Understanding adaptation and trade-offs in evolving yeast
Abstract/Contents
- Abstract
- Adaptation is a major driving force behind the observed bio-diversity in nature. Trade-offs as an essential concept in evolutionary biology, on the other hand, constrain the improvement of multiple traits simultaneously and thus limit the potential of adaptation. To understand how adaptation and trade-offs together shape the evolutionary processes, one needs to study the genetic and functional bases underlying both adaption and trade-offs, and how such genotypic and phenotypic changes affect organisms' fitness. Microbial experimental evolution approach offers an excellent framework to examine evolutionary processes under well-controlled conditions. However, existing techniques of characterizing adaptive events from experimental evolutions are often low-throughput and bias towards a subset of lineages with a high fitness and a high frequency in the population, preventing a comprehensive study of evolution. To overcome this limitation, a DNA barcode lineage-tracking system was previously developed in Saccharomyces cerevisiae, which enables the isolation and analysis of a large number of independently evolved adaptive clones. My work in graduate school has centered on combining the yeast lineage-tracking system, the experimental evolution approach, and the next-generation sequencing technique to investigate the evolutionary processes in a high-throughput and highly quantitative way. In Chapter 1, by measuring the fitness of thousands of evolved yeast clones and genome-wide sequencing hundreds of them, we constructed a comprehensive genotype-fitness map. Two major classes of adaptive events were observed in this serial transfer evolutionary condition, where cells experienced lag, fermentation and respiration phases within each growth cycle. In Chapter 2, we characterized the adaptive strategies of clones isolated in Chapter 1, by quantifying their fitness changes in physiologically distinct growth phases. Contrary to the common assumption that fitness benefits are acquired from an increased exponential growth rate, we instead showed that in batch serial transfer experiments, adaptive mutants' fitness gains can be dominated by benefits that are accrued in one growth cycle but not realized until the next growth cycle. In Chapter 3, we investigated whether and how yeast growth in physiologically distinct growth phases was constrained. We collected thousands of adaptive clones form multiple evolutionary conditions and measured their performance in different traits (phases of the growth cycle). The existence of an evolutionary inaccessible space was demonstrated between two pairs of traits, demonstrating the existence of trade-offs. Furthermore, by identifying the causative mutations of adaptive clones, we elucidated the underlying genetic basis of trade-offs
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2019; ©2019 |
| Publication date | 2019; 2019 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Li, Yuping, (Biologist) |
|---|---|
| Degree supervisor | Petrov, Dmitri Alex, 1969- |
| Degree supervisor | Sherlock, Gavin |
| Thesis advisor | Petrov, Dmitri Alex, 1969- |
| Thesis advisor | Sherlock, Gavin |
| Thesis advisor | Fisher, Daniel S |
| Thesis advisor | Fraser, Hunter B |
| Degree committee member | Fisher, Daniel S |
| Degree committee member | Fraser, Hunter B |
| Associated with | Stanford University, Department of Biology. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Yuping Li |
|---|---|
| Note | Submitted to the Department of Biology |
| Thesis | Thesis Ph.D. Stanford University 2019 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Yuping Li
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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