Biology subjects

Chawes, B.

Publications and source records attributed to Chawes, B..

4 recordsLinked to original sources

bioRxiv2023

A chemical structure and machine learning approach to assess the potential bioactivity of endogenous metabolites and their association with early-childhood hs-CRP levels

Metabolomics has gained much attraction due to its potential to reveal molecular disease mechanisms and present viable biomarkers. In this work we used a panel of untargeted serum metabolomes in 602 childhood patients of the COPSAC2010 mother-child cohort. The annotated part of the metabolome consists of 493 chemical compounds curated using automated procedures. Using predicted quantitative-structure-bioactivity relationships for the Tox21 database on nuclear receptors and stress response in cell lines, we created a filtering method for the vast number of quantified metabolites. The metabolites measured in childrens serums used here have predicted potential against the chosen target modelled targets. The targets from Tox21 have been used with quantitative structure-activity relationships (QSARs) and were trained for [~]7000 structures, saved as models, and then applied to 493 metabolites to predict their potential bioactivities. The models were selected based on strict accuracy criteria surpassing random effects. After application, 52 metabolites showed potential bioactivity based on structural similarity with known active compounds from the Tox21 set. The filtered compounds were subsequently used and weighted by their bioactive potential to show an association with early childhood hs-CRP levels at six months in a linear model supporting a physiological adverse effect on systemic low-grade inflammation. The significant metabolites were reported. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/567095v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@9c0216org.highwire.dtl.DTLVardef@4d26fborg.highwire.dtl.DTLVardef@13a2f0corg.highwire.dtl.DTLVardef@e6cbd1_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

Characterizing human postprandial metabolic response using multiway data analysis

Analysis of time-resolved postprandial metabolomics data can enhance our knowledge about human metabolism by providing a better understanding of similarities and differences in postprandial responses of individuals, with the potential to advance precision nutrition and medicine. Traditional data analysis methods focus on clustering methods relying on summaries of data across individuals or use univariate methods analyzing one metabolite at a time. However, they fail to provide a compact summary revealing the underlying patterns, i.e., groups of subjects, clusters of metabolites, and their temporal profiles. In this study, we analyze NMR (Nuclear Magnetic Resonance) spectroscopy measurements of plasma samples collected at multiple time points during a meal challenge test from 299 individuals from the COPSAC2000 cohort. We arrange the data as a three-way array: subjects by metabolites by time, and use the CAN-DECOMP/PARAFAC (CP) tensor factorization model to capture the underlying patterns. We analyze the fasting state data to reveal static patterns of subject group differences, and the fasting state-corrected postprandial data to reveal dynamic markers of group differences. Our analysis demonstrates that the CP model reveals replicable and biologically meaningful patterns capturing certain metabolite groups and their temporal profiles, and showing differences among males according to their body mass index (BMI). Furthermore, we observe that certain lipoproteins relate to the group difference differently in the fasting vs. dynamic state in males. While similar dynamic patterns are observed in response to the challenge test in males and females, the BMI-related group difference is only observed in males in the dynamic state.

systems biology↗

bioRxiv2023

The influence of early life exposures on the infant gut virome

Large cohort studies have contributed significantly to our understanding of the factors that influence the development of the bacterial component of the gut microbiome (GM) during the first years of life. However, the factors that shape the colonization by other important GM members such as the viral fraction remain more elusive. Most gut viruses are bacteriophages (phages), i.e., viruses attacking bacteria in a host specific manner, and to a lesser extent, but also widely present, eukaryotic viruses, including viruses attacking human cells. Here, we utilize the deeply phenotyped COPSAC2010 birth cohort consisting of 700 infants to investigate how social, pre-, peri- and postnatal factors may influence the gut virome composition at one year of age, where fecal virome data was available from 645 infants. Among the different exposures studied, having older siblings and living in an urban vs. rural area had the strongest impact on gut virome composition. Differential abundance analysis from a total of 16,118 viral operational taxonomic units (vOTUs) (mainly phages, but also 6.1% eukaryotic viruses) identified 2,105 vOTUs varying with environmental exposures, of which 5.9% were eukaryotic viruses and the rest was phages. Bacterial hosts for these phages were mainly predicted to be within the Bacteroidaceae, Prevotellaceae, and Ruminococcaceae families, as determined by CRISPR spacer matches. Spearman correlation coefficients indicated strong co-abundance trends of vOTUs and their targeted bacterial host, which underlined the predicted phage-host connections. Further, our findings show that some gut viruses encode important metabolic functions and how the abundance of genes encoding these functions is influenced by environmental exposures. Genes that were significantly associated with early life exposures were found in a total of 42 vOTUs. 18 of these vOTUs had their life styles predicted, with 17 of them having a temperate lifestyle. These 42 vOTUs carried genes coding for enzymes involved in alanine, aspartate and glutamate metabolism, glycolysis-gluconeogenesis, as well as fatty acid biosynthesis. The latter implies that these phages could be involved in the utilization and degradation of major dietary components and affect infant health by influencing the metabolic capacity of their bacterial host. Given the importance of the GM in early life for maturation of the immune system and maintenance of metabolic health, these findings provide a valuable source of information for understanding early life factors that predispose for autoimmune and metabolic disorders.

microbiology↗

bioRxiv2022

Analyzing postprandial metabolomics data using multiway models: A simulation study

BackgroundAnalysis of time-resolved postprandial metabolomics data can improve the understanding of metabolic mechanisms, potentially revealing biomarkers for early diagnosis of metabolic diseases and advancing precision nutrition and medicine. Postprandial metabolomics measurements at several time points from multiple subjects can be arranged as a subjects by metabolites by time points array. Traditional analysis methods are limited in terms of revealing subject groups, related metabolites, and temporal patterns simultaneously from such three-way data. ResultsWe introduce an unsupervised multiway analysis approach based on the CANDECOMP/PARAFAC (CP) model for improved analysis of postpran-dial metabolomics data guided by a simulation study. Because of the lack of ground truth in real data, we generate simulated data using a comprehensive human metabolic model. This allows us to assess the performance of CP models in terms of revealing subject groups and underlying metabolic processes. We study three analysis approaches: analysis of fasting-state data using Principal Component Analysis, T0-corrected data (i.e., data corrected by subtracting fasting-state data) using a CP model and full-dynamic (i.e., full postprandial) data using CP. Through extensive simulations, we demonstrate that CP models capture meaningful and stable patterns from simulated meal challenge data, revealing underlying mechanisms and differences between diseased vs. healthy groups. ConclusionsOur experiments show that it is crucial to analyze both fasting-state and T0-corrected data for understanding metabolic differences among subject groups. Depending on the nature of the subject group structure, the best group separation may be achieved by CP models of T0-corrected or full-dynamic data. This study introduces an improved analysis approach for postprandial metabolomics data while also shedding light on the debate about correcting baseline values in longitudinal data analysis.

systems biology↗