Genomic comparison of Italian honeybees (Apis Mellifera Ligustica) and Caucasian honeybees (Apis Mellifera Caucasica) for identifying signatures of selection

Document Type : Research Paper

Authors

1 Department of Animal Science, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran.

2 Animal Science Research Department, Ardabil Agricultural and Natural Resources Research and Education Center, AREEO, Ardabil, Iran.

3 Department of Molecular Biology, Virtual University of Pakistan, Lahore, Punjab, Pakistan.

10.22103/jab.2025.24103.1620

Abstract

The honeybee (Apis mellifera) is considered one of the most important insect species from an ecological and economic perspective. The honeybee provides an ideal model for utilizing population genomics to understand the evolutionary forces shaping the genomes of social insects. This study aimed to investigate and identify signatures of selection at the whole-genome level between the Italian and Caucasian honeybee breeds, considering the significance of these two breeds in honeybee-related production in Iran and globally.
Materials and Methods
The current study used whole-genome sequencing data of two subspecies of European honeybees available in the NCBI database. After applying quality control and filtration to the downloaded sequences, high-quality reads were aligned to the honeybee reference genome using BWA software. Subsequently, after applying various filters, high-quality SNPs were extracted using GATK software. In the next step, XP-EHH and Fst methods were employed to identify signatures of selection in the Italian honeybees compared to Caucasian bees. Additionally, to examine the genetic structure of the two studied breeds and to give a brief overview of their admixture and purity, the ADMIXTURE program was used.
Results
The analyses led to the identification of 847 genomic windows (containing 244 protein-coding genes) by the XP-EHH method, and 815 genomic windows (containing 439 protein-coding genes) by the Fst method. The results indicated that 19 genes were identified by both methods, and these genes were further investigated as final selection signatures. Among these genes, LOC72499, LOC551114, and LOC411919 were involved in immunity; LOC41390 in the foraging behaviors of workers; LOC413200 in cellular growth and development; LOC725885 in cellular differentiation, nursing, and foraging behaviors of bees as well as wing growth; LOC550886 in hygienic behaviors; LOC410393 in gut health and detoxification; and LOC408718 in growth of neural cells and the transformation of larvae into workers or queens. Identifying selection signatures can facilitate breeding strategies, disease management, and colony management. Furthermore, genomic information pertaining to various breeds enables the prediction of their behaviors in response to environmental challenges including climate changes.
Conclusion
Since traits such as behavior, foraging for food (nectar and pollen), cleanliness, colony defense, and immunity are among the most important and economically significant characteristics in honey bees, identifying the genes associated with these traits in the current study highlights the high potential of genomic data for better understanding the different breeds of honeybees.

Keywords

References

Alexander, D. H., Novembre, J., & Lange, K. (2009). Fast model-based estimation of ancestry in unrelated individuals. Genome Research, 19(9), 1655–1664. https://doi.org/10.1101/gr.094052.109
Arechavaleta-Velasco, M. E., Alcala-Escamilla, K., Robles-Rios, C., Tsuruda, J. M., & Hunt, G. J. (2012). Fine-scale linkage mapping reveals a small set of candidate genes influencing honey bee grooming behavior in response to Varroa mites. PLoS ONE, 7(11), Article e47269. https://doi.org/10.1371/journal.pone.0047269
Askari, N., Mohammadabadi, M., & Baghizadeh, A. (2011). ISSR markers for assessing DNA polymorphism and genetic characterization of cattle, goat, and sheep populations. Iranian Journal of Biotechnology, 9(3), 222–229. https://doi.org/10.5812/ijb.7228
Bolger, A. M., Lohse, M., & Usadel, B. (2014). Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics, 30(15), 2114–2120. https://doi.org/10.1093/bioinformatics/btu170
Boncristiani, D. L., Tauber, J. P., & Palmer-Young, E. C. (2021). Impacts of diverse natural products on honey bee viral loads and health. Applied Sciences, 11(22), Article 10732. https://doi.org/10.3390/app112210732
Browning, B. L., Tian, X., Zhou, Y., & Browning, S. R. (2021). Fast two-stage phasing of large-scale sequence data. American Journal of Human Genetics, 108(10), 1880–1890. https://doi.org/10.1016/j.ajhg.2021.08.005
Chandrasekaran, S., Ament, S. A., Eddy, J. A., Rodriguez-Zas, S. L., Schatz, B. R., Price, N. D., & Robinson, G. E. (2011). Behavior-specific changes in transcriptional modules lead to distinct and predictable neurogenomic states. Proceedings of the National Academy of Sciences, 108(44), 18020–18025. https://doi.org/10.1073/pnas.1114093108
Danecek, P., Auton, A., Abecasis, G., Albers, C. A., Banks, E., DePristo, M. A., Handsaker, R. E., Lunter, G., Marth, G. T., Sherry, S. T., McVean, G., Durbin, R., & 1000 Genomes Project Analysis Group. (2011). The variant call format and VCFtools. Bioinformatics, 27(15), 2156–2158. https://doi.org/10.1093/bioinformatics/btr330
Decio, P., Miotelo, L., Pereira, F. D. C., Roat, T. C., Marin-Morales, M. A., & Malaspina, O. (2021). Enzymatic responses in the head and midgut of Africanized Apis mellifera contaminated with a sublethal concentration of thiamethoxam. Ecotoxicology and Environmental Safety, 223, e112581. https://doi.org/10.1016/j.ecoenv.2021.112581
Di Pasquale, G., Salignon, M., Le Conte, Y., Belzunces, L. P., Decourtye, A., Kretzschmar, A., Suchail, S., Brunet, J.-L., & Alaux, C. (2013). Influence of pollen nutrition on honey bee health: Do pollen quality and diversity matter? PLoS ONE, 8(8), Article e72016. https://doi.org/10.1371/journal.pone.0072016
Foret, S., Kucharski, R., Pellegrini, M., Feng, S., Jacobsen, S. E., Robinson, G. E., & Maleszka, R. (2012). DNA methylation dynamics, metabolic fluxes, gene splicing, and alternative phenotypes in honey bees. Proceedings of the National Academy of Sciences, 109(13), 4968–4973. https://doi.org/10.1073/pnas.1202392109
Franck, P., Garnery, L., Celebrano, G., Solignac, M., & Cornuet, J. M. (2000). Hybrid origins of honeybees from Italy (Apis mellifera ligustica) and Sicily (A. m. sicula). Molecular Ecology, 9(7), 907–921. https://doi.org/10.1046/j.1365-294x.2000.00956.x
Ganapathy, A., & Ezekiel, U. (2019). Phytochemical modulation of miRNAs in colorectal cancer. Medicines, 6(2), Article 48. https://doi.org/10.3390/medicines6020048
Grozinger, C. M., & Zayed, A. (2020). Improving bee health through genomics. Nature Reviews Genetics, 21(5), 277–291. https://doi.org/10.1038/s41576-020-0210-7
Grozinger, C. M., Sharabash, N. M., Whitfield, C. W., & Robinson, G. E. (2003). Pheromone-mediated gene expression in the honey bee brain. Proceedings of the National Academy of Sciences, 100(Suppl. 2), 14519–14525. https://doi.org/10.1073/pnas.2335884100
Hedayat-Evrigh, N., Khalkhali-Evrigh, R., & Bakhtiarizadeh, M. R. (2020). Genome-wide identification and analysis of variants in domestic and wild Bactrian camels using whole-genome sequencing data. International Journal of Genomics, 2020, Article 2430846. https://doi.org/10.1155/2020/2430846
Huisken, J. L., & Rehan, S. M. (2023). Brain gene expression of foraging behavior and social environment in Ceratina calcarata. Genome Biology and Evolution, 15(7), Article evad117. https://doi.org/10.1093/gbe/evad117
Kandemir, I., Özkan, A., & Fuchs, S. (2011). Reevaluation of honeybee (Apis mellifera) microtaxonomy: A geometric morphometric approach. Apidologie, 42(5), 618–627. https://doi.org/10.1007/s13592-011-0063-3
Kang, Y., Wang, Z., An, K., Hou, Q., Zhang, Z., & Su, J. (2024). Introgression drives adaptation to the plateau environment in a subterranean rodent. BMC Biology, 22(1), Article 187. https://doi.org/10.1186/s12915-024-01986-y
Khalkhali-Evrigh, R., Hedayat, N., Seyedsharifi, R., Shakouri, M., & Ponnampalam, E. N. (2025). Genomic evidence of improved fertility and adaptation in Iranian domestic sheep attributed to introgression from Asiatic mouflon and urial. Scientific Reports, 15(1), e1185. https://doi.org/10.1038/s41598-025-85756-y
Kovac, H., Käfer, H., Stabentheiner, A., & Costa, C. (2014). Metabolism and upper thermal limits of Apis mellifera carnica and A. m. ligustica. Apidologie, 45(6), 664–677. https://doi.org/10.1007/s13592-014-0284-3
Li, H., & Durbin, R. (2009). Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics, 25(14), 1754–1760. https://doi.org/10.1093/bioinformatics/btp324
Li, W., Evans, J. D., Huang, Q., Rodríguez-García, C., Liu, J., Hamilton, M., Grozinger, C. M., Webster, T. C., Su, S., & Chen, Y. P. (2016). Silencing the honey bee (Apis mellifera) naked cuticle gene (nkd) improves host immune function and reduces Nosema ceranae infections. Applied and Environmental Microbiology, 82(22), 6779–6787. https://doi.org/10.1128/AEM.02105-16
Lin, X., Buff, E. M., Perrimon, N., & Michelson, A. M. (1999). Heparan sulfate proteoglycans are essential for FGF receptor signaling during Drosophila embryonic development. Development, 126(17), 3715–3723. https://doi.org/10.1242/dev.126.17.3715
Lou, R. N., & Therkildsen, N. O. (2022). Batch effects in population genomic studies with low-coverage whole-genome sequencing data: Causes, detection, and mitigation. Molecular Ecology Resources, 22(5), 1678–1692. https://doi.org/10.1111/1755-0998.13559
Ma, C., Xue, T., Peng, Q., Zhang, J., Guan, J., Ding, W., Li, Y., Xia, P., Zhou, L., Zhao, T., Wang, S., Quan, L., Li, C.-Y., & Liu, Y. (2023). A novel N6-deoxyadenine methyltransferase METL-9 modulates C. elegans immunity via dichotomous mechanisms. Cell Research, 33(8), 628–639. https://doi.org/10.1038/s41422-023-00826-y
Minozzi, G., Lazzari, B., De Iorio, M. G., Costa, C., Carpana, E., Crepaldi, P., Rizzi, R., Facchini, E., Gandini, G., Stella, A., & Pagnacco, G. (2021). Whole-genome sequence analysis of Italian honeybees (Apis mellifera). Animals, 11(5), Article 1311. https://doi.org/10.3390/ani11051311
Mohammadabadi, M. R., & Tohidinejad, F. (2017). Characteristics determination of Rheb gene and protein in Raini Cashmere goat. Iranian Journal of Applied Animal Science, 7(2), 289–295. https://doi.org/10.22099/IJAAS.2017.4076
Mohammadabadi, M. R., Kheyrodin, H., Afanasenko, V., Babenko, O., Klopenko, N., Kalashnik, O., & Buchkovska, V. (2024). The role of artificial intelligence in genomics. Journal of Agricultural Biotechnology, 15(2), 1–10. https://doi.org/10.22092/JAB.2024.355960.1411
Molodtsova, D. (2015). Molecular evolution of the brain transcription regulatory network affecting worker behaviour of honey bees (Apis mellifera) [Unpublished doctoral dissertation]. [Institution Name].
Nadeau, N. J., & Jiggins, C. D. (2010). A golden age for evolutionary genetics? Genomic studies of adaptation in natural populations. Trends in Genetics, 26(11), 484–492. https://doi.org/10.1016/j.tig.2010.08.004
Norouzy, A., Nassiry, M. R., Eftekhari Shahrody, F., Javadmanesh, A., Mohammad Abadi, M. R., & Sulimova, G. E. (2005). Identification of bovine leucocyte adhesion deficiency (BLAD) carriers in Holstein and Brown Swiss AI bulls in Iran. Russian Journal of Genetics, 41(12), 1409–1413. https://doi.org/10.1007/s11177-005-0243-9
Ou, J., Deng, H. M., Zheng, S. C., Huang, L. H., Feng, Q. L., & Liu, L. (2014). Transcriptomic analysis of developmental features of Bombyx mori wing disc during metamorphosis. BMC Genomics, 15, Article 820. https://doi.org/10.1186/1471-2164-15-820
Quinlan, A. R., & Hall, I. M. (2010). BEDTools: A flexible suite of utilities for comparing genomic features. Bioinformatics, 26(6), 841–842. https://doi.org/10.1093/bioinformatics/btq033
Saadatabadi, L. M., Mohammadabadi, M., Nanaei, H. A., Ghanatsaman, Z. A., Stavetska, R. V., Kalashnyk, O., Kochuk-Yashchenko, O. A., & Kucher, D. M. (2023). Unraveling candidate genes related to heat tolerance and immune response traits in some native sheep using whole-genome sequencing data. Small Ruminant Research, 225, Article 107018. https://doi.org/10.1016/j.smallrumres.2023.107018
Sabeti, P. C., Varilly, P., Fry, B., Lohmueller, J., Hostetter, E., Cotsapas, C., Xie, X., Byrne, Vinh, E. H., McCarroll, S. A., Gaudet, R., Schaffner, S. F., Lander, E. S., & The International HapMap Consortium. (2007). Genome-wide detection and characterization of positive selection in human populations. Nature, 449(7164), 913–918. https://doi.org/10.1038/nature06250
Sharma, R. (2014). Analysis of the FGF signalling pathway during embryogenesis of the red flour beetle Tribolium castaneum [Doctoral dissertation, Universität Rostock]. https://rosdok.uni-rostock.de/resolve/id/rosdok_document_0000002959
Shokri, S., Khezri, A., Mohammadabadi, M., & Kheyrodin, H. (2023). The expression of MYH7 gene in femur, humeral muscle, and back muscle tissues of fattening lambs of the Kermani breed. Agricultural Biotechnology Journal, 15(2), 217–236. https://doi.org/10.22103/jab.2023.21524.1486
Wragg, D., Marti-Marimon, M., Basso, B., Bidanel, J. P., Labarthe, E., Bouchez, O., Le Conte, Y., & Vignal, A. (2016). Whole-genome resequencing of honeybee drones to detect genomic selection in a population managed for royal jelly. Scientific Reports, 6, Article 27168. https://doi.org/10.1038/srep27168
Wragg, D., Techer, M. A., Canale-Tabet, K., Basso, B., Bidanel, J. P., Labarthe, E., Bouchez, O., Le Conte, Y., Clémencet, J., Delatte, H., & Vignal, A. (2018). Autosomal and mitochondrial adaptation following admixture: A case study on the honeybees of Reunion Island. Genome Biology and Evolution, 10(1), 220–238. https://doi.org/10.1093/gbe/evx247
Yıldız, B. İ., & Karabağ, K. (2022). Quantitation of neuroxin-1, ataxin-3, and atlastin genes related to grooming behavior in five races of honey bee, Apis mellifera L., 1758 (Hymenoptera: Apidae), in Turkey. Turkish Journal of Entomology, 46(1), 3–11. https://doi.org/10.16970/entoted.992984
Agricultural Biotechnology Journal
Volume 17, Issue 3
August 2025
Pages 27-48

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