Metabolomics profiles of premenopausal women are different based on O-desmethylangolensin metabotype

ABSTRACT Urinary O-desmethylangolensin (ODMA) concentrations provide a functional gut microbiome marker of dietary isoflavone daidzein metabolism to ODMA. Individuals who do not have gut microbial environments that produce ODMA have less favorable cardiometabolic and cancer risk profiles. Urinary metabolomics profiles were evaluated in relation to ODMA metabotypes within and between individuals over time. Secondary analysis of data was conducted from the BEAN2 trial, which was a cross-over study of premenopausal women consuming six months on a high- and a low-soy diet, each separated by a 1-month washout period. In all of the 672 samples in the study, 66 of the 84 women had the same ODMA metabotype at seven or all eight time points. Two or four urine samples per woman were selected based on temporal metabotypes in order to compare within and across individuals. Metabolomics assays for primary metabolism and biogenic amines were conducted in 60 urine samples from 20 women. Partial least-squares discriminant analysis was used to compare metabolomics profiles. For the same ODMA metabotype across different time points, no profile differences were detected. For changes in metabotype within individuals and across individuals with different metabotypes, distinct metabolomes emerged. Influential metabolites (variables importance in projection score>2) included several phenolic compounds, carnitine and derivatives, fatty acid and amino acid metabolites, and some medications. Based on the distinct metabolomes of producers vs. non-producers, the ODMA metabotype may be a marker of gut microbiome functionality broadly involved in nutrient and bioactive metabolism, and should be evaluated for relevance to precision nutrition initiatives.

lncRNA-Disease Association Prediction Based On Weight Matrix And Projection Score

Background: with the development of medical science, lncRNA, originally considered as a noise gene, has been found to participate in a variety of biological activities. Nowadays, more and more studies show that lncRNA is involved in various human diseases, such as gastric cancer, prostate cancer, lung cancer, etc. However, obtaining lncRNA-disease association only through biological experiments not only costs manpower and material resources, but also gains little. Therefore, it is very important to develop effective computational models for predicting lncRNA-disease association. Results: In this paper, a new lncRNA-disease association prediction model LDAP-WMPS based on weight distribution and projection score is proposed. Based on the existing research results of disease semantic similarity, the integrated lncRNA similarity matrix and the integrated disease similarity matrix are calculated according to the disease semantic similarity and the association information between data. On this basis, the weight algorithm is combined with the improved projection algorithm to predict the lncRNA-disease association through the known lncRNA-miRNA association and miRNA-disease association. The simulation results show that under the loocv framework, the AUC of LDAP-WMPS can reach 0.8822. Better than the latest results. Through the case study of adenocarcinoma and colorectal cancer, it is proved that LDAP-WMPS can effectively infer lncRNA-disease association. Conclusions: The simulation results show that LDAP-WMPS has good prediction performance, which is an important supplement to the research of lncRNA-disease association prediction without lncRNA-disease association data. Keywords: lncRNA-miRNA association, miRNA-disease association, disease semantic similarity, Integrated lncRNA similarity, integrated disease similarity, Weight allocation algorithm, Projection score.

The impact of exhaustive exercise on metabolic profiles in young men: a metabolomics approach

Background: Exercise-induced fatigue leads to reduction in the ability exert physical performance. Prolonged and intensive exercise stimulates several metabolic pathways to produce energy. The purpose of the present study was to investigate the impact of exhaustive exercise on metabolomic pathways. Methods: Nine young recreationally active men were recruited to this study. Participants performed step incremental maximal exercise until maximum exhaustion. Saliva samples were collected pre- and post-exercise (immediately after exercise cessation) using a salimetric oral swab, and salivary metabolites were analyzed using capillary electrophoresis and time-of-flight mass spectrometry. Results: Two hundred ten metabolites were detected, representing different clustering principle component structures between pre- and post-exercise. Orthogonal partial least squares discriminant analysis identified 29 metabolites with highly related variable importance for projection score and 16 metabolites significantly increased after exercise. Furthermore, increase in cyclohexylamine was positively correlated with an increase in fatigue on a visual analog scale. Conclusion: The present study demonstrated that exhaustive exercise changed the saliva metabolomic pathways related to energy production including glycolysis, lipolysis, amino acid metabolism, amines, and ketone bodies.

Urinary Metabolomics around Parturition Identifies Metabolite Alterations in Dairy Cows Affected Postpartum by Lameness: Preliminary Study

(1) Background: The objective of this study was to evaluate the urine of dairy cows for presence of metabolites with the potential to be used as screening biomarkers for lameness as well as to characterize pre-lame, lame, and post-lame cows from the metabolic prospective. (2) Methods: Six lame and 20 control healthy cows were used in this nested case-control study. Urinary 1H-NMR analysis was used to identify and measure metabolites at five time points including −8 and −4 weeks prepartum, lameness diagnosis week (1–3 weeks postpartum) as well as at +4 and +8 weeks after calving. (3) Results: A total of 90 metabolites were identified and measured in the urine. At −8 and −4 weeks, 27 prepartum metabolites were identified as altered, at both timepoints, with 19 and 5 metabolites excreted at a lower concentration, respectively. Additionally, a total of 8 and 22 metabolites were found at greater concentration in pre-lame cows at −8 and −4 weeks, respectively. Lame cows were identified to excrete, at lower concentrations, seven metabolites during a lameness event with the top five most important metabolites being Tyr, adipate, glycerate, 3-hydroxy-3-methylglutarate, and uracil. Alterations in urinary metabolites also were present at +4 and +8 weeks after calving with N-acetylaspartate, glutamine, imidazole, pantothenate, beta-alanine and trimethylamine, with the greatest VIP (variable importance in projection) score at +4 weeks; and hipurate, pantothenate 1,3-dihydroxyacetone, galactose, and Tyr, with the greatest VIP score at +8 weeks postpartum. (4) Conclusions: Overall, results showed that urine metabotyping can be used to identify cows at risk of lameness and to better characterize lameness from the metabolic prospective. However, caution should be taken in interpretation of the data presented because of the low number of replicates.

Discriminating complex networks through supervised NDR and Bayesian classifier

Discriminating complex networks is a particularly important task for the purpose of the systematic study of networks. In order to discriminate unknown networks exactly, a large set of network measurements are needed to be taken into account for comprehensively considering network properties. However, as we demonstrate in this paper, these measurements are nonlinear correlated with each other in general, resulting in a wide variety of redundant measurements which unintentionally explain the same aspects of network properties. To solve this problem, we adopt supervised nonlinear dimensionality reduction (NDR) to eliminate the nonlinear redundancy and visualize networks in a low-dimensional projection space. Though unsupervised NDR can achieve the same aim, we illustrate that supervised NDR is more appropriate than unsupervised NDR for discrimination task. After that, we perform Bayesian classifier (BC) in the projection space to discriminate the unknown network by considering the projection score vectors as the input of the classifier. We also demonstrate the feasibility and effectivity of this proposed method in six extensive research real networks, ranging from technological to social or biological. Moreover, the effectiveness and advantage of the proposed method is proved by the contrast experiments with the existing method.