Comparative phylogenetic tree reconstruction algorithms based on the BRCA1 gene in different species

Document Type : Research Paper

Author

Department of Animal Science, Faculty of Agriculture, Yasouj University, Yasouj, Iran

Abstract

Introduction: The BRCA1 gene is a pivotal candidate in current research endeavors, specifically drawing significant attention in studies related to bovine mammary tumors and human breast cancer. This gene is recognized for its fundamental biological role, as it encodes a protein that primarily functions as a tumor suppressor. Understanding the intricate evolutionary trajectory and the degrees of conservation or divergence of the BRCA1 gene across various species is of paramount importance. Such an understanding can provide profoundly crucial insights into its underlying functional mechanisms and can offer invaluable comparative perspectives directly relevant to the development and progression of diseases in both human populations and livestock. Considering the substantial and impactful economic consequences that mammary tumors exert on agricultural productivity within cattle farming, alongside the pervasive and persistent challenge posed by breast cancer in human health, a detailed phylogenetic analysis of the BRCA1 gene across a broad and representative spectrum of species becomes an imperative scientific undertaking. This research endeavor specifically aimed to meticulously reconstruct the evolutionary history of the BRCA1 gene across a wide array of selected species. By successfully achieving this, it sought to contribute significantly to a deeper and more comprehensive understanding of its functional preservation across evolutionary time. Moreover, it is intended to explore its potential practical applications, which notably include informing more effective breeding strategies designed to foster disease-resistant livestock and guiding the development of novel diagnostic approaches pertinent to improving human health.
Materials and methods: In this computational study, the coding sequence (CDS) region of the BRCA1 gene was precisely extracted from the genomes of 32 mammalian species from the NCBI database. The extracted sequences were then aligned using the BLAST algorithm and MEGA software. To reconstruct the evolutionary relationships, five phylogenetic methods were employed: DNAPARS, Pars, PhyML, Dnaml, and Proml. This comprehensive approach allowed for a multifaceted analysis of the gene's evolutionary history. The current version of Dnaml is faster than its previous iterations. This algorithm accounts for unequal predicted frequencies of the four DNA bases and allows for different predicted frequencies of transitions and transversions, along with various methods for incorporating different rates of interaction at distinct DNA sites. Key assumptions of this algorithm include the independent evolution of each DNA site and different lineages. Proml, optimized for protein data, offers robust statistical inference.
Results and discussion: The application of various selected phylogenetic algorithms consistently revealed that all methods clustered closely related species, such as Bos taurus and Ovis aries, into similar phylogenetic groups. This consistent grouping strongly confirms the conserved functional role of BRCA1 in DNA repair and immune regulation. However, significant differences in phylogenetic tree topology were observed. Maximum likelihood methods (PhyML/Dnaml) grouped humans (Homo sapiens) with primates, showing bootstrap values of 65-75%. In contrast, maximum parsimony methods (DNAPARS/Pars) revealed weaker associations for rodents, with bootstrap values of 50-60%. The Proml algorithm, based on Bayesian inference, also showed uncertainty in estimating branch lengths. These differences highlight the sensitivity of the methods to the heterogeneous evolutionary rate of BRCA1 and underscore the necessity of using combined mRNA and protein data to reduce methodological biases. The results showed that most mammals are grouped into closely related clusters; however, even within a single phylogeny, maximum likelihood did not always yield consistent topologies, hinting at complex evolutionary dynamics not fully captured by single gene analyses.
Conclusions: The findings of this study not only illuminate the intricate evolutionary history of BRCA1 but also possess practical applications in breeding disease-resistant livestock and in human breast cancer research. Future research should investigate the role of horizontal gene transfer and its influence on BRCA1 evolution. This study ultimately provides a framework for the evolutionary analysis of disease-resistance genes. Given the crucial role of the BRCA1 gene in mammary tumors and breast cancer, the results of this study can provide a better evolutionary perspective across different species and be utilized in diagnostic assays for both humans and livestock. The study consistently showed that in most procedures and algorithms, bovine and ovine species form a very close evolutionary cluster. Therefore, the evolutionary results for one species can be applied to another. The BRCA1 gene, essential for maintaining genomic stability through its role in DNA repair and cell cycle regulation, exhibits a high degree of evolutionary conservation, particularly in the N-terminal RING domain and C-terminal BRCT domains. This was demonstrated in this study, indicating that these vital regions are conserved among various vertebrate species, underscoring their functional importance.

Keywords

Main Subjects


Asif, M., & Khan, R. A. (2014a). Genetic markers associated with resistance to gastrointestinal nematodes in sheep. Tropical Animal Health and Production, 46(4), 807-814.
Asif, M., & Khan, R. A. (2014b). Genetic resistance to parasitic worms in livestock: A review. Journal of Animal Science and Biotechnology, 5(1), 1-10.
Ayatollahi, M., Hosseini Moghaddam, S. H., Mirhosseini, S. Z., & Ghavi Hossein-Zadeh, N. (2015). Study of Lactoferrin gene single nucleotide polymorphism and its relation with milk somatic cells in crossbred cows of Guilan province. Animal Production Research4(2), 87-94. [In Persian]
Bannerman, D. D., Springer, H. R., Paape, M. J., Kauf, A. C., & Goff, J. P. (2008). Evaluation of breed-dependent differences in the innate immune responses of Holstein and Jersey cows to Staphylococcus aureus intramammary infection. Journal of Dairy Research, 75(3), 291-301.
Benezra, M., Chevallier, N., Morrison, D. J., MacLachlan, T. K., El-Deiry, W. S., & Licht, J. D. (2003). BRCA1 augments transcription by the NF-κB transcription factor by binding to the Rel domain of the p65/RelA subunit. Journal of Biological Chemistry, 278(29), 26333-26341.
Bollback, J. P. (2002). Bayesian model adequacy and choice in phylogenetics. Molecular Biology and Evolution, 19(7), 1171-1180.
Buckley, N. E., Hosey, A. M., Gorski, J. J., Purcell, J. W., Mulligan, J. M., Harkin, D. P., & Mullan, P. B. (2007). BRCA1 regulates IFN-γ signaling through a mechanism involving the type I IFNs. Molecular Cancer Research, 5(3), 261-270.
Bull, J. J., Huelsenbeck, J. P., & Cunningham, C. W. (1997). Partitioning and combining data in phylogenetic analysis. Systematic Biology, 42, 384-397.
Caldart, E. T., Mata, H., Canal, C. W., & Ravazzolo, A. P. (2016). Phylogenetic analysis: basic concepts and its use as a tool for virology and molecular epidemiology. Acta Scientiae Veterinariae, 44, 20-20.
Charleston, M. A., Hendy, M. D., & Penny, D. (1994). The effects of sequence length, tree topology, and number of taxa on the performance of phylogenetic methods. Journal of Computational Biology, 1(2), 133-151.
Cheon, S., & Liang, F. (2008). Phylogenetic tree construction using sequential stochastic approximation Monte Carlo. BioSystems, 91(1), 94-107.
Deb, R., Kumar, A., Chakraborty, S., Verma, A. K., Tiwari, R., Dhama, K., ... & Kumar, S. (2013). Trends in diagnosis and control of bovine mastitis: a review. Pakistan Journal of Biological Sciences, 16(23), 1653-1661.
Fabreti, L. G., & Höhna, S. (2022). Bayesian inference of phylogeny is robust to substitution model over-parameterization. bioRxiv, 2022-02.
Farris, J. S. (1977). Phylogenetic analysis under Dollo's Law. Systematic Biology, 26(1), 77-88.
Farris, J. S. (1970). Methods for computing Wagner trees. Systematic Biology, 19(1), 83-92.
Felsenstein, J. (1978). Cases in which parsimony or compatibility methods will be positively misleading. Systematic Zoology, 27(4), 401-410.
Felsenstein, J. (2004). Inferring phylogenies Sinauer Associates Inc. Sunderland, MA.
Felsenstein, J., & Churchill, G. A. (1996). A Hidden Markov Model approach to variation among sites in rate of evolution. Molecular Biology and Evolution, 13(1), 93-104.
Friedman, L. S., & Hastie, N. D. (1994). The BRCA1 gene in hereditary breast and ovarian cancer. Nature Genetics, 7(3), 309-310.
Gonnet, G., & Scholl, R. (2009). Scientific Computation: Phylogenetic tree construction. doi: 10.1017/CBO9780511815027.009
Guindon, S., Dufayard, J. F., Lefort, V., Anisimova, M., Hordijk, W., & Gascuel, O. (2010). New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Systematic Biology, 59(3), 307-321.
Hall, B. G. (2005). Comparison of the accuracies of several phylogenetic methods using protein and DNA sequences. Molecular Biology and Evolution, 22(3), 792-802.
Hammoud, M., Santos, C. M. D., & Gois, J. P. (2021). Visual Comparison of phylogenetic trees through iPhyloC, a new interactive web-based framework. bioRxiv, 2021-05.
Hillis, D. M., Bull, J. J., White, M. E., Badgett, M. R., & Molineux, I. J. (1992). Experimental phylogenetics: generation of a known phylogeny. Science, 255(5044), 589-592.
Hopkins, M. J., & St. John, K. (2021). Incorporating hierarchical characters into phylogenetic analysis. Systematic Biology, 70(6), 1163-1180.
Huelsenbeck, J. P., Ronquist, F., Nielsen, R., & Bollback, J. P. (2001). Bayesian inference of phylogeny and its impact on evolutionary biology. Science, 294(5550), 2310-2314.
Huson, D. H., & Steel, M. (2004). Distances that perfectly mislead. Systematic Biology, 53(2), 327-332.
Ilie, D. E., Gavojdian, D., Kusza, S., NeamÈ›, R. I., Mizeranschi, A. E., Mihali, C. V., & Cziszter, L. T. (2023). Kompetitive allele specific pcr genotyping of 89 SNPs in romanian spotted and romanian brown cattle breeds and their association with clinical mastitis. Animals, 13(9), 1484.
Jabbir, F., Irfan, M., Mustafa, G., & Ahmad, H. I. (2019). Bioinformatics approaches to explore the phylogeny and role of BRCA1 in breast cancer. Critical Reviews™ in Eukaryotic Gene Expression, 29(6).
Keating, J. N., Sansom, R. S., Sutton, M. D., Knight, C. G., & Garwood, R. J. (2020). Morphological phylogenetics evaluated using novel evolutionary simulations. Systematic Biology, 69(5), 897-912.
Kluge, A. G., & Farris, J. S. (1969). Quantitative phyletics and the evolution of anurans. Systematic Biology, 18(1), 1-32.
Koshkarov, A., & Tahiri, N. (2024). Novel algorithm for comparing phylogenetic trees with different but overlapping taxa. Symmetry, 16(7), 790.
Lai, J., & Sarkar, I. N. (2021). A phylogenetic approach to analyze the conservativeness of BRCA1 and BRCA2 mutations. In AMIA Annual Symposium Proceedings (Vol. 2020, p. 677).
Li, D., Trotta, L., Marx, H. E., Allen, J. M., Sun, M., Soltis, D. E., ... & Baiser, B. (2019). For common community phylogenetic analyses, go ahead and use synthesis phylogenies. Ecology, 100(9), e02788.
Li, D. (2019). For common community phylogenetic analyses, go ahead and use synthesis phylogenies. Ecology, 100. doi: 10.1002/ecy.2788
Li, S., Pearl, D. K., & Doss, H. (2000). Phylogenetic tree construction using Markov chain Monte Carlo. Journal of the American Statistical Association, 95(450), 493-508.
Liu, P., Biller, P., Gould, M., & Colijn, C. (2022). Analyzing phylogenetic trees with a tree lattice coordinate system and a graph polynomial. Systematic Biology, 71(6), 1378-1390.
Marcet-Houben, M., & Gabaldón, T. (2011). TreeKO: a duplication-aware algorithm for the comparison of phylogenetic trees. Nucleic Acids Research, 39(10), e66-e66.
Nwezeobi, J., Onyegbule, O., Nkere, C., Onyeka, T., van Brunschot, S., Seal, S., & Colvin, J. (2020). Phylogenetic analysis v1. doi: 10.17504/protocols.io.bd44i8yw
Otu, H. H., & Sayood, K. (2003). A new sequence distance measure for phylogenetic tree construction. Bioinformatics, 19(16), 2122-2130.
Ouchi, T., Lee, S. W., Ouchi, M., Aaronson, S. A., & Horvath, C. M. (2000). Collaboration of signal transducer and activator of transcription 1 (STAT1) and BRCA1 in differential regulation of IFN-γ target genes. Proceedings of the National Academy of Sciences, 97(10), 5208-5213.
Paull, T. T., Cortez, D., Bowers, B., Elledge, S. J., & Gellert, M. (2001). Direct DNA binding by Brca1. Proceedings of the National Academy of Sciences, 98(11), 6086-6091.
Posada, D., & Buckley, T. R. (2004). Model selection and model averaging in phylogenetics: advantages of Akaike information criterion and Bayesian approaches over likelihood ratio tests. Systematic Biology, 53(5), 793-808.
Posada, D., & Crandall, K. A. (2001). Selecting models of nucleotide substitution: an application to human immunodeficiency virus 1 (HIV-1). Molecular Biology and Evolution, 18(6), 897-906.
Roehe, R., Georgieva, S., Zhang, W., & Shaw, S. E. (2007). Genetic variation in resistance to gastrointestinal nematodes in sheep. International Journal for Parasitology, 37(12), 1331–1340.
Rohlf, F. J., Chang, W. S., Sokal, R. R., & Kim, J. (1990). Accuracy of estimated phylogenies: effects of tree topology and evolutionary model. Evolution, 44(6), 1671-1684.
Ronquist, F., Teslenko, M., Van Der Mark, P., Ayres, D. L., Darling, A., Höhna, S., ... & Huelsenbeck, J. P. (2012). MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Systematic Biology, 61(3), 539-542.
Saitou, N., & Nei, M. (1987). The neighbor-joining method: a new method for reconstructing phylogenetic trees. Molecular Biology and Evolution, 4(4), 406-425.
Scott, R., & Gras, R. (2012, July). Comparing distance-based phylogenetic tree construction methods using an individual-based ecosystem simulation, EcoSim. In Artificial Life Conference Proceedings (pp. 105-110). One Rogers Street, Cambridge, MA 02142-1209, USA journals-info@ mit. edu: MIT Press.
Slater, G. J., Harmon, L. J., & Alfaro, M. E. (2012). Integrating fossils with molecular phylogenies improves inference of trait evolution. Evolution66(12), 3931-3944. doi: 10.1111/j.1558-5646.2012.01723.x
Swofford, D. L. (1996). Phylogenetic inference. Molecular Systematics, 2nd edition. Pp. 407-514.
Tolouei, E., & Behrouzi, M. (2016). Genetic resistance to parasitic worms in livestock: A review. Journal of Applied Animal Research, 44(3), 345-352.
Whelan, S., & Goldman, N. (2001). A general empirical model of protein evolution derived from multiple protein families using a maximum-likelihood approach. Molecular Biology and Evolution18(5), 691-699.
Wiens, J. J. (1998). Testing phylogenetic methods with tree congruence: phylogenetic analysis of polymorphic morphological characters in phrynosomatid lizards. Systematic Biology47(3), 427-444.
Wright, A. M. (2019). A systematist’s guide to estimating Bayesian phylogenies from morphological data. Insect Systematics and Diversity, 3(3), 2.
Xu, C. F., Brown, M. A., Nicolai, H., Chambers, J. A., Griffiths, B. L., & Solomon, E. (1997). Isolation and characterisation of the NBR2 gene which lies head-to-head with the human BRCA1 gene. Human Molecular Genetics6(7), 1057-1062.
Yang, R., Hu, S., & Gasbarre, L. C. (2004). Genetic basis of resistance to gastrointestinal nematodes in sheep. Animal Genetics, 35(4), 301-310.
Yang, Z. (2006). Computational molecular evolution. OUP Oxford..
Zou, S., Li, Q., Kong, L., Yu, H., & Zheng, X. (2011). Comparing the usefulness of distance, monophyly and character-based DNA barcoding methods in species identification: a case study of Neogastropoda. PLoS One, 6(10), e26619.