Testing the Anna Karenina Principle in Human Microbiome-Associated Diseases
Summary: The AKP (Anna Karenina principle), which refers to observations inspired by the opening line of Leo Tolstoy's Anna Karenina, “all happy families are all alike; each unhappy family is unhappy in its own way,” predicts that all “healthy” microbiomes are alike and each disease-associated...
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doaj-4bdbe31614a741d48651d3af52bbf1252020-11-25T02:44:17ZengElsevieriScience2589-00422020-04-01234Testing the Anna Karenina Principle in Human Microbiome-Associated DiseasesZhanshan (Sam) Ma0Computational Biology and Medical Ecology Lab, State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China; Corresponding authorSummary: The AKP (Anna Karenina principle), which refers to observations inspired by the opening line of Leo Tolstoy's Anna Karenina, “all happy families are all alike; each unhappy family is unhappy in its own way,” predicts that all “healthy” microbiomes are alike and each disease-associated microbiome is “sick” in its own way in human microbiome-associated diseases (MADs). The AKP hypothesis predicts the rise of heterogeneity/stochasticity in human microbiomes associated with dysbiosis due to MADs. We used the beta-diversity in Hill numbers and stochasticity analysis to detect AKP and anti-AKP effects. We tested the AKP with 27 human MAD studies and discovered that the AKP, anti-AKP, and non-AKP effects were exhibited in approximately 50%, 25%, and 25% of the MAD cases, respectively. Mechanistically, AKP effects are primarily influenced by highly dominant microbial species and less influenced by rare species. In contrast, all species appear to play equal roles in influencing anti-AKP effects. : Microbiology; Microbiome; In Silico Biology Subject Areas: Microbiology, Microbiome, In Silico Biologyhttp://www.sciencedirect.com/science/article/pii/S2589004220301917 |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhanshan (Sam) Ma |
spellingShingle |
Zhanshan (Sam) Ma Testing the Anna Karenina Principle in Human Microbiome-Associated Diseases iScience |
author_facet |
Zhanshan (Sam) Ma |
author_sort |
Zhanshan (Sam) Ma |
title |
Testing the Anna Karenina Principle in Human Microbiome-Associated Diseases |
title_short |
Testing the Anna Karenina Principle in Human Microbiome-Associated Diseases |
title_full |
Testing the Anna Karenina Principle in Human Microbiome-Associated Diseases |
title_fullStr |
Testing the Anna Karenina Principle in Human Microbiome-Associated Diseases |
title_full_unstemmed |
Testing the Anna Karenina Principle in Human Microbiome-Associated Diseases |
title_sort |
testing the anna karenina principle in human microbiome-associated diseases |
publisher |
Elsevier |
series |
iScience |
issn |
2589-0042 |
publishDate |
2020-04-01 |
description |
Summary: The AKP (Anna Karenina principle), which refers to observations inspired by the opening line of Leo Tolstoy's Anna Karenina, “all happy families are all alike; each unhappy family is unhappy in its own way,” predicts that all “healthy” microbiomes are alike and each disease-associated microbiome is “sick” in its own way in human microbiome-associated diseases (MADs). The AKP hypothesis predicts the rise of heterogeneity/stochasticity in human microbiomes associated with dysbiosis due to MADs. We used the beta-diversity in Hill numbers and stochasticity analysis to detect AKP and anti-AKP effects. We tested the AKP with 27 human MAD studies and discovered that the AKP, anti-AKP, and non-AKP effects were exhibited in approximately 50%, 25%, and 25% of the MAD cases, respectively. Mechanistically, AKP effects are primarily influenced by highly dominant microbial species and less influenced by rare species. In contrast, all species appear to play equal roles in influencing anti-AKP effects. : Microbiology; Microbiome; In Silico Biology Subject Areas: Microbiology, Microbiome, In Silico Biology |
url |
http://www.sciencedirect.com/science/article/pii/S2589004220301917 |
work_keys_str_mv |
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