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Drivers of interlineage variability in mitogenomic evolutionary rates in Platyhelminthes

Heredity volume 133, pages 276–286 (2024)Cite this article

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Abstract

Studies of forces driving interlineage variability in the evolutionary rates (both sequence and architecture) of mitochondrial genomes often produce contradictory results. Flatworms (Platyhelminthes) exhibit the fastest-evolving mitogenomic sequences among all bilaterian phyla. To test the effects of multiple factors previously associated with different aspects of mitogenomic evolution, we used mitogenomes of 223 flatworm species, phylogenetic multilevel regression models, and causal inference. Thermic host environment (endothermic vs. ectothermic) had nonsignificant impacts on both sequence evolution and mitogenomic size. Mitogenomic gene order rearrangements (GORR) were mostly positively correlated with mitogenomic size (R2 ≈ 20–30%). Longevity was not (negatively) correlated with sequence evolution in flatworms. The predominantly free-living “turbellaria” exhibited much shorter branches and faster-evolving mitogenomic architecture than parasitic Neodermata. As a result, “parasitism” had a strong explanatory power on the branch length variability (>90%), and there was a negative correlation between GORR and branch length. However, the stem branch of Neodermata comprised 63.6% of the total average branch length. This evolutionary period was also marked by a high rate of gene order rearrangements in the ancestral Neodermata. We discuss how this period of rapid evolution deep in the evolutionary history may have decoupled sequence evolution rates from longevity and GORR, and overestimated the explanatory power of “parasitism”. This study shows that impacts of variables often vary across lineages, and stresses the importance accounting for the episodic nature of evolutionary patterns in studies of mitogenomic evolution.

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Fig. 1: An overview of the dataset and tested variables.
Fig. 2: Statistical comparisons of flatworm mitogenomes.
Fig. 3: Causal effects of different parameters on branch length, gene order rearrangement rate (GORR), and mitogenome length inferred using brms.
Fig. 4: Correlation analyses.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (32360927, 32102840); the Key Project of Natural Science Foundation of Tibet (XZ202301ZR0028G); the Open Fund Project of Key Laboratory of Breeding Biotechnology and Sustainable Aquaculture (2023FB07); and the Start-up Funds of Introduced Talent in Lanzhou University (561120206). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We would like to thank Dr. Xiang Liu for helpful discussions and technical assistance.

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Authors and Affiliations

  1. State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, and College of Ecology, Lanzhou University, Lanzhou, 730000, China

    Ivan Jakovlić, Tong Ye, Yuying Shi, Yiwen Ma & Dong Zhang

  2. Key Laboratory of Aquaculture Disease Control, Ministry of Agriculture, and State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China

    Hong Zou, Gui-Tang Wang & Wen-Xiang Li

  3. National Agricultural Science Observing and Experimental Station of Chongqing, Chongqing, 401329, China

    Fengyue Zhu

  4. Yangtze River Fisheries Research Institute, Chinese Academy of Fishery Science, Wuhan, 430073, China

    Fengyue Zhu

  5. Key Laboratory of Biodiversity and Environment on the Qinghai-Tibetan Plateau, Ministry of Education, School of Ecology and Environment, Tibet University, Lhasa, 850011, China

    Dong Zhang

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  1. Ivan Jakovlić
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  2. Tong Ye
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  3. Hong Zou
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Contributions

IJ: conceptualization, data curation, formal analysis, investigation, methodology, validation, visualization, writing – original draft. TY, FZ, YS, YM: formal analysis, investigation, methodology, writing – review & editing. HZ: formal analysis, resources, validation, visualization, writing – review & editing. GTW: formal analysis, resources, validation, visualization, supervision, writing – review & editing. WXL: validation, funding acquisition, supervision, writing – review & editing. ZD: data curation, formal analysis, investigation, methodology, resources, software, validation, visualization, writing – review & editing.

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Correspondence to Dong Zhang.

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No approval of research ethics committees was required to accomplish the goals of this study because it was conducted on previously sequenced mitogenomes acquired from GenBank and included only unregulated invertebrate species.

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Jakovlić, I., Ye, T., Zou, H. et al. Drivers of interlineage variability in mitogenomic evolutionary rates in Platyhelminthes. Heredity 133, 276–286 (2024). https://doi.org/10.1038/s41437-024-00712-2

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