Advances in instrumentation and sample preparation techniques enable evermore in-depth molecular profiling to catalyze exciting research into complex biological processes. Current platforms survey biomolecular classes with varying depth. While sequencing is near comprehensive, and even enabled at single cell resolution, challenges remain in global metabolite surveys primarily due to the increased chemical diversity relative to other “omics” data types. At the same time, metabolism and the interaction of diverse biomolecules are increasingly recognized as vitally important components of many disease processes. Presented here is work describing the development and use of molecular interaction subnetworks for the functional interpretation of multi-omic data. Metabolic pathway-centric subnetworks for functional inference with protein or gene derived global profiling data were created from the integration of disparate network models: Protein- protein interaction (PPI) networks and metabolic models. The subnetworks were shown to increase mapping between metabolic pathways and the proteome, and the subnetwork- derived analysis shows dramatic improvement over primary enzymes alone with direct
metabolomic experimental measurements for validation of pathway findings. We illustrate the functional utility of integrating PPI data with metabolic models by finding network modules previously but independently implicated in disease. Specifically, the analysis reveals abundance increases in known oncogenes in response to changes in breast cancer metabolism. Additionally, we reveal cellular mechanisms related to metabolic stress observed in patient sera following viral SARS-CoV-2 infection, and metabolic changes in a model of heart disease, where the characteristic muscle fibers make in-depth proteomic profiling difficult. Functional network models were additionally used to compare the response of varying cell lines in response to viral infection, showing significant context- specific differences. All of these findings demonstrate the importance of functional models to help interpret multi-omic data. The implications of revealing the connections between metabolism and protein subnetwork rewiring may be profound; for example, suggesting metabolic pathway activity may be as important a biomarker as mutation status in cancer. This research points to a means of practically inferring metabolic state from proteomic data. We further describe the release of our open-source software to accelerate integrative multi-omic analysis in the broader research community. / 2023-06-16T00:00:00Z
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/42691 |
| Date | 16 June 2021 |
| Creators | Blum, Benjamin Coburn |
| Contributors | Emili, Andrew |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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