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Purpose There is an ongoing demand for easily accessible biomarkers that reflect the physiological and pathophysiological mechanisms of COPD. To test if an exercise challenge could help to identify clinically relevant metabolic biomarkers in COPD. Patients and methods We performed two constant-load exercise challenges separated by 4 weeks including smokers with COPD (n=23/19) and sex- and age-matched healthy smokers (n=23/20). Two hours after a standardized meal venous blood samples were obtained before, 5 mins after the start, at the end of submaximal exercise, and following a recovery of 20 mins. Data analysis was performed using mixed- effects model, with the metabolite level as a function of disease, time point and interaction terms and using each individual's resting level as reference. Results Exercise duration was longer in healthy smokers but lactate levels were comparable between groups at all four time points. Glucose levels were increased in COPD. Glutamine was lower, while glutamate and arginine were higher in COPD. Branched-chain amino acids showed a stronger decline during exercise in healthy smokers. Carnitine and the acyl-carnitines C16 and C18:1 were increased in COPD. These metabolite levels and changes were reproducible in the second challenge. Conclusion Higher serum glucose, evidence for impaired utilization of amino acids during exercise and a shift of energy metabolism to enhanced consumption of lipids could be early signs for a developing metabolic syndrome in COPD. In COPD patients, deviations of energy and nitrogen metabolism are amplified by an exercise challenge.

Purpose There is an ongoing demand for easily accessible biomarkers that reflect the physiological and pathophysiological mechanisms of COPD. To test if an exercise challenge could help to identify clinically relevant metabolic biomarkers in COPD. Patients and Methods We performed two constant-load exercise challenges separated by 4 weeks including smokers with COPD (n=23/19) and sex- and age-matched healthy smokers (n=23/20). Two hours after a standardized meal venous blood samples were obtained before, 5 mins after the start, at the end of submaximal exercise, and following a recovery of 20 mins. Data analysis was performed using mixed- effects model, with the metabolite level as a function of disease, time point and interaction terms and using each individual's resting level as reference. Results Exercise duration was longer in healthy smokers but lactate levels were comparable between groups at all four time points. Glucose levels were increased in COPD. Glutamine was lower, while glutamate and arginine were higher in COPD. Branched-chain amino acids showed a stronger decline during exercise in healthy smokers. Carnitine and the acyl-carnitines C16 and C18:1 were increased in COPD. These metabolite levels and changes were reproducible in the second challenge. Conclusion Higher serum glucose, evidence for impaired utilization of amino acids during exercise and a shift of energy metabolism to enhanced consumption of lipids could be early signs for a developing metabolic syndrome in COPD. In COPD patients, deviations of energy and nitrogen metabolism are amplified by an exercise challenge.

Machine learning is one important application in the area of health informatics, however classification methods for longitudinal data are still rare.The aim of this work is to analyze and classify differences in metabolite time series data between groups of individuals regarding their athletic activity.We propose a new ensemble-based 2-tier approach to classify metabolite time series data. The first tier uses polynomial fitting to generate a class prediction for each metabolite. An induced classifier (k-nearest-neighbor or naïve bayes) combines the results to produce a final prediction. Metabolite levels of 47 individuals undergoing a cycle ergometry test were measured using mass spectrometry.In accordance with our previous work the statistical results indicate strong changes over time. We found only small but systematic differences between the groups. However, our proposed stacking approach obtained a mean accuracy of 78% using 10-fold cross-validation.Our proposed classification approach allows a considerable classification performance for time series data with small differences between the groups.

International audience; Background: Various alterations in lipid metabolism have been observed in patients with chronic kidney disease (CKD).Objectives: To determine the levels of lipid species in plasma from CKD and hemodialysis (HD) patients and test their association with CKD severity and patient outcome.Methods: Seventy-seven patients with CKD stage 2 to HD were grouped into classes of CKD severity at baseline and followed-up for 3.5 years for the occurrence of transition to HD or death (combined outcome). Plasma levels of phosphatidylcholines (PCs), lysophosphatidylcholines (LPCs), sphingomyelins (SMs), and fatty acids were analyzed by flow-injection analysis coupled to tandem mass spectrometry or gas chromatography coupled with mass spectrometry. Kruskal Wallis rank tests and Cox regressions were used to analyze the association of lipids with CKD severity and the risk of combined outcome, respectively.Results: The plasma level of PCs, LPCs, and SMs was decreased in HD patients compared with nondialyzed CKD patients (all P < .05), whereas esterified and/or nonesterified fatty acids level did not change. Thirty-four lipids displayed significantly lower abundance in plasma of HD patients, whereas elaidic acid (C18:1ω9t) level was increased (P < .001). The total amount of LPCs and individual LPCs were associated with better outcome (P < .05). In particular, LPC 18:2 and LPC 20:3 were statistically associated with outcome in adjusted models (P < .05).Discussion: In HD patients, a reduction in plasma lipids is observed. Some of the alterations, namely reduced LPCs, were associated with the risk of adverse outcome. These changes could be related to metabolic dysfunctions.

The identification and interpretation of metabolic biomarkers is a challenging task. In this context, network-based approaches have become increasingly a key technology in systems biology allowing to capture complex interactions in biological systems. In this work, we introduce a novel network-based method to identify highly predictive biomarker candidates for disease. First, we infer two different types of networks: (i) correlation networks, and (ii) a new type of network called ratio networks. Based on these networks, we introduce scores to prioritize features using topological descriptors of the vertices. To evaluate our method we use an example dataset where quantitative targeted MS/MS analysis was applied to a total of 52 blood samples from 22 persons with obesity (BMI >30) and 30 healthy controls. Using our network-based feature selection approach we identified highly discriminating metabolites for obesity (F-score >0.85, accuracy >85%), some of which could be verified by the literature.

Motivation: Prostate cancer is the most prevalent tumor in males and its incidence is expected to increase as the population ages. Prostate cancer is treatable by excision if detected at an early enough stage. The challenges of early diagnosis require the discovery of novel biomarkers and tools for prostate cancer management. Results: We developed a novel feature selection algorithm termed as associative voting (AV) for identifying biomarker candidates in prostate cancer data measured via targeted metabolite profiling MS/MS analysis. We benchmarked our algorithm against two standard entropy-based and correlation-based feature selection methods [Information Gain (IG) and ReliefF (RF)] and observed that, on a variety of classification tasks in prostate cancer diagnosis, our algorithm identified subsets of biomarker candidates that are both smaller and show higher discriminatory power than the subsets identified by IG and RF. A literature study confirms that the highest ranked biomarker candidates identified by AV have independently been identified as important factors in prostate cancer development. Availability: The algorithm can be downloaded from the following http://biomed.umit.at/page.cfm?pageid=516 Contact: melanie.osl@umit.at

Metabolomics, also die systematische Identifizierung und Quantifizierung der niedermolekularen Stoffwechselprodukte in einer bestimmten Zelle, einem Gewebe oder einer Körperflüssigkeit, wird zunehmend als die aussagekräftigste Disziplin innerhalb der funktionellen Genomanalyse erkannt. Langjährige positive Erfahrungen in der Routinediagnostik angeborener Stoffwechselstörungen sowie verbesserte Empfindlichkeit und Reproduzierbarkeit quantitativer multiparametrischer Assays durch instrumentelle und experimentelle Innovationen lassen viele zusätzliche diagnostische Anwendungen in greifbare Nähe rücken. Dieser Artikel fasst die wissenschaftlichen und technologischen Hintergründe zusammen, die zum rasanten Aufschwung von Metabolomics in den vergangenen Jahren geführt haben, und beleuchtet anhand einiger Beispiele das Potential der hochauflösenden Stoffwechselanalytik für die Labormedizin der Zukunft.

Isotope correction of a profile is an important step in the analysis of mass spectrometry derived data. The problem is mathematically formulated as a system of linear equations which is general enough to include previous correction methods. For the solution of these equations when applied to the whole profile an efficient algorithm is developed. In experimental tests the resulting algorithm corrected the profile fast and successfully.

Chronic kidney disease (CKD) is an increasingly recognized burden for patients and health care systems with high (and growing) global incidence and prevalence, significant mortality, and disproportionately high treatment costs. Yet, the available diagnostic tools are either impractical in clinical routine or have serious shortcomings impeding a well-informed disease management although optimized treatment strategies with proven benefits for the patients have become available. Advances in bioanalytical technologies have facilitated studies that identified genomic, proteomic, and metabolic biomarker candidates, and confirmed some of them in independent cohorts. This review summarizes the CKD-related markers discovered so far, and focuses on compounds and pathways, for which there is quantitative data, substantiating evidence from translational research, and a mechanistic understanding of the processes involved. Also, multiparametric marker panels have been suggested that showed promising diagnostic and prognostic performance in initial analyses although the data basis from prospective trials is very limited. Large-scale studies, however, are underway and will provide the information for validating a set of parameters and discarding others. Finally, the path from clinical research to a routine application is discussed, focusing on potential obstacles such as the use of mass spectrometry, and the feasibility of obtaining regulatory approval for targeted metabolomics assays.

The objectives of this work were the classification of dynamic metabolic biomarker candidates and the modeling and characterization of kinetic regulatory mechanisms in human metabolism with response to external perturbations by physical activity. Longitudinal metabolic concentration data of 47 individuals from 4 different groups were examined, obtained from a cycle ergometry cohort study. In total, 110 metabolites (within the classes of acylcarnitines, amino acids, and sugars) were measured through a targeted metabolomics approach, combining tandem mass spectrometry (MS/MS) with the concept of stable isotope dilution (SID) for metabolite quantitation. Biomarker candidates were selected by combined analysis of maximum fold changes (MFCs) in concentrations and P-values resulting from statistical hypothesis testing. Characteristic kinetic signatures were identified through a mathematical modeling approach utilizing polynomial fitting. Modeled kinetic signatures were analyzed for groups with similar behavior by applying hierarchical cluster analysis. Kinetic shape templates were characterized, defining different forms of basic kinetic response patterns, such as sustained, early, late, and other forms, that can be used for metabolite classification. Acetylcarnitine (C2), showing a late response pattern and having the highest values in MFC and statistical significance, was classified as late marker and ranked as strong predictor (MFC = 1.97, P < 0.001). In the class of amino acids, highest values were shown for alanine (MFC = 1.42, P < 0.001), classified as late marker and strong predictor. Glucose yields a delayed response pattern, similar to a hockey stick function, being classified as delayed marker and ranked as moderate predictor (MFC = 1.32, P < 0.001). These findings coincide with existing knowledge on central metabolic pathways affected in exercise physiology, such as β-oxidation of fatty acids, glycolysis, and glycogenolysis. The presented modeling approach demonstrates high potential for dynamic biomarker identification and the investigation of kinetic mechanisms in disease or pharmacodynamics studies using MS data from longitudinal cohort studies., Author Summary Human metabolism is controlled through basic kinetic regulatory mechanisms, where the overall system aims to maintain a state of homeostasis. In response to external perturbations, such as environmental influences, nutrition or physical exercise, circulating metabolites show specific kinetic response patterns, which can be computationally modeled. In this work, we searched for dynamic metabolic biomarker candidates and analyzed specific kinetic mechanisms from longitudinal metabolic concentration data, obtained through a cycle ergometry stress test. In total, 110 metabolites measured from blood samples of 47 individuals were analyzed using tandem mass spectrometry (MS/MS). Dynamic biomarker candidates could be selected based on the amplitudes of changes in metabolite concentrations and the significance of statistical hypothesis testing. We were able to characterize specific kinetic patterns for groups of similarly behaving metabolites. Kinetic shape templates were identified, defining basic kinetic response patterns to physical exercise, such as sustained, early, late and other shape forms. The presented approach contributes to a better understanding of (patho)physiological biochemical mechanisms in human health, disease or during drug therapy, by offering tools for classifying dynamic biomarker candidates and for modeling and characterizing kinetic regulatory mechanisms from longitudinal experimental data.

Several naturally occurring variants of immunogenic T cell epitopes were identified within the hepatitis B surface antigen (HBsAg). The effect of these variants on the cellular immune response was studied in individuals vaccinated against HBV. Class-II restricted T-cell responses of 30 vaccinees were analyzed after stimulation of peripheral blood mononuclear cells (PBMCs) with 4 synthetic peptides representing the 4 T-cell epitopes of HBsAg known as of yet. The 2 epitopes P1 (aa 16-33) and P4 (aa 213-226) could be identified as the dominant ones in our vaccinees by proliferation assays and enzyme-linked immunospot assays. Responses to these epitopes were compared with responses to their naturally occurring variants found in HBV isolates of chronic virus carriers. Three of 11 variants of epitope P4 led to a complete loss of T-cell reactivity in 4 of 10 donors, all of whom reacted well to the corresponding wild-type sequence. The remaining 6 donors recognized these variants as well as the vaccine epitope. Similarly, 3 P1-variants of the 12 found induced only a significantly reduced reactivity in 4 of 10 donors, whereas they led to a normal response in the other 6 individuals. Stimulation of T cells also induced the secretion of antibody to HBsAg (anti-HBs) by specific B cells; however, those peptides that failed to activate T cells were also unable to cause any significant anti-HBs production. In conclusion, our results suggest an immune escape of certain mutant strains of HBV in vaccinated individuals could exist at the T-cell level.

The prevalence of hepatitis B virus markers was investigated in 5305 individuals considered to be representative for the adult German population. After adjustment of the data according to the age and sex distribution in the whole German population an anti-HBc prevalence of 8.71% (95% confidence interval, 7.94-9.48%) and an HBsAg carrier rate of 0.62% (95% confidence interval, 0.40-0.84%) were calculated. Anti-HBc prevalence increased with age from 4.12% in the youngest group to 15.66% in the 61-70-year-old. The percentage of HBsAg carriers showed a maximum of 1.12% in the 41-50-year-old individuals and decreased significantly in the older age groups. 1.40% (95% confidence interval, 1.08-1.72%) of individuals had anti-HBc only. There was a trend to higher rates of this pattern in males than in females; a significantly higher percentage of persons with anti-HBc only was found in anti-HBc-positive individuals below 31 years than in older individuals. Five participants with anti-HBc only (7.7%, or about 0.1% of the whole population) showed HBV-DNA despite the absence of HBsAg. 3.1% of anti-HBc positive individuals where also positive for anti-HCV, that was significantly higher than the percentage of anti-HCV-positives among persons without any HBV marker (0.46%). This study provides a comprehensive picture of the current hepatitis B situation in Germany, showing new data especially on the distribution of HBsAg in the general population and on the subgroup of individuals with anti-HBc only.

Chronic carriers of hepatitis B virus (HBV) usually show hepatitis B surface antigen (HBsAg) in their sera, which is considered the best marker for acute and chronic HBV infection. In some individuals, however, this antigen cannot be detected by routine serological assays despite the presence of virus in liver and peripheral blood. One reason for this lack of HBsAg might be mutations in the part of the molecule recognized by specific antibodies. To test this hypothesis, the HBV S gene sequences were determined of isolates from 33 virus carriers who were negative for HBsAg but showed antibodies against the virus core (anti-HBc) as the only serological marker of hepatitis B. Isolates from 36 HBsAg-positive patients served as controls. In both groups, a considerable number of novel mutations were found. In isolates from individuals with anti-HBc reactivity only, the variability of the major hydrophilic loop of HBsAg, the main target for neutralizing and diagnostic antibodies, was raised significantly when compared with the residual protein (22·6 vs 9·4 mutations per 1000 amino acids; PP

The laboratory diagnosis of hepatitis B virus (HBV) infection is based mainly on serological assays. Yet the detection and quantitation of viral DNA is necessary when addressing directly the question of infectivity or when monitoring the viral load during therapy. Standard hybridization assays allow for exact quantitation, but their sensitivity is limited to 105–106 viral genomes per ml of serum. The most sensitive tests for HBV DNA are nested PCR systems, which recognize virtually one molecule of the target DNA per reaction. However, these assays only provide very coarse quantitative statements. To take advantage of both methods, a new assay for HBV DNA is described based on the commercial TaqMan© system. This assay is capable of quantifying HBV DNA from the theoretical lower limit up to 1010 genome equivalents per ml of serum and, thus, covers the complete range of naturally occurring states of infections. The method was calibrated on the basis of serial plasmid dilutions and compared with a well-established nested PCR system. More than 100 HBV positive sera and serial dilutions of the Eurohep standard for both ad and ay subtypes were analyzed. The assay reliably detected all HBV positive samples. It shows minimal run-to-run deviations, allows for quantitation that covers eight orders of magnitude, and finally, completely avoids the risk of cross-contamination by PCR products. Thus, this technique combines the sensitivity of PCR amplification and the quantitation potential of hybridization tests and it is time efficient and safer.

HBsAg is the most important serological marker for acute or chronic hepatitis B. Nevertheless, there are reports of HBsAg-negative virus carriers, either with anti-HBc as the only marker for hepatitis B virus (HBV) infection or even positive for anti-HBs and anti-HBc. We report isolates from a patient, in which a deletion in the HBs-gene was associated with persisting viremia in the presence of anti-HBs. The 62-year-old female, infected most likely by her husband, had detectable markers of chronic active hepatitis B, such as HBsAg, HBeAg, and anti-HBc-IgM, for 2 years. The patient then seroconverted to anti-HBs, although HBeAg and anti-HBc-IgM remained detectable. At this time, semiquantitative polymerase chain reaction showed about 104 viral genomes per milliliter of serum. Direct sequencing of the amplified products revealed a major population of DNA molecules with a deletion of nucleotide 31 of the HBs-gene, which up to now has not been described. This deletion led to a frame-shift and introduced a stop-codon after 21 amino acids of the sHBsAg. We suspect that this deletion, and the resulting HBsAg lacking the major epitopes recognized by specific antibodies, could favor ongoing viral replication, despite the presence of anti-HBs. However, because the reading frame of the polymerase was also severely damaged by this deletion, it is assumed that a minor population of intact genomes was present to help in the formation of virus particles. J. Med. Virol. 58:105–110, 1999. © 1999 Wiley-Liss, Inc.

International audience; Chronic kidney disease (CKD) is part of a number of systemic and renal diseases and may reach epidemic proportions over the next decade. Efforts have been made to improve diagnosis and management of CKD. We hypothesised that combining metabolomic and proteomic approaches could generate a more systemic and complete view of the disease mechanisms. To test this approach, we examined samples from a cohort of 49 patients representing different stages of CKD. Urine samples were analysed for proteomic changes using capillary electrophoresis-mass spectrometry and urine and plasma samples for metabolomic changes using different mass spectrometry-based techniques. The training set included 20 CKD patients selected according to their estimated glomerular filtration rate (eGFR) at mild (59.9616.5 mL/min/1.73 m 2 ; n = 10) or advanced (8.964.5 mL/min/1.73 m 2 ; n = 10) CKD and the remaining 29 patients left for the test set. We identified a panel of 76 statistically significant metabolites and peptides that correlated with CKD in the training set. We combined these biomarkers in different classifiers and then performed correlation analyses with eGFR at baseline and follow-up after 2.860.8 years in the test set. A solely plasma metabolite biomarker-based classifier significantly correlated with the loss of kidney function in the test set at baseline and follow-up (r = 20.8031; p,0.0001 and r = 20.6009; p = 0.0019, respectively). Similarly, a urinary metabolite biomarker-based classifier did reveal significant association to kidney function (r = 20.6557; p = 0.0001 and r = 20.6574; p = 0.0005). A classifier utilising 46 identified urinary peptide biomarkers performed statistically equivalent to the urinary and plasma metabolite classifier (r = 20.7752; p,0.0001 and r = 20.8400; p,0.0001). The combination of both urinary proteomic and urinary and plasma metabolic biomarkers did not improve the correlation with eGFR. In conclusion, we found excellent association of plasma and urinary metabolites and urinary peptides with kidney function, and disease progression, but no added value in combining the different biomarkers data.

Increased pulmonary artery pressure is a well-known phenomenon of hypoxia and is seen in patients with chronic pulmonary diseases, and also in mountaineers on high altitude expedition. Different mediators are known to regulate pulmonary artery vessel tone. However, exact mechanisms are not fully understood and a multimodal process consisting of a whole panel of mediators is supposed to cause pulmonary artery vasoconstriction. We hypothesized that increased hypoxemia is associated with an increase in vasoconstrictive mediators and decrease of vasodilatators leading to a vasoconstrictive net effect. Furthermore, we suggested oxidative stress being partly involved in changement of these parameters. Oxygen saturation (Sao2) and clinical parameters were assessed in 34 volunteers before and during a Swiss research expedition to Mount Muztagh Ata (7549 m) in Western China. Blood samples were taken at four different sites up to an altitude of 6865 m. A mass spectrometry-based targeted metabolomic platform was used to detect multiple parameters, and revealed functional impairment of enzymes that require oxidation-sensitive cofactors. Specifically, the tetrahydrobiopterin (BH4)-dependent enzyme nitric oxide synthase (NOS) showed significantly lower activities (citrulline-to-arginine ratio decreased from baseline median 0.21 to 0.14 at 6265 m), indicating lower NO availability resulting in less vasodilatative activity. Correspondingly, an increase in systemic oxidative stress was found with a significant increase of the percentage of methionine sulfoxide from a median 6% under normoxic condition to a median level of 30% (p

We report a case of early-onset acute Guillain-Barre syndrome associated with reactivation of Epstein-Barr virus (EBV) infection after nonmyeloablative stem cell transplantation (NST). Reactivation of EBV infection preceded disease onset, and the virus load increased concomitantly with disease progression (doubling time, 2.7 days). This case raises concern about the expanding scope of manifestations associated with reactivation of EBV infection after NST.

The vast majority of this book deals with monogenic disorders which are relatively rare but have just one or a small number of characteristic genotypes and usually very pronounced clinical and biochemical phenotypes. In contrast, this chapter will try to discuss multifactorial diseases which are far more prevalent and pose a completely different kind of challenge both for the socio-economic systems and for biomedical research. As an example we will focus on chronic kidney disease (CKD) and relevant animal models thereof. In fact, together with diabetic retinopathy, myocardial infarction, and stroke, diabetic nephropathy is one of the most severe sequelae of type II diabetes mellitus (T2D) and, considering the obesity-related pandemic of T2D, will represent a major health issue in the decades to come (Mensah et al., 2004; James et al., 2010). Of course, all of these diseases have an important genetic component as demonstrated by pedigree analyses and a growing number of twin studies (Walder et al., 2003; Vaag & Poulsen, 2007). Still, with rare exceptions, this genetic component is rather seen as a predisposition for than as a cause of the actual disease. In particular, recent genome-wide association studies (GWAS) on large population-based cohorts have revealed a couple of single nucleotide polymorphisms (SNPs) that are significantly associated with T2D but the contribution of single SNPs to the individual’s risk of developing T2D are marginal (Groop & Lyssenko, 2009). To fully understand the interaction of the identified genetic loci and to appreciate the meaning of the genetic background in a personalized medicine approach, complex haplotypes would have to be analyzed, and this has not even been achieved in basic diabetes research, let alone in any clinical application. Yet, genetic research in diabetology has gained a new momentum in the last few years since it became obvious that a combination of GWAS with a more detailed phenotyping than just a generic diagnosis of T2D immediately led to improved statistics and to a much better biochemical plausibility of the findings (Gieger et al., 2008; Illig et al., 2010). Specifically, genome-wide significances could be achieved on much smaller cohorts than in classical GWAS rendering a more cost-efficient tool in biomedical research. The statistical power could be further improved by defining metabolic phenotypes based on the knowledge of the underlying biochemical pathways, e.g., by using groups of metabolites that are synthesized or degraded by the same enzymes or by calculating ratios of the concentrations of products

The broad view of the state of biological systems cannot be complete without the added value of integrating proteomic and genomic data with metabolite measurement. By definition, metabolomics aims at quantifying not less than the totality of small molecules present in a biofluid, tissue, organism, or any material beyond living systems. To cope with the complexity of the task, mass spectrometry (MS) is the most promising analytical environment to fulfill increasing appetite for more accurate and larger view of the metabolome while providing sufficient data generation throughput. Bioinformatics and associated disciplines naturally play a central role in bridging the gap between fast evolving technology and domain experts. Here, we describe the strategies to translate crude MS information into features characteristics of metabolites, and resources available to guide scientists along the metabolomics pipeline. A particular emphasis is put on pragmatic solutions to interpret the outcome of metabolomics experiments at the level of signal processing, statistical treatment, and biochemical understanding.

The broad view of the state of biological systems cannot be complete without the added value of integrating proteomic and genomic data with metabolite measurement. By definition, metabolomics aims at quantifying not less than the totality of small molecules present in a biofluid, tissue, organism, or any material beyond living systems. To cope with the complexity of the task, mass spectrometry (MS) is the most promising analytical environment to fulfill increasing appetite for more accurate and larger view of the metabolome while providing sufficient data generation throughput. Bioinformatics and associated disciplines naturally play a central role in bridging the gap between fast evolving technology and domain experts. Here, we describe the strategies to translate crude MS information into features characteristics of metabolites, and resources available to guide scientists along the metabolomics pipeline. A particular emphasis is put on pragmatic solutions to interpret the outcome of metabolomics experiments at the level of signal processing, statistical treatment, and biochemical understanding.

International audience; Nutrition plays an important role in human metabolism and health. However, it is unclear in how far self-reported nutrition intake reflects de facto differences in body metabolite composition. To investigate this question on an epidemiological scale we conducted a metabolomics study analyzing the association of self-reported nutrition habits with 363 metabolites quantified in blood serum of 284 male participants of the KORA population study, aged between 55 and 79 years. Using data from an 18-item food frequency questionnaire, the consumption of 18 different food groups as well as four derived nutrition indices summarizing these food groups by their nutrient content were analyzed for association with the measured metabolites. The self-reported nutrition intake index "polyunsaturated fatty acids" associates with a decrease in saturation of the fatty acid chains of glycero-phosphatidylcholines analyzed in serum samples. Using a principal component analysis dietary patterns highly associating with serum metabolite concentrations could be identified. The first principal component, which was interpreted as a healthy nutrition lifestyle, associates with a decrease in the degree of saturation of the fatty acid moieties of different glycero-phosphatidylcholines. In summary, this analysis shows that on a population level metabolomics provides the possibility to link self-reported nutrition habits to changes in human metabolic profiles and that the associating metabolites reflect the self-reported nutritional intake. Moreover, we could show that the strength of association increases when composed nutrition indices are used. Metabolomics may, thus, facilitate evaluating questionnaires and improving future questionnaire-based epidemiological studies on human health.

Identifying trait-associated genetic variation offers new prospects to reveal novel physiological pathways modulating complex traits. Taking advantage of a unique animal model, we identified the I442M mutation in the non-SMC condensin I complex, subunit G ( NCAPG) gene and the Q204X mutation in the growth differentiation factor 8 ( GDF8) gene as substantial modulators of pre- and/or postnatal growth in cattle. In a combined metabolomic and genotype association approach, which is the first respective study in livestock, we surveyed the specific physiological background of the effects of both loci on body-mass gain and lipid deposition. Our data provided confirming evidence from two historically and geographically distant cattle populations that the onset of puberty is the key interval of divergent growth. The locus-specific metabolic patterns obtained from monitoring 201 plasma metabolites at puberty mirror the particular NCAPG I442M and GDF8 Q204X effects and represent biosignatures of divergent physiological pathways potentially modulating effects on proportional and disproportional growth, respectively. While the NCAPG I442M mutation affected the arginine metabolism, the 204X allele in the GDF8 gene predominantly raised the carnitine level and had concordant effects on glycerophosphatidylcholines and sphingomyelins. Our study provides a conclusive link between the well-described growth-regulating functions of arginine metabolism and the previously unknown specific physiological role of the NCAPG protein in mammalian metabolism. Owing to the confirmed effect of the NCAPG/LCORL locus on human height in genome-wide association studies, the results obtained for bovine NCAPG might add valuable, comparative information on the physiological background of genetically determined divergent mammalian growth.

The effect of coffee consumption on human health is still discussed controversially. Here, we report results from a metabolomics study of coffee consumption, where we measured 363 metabolites in blood serum of 284 male participants of the Cooperative Health Research in the Region of Augsburg study population, aged between 55 and 79 years. A statistical analysis of the association of metabolite concentrations and the number of cups of coffee consumed per day showed that coffee intake is positively associated with two classes of sphingomyelins, one containing a hydroxy-group (SM(OH)) and the other having an additional carboxy-group (SM(OH,COOH)). In contrast, long- and medium-chain acylcarnitines were found to decrease with increasing coffee consumption. It is noteworthy that the concentration of total cholesterol also rises with an increased coffee intake in this study group. The association observed here between these hydroxylated and carboxylated sphingolipid species and coffee intake may be induced by changes in the cholesterol levels. Alternatively, these molecules may act as scavengers of oxidative species, which decrease with higher coffee intake. In summary, we demonstrate strong positive associations between coffee consumption and two classes of sphingomyelins and a negative association between coffee consumption and long- and medium-chain acylcarnitines.

Background The oxidation of fatty acids in mitochondria plays an important role in energy metabolism and genetic disorders of this pathway may cause metabolic diseases. Enzyme deficiencies can block the metabolism at defined reactions in the mitochondrion and lead to accumulation of specific substrates causing severe clinical manifestations. Ten of the disorders directly affecting mitochondrial fatty acid oxidation have been well-defined, implicating episodic hypoketotic hypoglycemia provoked by catabolic stress, multiple organ failure, muscle weakness, or hypertrophic cardiomyopathy. Additionally, syndromes of severe maternal illness (HELLP syndrome and AFLP) have been associated with pregnancies carrying a fetus affected by fatty acid oxidation deficiencies. However, little is known about fatty acids kinetics, especially during fasting or exercise when the demand for fatty acid oxidation is increased (catabolic stress). Results A computational kinetic network of 64 reactions with 91 compounds and 301 parameters was constructed to study dynamic properties of mitochondrial fatty acid β-oxidation. Various deficiencies of acyl-CoA dehydrogenase were simulated and verified with measured concentrations of indicative metabolites of screened newborns in Middle Europe and South Australia. The simulated accumulation of specific acyl-CoAs according to the investigated enzyme deficiencies are in agreement with experimental data and findings in literature. Investigation of the dynamic properties of the fatty acid β-oxidation reveals that the formation of acetyl-CoA – substrate for energy production – is highly impaired within the first hours of fasting corresponding to the rapid progress to coma within 1–2 hours. LCAD deficiency exhibits the highest accumulation of fatty acids along with marked increase of these substrates during catabolic stress and the lowest production rate of acetyl-CoA. These findings might confirm gestational loss to be the explanation that no human cases of LCAD deficiency have been described. Conclusion In summary, this work provides a detailed kinetic model of mitochondrial metabolism with specific focus on fatty acid β-oxidation to simulate and predict the dynamic response of that metabolic network in the context of human disease. Our findings offer insight into the disease process (e.g. rapid progress to coma) and might confirm new explanations (no human cases of LCAD deficiency), which can hardly be obtained from experimental data alone.

The rapidly evolving field of metabolomics aims at a comprehensive measurement of ideally all endogenous metabolites in a cell or body fluid. It thereby provides a functional readout of the physiological state of the human body. Genetic variants that associate with changes in the homeostasis of key lipids, carbohydrates, or amino acids are not only expected to display much larger effect sizes due to their direct involvement in metabolite conversion modification, but should also provide access to the biochemical context of such variations, in particular when enzyme coding genes are concerned. To test this hypothesis, we conducted what is, to the best of our knowledge, the first GWA study with metabolomics based on the quantitative measurement of 363 metabolites in serum of 284 male participants of the KORA study. We found associations of frequent single nucleotide polymorphisms (SNPs) with considerable differences in the metabolic homeostasis of the human body, explaining up to 12% of the observed variance. Using ratios of certain metabolite concentrations as a proxy for enzymatic activity, up to 28% of the variance can be explained (p-values 10−16 to 10−21). We identified four genetic variants in genes coding for enzymes (FADS1, LIPC, SCAD, MCAD) where the corresponding metabolic phenotype (metabotype) clearly matches the biochemical pathways in which these enzymes are active. Our results suggest that common genetic polymorphisms induce major differentiations in the metabolic make-up of the human population. This may lead to a novel approach to personalized health care based on a combination of genotyping and metabolic characterization. These genetically determined metabotypes may subscribe the risk for a certain medical phenotype, the response to a given drug treatment, or the reaction to a nutritional intervention or environmental challenge., Author Summary This paper reports what is, to the best of our knowledge, the first genome-wide association (GWA) study with metabolic traits as phenotypic traits. By simultaneous measurements of single nucleotide polymorphisms (SNPs) and serum concentrations of endogenous organic compounds in a human population, we identify genetically determined variants in metabolic phenotype (metabotype) that exhibit large effect sizes. Four of these polymorphisms are located in genes coding for well-characterized enzymes of the lipid metabolism. We find that individuals with different genotypes in these genes have significantly different metabolic capacities with respect to the synthesis of some polyunsaturated fatty acids, the beta-oxidation of short- and medium-chain fatty acids, and the breakdown of triglycerides. In this approach, the concept of the “genetically determined metabotype” as an intermediate phenotype is central, as it becomes a measurable quantity in the framework of GWA studies with metabolomics. The investigation of the genetically determined metabotypes in their biochemical context might help to better understand the pathogenesis of common diseases and gene–environment interactions. These findings could result in a step towards personalized health care and nutrition based on a combination of genotyping and metabolic characterization.

Metabolomics, i.e. the systematic identification and quantitation of low-molecular weight compounds in a certain cell, a tissue or a body fluid is increasingly recognized as the most relevant approach in functional genomics. Long-standing positive experience in routine diagnostics of inborn errors of metabolism and analytical innovations leading to improved sensitivity and reproducibility of quantitative multiparametric assays now pave the way for further diagnostic use of metabolomics. This article summarizes the scientific and technological background of the current boost in metabolomics and describes some use cases in which high-resolution metabolic analyses demonstrate their potential for future clinical applications.

Isotope correction of a profile is an important step in the analysis of mass spectrometry derived data. The problem is mathematically formulated as a system of linear equations which is general enough to include previous correction methods. For the solution of these equations when applied to the whole profile an efficient algorithm is developed. In experimental tests the resulting algorithm corrected the profile fast and successfully.

Metabolomics is a powerful tool for identifying both known and new disease-related perturbations in metabolic pathways. In preclinical drug testing, it has a high potential for early identification of drug off-target effects. Recent advances in high-precision high-throughput mass spectrometry have brought the metabolomic field to a point where quantitative, targeted, metabolomic measurements with ready-to-use kits allow for the automated in-house screening for hundreds of different metabolites in large sets of biological samples. Today, the field of metabolomics is, arguably, at a point where transcriptomics was about 5 yr ago. This being so, the field has a strong need for adapted bioinformatics tools and methods. In this paper we describe a systematic analysis of a targeted quantitative characterization of more than 800 metabolites in blood plasma samples from healthy and diabetic mice under rosiglitazone treatment. We show that known and new metabolic phenotypes of diabetes and medication can be recovered in a statistically objective manner. We find that concentrations of methylglutaryl carnitine are oppositely impacted by rosiglitazone treatment of both healthy and diabetic mice. Analyzing ratios between metabolite concentrations dramatically reduces the noise in the data set, allowing for the discovery of new potential biomarkers of diabetes, such as the N-hydroxyacyloylsphingosyl-phosphocholines SM(OH)28:0 and SM(OH)26:0. Using a hierarchical clustering technique on partial η2 values, we identify functionally related groups of metabolites, indicating a diabetes-related shift from lysophosphatidylcholine to phosphatidylcholine levels. The bioinformatics data analysis approach introduced here can be readily generalized to other drug testing scenarios and other medical disorders.

Exposure to nicotine during smoking causes a multitude of metabolic changes that are poorly understood. We quantified and analyzed 198 metabolites in 283 serum samples from the human cohort KORA (Cooperative Health Research in the Region of Augsburg). Multivariate analysis of metabolic profiles revealed that the group of smokers could be clearly differentiated from the groups of former smokers and non-smokers. Moreover, 23 lipid metabolites were identified as nicotine-dependent biomarkers. The levels of these biomarkers are all up-regulated in smokers compared to those in former and non-smokers, except for three acyl-alkyl-phosphatidylcholines (e.g. plasmalogens). Consistently significant results were further found for the ratios of plasmalogens to diacyl-phosphatidylcolines, which are reduced in smokers and regulated by the enzyme alkylglycerone phosphate synthase (alkyl-DHAP) in both ether lipid and glycerophospholipid pathways. Notably, our metabolite profiles are consistent with the strong down-regulation of the gene for alkyl-DHAP (AGPS) in smokers that has been found in a study analyzing gene expression in human lung tissues. Our data suggest that smoking is associated with plasmalogen-deficiency disorders, caused by reduced or lack of activity of the peroxisomal enzyme alkyl-DHAP. Our findings provide new insight into the pathophysiology of smoking addiction. Activation of the enzyme alkyl-DHAP by small molecules may provide novel routes for therapy.

Monitoring of Epstein-Barr virus (EBV) infection and reactivation in immunocompromized patients (e.g., after organ or bone-marrow transplantation) is based mainly on serological assays and detection of viral DNA. For further characterization of virus reactivation and monitoring of viral transcription we established real-time RT-PCR assays using TaqMan technology to sensitively quantify viral transcripts expressed at different times of the lytic cycle: for BZLF1, an immediate early transactivator initiating the transition from latency to lytic replication, for the DNA-polymerase BALF5 and for the major viral glycoprotein gp350/220 (BLLF1). RNA-isolation was optimized to eliminate contaminating DNA. Preparations were shown to be virtually DNA-free for up to 10(6) copies of RNA. With our PCR systems, it is possible to detect 10 copies of DNA or 100 copies of RNA per reaction as shown with serial dilutions of DNA-plasmids or in vitro transcribed RNA, respectively. This corresponds to a detection limit of 8 x 10(2) copies/10(6) peripheral blood mononuclear cells (PBMCs). Evaluation of this system showed that even in healthy carriers borderline levels of BLLF1 mRNA were sometimes detectable. In patients with acute infectious mononucleosis (IM) viral transcripts were regularly found in varying concentrations. Extremely high levels of all three mRNA species could be seen in a patient after bone-marrow transplantation monitored during an episode of lymphoproliferation which regressed during treatment with acyclovir and transfusion of donor T-cells. This sensitive and reproducible method to detect and quantify different transcripts of EBV can be used to closely monitor reactivation of EBV, e.g., in immunocompromized patients.

Quantitative determination of hepatitis B virus (HBV) DNA concentration is the most important measure for an estimation of the infectivity of an HBV positive health care worker and the basis for the decision whether he or she is allowed to perform exposure prone procedures. Thus questions are raised on how reliable quantitative HBV DNA assays are, and how stable is the HBV DNA concentration is in a healthy chronic HBV carrier. Therefore, in the present study two commercially available quantitative HBV DNA assays, the Amplicor HBV Monitor and the HBV Test Hybrid Capture II, and an "in house" quantitative HBV TaqMan PCR, analysing 101 sera of HBsAg positive patients for HBV DNA were compared. In addition, HBV DNA concentrations were followed in 14 healthy chronic carriers for up to 6 years. Despite a good overall correlation between the three tests considerable differences were found for the results of individual sera. Fifty-one percent of sera showed differences within one order of magnitude, 45% differed by a factor above 10 and 4% even by a factor of 100 and higher. The follow-up of the HBV DNA concentrations in 14 carriers showed in 7 carriers a rather stable course with variations within one order of magnitude, whereas in the other half the DNA concentrations fluctuated by factors between 10(2) and 10(6) over the observation period. Thus, viral load determinations in health care workers have to be interpreted with some caution, especially when they are the basis of far-reaching decisions.

Mutant hepatitis B virus with substitutions within the coding region for HBV surface antigen (HBsAg) has been found naturally in chronic carriers. It is therefore important to clarify whether the identified substitutions within the HBsAg have impact on the antigenicity and immunogenicity of HBsAg. A total of nine mutated HBV s-genes with single representative mutations were generated by site-directed mutagenesis and subcloned into an expression vector. The binding of polyclonal and monoclonal antibodies to these mutant HBsAg (mtHBsAg) was tested by immunofluorescence (IF) staining of cells transfected with the expression vectors. The amino acid (aa) substitutions like G145R, F134S, and C147W affected the binding of anti-HBs antibodies to corresponding mtHBsAg to different extents. The impact of aa substitutions G145R and F134S on the immunogenicity was accessed by genetic immunization of mice with vectors expressing middle HBsAg with the corresponding mutations. The immunized mice developed antibodies to recombinant HBsAg containing the HBV preS region and HBsAg-specific cytotoxic T-cell. However, the development of antibody response to wild-type small HBsAg was significantly impaired by the aa substitutions in HBsAg. Based on this fact, we further investigated whether the mtHBsAg with the aa substitution G145R is able to induce mutant-specific antibody responses. Strikingly, serum samples from mice immunized with mtHBsAg with G145R recognized plasma-derived mtHBsAg. Two mouse MAbs specific to mtHBsAg were generated. One MAb recognized mtHBsAg with G145R but not wild type and other mtHBsAg. We conclude that HBsAg with aa substitutions are immunogenic but may have a changed fine specificity.

Motivation: During the Bavarian newborn screening programme all newborns have been tested for about 20 inherited metabolic disorders. Owing to the amount and complexity of the generated experimental data, machine learning techniques provide a promising approach to investigate novel patterns in high-dimensional metabolic data which form the source for constructing classification rules with high discriminatory power. Results: Six machine learning techniques have been investigated for their classification accuracy focusing on two metabolic disorders, phenylketo nuria (PKU) and medium-chain acyl-CoA dehydrogenase deficiency (MCADD). Logistic regression analysis led to superior classification rules (sensitivity >96.8%, specificity >99.98%) compared to all investigated algorithms. Including novel constellations of metabolites into the models, the positive predictive value could be strongly increased (PKU 71.9% versus 16.2%, MCADD 88.4% versus 54.6% compared to the established diagnostic markers). Our results clearly prove that the mined data confirm the known and indicate some novel metabolic patterns which may contribute to a better understanding of newborn metabolism. Availability: WEKA machine learning package: www.cs.waikato.ac.nz/~ml/weka and statistical software package ADE-4: http://pbil.univ-lyon1.fr/ADE-4

Background Bioprocesses like the cell-based production of biologicals, i.e. mainly recombinant proteins and monoclonal antibodies, require optimal culture conditions to obtain a high yield of quality products. The performance of a bioreactor highly depends on the cell characteristics as well as on the composition of the cell culture medium and the process conditions. As the metabolic activity of the cells is very high during fermentation, the external and internal metabolite compositions vary tremendously throughout the process. The quantification of a wide range of metabolic substrates and products is a prerequisite to understand and optimize the underlying cell-based activities. Furthermore, metabolite quantification reveals the composition of biologically derived cell culture supplements, thus serving as a tool to monitor supplement quality or providing the base for the formulation of a chemically defined medium supplement.

In metabolomics, biomarker discovery is a highly data driven process and requires sophisticated computational methods for the search and prioritization of novel and unforeseen biomarkers in data, typically gathered in preclinical or clinical studies. In particular, the discovery of biomarker candidates from longitudinal cohort studies is crucial for kinetic analysis to better understand complex metabolic processes in the organism during physical activity. In this work we introduce a novel computational strategy that allows to identify and study kinetic changes of putative biomarkers using targeted MS/MS profiling data from time series cohort studies or other cross-over designs. We propose a prioritization model with the objective of classifying biomarker candidates according to their discriminatory ability and couple this discovery step with a novel network-based approach to visualize, review and interpret key metabolites and their dynamic interactions within the network. The application of our method on longitudinal stress test data revealed a panel of metabolic signatures, i.e., lactate, alanine, glycine and the short-chain fatty acids C2 and C3 in trained and physically fit persons during bicycle exercise. We propose a new computational method for the discovery of new signatures in dynamic metabolic profiling data which revealed known and unexpected candidate biomarkers in physical activity. Many of them could be verified and confirmed by literature. Our computational approach is freely available as R package termed BiomarkeR under LGPL via CRAN http://cran.r-project.org/web/packages/BiomarkeR/ .