Title |
Key Technologies for Progressing Discovery of Microbiome-Based Medicines
|
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Published in |
Frontiers in Microbiology, June 2021
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DOI | 10.3389/fmicb.2021.685935 |
Pubmed ID | |
Authors |
Remy B. Young, Vanessa R. Marcelino, Michelle Chonwerawong, Emily L. Gulliver, Samuel C. Forster |
Abstract |
A growing number of experimental and computational approaches are illuminating the "microbial dark matter" and uncovering the integral role of commensal microbes in human health. Through this work, it is now clear that the human microbiome presents great potential as a therapeutic target for a plethora of diseases, including inflammatory bowel disease, diabetes and obesity. The development of more efficacious and targeted treatments relies on identification of causal links between the microbiome and disease; with future progress dependent on effective links between state-of-the-art sequencing approaches, computational analyses and experimental assays. We argue determining causation is essential, which can be attained by generating hypotheses using multi-omic functional analyses and validating these hypotheses in complex, biologically relevant experimental models. In this review we discuss existing analysis and validation methods, and propose best-practice approaches required to enable the next phase of microbiome research. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Australia | 4 | 33% |
United States | 1 | 8% |
Italy | 1 | 8% |
Switzerland | 1 | 8% |
Unknown | 5 | 42% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 7 | 58% |
Members of the public | 5 | 42% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 66 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 15 | 23% |
Student > Ph. D. Student | 10 | 15% |
Student > Bachelor | 3 | 5% |
Professor > Associate Professor | 3 | 5% |
Student > Master | 3 | 5% |
Other | 4 | 6% |
Unknown | 28 | 42% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 14 | 21% |
Immunology and Microbiology | 7 | 11% |
Medicine and Dentistry | 5 | 8% |
Agricultural and Biological Sciences | 5 | 8% |
Pharmacology, Toxicology and Pharmaceutical Science | 2 | 3% |
Other | 2 | 3% |
Unknown | 31 | 47% |