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      Phylogenetic analyses suggest centipede venom arsenals were repeatedly stocked by horizontal gene transfer

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      Nature Communications
      Springer Science and Business Media LLC

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          Abstract

          Venoms have evolved over a hundred times in animals. Venom toxins are thought to evolve mostly by recruitment of endogenous proteins with physiological functions. Here we report phylogenetic analyses of venom proteome-annotated venom gland transcriptome data, assisted by genomic analyses, to show that centipede venoms have recruited at least five gene families from bacterial and fungal donors, involving at least eight horizontal gene transfer events. These results establish centipedes as currently the only known animals with venoms used in predation and defence that contain multiple gene families derived from horizontal gene transfer. The results also provide the first evidence for the implication of horizontal gene transfer in the evolutionary origin of venom in an animal lineage. Three of the bacterial gene families encode virulence factors, suggesting that horizontal gene transfer can provide a fast track channel for the evolution of novelty by the exaptation of bacterial weapons into animal venoms.

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          Most cited references75

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          MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

          We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
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            IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

            Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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              ModelFinder: Fast Model Selection for Accurate Phylogenetic Estimates

              Model-based molecular phylogenetics plays an important role in comparisons of genomic data, and model selection is a key step in all such analyses. We present ModelFinder, a fast model-selection method that greatly improves the accuracy of phylogenetic estimates. The improvement is achieved by incorporating a model of rate-heterogeneity across sites not previously considered in this context, and by allowing concurrent searches of model-space and tree-space.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Nature Communications
                Nat Commun
                Springer Science and Business Media LLC
                2041-1723
                December 2021
                February 05 2021
                December 2021
                : 12
                : 1
                Article
                10.1038/s41467-021-21093-8
                9eaa7631-9ecb-4be7-b75a-94beb2c71f74
                © 2021

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

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