Urinary metabolomics fingerprinting around parturition identifies metabolites that differentiate lame dairy cows from healthy ones.

Lameness is a very important disorder of periparturient dairy cows with implications on milk production and composition as well as with consequences on reproductive performance. The aetiology of lameness is not clear although there have been various hypotheses suggested over the years. The objective of this study was to metabotype the urine of dairy cows prior to, during and after the onset of lameness by evaluating at weeks -8, -4 pre-calving, the week of lameness diagnosis, and +4 and +8 weeks post-calving. We used a metabolomics approach to analyse urine samples collected from dairy cows around calving (6 cows with lameness v. 20 healthy control cows). A total of 153 metabolites were identified and quantified using an in-house MS library and classified into 6 groups including: 11 amino acids (AAs), 39 acylcarnitines (ACs), 3 biogenic amines (BAs), 84 glycerophospholipids, 15 sphingolipids and hexose. A total of 23, 36, 40, 23 and 49 metabolites were observed to be significantly different between the lame and healthy cows at -8 and -4 weeks pre-calving, week of lameness diagnosis as well as at +4 and +8 weeks post-calving, respectively. It should be noted that most of the identified metabolites were elevated; however, a few of them were also lower in lame cows. Overall, ACs and glycerophospholipids, specifically phosphatidylcholines (PCs), were the metabolite groups displaying the strongest differences in the urine of pre-lame and lame cows. Lysophosphatidylcholines (LysoPCs), although to a lesser extent than PCs, were altered at all time points. Alterations in urinary AA concentrations were also observed during the current study for four time points. During the pre-calving period, there was an observed elevation of arginine (-8 week), tyrosine (-8 week) and aspartate (-4 week), as well as a depression of urinary glutamate (-4 weeks). In the current study, it was additionally observed that concentrations of several sphingomyelins and one BA were altered in pre-lame and lame cows. Symmetric dimethylarginine was elevated at both -8 weeks pre-calving and the week of lameness diagnosis. Data showed that urinary fingerprinting might be a reliable methodology to be used in the future to differentiate lame cows from healthy ones.

Gamma-aminobutyric acid-producing lactobacilli positively affect metabolism and depressive-like behaviour in a mouse model of metabolic syndrome.

Metabolic and neuroactive metabolite production represents one of the mechanisms through which the gut microbiota can impact health. One such metabolite, gamma-aminobutyric acid (GABA), can modulate glucose homeostasis and alter behavioural patterns in the host. We previously demonstrated that oral administration of GABA-producing Lactobacillus brevis DPC6108 has the potential to increase levels of circulating insulin in healthy rats. Therefore, the objective of this study was to assess the efficacy of endogenous microbial GABA production in improving metabolic and behavioural outcomes in a mouse model of metabolic dysfunction. Diet-induced obese and metabolically dysfunctional mice received one of two GABA-producing strains, L. brevis DPC6108 or L. brevis DSM32386, daily for 12 weeks. After 8 and 10 weeks of intervention, the behavioural and metabolic profiles of the mice were respectively assessed. Intervention with both L. brevis strains attenuated several abnormalities associated with metabolic dysfunction, causing a reduction in the accumulation of mesenteric adipose tissue, increased insulin secretion following glucose challenge, improved plasma cholesterol clearance and reduced despair-like behaviour and basal corticosterone production during the forced swim test. Taken together, this exploratory dataset indicates that intervention with GABA-producing lactobacilli has the potential to improve metabolic and depressive- like behavioural abnormalities associated with metabolic syndrome in mice.

Validation of biomarkers of food intake-critical assessment of candidate biomarkers.

Biomarkers of food intake (BFIs) are a promising tool for limiting misclassification in nutrition research where more subjective dietary assessment instruments are used. They may also be used to assess compliance to dietary guidelines or to a dietary intervention. Biomarkers therefore hold promise for direct and objective measurement of food intake. However, the number of comprehensively validated biomarkers of food intake is limited to just a few. Many new candidate biomarkers emerge from metabolic profiling studies and from advances in food chemistry. Furthermore, candidate food intake biomarkers may also be identified based on extensive literature reviews such as described in the guidelines for Biomarker of Food Intake Reviews (BFIRev). To systematically and critically assess the validity of candidate biomarkers of food intake, it is necessary to outline and streamline an optimal and reproducible validation process. A consensus-based procedure was used to provide and evaluate a set of the most important criteria for systematic validation of BFIs. As a result, a validation procedure was developed including eight criteria, plausibility, dose-response, time-response, robustness, reliability, stability, analytical performance, and inter-laboratory reproducibility. The validation has a dual purpose: (1) to estimate the current level of validation of candidate biomarkers of food intake based on an objective and systematic approach and (2) to pinpoint which additional studies are needed to provide full validation of each candidate biomarker of food intake. This position paper on biomarker of food intake validation outlines the second step of the BFIRev procedure but may also be used as such for validation of new candidate biomarkers identified, e.g., in food metabolomic studies.

Targeted metabolomics: new insights into pathobiology of retained placenta in dairy cows and potential risk biomarkers.

A targeted quantitative metabolomics approach was used to study temporal changes of serum metabolites in cows that normally released their fetal membranes and those that retained the placenta. We identified and measured serum concentrations of 128 metabolites including amino acids, acylcarnitines, biogenic amines, glycerophospholipids, sphingolipids and hexose at -8 and -4 weeks before parturition, during the week of retained placenta (RP) diagnosis, and at +4 and +8 weeks after parturition. In addition, we aimed at identifying metabolite signatures of pre-RP in the serum that might be used as predictive biomarkers for risk of developing RP in dairy cows. Results revealed major alterations in the metabolite fingerprints of pre-RP cows starting as early as -8 weeks before parturition and continuing as far as +8 weeks after calving. Biomarker candidates found in this study are mainly biomarkers of inflammation which might not be specific to RP. Therefore, the relevance of serum Lys, Orn, acetylornithine, lysophophatidylcholine LysoPC a C28:0, Asp, Leu and Ile as potential serum biomarkers for prediction of risk of RP in dairy cows will have to be tested in the future. In addition, lower concentrations of LysoPCs, Trp, and higher kynurenine in the serum during prepartum and the week of occurrence of RP suggest involvement of inflammation in the pathobiology of RP.

Urine metabolic fingerprinting can be used to predict the risk of metritis and highlight the pathobiology of the disease in dairy cows.

INTRODUCTION: Metritis is an uterine pathology that causes economic losses for the dairy industry. It is associated with lower reproductive efficiency, increased culling rates, decreased milk production and increased veterinary costs. OBJECTIVES: To gain a more detailed view of the urine metabolome and to detect metabolite signature in cows with metritis. In addition, we aimed to identify early metabolites which can help to detect cows at risk to develop metritis in the future. METHODS: We used nuclear magnetic resonance spectroscopy starting at 8 and 4 weeks prior to the expected day of parturition, during the week of diagnosis of metritis, and at 4 and 8 weeks after diagnosis of metritis in Holstein dairy cows. RESULTS: At 8 weeks before parturition, pre-metritic cows had a total of 30 altered metabolites. Interestingly, 28 of them increased in urine when compared with control cows (P < 0.05). At 4 weeks before parturition, 34 metabolites were altered. At the week of diagnosis of metritis a total of 20 metabolites were altered (P < 0.05). The alteration continued at 4 and 8 weeks after diagnosis. CONCLUSIONS: The metabolic fingerprints in the urine of pre-metritic and metritic cows point toward excretion of multiple amino acids, tricarboxylic acid cycle metabolites and monosaccharides. Combination of galactose, leucine, lysine and panthotenate at 8 weeks before parturition might serve as predictive biomarkers for metritis.

Transcript analysis in two alfalfa salt tolerance selected breeding populations relative to a non-tolerant population.

With the growing limitations on arable land, alfalfa (a widely cultivated, low-input forage) is now being selected to extend cultivation into saline lands for low-cost biofeedstock purposes. Here, minerals and transcriptome profiles were compared between two new salinity-tolerant North American alfalfa breeding populations and a more salinity-sensitive western Canadian alfalfa population grown under hydroponic saline conditions. All three populations accumulated two-fold higher sodium in roots than shoots as a function of increased electrical conductivity. At least 50% of differentially expressed genes (p < 0.05) were down-regulated in the salt-sensitive population growing under high salinity, while expression remained unchanged in the saline-tolerant populations. In particular, most reduction in transcript levels in the salt-sensitive population was observed in genes specifying cell wall structural components, lipids, secondary metabolism, auxin and ethylene hormones, development, transport, signalling, heat shock, proteolysis, pathogenesis-response, abiotic stress, RNA processing, and protein metabolism. Transcript diversity for transcription factors, protein modification, and protein degradation genes was also more strongly affected in salt-tolerant CW064027 than in salt-tolerant Bridgeview and salt-sensitive Rangelander, while both saline-tolerant populations showed more substantial up-regulation in redox-related genes and B-ZIP transcripts. The report highlights the first use of bulked genotypes as replicated samples to compare the transcriptomes of obligate out-cross breeding populations in alfalfa.

The effect of haemolysis on the metabolomic profile of umbilical cord blood.

OBJECTIVES: Metabolomics is defined as the comprehensive study of all low molecular weight biochemicals, (metabolites) present in an organism. Using a systems biology approach, metabolomics in umbilical cord blood (UCB) may offer insight into many perinatal disease processes by uniquely detecting rapid biochemical pathway alterations. In vitro haemolysis is a common technical problem affecting UCB sampling in the delivery room, and can hamper metabolomic analysis. The extent of metabolomic alteration which occurs in haemolysed samples is unknown. DESIGN AND METHODS: Visual haemolysis was designated by the laboratory technician using a standardised haemolysis index colour chart. The metabolomic profile of haemolysed and non-haemolysed UCB serum samples from 69 healthy term infants was compared using both (1)H-NMR and targeted DI and LC-MS/MS approach. RESULTS: We identified 43 metabolites that are significantly altered in visually haemolysed UCB samples, acylcarnitines (n=2), glycerophospholipids (n=23), sphingolipids (n=7), sugars (n=3), amino acids (n=4) and Krebs cycle intermediates (n=4). CONCLUSION: This information will be useful for researchers in the field of neonatal metabolomics to avoid false findings in the presence of haemolysis, to ensure reproducible and credible results.

Urinary metabolomics for noninvasive detection of borderline and acute T cell-mediated rejection in children after kidney transplantation.

The goal of this study was to evaluate the utility of urinary metabolomics for noninvasive diagnosis of T cell-mediated rejection (TCMR) in pediatric kidney transplant recipients. Urine samples (n = 277) from 57 patients with surveillance or indication kidney biopsies were assayed for 134 unique metabolites by quantitative mass spectrometry. Samples without TCMR (n = 183) were compared to borderline tubulitis (n = 54) and TCMR (n = 30). Partial least squares discriminant analysis identified distinct classifiers for TCMR (area under receiver operating characteristic curve [AUC] = 0.892; 95% confidence interval [CI] 0.827-0.957) and borderline tubulitis (AUC = 0.836; 95% CI 0.781-0.892), respectively. Application of the TCMR classifier to borderline tubulitis samples yielded a discriminant score (-0.47 ± 0.33) mid-way between TCMR (-0.20 ± 0.34) and No TCMR (-0.80 ± 0.32) (p < 0.001 for all comparisons). Discriminant scoring for combined borderline/TCMR versus No TCMR (AUC = 0.900; 95% CI 0.859-0.940) applied to a validation cohort robustly distinguished between samples with (-0.08 ± 0.52) and without (-0.65 ± 0.54, p < 0.001) borderline/TCMR (p < 0.001). The TCMR discriminant score was driven by histological t-score, ct-score, donor-specific antibody and biopsy indication, and was unaffected by renal function, interstitial or microcirculatory inflammation, interstitial fibrosis or pyuria. These preliminary findings suggest that urinary metabolomics is a sensitive, specific and noninvasive tool for TCMR identification that is superior to serum creatinine, with minimal confounding by other allograft injury processes.

Identification of predictive biomarkers of disease state in transition dairy cows.

In dairy cows, periparturient disease states, such as metritis, mastitis, and laminitis, are leading to increasingly significant economic losses for the dairy industry. Treatments for these pathologies are often expensive, ineffective, or not cost-efficient, leading to production losses, high veterinary bills, or early culling of the cows. Early diagnosis or detection of these conditions before they manifest themselves could lower their incidence, level of morbidity, and the associated economic losses. In an effort to identify predictive biomarkers for postpartum or periparturient disease states in dairy cows, we undertook a cross-sectional and longitudinal metabolomics study to look at plasma metabolite levels of dairy cows during the transition period, before and after becoming ill with postpartum diseases. Specifically we employed a targeted quantitative metabolomics approach that uses direct flow injection mass spectrometry to track the metabolite changes in 120 different plasma metabolites. Blood plasma samples were collected from 12 dairy cows at 4 time points during the transition period (-4 and -1 wk before and 1 and 4 wk after parturition). Out of the 12 cows studied, 6 developed multiple periparturient disorders in the postcalving period, whereas the other 6 remained healthy during the entire experimental period. Multivariate data analysis (principal component analysis and partial least squares discriminant analysis) revealed a clear separation between healthy controls and diseased cows at all 4 time points. This analysis allowed us to identify several metabolites most responsible for separating the 2 groups, especially before parturition and the start of any postpartum disease. Three metabolites, carnitine, propionyl carnitine, and lysophosphatidylcholine acyl C14:0, were significantly elevated in diseased cows as compared with healthy controls as early as 4 wk before parturition, whereas 2 metabolites, phosphatidylcholine acyl-alkyl C42:4 and phosphatidylcholine diacyl C42:6, could be used to discriminate healthy controls from diseased cows 1 wk before parturition. A 3-metabolite plasma biomarker profile was developed that could predict which cows would develop periparturient diseases, up to 4 wk before clinical symptoms appearing, with a sensitivity of 87% and a specificity of 85%. This is the first report showing that periparturient diseases can be predicted in dairy cattle before their development using a multimetabolite biomarker model. Further research is warranted to validate these potential predictive biomarkers.

A metabolomics approach to uncover the effects of grain diets on rumen health in dairy cows.

Dairy cows fed high-grain diets during early lactation have a high incidence of metabolic disorders. However, the precise mechanism(s) of how grain feeding causes disease is not clear. In an effort to understand how this diet transition alters the rumen environment and potentially leads to certain metabolic disorders in dairy cattle, we undertook a comprehensive, quantitative metabolomic analysis of rumen fluid samples from dairy cows fed 4 different diets. Using a combination of proton nuclear magnetic resonance spectroscopy, gas chromatography-mass spectrometry, and direct flow injection tandem mass spectroscopy, we identified and quantified 93 metabolites in rumen samples taken from 8 dairy cows fed graded amounts of barley grain (i.e., 0, 15, 30, and 45% of diet dry matter). We also studied temporal changes in the rumen by studying metabolite concentration differences between the first day and the last day of each diet phase following the diet adaptation period. Multivariate analysis showed that rumen metabolites arising from the diet containing 45% barley grain were clearly different from those containing 0, 15, and 30% barley grain. Likewise, a clear separation of the metabolic composition of the ruminal fluid was evident at the beginning and at the end of each diet phase-contrary to the belief that 11 d are suitable for the adaptation of cows to high-grain diets. High-grain diets (>30%) resulted in increased rumen fluid concentrations of several toxic, inflammatory, and unnatural compounds including putrescine, methylamines, ethanolamine, and short-chain fatty acids. Perturbations in several amino acids (phenylalanine, ornithine, lysine, leucine, arginine, valine, and phenylacetylglycine) were also evident. The present study confirms and greatly extends earlier observations on dietary effects on rumen fluid composition and shows that the use of multiple metabolomic platforms permits a far more detailed understanding of metabolic causes and effects. These results may improve our understanding of diet-related rumen metabolism and the influence of grain on the overall health of dairy cattle.

Structural domains and main-chain flexibility in prion proteins.

In this study we describe a novel approach to define structural domains and to characterize the local flexibility in both human and chicken prion proteins. The approach we use is based on a comprehensive theory of collective dynamics in proteins that was recently developed. This method determines the essential collective coordinates, which can be found from molecular dynamics trajectories via principal component analysis. Under this particular framework, we are able to identify the domains where atoms move coherently while at the same time to determine the local main-chain flexibility for each residue. We have verified this approach by comparing our results for the predicted dynamic domain systems with the computed main-chain flexibility profiles and the NMR-derived random coil indexes for human and chicken prion proteins. The three sets of data show excellent agreement. Additionally, we demonstrate that the dynamic domains calculated in this fashion provide a highly sensitive measure of protein collective structure and dynamics. Furthermore, such an analysis is capable of revealing structural and dynamic properties of proteins that are inaccessible to the conventional assessment of secondary structure. Using the collective dynamic simulation approach described here along with a high-temperature simulations of unfolding of human prion protein, we have explored whether locations of relatively low stability could be identified where the unfolding process could potentially be facilitated. According to our analysis, the locations of relatively low stability may be associated with the beta-sheet formed by strands S1 and S2 and the adjacent loops, whereas helix HC appears to be a relatively stable part of the protein. We suggest that this kind of structural analysis may provide a useful background for a more quantitative assessment of potential routes of spontaneous misfolding in prion proteins.

Metabolomics: the principles and potential applications to transplantation.

This review provides a summary of the applications and potential applications of metabolite profiling (i.e. metabolomics) in monitoring organ transplants. While the concept of metabolomics is relatively new to organ transplantation, the idea of measuring metabolites as a quick, noninvasive probe of organ function is not. Indeed, metabolite measurements of serum creatinine have long been used to assess pre- and post-operative organ function. Over the past 10 years, a number of lesser-known, organ-specific metabolites have also been shown to be good diagnostic indicators of both organ function and viability. In general, metabolomics offers a complementary picture to what can be revealed via techniques based on genomics, proteomics or histology. Because metabolic changes typically happen within seconds or minutes after an 'event', whereas some transcript, protein abundance or tissue changes may take place over days or weeks, metabolomic measurements may offer a particularly useful and inexpensive diagnostic tool to monitor donor organ viability or to detect organ rejection. The excitement associated with metabolomics, however, must be tempered by the fact that the technology for rapid metabolite identification is still in its infancy, and that metabolites are but one part of a very complex picture pertaining to organ function.

Predicting subcellular localization of proteins using machine-learned classifiers.

MOTIVATION: Identifying the destination or localization of proteins is key to understanding their function and facilitating their purification. A number of existing computational prediction methods are based on sequence analysis. However, these methods are limited in scope, accuracy and most particularly breadth of coverage. Rather than using sequence information alone, we have explored the use of database text annotations from homologs and machine learning to substantially improve the prediction of subcellular location. RESULTS: We have constructed five machine-learning classifiers for predicting subcellular localization of proteins from animals, plants, fungi, Gram-negative bacteria and Gram-positive bacteria, which are 81% accurate for fungi and 92-94% accurate for the other four categories. These are the most accurate subcellular predictors across the widest set of organisms ever published. Our predictors are part of the Proteome Analyst web-service.

The BioTools Suite. A comprehensive suite of platform-independent bioinformatics tools.

The BioTools Suite is a set of three comprehensive, platform-independent software packages (PepTool, GeneTool, and ChromaTool) developed for sequence assembly and analysis. In addition to supporting a large number of standard bioinformatics functions, these programs also incorporate a number of useful innovations including uniform graphical-user interface (GUI) design, direct internet connectivity, a novel approach to feature annotation, and a variety of enhanced algorithms for large scale proteome and genome analysis. This article describes the key features, recent changes, and general operation of all three programs.

Production of an anti-prostate-specific antigen single-chain antibody fragment from Pichia pastoris.

Prostate-specific antigen (PSA) is a widely used marker for screening and monitoring prostate cancer. Because PSA levels are normally quite low, an antibody-based assay must be used to detect PSA. However, not all PSA-specific antibodies bind equally well to PSA or to its different isoforms. Therefore, a better understanding of how PSA interacts with PSA-specific antibodies is of considerable clinical interest. B80.3 is a widely used murine monoclonal anti-PSA antibody (IgG), which has very high affinity for both free and alpha-anti-chymotrypsin complexed PSA. More importantly, its gene sequence is known-making it one of only two anti-PSA antibodies that has been fully cloned and sequenced. To better elucidate the interaction between PSA and B80.3, a single-chain antibody fragment, derived from the variable domain of B80.3 (scFvB80), was cloned into a pPIC9 vector and expressed in Pichia pastoris. The secreted protein was purified using a three-step protocol beginning with a 50% ammonium sulfate precipitation step, followed by a T-gel thio-affinity step and concluding with a simple anion-exchange (DE52) filtration step. NMR studies indicate the protein is correctly folded while competitive enzyme-linked immunosorbant assays show that the purified scFvB80 has approximately 20% of the activity of the full-length B80.3 antibody. The protocol described here provides a quick and convenient route to prepare large quantities of very pure anti-PSA antibody fragments (15-20 mg/L culture medium) for detailed structural and biophysical characterization.

Prediction of genetic structure in eukaryotic DNA using reference point logistic regression and sequence alignment.

MOTIVATION: Current software tools are moderately effective in predicting genetic structure (exons, introns, intergenic regions, and complete genes) from raw DNA sequence data. Improvements in accuracy and speed are needed to deal with the increasing volume of data from large scale sequencing projects. RESULTS: We present a two-stage computer program to predict genetic structure in eukaryotic DNA. The first stage makes use of a novel statistical technique, called reference point logistic (RPL) regression, to calculate scores for potential functional sites. These site scores are combined with interval content, length, and state scores, via a Generalized Hidden Markov Model, to determine a combined score for each possible parse of a given DNA sequence into exons, introns, and intergenic regions. An optimal parse is found using a dynamic programming algorithm. In the second stage, protein sequence alignment methods are applied to improve the accuracy of the initial parse. Computation in the first stage of the program is very fast (1 s on a 360 MHz CPU for a 16 kb sequence) and its predictive accuracy typically matches or exceeds the best results reported for other methods (Sensitivity = 0.93 and Specificity = 0.93 for the Burset/Guigótest set). Computation in the second stage is slower, but the final predictions are more accurate (Sn = 0.97, Sp = 0.97). The program (called GRPL) can handle partial, single, and multi-gene sequences. The program is also capable of predicting the genetic structure of vertebrate, invertebrate, and plant DNA with nearly equal accuracy. Statistical techniques have also been introduced to model the effects of varying C+G content in a continuous manner and to control overfitting of parameters for smaller training sets. AVAILABILITY: An academic implementation of GRPL, compiled for SUN workstations, is available by anonymous ftp from snipe.pharmacy. ualberta.ca/pub. The training and test sets used in this work, together with supplementary material, can be found at the same location. A commercial implementation is available as a component of GeneTool (BioTools Inc., http://biotools.com).