Stratification of the gut microbiota composition landscape across the alzheimer's disease continuum in a Turkish cohort
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info:eu-repo/semantics/openAccessAttribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/Tarih
2022Yazar
Yıldırım, SüleymanNalbantoğlu, Özkan Ufuk
Bayraktar, Abdulahad
Ercan, Fatma Betül
Gündoğdu, Aycan
Velioğlu, Halil Aziz
Göl, Mehmet Fatih
Ekinci Soylu, Ayten
Koç, Fatma
Aslan Gülpınar, Ezgi
Soğukkanlı Kadak, Kübra
Arıkan, Muzaffer
Mardinoğlu, Adil
Koçak, Mehmet
Köseoğlu, Emel
Hanoğlu, Lütfü
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Yıldırım, S., Nalbantoğlu, Ö. U., Bayraktar, A., Ercan, F. B., Gündoğdu, A., Velioğlu, H. A. ... Hanoğlu, L. (2022). Stratification of the gut microbiota composition landscape across the alzheimer's disease continuum in a Turkish cohort. mSystems, 7(1). https://doi.org/10.1128/msystems.00004-22Özet
Alzheimer's disease (AD) is a heterogeneous disorder that spans a continuum with multiple phases, including preclinical, mild cognitive impairment, and dementia. Unlike for most other chronic diseases, human studies reporting on AD gut microbiota in the literature are very limited. With the scarcity of approved drugs for AD therapies, the rational and precise modulation of gut microbiota composition using diet and other tools is a promising approach to the management of AD. Such an approach could be personalized if an AD continuum can first be deconstructed into multiple strata based on specific microbiota features by using single or multiomics techniques. However, stratification of AD gut microbiota has not been systematically investigated before, leaving an important research gap for gut microbiota-based therapeutic approaches. Here, we analyze 16S rRNA amplicon sequencing of stool samples from 27 patients with mild cognitive impairment, 47 patients with AD, and 51 nondemented control subjects by using tools compatible with the compositional nature of microbiota. To stratify the AD gut microbiota community, we applied four machine learning techniques, including partitioning around the medoid clustering and fitting a probabilistic Dirichlet mixture model, the latent Dirichlet allocation model, and we performed topological data analysis for population-scale microbiome stratification based on the Mapper algorithm. These four distinct techniques all converge on Prevotella and Bacteroides stratification of the gut microbiota across the AD continuum, while some methods provided fine-scale resolution in stratifying the community landscape. Finally, we demonstrate that the signature taxa and neuropsychometric parameters together robustly classify the groups. Our results provide a framework for precision nutrition approaches aiming to modulate the AD gut microbiota.
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mSystemsCilt
7Sayı
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