Phage therapy is a promising method for the treatment of multi-drug-resistant bacterial infections. However, its long-term efficacy depends on understanding the evolutionary effects of the treatment. Current knowledge of such evolutionary effects is lacking, even in well-studied systems. We used the bacterium Escherichia coli C and its bacteriophage ΦX174, which infects cells using host lipopolysaccharide (LPS) molecules. We first generated 31 bacterial mutants resistant to ΦX174 infection. Based on the genes disrupted by these mutations, we predicted that these E. coli C mutants collectively produce eight unique LPS structures. We then developed a series of evolution experiments to select for ΦX174 mutants capable of infecting the resistant strains. During phage adaptation, we distinguished two types of phage resistance: one that was easily overcome by ΦX174 with few mutational steps (“easy” resistance), and one that was more difficult to overcome (“hard” resistance). We found that increasing the diversity of the host and phage populations could accelerate the adaptation of phage ΦX174 to overcome the hard resistance phenotype. From these experiments, we isolated 16 ΦX174 mutants that, together, can infect all 31 initially resistant E. coli C mutants. Upon determining the infectivity profiles of these 16 evolved phages, we uncovered 14 distinct profiles. Given that only eight profiles are anticipated if the LPS predictions are correct, our findings highlight that the current understanding of LPS biology is insufficient to accurately forecast the evolutionary outcomes of bacterial populations infected by phage.
Noise in expression of individual genes gives rise to variations in activity of cellular pathways and generates heterogeneity in cellular phenotypes. Phenotypic heterogeneity has important implications for antibiotic persistence, mutation penetrance, cancer growth and therapy resistance. Specific molecular features such as the presence of the TATA box sequence and the promoter nucleosome occupancy have been associated with noise. However, the relative importance of these features in noise regulation is unclear and how well these features can predict noise has not yet been assessed. Here through an integrated statistical model of gene expression noise in yeast we found that the number of regulating transcription factors (TFs) of a gene was a key predictor of noise, whereas presence of the TATA box and the promoter nucleosome occupancy had poor predictive power. With an increase in the number of regulatory TFs, there was a rise in the number of cooperatively binding TFs. In addition, an increased number of regulatory TFs meant more overlaps in TF binding sites, resulting in competition between TFs for binding to the same region of the promoter. Through modeling of TF binding to promoter and application of stochastic simulations, we demonstrated that competition and cooperation among TFs could increase noise. Thus, our work uncovers a process of noise regulation that arises out of the dynamics of gene regulation and is not dependent on any specific transcription factor or specific promoter sequence.
SummaryCellular processes driven by coordinated actions of individual genes generate cellular phenotypes. Stochastic variations in these processes lead to phenotypic heterogeneity that often has important implications for antibiotic persistence, mutation penetrance, cancer growth and anti-cancer drug resistance. However, the architecture of noise in cellular processes has remained largely unexplored even though expression noise in individual genes have been widely studied. Here we quantify noise in biological processes in yeast and through an integrated quantitative model show that the number of regulating transcription factors and their binding dynamics are the primary drivers of noise. Specifically, binding dynamics arising from competition and cooperation among TFs for promoter binding can predict a large fraction of noise variation. Our work reveals a novel mechanism of noise regulation that arises out of the dynamic nature of gene regulation and is not dependent on specific transcription factor or specific promoter sequence.
Extensive efforts have been made to understand the phenotypic diversity of lipopolysaccharide (LPS) structures through deletion and complementation experiments. However, this approach likely underestimates the available phenotypic diversity. To better explore LPS diversity, we generate LPS mutants in Escherichia coli C by selecting for resistance to φX174, a bacteriophage that relies solely on binding to core LPS to infect its host. An analysis of 31 E. coli C mutants that are resistant to φX174 reveals that each mutant carries at least one mutation in genes linked to core LPS biosynthesis or assembly. Based on which genes are mutated, we predict the core LPS structures of each bacterial mutant, and test our predictions by evolving phages to recognize each evolved LPS structure. We find that phages that evolved to infect the same predicted LPS structure were not always able to cross-infect each other's host, suggesting that core LPS structure diversity is higher than predicted. Similarly, phage genotype-phenotype maps can be constructed using the bacterial LPS mutant classes. For example, we demonstrate that a combination of two phage mutations leads to loss of the ability to infect wildtype E. coli C. Our results show that phages are a useful tool to study LPS structures, and conversely that the study of LPS structures helps to understand phage evolution and biology.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.