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Mapping Local Fitness Landscapes Over 50,000 Generations of Evolution- [electronic resource]
Mapping Local Fitness Landscapes Over 50,000 Generations of Evolution- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214100125
- ISBN
- 9798379614423
- DDC
- 574
- 저자명
- Limdi, Anurag K.
- 서명/저자
- Mapping Local Fitness Landscapes Over 50,000 Generations of Evolution - [electronic resource]
- 발행사항
- [S.l.]: : Harvard University., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(131 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
- 주기사항
- Includes supplementary digital materials.
- 주기사항
- Advisor: Baym, Michael H.
- 학위논문주기
- Thesis (Ph.D.)--Harvard University, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약Evolution is short-sighted; as a population adapts, natural selection can only act on existing genetic variation, a key tenet of Darwinian evolutionary theory. For asexually evolving organisms such as bacteria, only those genetic variants that arise in the mutational neighborhood of the starting population are visible to selection. Because effects of mutations are context dependent, accumulation of mutations during adaptation can modulate the access to and effects of yet-to-occur mutations. Therefore, the local mutational neighborhood can begin to look different over long periods of time. With the advent of modern genetic engineering and genomics tools, and ability to sequence at scale, we can create and measure effects of thousands of mutations and address a key question: how different are the local fitness landscapes before and after thousands of generations of adaptation to an environment?In this dissertation, I describe my work exploring how the properties of the local fitness landscape of loss of function mutations changes over the long-term evolution experiment (LTEE) in E. coli, and computational and experimental approaches to optimize fitness measurements from sequencing based fitness assays. In Chapter 2, I detail research where I construct highly saturated transposon insertion mutagenesis libraries in ancestral and evolved strains from the LTEE, finding that while the statistical properties of the fitness landscape do not change systematically over 50,000 generations of evolution, access to particular evolutionary paths changes consistently across the replicate evolving populations. In Chapter 3, I discuss limitations of pooled sequencing-based fitness assays, tradeoffs in resolving deleterious and near-neutral fitness effects in a single experiment given fixed total sequencing, and suggest best practices to tune design of fitness assay experiments for the effect sizes relevant to the specific biological question. And in Chapter 4, I describe the development of UMI-TnSeq, a unique molecular identifier enabled sequencing library preparation protocol, and apply this method to show that PCR amplification bias is not consequential for transposon insertion sequencing experiments. Lastly, in Chapter 5, I review my findings and speculate on promising future directions stemming from this research.
- 일반주제명
- Biology.
- 일반주제명
- Microbiology.
- 일반주제명
- Systematic biology.
- 일반주제명
- Evolution & development.
- 키워드
- Genomics
- 키워드
- LTEE
- 기타저자
- Harvard University Biology Molecular and Cellular
- 기본자료저록
- Dissertations Abstracts International. 84-12B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008240612s2023 us |||||||||||||||c||eng d■001000016931840
■00520240214100125
■006m o d
■007cr#unu||||||||
■020 ▼a9798379614423
■035 ▼a(MiAaPQ)AAI30425259
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a574
■1001 ▼aLimdi, Anurag K.▼0(orcid)0000-0002-4891-8254
■24510▼aMapping Local Fitness Landscapes Over 50,000 Generations of Evolution▼h[electronic resource]
■260 ▼a[S.l.]:▼bHarvard University. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(131 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 84-12, Section: B.
■500 ▼aIncludes supplementary digital materials.
■500 ▼aAdvisor: Baym, Michael H.
■5021 ▼aThesis (Ph.D.)--Harvard University, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aEvolution is short-sighted; as a population adapts, natural selection can only act on existing genetic variation, a key tenet of Darwinian evolutionary theory. For asexually evolving organisms such as bacteria, only those genetic variants that arise in the mutational neighborhood of the starting population are visible to selection. Because effects of mutations are context dependent, accumulation of mutations during adaptation can modulate the access to and effects of yet-to-occur mutations. Therefore, the local mutational neighborhood can begin to look different over long periods of time. With the advent of modern genetic engineering and genomics tools, and ability to sequence at scale, we can create and measure effects of thousands of mutations and address a key question: how different are the local fitness landscapes before and after thousands of generations of adaptation to an environment?In this dissertation, I describe my work exploring how the properties of the local fitness landscape of loss of function mutations changes over the long-term evolution experiment (LTEE) in E. coli, and computational and experimental approaches to optimize fitness measurements from sequencing based fitness assays. In Chapter 2, I detail research where I construct highly saturated transposon insertion mutagenesis libraries in ancestral and evolved strains from the LTEE, finding that while the statistical properties of the fitness landscape do not change systematically over 50,000 generations of evolution, access to particular evolutionary paths changes consistently across the replicate evolving populations. In Chapter 3, I discuss limitations of pooled sequencing-based fitness assays, tradeoffs in resolving deleterious and near-neutral fitness effects in a single experiment given fixed total sequencing, and suggest best practices to tune design of fitness assay experiments for the effect sizes relevant to the specific biological question. And in Chapter 4, I describe the development of UMI-TnSeq, a unique molecular identifier enabled sequencing library preparation protocol, and apply this method to show that PCR amplification bias is not consequential for transposon insertion sequencing experiments. Lastly, in Chapter 5, I review my findings and speculate on promising future directions stemming from this research.
■590 ▼aSchool code: 0084.
■650 4▼aBiology.
■650 4▼aMicrobiology.
■650 4▼aSystematic biology.
■650 4▼aEvolution & development.
■653 ▼aGenomics
■653 ▼aGenetic variation
■653 ▼aPopulation genetics
■653 ▼aGenetic engineering
■653 ▼aLTEE
■690 ▼a0306
■690 ▼a0423
■690 ▼a0412
■690 ▼a0410
■71020▼aHarvard University▼bBiology, Molecular and Cellular.
■7730 ▼tDissertations Abstracts International▼g84-12B.
■773 ▼tDissertation Abstract International
■790 ▼a0084
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16931840▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
