Identification of the genetic mechanisms governing mammalian nephron endowment
鉴定控制哺乳动物肾单位禀赋的遗传机制
基本信息
- 批准号:10247020
- 负责人:
- 金额:$ 5.1万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-09-12 至 2023-09-11
- 项目状态:已结题
- 来源:
- 关键词:AddressAlgorithmsBiological AssayCellsChildhoodChromosomesChronic Kidney FailureClinicalComplementDataDevelopmentDiagnosticEmbryoEndowmentEnvironmentEpithelialEquilibriumExhibitsFellowshipFiltrationFosteringGene Expression ProfileGenerationsGenesGeneticGenetic DeterminismGenotypeHumanHybridsHypertensionInbred StrainIndividualInner mitochondrial membraneInterventionInvestigationKidneyLecithinLipidsLod ScoreMediatingMesenchymalMesenchymeMetanephric DiverticulumMitochondriaModelingMorphologyMouse StrainsMusMutationNephrologyNephronsOrganogenesisOutcomePartner in relationshipPerinatalPhenotypePhysiciansPopulationPregnancyPremature BirthPrincipal InvestigatorProcessQuantitative Trait LociRenal TissueRenal functionReportingResearchResearch TrainingRespirationRiskScientistSeriesSignal TransductionStructureSystemTSC1 geneTestingTherapeuticTissuesTreesVariantWNT Signaling Pathwayburden of illnesscohortconditional mutantdesigndiagnostic screeningepithelial to mesenchymal transitionexperimental studygenomic locusmouse modelmutantmutant mouse modelnephrogenesisnephron progenitornovel diagnosticsnovel therapeutic interventionoffspringpostnatalprematureprenatalprogenitorprogramsself-renewalstem cellstherapeutic targettraittreatment strategy
项目摘要
PROJECT SUMMARY & ABSTRACT
Mammalian kidney function is critically dependent on the number of nephrons generated during renal
development. Nephrons are the filtration unit of the renal system and arise from a nephron progenitor cell (NPC)
population at the periphery of the developing tissue. NPCs interact with the surrounding ureteric bud (UB) and
stromal compartments, balancing self-renewal and differentiation into segmented nephron structures via a
mesenchymal-to-epithelial transition. Consequently, nephron endowment is a quantitative outcome determined
by several processes including UB branching and NPC dynamics. Two noteworthy aspects of mammalian renal
development are: (1) a 10-fold variation in nephron number (NN) between human kidneys from different
individuals, ranging from 200,000 to >2.5 million units per kidney and (2) the synchronous depletion of remaining
progenitors at postnatal day 3 in mice (gestational week 34-37 in humans). These facts pose compelling research
questions, as the genetic contributions to these aspects of renal organogenesis are not currently known. From
a clinical standpoint, a low nephron endowment, which is particularly prevalent in premature birth cohorts,
contributes to high blood pressure and chronic kidney disease (CKD). These conditions pose an immense
disease burden worldwide, particularly as there is no known postnatal generation of new nephrons. While various
genetic and perinatal factors are demonstrated to reduce NN, there remains a clear need to identify genetic
contributions to the variation in and upper limits of nephron endowment. The principal investigator herein has
identified that distinct mouse strains can be used to model and dissect the genetic basis of differences in nephron
number, as several inbred strains and diversity outbred hybrids exhibit distinct, consistent NN phenotypes.
Therefore, this proposal sets forth a strategy to identify and subsequently target genetic loci that modify NN
outcomes, leveraging QTL mapping algorithms, sequencing data and known gene expression patterns in renal
tissue. Secondarily, on a mechanistic basis, it is unclear whether NN variation arises from altered cessation
timing, intrinsic changes in NPC activity, or a combination thereof; cellular energetics and mitochondrial function
have been implicated. Thus, this proposal will also investigate a mitochondrial mutant mouse model, which
exhibits NN elevated above baseline littermate controls, to identify mechanisms by which nephrogenesis can be
enhanced. Collectively, by identifying targets and mechanisms that segregate with either high or low nephron
number, this research will contribute to the ability to develop diagnostic screens and interventional treatment
strategies for deficient nephrogenesis, respectively.
Comprehensively, this research plan will aptly be executed in the fulfillment of a fellowship research training plan
aimed at fostering the development of an independent physician-scientist in academic pediatric nephrology.
项目概要和摘要
哺乳动物的肾功能严重依赖于肾移植过程中产生的肾单位的数量。
发展肾单位是肾脏系统的过滤单位,起源于肾单位祖细胞(NPC)
在发育中的组织的外围的群体。NPC与周围的输尿管芽(UB)相互作用,
间质区室,平衡自我更新和分化成分段肾单位结构,通过
间充质-上皮转化。因此,肾单位禀赋是一个定量的结果,
包括UB分支和NPC动力学的几个过程。哺乳动物肾脏的两个值得注意的方面
发展是:(1)10倍的变化,肾单位数(NN)之间的人肾脏从不同的
个人,每个肾脏的单位从200,000到> 250万个单位不等,以及(2)剩余肾脏的同步消耗
小鼠出生后第3天(人类妊娠34-37周)的祖细胞。这些事实提出了引人注目的研究
问题,因为目前尚不清楚肾器官发生的这些方面的遗传贡献。从
从临床角度来看,肾单位禀赋低,这在早产人群中特别普遍,
导致高血压和慢性肾病(CKD)。这些条件构成了巨大的
这是世界范围内的疾病负担,特别是因为没有已知的出生后新肾单位的产生。虽然各种
遗传和围产期因素被证明可以减少NN,但仍然需要明确识别遗传因素。
对肾单位禀赋的变化和上限的贡献。本研究的主要研究者
确定不同的小鼠品系可用于模拟和剖析肾单位差异的遗传基础,
数量,因为几个近交系和多样性远交杂种表现出不同的,一致的NN表型。
因此,该建议提出了一种策略,以确定并随后靶向修饰NN的遗传基因座
结果,利用QTL作图算法,测序数据和已知的基因表达模式,在肾脏
组织.其次,在一个机械的基础上,目前还不清楚神经网络的变化是否来自改变停止
时间、NPC活性的内在变化或其组合;细胞能量学和线粒体功能
被牵连了因此,本提案还将研究线粒体突变小鼠模型,
显示NN升高高于基线同窝对照,以确定肾发生的机制,
增强总的来说,通过识别与高或低肾单位分离的靶点和机制,
这项研究将有助于开发诊断筛查和介入治疗的能力
肾发生缺陷的策略。
综合而言,本研究计划将在奖学金研究培训计划的实施中适当执行
旨在促进学术儿科肾病学的独立医生-科学家的发展。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Alison Jarmas其他文献
Alison Jarmas的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Alison Jarmas', 18)}}的其他基金
Identification of the genetic mechanisms governing mammalian nephron endowment
鉴定控制哺乳动物肾单位禀赋的遗传机制
- 批准号:
10478960 - 财政年份:2019
- 资助金额:
$ 5.1万 - 项目类别:
Identification of the genetic mechanisms governing mammalian nephron endowment
鉴定控制哺乳动物肾单位禀赋的遗传机制
- 批准号:
10019324 - 财政年份:2019
- 资助金额:
$ 5.1万 - 项目类别:
Identification of the genetic mechanisms governing mammalian nephron endowment
鉴定控制哺乳动物肾单位禀赋的遗传机制
- 批准号:
9906385 - 财政年份:2019
- 资助金额:
$ 5.1万 - 项目类别:
相似海外基金
CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
- 批准号:
2337776 - 财政年份:2024
- 资助金额:
$ 5.1万 - 项目类别:
Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
- 批准号:
2338816 - 财政年份:2024
- 资助金额:
$ 5.1万 - 项目类别:
Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
- 批准号:
2338846 - 财政年份:2024
- 资助金额:
$ 5.1万 - 项目类别:
Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
- 批准号:
2348261 - 财政年份:2024
- 资助金额:
$ 5.1万 - 项目类别:
Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
- 批准号:
2348346 - 财政年份:2024
- 资助金额:
$ 5.1万 - 项目类别:
Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
- 批准号:
2348457 - 财政年份:2024
- 资助金额:
$ 5.1万 - 项目类别:
Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
- 批准号:
2404989 - 财政年份:2024
- 资助金额:
$ 5.1万 - 项目类别:
Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
- 批准号:
2339310 - 财政年份:2024
- 资助金额:
$ 5.1万 - 项目类别:
Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
- 批准号:
2339669 - 财政年份:2024
- 资助金额:
$ 5.1万 - 项目类别:
Continuing Grant
DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
- 批准号:
EP/Y029089/1 - 财政年份:2024
- 资助金额:
$ 5.1万 - 项目类别:
Research Grant