Algorithmic approaches to systems biology, data integration, and evolution

National Library Of Medicine

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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. 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.