Analyse combinée des données

Nous avons ensuite réalisé la combinaison des données RMN 1H provenant des deux fractions extraites. Comme nous l’avons vu précédemment (§ 2.4.2), cette combinaison passe nécessairement par la transformation de chaque matrice de données en une matrice centrée réduite. La normalisation des données est précédée d’une ANOVA permettant à la fois d’éliminer les signaux correspondant au bruit spectral et aux variables non significativement différentes entre les quatre populations. Afin de ne pas influencer le résultat de l’ACP, la classification des quatre groupes d’échantillons est réalisée avec la méthode du k-means. La représentation des individus selon les composantes principales 1 et 2 est montré sur la figure 2.21. Maigres 10mois Obèses 10mois Obèses 4 mois Maigres 4 mois Maigres 10mois Obèses 10mois Obèses 4 mois Maigres 4 mois CP2 CP1

Figure 2.21: Représentation des individus suivant les composantes principales CP1 et CP2

après l’ACP issue de l’analyse des spectres RMN 1H des fractions hydro- et organo-soluble des extraits de tissu cardiaque. En noir: maigres 10 mois; en bleu: obèses 10 mois ; en rouge: maigres 4 mois et en vert: obèses 4 mois.

L’ACP réalisée sur l’ensemble des données spectrales recueillies à partir du tissu cardiaque nous permet de retrouver la tendance observée à la fois sur les extraits aqueux (à propos du rapport Tau/Cr) et sur les extraits organiques (dans l’ACP). Cette tendance est matérialisée par la flèche sur la figure 2.21. Les rats maigres 4 mois et les rats obèses 10 mois ont un comportement totalement différent. Par contre, les animaux maigres 10 mois et les obèses 4 mois ont un comportement similaire. Alors que les rats SHHF maigres entament les processus métaboliques aboutissant à l’insuffisance cardiaque à 10 mois, les rats SHHF obèses, quant à eux, semblent développer précocement ces mêmes processus, dès l’âge de 4 mois. Cette hypothèse est en accord avec le développement chronologique avéré de l’IC170.

On peut remarquer la présence d’un point du groupe des animaux maigres 4 mois assez écarté des quatre autres. Si l’on réalise l’ACP sans ce point, la tendance observée est conservée voire amplifiée. Nous avons donc décidé de garder malgré tout cette information.

Maigres 4 mois Maigres 10mois Obèses 4 mois Obèses 10 mois CP2 CP1

4.5 Conclusion.

Cette étude métabonomique confirme la précocité de l’insuffisance cardiaque chez le rat obèse. Cette hypothèse pourrait être confirmé en poursuivant l’étude métabolique sur des rats SHHF obèses et maigres à 14 mois. Les modifications métaboliques observées sont en accord avec un remodelage cardiaque qui s’accompagne d’une expansion de la matrice extracellulaire (fibrose)168 et d’une régénération musculaire169. L’analyse transcriptomique des mêmes individus ayant été parallèlement effectuée, nous avons le projet de combiner l’ensemble des données pour les soumettre à une analyse statistique multivariée.

Nous avons utilisé et évalué deux outils: la RMN HR-MAS, méthode d’analyse RMN adaptée à une grande diversité d’échantillons, et la Métabonomique, méthode de traitement statistique des données spectroscopiques pour la recherche de signatures métaboliques. Ces deux outils se sont avérés très puissants mais possèdent, comme tout procédé, certains biais.

Pour la RMN HR-MAS appliquée à l’étude des tissus biologiques, des problèmes principalement pratiques se posent: problèmes de prélèvement, obligation de congeler les échantillons ou encore risque biologique. La sensibilité des sondes HR-MAS, permettant de travailler avec de très faibles quantités de matière, leur donne tout de même un véritable attrait. Les microsondes actuellement proposées sont peut être l’alternative à envisager pour l’étude des tissus biologiques.

Concernant la Métabonomique appliquée aux données RMN, le principal souci que nous avons rencontré se situe au niveau du nombre d’échantillons, celui-ci étant généralement trop faible par rapport au nombre de variables étudiées. Mais l’utilisation des méthodes statistiques multivariées appliquées au grand nombre de données que nous possédons permet un gain de temps très important, par rapport à une analyse quantitative classique, en permettant de cibler certains des métabolites qui pourront ensuite être quantifiés.

L’utilisation de ces deux outils nous a permis de travailler sur une large gamme d’échantillons, que ce soit des animaux, des végétaux, des tissus biologiques ou encore des cellules et sur de nombreux sujets d’études biomédicales allant de l’étude de la néphroprotection par la pargyline, en passant par la recherche de marqueurs métaboliques de la radiorésistance des glioblastomes ou encore les effets de l’obésité chez le rat hypertendu.

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