Biology subjects

Braichenko, S.

Publications and source records attributed to Braichenko, S..

3 recordsLinked to original sources

bioRxiv2026

Genetic trans-effects link triglyceride-rich lipoproteins to blood pressure

Background: Genome-wide association studies (GWASs) have identified thousands of genomic regions in which common variants affect blood pressure, but have yielded limited mechanistic insight. This study used a novel method of genetic analysis based on identifying core genes on which the _trans_-effects of common variants coalesce to influence blood pressure. Methods: We undertook GWASs of mean arterial pressure (MAP) and body mass index (BMI) in 373,882 individuals aged less than 60 years in the Our Future Health study. We used summary statistics from GWASs of proteins on the SomaScan and Olink platforms to compute genome-wide aggregated trans-effects (GATE) scores for each protein, and tested for association of MAP with these scores. Results: The strongest GATE score association with MAP was for lipoprotein lipase (LpL, encoded by LPL_. Genetic up-regulation of circulating levels of LpL was associated with lower MAP but higher BMI. Associations of MAP and BMI with GATE scores for circulating levels of proteins encoded by three other genes involved in lipid handling -- CD300LG, ADIPOQ, TIMP4 -- were similar to those with LpL. GATE scores for all four of these proteins were inversely associated with measurements by NMR spectroscopy of plasma triglyceride in chylomicrons and extremely large VLDL particles. Conclusions: These results point to a key role in hypertension for proteins that regulate post-prandial clearance of triglyceride-rich lipoproteins, independently of adiposity. This is consistent with experimental studies showing that accumulation of lipids in endothelial cells impairs endothelial function.

genetics↗

bioRxiv2023

Polymorphism-aware models in RevBayes: Species trees, disentangling Balancing Selection and CG-biased gene conversion

The role of balancing selection is a long-standing evolutionary puzzle. Balancing selection is a crucial evolutionary process that maintains genetic variation (polymorphism) over extended periods of time; however, detecting it poses a significant challenge. Building upon the polymorphismaware phylogenetic models (PoMos) framework rooted in the Moran model, we introduce Po-MoBalance model. This novel approach is designed to disentangle the interplay of mutation, genetic drift, directional selection (GC-biased gene conversion), along with the previously unexplored balancing selection pressures on ultra-long timescales comparable with species divergence times by analysing multi-individual genomic and phylogenetic divergence data. Implemented in the open-source RevBayes Bayesian framework, PoMoBalance offers a versatile tool for inferring phylogenetic trees as well as quantifying various selective pressures. The novel aspect of our approach in studying balancing selection lies in PoMos ability to account for ancestral polymorphisms and incorporate parameters that measure frequency-dependent selection, allowing us to determine the strength of the effect and exact frequencies under selection. We implemented validation tests and assessed the model on the data simulated with SLiM and a custom Moran model simulator. Real sequence analysis of Drosophila populations reveals insights into the evolutionary dynamics of regions subject to frequency-dependent balancing selection, particularly in the context of sex-limited colour dimorphism in Drosophila erecta.

evolutionary biology↗

bioRxiv2021

Distinguishing between models of mammalian gene expression: telegraph-like models versus mechanistic models

Two-state models (telegraph-like models) have a successful history of predicting distributions of cellular and nascent mRNA numbers that can well fit experimental data. These models exclude key rate limiting steps, and hence it is unclear why they are able to accurately predict the number distributions. To answer this question, here we compare these models to a novel stochastic mechanistic model of transcription in mammalian cells that presents a unified description of transcriptional factor, polymerase and mature mRNA dynamics. We show that there is a large region of parameter space where the first, second and third moments of the distributions of the waiting times between two consecutively produced transcripts (nascent or mature) of two-state and mechanistic models exactly match. In this region, (i) one can uniquely express the two-state model parameters in terms of those of the mechanistic model, (ii) the models are practically indistinguishable by comparison of their transcript numbers distributions, and (iii) they are distinguishable from the shape of their waiting time distributions. Our results clarify the relationship between different gene expression models and identify a means to select between them from experimental data.

systems biology↗