Parkinson's disease; Mild cognitive impairment; Gut microbiome; Microbiology
Abstract :
[en] Gut microbiome differences between people with Parkinson's disease (PD) and control subjects without Parkinsonism are widely reported, but potential alterations related to PD with mild cognitive impairment (MCI) have yet to be comprehensively explored. We compared gut microbial features of PD with MCI (n = 58) to cognitively unimpaired PD (n = 60) and control subjects (n = 90) with normal cognition. Our results did not support a specific microbiome signature related to MCI in PD.
Research center :
Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) Luxembourg Centre for Systems Biomedicine (LCSB): Eco-Systems Biology (Wilmes Group) Luxembourg Centre for Systems Biomedicine (LCSB): Clinical & Experimental Neuroscience (Krüger Group) LIH - Luxembourg Institute of Health Faculty of Humanities, Education and Social Sciences (FHSE) Integrative Research Unit: Social and Individual Development (INSIDE) > PEARL Institute for Research on Socio-Economic Inequality (IRSEI)
AHO, Velma ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine > Systems Ecology > Team Paul WILMES
KLEE, Matthias ; University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Social Sciences (DSOC) > Socio-Economic Inequality
LANDOULSI, Zied ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core
Heintz-Buschart, Anna ; Swammerdam Institute of Life Sciences at University of Amsterdam, Amsterdam, the Netherlands
PAVELKA, Lukas ; University of Luxembourg ; Parkinson's Research Clinic, Centre Hospitalier de Luxembourg, Luxembourg, Luxembourg ; Transversal Translational Medicine, Luxembourg Institute of Health, Strassen, Luxembourg
KRÜGER, Rejko ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Translational Neuroscience ; Centre Hospitalier de Luxembourg, Luxembourg, Luxembourg > Parkinson’s Research Clinic ; Luxembourg Institute of Health, Strassen, Luxembourg > Transversal Translational Medicine ; Centre Hospitalier de Luxembourg, Luxembourg, Luxembourg > Department of Neurology
We would like to thank all participants of the Luxembourg Parkinson’s Study for their important support of our research. Furthermore, we acknowledge the joint effort of the National Centre of Excellence in Research on Parkinson’s Disease (NCER-PD) Consortium members from the partner institutions Luxembourg Centre for Systems Biomedicine, Luxembourg Institute of Health, Centre Hospitalier de Luxembourg, and Laboratoire National de Santé generally contributing to the Luxembourg Parkinson’s Study. Data used in the preparation of this manuscript were obtained from the National Centre of Excellence in Research on Parkinson’s Disease (NCER-PD). The National Centre of Excellence in Research on Parkinson’s Disease (NCER-PD) is funded by the Luxembourg National Research Fund (FNR/NCER13/BM/11264123). The work was supported by the PEARL program (FNR/P13/6682797 to R.K.), MotaSYN (12719684 to R.K.), MAMaSyn (to R.K.), the FNR/DFG Core INTER (ProtectMove, FNR11250962 to P.M.). P.W. acknowledges funding from the European Research Council under the European Union’s Horizon 2020 research and innovation program (no. 863664). A.K.L., R.K., and P.W. acknowledge the financial support of the Institute for Advanced Studies of the University of Luxembourg through an AUDACITY grant (ref. no. MCI-BIOME_2019). The funders played no role in the study design, data collection, analysis and interpretation of data, or the writing of this manuscript. For the purpose of open access, and in fulfillment of the obligations arising from the grant agreement of the Luxembourg National Research Fund, the authors have applied a Creative Commons Attribution 4.0 International (CC BY 4.0) license to any Author Accepted Manuscript version arising from this submission. The sequence data analyses for this study were carried out using the HPC facilities of the University of Luxembourg.
C. Baiano P. Barone L. Trojano G. Santangelo Prevalence and clinical aspects of mild cognitive impairment in Parkinson’s disease: a meta-analysis Mov. Disord. 2020 35 45 54 10.1002/mds.27902 31743500
F. Scheperjans et al. Gut microbiota are related to Parkinson’s disease and clinical phenotype Mov. Disord. 2015 30 350 358 10.1002/mds.26069 25476529
A. Heintz-Buschart et al. The nasal and gut microbiome in Parkinson’s disease and idiopathic rapid eye movement sleep behavior disorder Mov. Disord. 2018 33 88 98 1:CAS:528:DC%2BC1cXhtlKnu7k%3D 10.1002/mds.27105 28843021
F. Baldini et al. Parkinson’s disease-associated alterations of the gut microbiome predict disease-relevant changes in metabolic functions BMC Biol. 2020 18 1:CAS:528:DC%2BB3cXhtFahu7%2FP 10.1186/s12915-020-00775-7 32517799 7285525
H. Nishiwaki et al. Meta-analysis of gut dysbiosis in Parkinson’s disease Mov. Disord. 2020 35 1626 1635 1:CAS:528:DC%2BB3cXhvVCltrjF 10.1002/mds.28119 32557853
S. Romano et al. Meta-analysis of the Parkinson’s disease gut microbiome suggests alterations linked to intestinal inflammation Npj Park. Dis. 2021 7 27 1:CAS:528:DC%2BB3MXht1GisLvJ 10.1038/s41531-021-00156-z
S. Kleine Bardenhorst et al. Gut microbiota dysbiosis in Parkinson disease: A systematic review and pooled analysis Eur. J. Neurol. 2023 30 3581 3594 10.1111/ene.15671 36593694
T. Ren et al. Gut microbiota altered in mild cognitive impairment compared with normal cognition in sporadic Parkinson’s disease Front. Neurol. 2020 11 137 10.3389/fneur.2020.00137 32161568 7052381
M.I. Love W. Huber S. Anders Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 Genome Biol. 2014 15 10.1186/s13059-014-0550-8 25516281 4302049
H. Lin S.D. Peddada Analysis of compositions of microbiomes with bias correction Nat. Commun. 2020 11 1:CAS:528:DC%2BB3cXhsVSmtr3I 10.1038/s41467-020-17041-7 32665548 7360769
Russel, J. et al. DAtest: a framework for choosing differential abundance or expression method. Preprint at https://www.biorxiv.org/content/10.1101/241802v1 (2018).
A.J. Hughes S.E. Daniel L. Kilford A.J. Lees Accuracy of clinical diagnosis of idiopathic Parkinson’s disease: a clinico-pathological study of 100 cases J. Neurol. Neurosurg. Psychiatry 1992 55 181 184 1:STN:280:DyaK383is1Omsg%3D%3D 10.1136/jnnp.55.3.181 1564476 1014720
G. Hipp et al. The Luxembourg Parkinson’s Study: a comprehensive approach for stratification and early diagnosis Front. Aging Neurosci. 2018 10 326 10.3389/fnagi.2018.00326 30420802 6216083
I. Litvan et al. Diagnostic criteria for mild cognitive impairment in Parkinson’s disease: Movement Disorder Society Task Force guidelines Mov. Disord. 2012 27 349 356 10.1002/mds.24893 22275317 3641655
Z.S. Nasreddine et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment J. Am. Geriatr. Soc. 2005 53 695 699 10.1111/j.1532-5415.2005.53221.x 15817019
C.L. Tomlinson et al. Systematic review of levodopa dose equivalency reporting in Parkinson’s disease Mov. Disord. 2010 25 2649 2653 10.1002/mds.23429 21069833
C. Weißbecker B. Schnabel A. Heintz-Buschart Dadasnake, a Snakemake implementation of DADA2 to process amplicon sequencing data for microbial ecology GigaScience 2020 9 10.1093/gigascience/giaa135 33252655 7702218
M. Martin Cutadapt removes adapter sequences from high-throughput sequencing reads EMBnet. J. 2011 17 10 12 10.14806/ej.17.1.200
B.J. Callahan et al. DADA2: High-resolution sample inference from Illumina amplicon data Nat. Methods 2016 13 581 583 1:CAS:528:DC%2BC28XosVWitb4%3D 10.1038/nmeth.3869 27214047 4927377
Li, Heng. seqtk: a fast and lightweight tool for processing sequences in the FASTA or FASTQ format, https://github.com/lh3/seqtk (2023).
C. Quast et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools Nucleic Acids Res. 2013 41 D590 D596 1:CAS:528:DC%2BC38XhvV2ksb%2FN 10.1093/nar/gks1219 23193283
P.D. Schloss et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities Appl. Environ. Microbiol. 2009 75 7537 7541 1:CAS:528:DC%2BC3cXis1yltw%3D%3D 10.1128/AEM.01541-09 19801464 2786419
R Core Team. R: A Language and Environment for Statistical Computing, https://www.R-project.org/ (2023).
Ushey, K. & Wickham, H. renv: Project Environments, https://rstudio.github.io/renv/ (2023).
Xie, Y. knitr: A General-Purpose Package for Dynamic Report Generation in R, https://yihui.org/knitr/ (2023).
P.J. Mcmurdie S. Holmes phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data PLoS ONE 2013 8 e61217 1:CAS:528:DC%2BC3sXntVWht7w%3D 10.1371/journal.pone.0061217 23630581 3632530
Oksanen, J. et al. vegan: Community Ecology Package, https://CRAN.R-project.org/package=vegan (2022).
M.E. Ritchie et al. limma powers differential expression analyses for RNA-sequencing and microarray studies Nucleic Acids Res. 2015 43 e47 10.1093/nar/gkv007 25605792 4402510
