Benchmark and integration of resources for the estimation of human transcription factor activities

Garcia‐Alonso, Luz; Holland, Christian H.; Ibrahim, Mahmoud M.; Türei, Dénes; Sáez-Rodríguez, Julio

doi:10.1101/gr.240663.118

Cited by 742 publications

(760 citation statements)

References 83 publications

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“…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.…”

Section: Resultsmentioning

confidence: 99%

“…All mouse transcription factor - target gene interactions with quality scores A-D were obtained from DoRothEA v2 via the OmniPath Cytoscape app [26, 74, 75]. A subnetwork was generated consisting of differentially expressed stem cell signature genes and all their upstream TFs which were expressed in the transcriptomics dataset (normalised counts > 1 in ≥ 1 sample).…”

Section: Methodsmentioning

confidence: 99%

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

Kiu¹,

Treveil

Harnisch³

et al. 2020

Preprint

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SummaryBifidobacterium is an important gut microbiota member during early life that is associated with improved gut health. However, the underlying health-driving mechanisms are not well understood, particularly how Bifidobacterium may modulate the intestinal barrier via programming of intestinal epithelial cells (IECs). In this study, we sought to investigate the global impact of model strain Bifidobacterium breve UCC2003 on the neonatal IEC transcriptome, including gene regulation and pathway modulation. Small IECs from two-week-old neonatal mice administered B. breve UCC2003 for three consecutive days or PBS (control group) were subjected to global RNASeq, with various bioinformatic approaches used to determine differentially expressed genes, pathways and affected cell types between control and experimental groups. Whilst colonisation with B. breve had minimal impacts on the neonatal microbiota, we observed extensive regulation of the IEC transcriptome; ~4,000 genes significantly up-regulated, including key genes associated with epithelial barrier function. Enrichment of cell differentiation and cell proliferation pathways were observed, along with an overrepresentation of stem cell marker genes, indicating an increase in the regenerative potential of the epithelial layer. Expression of distinct immune-associated pathway members (e.g. Toll-like Receptors) were also affected after neonatal B. breve colonisation. In conclusion, B. breve UCC2003 plays a central role in driving universal transcriptomic changes in neonatal IECs that enhances cell replication, differentiation and growth, predominantly in the stem cell compartment. This study enhances our overall understanding of the benefits of B. breve in driving intestinal epithelium homeostatic development during early life, with potential avenues to develop novel live biotherapeutic products.

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“…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.…”

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

Kiu¹,

Treveil

Harnisch³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…S4B) but caused a performance drop. By including TFs labelled with confidence level C, we introduce regulons in our benchmark data that have not been thoroughly studied (Garcia-Alonso et al , 2018) . Hence, the drop of the performance is expected.…”

Section: Discussionmentioning

confidence: 99%

“…To evaluate if DoRothEA's regulon also holds true in mice, we next compared the performance of mouse-DoRothEA to the performance of dedicated mouse regulons from the TRRUST database (Han et al , 2018) . Human-DoRothEA was reconstructed by integrating different resources spanning from literature-curated databases to predictions of TF-target interactions (Garcia-Alonso et al , 2018) . Thereby, each TF is accompanied with a summary confidence level from A (high confidence) to E (low confidence) based on the amount of supporting TF's regulatory evidences.…”

Section: Benchmarking Dorotheamentioning

confidence: 99%

“…In this work we extended PROGENy with novel footprints of the signaling pathways Androgen, Estrogen and WNT. The second approach is DoRothEA, a resource matching TFs with their targets (Garcia-Alonso et al , 2018) , which allows us to estimate TF activity from gene expression data in humans by enriched regulon analysis (Alvarez et al , 2016) . We consider the targets of a TF also as footprints of a TF on gene expression.…”

Section: Introductionmentioning

confidence: 99%

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

Holland

Szalai

Sáez-Rodríguez

2019

Preprint

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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.

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Why do pathway methods work better than they should?

Szalai

Sáez-Rodríguez

2020

FEBS Letters

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Pathway analysis methods are frequently applied to cancer gene expression data to identify dysregulated pathways. These methods often infer pathway activity based on the expression of genes belonging to a given pathway, even though the proteins ultimately determine the activity of a given pathway. Furthermore, the association between gene expression levels and protein activities is not well‐characterized. Here, we posit that pathway‐based methods are effective not because of the correlation between expression and activity of members of a given pathway, but because pathway gene sets overlap with the genes regulated by transcription factors (TFs). Thus, pathway‐based methods do not inform about the activity of the pathway of interest but rather reflect changes in TF activities.

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Benchmark and integration of resources for the estimation of human transcription factor activities

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Cited by 742 publications

References 83 publications

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

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

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

Why do pathway methods work better than they should?

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