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

• 批准号: 10688922
• 负责人: Teresa Przytycka
• 金额: $ 156.78万
• 依托单位: NATIONAL LIBRARY OF MEDICINE
• 依托单位国家: 美国
• 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/10688922
• 关键词: Acute Renal Failure with Renal Papillary Necrosis; Adult; Affinity; Algorithmic Software; Algorithms; Atlases; Binding; Biological; Biological Process; Cell physiology; Cells; Chemicals; Collaborations; Communities; Complex; Computer Models; Computing Methodologies; DNA; Data; Dependence; Diagnostic; Disease; Drosophila genus; Early Diagnosis; Elements; Environmental Risk Factor; Evolution; Exposure to; Gene Dosage; Gene Expression; Gene Expression Profile; Gene Expression Regulation; Gene Mutation; Generations; Genes; Genetic; Genetic study; Genotype; Goblet Cells; Graph; Growth Factor Gene; Immune response; International; Iowa; Joints; Journals; LCN2 gene; Language; Lead; Legal patent; Link; Machine Learning; Malignant Neoplasms; Methods; Molecular; Mucins; Mutagens; Mutation; National Institute of Diabetes and Digestive and Kidney Diseases; Nature; Network-based; Nucleic Acids; Paper; Pathologic Mutagenesis; Pathway interactions; Pattern; Phenotype; Play; Preparation; Process; Property; Publishing; RNA; Regulatory Element; Reporting; Research; Resolution; Resources; Role; Science; Signal Transduction; Smoking; Specificity; Structure; Structure of parenchyma of lung; System; Systems Biology; Techniques; Technology; Temperature; Tissues; United States National Institutes of Health; Universities; Work; aptamer; base; biological systems; cancer genome; cell type; data integration; design; fly; gene function; graph theory; heterogenous data; insight; large datasets; large scale data; machine learning model; molecular recognition; novel; nuclease; response; sexual dimorphism; theories; tool

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. Recently we began to 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 preliminary 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. The analysis results with this approach are consistent with recent findings linking perturbations of these pathways to smoking but also provided additional novel insights. Our studies indicate that smoking changes expression of many genes and pathways, especially these relater to immune response. It also changes cell type composition in lung tissue increasing the number on mucin producing goblet cells and reducing the number of ciliated cells. Preliminary results of these studies are reported in BioRxiv. A journal submission is in preparation. To gain additional insights into 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 construction leverages a sparse partial correlation among other statistical techniques to uncover dominant influence relations between the activities of network nodes. When applied to cancer data, GenSigNet uncovered important connections between mutational signatures and cellular processes that were difficult to detect using previous approaches. Preliminary results of these studies are reported in BioRxiv. A journal submission is in preparation. 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 flagship paper of the consortium has been recently published in Science. The single-cell atlas of the entire adult includes 580,000 cells and more than 250 annotated cell types. Together with Brian Oliver's group at NIDDK, my group has lead the analysis of sexual dimorphism. This flagship paper will be followed with further in-depth analyses, including additional analyses of sexual dimorphism co-lead by our two groups. Finally, continuing our long-standing collaboration with Marit Nilsen-Hamilton (Iowa University and Aptalogic), we work on applying computational methods to aid aptamer design. Nucleic acid aptamers are emerging as the new generation molecular recognition elements for diagnostics based on their synthetic nature, stability under a wide range of temperatures and amenability to different sensing platforms. Aptamers can further be modified in sequence and chemically to increase their specificities and affinities for their target molecule and to enhance their stabilities in the presence of nucleases. We filed a joint Aptalogic- NIH patent application for NGAL aptamer. NGAL is an early predictor for acute kidney injury and NGAL aptamer might provide important tool for early detection.

Przytycka 的团队继续开发和应用计算方法，利用和整合大数据集，重点关注基因调控和疾病。 我继续研究癌症突变特征。癌症基因组中存在的大多数突变都是无害的过客突变。人们越来越认识到，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。最近，我们开始利用突变特征的概念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的暴露与健康组织中生物过程的关系，旨在了解这些诱变剂的暴露如何影响细胞和组织的功能。我们的初步结果表明，通过量化这些诱变剂的强度，可以利用突变特征来研究诱变环境因素对组织分子途径和细胞组成的影响。这种方法的分析结果与最近将这些途径的扰动与吸烟联系起来的发现一致，但也提供了额外的新颖见解。我们的研究表明，吸烟会改变许多基因和途径的表达，尤其是与免疫反应相关的基因和途径的表达。 它还改变肺组织中的细胞类型组成，增加产生粘蛋白的杯状细胞的数量并减少纤毛细胞的数量。 BioRxiv 报道了这些研究的初步结果。正在准备向期刊投稿。 为了进一步了解诱变过程和细胞水平变化之间的关系，我们开发了一种基于网络的方法 GenSigNet，它捕获基因表达和特征之间的关系。该结构利用其他统计技术之间的稀疏偏相关来揭示网络节点活动之间的主导影响关系。当应用于癌症数据时，GenSigNet 发现了突变特征和细胞过程之间的重要联系，而使用以前的方法很难检测到这些联系。 BioRxiv 报道了这些研究的初步结果。正在准备向期刊投稿。 我的小组还参加了国际飞细胞图谱联盟，该联盟为果蝇界提供了以单细胞分辨率研究遗传扰动和疾病的资源。该联盟的旗舰论文最近发表在《科学》杂志上。整个成人的单细胞图谱包括 580,000 个细胞和 250 多种带注释的细胞类型。 我的团队与 NIDDK 的 Brian Oliver 团队一起领导了性别二态性的分析。这篇旗舰论文之后将进行进一步深入的分析，包括由我们两个小组共同领导的对性二态性的额外分析。 最后，继续与 Marit Nilsen-Hamilton（爱荷华大学和 Aptalogic）的长期合作，我们致力于应用计算方法来辅助适体设计。 核酸适体因其合成性质、在广泛温度下的稳定性以及对不同传感平台的适用性而成为新一代诊断分子识别元件。适体可以进一步进行序列和化学修饰，以增加其对其靶分子的特异性和亲和力，并增强其在核酸酶存在下的稳定性。我们为 NGAL 适体提交了 Aptalogic 与 NIH 联合专利申请。 NGAL 是急性肾损伤的早期预测因子，NGAL 适配体可能为早期检测提供重要工具。