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 à la Faculté de médecine de l'Université de Montréal (UdeM) et chercheuse à l'Institut de cardiologie de Montréal (ICM). Elle est aussi chercheuse-boursière junior 2 du Fonds de recherche du Québec - Santé (FRQS) et responsable des programmes d'études supérieures en bio-informatique à l'UdeM.

Julie Hussin a été formée en génomique statistique et évolutive et possède une vaste expérience dans l'analyse de données multi-omiques issues de vastes cohortes populationnelles. Ses travaux en biologie computationnelle se concentrent principalement sur la génomique médicale et des populations, contribuant à plusieurs avancées méthodologiques dans ces domaines. Son travail interdisciplinaire vise à développer des outils novateurs pour la médecine de précision.

Ses projets de recherche se focalisent sur l'amélioration de la prédiction de risques et la gestion des maladies cardiométaboliques, en particulier dans le cas de l'insuffisance cardiaque. Les méthodologies utilisées dans son groupe intègrent différentes sources de données, notamment des données cliniques, génétiques, transcriptomiques, protéomiques et métabolomiques, pour permettre la découverte de nouvelles informations sur les déterminants biologiques des maladies cardiaques, notamment par des techniques d’apprentissage non supervisé. Dans le contexte de la pandémie de COVID-19, son équipe a également développé des approches d’analyse de données génétiques des virus, pour la surveillance virale et l’étude des interactions hôte-pathogène ainsi que l'évolution virale.

Ses intérêts de recherche comprennent également l'interprétabilité, la généralisation et l'équité des algorithmes d'apprentissage automatique dans la recherche en santé. Julie Hussin s'engage à promouvoir activement une IA équitable, sûre et transparente dans la recherche en santé et s'efforce d'assurer l'inclusivité et la représentativité des individus dans sa recherche, pour que son travail bénéficie à l'ensemble de la population. Elle partage son expertise en donnant plusieurs cours de bio-informatique et de génétique des populations, ainsi que d’apprentissage automatique en génomique. Avant de se joindre à l'Université de Montréal en tant que 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) et chercheuse postdoctorale invitée à l'Université McGill.

Étudiants actuels

Doctorat - UdeM
Doctorat - UdeM
Doctorat - UdeM
Maîtrise recherche - UdeM
Doctorat - UdeM
Co-superviseur⋅e :
Doctorat - UdeM
Co-superviseur⋅e :

Publications

Toward computing attributions for dimensionality reduction techniques
Matthew Scicluna
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
Fatima Mostefai
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
Fatima Mostefai
Jane Heffernan
Adrianne L. Jenner
Morgan Craig
Intra-host viral populations of SARS-CoV-2 in immunosuppressed patients with hematologic cancers
Dominique Fournelle
Fatima Mostefai
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
Alexander Tong
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
Alexander Tong
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
Fatima Mostefai
Isabel Gamache
Arnaud N’Guessan
Justin Pelletier
Jessie Huang
Carmen Lia Murall
Ahmad Pesaranghader
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
Fatima Mostefai
I. Gamache
Arnaud N’Guessan
Justin Pelletier
Jessie Huang
Carmen Lia Murall
Ahmad Pesaranghader
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
Sacha Morin
Raphael Poujol
Fatima Mostefai
Shawn M. Simpson
Jean-Christophe Grenier
Léa Kaufmann
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
Sacha Morin
Léa Kaufmann
Shawn M. Simpson
Raphael Poujol
Fatima Mostefai
Jean-Christophe Grenier
Henry Xing
Miguelle Sanchez
Ariane Yechouron
Ronald Racette
Ivan Pavlov
Martin Smith
Data-driven approaches for genetic characterization of SARS-CoV-2 lineages
Fatima Mostefai
Isabel Gamache
Jessie Huang
Arnaud N’Guessan
Justin Pelletier
Ahmad Pesaranghader
David J. Hamelin
Carmen Lia Murall
Raphael Poujol
Jean-Christophe Grenier
Martin Smith
Etienne Caron
Morgan Craig
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 understand the evolution of this virus in humans, and to assist in tracing infection pathways and designing preventive strategies, we present a set of computational tools that span phylogenomics, population genetics and machine learning approaches. 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, using 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, enabling real-time analyses. Furthermore, time series change of Tajima’s D provides a powerful metric of population expansion. Unsupervised learning techniques further highlight key steps in variant detection and facilitate the study of the role of this genomic variation in the context of SARS-CoV-2 infection, with Multiscale PHATE methodology identifying fine-scale structure in the SARS-CoV-2 genetic data that underlies the emergence of key lineages. The computational framework presented here is useful for real-time genomic surveillance of SARS-CoV-2 and could be applied to any pathogen that threatens the health of worldwide populations of humans and other organisms.
Multiscale PHATE Exploration of SARS-CoV-2 Data Reveals Multimodal Signatures of Disease
Manik Kuchroo
Jessie Huang
Patrick Wong
Jean-Christophe Grenier
Dennis Shung
Alexander Tong
Carolina Lucas
Jon Klein
Daniel B. Burkhardt
Scott Gigante
Abhinav Godavarthi
Benjamin Israelow
Tianyang Mao
Ji Eun Oh
Julio Silva
Takehiro Takahashi
Camila D. Odio
Arnau Casanovas-Massana
John Fournier
Shelli Farhadian … (voir 7 de plus)
Charles S. Dela Cruz
Albert I. Ko
F. Perry Wilson
Akiko Iwasaki