A bioinformatics approach reveals novel interactions of the OVOL transcription factors in the regulation of epithelial – mesenchymal cell reprogramming and cancer progression

Abstract Background Mesenchymal to Epithelial Transition (MET) plasticity is critical to cancer progression, and we recently showed that the OVOL transcription factors (TFs) are critical regulators of MET. Results of that work also posed the hypothesis that the OVOLs impact MET in a range of cancers. We now test this hypothesis by developing a model, OVOL Induced MET (OI-MET), and sub-model (OI-MET-TF), to characterize differential gene expression in MET common to prostate cancer (PC) and breast cancer (BC). Results In the OI-MET model, we identified 739 genes differentially expressed in both the PC and BC models. For this gene set, we found significant enrichment of annotation for BC, PC, cancer, and MET, as well as regulation of gene expression by AP1, STAT1, STAT3, and NFKB1. Focusing on the target genes for these four TFs plus the OVOLs, we produced the OI-MET-TF sub-model, which shows even greater enrichment for these annotations, plus significant evidence of cooperation among these five TFs. Based on known gene/drug interactions, we prioritized targets in the OI-MET-TF network for follow-on analysis, emphasizing the clinical relevance of this work. Reflecting these results back to the OI-MET model, we found that binding motifs for the TF pair AP1/MYC are more frequent than expected and that the AP1/MYC pair is significantly enriched in binding in cancer models, relative to non-cancer models, in these promoters. This effect is seen in both MET models (solid tumors) and in non-MET models (leukemia). These results are consistent with our hypothesis that the OVOLs impact cancer susceptibility by regulating MET, and extend the hypothesis to include mechanisms not specific to MET. Conclusions We find significant evidence of the OVOL, AP1, STAT1, STAT3, and NFKB1 TFs having important roles in MET, and more broadly in cancer. We prioritize known gene/drug targets for follow-up in the clinic, and we show that the AP1/MYC TF pair is a strong candidate for intervention.http://deepblue.lib.umich.edu/bitstream/2027.42/109509/1/12918_2013_Article_1293.pd

Characterising the Role of OVOL1 in Cell Growth Regulation

The placenta supports the exchange of nutrients and gases between mother and fetus. Trophoblasts are the parenchymal cells of the placenta and perform the vast majority of its functions. There are different types of trophoblasts derived from stem cells called cytotrophoblasts (CTs). The balance between CT proliferation and differentiation is important for placental development. OVO-like 1 (OVOL1) is a transcription factor expressed in many epithelial lineages undergoing differentiation, including human differentiating CTs. The molecular mechanisms through which OVOL1 represses proliferation and/or promotes differentiation are unknown. We hypothesize that OVOL1 interacts with specific HDACs to repress CT proliferation. Ectopically expressing OVOL1 in wild-type yeast caused a significant growth defect, this defect was rescued by deleting class II HDACs. Ectopically expressing OVOL1 in human CT cell-line (BeWo) caused a significant increase in expression of ERVFRD1, a gene associated with cytotrophoblast differentiation, indicating that expression of OVOL1 is sufficient to trigger upregulation of at least a subset of genes that regulate CT proliferation and differentiation. Together, our findings demonstrate that OVOL1 can repress cell proliferation in yeast, a feature requiring specific HDACs, and is sufficient to at least prime CT differentiation. The combination of yeast and mammalian models provides a new experimental platform to better characterize OVOL1 function in repressing CT differentiation, providing new insights into placental development and potential therapeutics for placenta-associated diseases

Caractérisation du facteur de transcription shavenbaby par approches génomiques chez la drosophile

Les facteurs de transcription à doigt de zinc de la famille OvoL, au nombre de trois chez les mammifères (OvoL1, 2 et 3), sont des acteurs majeurs de la régulation épithéliale durant le développement des vertébrés. Chez l'adulte, ils participent à la fois à la différenciation terminale des cellules épithéliales et au maintien de ce caractère pour l'homéostasie tissulaire. Pour ce faire, les facteurs OvoL inhibent l'action des facteurs pro-EMT (Transition Epithélio-Mésenchymateuse) ZEB et TWIST qui eux poussent vers l'état mésenchymateux. Ainsi, lors de cancer humain, OvoL1 empêche la transformation des cellules tumorales épithéliales en cellules invasives mésenchymateuses métastatiques. De plus, des maladies héréditaires, dont des pathologies sévères des yeux et de la peau, ont été associées à la dérégulation des gènes OvoL. A ce jour et malgré des intérêts biomédicaux potentiels, les mécanismes moléculaires qui soutiennent l'action des facteurs OvoL et notamment leurs gènes cibles spécifiques restent mal compris. Il est possible que la redondance fonctionnelle des facteurs OvoL chez les vertébrés complique l'accès à leur fonction. Chez la Drosophile (Drosophila melanogaster) les facteurs OvoL, fortement conservés au sein du règne animal, sont seulement représentés par le gène ovo/shavenbaby (ovo/svb), membre fondateur de cette famille. Les deux formes germinales, OvoA et OvoB soutiennent l'ovogenèse alors que la forme somatique, Svb, contrôle le remodelage tridimensionnel des cellules épithéliales. Svb est également nécessaire pour le maintien, la prolifération, mais également la différenciation des cellules souches digestives adultes. Svb est synthétisée sous une forme longue, appelée SvbREP qui, sous l'action conjuguée des petits peptides Polished-rice (Pri) et de l'Ubiquitine-E3-ligase Ubr3, est clivée par le protéasome pour obtenir une forme plus courte, SvbACT, transcriptionellement active. Pour comprendre le mode d'action individuel de chacune des deux formes de Svb, nous avons développé un modèle cellulaire (S2) permettant l'expression de l'une ou l'autre des deux formes. Nous avons effectué des expériences de génomique pour déterminer de façon globale le comportement de SvbAct et SvbRep. Pour cela, nous avons analysé leur profil de fixation à l'ADN (ChIP-seq) d'une part, et identifié les gènes cibles (RNA-seq) pour chaque forme d'autre part. L'intégration de ces données a permis de mettre en évidence que SvbREP et ACT se fixent sur les mêmes régions génomiques cis-régulatrices (enhancers) pour contrôler de manière antagoniste l'expression d'environ 250 gènes cibles directs. Nous avons également montré que Svb régule des groupes de gènes différents en fonction du type cellulaire au sein duquel il évolue et mis en évidence que l'environnement chromatinien contraint son activité. Mes travaux ont non seulement permis de mieux appréhender le mode de fixation de facteurs OvoL sur le génome, mais apportent une nouvelle perspective sur la nature de leurs gènes cibles impliqués dans le contrôle du cycle cellulaire et/ou l'adhésion cellulaire.The OvoL family of zinc finger transcription factors, three in number in mammals (OvoL1, 2 and 3) are major players in epithelial regulation during vertebrate development. In the adult, they participate in both the terminal differentiation of epithelial cells and the maintenance of this character for tissue homeostasis. To do this, OvoL factors inhibit the action of the pro-EMT (Epithelial-Mesenchymal Transition) factors ZEB and TWIST, which push towards the mesenchymal state. Thus, in human cancer, OvoL1 prevents the transformation of epithelial tumour cells into invasive metastatic mesenchymal cells. In addition, hereditary diseases, including severe eye and skin diseases, have been associated with the deregulation of OvoL genes. Currently, despite potential biomedical interests, the molecular mechanisms that underpin the action of OvoL factors, including their specific target genes, remain poorly understood. It is possible that the functional redundancy of OvoL factors in vertebrates complicates access to their function. In Drosophila (Drosophila melanogaster), OvoL factors are highly conserved in the animal kingdom and are represented only by the ovo/shavenbaby (ovo/svb) gene, a founding member of this family. The two germline forms, OvoA and OvoB, support oogenesis while the somatic form, Svb, controls three-dimensional epithelial cell remodelling. Svb is also required for the maintenance, proliferation and differentiation of adult digestive stem cells. Svb is synthesised in a long form, called SvbREP, which, under the combined action of the small Polished-rice peptides (Pri) and the Ubiquitin-E3 ligase Ubr3, is cleaved by the proteasome to obtain a shorter, transcriptionally active form, SvbACT. To understand the individual mode of action of each of the two forms of Svb, we developed a cell model (S2) that allows the expression of either form. We performed genomic experiments to determine the overall behaviour of SvbAct and SvbRep. To do this, we analysed their DNA binding profile (ChIP-seq) on the one hand, and identified the target genes (RNA-seq) for each form on the other. Integration of these data revealed that SvbREP and ACT bind to the same cis-regulatory genomic regions (enhancers) to antagonistically control the expression of approximately 250 direct target genes. We have also shown that Svb regulates different groups of genes depending on the cell type in which it evolves and that the chromatin environment constrains its activity. My work has not only provided a better understanding of how OvoL factors bind to the genome, but also provides a new perspective on the nature of their target genes involved in cell cycle control and/or cell adhesion

Loss of signal transducer and activator of transcription 1 is associated with prostate cancer recurrence

STAT1 loss has previously been implicated in cell line studies to modify prostate cancer cell growth and survival, however the clinical significance of this has not previously been established. This study investigated if STAT1 loss was associated with patient outcome measures and the phenotypic consequence of STAT1 silencing. STAT1 expression was assessed in two patient cohorts with localised (n = 78) and advanced prostate cancer at initial diagnosis (n = 39) by immunohistochemistry (IHC). Impact of STAT1 silencing on prostate cancer cells lines was assessed using Cell Death detection ELISA, TLDA gene signature apoptosis arrays, WST-1 assay, xCELLigence system, clonogenic assay, and wound healing assay. In the localised patient cohort, low expression of STAT1 was associated with shorter time to disease recurrence (3.8 vs 7.3 years, P = 0.02) and disease specific survival (6.6 vs 9.3 years, P = 0.05). In the advanced patient cohort, low expression was associated with shorter time to disease recurrence (2.0 vs 3.9 years, P = 0.001). When STAT1 was silenced in PC3 cells (AR negative) and LNCaP cells (AR positive) silencing did not influence levels of apoptosis in either cell line and had little effect on cell viability in the LNCaP cells. In contrast, STAT1 silencing in the PC3 cells resulted in a pronounced increase in cell viability (WST-1 assay: mock silenced vs STAT1 silenced, P < 0.001), clonagenicity (clonogenic assay: mock silenced vs STAT1 silenced, P < 0.001), and migration (wound healing: mock silenced vs STAT1 silenced, P < 0.001). In conclusion, loss of STAT1 may promote prostate cancer recurrence in AR negative patients via increasing cell viability

Theoretical and computational tools to model multistable gene regulatory networks

The last decade has witnessed a surge of theoretical and computational models to describe the dynamics of complex gene regulatory networks, and how these interactions can give rise to multistable and heterogeneous cell populations. As the use of theoretical modeling to describe genetic and biochemical circuits becomes more widespread, theoreticians with mathematics and physics backgrounds routinely apply concepts from statistical physics, non-linear dynamics, and network theory to biological systems. This review aims at providing a clear overview of the most important methodologies applied in the field while highlighting current and future challenges, and includes hands-on tutorials to solve and simulate some of the archetypical biological system models used in the field. Furthermore, we provide concrete examples from the existing literature for theoreticians that wish to explore this fast-developing field. Whenever possible, we highlight the similarities and differences between biochemical and regulatory networks and classical systems typically studied in non-equilibrium statistical and quantum mechanics.Comment: 73 pages, 12 figure