Portrait de Julie Hussin

Julie Hussin

Membre académique associé
Professeure adjointe, Université de Montréal
Sujets de recherche
Apprentissage automatique médical
Apprentissage multimodal
Apprentissage profond
Biologie computationnelle
Exploration des données

Biographie

Julie Hussin est professeure agrégée au Département de médecine de l’Université de Montréal (UdeM) et chercheuse à l’Institut de cardiologie de Montréal (ICM). Elle est titulaire d’une Chaire de recherche du Canada (niveau 2) en science responsable des données multi-omiques et préside les programmes d’études supérieures en bio-informatique à l’UdeM.

Formée en génomique statistique et évolutive, la Dre Hussin possède une vaste expérience dans l’analyse de grands ensembles de données multi-omiques provenant de cohortes de population. Ses travaux en biologie computationnelle s’inscrivent dans les domaines de la génomique médicale et de la génomique des populations, auxquels elle a apporté plusieurs avancées méthodologiques. Son programme de recherche interdisciplinaire vise à développer des outils novateurs pour la médecine de précision. Ses projets portent notamment sur l’amélioration de la prédiction et de la gestion du risque de maladies cardiométaboliques, en particulier l’insuffisance cardiaque.

Ses approches intègrent différents types de données — cliniques, génétiques, transcriptomiques, protéomiques et métabolomiques — afin de mieux comprendre les déterminants biologiques des maladies du cœur, notamment grâce à des techniques d’apprentissage non supervisé. Dans le contexte de la pandémie de COVID-19, son équipe a également dirigé le développement d’algorithmes de science des données pour analyser les données génétiques virales, soutenir la surveillance épidémiologique et étudier les interactions hôte-pathogène ainsi que l’évolution virale.

Ses travaux s’intéressent aussi à l’interprétabilité, à la généralisabilité et à l’équité des algorithmes d’apprentissage automatique appliqués à la recherche en santé. La Dre Hussin milite pour une intelligence artificielle équitable, sécuritaire et transparente dans la recherche en santé, et s’engage à favoriser l’inclusivité et la représentativité afin que ses travaux bénéficient à l’ensemble de la population.

Elle enseigne plusieurs cours de premier et de deuxième cycles en biologie computationnelle, en génétique des populations et en apprentissage automatique appliqué à la génomique. Avant de se joindre à l’UdeM comme professeure, elle a été boursière postdoctorale du Human Frontier Science Program au Wellcome Trust Centre for Human Genetics de l’Université d’Oxford (Linacre College), ainsi que chercheuse invitée à l’Université McGill.

Étudiants actuels

Collaborateur·rice de recherche - UdeM
Doctorat - UdeM
Doctorat - UdeM
Doctorat - UdeM
Co-superviseur⋅e :
Doctorat - UdeM
Co-superviseur⋅e :

Publications

Genetic landscape of an in vivo protein interactome
Savandara Besse
Tatsuya Sakaguchi
Louis Gauthier
Zahra Sahaf
Olivier Péloquin
Lidice Gonzalez
Xavier Castellanos-Girouard
Nazli Koçatug
Chloé Matta
Stephen W. Michnick
Adrian W.R. Serohijos
Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results
Sarah Cherkaoui
Sandra Therrien-Laperriere
Yann Ilboudo
Raphael Poujol
Pamela Mehanna
Melanie E. Garrett
Marilyn J. Telen
Allison E. Ashley-Koch
Pablo Bartolucci
John D. Rioux
Guillaume Lettre
Christine Des Rosiers
Matthieu Ruiz
Studies combining metabolomics and genetics, known as metabolite genome-wide association studies (mGWAS), have provided valuable insights in… (voir plus)to our understanding of the genetic control of metabolite levels. However, the biological interpretation of these associations remains challenging due to a lack of existing tools to annotate mGWAS gene-metabolite pairs beyond the use of conservative statistical significance threshold. Here, we computed the shortest reactional distance (SRD) based on the curated knowledge of the KEGG database to explore its utility in enhancing the biological interpretation of results from three independent mGWAS, including a case study on sickle cell disease patients. Results show that, in reported mGWAS pairs, there is an excess of small SRD values and that SRD values and p-values significantly correlate, even beyond the standard conservative thresholds. The added-value of SRD annotation is shown for identification of potential false negative hits, exemplified by the finding of gene-metabolite associations with SRD ≤1 that did not reach standard genome-wide significance cut-off. The wider use of this statistic as an mGWAS annotation would prevent the exclusion of biologically relevant associations and can also identify errors or gaps in current metabolic pathway databases. Our findings highlight the SRD metric as an objective, quantitative and easy-to-compute annotation for gene-metabolite pairs that can be used to integrate statistical evidence to biological networks.
Toward computing attributions for dimensionality reduction techniques
Jean-Christophe Grenier
Raphael Poujol
Selection for immune evasion in SARS-CoV-2 revealed by high-resolution epitope mapping and sequence analysis
Arnaud N’Guessan
Senthilkumar Kailasam
Raphael Poujol
Jean-Christophe Grenier
Nailya Ismailova
Paola Contini
Raffaele De Palma
Carsten Haber
Volker Stadler
Guillaume Bourque
B. Jesse Shapiro
Jörg H. Fritz
Ciriaco A. Piccirillo
The race to understand immunopathology in COVID-19: Perspectives on the impact of quantitative approaches to understand within-host interactions
Sonia Gazeau
Xiaoyan Deng
Hsu Kiang Ooi
Jane Heffernan
Adrianne L. Jenner
Morgan Craig
Intra-host viral populations of SARS-CoV-2 in immunosuppressed patients with hematologic cancers
Dominique Fournelle
Elsa Brunet-Ratnasingham
Raphael Poujol
Jean-Christophe Grenier
José Héctor Gálvez
Amélie Pagliuzza
Inès Levade
Sandrine Moreira
Simon Grandjean Lapierre
Nicolas Chomont
Daniel E. Kaufmann
Morgan Craig
Throughout the SARS-CoV-2 pandemic, several variants of concern (VOC) have been identified, many of which share recurrent mutations in the s… (voir plus)pike protein’s receptor binding domain (RBD). This region coincides with known epitopes and can therefore have an impact on immune escape. Protracted infections in immunosuppressed patients have been hypothesized to lead to an enrichment of such mutations and therefore drive evolution towards VOCs. Here, we show that immunosuppressed patients with hematologic cancers develop distinct populations with immune escape mutations throughout the course of their infection. Notably, by investigating the co-occurrence of substitutions on individual sequencing reads in the RBD, we found quasispecies harboring mutations that confer resistance to known monoclonal antibodies (mAbs) such as S:E484K and S:E484A. Furthermore, we provide the first evidence for a viral reservoir based on intra-host phylogenetics. Our results on viral reservoirs can shed light on protracted infections interspersed with periods where the virus is undetectable as well as an alternative explanation for some long-COVID cases. Our findings also highlight that protracted infections should be treated with combination therapies rather than by a single mAbs to clear pre-existing resistant mutations.
Multiscale PHATE identifies multimodal signatures of COVID-19
Manik Kuchroo
Je-chun Huang
Patrick W. Wong
Jean-Christophe Grenier
Dennis L. Shung
C. Lucas
J. Klein
Daniel B. Burkhardt
Scott Gigante
Abhinav Godavarthi
Bastian Rieck
Benjamin Israelow
Michael Simonov
Tianyang Mao
Ji Eun Oh
Julio Silva
Takehiro Takahashi
C. Odio
Arnau Casanovas‐massana … (voir 10 de plus)
John Byrne Fournier
Shelli F. Farhadian
C. D. Dela Cruz
A. Ko
Matthew Hirn
F. Wilson
Akiko Iwasaki
Multiscale PHATE identifies multimodal signatures of COVID-19
Manik Kuchroo
Je-chun Huang
Patrick Wong
Jean-Christophe Grenier
Dennis Shung
Carolina Lucas
Jon Klein
Daniel B. Burkhardt
Scott Gigante
Abhinav Godavarthi
Bastian Rieck
Benjamin Israelow
Michael Simonov
Tianyang Mao
Ji Eun Oh
Julio Silva
Takehiro Takahashi
Camila D. Odio
Arnau Casanovas-Massana … (voir 10 de plus)
John Fournier
Shelli Farhadian
Charles S. Dela Cruz
Albert I. Ko
Matthew Hirn
F. Perry Wilson
Akiko Iwasaki
Population Genomics Approaches for Genetic Characterization of SARS-CoV-2 Lineages
Isabel Gamache
Arnaud N’Guessan
Justin Pelletier
Carmen Lia Murall
Vanda Gaonac'h-Lovejoy
David J. Hamelin
Raphael Poujol
Jean-Christophe Grenier
Martin Smith
Etienne Caron
Morgan Craig
B. Jesse Shapiro
Population Genomics Approaches for Genetic Characterization of SARS-CoV-2 Lineages
I. Gamache
Arnaud N’Guessan
Justin Pelletier
Carmen Lia Murall
Vanda Gaonac'h-Lovejoy
David J. Hamelin
Raphael Poujol
Jean-Christophe Grenier
Martin W. Smith
Étienne Caron
Morgan Craig
B. Jesse Shapiro
The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19)… (voir plus), has been sequenced at an unprecedented scale leading to a tremendous amount of viral genome sequencing data. To assist in tracing infection pathways and design preventive strategies, a deep understanding of the viral genetic diversity landscape is needed. We present here a set of genomic surveillance tools from population genetics which can be used to better understand the evolution of this virus in humans. To illustrate the utility of this toolbox, we detail an in depth analysis of the genetic diversity of SARS-CoV-2 in first year of the COVID-19 pandemic. We analyzed 329,854 high-quality consensus sequences published in the GISAID database during the pre-vaccination phase. We demonstrate that, compared to standard phylogenetic approaches, haplotype networks can be computed efficiently on much larger datasets. This approach enables real-time lineage identification, a clear description of the relationship between variants of concern, and efficient detection of recurrent mutations. Furthermore, time series change of Tajima's D by haplotype provides a powerful metric of lineage expansion. Finally, principal component analysis (PCA) highlights key steps in variant emergence and facilitates the visualization of genomic variation in the context of SARS-CoV-2 diversity. The computational framework presented here is simple to implement and insightful for real-time genomic surveillance of SARS-CoV-2 and could be applied to any pathogen that threatens the health of populations of humans and other organisms.
Genomic epidemiology and associated clinical outcomes of a SARS-CoV-2 outbreak in a general adult hospital in Quebec
Bastien Paré
Marieke Rozendaal
Raphael Poujol
Shawn M. Simpson
Jean-Christophe Grenier
Henry Xing
Miguelle Sanchez
Ariane Yechouron
Ronald Racette
Ivan Pavlov
Martin Smith
Patient health records and whole viral genomes from an early SARS-CoV-2 outbreak in a Quebec hospital reveal features associated with favorable outcomes
Bastien Paré
Marieke Rozendaal
Shawn M. Simpson
Raphael Poujol
Jean-Christophe Grenier
Henry Xing
Miguelle Sanchez
Ariane Yechouron
Ronald Racette
Ivan Pavlov
Martin Smith