Genomic Analysis of Tissue and Cellular Heterogeneity in IPF
Genomic Analysis of Tissue and Cellular Heterogeneity in IPF
批准号:
10818884
负责人:
PANAGIOTIS V BENOS
金额:
$9.04万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2015
资助国家:
美国
项目状态:
未结题
起止时间:
2015-08-14 至 2026-06-30
关键词:
AddressAdultAffectAgeAlveolarAsthmaAttenuatedBioinformaticsBiomedical EngineeringCellsCellular biologyChildChromosomesChronicComputational BiologyComputer AnalysisComputing MethodologiesDataDevelopmentDiseaseDisease ProgressionDistalESR1 geneESR2 geneEndocrineEpitheliumEstrogensFemaleFibrosisFoundationsGene ExpressionGene Expression ProfileGenerationsGenesGenetic TranscriptionGenomicsGonadal Steroid HormonesGrantHeterogeneityHistologicHormone ReceptorHumanImageImmuneImmunohistochemistryIn Situ HybridizationLengthLigandsLungLung diseasesMapsMesenchymalModelingMolecular BiologyMolecular TargetMorbidity - disease rateNuclearOutcomePathogenesisPathologyPathway interactionsPatientsPerformancePersonsPhenotypePlayPopulationProcessProteomicsPulmonary FibrosisResolutionRoleSamplingSignal TransductionStructureStructure of parenchyma of lungSymptomsSystems BiologyTestosteroneTherapeutic InterventionTissuesValidationWorkcausal modelcell typecomparison controlcomputer sciencedata sharingdesigneffective interventionexperimental studyfibrogenesisfibrotic lung diseasehuman modelidiopathic pulmonary fibrosisinsightmalemale sex hormonesmembermenmiRNA expression profilingmicroCTmortalitymultidisciplinarynovelreceptorrepairedsexsexual dimorphismsingle cell technologysingle-cell RNA sequencingstatisticstelomeretooltranscriptome sequencingtranscriptomics
中文摘要
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英文摘要
PROJECT SUMMARY AND SUPPLEMENT TO R01
Idiopathic Pulmonary Fibrosis (IPF) is a chronic progressive lung disease with significant morbidity and
mortality. In the previous period of this grant, we performed bulk RNA-seq and microRNA profiling of microCT
defined differentially affected lung regions. This work led to identification of numerous molecular targets and
insights, development of computational methods, and development of a transcriptional model of fibrosis
progression. Using the powerful high-resolution technologies of single cell profiling, we generated a ‘map’ of all
human cells in patients with IPF, discovered novel, ectopic and aberrant cell populations, and replacement of
the distal alveolar cellular content with cells that usually populate the airways. These exciting findings are
foundations of this renewal application that focuses on identification of the signals that drive the changes we
identified, their sequence and their spatial organization. The hypothesis underlying this application is that
the unique histopathologic features of IPF reflect a disruption in the homeostatic cellular networks in
alveolar niche, that activates an aberrant but coordinated repair process that leads to the proximalization
of the distal lung. To address this hypothesis, we have assembled a multi-disciplinary team of experts in lung
fibrosis, genomics, proteomics, computational biology, computer science, cell and molecular biology, statistics,
imaging, bioengineering, pathology, and bioinformatics that will perform the following specific aims:
Specific Aim 1: To identify the specific sequence of changes in cell compositions and phenotypes during the
progression of fibrosis in the human IPF lung.
Specific Aim 2: To identify the changes in spatial relations, interactions, and connections between cellular
members of the fibrotic niche at different stages of fibrosis and progression of fibrosis.
Specific Aim 3: Generation of a systems biology model of human pulmonary fibrosis with a specific focus on
regulators of disease emergence and progression.
At the completion of this project, we will have a cell level, comprehensive transcriptional regulatory,
mechanistically relevant model of IPF based on the unique histological features of the disease. The model, the
discovered key regulatory modules and the accompanying data sharing, and dissemination tools will be useful
for understanding disease mechanisms and generation of novel, effective and precise therapeutic interventions.
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DOI:
10.1002/sim.7374
发表时间:
2017-09-30
期刊:
Statistics in medicine
影响因子:
2
作者:
[Sun J, Herazo-Maya JD, Kaminski N, Zhao H, Warren JL]
通讯作者:
Warren JL
DOI:
10.1111/resp.13302
发表时间:
2018-08
期刊:
Respirology (Carlton, Vic.)
影响因子:
--
作者:
[Tzouvelekis A, Herazo-Maya JD, Ryu C, Chu JH, Zhang Y, Gibson KF, Adonteng-Boateng PK, Li Q, Pan H, Cherry B, Ahmad F, Ford HJ, Herzog EL, Kaminski N, Fares WH]
通讯作者:
Fares WH
DOI:
10.1186/s12890-016-0356-4
发表时间:
2017-01-12
期刊:
BMC pulmonary medicine
影响因子:
3.1
作者:
[Vukmirovic M, Herazo-Maya JD, Blackmon J, Skodric-Trifunovic V, Jovanovic D, Pavlovic S, Stojsic J, Zeljkovic V, Yan X, Homer R, Stefanovic B, Kaminski N]
通讯作者:
Kaminski N
DOI:
10.1136/thoraxjnl-2018-211929
发表时间:
2019-03
期刊:
Thorax
影响因子:
10
作者:
[McDonough JE, Kaminski N, Thienpont B, Hogg JC, Vanaudenaerde BM, Wuyts WA]
通讯作者:
Wuyts WA
Regularized Latent Class Model for Joint Analysis of High-Dimensional Longitudinal Biomarkers and a Time-to-Event Outcome.
用于高维纵向生物标志物和事件时间结果联合分析的正则化潜在类模型。
DOI:
10.1111/biom.12964
发表时间:
2019
期刊:
Biometrics
影响因子:
1.9
作者:
[Sun,Jiehuan, Herazo-Maya,JoseD, Molyneaux,PhilipL, Maher,TobyM, Kaminski,Naftali, Zhao,Hongyu]
通讯作者:
Zhao,Hongyu
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