Prediction of Cross-resistance and Collateral Sensitivity by Gene Expression profiles and Genomic Mutations

Abstract

In adaptive evolution, an increase in fitness to an environment is frequently accompanied by changes in fitness to other environmental conditions, called cross-resistance and sensitivity. Although the networks between fitness changes affect the course of evolution substantially, the mechanisms underlying such fitness changes are yet to be fully elucidated. Herein, we performed high-throughput laboratory evolution of Escherichia coli under various stress conditions using an automated culture system, and quantified how the acquisition of resistance to one stressor alters the resistance to other stressors. We demonstrated that resistance changes could be quantitatively predicted based on changes in the transcriptome of the resistant strains. We also identified several genes and gene functions, for which mutations were commonly fixed in the strains resistant to the same stress, which could partially explain the observed cross-resistance and collateral sensitivity. The integration of transcriptome and genome data enabled us to clarify the bacterial stress resistance mechanisms.Laboratory evolution of microorganisms is a powerful approach to the elucidation of the nature of evolutionary dynamics 1,2 . Recent advances in measurement technology, including high-throughput sequencing, have enabled us to quantify phenotypic and genotypic changes during laboratory evolution, which have provided valuable information on the mechanisms and principles of adaptive evolution [3][4][5] . The impact of laboratory evolution has extended beyond the field of evolutionary biology into engineering and medicine. For example, by using laboratory evolution approaches, some candidate mutations that contribute to antibiotic resistance have been identified, and this sheds light on how to control the emergence of antibiotic-resistant strains [6][7][8][9] . Laboratory evolution has also become a widely used tool for bioengineering applications 10-13 -to generate cells with improved growth, production titer, and stress tolerance, which are essential for improving industrial microbial production.The evolutionary adaptation to a specific environment is frequently accompanied by changes in fitness in response to other environments. For example, it was demonstrated that the acquisition of resistance to one antibiotic can give rise to resistance to other drugs simultaneously, which is called cross-resistance, while it can also increase sensitivity to other drugs, which is called collateral sensitivity [14][15][16][17][18][19] . Such links between changes in fitness affect the course of evolution considerably. This can be utilized for predicting and controlling evolutionary dynamics, such as the suppression of resistance acquisition by use of multiple antibiotics in combination, with collateral sensitivity interactions. Such a "design" of evolutionary dynamics based on the links between fitness changes can contribute to the suppression of emerging multidrug-resistant pathogens [20][21][22] , and to the development of useful microorganisms for bioprodu...

Ciprofloxacin, amoxicillin, and aminoglycosides stimulate genetic and phenotypic changes in uropathogenic Escherichia coli strains

“…As we mentioned aboveit can result from various mechanism of drug resistance that could be induced by the stress-response of the E. coli cell. Antibiotic-induced stress inside the bacterial cell can change the gene expression profile and it regulates the bacterial metabolism, it also has an impact on other virulence properties of bacteria [59,60].…”

Evolutionary dimension reduction in phenotypic space

“…Cell model simulations with stochastic catalytic reaction dynamics have shown that that adaptive changes in chemical concentrations after evolution are restricted to the one-dimensional subspace of chemical composition [12,13]. Furthermore, repeated bacterial evolution experiments have suggested that changes in chemical concentrations follow the same low-dimensional paths despite differences in genetic changes [14,15]. The hypothesis that evolution to increase fitness leads to this dimension reduction seen in phenotypic change explains the experimental results of common proportionality.…”

CNV-BAC: Copy Number Variation Detection in Bacterial Circular Genome

“…We consider three bacterial sequencing datasets, (1) 55 Escherichia coli ( E. coli ) MDS42 cells (Horinouchi, et al, 2017), (2) 85 Staphylococcus aureus ( S. aureus ) strains (Copin, et al, 2019), and (3) 50 Lactobacillus casei Zhang ( L. casei Zhang ) (Wang, et al, 2017). After mapping, we calculate the Spearman’s rank correlation between the RD O i and the bin’s distance to the replication origin d i .…”