Graduate Students
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.
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.
Maxime Tixhon
Visiting Graduate Student
Education
BSc, Institut Paul Lambin, 2015
MSc, Université Libre de Bruxelles, 2020
Contact info
maximeo_tixhon@dfci.harvard.edu
Thanks to the rise of next generation sequencing technologies, the last few decades have produced an enormous catalog of human genomic variants. These efforts bring amazing opportunities to unravel the wonders of molecular biology and revolutionize human genetics. The widespread use of genome and exome sequencing has enabled the scientific community to identify variants present in individuals affected with disease with greater and greater frequency. However, understanding which and how these variants lead to disease is still an on-going challenge. Linking genotype to phenotype is one of the main approaches to deepen our knowledge of biology.
One can think about molecular biology in terms of interactions. Gene products, proteins, interact with each other to create a vast intricate network of biophysical interactions, or so-called “interactome”. Assessing the effects of variants on this interactome has already proved useful to shed light on their role in Mendelian diseases. Still, a lot of diseases are complex, or polygenic, involving a huge number of genes, making it more challenging to functionalize variants.
Autism Spectrum Disorders are a group of neurodevelopmental diseases that affect 2.5% of the worldwide population. It’s a heterogeneous disease that can impose a heavy on the affected individuals as well as caretakers. My graduate project will focus on functionalizing ASD-related variants, generating comparative profiles of protein stability and the ability to mediate macromolecular interactions with protein partners. These profiles will enhance both our ability to identify true disease-causing variants and provide insight into the underlying disease mechanisms to lead the way for new and better diagnostic and classification tools as well as novel therapeutic approaches.
Visiting Graduate Student
Education
BSc, Institut Paul Lambin, 2015
MSc, Université Libre de Bruxelles, 2020
Contact info
maximeo_tixhon@dfci.harvard.edu
Thanks to the rise of next generation sequencing technologies, the last few decades have produced an enormous catalog of human genomic variants. These efforts bring amazing opportunities to unravel the wonders of molecular biology and revolutionize human genetics. The widespread use of genome and exome sequencing has enabled the scientific community to identify variants present in individuals affected with disease with greater and greater frequency. However, understanding which and how these variants lead to disease is still an on-going challenge. Linking genotype to phenotype is one of the main approaches to deepen our knowledge of biology.
One can think about molecular biology in terms of interactions. Gene products, proteins, interact with each other to create a vast intricate network of biophysical interactions, or so-called “interactome”. Assessing the effects of variants on this interactome has already proved useful to shed light on their role in Mendelian diseases. Still, a lot of diseases are complex, or polygenic, involving a huge number of genes, making it more challenging to functionalize variants.
Autism Spectrum Disorders are a group of neurodevelopmental diseases that affect 2.5% of the worldwide population. It’s a heterogeneous disease that can impose a heavy on the affected individuals as well as caretakers. My graduate project will focus on functionalizing ASD-related variants, generating comparative profiles of protein stability and the ability to mediate macromolecular interactions with protein partners. These profiles will enhance both our ability to identify true disease-causing variants and provide insight into the underlying disease mechanisms to lead the way for new and better diagnostic and classification tools as well as novel therapeutic approaches.