Dataset Information

HBeeID: a molecular tool that identifies honey bee subspecies from different geographic populations.

ABSTRACT:

Background

Honey bees are the principal commercial pollinators. Along with other arthropods, they are increasingly under threat from anthropogenic factors such as the incursion of invasive honey bee subspecies, pathogens and parasites. Better tools are needed to identify bee subspecies. Genomic data for economic and ecologically important organisms is increasing, but in its basic form its practical application to address ecological problems is limited.

Results

We introduce HBeeID a means to identify honey bees. The tool utilizes a knowledge-based network and diagnostic SNPs identified by discriminant analysis of principle components and hierarchical agglomerative clustering. Tests of HBeeID showed that it identifies African, Americas-Africanized, Asian, and European honey bees with a high degree of certainty even when samples lack the full 272 SNPs of HBeeID. Its prediction capacity decreases with highly admixed samples.

Conclusion

HBeeID is a high-resolution genomic, SNP based tool, that can be used to identify honey bees and screen species that are invasive. Its flexible design allows for future improvements via sample data additions from other localities.

SUBMITTER: Donthu R

PROVIDER: S-EPMC11348773 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Publications

HBeeID: a molecular tool that identifies honey bee subspecies from different geographic populations.

Donthu Ravikiran R Marcelino Jose A P JAP Giordano Rosanna R Tao Yudong Y Weber Everett E Avalos Arian A Band Mark M Akraiko Tatsiana T Chen Shu-Ching SC Reyes Maria P MP Hao Haiping H Ortiz-Alvarado Yarira Y Cuff Charles A CA Claudio Eddie Pérez EP Soto-Adames Felipe F Smith-Pardo Allan H AH Meikle William G WG Evans Jay D JD Giray Tugrul T Abdelkader Faten B FB Allsopp Mike M Ball Daniel D Morgado Susana B SB Barjadze Shalva S Correa-Benitez Adriana A Chakir Amina A Báez David R DR Chavez Nabor H M NHM Dalmon Anne A Douglas Adrian B AB Fraccica Carmen C Fernández-Marín Hermógenes H Galindo-Cardona Alberto A Guzman-Novoa Ernesto E Horsburgh Robert R Kence Meral M Kilonzo Joseph J Kükrer Mert M Le Conte Yves Y Mazzeo Gaetana G Mota Fernando F Muli Elliud E Oskay Devrim D Ruiz-Martínez José A JA Oliveri Eugenia E Pichkhaia Igor I Romane Abderrahmane A Sanchez Cesar Guillen CG Sikombwa Evans E Satta Alberto A Scannapieco Alejandra A AA Stanford Brandi B Soroker Victoria V Velarde Rodrigo A RA Vercelli Monica M Huang Zachary Z

BMC bioinformatics 20240827 1

<h4>Background</h4>Honey bees are the principal commercial pollinators. Along with other arthropods, they are increasingly under threat from anthropogenic factors such as the incursion of invasive honey bee subspecies, pathogens and parasites. Better tools are needed to identify bee subspecies. Genomic data for economic and ecologically important organisms is increasing, but in its basic form its practical application to address ecological problems is limited.<h4>Results</h4>We introduce HBeeID ...[more]

PMID: 39192185

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Project description:We aimed to examine mechanistically the observed foraging differences across two honey bee, Apis mellifera, subspecies using the proboscis extension response assay. Specifically, we compared differences in appetitive reversal learning ability between honey bee subspecies: Apis mellifera caucasica (Pollman), and Apis mellifera syriaca (Skorikov) in a "common garden" apiary. It was hypothesized that specific learning differences could explain previously observed foraging behavior differences of these subspecies: A.m. caucasica switches between different flower color morphs in response to reward variability, and A.m. syriaca does not switch. We suggest that flower constancy allows reduced exposure by minimizing search and handling time, whereas plasticity is important when maximizing harvest in preparation for long winter is at a premium. In the initial or Acquisition phase of the test we examined specifically discrimination learning, where bees were trained to respond to a paired conditioned stimulus with an unconditioned stimulus and not to respond to a second conditioned stimulus that is not followed by an unconditioned stimulus. We found no significant differences among the subspecies in the Acquisition phase in appetitive learning. During the second, Reversal phase of the experiment, where flexibility in association was tested, the paired and unpaired conditioned stimuli were reversed. During the Reversal phase A.m. syriaca showed a reduced ability to learn the reverse association in the appetitive learning task. This observation is consistent with the hypothesis that A.m. syriaca foragers cannot change the foraging choice because of lack of flexibility in appetitive associations under changing contingencies. Interestingly, both subspecies continued responding to the previously rewarded conditioned stimulus in the reversal phase. We discuss potential ecological correlates and molecular underpinnings of these differences in learning across the two subspecies. In addition, in a supplemental experiment we demonstrated that these differences in appetitive reversal learning do not occur in other learning contexts.

Dataset Information

HBeeID: a molecular tool that identifies honey bee subspecies from different geographic populations.

Background

Results

Conclusion

Publications

HBeeID: a molecular tool that identifies honey bee subspecies from different geographic populations.

Similar Datasets

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