Microbiome-based biomarkers to guide personalized microbiome-based therapies for Parkinson's disease

<p><strong>Abstract: </strong>We address an unmet challenge in Parkinson&rsquo;s disease: the lack of biomarkers to identify the right patients for the right therapy, which is a main reason clinical trials for disease modifying treatments have all failed. The gut microbiome is a new target for treatment of neurodegenerative diseases. Our aim was to develop microbiome-based biomarkers to guide patient selection for microbiome-based clinical trials. We used microbial taxa that are robustly associated with PD across studies and at high significance as dysbiotic features of PD. &nbsp;Using individual-level taxonomic relative abundance data, we classified patients according to their dysbiotic features, effectively defining microbiome-based subtypes of PD. &nbsp;We show that not all persons with PD have a dysbiotic microbiome, and not all dysbiotic PD microbiomes have the same features. Grounded in robust and reproducible data from differential abundance studies, we propose an intuitive and easily modifiable method to identify the optimal candidates for microbiome-based clinical trials, and subsequently, for treatments that are personalized for each individual&rsquo;s dysbiotic features. We demonstrate the method for PD. The concept, and the method, is generalizable for any disease with a microbiome component.</p> <p><strong>Zenodo</strong>&nbsp;<strong>content: </strong>In this Zenodo archive we provide (a) the method described step by step, which can be implemented in Microsoft Excel (we used v.16.84 (RRID:SCR_016137)&nbsp;<a href="https://www.microsoft.com/en-gb/">https://www.microsoft.com/en-gb/</a>) or in R (we used v4.3.3 (RRID:SCR_001905) <a title="https://www.r-project.org/ Cmd+Click or tap to follow the link" href="https://www.r-project.org/">https://www.r-project.org/</a>); and&nbsp;(b) data used to generate the results, tables and figures (except figure 2). Data for creating figure 2 can be found in source data&nbsp;(doi: 10.5281/zenodo.7246185) and the method is described by Wallen et al 2022&nbsp;(DOI:&nbsp;<u><a href="https://doi.org/10.1038/s41467-022-34667-x" target="_blank" rel="noopener">10.1038/s41467-022-34667-x</a></u>). All data used here were extracted from the original source data reported by<strong> </strong>Wallen et. al. 2022 (DOI:&nbsp;<u><a href="https://doi.org/10.1038/s41467-022-34667-x" target="_blank" rel="noopener">10.1038/s41467-022-34667-x</a></u>) which can be found on Zenodo (DOI: 10.5281/zenodo.7246185).</p>