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.
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•Guidelines for reproducible stroke-microbiota research in patients and animal models•Current best practices for 16S rRNA profiling and analysis•Easy-to-use, freely available bioinformatics pipeline for gut microbiota analysis
Neuroscience; Clinical neuroscience; Microbiome
Investigations on the microbiota in neurological diseases such as stroke are increasingly common; however, stroke researchers may have limited experience with designing such studies. Here, we ...describe a protocol to conduct a stroke microbiota study in mice, from experimental stroke surgery and sample collection to data analysis. We provide details on sample processing and sequencing and provide a reproducible data analysis pipeline. In doing so, we hope to enable researchers to conduct robust studies and facilitate identification of stroke-associated microbial signatures.
For complete details on the use and execution of this protocol, please refer to Sorbie et al. (2022).1
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•Detailed instructions and walkthrough of the tMCAO murine stroke model•Simple and reproducible infarct volumetry and visualization•Sample collection and DNA extraction for 16S-based microbiota analysis•Easy-to-use, bioinformatic pipeline, with a choice of programming language (R/Python).
Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.
Investigations on the microbiota in neurological diseases such as stroke are increasingly common; however, stroke researchers may have limited experience with designing such studies. Here, we describe a protocol to conduct a stroke microbiota study in mice, from experimental stroke surgery and sample collection to data analysis. We provide details on sample processing and sequencing and provide a reproducible data analysis pipeline. In doing so, we hope to enable researchers to conduct robust studies and facilitate identification of stroke-associated microbial signatures.
Activating transcription factor 6 (ATF6) regulates endoplasmic reticulum stress. We studied whether ATF6 contributes to the development of colorectal cancer (CRC) using tissue from patients and ...transgenic mice.
We analyzed data from 541 patients with CRC in The Cancer Genome Atlas database for genetic variants and aberrant expression levels of unfolded protein response genes. Findings were validated in a cohort of 83 patients with CRC in Germany. We generated mice with intestinal epithelial cell–specific expression of the active form of Atf6 (nATF6IEC) from 2 alleles (homozygous), mice with expression of nATF6IEC from 1 allele (heterozygous), and nATF6IECfl/fl mice (controls). All nATF6IEC mice were housed under either specific-pathogen–free or germ-free conditions. Cecal microbiota from homozygous nATF6IEC mice or control mice was transferred into homozygous nATF6IEC mice or control mice. nATF6IEC mice were crossed with mice with disruptions in the myeloid differentiation primary response gene 88 and toll-like receptor adaptor molecule 1 gene (Myd88/Trif-knockout mice). Intestinal tissues were collected from mice and analyzed by histology, immunohistochemistry, immunoblots, gene expression profiling of unfolded protein response and inflammatory genes, array-based comparative genome hybridization, and 16S ribosomal RNA gene sequencing.
Increased expression of ATF6 was associated with reduced disease-free survival times of patients with CRC. Homozygous nATF6IEC mice developed spontaneous colon adenomas at 12 weeks of age. Compared with controls, homozygous nATF6IEC mice had changes in the profile of their cecal microbiota, increased proliferation of intestinal epithelial cells, and loss of the mucus barrier—all preceding tumor formation. These mice had increased penetration of bacteria into the inner mucus layer and activation of signal transducer and activator of transcription 3, yet inflammation was not observed at the pretumor or tumor stages. Administration of antibiotics to homozygous nATF6IEC mice greatly reduced tumor incidence, and germ-free housing completely prevented tumorigenesis. Analysis of nATF6IEC MyD88/TRIF-knockout mice showed that tumor initiation and growth required MyD88/TRIF-dependent activation of signal transducer and activator of transcription 3. Transplantation of cecal microbiota from nATF6IEC mice and control mice, collected before tumor formation, caused tumor formation in ex–germ-free nATF6IEC mice.
In patients with CRC, ATF6 was associated with reduced time of disease-free survival. In studies of nATF6IEC mice, we found sustained intestinal activation of ATF6 in the colon to promote dysbiosis and microbiota-dependent tumorigenesis.
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Machine learning (ML) has become an essential asset for the life sciences and medicine. We selected 250 articles describing ML applications from 17 journals sampling 26 different fields between 2011 ...and 2016. Independent evaluation by two readers highlighted three results. First, only half of the articles shared software, 64% shared data and 81% applied any kind of evaluation. Although crucial for ensuring the validity of ML applications, these aspects were met more by publications in lower-ranked journals. Second, the authors’ scientific backgrounds highly influenced how technical aspects were addressed: reproducibility and computational evaluation methods were more prominent with computational co-authors; experimental proofs more with experimentalists. Third, 73% of the ML applications resulted from interdisciplinary collaborations comprising authors from at least two of the three disciplines: computational sciences, biology, and medicine. The results suggested collaborations between computational and experimental scientists to generate more scientifically sound and impactful work integrating knowledge from both domains. Although scientifically more valid solutions and collaborations involving diverse expertise did not correlate with impact factors, such collaborations provide opportunities to both sides: computational scientists are given access to novel and challenging real-world biological data, increasing the scientific impact of their research, and experimentalists benefit from more in-depth computational analyses improving the technical correctness of work.Applications of machine learning in the life sciences and medicine require expertise in computational methods and in scientific subject matter. The authors surveyed articles in the life sciences that included machine learning applications, and found that interdisciplinary collaborations increased the scientific validity of published research.
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, while 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 due 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.
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