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Purifying selection acting on the sequence of highly expressed proteins in the microevolution

ABSTRACT: Constraints on the rate of protein sequence evolution have been a central question in evolutionary biology. Orthology analysis between closely related species has revealed that the evolutionary speed is constrained by the expression level, with highly expressed proteins evolving slowly. This negative correlation between expression levels and evolutionary rates (known as the E-R anticorrelation) has already been widely observed in past macroevolution between species, for example, between Escherichia coli and Salmonella. However, it remains unclear whether this seemingly general law also governs microevolution, including past and de novo, within a species. To explore the ubiquitous impact of expression level on molecular evolution in bacteria, we analysed genome sequences for 99 strains of E. coli for microevolution in nature. We also analysed genomic mutations accumulated under laboratory conditions as a model of de novo microevolution. Here, we show that the E-R anticorrelation is significant in both past microevolution and de novo evolution in E. coli. Our data also indicate that purifying selection acting on highly expressed genes contributes to the ubiquity of the E-R anticorrelation. This study confirmed ongoing purifying selection acting on highly expressed genes, and implied that expression level has a ubiquitous impact on the speed of molecular diversification evolution in bacteria.

ORGANISM(S): Escherichia coli

PROVIDER: GSE189008 | GEO | 2022/09/01

REPOSITORIES: GEO

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Project description:Recent advances in transcriptome sequencing have enabled the discovery of thousands of long non-coding RNAs (lncRNAs) across many species. Though several lncRNAs have been shown to play important roles in diverse biological processes, the functions and mechanisms of most lncRNAs remain unknown. Two significant obstacles lie between transcriptome sequencing and functional characterization of lncRNAs: identifying truly non-coding genes from de novo reconstructed transcriptomes, and prioritizing the hundreds of resulting putative lncRNAs for downstream experimental interrogation. We present slncky, a computational lncRNA discovery tool that produces a high-quality set of lncRNAs from RNA-sequencing data and further uses evolutionary constraint to prioritize lncRNAs that are likely to be functionally important. Our automated filtering pipeline is comparable to manual curation efforts and more sensitive than previously published computational approaches. Furthermore, we develop a sensitive alignment pipeline for aligning lncRNA loci and propose new evolutionary metrics relevant for analyzing sequence and transcript evolution. Our analysis reveals that evolutionary selection acts in several distinct patterns, and uncovers two notable classes of intergenic lncRNAs: one showing strong purifying selection on RNA sequence and another where constraint is restricted to the regulation but not the sequence of the transcript. To study a comprehensive and comparable set of lncRNAs expressed in the pluripotent state, we analyzed RNA-Seq data from pluripotent cells derived from several strains and species, and grown in two physiological conditions. First we derived ânaÃ¯veâ ES cells (ESCs) from each of three different mice strains: 129SvEv, NOD, and Mus musculus castaneus (cast) mouse, a wild mouse subspecies originally from Thailand, as well as naÃ¯ve induced pluripotent stem (iPS) cells from rat and human. Next, to facilitate comparisons across pluripotent cells from different species, we also cultured mouse and rat cells in âprimedâ epiblast stem cell (EpiSC) culture conditions, since human iPS cells in culture are much more similar molecularly and functionally to mouse primed EpiSCs rather than to a ground state naÃ¯ve ESCs. We polyA selected RNA from each cell type and deeply sequenced on HiSeq2500

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Purifying selection acting on the sequence of highly expressed proteins in the microevolution

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