Using Microfluidic Affinity Analysis to Probe Transcriptional Regulation
Using Microfluidic Affinity Analysis to Probe Transcriptional Regulation
批准号:
9021659
负责人:
Polly Morrell Fordyce
金额:
$24.9万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2012
资助国家:
美国
项目状态:
已结题
起止时间:
2012-09-15 至 2017-12-31
关键词:
AchievementAddressAffectAffinityAwardBehaviorBindingBiological SciencesBiologyBiotechnologyBoxingCell physiologyCellsCodeCollaborationsComplexDNADNA SequenceDNA-Binding ProteinsDataDevelopmentDiseaseEnsureEvolutionFunctional disorderFundingGene ExpressionGene Expression RegulationGenesGenetic CodeGenetic PolymorphismGenetic TranscriptionGenomicsGlucocorticoid ReceptorGoalsHuman GenomeHuman Genome ProjectImmunoprecipitationIn VitroIndividualInstructionLaboratoriesLeadLibrariesLigandsLocationMapsMeasurementMeasuresMicrofluidicsModelingNucleosomesOligonucleotidesOrganOrganismPatternProteinsPublic HealthRecruitment ActivityRegulationRegulator GenesRegulatory ElementResourcesRoleSignal TransductionSpecific qualifier valueSpecificityStereotypingSystemThermodynamicsTimeTissuesTrainingTranscriptional RegulationTransgenesUniversitiesVariantWorkbasecareercell typedesigngene therapygenome-widehuman diseasehuman genome sequencingimprovedin vivomodel buildingnovelpreferenceprogramsreconstructionresearch studyresponsesuccesssynthetic biologytranscription factor
中文摘要
描述(申请人提供):在一个分水岭的成就中,人类基因组计划(HGP)最近对整个人类基因组进行了测序,提供了关于潜在基因和调控序列的丰富信息。尽管取得了这一成功,但基因组序列如何具体规定复杂生物体的行为和发育仍在很大程度上不得而知。细胞内的基因表达受到严格的调控,许多基因只在特定的环境条件下或在发育过程中的固定时间点表达。下一个重大挑战在于对调控序列如何决定基因表达的机械理解,最终目标是能够从序列中定量预测表达水平。解决这一挑战将对生物学产生深远的影响,阐明调控序列的变化如何导致转录功能障碍和疾病,并改进用于基因治疗的转基因的合理设计。基因表达的调节主要是通过结合特定基因组位点上的转录因子来完成的。
一旦结合,转录因子可以招募或阻止一般的转录机制,从而激活或抑制转录。大多数主要的转录调控模型都建立在热力学原理的基础上,需要关于体内转录因子浓度的信息,以及它们对不同DNA序列的亲和力。尽管结合亲和力发挥了这种核心作用,但由于缺乏生物物理数据,到目前为止,实验一直被迫从全基因组的占用和表达测量中推断亲和力。利用最近开发的微流控系统,该系统允许高通量测量相互作用亲和力,该提议试图系统地研究转录的热力学。
在多个尺度上的调控,从转录因子和目标序列之间的个体相互作用到调控基因座上DNA结合蛋白组装的成核。实验将依次集中在:(1)蛋白质残基和DNA碱基之间的特定接触如何决定相互作用亲和力;(2)细胞特异性信号如何修改这些相互作用以决定组织特异性表达模式;(3)调控DNA序列和转录因子的进化变化如何在进化过程中重新连接转录网络以驱动表型变化;以及(4)转录因子之间的合作和竞争如何影响结合模式以影响基因表达。来自这些实验的数据将提供构建转录调控的基础上的、定量的模型所需的关键信息,并提高我们从调控序列预测基因表达的能力。这项K99奖提供的资金将为私人投资公司Polly Fordyce提供关键资源,使其能够接受两年的额外生物科学正规培训,并确保成功过渡到独立的职业生涯。
英文摘要
DESCRIPTION (provided by applicant): In a watershed achievement, the Human Genome Project (HGP) recently sequenced the entire human genome, providing a wealth of information about potential genes and regulatory sequences. Despite this success, exactly how genomic sequence specifies the behavior and development of complex organisms remains largely unknown. Gene expression within cells is tightly regulated, with many genes expressed only under certain environmental conditions or at stereotyped time points during development. The next great challenge lies in developing a mechanistic understanding of how regulatory sequences dictate gene expression, with the ultimate goal of being able to quantitatively predict expression levels from sequence. Solving this challenge would have far-reaching impacts in biology, elucidating how changes in regulatory sequence can lead to transcriptional dysfunction and disease and improving rational design of transgenes for gene therapy. Regulation of gene expression is accomplished primarily via binding of transcription factors at specific genomic loci.
Once bound, transcription factors can either recruit or block the general transcription machinery, thereby activating or repressing transcription. Most leading models of transcriptional regulation are built upon thermodynamic principles, and require information about transcription factor concentrations in vivo and their affinities for different DNA sequences. Despite this central role for binding affinities, experiments to date have been forced to infer affinities from genome-wide occupancy and expression measurements due to a lack of biophysical data. Using a recently developed microfluidic system that permits the high-throughput measurement of interaction affinities, this proposal seeks to systematically investigate the thermodynamics of transcriptional
regulation at multiple scales, from individual interactions between transcription factors and target sequences to the nucleation of assemblies of DNA binding proteins at regulatory loci. Experiments will focus on, in turn: (1) how particular contacts between protein residues and DNA bases determine interaction affinities; (2) how cell-specific signals modify these interactions to dictate tissue-specific expression patterns; (3) how evolutionary changes in both regulatory DNA sequences and transcription factors rewire transcriptional networks during evolution to drive phenotypic change; and (4) how cooperativity and competition between transcription factors affect binding patterns to influence gene expression. Data from these experiments will provide crucial information required to construct ground-up, quantitative models of transcriptional regulation and increase our ability to predict gene expression from regulatory sequence. The funding provided by this K99 award would provide crucial resources for the PI, Polly Fordyce, to receive 2 years of additional formal training in the biological sciences and ensure a successful transition to an independent career.
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