Graduate Students
Julien Olivet
Visiting Graduate Student
education
BSc, University of Namur, Belgium, 2013
MSc, University of Namur, Belgium, 2015
PhD, University of Liege, Belgium, present
contact Info
Julien_Olivet@dfci.harvard.edu
After graduating with a MS in Chemistry of Life and Nanomaterials in 2015, I moved to CCSB to conduct my PhD research project in the fields of Chemical Biology and Edgetics. My PhD thesis, supervised by Prof. Marc Vidal and Dr. Jean-Claude Twizere (GIGA-Research Center, University of Liège, Belgium), is focused on the development of high-throughput platforms for the systematic discovery of chemical modulators of protein-protein interactions (MPPIs) (see section Drug Screening and Druggability of the Interactome).
In particular, I am using two drug screening approaches to discover MPPIs, both implemented in in cellulo formats. First, a “phenotypic screening” approach in which PPIs involved in the S. cerevisiae histone deacetylase Rpd3 complex are targeted. Second, a “binary PPI assay” approach (based on the Reverse Yeast Two-Hybrid technology) in which interactions from CCSB-Interactome maps serve as potential drug targets. All experiments for the following up of primary hits are conducted with Dr. Soon Gang Choi, and involve the use of binary PPI assays such as NanoLuc Two-Hybrid, as well as biochemical techniques.
Visiting Graduate Student
education
BSc, University of Namur, Belgium, 2013
MSc, University of Namur, Belgium, 2015
PhD, University of Liege, Belgium, present
contact Info
Julien_Olivet@dfci.harvard.edu
After graduating with a MS in Chemistry of Life and Nanomaterials in 2015, I moved to CCSB to conduct my PhD research project in the fields of Chemical Biology and Edgetics. My PhD thesis, supervised by Prof. Marc Vidal and Dr. Jean-Claude Twizere (GIGA-Research Center, University of Liège, Belgium), is focused on the development of high-throughput platforms for the systematic discovery of chemical modulators of protein-protein interactions (MPPIs) (see section Drug Screening and Druggability of the Interactome).
In particular, I am using two drug screening approaches to discover MPPIs, both implemented in in cellulo formats. First, a “phenotypic screening” approach in which PPIs involved in the S. cerevisiae histone deacetylase Rpd3 complex are targeted. Second, a “binary PPI assay” approach (based on the Reverse Yeast Two-Hybrid technology) in which interactions from CCSB-Interactome maps serve as potential drug targets. All experiments for the following up of primary hits are conducted with Dr. Soon Gang Choi, and involve the use of binary PPI assays such as NanoLuc Two-Hybrid, as well as biochemical techniques.
Georges Coppin
Visiting Graduate Student
education
BSc, Université Libre de Bruxelles, 2014
MSc, Université Libre de Bruxelles, 2016
contact Info
Georges_Coppin@dfci.harvard.edu
Since the human genome was first sequenced in 2003, the number of available sequenced genomes has exploded, resulting in identification of a plethora of genetic variants. But linking genotype and phenotype, already a non-trivial task, is further complicated as most of these variants are rare and hence lack the statistical power required to perform phenotypic association. As a result, there is a massive effort in the scientific community to come up with orthogonal methodologies to annotate the functional consequence of these variants, in a high-throughput manner. A large number of tools are available that assess the deleteriousness of these rare variants. However, they rely on existing information like known disease-association or conservation to predict the negative effect of these variants, and hence are liable to misinterpretation. In addition, since most of the high-throughput methodologies simply predict the negative impact of variants, they provide little information about the underlying disease biology.
We are interested in understanding the pathogenicity of these variants, and their molecular implications, by studying their impact on unbiased, comprehensive biophysical networks like the binary protein-protein interactome. In the last decade, several protein networks have been released, each of which provide unique insights into various biological processes. Integrating these with other quantitative molecular estimates like expression levels will help us understand the cellular dynamics in a systematic manner. Moreover, we will experimentally generate thousands of alleles carrying different disease variants, and compare their interaction profiles with reference alleles to understand how perturbations of networks can result in different disease pathologies.
Florent Laval
Visiting Graduate Student
education
BSc, Université de Liège, Belgium, 2015
MSc, Université de Liège, Belgium, 2017
PhD, Université de Liège, Belgium, present
contact Info
Florent_Laval@dfci.harvard.edu
Traditional forward and reverse genetic approaches have considered genes as discrete units of function for decades. Null mutations and gene deletions provide an understanding of phenotypes that result from the complete absence of gene products. Most diseases, however, stem from single nucleotide polymorphisms, suggesting that gene deletions do not accurately reflect the molecular mechanisms that underlie human disorders. Furthermore, the polygenic regulation of biological functions that are mediated through complex networks of biophysical interactions emphasizes the importance of investigating these interactions in greater depth. Accordingly, efforts to create comprehensive biophysical interaction or “interactome” networks by systematically mapping all protein-protein interactions in the cell have been a major research focus. While these reference interactome maps provide a valuable resource for the study of protein function, such interaction networks merely represent static snapshots of complex macromolecule assemblies, lacking textured information regarding their dynamics or context-dependent functions. Furthermore, these reference maps fall short of describing the impact of interaction perturbations, as subtle dysfunctions within a network resulting from gene mutation can potentially result in major systemic consequences. Besides, the complete loss of a gene product does not necessarily lead to the same phenotypic outcome as an interaction-specific or edge-specific or “edgetic” perturbation of a protein-protein interaction. While gene knockout approaches are convenient for describing the impact of gross disruptions in organisms, an edge-centered approach can dissect the complexities of biological systems. It therefore follows that a requirement for understanding these biological systems is the consideration of each individual interaction within a network. This emphasizes the importance of developing a higher resolution genetic approach that focuses on gene products as interacting entities.
My graduate project thus aims to devise a systematic platform to dissect interactions within protein networks by using a mutagenic approach with a yeast complex involved in transcriptional repression as a model.
Visiting Graduate Student
education
BSc, Université de Liège, Belgium, 2015
MSc, Université de Liège, Belgium, 2017
PhD, Université de Liège, Belgium, present
contact Info
Florent_Laval@dfci.harvard.edu
Traditional forward and reverse genetic approaches have considered genes as discrete units of function for decades. Null mutations and gene deletions provide an understanding of phenotypes that result from the complete absence of gene products. Most diseases, however, stem from single nucleotide polymorphisms, suggesting that gene deletions do not accurately reflect the molecular mechanisms that underlie human disorders. Furthermore, the polygenic regulation of biological functions that are mediated through complex networks of biophysical interactions emphasizes the importance of investigating these interactions in greater depth. Accordingly, efforts to create comprehensive biophysical interaction or “interactome” networks by systematically mapping all protein-protein interactions in the cell have been a major research focus. While these reference interactome maps provide a valuable resource for the study of protein function, such interaction networks merely represent static snapshots of complex macromolecule assemblies, lacking textured information regarding their dynamics or context-dependent functions. Furthermore, these reference maps fall short of describing the impact of interaction perturbations, as subtle dysfunctions within a network resulting from gene mutation can potentially result in major systemic consequences. Besides, the complete loss of a gene product does not necessarily lead to the same phenotypic outcome as an interaction-specific or edge-specific or “edgetic” perturbation of a protein-protein interaction. While gene knockout approaches are convenient for describing the impact of gross disruptions in organisms, an edge-centered approach can dissect the complexities of biological systems. It therefore follows that a requirement for understanding these biological systems is the consideration of each individual interaction within a network. This emphasizes the importance of developing a higher resolution genetic approach that focuses on gene products as interacting entities.
My graduate project thus aims to devise a systematic platform to dissect interactions within protein networks by using a mutagenic approach with a yeast complex involved in transcriptional repression as a model.