Evidence map›Paper›PMID 29703247›Full record

ArticleMicrobiome2018

Neutral models of short-term microbiome dynamics with host subpopulation structure and migration limitation.

Qinglong Zeng, Allen Rodrigo

Abstract read
In one paragraph

Article in Microbiome, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Article
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  5. Review
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  9. Article
  10. Scales of persistence: transmission and the microbiome.Current opinion in microbiology · 2019
    Review
  11. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Qinglong ZengDepartment of Biology, Duke University, Durham, NC, USA. qinglong.zeng@duke.edu.ORCID 0000-0002-8955-7248
Allen RodrigoResearch School of Biology, The Australian National University, Canberra, ACT, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMost empirical studies tend to focus on microbiome dynamics within hosts or microbiome compositional differences between hosts over short periods. However, there is still a dearth of formal models that allow us to investigate the observed short-term dynamics of microbiomes under a unified ecological and evolutionary framework. In our previous study, we developed a computational agent-based neutral framework that simulates microbiome dynamics spanning many host generations with the added dimension of a genealogy of hosts. Although this long-term framework revealed interesting microbial diversity patterns under a simple but plausible evolutionary process and provided a platform for future elaboration of more complex systems, it does not allow us to explore microbiome dynamics within a single host generation.

methodsIn this paper, we developed a computational, agent-based, forward-time framework of microbiome dynamics within a single host generation. As we have done under our neutral long-term models, we incorporate neutral processes of environmental microbiome assembly and microbe acquisition from parents and environment. We also incorporate a Moran genealogical model of hosts, so that the dynamics of microbiome evolution can be studied within a single host generation. Furthermore, we allow host subpopulation structure and host migration to affect microbiome recruitment.

resultsWe show that microbiome diversity within hosts increases monotonically with increases in environmental contribution, while microbiome diversity between hosts increases with increasing parental inheritance. Host population division and dispersal limitation under high host contribution further shaped the patterns by elevating microbiome differences between hosts and depressing microbial diversity within hosts. Microbiome diversity within the whole population showed strong temporal stability regardless of the modes of microbiome acquisition and subpopulation structures.

conclusionsWe present a computational framework that integrates various processes including host genealogy, microbe recruitment, and host dispersal limitation acting on the short-term dynamics of microbiomes. Our framework demonstrates that the neutral dynamics of microbiomes within a population of hosts is strongly influenced by transmission mode and shared environment.

Indexed as

Computer SimulationModels, BiologicalBiological EvolutionComputational BiologyHost Microbial InteractionsHumansMicrobiotaDispersal limitationDiversityMetacommunityMicrobiomeTemporal dynamics

Identifiers

PMID29703247
PMCPMC5921780

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.