Transfer of regulatory knowledge from human to mouse for functional genomics analysis

Abstract

Transcriptome profiling followed by differential gene expression analysis often leads to unclear lists of genes which are hard to analyse and interpret. Functional genomic tools are powerful approaches for downstream analysis, as they summarize the large and noisy gene expression space in a smaller number of biological meaningful features. In particular, methods that estimate the activity of processes by mapping transcripts level to process members are popular. However, footprints of either a pathway or transcription factor (TF) on gene expression show superior performance over mapping-based gene sets. These footprints are largely developed for human and their usability in the broadly-used model organism Mus musculus is uncertain. Evolutionary conservation of the gene regulatory system suggests that footprints of human pathways and TFs can functionally characterize mice data. In this paper we analyze this hypothesis. We perform a comprehensive benchmark study exploiting two state-of-the-art footprint methods, DoRothEA and an extended version of PROGENy. These methods infer TF and pathway activity, respectively. Our results show that both can recover mouse perturbations, confirming our hypothesis that footprints are conserved between mice and humans. Subsequently, we illustrate the usability of PROGENy and DoRothEA by recovering pathway/TF-disease associations from newly generated disease sets. Additionally, we provide pathway and TF activity scores for a large collection of human and mouse perturbation and disease experiments (2,374). We believe that this resource, available for interactive exploration and download (https://saezlab.shinyapps.io/footprint_scores/), can have broad applications including the study of diseases and therapeutics. 2.

Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data

“…However, both PROGENy and GO-GSEA performed poorly for some pathways, e.g., WNT pathway. We reason that this observation might be due to the quality of the corresponding benchmark data [33]. Given this fact and that GO-GSEA cannot handle low gene coverage (in our hands), we concluded that this approach is not suitable for scRNA-seq analysis.…”

“…PROGENy is a tool that infers pathway activity for 14 signaling pathways (Androgen, Estrogen, EGFR, Hypoxia, JAK-STAT, MAPK, NFkB, PI3K, p53, TGFb, TNFa, Trail, VEGF, and WNT) from gene expression data [12,33]. By default pathway activity inference is based on gene sets comprising the top 100 most responsive genes upon corresponding pathway perturbation, which we refer to as footprint genes of a pathway.…”

Bifidobacterium breveUCC2003 induces a distinct global transcriptomic programme in neonatal murine intestinal epithelial cells

“…5d). Using the TF-target gene database, DoRothEA, we identified expressed TFs known to regulate these genes [26, 27]. Five genes had no known and expressed regulator, thus were excluded.…”

Influence of Liver Fibrosis on Lobular Zonation

“…Pathway analysis with PROGENy. Pathway activity scores were calculated with the functional genomics tools PROGENy [26,27]. While classical pathway analysis methods (e.g., Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis) rely on gene sets containing genes of pathway members PROGENy exploits so called "footprint gene sets" containing not the pathway members but the most responsive genes upon corresponding pathway perturbation.…”

“…Transcription factor (TF) analysis with DoRothEA. DoRothEA is a high-quality data resource of TF-target interactions (regulons) [26,28]. Coupling DoRothEA regulons with a statistical method allows to infer TF activity from the expression of its transcriptional targets.…”