Biology subjects

Serohijos, A. W. R.

Publications and source records attributed to Serohijos, A. W. R..

3 recordsLinked to original sources

bioRxiv2023

Genetic landscape of an in vivo protein interactome

Protein-protein interaction (PPI) networks accurately map environmental perturbations to their molecular consequences in cells, but effects of genome-wide genetic variation on PPIs remain unknown. We hypothesized that PPI networks integrate genetic and environmental effects, potentially defining biochemical mechanisms underlying complex polygenic traits. Here, we measured 61 PPIs in inbred strains of Saccharomyces cerevisiae with [~]12,000 single-nucleotide polymorphisms (SNPs) across the genome. Unlike mRNA expression and protein abundance that are primarily affected by SNPs local (in "cis") to a gene, PPIs are predominantly affected by SNPs far (in "trans") to the genomic loci of the interacting proteins. However, consistent with the PPI networks small-world characteristic, these transacting SNPs are in neighboring genes in the network. We likewise discovered SNPs in non-coding RNAs and post-transcriptional regulators (3 UTRs) with, counterintuitively, larger PPI-modulating effects than SNPs within protein-coding regions. Finally, we inferred known and novel mechanisms of action for yeast and human drugs. HIGHLIGHTSO_LIProtein-interaction quantitative trait locus ("piQTL") mapping reveals sensitivity of in vivo PPIs to polymorphisms across the yeast genome C_LIO_LITrans-piQTLs significantly outnumber and are stronger than cis-piQTLs C_LIO_LISNPs in non-coding RNAs and 3 UTRs have comparable effects to PPI as SNPs in coding regions C_LIO_LIpiQTL mapping reveals known and novel mechanism of yeast and human drugs C_LI

genomics↗

bioRxiv2023

Neutral Drift and Threshold Selection Promote Phenotypic Variation

Phenotypic variations within a population exist on different scales of biological organization and play a central role in evolution by providing adaptive capacity at the population-level. Thus, the question of how evolution generates phenotypic variation within an evolving population is fundamental in evolutionary biology. Here we address this question by performing experimental evolution of an antibiotic resistance gene, VIM-2 {beta}-lactamase, combined with diverse biochemical assays and population genetics. We found that neutral drift, i.e., evolution under a static environment, with a low antibiotic concentration can promote and maintain significant phenotypic variation within the population with >100-fold differences in resistance strength. We developed a model based on the phenotype-environment-fitness landscape generated with >5,000 VIM-2 variants, and demonstrated that the combination of "mutation-selection balance" and "threshold-like fitness-phenotype relationship" is sufficient to explain the generation of large phenotypic variation within the evolving population. Importantly, high-resistance conferring variants can emerge during neutral drift, without being a product of adaptation. Our findings provide a novel and simple mechanistic explanation for why most genes in nature, and by extension, systems and organisms, inherently exhibit phenotypic variation, and thus, population-level evolvability.

evolutionary biology↗

bioRxiv2019

Chromosomal barcoding of E. coli populations reveals lineage diversity dynamics at high resolution

Evolutionary dynamics in large asexual populations is strongly influenced by multiple competing beneficial lineages, most of which segregate at very low frequencies. However, technical barriers to tracking a large number of these rare lineages have so far prevented a detailed elucidation of evolutionary dynamics in large bacterial populations. Here, we overcome this hurdle by developing a chromosomal barcoding technique that allows simultaneous tracking of [~]450,000 distinct lineages in E. coli. We used this technique to gather insights into the evolutionary dynamics of large (>107 cells) E. coli populations propagated for [~]420 generations in the presence of sub-inhibitory concentrations of common antibiotics. By deep sequencing the barcodes, we reconstructed trajectories of individual lineages at high frequency resolution (< 10-5). Using quantitative tools from ecology, we found that populations lost lineage diversity at distinct rates corresponding to their antibiotic regimen. Additionally, by quantifying the reproducibility of these dynamics across replicate populations, we found that some lineages had similar fates over independent experiments. Combined with an analysis of individual lineage trajectories, these results suggest how standing genetic variation and new mutations may contribute to adaptation to sub-inhibitory antibiotic levels. Altogether, our results demonstrate the power of high-resolution barcoding in studying the dynamics of bacterial evolution.

evolutionary biology↗