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. 2022 Feb 26;25(4):103998.
doi: 10.1016/j.isci.2022.103998. eCollection 2022 Apr 15.

Increasing transparency and reproducibility in stroke-microbiota research: A toolbox for microbiota analysis

Adam Sorbie  1 , Rosa Delgado Jiménez  1 , Corinne Benakis  1
Affiliations

Increasing transparency and reproducibility in stroke-microbiota research: A toolbox for microbiota analysis

Adam Sorbie et al. iScience. .

Abstract

Homeostasis of gut microbiota is crucial in maintaining human health. Alterations, or "dysbiosis," are increasingly implicated in human diseases, such as cancer, inflammatory bowel diseases, and, more recently, neurological disorders. In ischemic stroke patients, gut microbial profiles are markedly different compared to healthy controls, whereas manipulation of microbiota in animal models of stroke modulates outcome, further implicating microbiota in stroke pathobiology. Despite this, evidence for the involvement of specific microbes or microbial products and microbial signatures have yet to be identified, likely owing to differences in methodology, data analysis, and confounding variables between different studies. Here, we provide a set of guidelines to enable researchers to conduct high-quality, reproducible, and transparent microbiota studies, focusing on 16S rRNA sequencing in the emerging subfield of the stroke-microbiota. In doing so, we aim to facilitate novel and reproducible associations between the microbiota and brain diseases, including stroke, and translation into clinical practice.

Keywords: Clinical neuroscience; Microbiome; Neuroscience.

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Conflict of interest statement

The authors declare no competing interests.

Figures

None
Graphical abstract
Figure 1
Figure 1
The gut microbiota in ischemic stroke A summary of current knowledge on the involvement of the gut microbiota in stroke and outstanding questions which microbiota signatures identified by 16S rRNA sequencing can answer (created with BioRender.com)
Figure 2
Figure 2
Data analysis pipeline Overview of the data-analysis pipelines provided with this study, displaying each step in the analysis pipeline and the software used. Some examples of the kinds of figures which can generated with our pipeline are highlighted in the last step. Two versions of the same pipeline are provided, one written in R (left) and one in python via QIIME2 (right), which wraps the individual analysis steps in one software package (created with BioRender.com)
Figure 3
Figure 3
Disruption of community structure and composition post-stroke (A) Representative cresyl violet stained sections, 3 days after tMCAO. Scale bar: 4 mm (B) Alpha diversity measurements between stroke and sham mice showing richness (left), Shannon effective (center), and Faith’s phylogenetic diversity (right). Individual points dots represent a single mouse. Error bars represent the median +/− 1.5 multiplied by the IQR. Outliers are highlighted by an empty square (C) Non-metric multidimensional scaling plot of Generalized UniFrac distance colored by group (Adonis PERMANOVA R2 = 0.12, p-value = 0.018) (D) Family-level relative abundance in sham and stroke mice. Low abundant families were grouped with each other (E) Significant differentially abundant taxa between stroke and sham mice, identified by ANCOM-BC (corrected p-value <0.05). Log2 Fold-change between conditions is shown on the x axis
See this image and copyright information in PMC

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