Maladie de Huntington : L’alliance de la génomique et de l’intelligence artificielle met en avant un rôle central des pertes de résilience neuronale et une feuille de route pour les empêcher
Les cellules de notre corps sont naturellement capables de résister aux maladies grâce à
l'homéostasie cellulaire, une série de mécanismes adaptatifs qui réparent les dommages
cellulaires, impliquant des centaines de gènes qui rendent nos cellules résilientes. Bien que
l'homéostasie recèle un grand potentiel de protection des neurones dans le vieillissement et
les maladies neurodégénératives, l'importance et la dynamique des mécanismes
homéostatiques dans les maladies neurodégénératives sont restées insaisissables en raison
des difficultés d'étude de différents types de cellules dans le cerveau des mammifères.
Récemment, des technologies de criblage génomique ont été utilisées pour interroger
comment les différentes cellules du cerveau utilisent des centaines de gènes pour moduler les
processus neurodégénératifs. La complexité de ces données les rend cependant difficiles à
analyser. En collaboration avec l’équipe de Myriam Heiman au MIT qui a obtenu ces données
aux USA et le Laboratoire Jacques-Louis Lions (Sorbonne Université/CNRS), l’équipe de
recherche a développé une approche mathématique capable d'identifier précisément les
groupes de gènes utilisés dans le cerveau de modèles murins pour contrer les effets toxiques
de la huntingtine mutante, le gène de la maladie de Huntington (MH), au fil du temps et dans
plusieurs types de neurones composant le striatum, une région du cerveau fortement affectée
par la maladie de Huntington.
Les scientifiques ont testé si la mort neuronale dans cette maladie serait principalement due
au renforcement des réponses pathogènes ou à la perte des réponses homéostatiques.
Répondre à cette question pourrait considérablement modifier notre point de vue sur la
meilleure façon de contrer la progression de la maladie de Huntington. Cela pourrait fournir
des biomarqueurs pour savoir si une intervention thérapeutique protège le cerveau en
bloquant la pathogenèse ou en augmentant l'homéostasie, améliorant ainsi la précision des
études précliniques.
Pour ce faire, ils ont mis au point Geomic, une méthode d’apprentissage automatique basée
sur la géométrie des données génomiques permettant d’analyser la forme (courbes, surfaces)
de l'expression des gènes, et de cartographier la dynamique temporelle des réponses
homéostatiques et pathogéniques. De manière inattendue, cette carte montre que la plupart
des réponses pathogéniques sont atténuées avec le temps et, qui plus est, que la plupart des
réponses homéostatiques diminuent. Ces résultats suggèrent que la mort neuronale dans la
maladie de Huntington serait principalement due à la perte des réponses moléculaires
homéostatiques et non au renforcement des réponses moléculaires pathogéniques, soulignant
l'importance des processus homéostatiques dans l’évolution de la maladie.
Ces résultats apportent un cadre conceptuel pour explorer le développement de stratégies
thérapeutiques axées sur le rétablissement des capacités de résistance des cellules
cérébrales à la maladie de Huntington. Ils fournissent une feuille de route pour sélectionner
des cibles thérapeutiques pour rétablir la résilience neuronale et des biomarqueurs pour
surveiller si les médicaments émergents peuvent engager des mécanismes homéostatiques
pour être efficaces, et pour utiliser ces outils dans des modèles expérimentaux de cette
maladie. Les conclusions ouvrent la voie à des applications de Geomic à l'analyse des
données ‘omiques’ dans plusieurs autres maladies, notamment d'autres maladies
neurodégénératives.
Source : INSERM
Commentaires 27 réactions
04/03/2021 11:41
Thibaut*****
Excellent article qui m'aide à en apprendre beaucoup sur la maladie. site de conseils sur le TDAH.
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05/05/2021 10:48
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