Although many mutations contributing to antibiotic resistance have been identified, the relationship between the mutations and the related phenotypic changes responsible for the resistance has yet to ...be fully elucidated. To better characterize phenotype-genotype mapping for drug resistance, here we analyse phenotypic and genotypic changes of antibiotic-resistant Escherichia coli strains obtained by laboratory evolution. We demonstrate that the resistances can be quantitatively predicted by the expression changes of a small number of genes. Several candidate mutations contributing to the resistances are identified, while phenotype-genotype mapping is suggested to be complex and includes various mutations that cause similar phenotypic changes. The integration of transcriptome and genome data enables us to extract essential phenotypic changes for drug resistances.
•Laboratory evolution has provided detailed information on the evolution of drug resistance.•Phenotype/genotype data reveal evolutionary constraint in the development of resistance.•Evolutionary ...constraints make it possible to predict and control the evolution of resistance.
The emergence of antibiotic-resistant bacteria is a serious public concern. To deal with this problem, recent advances in technology and the use of laboratory evolution experiments have provided valuable information on the phenotypic and genotypic changes that occur during the evolution of resistance. These studies have demonstrated the existence of evolutionary constraints on the development of drug-resistance, which suggests predictability in its evolution. In this review, we focus on the possibility to predict and control the evolution of antibiotic resistance, based on quantitative analysis of phenotypic and genotypic changes observed in bacterial laboratory evolution. We emphasize the key challenges in evolutionary biology that will contribute to the development of appropriate treatment strategies for preventing resistance evolution.
The genetic code refers to a rule that maps 64 codons to 20 amino acids. Nearly all organisms, with few exceptions, share the same genetic code, the standard genetic code (SGC). While it remains ...unclear why this universal code has arisen and been maintained during evolution, it may have been preserved under selection pressure. Theoretical studies comparing the SGC and numerically created hypothetical random genetic codes have suggested that the SGC has been subject to strong selection pressure for being robust against translation errors. However, these prior studies have searched for random genetic codes in only a small subspace of the possible code space due to limitations in computation time. Thus, how the genetic code has evolved, and the characteristics of the genetic code fitness landscape, remain unclear. By applying multicanonical Monte Carlo, an efficient rare-event sampling method, we efficiently sampled random codes from a much broader random ensemble of genetic codes than in previous studies, estimating that only one out of every 1020 random codes is more robust than the SGC. This estimate is significantly smaller than the previous estimate, one in a million. We also characterized the fitness landscape of the genetic code that has four major fitness peaks, one of which includes the SGC. Furthermore, genetic algorithm analysis revealed that evolution under such a multi-peaked fitness landscape could be strongly biased toward a narrow peak, in an evolutionary path-dependent manner.
Understanding the constraints that shape the evolution of antibiotic resistance is critical for predicting and controlling drug resistance. Despite its importance, however, a systematic investigation ...of evolutionary constraints is lacking. Here, we perform a high-throughput laboratory evolution of Escherichia coli under the addition of 95 antibacterial chemicals and quantified the transcriptome, resistance, and genomic profiles for the evolved strains. Utilizing machine learning techniques, we analyze the phenotype-genotype data and identified low dimensional phenotypic states among the evolved strains. Further analysis reveals the underlying biological processes responsible for these distinct states, leading to the identification of trade-off relationships associated with drug resistance. We also report a decelerated evolution of β-lactam resistance, a phenomenon experienced by certain strains under various stresses resulting in higher acquired resistance to β-lactams compared to strains directly selected by β-lactams. These findings bridge the genotypic, gene expression, and drug resistance gap, while contributing to a better understanding of evolutionary constraints for antibiotic resistance.
Abstract
Operons are a hallmark of the genomic and regulatory architecture of prokaryotes. However, the mechanism by which two genes placed far apart gradually come close and form operons remains to ...be elucidated. Here, we propose a new model of the origin of operons: Mobile genetic elements called insertion sequences can facilitate the formation of operons by consecutive insertion–deletion–excision reactions. This mechanism barely leaves traces of insertion sequences and thus difficult to detect in nature. In this study, as a proof-of-concept, we reproducibly demonstrated operon formation in the laboratory. The insertion sequence IS3 and the insertion sequence excision enhancer are genes found in a broad range of bacterial species. We introduced these genes into insertion sequence-less Escherichia coli and found that, supporting our hypothesis, the activity of the two genes altered the expression of genes surrounding IS3, closed a 2.7 kb gap between a pair of genes, and formed new operons. This study shows how insertion sequences can facilitate the rapid formation of operons through locally increasing the structural mutation rates and highlights how coevolution with mobile elements may shape the organization of prokaryotic genomes and gene regulation.
The fitness landscape represents the complex relationship between genotype or phenotype and fitness under a given environment, the structure of which allows the explanation and prediction of ...evolutionary trajectories. Although previous studies have constructed fitness landscapes by comprehensively studying the mutations in specific genes, the high dimensionality of genotypic changes prevents us from developing a fitness landscape capable of predicting evolution for the whole cell. Herein, we address this problem by inferring the phenotype-based fitness landscape for antibiotic resistance evolution by quantifying the multidimensional phenotypic changes, i.e., time-series data of resistance for eight different drugs. We show that different peaks of the landscape correspond to different drug resistance mechanisms, thus supporting the validity of the inferred phenotype-fitness landscape. We further discuss how inferred phenotype-fitness landscapes could contribute to the prediction and control of evolution. This approach bridges the gap between phenotypic/genotypic changes and fitness while contributing to a better understanding of drug resistance evolution.
The analysis of RNA-Seq data from individual differentiating cells enables us to reconstruct the differentiation process and the degree of differentiation (in pseudo-time) of each cell. Such analyses ...can reveal detailed expression dynamics and functional relationships for differentiation. To further elucidate differentiation processes, more insight into gene regulatory networks is required. The pseudo-time can be regarded as time information and, therefore, single-cell RNA-Seq data are time-course data with high time resolution. Although time-course data are useful for inferring networks, conventional inference algorithms for such data suffer from high time complexity when the number of samples and genes is large. Therefore, a novel algorithm is necessary to infer networks from single-cell RNA-Seq during differentiation.
In this study, we developed the novel and efficient algorithm SCODE to infer regulatory networks, based on ordinary differential equations. We applied SCODE to three single-cell RNA-Seq datasets and confirmed that SCODE can reconstruct observed expression dynamics. We evaluated SCODE by comparing its inferred networks with use of a DNaseI-footprint based network. The performance of SCODE was best for two of the datasets and nearly best for the remaining dataset. We also compared the runtimes and showed that the runtimes for SCODE are significantly shorter than for alternatives. Thus, our algorithm provides a promising approach for further single-cell differentiation analyses.
The R source code of SCODE is available at https://github.com/hmatsu1226/SCODE.
hirotaka.matsumoto@riken.jp.
Supplementary data are available at Bioinformatics online.
Spirosoma linguale is a gram-negative, coiled bacterium belonging to the family Cytophagaceae. Its coiled morphology is unique in contrast to closely related bacteria belonging to the genus ...Spirosoma, which have a short, rod-shaped morphology. The mechanisms that generate unique cell morphology are still enigmatic. In this study, using the Spirosoma linguale ATCC33905 strain, we isolated β-lactam (cefoperazone and amoxicillin)-resistant clones. These clones showed two different cell morphological changes: relatively loosely curved cells or small, horseshoe-shaped cells. Whole-genome resequencing analysis revealed the genetic determinants of β-lactam resistance and changes in cell morphology. The loose-curved clones commonly had mutations in Slin_5958 genes encoding glutamyl-tRNA amidotransferase B subunit, whereas the small, horseshoe-shaped clones commonly had mutations in either Slin_5165 or Slin_5509 encoding pyruvate dehydrogenase (PDH) components. Two clones, CFP1ESL11 and CFL5ESL4, which carried only one mutation in Slin_5958, showed almost perfectly straight, rod-shaped cells in the presence of amoxicillin. This result suggests that penicillin-binding proteins targeted by amoxicillin play an important role in the formation of a coiled morphology in this bacterium. In contrast, supplementation with acetate did not rescue the growth defect and abnormal cell size of the CFP5ESL9 strain, which carried only one mutation in Slin_5509. These results suggest that PDH is involved in cell-size maintenance in this bacterium.
The lack of understanding of stem cell differentiation and proliferation is a fundamental problem in developmental biology. Although gene regulatory networks (GRNs) for stem cell differentiation have ...been partially identified, the nature of differentiation dynamics and their regulation leading to robust development remain unclear. Herein, using a dynamical system modeling cell approach, we performed simulations of the developmental process using all possible GRNs with a few genes, and screened GRNs that could generate cell type diversity through cell-cell interactions. We found that model stem cells that both proliferated and differentiated always exhibited oscillatory expression dynamics, and the differentiation frequency of such stem cells was regulated, resulting in a robust number distribution. Moreover, we uncovered the common regulatory motifs for stem cell differentiation, in which a combination of regulatory motifs that generated oscillatory expression dynamics and stabilized distinct cellular states played an essential role. These findings may explain the recently observed heterogeneity and dynamic equilibrium in cellular states of stem cells, and can be used to predict regulatory networks responsible for differentiation in stem cell systems.
Antibiotic treatment generally results in the selection of resistant bacterial strains, and the dynamics of resistance evolution is dependent on complex interactions between cellular components. To ...better characterize the mechanisms of antibiotic resistance and evaluate its dependence on gene regulatory networks, we performed systematic laboratory evolution of Escherichia coli strains with single-gene deletions of 173 transcription factors under three different antibiotics. This resulted in the identification of several genes whose deletion significantly suppressed resistance evolution, including arcA and gutM. Analysis of double-gene deletion strains suggested that the suppression of resistance evolution caused by arcA and gutM deletion was not caused by epistatic interactions with mutations known to confer drug resistance. These results provide a methodological basis for combinatorial drug treatments that may help to suppress the emergence of resistant pathogens by inhibiting resistance evolution.
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