Marco Antonio Mendoza Parra PhD / HDR; Chargé de recherche (CRCN) CNRS
Born in 1978 in Bolivia, I have followed my basic education in my country until 1997, year in which I have been awarded a scholarship from the “Simon I. Patiño” Foundation to perform my undergraduate studies at the university of Geneva-Switzerland. In 2003 I have obtained a master's degree in Biochemistry from this university; then I have joined the IMP/Vienna Biocenter international Ph.D. program in Austria where I have been trained in meiotic recombination in the laboratory of Dr. Franz Klein (Max F. Perutz Laboratories).
Vienna Biocenter Campus; Austria
During this period I got interested in understanding the budding yeast chromosomes' organization and its interplay with molecular mechanisms involved in meiotic recombination. As part of a collaborative project, in 2005 I have joined the laboratory of Katsuhiko Shirahige at the Tokyo Institute of Technology with the aim of being educated in the use of chromatin immunoprecipitation combined with microarrays hybridization (ChIP-chip assays).
This stay in Japan became a reference in my scientific path because it opened my mind towards the use of global approaches for studying biological systems. We have used this technology for mapping the global localization of various meiotic recombination initiation components, demonstrating that while they produce DNA double-strand breaks in uncondensed loop regions for recombination initiation, they are found parked in chromosome axis structures from where uncondensed loops arise. This important discovery gave rise to an article in Cell (Panizza et al; 2011) which has been cited more than 100 times since its publication.
At the end of my PhD training in 2008, I have joined the laboratory of Dr. Hinrich Gronemeyer (IGBMC; Strasbourg France) to address during this post-doctoral training the role of the RAR/RXR nuclear receptors in the process of Retinoic acid(RA)-induced differentiation. My contribution to this field aimed at providing a global view of the sites where these factors are located and their influence in transcriptional regulation. Importantly, at that time the use of deep sequencing technologies was just at their beginning, thus I have directly participated at the setup and development of such approaches both from the wet-lab, but also from the data processing.
IGBMC; Strasbourg France
Despite my absence of pure bioinformatics training, I was early on concerned by the necessity of having optimal methods to extract the biological information embedded in the generated datasets. In this context, I have driven over the years the development of various tools dedicated to the analysis of ChIP-sequencing and related datasets; among them Polyphemus (a normalization approach for ChIP-seq datasets; NAR; 2011); MeDiChISeq (a peak caller algorithm presenting enhanced performance; BMC Genomics; 2013); NGS-QC Generator, a quality control system for ChIP-seq and enrichment-related datasets (NAR; 2013; Genomics Data 2014; Statistical Genomics: Methods and Protocols 2016; F1000Research 2016); and more recently qcGenomics, a web platform for retrieving, visualizing, comparing and integrating publicly available ChIP-seq and related datasets (Life Sci Alliance 2019).
The novelty of my contributions in the field of retinoids is based on the use of integrative approaches; i.e the combination of temporal ChIP-seq and transcriptome readouts and complemented with in silico transcription factors annotations; to reconstruct the RA-driven regulome in the differentiation model system under study. Hence, in 2011 I have reconstructed the first the endodermal differentiation regulome on F9 cells driven by RA treatment (Molecular Systems Biology 2011). This work has been cited more than 200 times since its publication and paved the way for my current studies focused on the characterization of the gene regulatory networks (GRNs) governing cell fate decisions.
In 2016 I have extended these efforts to a comparative study between then RA-driven regulome during endodermal and neuronal differentiation, giving rise to the identification of major transcription factor (TF) players implicated on n neurogenesis (Genome Res 2016). To do so, I have developed a computational strategy allowing to model RA-signaling propagation over reconstructed GRNs, which from a generic point of view, allows inferring the capacity of any TF to drive transcription regulation signaling leading to cell fate transitions. As a consequence, this concept has been released as a computational tool allowing to (i) reconstruct GRNs from temporal gene expression readouts; (ii) infer master regulator transcription factors. (TETRAMER; Nat Sys Bio & App 2018).
In September 2018, I have set up my independent team SysFate (Systems Biology of cell Fate decisions).