1) The TF2Network tool integrates various experimental and computational data sources to build gene regulatory networks in Arabidopsis thaliana, including over 1700 transcription factor binding site profiles. 2) It predicts potential transcription factor regulators for sets of co-expressed or functionally related genes, and evaluates the predictions against benchmarks. Integrating additional data sources like protein-protein interactions helps reveal cooperative transcription factor regulation. 3) Applying this approach to functional gene sets relates thousands of previously uncharacterized Arabidopsis genes to biological processes, and identifies brassica-specific genes and novel regulators involved in flower development.

1. TF2Network: unravelling gene regulatory
networks and transcription factor functions in
Arabidopsis thaliana
KlaasVandepoele
Gordon Research Conference
Dynamic Plant Systems
June 10 - 15, 2018
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plaza_genomics

2. Experimental characterization of regulatory DNA
ENCODE; Sullivan et al., 2015
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Gene Regulatory Networks analysis
• IdentifyTFs active in specific cellular conditions
• Identify new gene functions, both forTFs and target genes
• Study network structure and identify cooperativeTF regulation

3. Mapping regulatoryTF – target gene interactions
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Mejia-Guerra et al., 2012
EMSA
eY1H
SELEX
PBM
ChIP-Seq
DAP-Seq
DNaseI
ATAC-Seq

4. Computational analysis ofTF binding sites (motifs)
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• Known transcription factor (TF) binding sites
1. Limited information about binding site for mostTFs
2. Variety in qualityTF binding site models
3. Motif redundancy & multi-geneTF families
4. How to identify functionalTF binding sites?
 Simple motif mapping yields many False positives
 Not everyTATA is a ‘TATA’
Database #motifs Species
PLACE 469 Vascular plants
AGRIS 99 Arabidopsis thaliana
AtProbe 172 Arabidopsis thaliana
CisBP >1000 Cross-species
DAP-Seq >500 Arabidopsis thaliana
JASPAR2018 >500 Cross-species

5. TF2Network: inferringTF-target interactions using
publicly available binding site information
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 Data sources
 Experimental TF binding site
information (1793 PWMs - 916TFs)
 Enrichment statistics using quality-
based filtering ofTF binding site
matches
 Integration experimental datasets
 TF2Network predicts potentialTF regulators for a set of co-expressed or functionally
related genes in A. thaliana
CisBP, Franco-Zorrilla et al. , Plant Cistrome
Database, JASPAR 2016, UNIPROBE, AGRIS
and AthaMAP

6. IntegrationTF binding site information
TheTF2Network tool is available at http://bioinformatics.psb.ugent.be/webtools/TF2Network/

7. Integration gene functions (Gene Ontology)
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8. Integration gene functions (Gene Ontology)
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9. Integration experimental protein-DNA
interactions
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Experimental data
• >170,883 protein-DNA interactions (679TFs)
• 76,236 protein-protein interactions
• 13M co-expression interactions (RNA-Seq)

10. Integration protein-protein and co-expression
interactions
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Experimental data
• >170,883 protein-DNA interactions (679TFs)
• 76,236 protein-protein interactions
• 13M co-expression interactions

11. Evaluation recovery correct regulators
‘ChIP genes’ benchmark 24TFs
500 input genes
‘DE genes after TF perturbation’
benchmark 23TFs; 500 input
genes
Three gene region types:
• Long: complete upstream + exon-masked gene body + 1 kb downstream
• Intermediate: 1kb upstream + exon-masked gene body + 500 bp downstream
• Short: 500 bp upstream core promoter
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12. Protein-protein interactions (PPIs) between predicted
regulators reveal cooperative TF regulation
Intra-family PPI
Inter-family PPI
• Starting from DE genes for perturbed TF
recovery of perturbed TF as top-ranked regulator
recovery of cooperative TFs showing physical interactions with perturbed TF
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13. Systematic regulatory annotation of Arabidopsis genes
• Functional regulons combine known GO gene functions with co-expression
information
• Global recovery of correct regulators for 471TFs covering different biological processes
defense response (15TFs), plant organ development (12TFs), hormone-mediated signaling
pathway (8TFs).
• Focusing on correct GO-TF regulators: 6,498/11,447 unknown Arabidopsis genes are part of
functional regulons
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14. Brassicaceae-specific genes involved in flower development
• 43 unknown genes part of ‘flower development’ regulons with ≥3
incoming flowering relatedTFs
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AT1G06420 Unknown protein; conserved in 6 Brassicaceae species
Predicted regulators: AT2G45660 (AGL20);AT4G38000 (DOF4.7);AT5G13790 (AGL15);
AT5G20240 (PI)
AT1G05540 Protein of unknown function (DUF295); conserved in 7 Brassicaceae species
Predicted regulators: AT5G10140 (AGL25);AT5G13790 (AGL15);AT5G20240 (PI)

15. Brassicaceae-specific genes involved in flower development
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AT1G06420
BAR eFP browser:
Klepikova RNA-Seq atlas

16. New regulators involved in flower development
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AT5G12440 – CCCH-type zinc fingerfamily protein
Predicted regulators: AT2G45660 (AGL20), AT4G38000 (DOF4.7),
AT5G20240 (PI),AT1G17920 (HDG20), AT3G21890 (BBX31)
BAR eFP browser:
Klepikova RNA-Seq atlas

17. New regulators involved in flower development
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AT5G20420 – SNF2-related protein
Predicted regulators: AT4G38000 (DOF4.7); AT5G13790 (AGL15)
BAR eFP browser:
Klepikova RNA-Seq atlas

18. Conclusions
• Different experimental –omics data types as well as integrativeTF binding site
methods contribute significantly to the improved delineation of gene regulatory
networks
• Combined with high-throughput PPI data,TF binding site-based regulatory
networks offer new insights about cooperativeTF regulation
• Functional regulons are a starting point for
the systematic functional and regulatory annotation of all genes in a genome
new biological discoveries
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Dries VaneechoutteShubhada Kulkarni
Kulkarni et al. Nucleic Acids Res. 2018 Apr 6;46(6):e31

19. Confirming known and predicting new TF
functions
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