Modeling in vivo Protein-DNA Interactions from High-Throughput Data MP1/1

根据高通量数据 MP1/1 体内蛋白质-DNA 相互作用建模

基本信息

批准号：
8137917
负责人：
PANAGIOTIS V BENOS
金额：
$ 46.53万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2007
资助国家：
美国
起止时间：
2007-09-15 至 2013-09-14
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8137917
关键词：
Affect Affinity Algorithms Apoptosis Binding Binding Sites Biochemical Biological Biomedical Research Cell Proliferation Cell model Cells Characteristics Complex Computational Biology Computer Simulation Computer software Coupled DNA DNA Binding DNA Polymerase II DNA-Protein Interaction Data Data Analyses Data Set Databases Dependency Development Disease Distant Environment Epithelial Epithelial Cells Eukaryota Evaluation Family member Fibroblasts Fibrosis Gene Expression Gene Expression Microarray Analysis Gene Expression Regulation Gene Targeting General Transcription Factors Genes Genetic Transcription Genome Genomics Goals Growth Factor Hybridization Array In Vitro Inflammatory Internet Investigation Laboratories Learning Life Ligation Lung MADH3 gene MADH4 gene Malignant Neoplasms Mediating Methodology Methods Mining Modeling Noise Online Systems Ontology Pathway interactions Pattern Phenotype Population Database Positioning Attribute Process Protein Family Proteins Regulation Regulator Genes Regulatory Element Reporting Research Personnel Scheme Signal Transduction Site Specificity Statistical Data Interpretation Stimulus Testing Time Transcription Initiation Site Transforming Growth Factors Validation Work Wound Healing base biological systems cell type chromatin immunoprecipitation combinatorial design essays genetic regulatory protein genome-wide graphical user interface high throughput screening in vivo in vivo Model interest membrane-associated placental tissue protein 1 preference programs promoter relational database research study response tool transcription factor web interface

项目摘要

DESCRIPTION (provided by applicant): The control of gene expression is the most fundamental process in the life of any cell and it is primarily mediated (at the single gene level) by transcription factors, the DMA-binding regulatory proteins. It has been reported that the DMA target recognition in vivo sometimes differs from the in vitro-based models. Understanding the mechanisms that govern the specific DMA recognition in a cellular environment will profoundly augment our understanding of the mechanisms of transcription factor function and will also have a major impact in biomedical research. Furthermore, it becomes apparent that new motif finding algorithms need to be developed that specifically for high-throughput protein-DNA in vivo interaction data. The immediate goal of the proposed work is to develop the methodologies and tools to efficiently analyze high-throughput in vivo protein-DNA association data (like ChIP on chip) and identify the biologically important cis-regulatory elements. The more distant goal is to understand the rules that govern the interactions of transcription factors with their genomic DMA targets. The proposed activity aims, initially, to develop such a new motif finding software by expanding and testing various methods and strategies. Tests will be based on artificial and "real" data and the strengths and weaknesses of the various methods will be assessed. The best performing methods will be used to analyze existing and new ChIP on chip data, and predict the cis-regulatory motifs, which they will be subsequently confirmed with biochemical methods. Example transcription factors will be used to study the effect of particular cis-regulatory modules on gene expression with a goal of developing the methodology that will allow for complete computational models of gene regulation to be built. Finally, a database and web-interface will be developed on and around the tools and the data we will produce that ill allow for efficient data dissemination, analysis and mining. To accomplish these goals a combination of biochemical experimentation and computational algorithmic development is needed. Chromatin immunoprecipitation experiments will be coupled with promoter microarray hybridization (ChlP-on-chip) to identify possible targets for TGFbetal-induced transcription factors in primary lung cells. The data will be analyzed statistically to infer the appropriate quantitative models of the transcription factor binding. Publicly available and newly generated gene expression data will also be analyzed statistically to assess the effect of certain cis-regulatory modules in the expression of the downstream genes.

描述（由申请人提供）：基因表达的控制是任何细胞生命中最基本的过程，它主要由转录因子（DMA 结合调节蛋白）介导（在单基因水平）。据报道，体内 DMA 目标识别有时与体外模型不同。了解细胞环境中特定 DMA 识别的控制机制将深刻增强我们对转录因子功能机制的理解，也将对生物医学研究产生重大影响。此外，很明显需要开发新的基序寻找算法，专门用于高通量蛋白质-DNA 体内相互作用数据。拟议工作的直接目标是开发方法和工具来有效分析高通量体内蛋白质-DNA 关联数据（如芯片上的 ChIP）并识别生物学上重要的顺式调控元件。更遥远的目标是了解控制转录因子与其基因组 DMA 靶标相互作用的规则。拟议的活动最初旨在通过扩展和测试各种方法和策略来开发这样一种新的主题查找软件。测试将基于人工和“真实”数据，并将评估各种方法的优点和缺点。性能最佳的方法将用于分析现有和新的 ChIP 芯片数据，并预测顺式调控基序，随后将通过生化方法进行确认。示例转录因子将用于研究特定顺式调控模块对基因表达的影响，目标是开发能够建立完整的基因调控计算模型的方法。最后，将在我们将产生的工具和数据上或周围开发数据库和网络界面，以实现高效的数据传播、分析和挖掘。为了实现这些目标，需要将生化实验和计算算法开发相结合。染色质免疫沉淀实验将与启动子微阵列杂交（ChlP-on-chip）相结合，以确定原代肺细胞中 TGFbeta 诱导的转录因子的可能靶标。对数据进行统计分析，以推断转录因子结合的适当定量模型。还将对公开的和新生成的基因表达数据进行统计分析，以评估某些顺式调控模块对下游基因表达的影响。

项目成果

期刊论文数量（13）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

miR-199a-5p Is upregulated during fibrogenic response to tissue injury and mediates TGFbeta-induced lung fibroblast activation by targeting caveolin-1.

DOI：
10.1371/journal.pgen.1003291
发表时间：
2013
期刊：
PLoS genetics
影响因子：
4.5
作者：
Lino Cardenas CL;Henaoui IS;Courcot E;Roderburg C;Cauffiez C;Aubert S;Copin MC;Wallaert B;Glowacki F;Dewaeles E;Milosevic J;Maurizio J;Tedrow J;Marcet B;Lo-Guidice JM;Kaminski N;Barbry P;Luedde T;Perrais M;Mari B;Pottier N
通讯作者：
Pottier N

Biomarkers in idiopathic pulmonary fibrosis.

DOI：
10.1097/mcp.0b013e328356d03c
发表时间：
2012-09
期刊：
Current opinion in pulmonary medicine
影响因子：
3.3
作者：
Zhang Y;Kaminski N
通讯作者：
Kaminski N

Features of mammalian microRNA promoters emerge from polymerase II chromatin immunoprecipitation data.

DOI：
10.1371/journal.pone.0005279
发表时间：
2009
期刊：
PloS one
影响因子：
3.7
作者：
Corcoran DL;Pandit KV;Gordon B;Bhattacharjee A;Kaminski N;Benos PV
通讯作者：
Benos PV

HHMMiR: efficient de novo prediction of microRNAs using hierarchical hidden Markov models.

DOI：
10.1186/1471-2105-10-s1-s35
发表时间：
2009-01-30
期刊：
BMC bioinformatics
影响因子：
3
作者：
Kadri S;Hinman V;Benos PV
通讯作者：
Benos PV

Global methylation patterns in idiopathic pulmonary fibrosis.

DOI：
10.1371/journal.pone.0033770
发表时间：
2012
期刊：
PloS one
影响因子：
3.7
作者：
Rabinovich EI;Kapetanaki MG;Steinfeld I;Gibson KF;Pandit KV;Yu G;Yakhini Z;Kaminski N
通讯作者：
Kaminski N