Structure function relationships from deep mutational scanning in human cardiomyopathy
人类心肌病深度突变扫描的结构功能关系
基本信息
- 批准号:10576926
- 负责人:
- 金额:$ 67.87万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-01-01 至 2024-12-31
- 项目状态:已结题
- 来源:
- 关键词:3-DimensionalATP phosphohydrolaseAllelesAmino Acid SequenceArrhythmiaBiological AssayBiological ModelsCardiacCardiomyopathiesCardiovascular DiseasesCell SizeCell modelCellsChemicalsClassificationClinicClinicalClustered Regularly Interspaced Short Palindromic RepeatsCodeComplexCustomCytidine DeaminaseDataData SetDimensionsDiseaseDisease modelEngineeringEvaluationFluorescenceGenerationsGenesGenetic TechniquesGenetic TranscriptionGenetic VariationGenomeGenomicsGoalsHealthHeartHeart failureHumanHuman GeneticsHypertrophic CardiomyopathyHypertrophyIn SituIndividualInheritedIntegraseLibrariesMapsMeasurementMendelian disorderMethodsMicrofluidicsMolecularMolecular MotorsMolecular StructureMutagenesisMutagensMutationNatural experimentOligonucleotidesOptical MethodsPathogenicityPatientsPatternPhenotypePopulationPopulation GeneticsProtein BiochemistryProtein ChemistryProteinsReporterSarcomeresScanningSortingStatistical ModelsStructureStructure-Activity RelationshipSudden DeathSystemTechnologyTest ResultTestingTimeTranscriptVariantadjudicationcausal variantcell motilitydeep sequencinggene functiongenetic testinggenetic variantheart cellhuman diseaseindividual patientinduced pluripotent stem cell derived cardiomyocytesinnovationmutantmutation screeningnovelnucleaseprogramsprotein functionprotein protein interactionprotein structuresingle-cell RNA sequencingstoichiometrysudden cardiac deathtooltranscriptomics
项目摘要
PROJECT SUMMARY
The natural experiment of human genetic variation can be used to infer structure-function relationships for key
disease genes. We have previously demonstrated that population-scale genetic variation data can be
harnessed to illuminate structure-function relationships for genes causative of the Mendelian disease
hypertrophic cardiomyopathy. However, due to the rarity of individual causative variants, population genetics is
ultimately limiting to the goal of understanding the functional importance of the entire coding region of any
specific gene. There is an urgent need for experimental alternatives. Here, we propose to introduce targeted
genetic variation into human induced pluripotent stem cell derived cardiomyocytes (iPSC-CM) at scale (Aim 1).
We propose two complementary strategies for deep mutational scanning of the most common genes causing
hypertrophic cardiomyopathy, MYH7, MYBPC3 and TNNT2. The first, CRISPR-X, is a fusion of a cytidine
deaminase (AID) with nuclease-inactive Cas9 (dCas9), and provides targeted mutational coverage in situ. The
second, POPcode, uses a uracilated gene template and a set of mutant oligos to create an allelic library, which
is then integrated into the genome using a Dual-Integrase Cassette Exchange (DICE). To characterize these
cells, we further develop a custom microfluidics-based, fast optical method to phenotype single cells in real
time (Aim 2). Predictions of pathogenicity according to both cell size and a fluorescence marker of the
hypertrophy expression program will be mapped to 3D protein structures using our spatial scanning approach
and tested against gold standard adjudicated patient variant data. Finally, we will investigate variant-specific
mechanisms of disease using single cell RNA sequencing to assess the effect of each variant on allelic
stoichiometry and transcriptional programming, as well as protein biochemistry to assess sarcomere protein
interaction and power generation (Aim 3). In summary, we plan comprehensive evaluation of all potential
coding variation in the most frequently causative genes for the most common Mendelian cardiovascular
disease. Using innovative phenotyping tools and novel statistical approaches to the integration of population
and cellular data, we aim to understand the structure and function of these genes in health and disease,
providing an experimental basis for the classification of genetic variants in the clinical setting.
项目总结
项目成果
期刊论文数量(5)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Deconvoluting complex correlates of COVID-19 severity with a multi-omic pandemic tracking strategy.
- DOI:10.1038/s41467-022-32397-8
- 发表时间:2022-08-30
- 期刊:
- 影响因子:16.6
- 作者:Parikh, Victoria N.;Ioannidis, Alexander G.;Jimenez-Morales, David;Gorzynski, John E.;De Jong, Hannah N.;Liu, Xiran;Roque, Jonasel;Cepeda-Espinoza, Victoria P.;Osoegawa, Kazutoyo;Hughes, Chris;Sutton, Shirley C.;Youlton, Nathan;Joshi, Ruchi;Amar, David;Tanigawa, Yosuke;Russo, Douglas;Wong, Justin;Lauzon, Jessie T.;Edelson, Jacob;Montserrat, Daniel Mas;Kwon, Yongchan;Rubinacci, Simone;Delaneau, Olivier;Cappello, Lorenzo;Kim, Jaehee;Shoura, Massa J.;Raja, Archana N.;Watson, Nathaniel;Hammond, Nathan;Spiteri, Elizabeth;Mallempati, Kalyan C.;Montero-Martin, Gonzalo;Christle, Jeffrey;Kim, Jennifer;Kirillova, Anna;Seo, Kinya;Huang, Yong;Zhao, Chunli;Moreno-Grau, Sonia;Hershman, Steven G.;Dalton, Karen P.;Zhen, Jimmy;Kamm, Jack;Bhatt, Karan D.;Isakova, Alina;Morri, Maurizio;Ranganath, Thanmayi;Blish, Catherine A.;Rogers, Angela J.;Nadeau, Kari;Yang, Samuel;Blomkalns, Andra;O'Hara, Ruth;Neff, Norma F.;DeBoever, Christopher;Szalma, Sandor;Wheeler, Matthew T.;Gates, Christian M.;Farh, Kyle;Schroth, Gary P.;Febbo, Phil;DeSouza, Francis;Cornejo, Omar E.;Fernandez-Vina, Marcelo;Kistler, Amy;Palacios, Julia A.;Pinsky, Benjamin A.;Bustamante, Carlos D.;Rivas, Manuel A.;Ashley, Euan A.
- 通讯作者:Ashley, Euan A.
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Euan A Ashley其他文献
Artificial Intelligence in Molecular Medicine. Reply.
分子医学中的人工智能。
- DOI:
10.1056/nejmc2308776 - 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Bruna Gomes;Euan A Ashley - 通讯作者:
Euan A Ashley
Prediction of diagnosis and diastolic filling pressure by AI-enhanced cardiac MRI: a modelling study of hospital data.
通过人工智能增强心脏 MRI 预测诊断和舒张充盈压:医院数据的建模研究。
- DOI:
10.1016/s2589-7500(24)00063-3 - 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
D. Lehmann;Bruna Gomes;Niklas Vetter;Olivia Braun;Ali Amr;Thomas Hilbel;Jens Müller;Ulrich Köthe;Christoph Reich;E. Kayvanpour;F. Sedaghat;Manuela Meder;J. Haas;Euan A Ashley;Wolfgang Rottbauer;D. Felbel;Raffi Bekeredjian;H. Mahrholdt;Andreas Keller;P. Ong;Andreas Seitz;H. Hund;N. Geis;F. André;Sandy Engelhardt;Hugo A Katus;Norbert Frey;Vincent Heuveline;Benjamin Meder - 通讯作者:
Benjamin Meder
Euan A Ashley的其他文献
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{{ truncateString('Euan A Ashley', 18)}}的其他基金
Diagnosing the Unknown for Care and Advancing Science (DUCAS)
诊断未知的护理和推进科学 (DUCAS)
- 批准号:
10682163 - 财政年份:2023
- 资助金额:
$ 67.87万 - 项目类别:
Diagnosing the Unknown for Care and Advancing Science (DUCAS)
诊断未知的护理和推进科学 (DUCAS)
- 批准号:
10872436 - 财政年份:2023
- 资助金额:
$ 67.87万 - 项目类别:
Systematically mapping variant effects for cardiovascular genes
系统地绘制心血管基因的变异效应
- 批准号:
10501975 - 财政年份:2022
- 资助金额:
$ 67.87万 - 项目类别:
Center for Undiagnosed Diseases at Stanford Administrative Supplement
斯坦福大学未确诊疾病中心行政增刊
- 批准号:
10677455 - 财政年份:2022
- 资助金额:
$ 67.87万 - 项目类别:
Structure function relationships from deep mutational scanning in human cardiomyopathy
人类心肌病深度突变扫描的结构功能关系
- 批准号:
10083762 - 财政年份:2020
- 资助金额:
$ 67.87万 - 项目类别:
Structure function relationships from deep mutational scanning in human cardiomyopathy
人类心肌病深度突变扫描的结构功能关系
- 批准号:
9884435 - 财政年份:2020
- 资助金额:
$ 67.87万 - 项目类别:
Structure function relationships from deep mutational scanning in human cardiomyopathy
人类心肌病深度突变扫描的结构功能关系
- 批准号:
10364603 - 财政年份:2020
- 资助金额:
$ 67.87万 - 项目类别:
What comes next? Engaging stakeholders in governance of participant data and relationships during the sunset of large genomic medicine research initiatives
接下来是什么?
- 批准号:
10162151 - 财政年份:2018
- 资助金额:
$ 67.87万 - 项目类别: