Hoefsloot, H. C. | bioRxiv Science

bioRxiv2024

Longitudinal metabolomics data analysis informed bymechanistic models

MotivationMetabolomics measurements are noisy, often characterized by a small sample size and missing entries. While data-driven methods have shown promise in terms of analyzing metabolomics data, e.g., revealing biomarkers of various phenotypes, metabolomics data analysis can significantly benefit from incorporating prior information about metabolic mechanisms. In this paper, we introduce a novel data analysis approach where data-driven methods are guided by prior information through joint analysis of simulated data generated using a human metabolic model and real metabolomics measurements. ResultsWe arrange time-resolved metabolomics measurements of plasma samples collected during a meal challenge test from the COPSAC2000 cohort as a third-order tensor: subjects by metabolites by time samples. Simulated challenge test data generated using a human whole-body metabolic model is also arranged as a third-order tensor: virtual subjects by metabolites by time samples. Real and simulated data sets are coupled in the metabolites mode and jointly analyzed using coupled tensor factorizations to reveal the underlying patterns. Our experiments demonstrate that joint analysis of simulated and real data has a better performance in terms of pattern discovery achieving higher correlations with a BMI (body mass index)-related phenotype compared to the analysis of only real data in males while in females, the performance is comparable. We also demonstrate the advantages of such a joint analysis approach in the presence of incomplete measurements and its limitations in the presence of wrong prior information. AvailabilityThe code for joint analysis of real and simulated metabolomics data sets is released as a GitHub repository. Simulated data can also be accessed using the GitHub repo. Real measurements of plasma samples are not publicly available. Data may be shared by COPSAC through a collaboration agreement. Data access requests should be directed to Morten A. Rasmussen (morten.arendt@dbac.dk).

bioinformatics↗

bioRxiv2020

In plants distal regulatory sequences overlap with unmethylated rather than low-methylated regions, in contrast to mammals

BackgroundDNA methylation is an important factor in the regulation of gene expression and genome stability. High DNA methylation levels are associated with transcriptional repression. In mammalian systems, unmethylated, low methylated and fully methylated regions (UMRs, LMRs, and FMRs, respectively) can be distinguished. UMRs are associated with proximal regulatory regions, while LMRs are associated with distal regulatory regions. Although DNA methylation is mainly limited to the CG context in mammals, while it occurs in CG, CHG and CHH contexts in plants, UMRs and LMRs were expected to occupy similar genomic sequences in both mammals and plants. ResultsThis study investigated major model and crop plants such as Arabidopsis thaliana, tomato (Solanum lycopersicum), rice (Oryza sativa) and maize (Zea mays), and shows that plant genomes can also be subdivided in UMRs, LMRs and FMRs, but that LMRs are mainly present in the CHG context rather than the CG context. Strikingly, the identified CHG LMRs were enriched in transposable elements rather than regulatory regions. Maize candidate regulatory regions overlapped with UMRs. LMRs were enriched for heterochromatic histone modifications and depleted for DNase accessibility and H3K9 acetylation. CHG LMRs form a distinct, abundant cluster of loci, indicating they have a different role than FMRs. ConclusionsBoth mammalian and plant genomes can be segmented in three distinct classes of loci, UMRs, LMRs and FMRs, indicating similar underlying mechanisms. Unlike in mammals, distal regulatory sequences in plants appear to overlap with UMRs instead of LMRs. Our data indicate that LMRs in plants have a different function than those in mammals.

cell biology↗