| Autor: |
Yıldırım S; Department of Medical Microbiology, Istanbul Medipol Universitygrid.411781.a International School of Medicine, Istanbul, Turkey.; Regenerative and Restorative Medicine Research Center (REMER), Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol Universitygrid.411781.a, Istanbul, Turkey., Nalbantoğlu ÖU; Department of Computer Engineering, Erciyes Universitygrid.411739.9, Kayseri, Turkey.; Genome and Stem Cell Center (GenKok), Erciyes Universitygrid.411739.9, Kayseri, Turkey., Bayraktar A; Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, United Kingdom., Ercan FB; Graduate Program in Neuroscience, Istanbul Medipol Universitygrid.411781.a International School of Medicine, Istanbul, Turkey., Gündoğdu A; Genome and Stem Cell Center (GenKok), Erciyes Universitygrid.411739.9, Kayseri, Turkey.; Department of Microbiology and Clinical Microbiology, Erciyes Universitygrid.411739.9 School of Medicine, Kayseri, Turkey., Velioğlu HA; Regenerative and Restorative Medicine Research Center (REMER), Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol Universitygrid.411781.a, Istanbul, Turkey.; Graduate Program in Neuroscience, Istanbul Medipol Universitygrid.411781.a International School of Medicine, Istanbul, Turkey., Göl MF; Department of Microbiology and Clinical Microbiology, Erciyes Universitygrid.411739.9 School of Medicine, Kayseri, Turkey., Soylu AE; Department of Microbiology and Clinical Microbiology, Erciyes Universitygrid.411739.9 School of Medicine, Kayseri, Turkey., Koç F; Department of Medical Microbiology, Istanbul Medipol Universitygrid.411781.a International School of Medicine, Istanbul, Turkey., Gülpınar EA; Department of Microbiology and Clinical Microbiology, Erciyes Universitygrid.411739.9 School of Medicine, Kayseri, Turkey., Kadak KS; Graduate Program in Neuroscience, Istanbul Medipol Universitygrid.411781.a International School of Medicine, Istanbul, Turkey., Arıkan M; Regenerative and Restorative Medicine Research Center (REMER), Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol Universitygrid.411781.a, Istanbul, Turkey., Mardinoğlu A; Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, United Kingdom.; Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden., Koçak M; Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA., Köseoğlu E; Department of Neurology, School of Medicine, Erciyes Universitygrid.411739.9, Kayseri, Turkey., Hanoğlu L; Department of Neurology, School of Medicine, Istanbul Medipol Universitygrid.411781.a, Istanbul, Turkey. |
| Abstrakt: |
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. IMPORTANCE The prevalence of AD worldwide is estimated to reach 131 million by 2050. Most disease-modifying treatments and drug trials have failed, due partly to the heterogeneous and complex nature of the disease. Recent studies demonstrated that gut dybiosis can influence normal brain function through the so-called "gut-brain axis." Modulation of the gut microbiota, therefore, has drawn strong interest in the clinic in the management of the disease. However, there is unmet need for microbiota-informed stratification of AD clinical cohorts for intervention studies aiming to modulate the gut microbiota. Our study fills in this gap and draws attention to the need for microbiota stratification as the first step for microbiota-based therapy. We demonstrate that while Prevotella and Bacteroides clusters are the consensus partitions, the newly developed probabilistic methods can provide fine-scale resolution in partitioning the AD gut microbiome landscape. |
