A peptide toxin in ant venom mimics vertebrate EGF-like hormones to cause long-lasting hypersensitivity in mammals

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Significance

The targeting of mammalian ErbB receptor signaling by a venom toxin to cause hypersensitivity is a mode of action that has not previously been described. Natural selection of a defensive toxin to target ErbB signaling provides compelling independent evidence for a fundamental role of this receptor and its ligands in mammalian pain. The evolution of a toxin in ant venom to mimic a vertebrate nociceptive hormone serves as an example of both convergent evolution and molecular mimicry, illustrating how natural selection can shape the gene product of one organism to resemble that of another.

Abstract

Venoms are excellent model systems for studying evolutionary processes associated with predator–prey interactions. Here, we present the discovery of a peptide toxin, MIITX ₂-Mg1a, which is a major component of the venom of the Australian giant red bull ant Myrmecia gulosa and has evolved to mimic, both structurally and functionally, vertebrate epidermal growth factor (EGF) peptide hormones. We show that Mg1a is a potent agonist of the mammalian EGF receptor ErbB1, and that intraplantar injection in mice causes long-lasting hypersensitivity of the injected paw. These data reveal a previously undescribed venom mode of action, highlight a role for ErbB receptors in mammalian pain signaling, and provide an example of molecular mimicry driven by defensive selection pressure.

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

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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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A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.

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IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

Lam-Tung Nguyen, Heiko Schmidt, Arndt von Haeseler … (2014)

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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Author and article information

Journal

Journal ID (nlm-ta): Proc Natl Acad Sci U S A

Journal ID (iso-abbrev): Proc Natl Acad Sci U S A

Journal ID (hwp): pnas

Journal ID (publisher-id): PNAS

Title: Proceedings of the National Academy of Sciences of the United States of America

Publisher: National Academy of Sciences

ISSN (Print): 0027-8424

ISSN (Electronic): 1091-6490

Publication date (Electronic): 7 February 2022

Publication date (Print): 15 February 2022

Publication date PMC-release: 7 February 2022

Volume: 119

Issue: 7

Electronic Location Identifier: e2112630119

Affiliations

[1] ^aInstitute for Molecular Bioscience, The University of Queensland , Brisbane, QLD 4072, Australia;

[2] ^bAustralian Research Council Centre of Excellence for Innovations in Peptide and Protein Science, The University of Queensland , Brisbane, QLD 4072, Australia;

[3] ^cMonash Institute of Pharmaceutical Sciences, Monash University , Melbourne, VIC 3052, Australia;

[4] ^dSchool of Biomedical Sciences, The University of Queensland , Brisbane, QLD 4072, Australia;

[5] ^eCentre for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology , 7491 Trondheim, Norway;

[6] ^fCentre for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo , 0316 Oslo, Norway;

[7] ^gAustralian Research Council Centre for Fragment-Based Design, Monash University , Parkville, VIC 3052, Australia;

[8] ^hSchool of Pharmacy, The University of Queensland , Brisbane, QLD 4102, Australia

Author notes

¹To whom correspondence may be addressed. Email: sam.robinson@ 123456uq.edu.au .

Edited by Greg Neely, The University of Sydney Charles Perkins Centre, Camperdown, NSW, Australia; received July 11, 2021; accepted January 10, 2022 by Editorial Board Member Douglas J. Futuyma

Author contributions: S.D.R. designed research; D.A.E., N.J.S., B.K., J.J.B., Y.K.-Y.C., H.S., A.M., E.A.B.U., and S.D.R. performed research; M.E.R., R.S.N., W.G.T., I.V., G.F.K., and S.D.R. contributed new reagents/analytic tools; D.A.E., E.A.B.U., and S.D.R. analyzed data; and S.D.R. wrote the paper.