A flexible ontology for inference of emergent whole cell function from relationships between subcellular processes

Abstract

Abstract:Whole cell responses arise from coordinated interactions between diverse human gene products functioning within various pathways underlying sub-cellular processes (SCP). Lower level SCPs interact to form higher level SCPs, often in a context specific manner to give rise to whole cell function. We sought to determine if capturing such relationships enables us to describe the emergence of whole cell functions from interacting SCPs. We developed the "Molecular Biology of the Cell" ontology based on standard cell biology and biochemistry textbooks and review articles. Currently, our ontology contains 5,385 genes, 753 SCPs and 19,180 expertly curated gene-SCP associations. Our algorithm to populate the SCPs with genes enables extension of the ontology on demand and the adaption of the ontology to the continuously growing cell biological knowledge. Since whole cell responses most often arise from the coordinated activity of multiple SCPs, we developed a dynamic enrichment algorithm that flexibly predicts SCP-SCP relationships beyond the current taxonomy. This algorithm enables us to identify interactions between SCPs as a basis for higher order function in a context dependent manner, allowing us to provide a detailed description of how SCPs together can give rise to whole cell functions. We conclude that this ontology can, from omics data sets, enable the development of detailed multidimensional SCP networks for predictive modeling of emergent whole cell functions.

Genomic signatures defining responsiveness to allopurinol and combination therapy for lung cancer identified by systems therapeutics analyses

“…In resistant cells, fatty acid catabolic processes and other functions related to metabolism of lipids are likely to be dominant. Gene signatures (combined and separately) also were subjected to pathway enrichment analysis using Molecular Biology of the Cell Ontology (Hansen et al ., ), (Fig. A).…”

“…The sets of gene signatures (allopurinol sensitivity and allopurinol resistance) were used for gene set enrichment analysis by Enricher [Gene Ontology (GO) and WikiPathways) and MBC ontology (Hansen et al ., ; Kuleshov et al ., ).…”

A reference tissue atlas for the human kidney

“…To overcome the inherent challenges of multiomics integration and assay dependent divergence, we employed dynamic enrichment analysis 6 and network mapping 7 . We evaluated the convergence of subcellular processes (SCPs) and pathways that are overrepresented in different cell types or subsegments within the kidney (in comparison to the other cell types or subsegments), using single cell RNASeq data from PREMIERE TIS (Michigan, Princeton, Broad) 8 , single nucleus RNASeq data from UCSD/WU TIS 9 , Laser microdissected (LMD) bulk RNASeq (Supplementary Table 2) and LMD proteomics ( Supplementary Table 3) from the OSU/IU TIS, Near Single Cell (NSC) proteomics from the UCSF TIS (Supplementary Table 4) and spatial metabolomics from the UTHSA-PNNL-EMBL TIS ( Supplementary Table 5A/B/C from 3 different participants).…”

“…Top DEGs and DEPs for each podocyte cluster/glomerulus, proximal tubule cell cluster/tubulointerstitium and principal cell cluster/collecting duct subsegment were separately subjected to standard enrichment analysis using Gene Ontology Biological Processes (GO BPs) or the Molecular Biology of the Cell Ontology (MBCO) level-3 subcellular processes (SCPs) 6 and Fisher's Exact Test. An open access ontological framework for this analysis can be found at https://github.com/SBCNY/Molecular-Biology-of-the-Cell.…”

“…Additionally, we subjected the top DEGs and DEPs to dynamic enrichment analysis using MBCO 6 . The annotated interconnected hierarchy of MBCO is enriched using a unique algorithm that infers relationships between functionally related SCPs.…”