Individual Metabolic Profiles Research Articles

Metabolites from midtrimester plasma of pregnant patients at high risk for preterm birth

There is an increased awareness regarding the association between exposure to environmental contaminants and adverse pregnancy outcomes including preterm birth. Whether an individual's metabolic profile can be utilized during pregnancy to differentiate the subset of patients who are ultimately destined to delivered preterm remains uncertain but could have MEANINGFUL clinical implications. We sought to objectively quantify metabolomic profiles of patients at high risk of preterm birth by evaluating midtrimester maternal plasma and to measure whether endogenous metabolites and exogenous environmental substances differ among those who ultimately deliver preterm compared with those who deliver at term. This was a case-control analysis from a prospective cohort of patients carrying a singleton, nonanomalous gestation who were at high risk of spontaneous preterm birth. Subjects with a plasma blood sample drawn at <28 weeks' gestation and no evidence of preterm labor at the time of enrollment were included. Metabolites were extracted from frozen samples, and metabolomic analysis was performed using liquid chromatography/mass spectrometry. The primary outcome was preterm birth at 16.0 to 36.9 weeks' gestation. A total of 42 patients met the inclusion criteria. Of these, 25 (59.5%) delivered preterm at <37 weeks' gestation, at a median of 30.14 weeks' gestation (interquartile range, 28.14-34.14). A total of 812 molecular features differed between preterm birth cases and term controls with a minimum fold change of 1.2 and P<.05. Of these, 570 of 812 (70.1%) were found in higher abundances in preterm birth cases; the other 242 of 812 (29.9%) were in higher abundance in term birth controls. The identity of the small molecule/compound represented by the molecular features differing statistically between preterm birth cases and term controls was identified as ranging from those involved with endogenous metabolic pathways (including lipid catabolism, steroids, and steroid-related molecules) to exogenous exposures (including avocadyne, diosgenin, polycyclic aromatic hydrocarbons, acetaminophen metabolites, aspartame, and caffeine). Random forest analyses evaluating the relative contribution of each of the top 30 compounds in differentiating preterm birth and term controls accurately classified 21 of 25 preterm birth cases (84%). Both endogenous metabolites and exogenous exposures differ in maternal plasma in the midtrimester among patients who ultimately delivered preterm compared with those who deliver at term.

Scrophularia ningpoensis Hemsl: a review of its phytochemistry, pharmacology, quality control and pharmacokinetics

Scrophularia ningpoensis Hemsl (SNH) is a commonly used medicinal plant in East Asia. Scrophulariae Radix (SR) is the dried roots of SNH, and is one of the most commonly used medicinal parts of SNH, and is an essential traditional medicine and widely used in East Asia for more than 2000 years. SR is used for clearing away heat and cooling blood, nourishing Yin and reducing fire, detoxicating and resolving a mass. The purpose of this paper is to systematically review the phytochemistry, pharmacology, quality control and pharmacokinetics of SNH based on the surveyed and summarized literature. Up to now, iridoids, phenolic glycosides, phenolic acids, alkaloids, flavonoids, triterpenes and other compounds have been isolated and identified from SNH. The extract and chemical components of SNH exerts multiple pharmacological effects, such as hepatoprotective effect, anti-inflammatory effect, neuroprotective effect, anti-ventricular remodeling effect and other activities. Various methods have been developed for the quality control of SNH, mainly for SR. Some bioactive compounds in SNH exhibited different pharmacokinetic behaviours and individual metabolic transformation profiles. This review will contribute to understanding the correlation between the pharmacological activities and the traditional usage of SNH, and useful to rational use and drug development in the future.

Body condition score at lambing and performance of Santa Inês ewes with an offspring during lactation

The main objective of this research work was to evaluate the performance of Santa Inês ewes according to their body condition score (BCS) at lambing. Data from 135 adult ewes, with singletons, were used. Ewes were divided into three distinct groups: 1) Bellow average: BCS less than 2.5 points (n = 44); 2) Average: BCS between 2.5 and 3.0 (n = 53); and 3) Above average: BCS greater than 3.0 (n = 38). The scale for BCS varied from 1 (very poor) to 5 (very fat). The individual BW, BCS, metabolic profile, milk production and composition of ewes as well as their lambs’ performance until weaning were considered. Metabolic profile was monitored at -7, +7 and +60 days relative to lambing. It was observed that ewes with higher BCS were also heavier; however, there were no differences among the groups for weights of lambs at birth and weaning (70 days). As there was no difference in milk production and composition, the average daily weight gain of the lambs was also similar for the different BCS groups. The metabolic profile (glucose, ?-hydroxibutyrate, cholesterol, creatinine, urea, hemoglobin, albumin, total protein and aspartate aminotransferase - AST) was also similar among the groups. However, when the metabolic profile was compared among the different physiological stages relative to lambing, some differences were observed. For example, ?-hydroxibutyrate was greater during the peripartum period, and glucose was greater in the lactation phase. As conclusion we could say that even in a situation of poor BCS, Santa Inês ewes have the physiological capacity to adapt themselves to the negative energy balance like those of the periparturient period. It seems that irrespective of the BCS, ewes prioritized lactation and were able to produce enough milk to accordingly nurse their lambs

Serum Myo-Inositol, Dimethyl Sulfone, and Valine in Combination with Creatinine Allow Accurate Assessment of Renal Insufficiency-A Proof of Concept.

Evaluation of renal dysfunction includes estimation of glomerular filtration rate (eGFR) as the initial step and subsequent laboratory testing. We hypothesized that combined analysis of serum creatinine, myo-inositol, dimethyl sulfone, and valine would allow both assessment of renal dysfunction and precise GFR estimation. Bio-banked sera were analyzed using nuclear magnetic resonance spectroscopy (NMR). The metabolites were combined into a metabolite constellation (GFRNMR) using n = 95 training samples and tested in n = 189 independent samples. Tracer-measured GFR (mGFR) served as a reference. GFRNMR was compared to eGFR based on serum creatinine (eGFRCrea and eGFREKFC), cystatin C (eGFRCys-C), and their combination (eGFRCrea-Cys-C) when available. The renal biomarkers provided insights into individual renal and metabolic dysfunction profiles in selected mGFR-matched patients with otherwise homogenous clinical etiology. GFRNMR correlated better with mGFR (Pearson correlation coefficient r = 0.84 vs. 0.79 and 0.80). Overall percentages of eGFR values within 30% of mGFR for GFRNMR matched or exceeded those for eGFRCrea and eGFREKFC (81% vs. 64% and 74%), eGFRCys-C (81% vs. 72%), and eGFRCrea-Cys-C (81% vs. 81%). GFRNMR was independent of patients’ age and sex. The metabolite-based NMR approach combined metabolic characterization of renal dysfunction with precise GFR estimation in pediatric and adult patients in a single analytical step.

Machine learning to determine relative contribution of modifiable and non-modifiable risk factors of major eye diseases

AimsTo use machine learning (ML) to determine the relative contributions of modifiable and non-modifiable clinical, metabolic, genetic, lifestyle and socioeconomic factors on the risk of major eye diseases.MethodsWe conducted analyses...

Optimisation of a metabotype approach to deliver targeted dietary advice

BackgroundTargeted nutrition is defined as dietary advice tailored at a group level. Groups known as metabotypes can be identified based on individual metabolic profiles. Metabotypes have been associated with differential responses to diet, which support their use to deliver dietary advice. We aimed to optimise a metabotype approach to deliver targeted dietary advice by encompassing more specific recommendations on nutrient and food intakes and dietary behaviours.MethodsParticipants (n = 207) were classified into three metabotypes based on four biomarkers (triacylglycerol, total cholesterol, HDL-cholesterol and glucose) and using a k-means cluster model. Participants in metabotype-1 had the highest average HDL-cholesterol, in metabotype-2 the lowest triacylglycerol and total cholesterol, and in metabotype-3 the highest triacylglycerol and total cholesterol. For each participant, dietary advice was assigned using decision trees for both metabotype (group level) and personalised (individual level) approaches. Agreement between methods was compared at the message level and the metabotype approach was optimised to incorporate messages exclusively assigned by the personalised approach and current dietary guidelines. The optimised metabotype approach was subsequently compared with individualised advice manually compiled.ResultsThe metabotype approach comprised advice for improving the intake of saturated fat (69% of participants), fibre (66%) and salt (18%), while the personalised approach assigned advice for improving the intake of folate (63%), fibre (63%), saturated fat (61%), calcium (34%), monounsaturated fat (24%) and salt (14%). Following the optimisation of the metabotype approach, the most frequent messages assigned to address intake of key nutrients were to increase the intake of fruit and vegetables, beans and pulses, dark green vegetables, and oily fish, to limit processed meats and high-fat food products and to choose fibre-rich carbohydrates, low-fat dairy and lean meats (60–69%). An average agreement of 82.8% between metabotype and manual approaches was revealed, with excellent agreements in metabotype-1 (94.4%) and metabotype-3 (92.3%).ConclusionsThe optimised metabotype approach proved capable of delivering targeted dietary advice for healthy adults, being highly comparable with individualised advice. The next step is to ascertain whether the optimised metabotype approach is effective in changing diet quality.

SUN-367 Changes in Bone and Glucose Metabolism in Patients Post Solid Organ Transplant

Introduction:Solid organ transplantation has emerged as a pivotal therapeutic option for various organ failures and has gained more popularity with newer technologies and better immunosuppressive options.However, immunosuppressive therapies for survival of solid organ transplant is also associated with various metabolic complications with changes in bone and glucose metabolism.The aim of our study is to review the changes in bone and glucose metabolism in post solid organ transplant recipient Veterans.Methods:Single center, retrospective study with subjects who had solid organ transplant conducted at William Jennings Bryan Dorn Veteran Hospital in Columbia, South Carolina. All available subjects who had solid organ transplant between January 1, 2008 till December 31st, 2017 and had at least one post-transplant followed up visit were included. Data was collected from computerized patient record system after approval by Institutional Review board (IRB) and Research and development. Collected data included age, sex, BMI, Laboratory data, Medications, Bone mineral density (BMD) by DXA, Diabetes status and medications pre and post operatively.Results:Data collected include 227 patients with solid organ transplants. Out of those, only 88 had BMD evaluation and only 45 had follow up BMD. Out of 88 with baseline BMD, 16 had osteoporosis, 36 had osteopenia and 36 had normal BMD. Although 51 were on Bisphosphonates, many of them did not have follow up DXA scans. 157 were receiving Vitamin D supplementation but very few had levels checked.A total of 158 patients had Diabetes, with 95 having pre-existing diabetes and 52 were diagnosed post transplantation. The time of onset was unknown in 11 patients. Majority of patients with pre-existing diabetes required intensification of their medications for diabetes to achieve optimal glycemic control.DiscussionA multitude of factors including type of transplant, individual pre-operative metabolic profiles, choice of immunosuppressive agents and certain infections increase the risk of these metabolic complications. Given the complex post-operative care, issues with immunosuppressive agents and other comorbidities, metabolic bone disease and other complications may go unnoticed and under recognized which may later lead to higher risk of fractures, morbidity and mortality.ConclusionThis study highlights the importance of monitoring prudently for metabolic changes after solid organ transplantation. Early identification and aggressive management of these complications may help decrease morbidity and mortality related to fractures and sub-optimal glycemic control.

Current metabolic perspective on malnutrition in obesity: towards more subgroup-based nutritional approaches?

Lifestyle intervention may be effective in reducing type 2 diabetes mellitus incidence and cardiometabolic risk. A more personalised nutritional approach based on an individual or subgroup-based metabolic profile may optimise intervention outcome. Whole body insulin resistance (IR) reflects defective insulin action in tissues such as muscle, liver, adipose tissue, gut and brain, which may precede the development of cardiometabolic diseases. IR may develop in different organs but the severity may vary between organs. Individuals with more pronounced hepatic IR have a distinct plasma metabolome and lipidome profile as compared with individuals with more pronounced muscle IR. Additionally, genes related to extracellular modelling were upregulated in abdominal subcutaneous adipose tissue in individuals with more pronounced hepatic IR, whilst genes related to inflammation as well as systemic low-grade inflammation were upregulated in individuals with primarily muscle IR. There are indications that these distinct IR phenotypes may also respond differentially to dietary macronutrient composition. Besides metabolic phenotype, microbial phenotype may be of importance in personalising the response to diet. In particular fibres or fibre mixtures, leading to a high distal acetate and SCFA production may have more pronounced effects on metabolic health. Notably, individuals with prediabetes may have a reduced response to diet-induced microbiota modulation with respect to host insulin sensitivity and metabolic health outcomes. Overall, we need more research to relate metabolic subphenotypes to intervention outcomes to define more optimal diets for individuals with or predisposed to chronic metabolic diseases.

FastMM: an efficient toolbox for personalized constraint-based metabolic modeling

BackgroundConstraint-based metabolic modeling has been applied to understand metabolism related disease mechanisms, to predict potential new drug targets and anti-metabolites, and to identify biomarkers of complex diseases. Although the state-of-art modeling toolbox, COBRA 3.0, is powerful, it requires substantial computing time conducting flux balance analysis, knockout analysis, and Markov Chain Monte Carlo (MCMC) sampling, which may limit its application in large scale genome-wide analysis.ResultsHere, we rewrote the underlying code of COBRA 3.0 using C/C++, and developed a toolbox, termed FastMM, to effectively conduct constraint-based metabolic modeling. The results showed that FastMM is 2~400 times faster than COBRA 3.0 in performing flux balance analysis and knockout analysis and returns consistent outputs. When applied to MCMC sampling, FastMM is 8 times faster than COBRA 3.0. FastMM is also faster than some efficient metabolic modeling applications, such as Cobrapy and Fast-SL. In addition, we developed a Matlab/Octave interface for fast metabolic modeling. This interface was fully compatible with COBRA 3.0, enabling users to easily perform complex applications for metabolic modeling. For example, users who do not have deep constraint-based metabolic model knowledge can just type one command in Matlab/Octave to perform personalized metabolic modeling. Users can also use the advance and multiple threading parameters for complex metabolic modeling. Thus, we provided an efficient and user-friendly solution to perform large scale genome-wide metabolic modeling. For example, FastMM can be applied to the modeling of individual cancer metabolic profiles of hundreds to thousands of samples in the Cancer Genome Atlas (TCGA).ConclusionFastMM is an efficient and user-friendly toolbox for large-scale personalized constraint-based metabolic modeling. It can serve as a complementary and invaluable improvement to the existing functionalities in COBRA 3.0. FastMM is under GPL license and can be freely available at GitHub site: https://github.com/GonghuaLi/FastMM.

Autophagy in Adipocyte Browning: Emerging Drug Target for Intervention in Obesity.

Autophagy, lipophagy, and mitophagy are considered to be the major recycling processes for protein aggregates, excess fat, and damaged mitochondria in adipose tissues in response to nutrient status-associated stress, oxidative stress, and genotoxic stress in the human body. Obesity with increased body weight is often associated with white adipose tissue (WAT) hypertrophy and hyperplasia and/or beige/brown adipose tissue atrophy and aplasia, which significantly contribute to the imbalance in lipid metabolism, adipocytokine secretion, free fatty acid release, and mitochondria function. In recent studies, hyperactive autophagy in WAT was observed in obese and diabetic patients, and inhibition of adipose autophagy through targeted deletion of autophagy genes in mice improved anti-obesity phenotypes. In addition, active mitochondria clearance through activation of autophagy was required for beige/brown fat whitening – that is, conversion to white fat. However, inhibition of autophagy seemed detrimental in hypermetabolic conditions such as hepatic steatosis, atherosclerosis, thermal injury, sepsis, and cachexia through an increase in free fatty acid and glycerol release from WAT. The emerging concept of white fat browning–conversion to beige/brown fat–has been controversial in its anti-obesity effect through facilitation of weight loss and improving metabolic health. Thus, proper regulation of autophagy activity fit to an individual metabolic profile is necessary to ensure balance in adipose tissue metabolism and function, and to further prevent metabolic disorders such as obesity and diabetes. In this review, we summarize the effect of autophagy in adipose tissue browning in the context of obesity prevention and its potential as a promising target for the development of anti-obesity drugs.

Simulation and Reconstruction of Metabolite-Metabolite Association Networks Using a Metabolic Dynamic Model and Correlation Based Algorithms.

Biological networks play a paramount role in our understanding of complex biological phenomena, and metabolite-metabolite association networks are now commonly used in metabolomics applications. In this study we evaluate the performance of several network inference algorithms (PCLRC, MRNET, GENIE3, TIGRESS, and modifications of the MRNET algorithm, together with standard Pearson's and Spearman's correlation) using as a test case data generated using a dynamic metabolic model describing the metabolism of arachidonic acid (consisting of 83 metabolites and 131 reactions) and simulation individual metabolic profiles of 550 subjects. The quality of the reconstructed metabolite-metabolite association networks was assessed against the original metabolic network taking into account different degrees of association among the metabolites and different sample sizes and noise levels. We found that inference algorithms based on resampling and bootstrapping perform better when correlations are used as indexes to measure the strength of metabolite-metabolite associations. We also advocate for the use of data generated using dynamic models to test the performance of algorithms for network inference since they produce correlation patterns that are more similar to those observed in real metabolomics data.

Novel approaches for the assessment of relative body weight and body fat in diagnosis and treatment of anorexia nervosa: A cross-sectional study

Anorexia nervosa (AN) is a severe psychosomatic disease that seriously affects nutritional status. Therapeutic approaches primarily aim for rapid weight restoration by high caloric diets and activity restriction. This often promotes abdominal body fat gain, which potentially negatively influences the patient's compliance and increases the risk of relapse. This study focused on the evaluation of body weight and subcutaneous adipose tissue (SAT) in AN patients by novel approaches. The SAT of AN patients (n=18, body mass index (BMI) 15.3±1.3kg/m2) was determined by a highly accurate and reliable ultrasound method. The sum of SAT thicknesses of eight sites (DINCL) was calculated. Individual metabolic profiles were analyzed. The mass index (MI), which considers body proportions, was used in addition to BMI. Additional to the standard laboratory diagnostics, dermal carotenoids measured by resonance Raman spectroscopy, leptin, and oxidative stress indicators were determined. The mean MI was 15.7±1.4kg/m2. The DINCL considerably differed between individuals with the same BMI. Half of the patients (Group 1) had low DINCL: 1.3-28.4mm, and Group 2 showed values up to 58.2mm (corresponding to approximately 6kg SAT mass). The two group means differed by more than 300% (P < 0.001). Accordingly, leptin levels significantly differed (P < 0.001). Mean SAT thicknesses were significantly higher in Group 2at all eight sites. The groups also significantly differed in two oxidative stress parameters: total antioxidative capacity, malondialdehyde-modified low density lipoprotein immunoglobulin M (MDA-LDL IgM), and in the carotenoid level. Half of the patients had sufficiently high fat mass, despite very low BMI. Consequently, their muscle (and other organ) masses must have been extremely low. Diagnostic criteria and treatment protocols for AN should consider each patient's body composition. In addition to dietary treatments, muscle training at low energy turnover rates may be essential for avoiding unnecessary body fat gain, better treatment results, and long-term recovery.

Serum metabolomics using ultra performance liquid chromatography coupled to mass spectrometry in lactating dairy cows following a single dose of sporidesmin

IntroductionPhotosensitization is a common clinical sign in cows suffering from liver damage caused by the mycotoxin sporidesmin. This disease, called facial eczema (FE), is of major importance in New Zealand. Current techniques for diagnosing animals with subclinical sporidesmin-induced liver damage (i.e. without photosensitization) are nonspecific. In addition, little is known of the mechanisms involved in sporidesmin resistance, nor the early effects seen following low-dose sporidesmin intoxication.ObjectiveThe objective of this study was to identify individual metabolites or metabolic profiles that could be used as serum markers for early stage FE in lactating cows.MethodsResults are presented from a 59-day sporidesmin challenge in Friesian-cross dairy cows. Serum metabolite profiles were obtained using reversed phase ultra-performance liquid chromatography (UPLC) electrospray ionization mass spectrometry (MS) and UPLC tandem MS. Multivariate and time series analyses were used to assess the data.ResultsStatistical analysis, both with and without the temporal component, could distinguish the profiles of animals with clinical signs from the others, but not those affected subclinically. An increase in the concentrations of a combination of taurine- and glycine-conjugated secondary bile acids (BAs) was the most likely cause of the separation. This is the first time that MS methods have been applied to FE and that bile acids changes have been detected in cattle exposed to sporidesmin.ConclusionsIt is well known that BA concentrations increase during cholestasis due to damage to bile ducts and leakage of the bile. This is the first study to investigate metabolomic changes in serum following a sporidesmin challenge. Further work to establish the significance of the elevation of individual BAs concentrations in the serum of early-stage sporidesmin-poisoned cows is necessary.

P217 Serum metabolic profile of creatine correlates with repeated motor evoked potentials: A study on TMS-induced repetition suppression

Objectives The motor evoked potentials (MEPs) induced by transcranial magnetic stimulation (TMS) mainly reflect the electrophysiological characteristics of the motor system. Although the MEP amplitude is a peripheral response, it can be applied to study several neural phenomena, such as repetition suppression (RS). In RS, a train of TMS pulses is repeated and the induced MEP amplitudes suppress after the first MEP. RS is suggested to be a general response of the brain to repeated stimuli. When measuring the RS, the peripheral muscles contract frequently and thereby in this study, we evaluated whether RS is influenced by the muscle metabolism. Methods The metabolic profiles of 73 subjects (37 females) were measured from serum samples and TMS was targeted on the cortical representation of the right abductor pollicis brevis muscle on the primary motor cortex. The RS protocol included repeating trains of 4 pulses at 110% of the resting motor threshold with a 1s inter-stimulus interval and 20s inter-train interval. Results Serum creatine levels were found to correlate negatively with the magnitude of RS ( r = −0.43, p = 0.0002). Discussion Creatine is an important metabolite relating to energy requiring situations in the muscles. Therefore, the results suggest that large amounts of fast energy reservoir may be needed to keep up the muscle contraction in response to repeated TMS pulses. Conclusions Subjects with different metabolic profiles of creatine responded differently to repeated TMS pulses. Significance The possible influence of different individual metabolic profiles on repeated MEPs should be taken into account in TMS motor paradigms involving pulse repetition.

The Value of Biosamples in Smoking Cessation Trials: A Review of Genetic, Metabolomic, and Epigenetic Findings.

Human genetic research has succeeded in definitively identifying multiple genetic variants associated with risk for nicotine dependence and heavy smoking. To build on these advances, and to aid in reducing the prevalence of smoking and its consequent health harms, the next frontier is to identify genetic predictors of successful smoking cessation and also of the efficacy of smoking cessation treatments ("pharmacogenomics"). More broadly, additional biomarkers that can be quantified from biosamples also promise to aid "Precision Medicine" and the personalization of treatment, both pharmacological and behavioral. To motivate ongoing and future efforts, here we review several compelling genetic and biomarker findings related to smoking cessation and treatment. These Key results involve genetic variants in the nicotinic receptor subunit gene CHRNA5, variants in the nicotine metabolism gene CYP2A6, and the nicotine metabolite ratio. We also summarize reports of epigenetic changes related to smoking behavior. The results to date demonstrate the value and utility of data generated from biosamples in clinical treatment trial settings. This article cross-references a companion paper in this issue that provides practical guidance on how to incorporate biosample collection into a planned clinical trial and discusses avenues for harmonizing data and fostering consortium-based, collaborative research on the pharmacogenomics of smoking cessation. Evidence is emerging that certain genotypes and biomarkers are associated with smoking cessation success and efficacy of smoking cessation treatments. We review key findings that open potential avenues for personalizing smoking cessation treatment according to an individual's genetic or metabolic profile. These results provide important incentive for smoking cessation researchers to collect biosamples and perform genotyping in research studies and clinical trials.

Mixing omics: combining genetics and metabolomics to study rheumatic diseases.

Metabolomics is an exciting field in systems biology that provides a direct readout of the biochemical activities taking place within an individual at a particular point in time. Metabolite levels are influenced by many factors, including disease status, environment, medications, diet and, importantly, genetics. Thanks to their dynamic nature, metabolites are useful for diagnosis and prognosis, as well as for predicting and monitoring the efficacy of treatments. At the same time, the strong links between an individual's metabolic and genetic profiles enable the investigation of pathways that underlie changes in metabolite levels. Thus, for the field of metabolomics to yield its full potential, researchers need to take into account the genetic factors underlying the production of metabolites, and the potential role of these metabolites in disease processes. In this Review, the methodological aspects related to metabolomic profiling and any potential links between metabolomics and the genetics of some of the most common rheumatic diseases are described. Links between metabolomics, genetics and emerging fields such as the gut microbiome and proteomics are also discussed.

Inference of cancer mechanisms through computational systems analysis.

Large amounts of metabolomics data have been accumulated to study metabolic alterations in cancer that allow cancer cells to synthesize molecular materials necessary for cell growth and proliferation. Although metabolic reprogramming in cancer was discovered almost a century ago, the underlying biochemical mechanisms are still unclear. We show that metabolomics data can be used to infer likely biochemical mechanisms associated with cancer. The proposed inference method is data-driven and quite generic; its efficacy is demonstrated by the analysis of changes in purine metabolism of human renal cell carcinoma. The method and results are essentially unbiased and tolerate noise in the data well. The proposed method correctly identified and accurately quantified primary enzymatic alterations in cancer, and these account for over 80% of the metabolic alterations in the investigated carcinoma. Interestingly, the two primary action sites are not the most sensitive reaction steps in purine metabolism, which implies that sensitivity analysis is not a valid approach for identifying cancer targets. The proposed method exhibits statistically high precision and robustness even for analyses of moderately incomplete metabolomics data. By permitting analyses of individual metabolic profiles, the method may become a tool of personalized precision medicine.

Metabolic profiling of breast cancer: Differences in central metabolism between subtypes of breast cancer cell lines.

Although the concept of aerobic glycolysis in cancer was already reported in the 1930s by Otto Warburg, the understanding of metabolic pathways remains challenging especially due to the heterogeneity of cancer. In consideration of four different time points (1, 2, 4, and 7 days of incubation), GC-MS profiling of metabolites was performed on cell extracts and supernatants of breast cancer cell lines (MDA-MB-231, -453, BT-474) with different sub classification and the breast epithelial cell line MCF-10A. To the exclusion of trypsinization, direct methanolic extraction, cell scraping and cell disruption was executed to obtain central metabolites. Major differences in biochemical pathways have been observed in the breast cancer cell lines compared to the breast epithelial cell line, as well as between the breast cancer cell lines themselves. Characteristics of breast cancer subtypes could be correlated to their individual metabolic profiles. PLS-DA revealed the discrimination of breast cancer cell lines from MCF-10A based on elevated amino acid levels. The observed metabolic signatures have great potential as biomarker for breast cancer as well as an improved understanding of subtype specific phenomenons of breast cancer.

Hepatic metabolomic investigation of the North American black bear (Ursus americanus) using 1H-NMR spectroscopy

The growing field of metabolomics examines the end products of metabolism, metabolites, to determine physiological processes at a cellular level. Nuclear magnetic resonance (NMR) spectroscopy affords advantages such as noninvasive sample collection, minimal or no sample preparation, and conservation of samples. The objectives of this study were to determine the feasibility of NMR-based metabolomics as a screening tool for evaluating changes in North American black bear (Ursus americanus) metabolism, establish a baseline metabolic profile for the black bear, and determine if there was any significant variation between individual metabolic profiles of hunter-killed bears. Hepatic samples were collected from 14 legally, hunter-killed black bears. The samples were frozen, homogenized, and extracted. 1H NMR spectra were collected and analyzed. Over 30 metabolites were identified, including those involved with protein, lipid, and carbohydrate metabolism. Principal components analysis demonstrated a separation among the bears by general age groups, based on differences in several energy and amino acid biomarkers, as well as lactate. This difference may be due to variation in growth and body composition with age. NMR-based metabolomics proved to be a practical technique for identifying components of black bear metabolism.

Novel individual metabolic profile characterizes the protein kinase B-alpha (pkbα−/−) in vivo model

Context: Type 2 diabetes and associated co-morbidities run epidemic waves worldwide. Since pathophysiological constellations are individual and display a wide spread of dysmetabolic profiles personalized health care assessments start to emerge. Therefore, we established a specific in silico assessment tool targeting metabolic characterizations individually. Materials and methods: Values obtained from oral glucose and intraperitoneal insulin tolerance tests performed on pkbα−/− mice (KO) as well as age- and gender-matched controls (WT) were analysed using our established in silico model. Results: Generally, male pkbα−/− mice (KO) presented significantly increased insulin sensitivity at an age of 6 months compared with age-matched WTs (p = 0.036). Female KO and WT groups displayed improved glucose sensitivities compared with age-matched males (for WT p ≤ 0.011). Discussion and conclusion: Specific metabolic characterization should be assessed individually. Therefore, our in silico model enables novel insights inaugurating specific primary preventive strategies targeting individual metabolic profiling precisely.