Algorithmic Annotation of Functional Roles for Components of 3,044 Human Molecular Pathways

Abstract

Current methods of high-throughput molecular and genomic analyses enabled to reconstruct thousands of human molecular pathways. Knowledge of molecular pathways structure and architecture taken along with the gene expression data can help interrogating the pathway activation levels (PALs) using different bioinformatic algorithms. In turn, the pathway activation profiles can characterize molecular processes, which are differentially regulated and give numeric characteristics of the extent of their activation or inhibition. However, different pathway nodes may have different functions toward overall pathway regulation, and calculation of PAL requires knowledge of molecular function of every node in the pathway in terms of its activator or inhibitory role. Thus, high-throughput annotation of functional roles of pathway nodes is required for the comprehensive analysis of the pathway activation profiles. We proposed an algorithm that identifies functional roles of the pathway components and applied it to annotate 3,044 human molecular pathways extracted from the Biocarta, Reactome, KEGG, Qiagen Pathway Central, NCI, and HumanCYC databases and including 9,022 gene products. The resulting knowledgebase can be applied for the direct calculation of the PALs and establishing large scale profiles of the signaling, metabolic, and DNA repair pathway regulation using high throughput gene expression data. We also provide a bioinformatic tool for PAL data calculations using the current pathway knowledgebase.

Keywords: DNA repair pathways; functional algorithmic annotation; human molecular pathway regulation; metabolic pathways; proteomics; signaling pathways; transcriptomics.

Figure 1

(A) Growth Hormone Signaling Pathway with highlighted Glucose Uptake micropathway. (B) Glucose Uptake micropathway obtained from Growth Hormone Signaling Pathway. (C) N+M values for all vertices of Growth Hormone Signaling Pathway graph. The vertices with maximal N+M values are highlighted in blue, these vertices are equal major node candidates and get Activator/Repressor Role (ARR) = 1. Different edge colors indicate edge attribute: green is for “activation,” red is for “inhibition.” Structure of the Growth Hormone Signaling Pathway is derived from Qiagen Pathway Central. Yellow vertices on panel 1A indicate micropathway Glucose Uptake within Growth Hormone Signaling Pathway.

Figure 2

Node activation of Growth Hormone Signaling Pathway for gastric cancer sample GC.11_S19_R1_001 from Sorokin et al. (2020b). Node activation is a sum of logarithmic case-to-norm ratio (CNR) for all genes in the node. CNR is ratio of expression levels in tumor sample and averaged normal sample. The RNA sequencing tumor profile (gastric cancer) was obtained from Sorokin et al. (2020b). The RNA sequencing profiles of normal gastric tissue were obtained from Oncobox Atlas of Normal Tissue Expression (ANTE) data (Suntsova et al., 2019). Different edge colors indicate edge attribute: green is for “activation,” red is for “inhibition.” Structure of the Growth Hormone Signaling Pathway is derived from Qiagen Pathway Central.