Biology subjects

Weisse, A. Y.

Publications and source records attributed to Weisse, A. Y..

4 recordsLinked to original sources

bioRxiv2026

Stochastic gene expression drives state switching and phenotypic heterogeneity in RNA repair

Bacterial populations often exhibit variability in cellular responses to antibiotics which can affect treatment success and survival outcomes. This heterogeneity suggests that genetically identical cells can occupy different states of susceptibility within the same population. Better understanding of these dynamic responses is essential for determining how cells respond to stress and survive antibiotic-induced damage. In this work, we investigate heterogeneity in antibiotic responses driven by the Rtc RNA repair system in E. coli. We develop a stochastic model of Rtc-mediated RNA repair to identify the drivers of heterogeneity at the single-cell level. We observe bimodality in cellular translational capacity and a transient memory in predicted responses to RNA damage, demonstrating how intrinsic stochasticity in Rtc-regulation can lead to phenotypic heterogeneity. Classifying single-cell trajectories, we predict drivers of state switching between the resistant and susceptible states, finding that stochastic expression of the rtc regulator determines entry into a resistant cell state, while dilution drives the system back into a susceptible state. These results indicate that heterogeneity in RNA repair emerges from stochastic switching between states, offering a route to understanding how individual cells survive in the presence of translation-targeting antibiotics.

bioinformatics↗

bioRxiv2024

A mechanistic model of Rtc-regulated RNA repair suggests molecular targets to potentiate antibiotic effects

RNA is susceptible to damage from various internal and external factors. Given the integral role of RNA, its repair is essential for maintaining proper cell function. A highly conserved RNA repair system, the Rtc system, maintains core RNA components of the translational apparatus. In E. coli, Rtc expression is induced upon various stresses, including exposure to ribosome-targeting antibiotics. Its expression enables cells to rescue growth and survive treatment by conferring transient resistance to these antibiotics. The mechanisms by which Rtc-induced resistance arises are largely unknown. Here, we develop and analyse a computational model of Rtc-regulated maintenance of long-lived RNAs that form part of the translational apparatus. Our model analysis and experimental validation provide evidence of cell-to-cell heterogeneity in rtc expression and effects on the translational capacity of cells, indicating that individual cell fates determine antibiotic efficacy and thus rtc may induce a form of heteroresistance. Through further analysis and quantification of single-cell ribosome levels, we then identify targets within the Rtc system that can reduce translational capacity of cells, rendering cells more susceptible and so potentiating antibiotic effects. Our results provide novel tools for the systems analysis of Rtc-regulated RNA repair, they reveal a complex response underpinning resistance conferred by the innate repair system, and they suggest novel avenues to address resistance in E. coli and related clinical pathogens.

systems biology↗

bioRxiv2023

From Proteins to Ligands: Decoding Deep Learning Methods for Binding Affinity Prediction

Accurate in silico prediction of protein-ligand binding affinity is important in the early stages of drug discovery. Deep learning-based methods exist but have yet to overtake more conventional methods such as giga-docking largely due to their lack of generalisability. To improve generalizability we need to understand what these models learn from input protein and ligand data. We systematically investigated a sequence-based deep learning framework to assess the impact of protein and ligand encodings on predicting binding affinities for commonly used kinase data sets. The role of proteins is studied using convolutional neural network-based encodings obtained from sequences and graph neural network-based encodings enriched with structural information from contact maps. Ligand-based encodings are generated from graph-neural networks. We test different ligand perturbations by randomizing node and edge properties. For proteins we make use of 3 different protein contact generation methods (AlphaFold2, Pconsc4, and ESM-1b) and compare these with a random control. Our investigation shows that protein encodings do not substantially impact the binding predictions, with no statistically significant difference in binding affinity for KIBA in the investigated metrics (concordance index, Pearsons R Spearmans Rank, and RMSE). Significant differences are seen for ligand encodings with random ligands and random ligand node properties, suggesting a much bigger reliance on ligand data for the learning tasks. Using different ways to combine protein and ligand encodings, did not show a significant change in performance. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/551483v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@117f37forg.highwire.dtl.DTLVardef@173427eorg.highwire.dtl.DTLVardef@126a39org.highwire.dtl.DTLVardef@fbcc6d_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2019

Growth defects and loss-of-function in synthetic gene circuits

Synthetic gene circuits perturb the physiology of their cellular host. The extra load on endogenous processes shifts the equilibrium of resource allocation in the host, leading to slow growth and reduced biosynthesis. Here we built integrated host-circuit models to quantify growth defects caused by synthetic gene circuits. Simulations reveal a complex relation between circuit output and cellular capacity for gene expression. For weak induction of heterologous genes, protein output can be increased at the expense of growth defects. Yet for stronger induction, cellular capacity reaches a tipping point, beyond which both gene expression and growth rate drop sharply. Extensive simulations across various growth conditions and large regions of the design space suggest that the critical capacity is a result of ribosomal scarcity. We studied the impact of growth defects on various gene circuits and transcriptional logic gates, which highlights the extent to which cellular burden can limit, shape and even break down circuit function. Our approach offers a comprehensive framework to assess the impact of host-circuit interactions in silico, with wide-ranging implications for the design and optimization of bacterial gene circuits.

synthetic biology↗