Algorithmic approaches to systems biology, data integration, and evolution

系统生物学、数据集成和进化的算法方法

基本信息

批准号：
10927048
负责人：
Teresa Przytycka
金额：
$ 141.1万
依托单位：
NATIONAL LIBRARY OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
资助国家：
美国
起止时间：
至
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10927048
关键词：
Address Adult Algorithmic Software Algorithms Atlases Binding Biological Biological Process Body part Cell Lineage Cell Nucleus Cell physiology Cells Communities Complex Computational Biology Computer Models Computer software Computing Methodologies DNA DNA Binding DNA Structure Data Data Set Dependence Disease Drosophila genus Elements Environment Environmental Exposure Environmental Risk Factor Etiology Evolution Exposure to Gene Dosage Gene Expression Gene Expression Regulation Gene Mutation Genes Genetic Genetic study Genome Genotype Graph Growth Factor Hand Immune system International Journals Knowledge Language Link Machine Learning Malignant Neoplasms Measures Medicine Methods Modeling Molecular Molecular Conformation Mutagens Mutation National Institute of Diabetes and Digestive and Kidney Diseases Network-based Nucleotide Excision Repair Paper Pathologic Mutagenesis Pathway interactions Pattern Performance Phenotype Play Process Property Publishing RNA Regulatory Element Regulatory T-Lymphocyte Reporting Research Resolution Resources Role Science Signal Transduction Smoking Structure Structure of parenchyma of lung System Systems Biology Techniques Technology Testis Tissues Variant Work adult stem cell aptamer biological systems cancer biomarkers cancer genome cell type comparative data integration driver mutation experimental study fly gene function gene regulatory network genetic signature graph theory heterogenous data homologous recombination improved insight interest large datasets large scale data machine learning model malignant breast neoplasm member network models novel reconstruction response sensor sex sexual dimorphism simulation single-cell RNA sequencing success theories tool transcription factor trend tumor tumor progression

项目摘要

Przytycka's group continued to develop and apply computational methods that utilize and integrate large data sets with a focus on gene regulation and diseases. I continued the research on mutation signatures in cancer. Most of the mutations present in cancer genomes are harmless passenger mutations. It has been increasingly appreciated that analyses of the patterns of these mutations can provide useful information regarding mutational processes acting on cancer genomes. We leverage the concept mutational signatures to study the relationship of environmental factors, such as smoking and cellular processes in specific tissues. Integrating gene expression and mutational signatures, we examined the relationship of the exposure to smoking and other mutagens with biological processes in healthy tissues, aiming to understand how the exposure to these mutagens impact functioning of cells and tissues. Our results demonstrated that mutational signatures can be utilized to study the impact of mutagenic environmental factors on molecular pathways and cellular compositions of tissues by allowing a quantification of the strength of these mutagens (reported in the paper Mutational Signatures as Sensors of Environmental Exposures: Analysis of Smoking-Induced Lung Tissue Remodeling 1) To gain a more detailed knowledge about the relationships between mutagenic processes and cellular-level changes we developed a network-based approach, GenSigNet, that captures the relations between gene expression and signatures. The GeneSigNet method allows to construct an influence network among genes and mutational signatures. The approach leverages sparse partial correlation among other statistical techniques to uncover dominant influence relations between the activities of network nodes. Applying GeneSigNet to cancer data sets, we uncovered important relations between mutational signatures and several cellular processes that can shed light on cancer-related processes. Our results are consistent with previous findings, such as the impact of homologous recombination deficiency on clustered APOBEC mutations in breast cancer. The network identified by GeneSigNet also suggest an interaction between APOBEC hypermutation and activation of regulatory T Cells (Tregs), as well as a relation between APOBEC mutations and changes in DNA conformation. GeneSigNet also exposed a possible link between the SBS8 signature of unknown etiology and the Nucleotide Excision Repair (NER) pathway. GeneSigNet provides a new and powerful method to reveal the relation between mutational signatures and gene expression. The results of these studies are reported in the paper Influence network model uncovers relations between biological processes and mutational signature published in Genome Medicine 2. Focusing on cancer driver mutations, we published in journal Trends in Medicine a review (Cancer driver mutations: predictions and reality) that summarize recent efforts to identify driver mutations in cancer and annotate their effects. We underline the success of computational methods to predict driver mutations in finding novel cancer biomarkers 3. This year we also initiated research related to tumor evolution is particular interest in the role of the environment including immune system. Our preliminary studies are have reported in BioRxiv paper Exploring tumor-normal cross-talk with TranNet: role of the environment in tumor progression. Focusing on gene expression evolution, we developed EvoGeneX, a computationally efficient implementation of the OU-based method that models within-species variation. Using extensive simulations, we show that modeling within-species variations and appropriate selection of species improve the performance of the model. Further, to facilitate a comparative analysis of expression evolution, we introduced a formal measure of evolutionary expression divergence for a group of genes using the rate and the asymptotic level of divergence. With these tools in hand, we performed the first-ever analysis of the evolution of gene expression across different body-parts, species, and sexes. (Stochastic Modeling of Gene Expression Evolution Uncovers Tissue- and Sex-Specific Properties of Expression Evolution in the Drosophila Genus published in Journal of Computational Biology. ) We now apply the approach developed in this paper to cancer evolution. My group also participates in the international Fly Cell Atlas Consortium that provides a resource for the Drosophila community to study genetic perturbations and diseases at single-cell resolution. The single-cell atlas of the entire adult includes 580,000 cells and more than 250 annotated cell types. Following the flagship paper of the consortium has been published previous year in Science we contributed to the eLife paper Emergent dynamics of adult stem cell lineages from single nucleus and single cell RNA-Seq of Drosophila testes 5. In addition together with Brian Oliver's group at NIDDK, and my former group member Soumitra Pal utilize Fly Cell Atlas data to study of sexual dimorphism in fly. Also with my former group member Yijie Wang we continued to work on developing new method for construction of regulatory networks. The inference of Gene Regulatory Networks (GRNs) is one of the key challenges in systems biology. Leading algorithms utilize, in addition to gene expression, prior knowledge such as Transcription Factor (TF) DNA binding motifs or results of TF binding experiments. However, such prior knowledge is typically incomplete, therefore, integrating it with gene expression to infer GRNs remains difficult. To address this challenge, we introduce NetREX-CFRegulatory Network Reconstruction using EXpression and Collaborative Filteringa GRN reconstruction approach that brings together Collaborative Filtering to address the incompleteness of the prior knowledge and a biologically justified model of gene expression (sparse Network Component Analysis based model). 6. We also have a long lasting collaborative research on non B-DNA structure with David Levens 7. In addition Jan Hoinka in the group continues to maintain previously developed Aptamer analysis software 8.

Przytycka的小组继续开发和应用计算方法，该方法利用和整合大型数据集，重点是基因调节和疾病。我继续研究癌症中突变特征。癌症基因组中存在的大多数突变是无害的乘客突变。越来越多地认为，对这些突变模式的分析可以提供有关作用于癌症基因组的突变过程的有用信息。我们利用概念突变特征来研究环境因素的关系，例如吸烟和特定组织中的细胞过程。整合基因表达和突变特征，我们研究了吸烟和其他诱变剂与健康组织中生物过程的关系，旨在了解这些诱变者的暴露如何影响细胞和组织的功能。我们的结果表明，可以利用突变特征来研究诱变环境因素对组织的分子途径和细胞组成的影响，通过允许对这些诱变剂的强度进行量化（报道在纸张突变中报道作为环境暴露的传感器：分析吸烟诱导的肺组织重塑1）。为了获得有关诱变过程与细胞级别变化之间关系的更详细的知识，我们开发了一种基于网络的方法Gensignet，该方法捕获了基因表达与特征之间的关系。 Genesignet方法允许在基因和突变特征之间构建影响网络。该方法利用其他统计技术之间的稀疏部分相关性，以发现网络节点活动之间的主要影响关系。将Genesignet应用于癌症数据集，我们发现了突变特征与几个可以揭示与癌症相关过程的细胞过程之间的重要关系。我们的结果与以前的发现是一致的，例如同源重组缺乏对乳腺癌聚集的APOBEC突变的影响。 Genesignet鉴定的网络还表明，APOBEC过度spry量与调节性T细胞的激活（Tregs）之间存在相互作用，以及APOBEC突变与DNA构象变化之间的关系。 Genesignet还暴露了未知病因的SBS8特征与核苷酸切除修复（NER）途径之间的可能联系。 Genesignet提供了一种新的强大方法，可以揭示突变特征与基因表达之间的关系。这些研究的结果在论文影响网络模型中报道了生物学过程与发表在基因组医学上的突变签名之间的关系2。为了关注癌症驱动器突变，我们在医学的期刊趋势中发表了一项综述（癌症驱动突变：预测和现实），总结了最近识别癌症驱动因素突变并注释其影响的努力。我们强调了计算方法在发现新型癌症生物标志物3中预测驱动突变的成功3。今年，我们还启动了与肿瘤进化有关的研究，这对包括免疫系统在内的环境的作用特别感兴趣。我们的初步研究已在Biorxiv论文中报道，探索与Trannet的肿瘤正常交叉对话：环境在肿瘤进展中的作用。为了关注基因表达演化，我们开发了Evogenex，这是一种基于OU的方法的计算有效实现，该方法模型内部变化。使用广泛的模拟，我们表明建模物种内部变化和物种的适当选择可以改善模型的性能。此外，为了促进对表达演化的比较分析，我们使用速率和差异水平引入了一组基因进化表达差异的形式量度。借助这些工具，我们对不同身体部位，物种和性别的基因表达的演变进行了首次分析。（基因表达进化的随机建模发现在计算生物学杂志上发表的果蝇属中表达进化的组织和性别特异性特性。）我们现在将本文中开发的方法应用于癌症的进化。我的小组还参加了国际飞池地图集联盟，为果蝇社区提供了以单细胞分辨率研究遗传扰动和疾病的资源。整个成年人的单细胞地图集包括580,000个细胞和250多个带注释的细胞类型。遵循该联盟的旗舰论文在上一年发表了科学上的发表，我们还为果蝇睾丸的单核和单细胞RNA-seq的成年干细胞谱系的新兴动态做出了贡献。同样，与我的前小组成员Yijie Wang一起，我们继续致力于开发建造监管网络的新方法。基因调节网络（GRN）的推断是系统生物学的主要挑战之一。领先的算法除了基因表达外，还利用了先验知识，例如转录因子（TF）DNA结合基序或TF结合实验的结果。但是，这种先验知识通常是不完整的，因此，将其与基因表达相结合以推断GRN仍然很困难。为了应对这一挑战，我们使用表达和协作过滤AGRN重建方法介绍了NetRex-crenculation网络重建方法，该方法将协作过滤汇集在一起，以解决先验知识的不完整以及基因表达的生物合理模型（基于稀疏网络组件分析模型）。 6。我们还与David Levens 7进行了对非B-DNA结构的持久协作研究。此外，该小组中的Jan Hoinka还继续维护先前开发的Aptamer Analition Software 8。

项目成果

期刊论文数量（44）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Subpopulation Detection and Their Comparative Analysis across Single-Cell Experiments with scPopCorn.

DOI：
10.1016/j.cels.2019.05.007
发表时间：
2019-06
期刊：
Cell systems
影响因子：
9.3
作者：
Yijie Wang;Jan Hoinka;T. Przytycka
通讯作者：
Yijie Wang;Jan Hoinka;T. Przytycka

ARG-walker: inference of individual specific strengths of meiotic recombination hotspots by population genomics analysis.

DOI：
10.1186/1471-2164-16-s12-s1
发表时间：
2015
期刊：
BMC genomics
影响因子：
4.4
作者：
Chen H;Yang P;Guo J;Kwoh CK;Przytycka TM;Zheng J
通讯作者：
Zheng J

Modeling information flow in biological networks.

DOI：
10.1088/1478-3975/8/3/035012
发表时间：
2011-06
期刊：
Physical biology
影响因子：
2
作者：
Kim YA;Przytycki JH;Wuchty S;Przytycka TM
通讯作者：
Przytycka TM

AptaBlocks Online: A Web-Based Toolkit for the In Silico Design of Oligonucleotide Sticky Bridges.

AptaBlocks Online：基于网络的寡核苷酸粘桥计算机设计工具包。

DOI：
10.1089/cmb.2019.0470
发表时间：
2020
期刊：
Journal of computational biology : a journal of computational molecular cell biology
影响因子：
0
作者：
Hoinka,Jan;Wang,Yijie;Przytycka,TeresaM
通讯作者：
Przytycka,TeresaM

Cancer driver mutations: predictions and reality.