Evidence map›Paper›PMID 42902431›Full record

ArticleAnimal microbiome2026

Spatiotemporal plasticity of the gut microbiome facilitates redbelly tilapia (Coptodon zillii) invasion in China.

Shoujie Tang, Ying Xing, Zhiyi Yan, Kenneth Nyirenda, Jinliang Zhao

Abstract read
In one paragraph

Article in Animal microbiome, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Shoujie TangKey Laboratory of Freshwater Aquatic Genetic Resources, Ministry of Agriculture and Rural Affairs, Shanghai Ocean University, Shanghai, 201306, China.
Ying XingKey Laboratory of Freshwater Aquatic Genetic Resources, Ministry of Agriculture and Rural Affairs, Shanghai Ocean University, Shanghai, 201306, China.
Zhiyi YanKey Laboratory of Freshwater Aquatic Genetic Resources, Ministry of Agriculture and Rural Affairs, Shanghai Ocean University, Shanghai, 201306, China.
Kenneth NyirendaKey Laboratory of Freshwater Aquatic Genetic Resources, Ministry of Agriculture and Rural Affairs, Shanghai Ocean University, Shanghai, 201306, China.
Jinliang ZhaoKey Laboratory of Freshwater Aquatic Genetic Resources, Ministry of Agriculture and Rural Affairs, Shanghai Ocean University, Shanghai, 201306, China. jlzhao@shou.edu.cn.

Funding

the National Key Research and Development Program of China 2022YFC2601302
6 · The paper itself

Abstract

backgroundThe redbelly tilapia (Coptodon zillii) is a highly invasive fish species posing serious ecological risks to freshwater systems in China. To uncover mechanisms behind its invasion success, this study investigated spatiotemporal plasticity of its gut microbiome and explored the drivers of such plasticity. Using high-throughput 16 S rRNA sequencing, gut microbiota of C. zillii from five water bodies (two rivers, three reservoirs) and from Shuikou Reservoir across four seasons were compared.

resultsAmong populations from different habitats, 6 of the top 10 abundant phyla and 17 of the top 30 genera varied significantly (P < 0.05). Gut microbiota differed significantly (P < 0.05) in all pairwise population comparisons. Alpha diversity indices also showed significant differences among some populations (P < 0.05). Of 24 digestion-related KEGG pathways identified, 23 exhibited significant abundance variations across populations. Relative gene abundances of beta-glucosidase (P = 0.0025) and chitinase (P = 0.022) also differed significantly. Water temperature, dissolved oxygen, and conductivity were key environmental drivers of gut microbiota variation. Notably, microbial community assembly was predominantly governed by deterministic processes (> 60%), with heterogeneous selection contributing 42%-63%. In seasonal samples from Shuikou Reservoir, 6 of the top 10 phyla and 15 of the top 30 genera showed significant seasonal variation (P < 0.05). PERMANOVA indicated significant differences (P < 0.05) in all pairwise seasonal comparisons. Of 19 digestion-related KEGG pathways, 13 exhibited significant abundance variations across seasons. Chitinase-coding gene abundance varied significantly among populations (P < 0.0001). Key factors driving seasonal differentiation were pH, conductivity, turbidity degree, and total nitrogen.

conclusionsThe gut microbiome of invasive C. zillii displays significant spatiotemporal variation in structure, diversity, and functional gene composition. This pronounced plasticity-rather than a stable, specialized microbiota-likely serves as a key adaptive strategy, enabling the species to thrive in heterogeneous environments.

Indexed as

Coptodon zilliiEnvironmental adaptationGut microbiomeInvasive speciesMicrobial plasticitySpatiotemporal variation

Identifiers

PMID42902431
PMCPMC13685089

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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.