Diet changes due to urbanization in South Africa are linked to microbiome and metabolome signatures of Westernization and colorectal cancer.

Transition from traditional high-fiber to Western diets in urbanizing communities of Sub-Saharan Africa is associated with increased risk of non-communicable diseases (NCD), exemplified by colorectal cancer (CRC) risk. To investigate how urbanization gives rise to microbial patterns that may be amenable by dietary intervention, we analyzed diet intake, fecal 16 S bacteriome, virome, and metabolome in a cross-sectional study in healthy rural and urban Xhosa people (South Africa). Urban Xhosa individuals had higher intakes of energy (urban: 3,578 ± 455; rural: 2,185 ± 179 kcal/d), fat and animal protein. This was associated with lower fecal bacteriome diversity and a shift from genera favoring degradation of complex carbohydrates (e.g., Prevotella) to taxa previously shown to be associated with bile acid metabolism and CRC. Urban Xhosa individuals had higher fecal levels of deoxycholic acid, shown to be associated with higher CRC risk, but similar short-chain fatty acid concentrations compared with rural individuals. Fecal virome composition was associated with distinct gut bacterial communities across urbanization, characterized by different dominant host bacteria (urban: Bacteriodota; rural: unassigned taxa) and variable correlation with fecal metabolites and dietary nutrients. Food and skin microbiota samples showed compositional differences along the urbanization gradient. Rural-urban dietary transition in South Africa is linked to major changes in the gut microbiome and metabolome. Further studies are needed to prove cause and identify whether restoration of specific components of the traditional diet will arrest the accelerating rise in NCDs in Sub-Saharan Africa.

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.

Artificial intelligence and amniotic fluid multiomics: prediction of perinatal outcome in asymptomatic women with short cervix.

OBJECTIVE: To evaluate the application of artificial intelligence (AI), i.e. deep learning and other machine-learning techniques, to amniotic fluid (AF) metabolomics and proteomics, alone and in combination with sonographic, clinical and demographic factors, in the prediction of perinatal outcome in asymptomatic pregnant women with short cervical length (CL). METHODS: AF samples, which had been obtained in the second trimester from asymptomatic women with short CL (< 15 mm) identified on transvaginal ultrasound, were analyzed. CL, funneling and the presence of AF 'sludge' were assessed in all cases close to the time of amniocentesis. A combination of liquid chromatography coupled with mass spectrometry and proton nuclear magnetic resonance spectroscopy-based metabolomics, as well as targeted proteomics analysis, including chemokines, cytokines and growth factors, was performed on the AF samples. To determine the robustness of the markers, we used six different machine-learning techniques, including deep learning, to predict preterm delivery < 34 weeks, latency period prior to delivery < 28 days after amniocentesis and requirement for admission to a neonatal intensive care unit (NICU). Omics biomarkers were evaluated alone and in combination with standard sonographic, clinical and demographic factors to predict outcome. Predictive accuracy was assessed using the area under the receiver-operating characteristics curve (AUC) with 95% CI, sensitivity and specificity. RESULTS: Of the 32 patients included in the study, complete omics, demographic and clinical data and outcome information were available for 26. Of these, 11 (42.3%) patients delivered ≥ 34 weeks, while 15 (57.7%) delivered < 34 weeks. There was no statistically significant difference in CL between these two groups (mean ± SD, 11.2 ± 4.4 mm vs 8.9 ± 5.3 mm, P = 0.31). Using combined omics, demographic and clinical data, deep learning displayed good to excellent performance, with an AUC (95% CI) of 0.890 (0.810-0.970) for delivery < 34 weeks' gestation, 0.890 (0.790-0.990) for delivery < 28 days post-amniocentesis and 0.792 (0.689-0.894) for NICU admission. These values were higher overall than for the other five machine-learning methods, although each individual machine-learning technique yielded statistically significant prediction of the different perinatal outcomes. CONCLUSIONS: This is the first study to report use of AI with AF proteomics and metabolomics and ultrasound assessment in pregnancy. Machine learning, particularly deep learning, achieved good to excellent prediction of perinatal outcome in asymptomatic pregnant women with short CL in the second trimester. Copyright © 2018 ISUOG. Published by John Wiley & Sons Ltd.

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.

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.

Metabolomic identification of diagnostic serum-based biomarkers for advanced stage melanoma.

INTRODUCTION: Melanoma is a highly aggressive malignancy and is currently one of the fastest growing cancers worldwide. While early stage (I and II) disease is highly curable with excellent prognosis, mortality rates rise dramatically after distant spread. We sought to identify differences in the metabolome of melanoma patients to further elucidate the pathophysiology of melanoma and identify potential biomarkers to aid in earlier detection of recurrence. METHODS: Using 1H NMR and DI-LC-MS/MS, we profiled serum samples from 26 patients with stage III (nodal metastasis) or stage IV (distant metastasis) melanoma and compared their biochemical profiles with 46 age- and gender-matched controls. RESULTS: We accurately quantified 181 metabolites in serum using a combination of 1H NMR and DI-LC-MS/MS. We observed significant separation between cases and controls in the PLS-DA scores plot (permutation test p-value = 0.002). Using the concentrations of PC-aa-C40:3, DL-carnitine, octanoyl-L-carnitine, ethanol, and methylmalonyl-L-carnitine we developed a diagnostic algorithm with an AUC (95% CI) = 0.822 (0.665-0.979) with sensitivity and specificity of 100 and 56%, respectively. Furthermore, we identified arginine, proline, tryptophan, glutamine, glutamate, glutathione and ornithine metabolism to be significantly perturbed due to disease (p < 0.05). CONCLUSION: Targeted metabolomic analysis demonstrated significant differences in metabolic profiles of advanced stage (III and IV) melanoma patients as compared to controls. These differences may represent a potential avenue for the development of multi-marker serum-based assays for earlier detection of recurrences, allow for newer, more effective targeted therapy when tumor burden is less, and further elucidate the pathophysiologic changes that occur in melanoma.

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.

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.

Epidemiology and costs associated with norovirus outbreaks in NHS Lothian, Scotland 2007-2009.

Healthcare-associated gastroenteritis outbreaks are becoming more common and are recognized challenges in hospital and community settings. In Edinburgh [NHS (National Health Service) Lothian], all the hospitals and the community were actively monitored for outbreaks of gastroenteritis from September 2007 to June 2009. In total, 1732 patients and 599 healthcare staff were affected in 192 unit outbreaks. In the acute sector, 1368 patients (0.99 cases/1000 inpatient bed-days) and 406 healthcare staff (0.29 cases/1000 inpatient bed-days) were affected in 155 unit outbreaks (0.23 unit outbreaks/day). Noroviruses were detected in 142 outbreaks (74%); 50 were not laboratory confirmed but were presumed to be noroviruses on epidemiological grounds. The closure of affected units to new admissions resulted in the loss of 3678 bed-days. By extrapolation, lost bed-days and staff absence due to gastroenteritis outbreaks cost NHS Lothian £1.2 million for the two norovirus seasons. Outbreaks in which the affected unit was closed within the first three days of recognizing the index case were contained in a mean of six days, and outbreaks in units that were closed later persisted for a mean of seven days; this difference was not statistically significant. Rapid implementation of control measures was effective in the control of outbreaks.

Phenol-Explorer: an online comprehensive database on polyphenol contents in foods.

A number of databases on the plant metabolome describe the chemistry and biosynthesis of plant chemicals. However, no such database is specifically focused on foods and more precisely on polyphenols, one of the major classes of phytochemicals. As antioxidants, polyphenols influence human health and may play a role in the prevention of a number of chronic diseases such as cardiovascular diseases, some cancers or type 2 diabetes. To determine polyphenol intake in populations and study their association with health, it is essential to have detailed information on their content in foods. However this information is not easily collected due to the variety of their chemical structures and the variability of their content in a given food. Phenol-Explorer is the first comprehensive web-based database on polyphenol content in foods. It contains more than 37,000 original data points collected from 638 scientific articles published in peer-reviewed journals. The quality of these data has been evaluated before they were aggregated to produce final representative mean content values for 502 polyphenols in 452 foods. The web interface allows making various queries on the aggregated data to identify foods containing a given polyphenol or polyphenols present in a given food. For each mean content value, it is possible to trace all original content values and their literature sources. Phenol-Explorer is a major step forward in the development of databases on food constituents and the food metabolome. It should help researchers to better understand the role of phytochemicals in the technical and nutritional quality of food, and food manufacturers to develop tailor-made healthy foods. Database URL: http://www.phenol-explorer.eu.

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.

The transcriptome of the implant biopsy identifies donor kidneys at increased risk of delayed graft function.

Improved assessment of donor organ quality at time of transplantation would help in management of potentially usable organs. The transcriptome might correlate with risk of delayed graft function (DGF) better than conventional risk factors. Microarray results of 87 consecutive implantation biopsies taken postreperfusion in 42 deceased (DD) and 45 living (LD) donor kidneys were compared to clinical and histopathology-based scores. Unsupervised analysis separated the 87 kidneys into three groups: LD, DD1 and DD2. Kidneys in DD2 had a greater incidence of DGF (38.1 vs. 9.5%, p < 0.05) than those in DD1. Clinical and histopathological risk scores did not discriminate DD1 from DD2. A total of 1051 transcripts were differentially expressed between DD1 and DD2, but no transcripts separated DGF from immediate graft function (adjusted p < 0.01). Principal components analysis revealed a continuum from LD to DD1 to DD2, i.e. from best to poorest functioning kidneys. Within DD kidneys, the odds ratio for DGF was significantly increased with a transcriptome-based score and recipient age (p < 0.03) but not with clinical or histopathologic scores. The transcriptome reflects kidney quality and susceptibility to DGF better than available clinical and histopathological scoring systems.

Microarray analysis of rejection in human kidney transplants using pathogenesis-based transcript sets.

Microarrays offer potential for objective diagnosis and insights into pathogenesis of allograft rejection. We used mouse transplants to annotate pathogenesis-based transcript sets (PBTs) that reflect major biologic events in allograft rejection-cytotoxic T-cell infiltration, interferon-gamma effects and parenchymal deterioration. We examined the relationship between PBT expression, histopathologic lesions and clinical diagnoses in 143 consecutive human kidney transplant biopsies for cause. PBTs correlated strongly with one another, indicating that transcriptome disturbances in renal transplants have a stereotyped internal structure. This disturbance was continuous, not dichotomous, across rejection and nonrejection. PBTs correlated with histopathologic lesions and were the highest in biopsies with clinically apparent rejection episodes. Surprisingly, antibody-mediated rejection had changes similar to T-cell mediated rejection. Biopsies lacking PBT disturbances did not have rejection. PBTs suggested that some current Banff histopathology criteria are unreliable, particularly at the cut-off between borderline and rejection. Results were validated in 51 additional biopsies. Thus many transcriptome changes previously described in rejection are features of a large-scale disturbance characteristic of rejection but occurring at lower levels in many forms of injury. PBTs represent a quantitative measure of the inflammatory disturbances in organ transplants, and a new window on the mechanisms of these changes.

Transplant glomerulopathy, late antibody-mediated rejection and the ABCD tetrad in kidney allograft biopsies for cause.

To define the relative frequency of phenotypes of transplant glomerulopathy, we retrospectively reviewed the findings in 1036 biopsies for clinical indications from 1320 renal transplant patients followed in our clinics between 1997 and 2005. Transplant glomerulopathy, defined by double contours of glomerular basement membranes (D), was diagnosed in 53 biopsies (5.1%) from 41 patients (3.1%) at a median of 5.5 years post-transplant (range 3.8-381 months). In cases with D, we studied the frequency of circulating anti-HLA alloantibody (A), peritubular capillary basement membrane multilayering (B) and peritubular capillary C4d deposition (C). B was present in 48 (91%) of D biopsies. C4d staining by indirect immunofluorescence was detected in 18 of 50 D biopsies studied (36%). By Flow PRA Screening or ELISA, A was detected in 33 (70%) in 47 D cases with available sera, of which 28/33 or 85% were donor-specific. Class II (13/33) or class I and II (17/33) were more common than class I (3/33) antibodies. Thus 73% of transplant glomerulopathy has evidence of alloantibody-mediated injury (A and/or C), with ABCD and ABD being the common phenotypes in biopsies for cause. The remaining 27%, mostly BD, may be a different disease or a stage in which A and C are undetectable.

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.