Ontology highlight
ABSTRACT:
PROVIDER: PRJNA667609 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR12778727_1.fastq.gz | Fastqsanger.gz | |||
| SRR12778727_2.fastq.gz | Fastqsanger.gz | |||
| SRR12778728_1.fastq.gz | Fastqsanger.gz | |||
| SRR12778728_2.fastq.gz | Fastqsanger.gz | |||
| SRR12778729_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.
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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.
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Project description:Small cell lung cancer (SCLC) tumors comprise heterogeneous mixtures of cell states, categorized into neuroendocrine (NE) and non-neuroendocrine (non-NE) transcriptional subtypes. NE to non-NE state transitions, fueled by plasticity, likely underlie adaptability to treatment and dismal survival rates. Here, we apply an archetypal analysis to model plasticity by recasting SCLC phenotypic heterogeneity through multi-task evolutionary theory. Cell line and tumor transcriptomics data fit well in a five-dimensional convex polytope whose vertices optimize tasks reminiscent of pulmonary NE cells, the SCLC normal counterparts. These tasks, supported by knowledge and experimental data, include proliferation, slithering, metabolism, secretion, and injury repair, reflecting cancer hallmarks. SCLC subtypes, either at the population or single-cell level, can be positioned in archetypal space by bulk or single-cell transcriptomics, respectively, and characterized as task specialists or multi-task generalists by the distance from archetype vertex signatures. In the archetype space, modeling single-cell plasticity as a Markovian process along an underlying state manifold indicates that task trade-offs, in response to microenvironmental perturbations or treatment, may drive cell plasticity. Stifling phenotypic transitions and plasticity may provide new targets for much-needed translational advances in SCLC.
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Project description:Intratumoral heterogeneity underlies cancer treatment resistance, but approaches to neutralize it remain elusive. Here, we recast heterogeneity in a systems perspective that considers cancer cell functional tasks inherited from cells of origin. We apply Archetype Analysis to bulk transcriptomics data from small cell lung cancer (SCLC), which forms tumors composed of neuroendocrine (NE) and non-neuroendocrine (non-NE) transcriptional subtypes. SCLC subtypes fit well in a 5-dimensional polytope whose vertices optimize tasks reminiscent of pulmonary NE cells, the SCLC normal counterpart, and include injury repair, slithering, and chemosensation. SCLC cells near a vertex are specialists for a task, while more distant cells are generalists, bearing gene signatures of multiple archetypes. Evolutionary theory and dynamical systems modeling suggest a division of labor strategy for adaptation to treatment, based on task trade-offs amongst specialists and generalists. Cell Transport Potential, a metric derived from single-cell RNA velocity, uncovers plasticity trends from specialists to generalists, and NE to non-NE subtypes. Transcription factor network simulations indicate that MYC overexpression increases plasticity by de-stabilizing NE subtypes. Framing heterogeneity in archetype space provides insights into transformative cancer treatments aimed at tumor cell plasticity.
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Project description:The evolution of phenotypic plasticity in response to temperature stress
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