Algorithmic approaches to systems biology, data integration, and evolution

National Library Of Medicine

Investigators

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Abstract

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.