A computational framework to explore cellular response mechanisms from multi-omics datasets

Abstract

1 † These authors contributed equally to this work.Recent technological advances have made it feasible to collect multi-condition transcriptome and proteome time-courses of cellular response to perturbation. The increasing size and complexity of these datasets impedes mechanism of action discovery due to challenges in data management, analysis, visualization, and interpretation. Here, we introduce MAG-INE, a software framework to explore complex time-course multi-omics datasets and build mechanistic hypotheses of dynamic cellular response. MAGINE combines data management, enrichment, and network analysis and visualization within an interactive, Jupyter notebookbased environment to enable human-in-the-loop inquiry of complex datasets. We demonstrate how measurements from HL-60 cellular response to bendamustine treatment can be used to build a mechanistic, multi-resolution description of cellular commitment to fate. We present a systems-level description of signal execution from cellular DNA-damage response, to cell cycle arrest, and eventual commitment to apoptosis, mediated by over 2 000 biochemical species. We further show that MAGINE can reveal unexpected, non-canonical effects of bendamustine treatment, including disruption of cellular pathways relevant to HIV infection response. MAGINE is available from https://github.com/lolab-vu/magine. IntroductionCellular response to perturbations can elicit molecular responses across multiple processes such as gene expression modulation, changes in protein and metabolic activity, and in extreme cases, 3 changes in DNA structure (e.g., mutations). Modern, accessible technologies-most notably mass spectrometry (MS) and RNA-sequencing (RNAseq)-have enabled the measurement of biochemical interactions at molecular resolution for whole cellular genomes, proteomes, and metabolomes 1-3 . Recent work by our labs and others have already shown the potential of these kinds of datasets to gain a systems-level understanding of cellular response mechanisms to perturbations, with measurements that can easily number in the thousands to millions of data points 4-6 . Although these measurements in principle contain the molecular details necessary to formulate mechanistic hypotheses about cellular response to perturbations, the analysis of these datasets currently entails multiple tools, most notably enrichment-and network-based methods.Enrichment analysis can provide insights about relevant cellular processes by comparing multiple experimental conditions following perturbations such as drug treatments 7, 8 . This approach can be used to identify biological processes with altered activity by identifying groups of genes or proteins that are up-or down-regulated following treatment. Unfortunately, for the purposes of mechanistic exploration, these approaches fail to provide insights about molecular interactions that could drive a specific cellular process. For the purposes of large multi-omics and multi-experiment exploration, popular web-based enrichment analysis tools such as EnrichR 9 and ...

Spatially Targeted Proteomics of the Host–Pathogen Interface during Staphylococcal Abscess Formation

“…These proteins were determined using external pathway mapping tools and entered into Reactome, 105 and the resulting list of proteins associated with metabolism were input into SIMONE 106 as seed proteins. This tool constructs networks using the MAGINE framework, 107 which derives protein connection information from multiple databases (Figure S2). By uncovering how these proteins are connected, pathways can be predicted from spatiotemporal proteomics data.…”