Resistance to pirimiphos-methyl in West African Anopheles is spreading via duplication and introgression of the Ace1 locus
Autoři:
Xavier Grau-Bové aff001; Eric Lucas aff001; Dimitra Pipini aff001; Emily Rippon aff001; Arjèn E. van ‘t Hof aff001; Edi Constant aff002; Samuel Dadzie aff003; Alexander Egyir-Yawson aff004; John Essandoh aff001; Joseph Chabi aff003; Luc Djogbénou aff001; Nicholas J. Harding aff006; Alistair Miles aff006; Dominic Kwiatkowski aff006; Martin J. Donnelly aff001; David Weetman aff001;
Působiště autorů:
Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, United Kingdom
aff001; Centre Suisse de Recherches Scientifiques en Côte d’Ivoire, Abidjan, Côte d’Ivoire
aff002; Department of Parasitology, Noguchi Memorial Institute for Medical Research, University of Ghana, Accra, Ghana
aff003; Department of Biomedical Sciences, University of Cape Coast, Cape Coast, Ghana
aff004; Institut Régional de Santé Publique, Université d’Abomey-Calavi, Benin
aff005; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
aff006; Wellcome Sanger Institute, Hinxton, United Kingdom
aff007
Vyšlo v časopise:
Resistance to pirimiphos-methyl in West African Anopheles is spreading via duplication and introgression of the Ace1 locus. PLoS Genet 17(1): e1009253. doi:10.1371/journal.pgen.1009253
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pgen.1009253
Souhrn
Vector population control using insecticides is a key element of current strategies to prevent malaria transmission in Africa. The introduction of effective insecticides, such as the organophosphate pirimiphos-methyl, is essential to overcome the recurrent emergence of resistance driven by the highly diverse Anopheles genomes. Here, we use a population genomic approach to investigate the basis of pirimiphos-methyl resistance in the major malaria vectors Anopheles gambiae and A. coluzzii. A combination of copy number variation and a single non-synonymous substitution in the acetylcholinesterase gene, Ace1, provides the key resistance diagnostic in an A. coluzzii population from Côte d’Ivoire that we used for sequence-based association mapping, with replication in other West African populations. The Ace1 substitution and duplications occur on a unique resistance haplotype that evolved in A. gambiae and introgressed into A. coluzzii, and is now common in West Africa primarily due to selection imposed by other organophosphate or carbamate insecticides. Our findings highlight the predictive value of this complex resistance haplotype for phenotypic resistance and clarify its evolutionary history, providing tools to for molecular surveillance of the current and future effectiveness of pirimiphos-methyl based interventions.
Klíčová slova:
Anopheles gambiae – Genomics – Ghana – Haplotypes – Insecticides – Introgression – Invertebrate genomics – Phylogenetic analysis
Zdroje
1. Oxborough RM. Trends in US President’s Malaria Initiative-funded indoor residual spray coverage and insecticide choice in sub-Saharan Africa (2008–2015): urgent need for affordable, long-lasting insecticides. Malar J. 2016;15: 146. doi: 10.1186/s12936-016-1201-1 26957210
2. Dengela D, Seyoum A, Lucas B, Johns B, George K, Belemvire A, et al. Multi-country assessment of residual bio-efficacy of insecticides used for indoor residual spraying in malaria control on different surface types: results from program monitoring in 17 PMI/USAID-supported IRS countries. Parasit Vectors. 2018;11: 71. doi: 10.1186/s13071-017-2608-4 29382388
3. World Health Organization. Report of the Sixteenth WHOPES Working Group Meeting. Genèva; 2013.
4. World Health Organization. Global report on insecticide resistance in malaria vectors: 2010–2016. Genèva; 2018.
5. Sherrard-Smith E, Griffin JT, Winskill P, Corbel V, Pennetier C, Djénontin A, et al. Systematic review of indoor residual spray efficacy and effectiveness against Plasmodium falciparum in Africa. Nat Commun. 2018;9: 4982. doi: 10.1038/s41467-018-07357-w 30478327
6. van den Berg H, Zaim M, Yadav RS, Soares A, Ameneshewa B, Mnzava A, et al. Global Trends in the Use of Insecticides to Control Vector-Borne Diseases. Environ Health Perspect. 2012;120: 577–582. doi: 10.1289/ehp.1104340 22251458
7. Ranson H, N’Guessan R, Lines J, Moiroux N, Nkuni Z, Corbel V. Pyrethroid resistance in African anopheline mosquitoes: what are the implications for malaria control? Trends Parasitol. 2011;27: 91–98. doi: 10.1016/j.pt.2010.08.004 20843745
8. Wagman J, Gogue C, Tynuv K, Mihigo J, Bankineza E, Bah M, et al. An observational analysis of the impact of indoor residual spraying with non-pyrethroid insecticides on the incidence of malaria in Ségou Region, Mali: 2012–2015. Malar J. 2018;17. doi: 10.1186/s12936-017-2160-x 29316929
9. Abong’o B, Gimnig JE, Torr SJ, Longman B, Omoke D, Muchoki M, et al. Impact of indoor residual spraying with pirimiphos-methyl (Actellic 300CS) on entomological indicators of transmission and malaria case burden in Migori County, western Kenya. Sci Rep. 2020;10: 1–14. doi: 10.1038/s41598-020-61350-2 32161302
10. Kisinza WN, Nkya TE, Kabula B, Overgaard HJ, Massue DJ, Mageni Z, et al. Multiple insecticide resistance in Anopheles gambiae from Tanzania: A major concern for malaria vector control. Malar J. 2017;16: 439. doi: 10.1186/s12936-017-2087-2 29084560
11. Chukwuekezie O, Nwosu E, Nwangwu U, Dogunro F, Onwude C, Agashi N, et al. Resistance status of Anopheles gambiae (s.l.) to four commonly used insecticides for malaria vector control in South-East Nigeria. Parasites and Vectors. 2020;13. doi: 10.1186/s13071-020-3896-7 31924262
12. Oakeshott JG, Devonshire AL, Claudianos C, Sutherland TD, Horne I, Campbell PM, et al. Comparing the organophosphorus and carbamate insecticide resistance mutations in cholin- and carboxyl-esterases. Chem Biol Interact. 2005;157–158: 269–275. doi: 10.1016/j.cbi.2005.10.041 16289012
13. Weill M, Lutfalla G, Mogensen K, Chandre F, Berthomieu A, Berticat C, et al. Insecticide resistance in mosquito vectors. Nature. 2003;423: 136–137. doi: 10.1038/423136b 12736674
14. Weill M, Malcolm C, Chandre F, Mogensen K, Berthomieu A, Marquine M, et al. The unique mutation in ace-1 giving high insecticide resistance is easily detectable in mosquito vectors. Insect Mol Biol. 2004;13: 1–7. doi: 10.1111/j.1365-2583.2004.00452.x 14728661
15. Feyereisen R, Dermauw W, Van Leeuwen T. Genotype to phenotype, the molecular and physiological dimensions of resistance in arthropods. Pestic Biochem Physiol. 2015;121: 61–77. doi: 10.1016/j.pestbp.2015.01.004 26047113
16. Greenblatt HM, Guillou C, Guénard D, Argaman A, Botti S, Badet B, et al. The complex of a bivalent derivative of galanthamine with torpedo acetylcholinesterase displays drastic deformation of the active-site gorge: implications for structure-based drug design. J Am Chem Soc. 2004;126: 15405–11. doi: 10.1021/ja0466154 15563167
17. Cheung J, Mahmood A, Kalathur R, Liu L, Carlier PR. Structure of the G119S Mutant Acetylcholinesterase of the Malaria Vector Anopheles gambiae Reveals Basis of Insecticide Resistance. Structure. 2018;26: 130–136.e2. doi: 10.1016/j.str.2017.11.021 29276037
18. Bourguet D, Roig A, Toutant JP, Arpagaus M. Analysis of molecular forms and pharmacological properties of acetylcholinesterase in several mosquito species. Neurochem Int. 1997;31: 65–72. doi: 10.1016/s0197-0186(96)00118-0 9185166
19. Labbé P, Berthomieu A, Berticat C, Alout H, Raymond M, Lenormand T, et al. Independent Duplications of the Acetylcholinesterase Gene Conferring Insecticide Resistance in the Mosquito Culex pipiens. Mol Biol Evol. 2007;24: 1056–1067. doi: 10.1093/molbev/msm025 17283366
20. Dabiré KR, Diabaté A, Namontougou M, Djogbenou L, Kengne P, Simard F, et al. Distribution of insensitive acetylcholinesterase (ace-1R) in Anopheles gambiae s.l. populations from Burkina Faso (West Africa). Trop Med Int Heal. 2009;14: 396–403. doi: 10.1111/j.1365-3156.2009.02243.x 19254231
21. Ahoua Alou LP, Koffi AA, Adja MA, Tia E, Kouassi PK, Koné M, et al. Distribution of ace-1R and resistance to carbamates and organophosphates in Anopheles gambiae s.s. populations from Côte d’Ivoire. Malar J. 2010;9: 167. doi: 10.1186/1475-2875-9-167 20553593
22. Essandoh J, Yawson AE, Weetman D. Acetylcholinesterase (Ace-1) target site mutation 119S is strongly diagnostic of carbamate and organophosphate resistance in Anopheles gambiae s.s. and Anopheles coluzzii across southern Ghana. Malar J. 2013;12: 404. doi: 10.1186/1475-2875-12-404 24206629
23. Ngufor C, Chouaïbou M, Tchicaya E, Loukou B, Kesse N, N’Guessan R, et al. Combining organophosphate-Treated wall linings and long-lasting insecticidal nets fails to provide additional control over long-lasting insecticidal nets alone against multiple insecticide-resistant Anopheles gambiae in Côte d’Ivoire: An experimental hut. Malar J. 2014;13: 1–10. doi: 10.1186/1475-2875-13-1 24383426
24. Weetman D, Mitchell SN, Wilding CS, Birks DP, Yawson AE, Essandoh J, et al. Contemporary evolution of resistance at the major insecticide target site gene Ace-1 by mutation and copy number variation in the malaria mosquito Anopheles gambiae. Mol Ecol. 2015;24: 2656–2672. doi: 10.1111/mec.13197 25865270
25. Elanga-Ndille Nouage, Ndo Binyang, Assatse Nguiffo-Nguete, et al. The G119S Acetylcholinesterase (Ace-1) Target Site Mutation Confers Carbamate Resistance in the Major Malaria Vector Anopheles gambiae from Cameroon: A Challenge for the Coming IRS Implementation. Genes (Basel). 2019;10: 790. doi: 10.3390/genes10100790 31614683
26. Feng X, Yang C, Yang Y, Li J, Lin K, Li M, et al. Distribution and frequency of G119S mutation in ace-1 gene within Anopheles sinensis populations from Guangxi, China. Malar J. 2015;14: 470. doi: 10.1186/s12936-015-1000-0 26608572
27. Djogbénou L, Chandre F, Berthomieu A, Dabiré R, Koffi A, Alout H, et al. Evidence of Introgression of the ace-1R Mutation and of the ace-1 Duplication in West African Anopheles gambiae s. s. Carter DA, editor. PLoS One. 2008;3: e2172. doi: 10.1371/journal.pone.0002172 18478097
28. Edi C V., Djogbénou L, Jenkins AM, Regna K, Muskavitch MAT, Poupardin R, et al. CYP6 P450 Enzymes and ACE-1 Duplication Produce Extreme and Multiple Insecticide Resistance in the Malaria Mosquito Anopheles gambiae. Zhang J, editor. PLoS Genet. 2014;10: e1004236. doi: 10.1371/journal.pgen.1004236 24651294
29. Assogba BS, Djogbénou LS, Milesi P, Berthomieu A, Perez J, Ayala D, et al. An ace-1 gene duplication resorbs the fitness cost associated with resistance in Anopheles gambiae, the main malaria mosquito. Sci Rep. 2015;5: 14529. doi: 10.1038/srep14529 26434951
30. Assogba BS, Milesi P, Djogbénou LS, Berthomieu A, Makoundou P, Baba-Moussa LS, et al. The ace-1 Locus Is Amplified in All Resistant Anopheles gambiae Mosquitoes: Fitness Consequences of Homogeneous and Heterogeneous Duplications. Barton N, editor. PLOS Biol. 2016;14: e2000618. doi: 10.1371/journal.pbio.2000618 27918584
31. Assogba BS, Alout H, Koffi A, Penetier C, Djogbénou LS, Makoundou P, et al. Adaptive deletion in resistance gene duplications in the malaria vector Anopheles gambiae. Evol Appl. 2018;11: 1245–1256. doi: 10.1111/eva.12619 30151037
32. The Anopheles gambiae 1000 Genomes Consortium. Genome variation and population structure among 1142 mosquitoes of the African malaria vector species Anopheles gambiae and Anopheles coluzzii. Genome Res. 2020;30: 1533–1546. doi: 10.1101/gr.262790.120 32989001
33. Miles A, Harding NJ, Bottà G, Clarkson CS, Antão T, Kozak K, et al. Genetic diversity of the African malaria vector Anopheles gambiae. Nature. 2017;552: 96–100. doi: 10.1038/nature24995 29186111
34. Lucas ER, Miles A, Harding NJ, Clarkson CS, Lawniczak MKN, Kwiatkowski DP, et al. Whole-genome sequencing reveals high complexity of copy number variation at insecticide resistance loci in malaria mosquitoes. Genome Res. 2019;29: 1250–1261. doi: 10.1101/gr.245795.118 31345938
35. Bass C, Nikou D, Vontas J, Williamson MS, Field LM. Development of high-throughput real-time PCR assays for the identification of insensitive acetylcholinesterase (ace-1R) in Anopheles gambiae. Pestic Biochem Physiol. 2010;96: 80–85. doi: 10.1016/J.PESTBP.2009.09.004
36. Yi X, Liang Y, Huerta-Sanchez E, Jin X, Cuo ZXP, Pool JE, et al. Sequencing of 50 Human Exomes Reveals Adaptation to High Altitude. Science. 2010;329: 75–78. doi: 10.1126/science.1190371 20595611
37. Malaspinas AS, Westaway MC, Muller C, Sousa VC, Lao O, Alves I, et al. A genomic history of Aboriginal Australia. Nature. Nature Publishing Group; 2016. pp. 207–214. doi: 10.1038/nature18299 27654914
38. Rahman A, Hallgrímsdóttir I, Eisen M, Pachter L. Association mapping from sequencing reads using k-mers. Elife. 2018;7. doi: 10.7554/eLife.32920 29897334
39. Garud NR, Messer PW, Buzbas EO, Petrov DA. Recent Selective Sweeps in North American Drosophila melanogaster Show Signatures of Soft Sweeps. Copenhaver GP, editor. PLOS Genet. 2015;11: e1005004. doi: 10.1371/journal.pgen.1005004 25706129
40. Messer PW, Petrov DA. Population genomics of rapid adaptation by soft selective sweeps. Trends Ecol Evol. 2013;28: 659–669. doi: 10.1016/j.tree.2013.08.003 24075201
41. Durand EY, Patterson N, Reich D, Slatkin M. Testing for Ancient Admixture between Closely Related Populations. Mol Biol Evol. 2011;28: 2239–2252. doi: 10.1093/molbev/msr048 21325092
42. Patterson N, Moorjani P, Luo Y, Mallick S, Rohland N, Zhan Y, et al. Ancient Admixture in Human History. Genetics. 2012;192: 1065–1093. doi: 10.1534/genetics.112.145037 22960212
43. Alout H, Berthomieu A, Cui F, Tan Y, Berticat C, Qiao C, et al. Different Amino-Acid Substitutions Confer Insecticide Resistance Through Acetylcholinesterase 1 Insensitivity in Culex vishnui and Culex tritaeniorhynchus (Diptera: Culicidae) from China. J Med Entomol. 2007;44: 463–469. doi: 10.1603/0022-2585(2007)44[463:dascir]2.0.co;2 17547232
44. Dabiré RK, Namountougou M, Diabaté A, Soma DD, Bado J, Toé HK, et al. Distribution and Frequency of kdr Mutations within Anopheles gambiae s.l. Populations and First Report of the Ace.1G119S Mutation in Anopheles arabiensis from Burkina Faso (West Africa). Brooke B, editor. PLoS One. 2014;9: e101484. doi: 10.1371/journal.pone.0101484 25077792
45. Liebman KA, Pinto J, Valle J, Palomino M, Vizcaino L, Brogdon W, et al. Novel mutations on the ace-1 gene of the malaria vector Anopheles albimanus provide evidence for balancing selection in an area of high insecticide resistance in Peru. Malar J. 2015;14: 74. doi: 10.1186/s12936-015-0599-1 25889700
46. Labbé P, Milesi P, Yébakima A, Pasteur N, Weill M, Lenormand T. Gene-dosage effects on fitness in recent adaptive duplications: ACE-1 in the mosquito culex pipiens. Evolution (N Y). 2014;68: 2092–2101. doi: 10.1111/evo.12372 24494966
47. Edi CA V, Koudou BG, Bellai L, Adja AM, Chouaibou M, Bonfoh B, et al. Long-term trends in Anopheles gambiae insecticide resistance in Côte d’Ivoire. Parasit Vectors. 2014;7: 500. doi: 10.1186/s13071-014-0500-z 25429888
48. Djogbénou LS, Assogba B, Essandoh J, Constant EA V., Makoutodé M, Akogbéto M, et al. Estimation of allele-specific Ace-1 duplication in insecticide-resistant Anopheles mosquitoes from West Africa. Malar J. 2015;14: 507. doi: 10.1186/s12936-015-1026-3 26682913
49. Weetman D, Donnelly MJ. Evolution of insecticide resistance diagnostics in malaria vectors. Trans R Soc Trop Med Hyg. 2015;109: 291–3. doi: 10.1093/trstmh/trv017 25740955
50. Oxborough RM, Seyoum A, Yihdego Y, Dabire R, Gnanguenon V, Wat’Senga F, et al. Susceptibility testing of Anopheles malaria vectors with the neonicotinoid insecticide clothianidin; Results from 16 African countries, in preparation for indoor residual spraying with new insecticide formulations. Malar J. 2019;18: 264. doi: 10.1186/s12936-019-2888-6 31370898
51. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25: 1754–1760. doi: 10.1093/bioinformatics/btp324 19451168
52. Van der Auwera GA, Carneiro MO, Hartl C, Poplin R, del Angel G, Levy-Moonshine A, et al. From fastQ data to high-confidence variant calls: The genome analysis toolkit best practices pipeline. Curr Protoc Bioinforma. 2013;11: 11.10.1. doi: 10.1002/0471250953.bi1110s43 25431634
53. Delaneau O, Howie B, Cox AJ, Zagury JF, Marchini J. Haplotype estimation using sequencing reads. Am J Hum Genet. 2013;93: 687–696. doi: 10.1016/j.ajhg.2013.09.002 24094745
54. Cingolani P, Platts A, Wang LL, Coon M, Nguyen T, Wang L, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin). 2012;6: 80–92. doi: 10.4161/fly.19695 22728672
55. The Anopheles gambiae 1000 Genomes Consortium. Ag1000G Phase 2 AR1 data release. In: MalariaGEN [Internet]. 2017. Available: https://www.malariagen.net/data/ag1000g-phase-2-ar1
56. Giraldo-Calderón GI, Emrich SJ, MacCallum RM, Maslen G, Dialynas E, Topalis P, et al. VectorBase: an updated bioinformatics resource for invertebrate vectors and other organisms related with human diseases. Nucleic Acids Res. 2015;43: D707–D713. doi: 10.1093/nar/gku1117 25510499
57. Fanello C, Santolamazza F, Della Torre A. Simultaneous identification of species and molecular forms of the Anopheles gambiae complex by PCR-RFLP. Med Vet Entomol. 2002;16: 461–464. doi: 10.1046/j.1365-2915.2002.00393.x 12510902
58. World Health Organization. Test procedures for insecticide resistance monitoring in malaria vector mosquitoes. 2nd ed. WHO. Genèva: World Health Organization; 2018.
59. Warnes GR, Bolker B, Lumley T, from Randall C. Johnson are Copyright SAIC-Frederick RCJC, by the Intramural Research Program IF, of the NIH, et al. gmodels: Various R Programming Tools for Model Fitting. 2018.
60. Schaarschmidt F, Gerhard D. pairwiseCI: Confidence Intervals for Two Sample Comparisons. 2019.
61. Santolamazza F, Mancini E, Simard F, Qi Y, Tu Z, della Torre A. Insertion polymorphisms of SINE200 retrotransposons within speciation islands of Anopheles gambiae molecular forms. Malar J. 2008;7: 163. doi: 10.1186/1475-2875-7-163 18724871
62. Chabi J, Van’t Hof A, N’dri LK, Datsomor A, Okyere D, Njoroge H, et al. Rapid high throughput SYBR green assay for identifying the malaria vectors Anopheles arabiensis, Anopheles coluzzii and Anopheles gambiae s.s. Giles. PLoS One. 2019;14: e0215669. doi: 10.1371/journal.pone.0215669 31002694
63. Rogers AR, Huff C. Linkage disequilibrium between loci with unknown phase. Genetics. 2009;182: 839–44. doi: 10.1534/genetics.108.093153 19433632
64. Miles A, Harding N. scikit-allel. 2017. doi: 10.5281/zenodo.822784
65. Jones E, Oliphant T, Peterson P, others. SciPy: Open source scientific tools for Python. 2019.
66. Ellson J, Gansner E, Hu Y, Janssen E, North S. Graphviz—Graph Visualization Software. Available: https://www.graphviz.org/about/
67. Clarkson CS, Miles A, Harding NJ, Weetman D, Kwiatkowski D, Donnelly M, et al. The genetic architecture of target-site resistance to pyrethroid insecticides in the African malaria vectors Anopheles gambiae and Anopheles coluzzii. BioRxiv. 2018. doi: 10.1101/323980
68. Clarkson C, Miles A. Hapclust. 2018. Available: https://github.com/malariagen/agam-vgsc-report
69. Fontaine MC, Pease JB, Steele A, Waterhouse RM, Neafsey DE, Sharakhov I V., et al. Extensive introgression in a malaria vector species complex revealed by phylogenomics. Science. 2015;347: 1258524. doi: 10.1126/science.1258524 25431491
70. Nguyen L-TT, Schmidt HA, Von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32: 268–274. doi: 10.1093/molbev/msu300 25371430
71. Kalyaanamoorthy S, Minh BQ, Wong TKF, Von Haeseler A, Jermiin LS. ModelFinder: Fast model selection for accurate phylogenetic estimates. Nat Methods. 2017;14: 587–589. doi: 10.1038/nmeth.4285 28481363
72. Hoang DT, Chernomor O, von Haeseler A, Minh BQ, Vinh LS. UFBoot2: Improving the Ultrafast Bootstrap Approximation. Mol Biol Evol. 2018;35: 518–522. doi: 10.1093/molbev/msx281 29077904
73. Minh BQ, Nguyen MAT, von Haeseler A. Ultrafast approximation for phylogenetic bootstrap. Mol Biol Evol. 2013;30: 1188–95. doi: 10.1093/molbev/mst024 23418397
74. Revell LJ. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol Evol. 2012;3: 217–223. doi: 10.1111/j.2041-210X.2011.00169.x
75. Paradis E, Schliep K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Schwartz R, editor. Bioinformatics. 2019;35: 526–528. doi: 10.1093/bioinformatics/bty633 30016406
76. Cavalli-Sforza L. Human Diversity. Proceedings of the 12th International Congress of Genetics. 1969. pp. 405–416.
77. Hudson RR, Slatkin M, Maddison WP. Estimation of levels of gene flow from DNA sequence data. Genetics. 1992;132: 583–9. 1427045
78. Bhatia G, Patterson N, Sankararaman S, Price AL. Estimating and interpreting FST: The impact of rare variants. Genome Res. 2013;23: 1514–1521. doi: 10.1101/gr.154831.113 23861382
79. Klaus B, Strimmer. K. fdrtool: Estimation of (Local) False Discovery Rates and Higher Criticism. 2015.
80. Patterson N, Price AL, Reich D. Population Structure and Eigenanalysis. PLoS Genet. 2006;2: e190. doi: 10.1371/journal.pgen.0020190 17194218
81. Marçais G, Kingsford C. A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics. 2011;27: 764–770. doi: 10.1093/bioinformatics/btr011 21217122
82. R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria; 2017. Available: https://www.r-project.org/
83. Kriventseva E V, Kuznetsov D, Tegenfeldt F, Manni M, Dias R, Simão FA, et al. OrthoDB v10: sampling the diversity of animal, plant, fungal, protist, bacterial and viral genomes for evolutionary and functional annotations of orthologs. Nucleic Acids Res. 2019;47: D807–D811. doi: 10.1093/nar/gky1053 30395283
84. Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2014;12: 59–60. doi: 10.1038/nmeth.3176 25402007
85. Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30: 772–80. doi: 10.1093/molbev/mst010 23329690
86. Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics. 2009;25: 1972–3. doi: 10.1093/bioinformatics/btp348 19505945
87. Le SQ, Gascuel O. An improved general amino acid replacement matrix. Mol Biol Evol. 2008;25: 1307–20. doi: 10.1093/molbev/msn067 18367465
88. Geneious. Geneious. 2019. Available: www.geneious.com
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