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Reimaging the Tree of Life Using a Mass Based Phylonumerics Approach

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Abstract

Evolutionary relationships among and across species are almost exclusively examined through phylogenetic analysis based on trees constructed using gene sequences. Such trees are not without their limitations. Here we show that sets of numbers representing the masses of peptide segments within proteins encoded by those genes can also be used to construct trees of life employing a phylonumerics approach. A purpose built algorithm developed in this laboratory has been used to construct so-called mass trees from these numerical mass map datasets for hypothetical proteins encoded by the 16S/18S gene across all forms of life. A mass and conventional sequence tree built from 736 proteins across all six kingdoms of life (comprising 201 animals, 26 plants, 12 fungi, 13 protists, 236 archaea and 248 bacteria) show considerable similarity and demonstrate the broad viability of a phylonumerics approach for displaying and studying biodiversity and evolutionary history. A visual and computational comparison of mass trees and conventional sequence based trees, using several tree comparison algorithms, demonstrates that the former represent a reliable and effective means to study organismal evolution without the need for gene or protein sequences nor their alignment. The phylonumerics approach can also display mutational differences along the branches of these trees to allow for the study of molecular mechanisms that drive evolutionary change.

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References

  • Akand, E. H., & Downard, K. M. (2017). Mutational analysis employing a phylogenetic mass tree approach in a study of the evolution of the influenza virus. Molecular Phylogenetics and Evolution,112, 209–217.

    Article  Google Scholar 

  • Akand, E. H., & Downard, K. M. (2018a). Identification of epistatic mutations and insights into the evolution of the influenza virus using a mass-based protein phylogenetic approach. Molecular Phylogenetics and Evolution,121, 132–138.

    Article  CAS  Google Scholar 

  • Akand, E. H., & Downard, K. M. (2018b). Ancestral and compensatory mutations that promote antiviral resistance in influenza N1 neuraminidase revealed by a phylonumerics approach. Journal of Molecular Evolution,86, 546–553.

    Article  CAS  Google Scholar 

  • Akand, E. H., & Downard, K. M. (2019). Mechanisms of antiviral resistance in influenza neuraminidase revealed by a mass spectrometry based phylonumerics approach. Molecular Phylogenetics and Evolution,135, 286–296.

    Article  CAS  Google Scholar 

  • Bogdanowicz, D., Giaro, K., & Wróbel, B. (2012). TreeCmp: comparison of trees in polynomial time. Evolutionary Bioinformatics,8, 475–487.

    Article  Google Scholar 

  • Bromham, L. (2016). An introduction to molecular evolution and phylogenetics. Oxford: Oxford University Press.

    Google Scholar 

  • Bujnicki, J. M. (2000). Phylogenomic analysis of 16S rRNA:(guanine-N2) methyltransferases suggests new family members and reveals highly conserved motifs and a domain structure similar to other nucleic acid amino-methyltransferases. The FASEB Journal,14, 2365–2368.

    Article  CAS  Google Scholar 

  • Chamary, J. V., & Hurst, L. D. (2009). The price of silent mutations. Scientific American,300, 46–53.

    Article  CAS  Google Scholar 

  • Delsuc, F., Brinkmann, H., & Philippe, H. (2005). Phylogenomics and the reconstruction of the tree of life. Nature Reviews Genetics,6, 361–375.

    Article  CAS  Google Scholar 

  • Doolittle, W. F. (1999). Phylogenetic classification and the universal tree. Science,284, 2124–2129.

    Article  CAS  Google Scholar 

  • Downard, K. M. (2004). Biological mass spectrometry. In Mass spectrometry—A foundation course (Chap. 7). Cambridge: Royal Society of Chemistry.

  • Downard, K. M. (2020). Mass-based protein phylogenetic approach to identify epistasis. In K.-C. Wong (Ed.), Epistasis: Methods and protocols. Methods in molecular biology. New Jersey: Springer.

    Google Scholar 

  • Evans, J., Sheneman, L., & Foster, J. A. (2006). Relaxed neighbor joining: A fast distance-based phylogenetic tree construction method. Journal of Molecular Evolution,62, 785–792.

    Article  CAS  Google Scholar 

  • Fernandes, N. D., & Downard, K. M. (2014). Incorporation of a proteotyping approach using mass spectrometry for the surveillance of the influenza virus in cell culture. Journal of Clinical Microbiology,52, 725–735.

    Article  CAS  Google Scholar 

  • Gupta, R. S. (1998). Protein phylogenies and signature sequences: A reappraisal of evolutionary relationships among archaebacteria, eubacteria, and eukaryotes. Microbiology and Molecular Biology Reviews,62, 1435–1491.

    Article  CAS  Google Scholar 

  • Hall, B. G. (2005). Comparison of the accuracies of several phylogenetic methods using protein and DNA sequences. Molecular Biology and Evolution,22, 792–802.

    Article  CAS  Google Scholar 

  • Hall, B. G. (2011). Phylogenetic trees made easy: A how to manual (4th ed.). Sunderland, MA: Sinauer Associates.

    Google Scholar 

  • Isenbarger, T. A. (2008). The most conserved genome segments for life detection on Earth and other planets. Origins of Life and Evolution of the Biosphere,38, 517–533.

    Article  CAS  Google Scholar 

  • Le, S. Q., & Gascuel, O. (2010). Accounting for solvent accessibility and secondary structure in protein phylogenetics is clearly beneficial. Systematic Biology,59, 277–287.

    Article  CAS  Google Scholar 

  • Lecointre, G., & Hervé, L. G. (2006). The tree of life: A phylogenetic classification. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Lee, K. M., Kivelä, S. M., Ivanov, V., Hausmann, A., Kaila, L., Wahlberg, N., et al. (2018). Information dropout patterns in restriction site associated DNA phylogenomics and a comparison with multilocus sanger data in a species-rich moth genus. Systematic Biology,67, 925–939.

    Article  CAS  Google Scholar 

  • Lemey, P., Salemi, M., & Vandamme, M.-A. (2009). The phylogenetic handbook: A practical approach to phylogenetic analysis and hypothesis testing (2nd ed.). Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Lun, A. T. L., Swaminathan, K., Wong, J. W. H., & Downard, K. M. (2013). Mass trees—A new phylogenetic approach and algorithm to chart evolutionary history with mass spectrometry. Analytical Chemistry,85, 5475–5482.

    Article  CAS  Google Scholar 

  • Nadler, S. A. (1995). Advantages and disadvantages of molecular phylogenetics: A case study of ascaridoid nematodes. Journal of Nematology,27, 423–432.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Nye, T. M. W., Lio, P., & Gilks, W. R. (2006). A novel algorithm and web-based tool for comparing two alternative phylogenetic trees. Bioinformatics,22, 117–119.

    Article  CAS  Google Scholar 

  • Pappin, D. J., Hojrup, P., & Bleasby, A. J. (1993). Rapid identification of proteins by peptide-mass fingerprinting. Current Biology,3, 327–332.

    Article  CAS  Google Scholar 

  • Pennisi, E. (1999). Is it time to uproot the tree of life? Science,284, 1305–1307.

    Article  CAS  Google Scholar 

  • Penny, D., & Hendy, M. (1986). Estimating the reliability of evolutionary trees. Molecular Biology and Evolution,3, 403–417.

    CAS  PubMed  Google Scholar 

  • Rambaut, A. (2016). FigTree algorithm, v.1.4.3. https://tree.bio.ed.ac.uk/software/figtree/.

  • Saitou, N., & Imanishi, T. (1989). Relative efficiencies of the Fitch-Margoliash, maximum parsimony, maximum-likelihood, minimum-evolution, and neighbor-joining methods of phylogenetic tree construction in obtaining the correct tree. Molecular Biology and Evolution,6, 514–525.

    CAS  Google Scholar 

  • Sanderson, M. J., & Driskell, A. C. (2003). The challenge of constructing large phylogenetic trees. Trends in Plant Science,8, 374–378.

    Article  CAS  Google Scholar 

  • Schwahn, A. B., Wong, J. W. H., & Downard, K. M. (2009). Subtyping of the influenza virus by high resolution mass spectrometry. Analytical Chemistry,81, 3500–3506.

    Article  CAS  Google Scholar 

  • Sheneman, L., Evans, J., & Foster, J. A. (2006). Clearcut: A fast implementation of relaxed neighbor joining. Bioinformatics,22, 2823–2834.

    Article  CAS  Google Scholar 

  • Soria-Carrasco, V., Talavera, G., Igea, J., & Castresana, J. (2007). The K tree score: Quantification of differences in the relative branch length and topology of phylogenetic trees. Bioinformatics,23, 2954–2956.

    Article  CAS  Google Scholar 

  • Stackebrandt, E. (2009). Phylogeny based on 16S rRNA/DNA. In E. Stackebrandt (Ed.), Encyclopedia of life sciences (ELS). Chichester: Wiley.

    Google Scholar 

  • Swaminathan, K., & Downard, K. M. (2014). Evolution of influenza neuraminidase and the detection of antiviral resistant strains using mass trees. Analytical Chemistry,86, 629–637.

    Article  CAS  Google Scholar 

  • Wei, X., McCune, B., Lumbsch, H. T., Li, H., Leavitt, S., Yamamoto, Y., et al. (2016). Limitations of species delimitation based on phylogenetic analyses: A case study in the hypogymnia hypotrypa group (Parmeliaceae, Ascomycota). PLoS ONE,11, e0163664.

    Article  Google Scholar 

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Acknowledgements

The authors acknowledge access to the Katana high-performance computational cluster at the University of New South Wales that was used to run the MassTree algorithm.

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Authors and Affiliations

  1. Infectious Disease Responses Laboratory, POWCS, Medicine, University of New South Wales, Sydney, NSW, 2052, Australia

    Elma H. Akand & Kevin M. Downard

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  1. Elma H. Akand
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  2. Kevin M. Downard
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Correspondence to Kevin M. Downard.

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The authors declare that they have no conflict of interest.

Electronic supplementary material

Below is the link to the electronic supplementary material.

11692_2020_9490_MOESM1_ESM.pdf

Supplementary Figure 1—High resolution version of the mass tree of Figure 2 that can be expanded to view detail (PDF 75 kb)

11692_2020_9490_MOESM2_ESM.pdf

Supplementary Figure 2—High resolution version of the sequence tree of Figure 2 that can be expanded to view detail (PDF 75 kb)

11692_2020_9490_MOESM3_ESM.pdf

Supplementary Figure 3—Side-by-side comparison of single kingdom mass and sequence (left to right) trees for animal, archaea and bacteria (PDF 4012 kb)

11692_2020_9490_MOESM4_ESM.pdf

Supplementary Figure 4—Side-by-side comparison of single kingdom mass and sequence (left to right) trees for fungi, plant and protist (PDF 2019 kb)

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Akand, E.H., Downard, K.M. Reimaging the Tree of Life Using a Mass Based Phylonumerics Approach. Evol Biol 47, 76–84 (2020). https://doi.org/10.1007/s11692-020-09490-1

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  • DOI: https://doi.org/10.1007/s11692-020-09490-1

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