saliva. We have also discussed potential quantitative proteomic approaches to the discovery of saliva protein biomarkers for human oral and systemic diseases. With the fast development of mass spectrometry and proteomic technologies, we are enthusiastic that saliva protein biomarkers will be developed for clinical diagnosis and prognosis of human diseases in the future.     

Placenta proteome analysis from Down syndrome pregnancies for biomarker discovery

Down syndrome is one of the most frequent chromosomal disorders, with a prevalence of approximately 1/500 to 1/800, depending on the maternal age distribution of the pregnant population. However, few reliable protein biomarkers have been used in the diagnosis of this disease. Recent progress in quantitative proteomics has offered opportunities to discover biomarkers for tracking the progression and for understanding the molecular mechanisms of Down syndrome. In the present study, placental samples were analyzed by fluorescence two-dimensional differential gel electrophoresis (2D-DIGE) and differentially expressed proteins were identified by matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS). In total, 101 proteins have been firmly identified representing 80 unique gene products. These proteins mainly function in cytoskeleton structure and regulation (such as vimentin and Profilin-1). Additionally, our quantitative proteomics approach has identified numerous previously reported Down syndrome markers, such as myelin protein. Here we present several Down syndrome biomarkers including galectin-1, ataxin-3 and sprouty-related EVH1 domain-containing protein 2 (SPRED2), which have not been reported elsewhere and may be associated with the progression and development of the disease. In summary, we report a comprehensive placenta-based proteomics approach for the identification of potential biomarkers for Down syndrome, in which serum amyloid P-component (APCS) and ataxin-3 have been shown to be up-regulated in the maternal peripheral plasma of Down syndrome cases. The potential of utilizing these markers for the prognosis and screening of Down syndrome warrants further investigation.     

Translational biomarker discovery in clinical metabolomics: an introductory tutorial

Metabolomics is increasingly being applied towards the identification of biomarkers for disease diagnosis, prognosis and risk prediction. Unfortunately among the many published metabolomic studies focusing on biomarker discovery, there is very little consistency and relatively little rigor in how researchers select, assess or report their candidate biomarkers. In particular, few studies report any measure of sensitivity, specificity, or provide receiver operator characteristic (ROC) curves with associated confidence intervals. Even fewer studies explicitly describe or release the biomarker model used to generate their ROC curves. This is surprising given that for biomarker studies in most other biomedical fields, ROC curve analysis is generally considered the standard method for performance assessment. Because the ultimate goal of biomarker discovery is the translation of those biomarkers to clinical practice, it is clear that the metabolomics community needs to start “speaking the same language” in terms of biomarker analysis and reporting-especially if it wants to see metabolite markers being routinely used in the clinic. In this tutorial, we will first introduce the concept of ROC curves and describe their use in single biomarker analysis for clinical chemistry. This includes the construction of ROC curves, understanding the meaning of area under ROC curves (AUC) and partial AUC, as well as the calculation of confidence intervals. The second part of the tutorial focuses on biomarker analyses within the context of metabolomics. This section describes different statistical and machine learning strategies that can be used to create multi-metabolite biomarker models and explains how these models can be assessed using ROC curves. In the third part of the tutorial we discuss common issues and potential pitfalls associated with different analysis methods and provide readers with a list of nine recommendations for biomarker analysis and reporting. To help readers test, visualize and explore the concepts presented in this tutorial, we also introduce a web-based tool called ROCCET (ROC Curve Explorer & Tester, http://www.roccet.ca). ROCCET was originally developed as a teaching aid but it can also serve as a training and testing resource to assist metabolomics researchers build biomarker models and conduct a range of common ROC curve analyses for biomarker studies.     

Strategies for biomarker discovery in fibrotic disease

The discovery and development of biomarkers for fibrotic diseases have potential utility in clinical decision-making as well as in pharmaceutical research and development. This review describes strategies for identifying diagnostic, prognostic and theranostic biomarkers. A range of technologies and platforms for biomarker discovery are highlighted, including several with specific relevance for fibrosis. Some challenges specific to fibrotic diseases are outlined including; benchmarking biomarkers against imperfect clinical measures of fibrosis, the complexity resulting from diverse aetiologies and target organs, and the availability of samples (including biopsy) from well-characterised patients with fibrotic disease. To overcome these challenges collaboration amongst clinical specialities as well as between academia and industry is essential. This article is part of a Special Issue entitled: Fibrosis: Translation of basic research to human disease.     

Advances in top-down proteomics for disease biomarker discovery

Top-down mass spectrometry strategies allow identification and characterization of proteins and protein networks by direct fragmentation. These analytical processes involve a panel of fragmentation mechanisms, some of which preserve protein post-translational modifications. Thus top-down is of special interest in clinical biochemistry to probe modified proteins as potential disease biomarkers. This review describes separating methods, mass spectrometry instrumentation, bioinformatics, and theoretical aspects of fragmentation mechanisms used for top-down analysis. The biological interest of this strategy is extensively reported regarding the characterization of post-translational modifications in biochemical pathways and the discovery of biomarkers. One has to bear in mind that quantitative aspects that are beyond the focus of this review are also of critical important for biomarker discovery. The constant evolution of technologies makes top-down strategies crucial players in clinical and basic proteomics.     

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     

Pairwise protein expression classifier for candidate biomarker discovery for early detection of human disease prognosis

ABSTRACT: BACKGROUND: An approach to molecular classification based on the comparative expression of protein pairs is presented.The method overcomes some of the present limitations in using peptide intensity data for class prediction forproblems such as the detection of a disease, disease prognosis, or for predicting treatment response. Dataanalysis is particularly challenging in these situations due to sample size (typically tens) being much smallerthan the large number of peptides (typically thousands). Methods based upon high dimensional statisticalmodels, machine learning or other complex classifiers generate decisions which may be very accurate butcan be complex and difficult to interpret in simple or biologically meaningful terms. A classificationscheme, called ProtPair, is presented that generates simple decision rules leading to accurate classificationwhich is based on measurement of very few proteins and requires only relative expression values, providingspecific targeted hypotheses suitable for straightforward validation. RESULTS: ProtPair has been tested against clinical data from 21 patients following a bone marrow transplant, 13 ofwhich progress to idiopathic pneumonia syndrome (IPS). The approach combines multiple peptide pairsoriginating from the same set of proteins, with each unique peptide pair providing an independent measureof discriminatory power. The prediction rate of the ProtPair for IPS study as measured by leave-one-out CVis 69.1%, which can be very beneficial for clinical diagnosis as it may flag patients in need of closer monitoring. The "top ranked" proteins provided by ProtPair are known to be associated with the biologicalprocesses and pathways intimately associated with known IPS biology based on mouse models. CONCLUSIONS: An approach to biomarker discovery, called ProtPair, is presented. ProtPair is based on the differentialexpression of pairs of peptides and the associated proteins. Using mass spectrometry data from "bottom up"proteomics methods, functionally related proteins/peptide pairs exhibiting co-ordinated changes expressionprofile are discovered, which represent a signature for patients progressing to various disease conditions.The method has been tested against clinical data from patients progressing to idiopthatic pneumoniasyndrome (IPS) following a bone marrow transplant. The data indicates that patients with improperregulation in the concentration of specific acute phase response proteins at the time of bone marrowtransplant are highly likely to develop IPS within few weeks. The results lead to a specific set of proteinpairs that can be efficiently verified by investigating the pairwise abundance change in independent cohortsusing ELISA or targeted mass spectrometry techniques. This generalized classifier can be extended to otherclinical problems in a variety of contexts.     

A proton nuclear magnetic resonance metabonomics approach for biomarker discovery in nonalcoholic fatty liver disease

This study was undertaken to discover novel biomarkers for the noninvasive early diagnosis of nonalcoholic fatty liver disease (NAFLD). A methionine and choline deficient (MCD) diet was used to represent different stages of NAFLD in male C57BL/6 mice. (1)H NMR spectroscopy and principal components analysis (PCA) were used to investigate the time-related biochemical changes in mice sera induced by the MCD diet. Many serum metabolites' concentrations changed between control and MCD-fed mice. Hierarchical cluster analysis (HCA) and artificial neural networks (ANNs) were used to select the least number of metabolites to be used for the noninvasive diagnosis of various stages of NAFLD; four potential biomarkers, serum glucose, lactate, glutamate/glutamine, and taurine were selected. To verify the diagnostic accuracy of these selected metabolites, their serum concentrations were measured in healthy controls (n = 28), NAFLD patients with steatosis (n = 15), steatosis patients with necro-inflammatory disease (n = 11), and NASH patients (n = 6). On the basis of results from MCD-fed mice model, clinical tests, and previous reports, we propose using the levels of the four metabolites for diagnosing NAFLD at various stages. Furthermore, the probability of developing NAFLD at a particular stage was assessed by multinomial logistic regression (MLR) based on the clinical results of the four serum metabolites.     

Advances in proximal fluid proteomics for disease biomarker discovery

Although serum/plasma has been the preferred source for identification of disease biomarkers, these efforts have been met with little success, in large part due the relatively small number of highly abundant proteins that render the reliable detection of low abundant disease-related proteins challenging due to the expansive dynamic range of concentration of proteins in this sample. Proximal fluid, the fluid derived from the extracellular milieu of tissues, contains a large repertoire of shed and secreted proteins that are likely to be present at higher concentrations relative to plasma/serum. It is hypothesized that many, if not all, proximal fluid proteins exchange with peripheral circulation, which has provided significant motivation for utilizing proximal fluids as a primary sample source for protein biomarker discovery. The present review highlights recent advances in proximal fluid proteomics, including the various protocols utilized to harvest proximal fluids along with detailing the results from mass spectrometry- and antibody-based analyses.     

New approaches for biomarker discovery: the search for liver fibrosis markers in hepatitis C patients

Despite many shortcomings, liver biopsy is regarded as the gold standard for assessing liver fibrosis. A less invasive and equally or more reliable approach would constitute a major advancement in the field. Proteomics can aid discovery of novel serological markers and these proteins can be measured in patient blood. A major challenge of discovering biomarkers in serum is the presence of highly abundant serum proteins, which restricts the levels of total protein loaded onto gels and limits the detection of low abundance features. To overcome this problem, we used two-dimensional gel electrophoresis (2-DE) over a narrow pH 3-5.6 range since this lies outside the range of highly abundant albumin, transferrin and immunoglobulins. In addition, we used in-solution isoelectric focusing followed by SDS-PAGE to find biomarkers in hepatitis C induced liver cirrhosis. Using the pH 3-5.6 range for 2-DE, we achieved improved representation of low abundance features and enhanced separation. We found in-solution isoelectric focusing to be beneficial for analyzing basic, high molecular weight proteins. Using this method, the beta chains of both complement C3 and C4 were found to decrease in serum from hepatitis C patients with cirrhosis, a change not observed previously by 2-DE. We present two proteomics approaches that can aid in the discovery of clinical biomarkers in various diseases and discuss how these approaches have helped to identify 23 novel biomarkers for hepatic fibrosis.     

Proteomic biomarker discovery for the monogenic disease cystic fibrosis

Proteomics was initially viewed as a promising new scientific discipline to study complex disorders such as polygenic, infectious and environment-related diseases. However, the first attempts to understand a monogenic disease such as cystic fibrosis (CF) by proteomics-based approaches have proved quite rewarding. In CF, the impairment of a unique protein, the CF transmembrane conductance regulator, does not completely explain the complex and variable CF clinical phenotype. The great advances in our knowledge about the molecular and cellular consequences of such impairment have not been sufficient to be translated into effective treatments, and CF patients are still dying due to chronic progressive lung dysfunction. The progression of proteomics application in CF will certainly unravel new proteins that could be useful as biomarkers either to elucidate CF basic mechanisms and to better monitor the disease progression, or to promote the development of novel therapeutic strategies against CF. This review will summarize the recent technological advances in proteomics and the first results of its application to address the most important issues in the CF field.     

Proteomic biomarker discovery for the monogenic disease cystic fibrosis

Proteomics was initially viewed as a promising new scientific discipline to study complex disorders such as polygenic, infectious and environment-related diseases. However, the first attempts to understand a monogenic disease such as cystic fibrosis (CF) by proteomics-based approaches have proved quite rewarding. In CF, the impairment of a unique protein, the CF transmembrane conductance regulator, does not completely explain the complex and variable CF clinical phenotype. The great advances in our knowledge about the molecular and cellular consequences of such impairment have not been sufficient to be translated into effective treatments, and CF patients are still dying due to chronic progressive lung dysfunction. The progression of proteomics application in CF will certainly unravel new proteins that could be useful as biomarkers either to elucidate CF basic mechanisms and to better monitor the disease progression, or to promote the development of novel therapeutic strategies against CF. This review will summarize the recent technological advances in proteomics and the first results of its application to address the most important issues in the CF field.     

Serum metabolomics analysis of patients with polycystic ovary syndrome by mass spectrometry

Polycystic ovary syndrome (PCOS) is a set of symptoms caused by elevated androgens (male hormones) in females. PCOS is the most common endocrine disorder among women between 18 and 44 years. Currently, the pathogenesis of PCOS remains unclear. Liquid chromatography–mass spectrometry (LC/MS)‐based metabolomics is becoming more and more useful for medical research, especially in revealing the mechanism of the disease. The aim of this study was to investigate the difference of serum metabolic profiles in patients with PCOS and healthy control to better understand the mechanism of this disease. Ten patients with PCOS and 10 healthy people were recruited for this study. The serum samples were collected for LC/MS analysis. Multivariate statistical analysis was performed to discover and identify the potential biomarkers. Six biomarkers were found and identified. The biomarkers belonged to different metabolic pathway including lipid metabolism, carnitine metabolism, androgen metabolism, and bile acid metabolism. Those biomarkers also played different roles in disease progression. Metabolomics is a powerful tool used in research of the mechanism involved in this disease to provide useful information for better understanding of PCOS.     

Human biomarker discovery and predictive models for disease progression for idiopathic pneumonia syndrome following allogeneic stem cell transplantation

Allogeneic hematopoietic stem cell transplantation (SCT) is the only curative therapy for many malignant and nonmalignant conditions. Idiopathic pneumonia syndrome (IPS) is a frequently fatal complication that limits successful outcomes. Preclinical models suggest that IPS represents an immune mediated attack on the lung involving elements of both the adaptive and the innate immune system. However, the etiology of IPS in humans is less well understood. To explore the disease pathway and uncover potential biomarkers of disease, we performed two separate label-free, proteomics experiments defining the plasma protein profiles of allogeneic SCT patients with IPS. Samples obtained from SCT recipients without complications served as controls. The initial discovery study, intended to explore the disease pathway in humans, identified a set of 81 IPS-associated proteins. These data revealed similarities between the known IPS pathways in mice and the condition in humans, in particular in the acute phase response. In addition, pattern recognition pathways were judged to be significant as a function of development of IPS, and from this pathway we chose the lipopolysaccaharide-binding protein (LBP) protein as a candidate molecular diagnostic for IPS, and verified its increase as a function of disease using an ELISA assay. In a separately designed study, we identified protein-based classifiers that could predict, at day 0 of SCT, patients who: 1) progress to IPS and 2) respond to cytokine neutralization therapy. Using cross-validation strategies, we built highly predictive classifier models of both disease progression and therapeutic response. In sum, data generated in this report confirm previous clinical and experimental findings, provide new insights into the pathophysiology of IPS, identify potential molecular classifiers of the condition, and uncover a set of markers potentially of interest for patient stratification as a basis for individualized therapy.     

Data-driven analysis approach for biomarker discovery using molecular-profiling technologies

High-throughput molecular-profiling technologies provide rapid, efficient and systematic approaches to search for biomarkers. Supervised learning algorithms are naturally suited to analyse a large amount of data generated using these technologies in biomarker discovery efforts. The study demonstrates with two examples a data-driven analysis approach to analysis of large complicated datasets collected in high-throughput technologies in the context of biomarker discovery. The approach consists of two analytic steps: an initial unsupervised analysis to obtain accurate knowledge about sample clustering, followed by a second supervised analysis to identify a small set of putative biomarkers for further experimental characterization. By comparing the most widely applied clustering algorithms using a leukaemia DNA microarray dataset, it was established that principal component analysis-assisted projections of samples from a high-dimensional molecular feature space into a few low dimensional subspaces provides a more effective and accurate way to explore visually and identify data structures that confirm intended experimental effects based on expected group membership. A supervised analysis method, shrunken centroid algorithm, was chosen to take knowledge of sample clustering gained or confirmed by the first step of the analysis to identify a small set of molecules as candidate biomarkers for further experimentation. The approach was applied to two molecular-profiling studies. In the first study, PCA-assisted analysis of DNA microarray data revealed that discrete data structures exist in rat liver gene expression and correlated with blood clinical chemistry and liver pathological damage in response to a chemical toxicant diethylhexylphthalate, a peroxisome-proliferator-activator receptor agonist. Sixteen genes were then identified by shrunken centroid algorithm as the best candidate biomarkers for liver damage. Functional annotations of these genes revealed roles in acute phase response, lipid and fatty acid metabolism and they are functionally relevant to the observed toxicities. In the second study, 26 urine ions identified from a GC/MS spectrum, two of which were glucose fragment ions included as positive controls, showed robust changes with the development of diabetes in Zucker diabetic fatty rats. Further experiments are needed to define their chemical identities and establish functional relevancy to disease development.