Xavier Gidrol

New p63 targets in keratinocytes identified by a genome‐wide approach

p63 is a developmentally regulated transcription factor related to p53. It is involved in the development of ectodermal tissues, including limb, skin and in general, multilayered epithelia. The ΔNp63α isoform is thought to play a ‘master’ role in the asymmetric division of epithelial cells. It is also involved in the pathogenesis of several human diseases, phenotypically characterized by ectodermal dysplasia. Our understanding of transcriptional networks controlled by p63 is limited, owing to the low number of bona fide targets. To screen for new targets, we employed chromatin immunoprecipitation from keratinocytes (KCs) coupled to the microarray technology, using both CpG islands and promoter arrays. The former revealed 96 loci, the latter yielded 85 additional genes. We tested 40 of these targets in several functional assays, including: (i) in vivo binding by p63 in primary KCs; (ii) expression analysis in differentiating HaCaT cells and in cells overexpressing ΔNp63α; (iii) promoter transactivation and (iv) immunostaining in normal tissues, confirming their regulation by p63. We discovered several new specific targets whose functional categorization links p63 to cell growth and differentiation.

Evolutionary approaches for the reverse-engineering of gene regulatory networks: A study on a biologically realistic dataset

BackgroundInferring gene regulatory networks from data requires the development of algorithms devoted to structure extraction. When only static data are available, gene interactions may be modelled by a Bayesian Network (BN) that represents the presence of direct interactions from regulators to regulees by conditional probability distributions. We used enhanced evolutionary algorithms to stochastically evolve a set of candidate BN structures and found the model that best fits data without prior knowledge.ResultsWe proposed various evolutionary strategies suitable for the task and tested our choices using simulated data drawn from a given bio-realistic network of 35 nodes, the so-called insulin network, which has been used in the literature for benchmarking. We assessed the inferred models against this reference to obtain statistical performance results. We then compared performances of evolutionary algorithms using two kinds of recombination operators that operate at different scales in the graphs. We introduced a niching strategy that reinforces diversity through the population and avoided trapping of the algorithm in one local minimum in the early steps of learning. We show the limited effect of the mutation operator when niching is applied. Finally, we compared our best evolutionary approach with various well known learning algorithms (MCMC, K2, greedy search, TPDA, MMHC) devoted to BN structure learning.ConclusionWe studied the behaviour of an evolutionary approach enhanced by niching for the learning of gene regulatory networks with BN. We show that this approach outperforms classical structure learning methods in elucidating the original model. These results were obtained for the learning of a bio-realistic network and, more importantly, on various small datasets. This is a suitable approach for learning transcriptional regulatory networks from real datasets without prior knowledge.

Id2 Reverses Cell Cycle Arrest Induced by γ-Irradiation in Human HaCaT Keratinocytes

Id2 plays a key role in epithelial cells, regulating differentiation, the cell cycle, and proliferation. Because human skin constantly renews itself and is the first target of irradiation, it is of primary interest to evaluate whether such a gene may be regulated in keratinocytes exposed to ionizing radiation. We show here that Id2 is induced in response to γ-irradiation and have investigated the consequence of this regulation on cell fate. Using RNA interference, we observed that Id2 extinction significantly reduces cell growth in human keratinocytes through the control of the G1-S transition of the cell cycle. We have investigated whether the impact of Id2 on the cell cycle may have a physiological role on the cells ability to cope with radiative stress. Indeed, when Id2 is down-regulated through interfering RNA, cells are more sensitive to irradiation. Conversely, when Id2 is overexpressed, this somehow protects the cell. We propose that Id2 favors reentering the cell cycle after radiation-induced cell cycle arrest to permit the recovery of keratinocytes exposed to ionizing radiation.

Natural Single-Nucleosome Epi-Polymorphisms in Yeast

Epigenomes commonly refer to the sequence of presence/absence of specific epigenetic marks along eukaryotic chromatin. Complete histone-borne epigenomes have now been described at single-nucleosome resolution from various organisms, tissues, developmental stages, or diseases, yet their intra-species natural variation has never been investigated. We describe here that the epigenomic sequence of histone H3 acetylation at Lysine 14 (H3K14ac) differs greatly between two unrelated strains of the yeast Saccharomyces cerevisiae. Using single-nucleosome chromatin immunoprecipitation and mapping, we interrogated 58,694 nucleosomes and found that 5,442 of them differed in their level of H3K14 acetylation, at a false discovery rate (FDR) of 0.0001. These Single Nucleosome Epi-Polymorphisms (SNEPs) were enriched at regulatory sites and conserved non-coding DNA sequences. Surprisingly, higher acetylation in one strain did not imply higher expression of the relevant gene. However, SNEPs were enriched in genes of high transcriptional variability and one SNEP was associated with the strength of gene activation upon stimulation. Our observations suggest a high level of inter-individual epigenomic variation in natural populations, with essential questions on the origin of this diversity and its relevance to gene x environment interactions.