Main Manuscript for Contrasting transcriptome-wide variation in SNP diversity among conifers and decoupled patterns of adaptive convergence between molecular and functional levels
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ABSTRACT:
PROVIDER: PRJEB64176 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR11696114_1.fastq.gz | Fastqsanger.gz | |||
| ERR11696114_2.fastq.gz | Fastqsanger.gz | |||
| ERR11694723_1.fastq.gz | Fastqsanger.gz | |||
| ERR11694723_2.fastq.gz | Fastqsanger.gz | |||
| ERR11696536_1.fastq.gz | Fastqsanger.gz |
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Project description:Although the relationship between phenotypic plasticity and evolutionary dynamics has attracted large interest, very little is known about the contribution of phenotypic plasticity to adaptive evolution. In this study, we analyzed phenotypic and genotypic changes in E. coli cells during adaptive evolution to ethanol stress. To quantify the phenotypic changes, transcriptome analyses were performed. We previously obtained 6 independently evolved ethanol tolerant E. coli strains, strains A through F, by culturing cells under 5% ethanol stress for about 1000 generations and found a significantly larger growth rate than the parent strains (Horinouchi et al, 2010, PMID: 20955615). To elucidate the phenotypic changes that occurred during adaptive evolution, we quantified the time-series of the expression changes by microarray analysis. Starting from frozen stocks obtained at 6 time points (0, 384, 744, 1224, 1824 and 2496 hours) in laboratory evolution, cells were cultured under 5% ethanol stress, and mRNA samples were obtained in the exponential growth phase for microarray analysis.
2014-07-21 | E-GEOD-59050 | biostudies-arrayexpress
Project description:BackgroundBacterial cells have a remarkable ability to adapt to environmental changes, a phenomenon known as adaptive evolution. During adaptive evolution, phenotype and genotype dynamically changes; however, the relationship between these changes and associated constraints is yet to be fully elucidated.ResultsIn this study, we analyzed phenotypic and genotypic changes in Escherichia coli cells during adaptive evolution to ethanol stress. Phenotypic changes were quantified by transcriptome and metabolome analyses and were similar among independently evolved ethanol tolerant populations, which indicate the existence of evolutionary constraints in the dynamics of adaptive evolution. Furthermore, the contribution of identified mutations in one of the tolerant strains was evaluated using site-directed mutagenesis. The result demonstrated that the introduction of all identified mutations cannot fully explain the observed tolerance in the tolerant strain.ConclusionsThe results demonstrated that the convergence of adaptive phenotypic changes and diverse genotypic changes, which suggested that the phenotype-genotype mapping is complex. The integration of transcriptome and genome data provides a quantitative understanding of evolutionary constraints.
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Project description:BackgroundNumerous genome-wide scans conducted by genotyping previously ascertained single-nucleotide polymorphisms (SNPs) have provided candidate signatures for positive selection in various regions of the human genome, including in genes involved in pigmentation traits. However, it is unclear how well the signatures discovered by such haplotype-based test statistics can be reproduced in tests based on full resequencing data. Four genes (oculocutaneous albinism II (OCA2), tyrosinase-related protein 1 (TYRP1), dopachrome tautomerase (DCT), and KIT ligand (KITLG)) implicated in human skin-color variation, have shown evidence for positive selection in Europeans and East Asians in previous SNP-scan data. In the current study, we resequenced 4.7 to 6.7 kb of DNA from each of these genes in Africans, Europeans, East Asians, and South Asians.ResultsApplying all commonly used neutrality-test statistics for allele frequency distribution to the newly generated sequence data provided conflicting results regarding evidence for positive selection. Previous haplotype-based findings could not be clearly confirmed. Although some tests were marginally significant for some populations and genes, none of them were significant after multiple-testing correction. Combined P values for each gene-population pair did not improve these results. Application of Approximate Bayesian Computation Markov chain Monte Carlo based to these sequence data using a simple forward simulator revealed broad posterior distributions of the selective parameters for all four genes, providing no support for positive selection. However, when we applied this approach to published sequence data on SLC45A2, another human pigmentation candidate gene, we could readily confirm evidence for positive selection, as previously detected with sequence-based and some haplotype-based tests.ConclusionsOverall, our data indicate that even genes that are strong biological candidates for positive selection and show reproducible signatures of positive selection in SNP scans do not always show the same replicability of selection signals in other tests, which should be considered in future studies on detecting positive selection in genetic data.
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