Interrogation of a Context-Specific Transcription Factor Network Identifies Novel Regulators of Pluripotency

Abstract

The predominant view of pluripotency regulation proposes a stable ground state with coordinated expression of key transcription factors (TFs) that prohibit differentiation. Another perspective suggests a more complexly regulated state involving competition between multiple lineage-specifying TFs that define pluripotency. These contrasting views were developed from extensive analyses of TFs in pluripotent cells in vitro. An experimentally-validated, genome-wide repertoire of the regulatory interactions that control pluripotency within the in vivo cellular contexts is yet to be developed. To address this limitation, we assembled a TF interactome of adult human male germ cell tumors (GCTs) using the Algorithm for the Accurate Reconstruction of Cellular Pathways (ARACNe) to analyze gene expression profiles of 141 tumors comprising pluripotent and differentiated subsets. The network (GCTNet) comprised 1305 TFs, and its Ingenuity Pathway analysis identified pluripotency and embryonal development as the top functional pathways. We experimentally validated GCTNet by functional (silencing) and biochemical (ChIP-seq) analysis of the core pluripotency regulatory TFs POU5F1, NANOG, and SOX2 in relation to their targets predicted by ARACNe. To define the extent of the in vivo pluripotency network in this system, we ranked all TFs in the GCTNet according to sharing of ARACNe-predicted targets with those of POU5F1 and NANOG using an Odds-Ratio analysis method. To validate this network, we silenced the top 10 TFs in the network in H9 ES cells. Silencing of each led to downregulation of pluripotency and induction of lineage; 7 of the 10 TFs were identified as pluripotency regulators for the first time.

The recurrent architecture of tumour initiation, progression and drug sensitivity

“…In addition, their use in non-cancer phenotypes has helped to elucidate an equivalent disease checkpoint architecture in neurological phenotypes — including amyotrophic lateral sclerosis (ALS) 53 , Alzheimer disease 7,54 , Parkinson disease 55 and alcohol addiction 56 — and in developmental phenotypes, from regulation of germinal centre formation 39 to stem cell pluripotency 57 . As briefly summarized below, these examples further outline the role of tumour checkpoint MRs in regulating disease dystasis and their unique nature compared with their physiological counterpart involved in homeostatic control.…”

“…Postgenomic research has seen intense interest in the systematic dissection of the transcriptional and signalling logic of cancer cells (henceforth termed regulatory logic) 48,53,55–57 , including its transcriptional 58–60 , post-transcriptional 61–63 and post-translational 64–68 layers. These methods were first developed and validated in bacteria 69 and yeast 70,71 and then extended to mammalian cells 21,59 , thus complementing pre-existing literature-curated models 72 and large-scale experimental assays 73 .…”

“…These methods were first developed and validated in bacteria 69 and yeast 70,71 and then extended to mammalian cells 21,59 , thus complementing pre-existing literature-curated models 72 and large-scale experimental assays 73 . The Algorithm for the Accurate Reconstruction of Cellular Networks (ARACNe) 59 was the first of its kind to be experimentally validated in mammalian cells 10,39,57,74 and has been among the most frequently used to infer regulatory targets for MR analysis. However, various alternative approaches have also been popular, from Bayesian networks 75 to co-expression network integration with chromatin immunoprecipitation–sequencing (ChIP–seq) assays 47 .…”

A Systems Approach to Drug Discovery in Alzheimer's Disease

“…Following reverse engineering analysis with these algorithms, each TF signaling protein and microRNA represented in an interactome is causally associated with a regulon containing dozens to hundreds of highly accurate and cell-context-specific targets and substrates. For instance, predictions by the ARACNe, MINDy, PrePPI algorithms have been typically validated at a very high rate (~70%) by chromatin immunoprecipitation, gene expression profiling following silencing, coimmunoprecipitation and other relevant assays [12,19,24,25,34,35]. Other systems approaches have also been applied successfully in AD [36][37][38][39], and our focus here is to maintain clarity rather than review the entire literature.…”

“…Indeed, virtually none of the regulators that were experimentally validated were significantly differentially expressed at the RNA level and yet they were confirmed as individual or synergistic phenotypic drivers following identification by regulatory network analysis, thus elucidating novel mechanisms of disease initiation/ progression [4,12,13,[41][42][43][44][45][46], chemosensitivity [15,28], and normal physiologic regulation [14]. Lately, we have successfully extended these methodologies to the study of neurological, neurodevelopmental, and stem cell phenotypes [35,42,47], and, more recently, to neurodegenerative phenotypes, resulting in a series of manuscripts, currently in review, where we report on the elucidation and experimental validation of novel genes mediating neurotoxicity in ALS [48], as well as biomarkers of AD progression [9].…”