Executable pathway analysis using ensemble discrete-state modeling for large-scale data

Abstract

Pathway analysis is widely used to gain mechanistic insights from high-throughput omics data. However, most existing methods do not consider signal integration represented by pathway topology, resulting in enrichment of convergent pathways when downstream genes are modulated. Incorporation of signal flow and integration in pathway analysis could rank the pathways based on modulation in key regulatory genes. This implementation can be facilitated for large-scale data by discrete state network modeling due to simplicity in parameterization. Here, we model cellular heterogeneity using discrete state dynamics and measure pathway activities in cross-sectional data. We introduce a new algorithm, Boolean Omics Network Invariant-Time Analysis (BONITA), for signal propagation, signal integration, and pathway analysis. Our signal propagation approach models heterogeneity in transcriptomic data as arising from intercellular heterogeneity rather than intracellular stochasticity, and propagates binary signals repeatedly across networks. Logic rules defining signal integration are inferred by genetic algorithm and are refined by local search. The rules determine the impact of each node in a pathway, which is used to score the probability of the pathway’s modulation by chance. We have comprehensively tested BONITA for application to transcriptomics data from translational studies. Comparison with state-of-the-art pathway analysis methods shows that BONITA has higher sensitivity at lower levels of source node modulation and similar sensitivity at higher levels of source node modulation. Application of BONITA pathway analysis to previously validated RNA-sequencing studies identifies additional relevant pathways in in-vitro human cell line experiments and in-vivo infant studies. Additionally, BONITA successfully detected modulation of disease specific pathways when comparing relevant RNA-sequencing data with healthy controls. Most interestingly, the two highest impact score nodes identified by BONITA included known drug targets. Thus, BONITA is a powerful approach to prioritize not only pathways but also specific mechanistic role of genes compared to existing methods. BONITA is available at: https://github.com/thakar-lab/BONITA.

Impact of vaccine type on HIV-1 vaccine elicited antibody durability and B cell gene signature

“…The FCRL genes were incorporated in BCR signaling pathways by mining primary literature 27 , 28 . Boolean Omics Network Invariant-Time Analysis (BONITA), which performs discrete-state modeling identified BCR pathway with FCRL genes to be significant at p 29 between MVA and protein boosted groups. PTPN6 and PTPN11 inhibit BCR signaling and were identified to have low impact on regulation of BCR pathway by BONITA irrespective of FCRL genes.…”

“…Other genes included VSTM1 which interacts with Fc receptors and CLEC10A which is expressed in highly active thymic B cells 30 . We further sought to understand molecular pathways underlying changes in gene expression between durable and transient responders using BONITA software 29 . The RIG-I-like receptor and Cytosolic DNA sensing pathways were significantly different and were highly expressed in transient vs. durable vaccine responders at peak in MVA-boosted HVTN 094-T2 ( p , Additional Data Table S3 ).…”

“…Finally, gene-set enrichment analysis of KEGG, Biocarta and reactome gene-sets from MSigDB was performed using hypergeometric test and qusage 29 , 87 – 92 with Benjamini-Hochberg correction. Pathway analysis of KEGG was performed using Boolean omics network invariant-time analysis (BONITA) 29 , 89 . BONITA was additionally used to assess the B Cell Receptor Signaling Pathway from which was taken from Wikipathways 26 but originally created in NetSlim 25 .…”

“…Separately, genes correlated in both MVA and HVTN105 were identified at a less stringent p < 0.05 in each group (MVA and HVTN105). Finally, gene-set enrichment analysis of KEGG, Biocarta and reactome gene-sets from MSigDB was performed using hypergeometric test and qusage 29,[87][88][89][90][91][92] with Benjamini-Hochberg correction. Pathway analysis of KEGG was performed using Boolean omics network invariant-time analysis (BONITA) 29,89 .…”

“…Scientific RepoRtS | (2020) 10:13031 | https://doi.org/10.1038/s41598-020-69007-w www.nature.com/scientificreports/ used to assess the B Cell Receptor Signaling Pathway from which was taken from Wikipathways 26 but originally created in NetSlim 25 . This pathway was manually converted into the format necessary for use with BONITA 29 . FCRL genes interact through immunoreceptor tyrosine-based activating motif (ITAM) and immunoreceptor tyrosine-based inhibitory motifs (ITIM) with LYN and PTPN molecules, respectively 27,28 .…”