title: LLM3D: a log-linear modeling-based method to predict functional gene regulatory interactions from genome-wide expression data.
abstract: All cellular processes are regulated by condition-specific and time-dependent interactions between transcription factors and their target genes. While in simple organisms, e.g. bacteria and yeast, a large amount of experimental data is available to support functional transcription regulatory interactions, in mammalian systems reconstruction of gene regulatory networks still heavily depends on the accurate prediction of transcription factor binding sites. Here, we present a new method, log-linear modeling of 3D contingency tables (LLM3D), to predict functional transcription factor binding sites. LLM3D combines gene expression data, gene ontology annotation and computationally predicted transcription factor binding sites in a single statistical analysis, and offers a methodological improvement over existing enrichment-based methods. We show that LLM3D successfully identifies novel transcriptional regulators of the yeast metabolic cycle, and correctly predicts key regulators of mouse embryonic stem cell self-renewal more accurately than existing enrichment-based methods. Moreover, in a clinically relevant in vivo injury model of mammalian neurons, LLM3D identified peroxisome proliferator-activated receptor γ (PPARγ) as a neuron-intrinsic transcriptional regulator of regenerative axon growth. In conclusion, LLM3D provides a significant improvement over existing methods in predicting functional transcription regulatory interactions in the absence of experimental transcription factor binding data.
authors.affiliation: Department of Mathematics, Faculty of Sciences, VU University, De Boelelaan 1081, 1081 HV Amsterdam, The Netherlands.
authors.forename: Geert
authors.initials: G
authors.lastname: Geeven
authors.forename: Harold D
authors.initials: HD
authors.lastname: Macgillavry
authors.forename: Ruben
authors.initials: R
authors.lastname: Eggers
authors.forename: Marion M
authors.initials: MM
authors.lastname: Sassen
authors.forename: Joost
authors.initials: J
authors.lastname: Verhaagen
authors.forename: August B
authors.initials: AB
authors.lastname: Smit
authors.forename: Mathisca C M
authors.initials: MC
authors.lastname: de Gunst
authors.forename: Ronald E
authors.initials: RE
authors.lastname: van Kesteren
mesh_terms: D000818:Animals; D001665:Binding Sites; D002460:Cell Line; D053595:Embryonic Stem Cells / Q000378:metabolism; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D016678:Genome; D016014:Linear Models; D051379:Mice; D009416:Nerve Regeneration / Q000235:genetics; D009474:Neurons / Q000378:metabolism; D047495:PPAR gamma / Q000378:metabolism; D051381:Rats; D017208:Rats, Wistar; D014157:Transcription Factors / Q000378:metabolism*; D015003:Yeasts / Q000235:genetics / Q000378:metabolism
chemical_list: D047495:PPAR gamma; D014157:Transcription Factors