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Investigating the microbial and immune mechanisms of depressive-like behaviour in a humanized mouse model of MDD

Major depressive disorder (MDD) is a highly heterogeneous disorder, with some patients displaying immune activation and altered intestinal microbiota composition when compared to healthy controls. In recent years, the transfer of fecal microbiota pooled from several MDD patients has been used to model depression in recipient rodents. However, we have previously observed the induction of donor-specific phenotypes in mice receiving microbiota from individual irritable bowel syndrome and generalized anxiety disorder patients. Therefore, we assessed the efficacy of fecal microbiota transplant (FMT) using individual versus pooled MDD patient microbiota to induce depressive-like behaviour in recipient rodents. We observed that pooling microbiota from several patients abrogated microbial features unique to individual donors. Mice that received pooled microbiota displayed different behavioural and immune phenotypes when compared to mice that received individual patient microbiota. Two individual MDD microbiota donors, patients MDD1 and MDD5, altered the behaviour of recipient mice when compared to controls. We identified several microbial species that may underlie the anxiety- and depressive-like behaviours observed in MDD1 and MDD5 mice. Additionally, altered expression of neural and immune genes was observed along the gut-brain axis of mice colonized with MDD1 microbiota. As microglia activation may play a role in our model, we developed a protocol for the isolation and phenotyping of adult mouse microglia that will facilitate future research efforts. Overall, our results demonstrate the heterogeneity of the microbial underpinnings of MDD and support the use of individual patient microbiota in future FMT experiments. / Thesis / Master of Science (MSc)

Identiferoai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/25717
Date January 2020
CreatorsHanuschak, Jennifer
ContributorsBercik, Premysl, Medical Sciences
Source SetsMcMaster University
LanguageEnglish
Detected LanguageEnglish
TypeThesis

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