Pozitif Seçilim Analizi, Böceklerde Yaşam Tarzına ve Soya Özgü Moleküler Evrimin İzlerini Ortaya Çıkarmaktadır

Böcekler, en çeşitli ve hızlı evrim geçiren organizmalar arasındadır ve bu, böceklerin neredeyse tüm ekosistemlere uyum sağlamalarına izin vermektedir. Başarılı bir adaptasyon, zorlu çevre koşullarının üstesinden gelmeyi gerektirir. Başarılı adaptasyonun altında yatan bilinen en iyi moleküler mekanizma pozitif seçilimdir. Bu mekanizma, yeni faydalı mutasyonlar kazanarak ve bu faydalı mutasyonları üreme yoluyla popülasyonlarda yeni nesillere aktararak türlerin lehine olmaktadır. Bu çalışmada 6 takım ve iki başkalaşım grubuna ait toplam 12 böcek türü kullanılmıştır. Bu böceklerde ve ortak atalarında adaptif pozitif seçilim toplam 535 bire bir tek kopya ortolog genlerin kodlayan dizileri kullanılarak incelenmiştir. En fazla pozitif seçilime maruz kalmış gen sayısı Onthaphagus taurus ve Dendroctanus ponderosae'de, en düşük pozitif seçilime uğramış gen sayısı ise bir homeopteran türü olan Acyrthosiphon pisum'da bulunmuştur. Soya dayalı analizlerde ise, en yüksek sayıda pozitif seçilime uğramış gen Lepidoptera ve Diptera takımlarının ortak atasında ve onları takiben Hymenoptera'yı Homoptera ve Isoptera takımlarının yakın zamandaki ortak atasından ayıran atada tespit edilmiştir. Sindirim, oksidatif indirgeme, transkripsiyon ve translasyon gibi temel biyolojik süreçte yer alan genler, pozitif olarak seçilen ortak genler arasındadır. Yaşam tarzı ve soya özgü genlerin pozitif seçilim altında olduğu bulunmuştur.

Anahtar Kelimeler:

Böcekler, Genom, Moleküler evrim

Analysis of Positive Selection Provides Insights into Lifestyle- and Lineage-Specific Patterns of Molecular Evolution in Insects

Insects are among the most divergent and most rapidly evolving species, which allow them to adapt to virtually all ecosystems. Successful adaptation requires overcome of challenging environmental conditions. The best-known molecular mechanism underlying successful adaptation is positive selection. This mechanism favors in species by gaining new beneficial mutations and transferring these beneficial mutations to new generations in populations via reproduction. In this study, a total of 12 insect species belonging to 6 orders and two morphogenesis groups were used to investigate positive adaptive selection in insects and their common ancestors using a total of 535 one-to-one single-copy ortholog genes. The highest number of the positively selected gene was found in Onthaphagus taurus and Dendroctanus ponderosae, and the lowest number of positively selected genes were found in a homopteran species, Acyrthosiphon pisum. The highest number of positively selected genes was detected in the common ancestor of the orders Lepidoptera and Diptera, followed by the node that separated Hymenoptera from a recent common ancestor of the orders Homoptera and Isoptera. Genes involved in the fundamental biological process digestion, oxidative reduction, transcription, and translation were among the core positively selected genes. Lifestyle and lineage-specific genes were found to be under positive selection.

Keywords:

Insects, Genome, Molecular evolution,

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[1]G. Zhang, H. Wang, J. Shi, X.Wang, H.Zheng, GK. Wong, T. Clark, W. Wang, J. Wang, L. Kang, “Identification and characterization of insect-specific proteins by genome data analysis,” BMC Genomics, vol. 8, p. 93, 2007.
[2]M.W. Gaunt, M.A. Miles, “An insect molecular clock dates the origin of the insects and accords with palaeontological and biogeographic landmarks,” Molecular Biology and Evolution, vol. 19, no. 5, pp. 748-61, 2002.
[3]C. Bleuven, C. R. Landry, “Molecular and cellular bases of adaptation to a changing environment in microorganisms,” Proceedings of the Royal Society B:Biological Sciences, vol. 283, no. 1841, 2016.
[4]J. H. Laity, B. M. Lee, P. E. Wright, “Zinc finger proteins: new insights into structural and functional diversity,” Current opinion in structural biology, vol. 11, no. 1, pp. 39-46, 2001.
[5]R. Feyereisen, “Insect P450 enzymes,” Annual Review of Entomology, vol. 44, pp. 507-33, 1999. [6]N. Liu, T. Li, Y. Wang, S. Liu, “G-Protein Coupled Receptors (GPCRs) in Insects-A Potential Target for New Insecticide Development,” Molecules, vol. 26, no. 10, 2021.
[7]H. Weigand, F. Leese, “Detecting signatures of positive selection in non-model species using genomic data,” Zoological Journal of the Linnean Society, vol. 184, no. 2, pp. 528-583, 2018.
[8]M. Molles, A. Sher, “Ecology: Concepts and Applications”, 8e, Graw Hill 2019.
[9]Z. Yang, “PAML 4: phylogenetic analysis by maximum likelihood,” Molecular biology and evolution, vol. 24, no. 8, pp. 1586-1591, 2007.
[10]F. Li, M.Li, K.He, C. Huang, Y. Zhou, Z. Li, J.R. Walters, “ Insect genomes: progress and challenges,” Insect Molecular Biology, vol. 28, no. 6, pp. 739-758, 2019. [11]J. Roux, E. Privman, S. Moretti, J. T. Daub, M. Robinson-Rechavi, L. Keller, “Patterns of positive selection in seven ant genomes,” Molecular biology and evolution, vol. 31, no. 7, pp. 1661-85, 2014.
[12]K. M. Kapheim et al., “Social evolution. Genomic signatures of evolutionary transitions from solitary to group living,” Science, vol. 348, no. 6239, pp. 1139-43, 2015.
[13]B. A. Harpur, C.F. Kent, D.Molodtsova, J.M.D. Lebon, A.S. Alqarni, A.A. Owayss, A.Zayed, “Population genomics of the honey bee reveals strong signatures of positive selection on worker traits,” Proceedings of the National Academy of Sciences of the United States of America, vol. 111, no. 7, pp. 2614-9, 2014.
[14]D. M. Emms, S. Kelly, “OrthoFinder: phylogenetic orthology inference for comparative genomics,” Genome Biology, vol. 20, no. 1, pp. 238, 2019.
[15]K. Katoh, D. M. Standley, “MAFFT: iterative refinement and additional methods,” Methods in Molecular Biology, vol. 1079, pp. 131-46, 2014.
[16]S. Capella-Gutierrez, J. M. Silla-Martinez, T. Gabaldon, “trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses,” Bioinformatics, vol. 25, no. 15, pp. 1972-3, 2009.
[17]A. Stamatakis, “RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies,” Bioinformatics, vol. 30, no. 9, pp. 1312-3, 2014.
[18]J. Huerta-Cepas, J. Dopazo, T. Gabaldon, “ETE: a python Environment for Tree Exploration,” BMC Bioinformatics, vol. 11, p. 24, 2010. [19]A. Dabney, J. D. Storey, G. Warnes, “qvalue: Q-value estimation for false discovery rate control,” Rv.2.22, vol. 1, no. 0, 2010.
[20]R. C. Team, “R: A language and environment for statistical computing,” 2013.
[21]P. Jones, D. Binns, H.Y Chang, M. Frase, W.Li, C. McAnulla, H.McWilliam, J.Maslen, A.Mitchell, G. Nuka, S.Pesseat, A.F. Quinn, A. Sangrador-Vegas, M. Scheremetjew, S.Y. Yong, R.Lopez, S. Hunter, “ InterProScan 5: genome-scale protein function classification,” Bioinformatics, vol. 30, no. 9, pp. 1236-40, 2014. [22]R. Apweiler, A. Bairoch, C.H. Wu, W.C. Baerker, B. Boeckmann, S.Ferro, E. Gasteiger, H. Huang, R. Lopez, M. Magrane, M.J. Martin, D.A. Natale, C. O'Donovan, N. Redaschi, L.SL. Yeh, “ UniProt: the Universal Protein knowledgebase,” Nucleic Acids Research, vol. 32, no. 47, pp. D115-9, 2004.
[23]B. Boeckmann, A. Bairoch, R. Apweiler, M.C. Blatter, A. Estreicher, E. Gasteiger, M.J. Martin, K. Michoud, C. O'Donovan, I. Phan, S. Pilbout, M. Schneider, “The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003,” Nucleic Acids Research, vol. 31, no. 1, pp. 365 370, 2003.
[24]R.D. Finn, J. Tate, J. Mistry, P.C. Coggill, S.J. Sammut, H.R. Hotz, G. Ceric, K. Forslud, S.R. Eddy, E.L.L. Sonnhammer, A. Bateman, “The Pfam protein families database,” Nucleic Acids Research, vol. 32, no. 40, pp. D138-41, 2004.
[25]A. Conesa, S. Gotz, “Blast2GO: A comprehensive suite for functional analysis in plant genomics,” International Journal of Plant Genomics, vol. 2008, pp. 619832, 2008.