Furusawa, C. | bioRxiv Science

bioRxiv2017

High-Throughput Laboratory Evolution Of Escherichia coli Under Multiple Stress Environments

Bacterial cells have a remarkable capacity to adapt and to evolve to environmental changes. Although many mutations contributing to adaptive evolution have been identified, the relationship between the mutations and the phenotypic changes responsible for fitness gain has yet to be fully elucidated. For a better understanding of phenotype-genotype relationship in evolutionary dynamics, we performed high-throughput laboratory evolution of Escherichia coli under various stress conditions using an automated culture system. One measure of phenotype, transcriptome analysis, revealed that the expression changes which occurred during the evolution were generally similar among the strains evolved in the same stress environment. We also found several genes and gene functions for which mutations were commonly fixed in the strains resistant to the same stress, and whose effects on resistance were verified experimentally. We demonstrated that the integration of transcriptome and genome data enables us to extract the mechanisms for stress resistance.\n\nAuthor summaryUnderstanding the relationship between phenotypic and genetic changes is a fundamental goal in evolutionary biology, which can provide insights into the past and future evolutionary trajectories. Evolution of microorganisms in a laboratory has been the primary approach to clarify the mappings of phenotypic and genotypic changes. Here, we performed high-throughput laboratory evolution with bacteria using an automated culture system, to quantify phenotypic and genotypic changes occurred under various stress conditions. We identified various stress-specific gene expression changes and mutations, and contributions of them to fitness gain were validated. These results demonstrated that the integration of phenotypic and genotypic changes makes it possible to extract the mechanisms for stress resistance evolution, which will contribute to bioengineering applications.

evolutionary biology↗

bioRxiv2017

Formation Of Dominant Mode By Evolution In Biological Systems

A reduction in high-dimensional phenotypic states to a few degrees of freedom is essential to understand biological systems. One possible origin of such a reduction (as recently discussed) is the steady growth of cells that constrains each components replication rate. Here, in contrast, our aim is to investigate consequences of evolutionary robustness, which is shown to cause a stronger dimensional reduction in possible phenotypic changes in response to a variety of environmental conditions. First, we examined global protein expression changes in Escherichia coli after various environmental perturbations. Remarkably, they were proportional across components, across different types of environmental conditions, while the proportion coefficient corresponded to the change in growth rate. Because such global proportionality is not generic to all systems under a condition of steady growth, a new conceptual framework is then needed. We hypothesized that such proportionality is a result of evolution. To test this hypothesis, we analyzed a cell model--with a huge number of components, that reproduces itself via a catalytic reaction network--and confirmed that common proportionality in the concentrations of all components is shaped through evolutionary processes to maximize cell growth (and therefore fitness) under a given environmental condition. Furthermore, we found that the changes in concentration across all components in response to environmental and evolutionary changes are constrained to the changes along a one-dimensional major axis within a huge-dimensional state space. On the basis of these observations, we propose a theory in which high-dimensional phenotypic changes after evolution are constrained to the points near a one-dimensional major axis that correlates with the growth rate, to achieve both evolutionary robustness and plasticity. By formulating this proposition in terms of dynamical systems, broad experimental and numerical results on phenotypic changes caused by evolution and adaptation are coherently explained.\n\nSummaryCells generally consist of thousands of components whose abundance levels change through adaptation and evolution. Accordingly, each steady cell state can be represented as a point in a high-dimensional space of component concentrations. In the context of equilibrium statistical thermodynamics, even though the state space is high-dimensional, macroscopic description only by a few degrees of freedom is possible for equilibrium systems; however, such characterization by a few degrees of freedom has not yet been achieved for cell systems. Given that they are not in equilibrium, we need some other constraint to be imposed. Here, by restricting our focus to a cellular state with steady growth that is achieved after evolution,we examine how the expression levels of its several components change under different environmental conditions. On the basis of analysis of protein expression levels in recent bacterial experiments as well as the results of simulations using a toy cell model consisting of thousands of components that are reproduced by catalytic reactions, we found that adaptation and evolutionary paths in high-dimensional state space are constrained to changes along a one-dimensional curve, representing a major axis for all the observed changes. Moreover, this one-dimensional structure emerges only after evolution and is not applicable to any system showing steady growth. This curve is determined by the growth rate of a cell, and thus it is possible to describe an evolved system by means of a growth rate function. All the observed results are consistent with the hypothesis that changes in high-dimensional states are nearly confined to the major axis in response to environmental, evolutionary, and stochastic perturbations. This description opens up the possibility to characterize a cell state as a macroscopic growth rate, as is the case for the thermodynamic potential. This approach can provide estimates of which phenotypic changes are theoretically more evolvable, as predicted simply from their observed environmental responses.

biophysics↗

bioRxiv2016

SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation

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.\n\nIn 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.\n\nThe R source code of SCODE is available at https://github.com/hmatsu1226/SCODE.

bioinformatics↗