St. John, P. | bioRxiv Science

bioRxiv2024

Bitter taste genetics and oral health in Canadian Longitudinal Study on Aging

This study aimed to investigate the association of single nucleotide polymorphisms (SNPs) in 25 Bitter Taste Receptor genes (TAS2Rs) and 12 TAS2R pseudogenes with self-reported oral health outcomes in the Canadian Longitudinal Study on Aging (CLSA) cohort. Following quality control, 124 SNPs with a minor allele frequency > 0.01 and 21,991 individuals of European ancestry were included in the analysis. Fifteen SNPs in TAS2R8, 9, 13, 14, 20, and 50 were significantly associated with self-reported sore jaw muscles, a symptom commonly linked to temporomandibular disorders (TMDs). TAS2R20 exhibited the highest number of associated SNPs. Structure-function analysis suggests that variants in TAS2R20 may contribute to this symptom by altering ligand interactions. These findings highlight the potential for TAS2R genetic screening to identify individuals at elevated risk for TMD, supporting the development of personalized treatment strategies and advancing our understanding of TMD genetic risk factors.

genetics↗

bioRxiv2018

Bayesian inference of metabolic kinetics from genome-scale multiomics data

Modern biological tools generate a wealth of data on metabolite and protein concentrations that can be used to help inform new strain designs. However, integrating these data sources to generate predictions of steady-state metabolism typically requires a kinetic description of the enzymatic reactions that occur within a cell. Parameterizing these kinetic models from biological data can be computationally difficult, especially as the amount of data increases. Robust methods must also be able to quantify the uncertainty in model parameters as a function of the available data, which can be particularly computationally intensive. The field of Bayesian inference offers a wide range of methods for estimating distributions in parameter uncertainty. However, these techniques are poorly suited to kinetic metabolic modeling due to the complex kinetic rate laws typically employed and the resulting dynamic system that must be solved. In this paper, we employ linear-logarithmic kinetics to simplify the calculation of steady-state flux distributions and enable efficient sampling and variational inference methods. We demonstrate that detailed information on the posterior distribution of kinetic model parameters can be obtained efficiently at a variety of different problem scales, including large-scale kinetic models trained on multiomics datasets. These results allow modern Bayesian machine learning tools to be leveraged in understanding biological data and developing new, efficient strain designs.

systems biology↗