Biology subjects

Sahaf, Z.

Publications and source records attributed to Sahaf, Z..

2 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↗

bioRxiv2022

Intra- and inter-species interactions drive early phases of invasion in mice gut microbiota

The stability and dynamics of ecological communities are dictated by interaction networks typically quantified at the level of species.1-10 But how such networks are influenced by intra-species variation (ISV) is poorly understood.11-14 Here, we use ~500,000 chromosomal barcodes to track high-resolution intra-species clonal lineages of Escherichia coli invading mice gut with the increasing complexity of gut microbiome: germ-free, antibiotic-perturbed, and innate microbiota. By co-clustering the dynamics of intra-species clonal lineages and those of gut bacteria from 16S rRNA profiling, we show the emergence of complex time-dependent interactions between E. coli clones and resident gut bacteria. With a new approach, dynamic covariance mapping (DCM), we differentiate three phases of invasion in susceptible communities: 1) initial loss of community stability as E. coli enters; 2) recolonization of some gut bacteria; and 3) recovery of stability with E. coli coexisting with resident bacteria in a quasi-steady state. Comparison of the dynamics, stability and fitness from experimental replicates and different cohorts suggest that phase 1 is driven by mutations in E. coli before colonization, while phase 3 is by de novo mutations. Our results highlight the transient nature of interaction networks in microbiomes driven by the persistent coupling of ecological and evolutionary dynamics. One-Sentence SummaryHigh-resolution lineage tracking and dynamic covariance mapping (DCM) define three distinct phases during early gut microbiome invasion.

evolutionary biology↗