Apis mellifera
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ABSTRACT:
PROVIDER: PRJNA12604 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Honey bee linkage map from the Hunt lab at Purdue University
| PRJNA13367 | ENA
Project description:Here we present the first characterisation of small RNAs in honey bee reproductive tissues. We conclude that small RNAs are likely to play an integral role in honey bee gametogenesis and reproduction and provide a plausible mechanism for parent-of origin-effects on gene expression and reproductive physiology. present in honey bee reproductive tissues: ovaries, spermatheca, semen, fertilised and unfertilised eggs, and testes.
2022-04-13 | GSE182720 | GEO
Project description:It is estimated that animals pollinate 87.5% of flowering plants worldwide and that managed honey bees (Apis mellifera) account for 30-50% of this ecosystem service to agriculture. In addition to their important role as pollinators, honey bees are well-established insect models for studying learning and memory, behaviour, caste differentiation, epigenetic mechanisms, olfactory biology, sex determination and eusociality. Despite their importance to agriculture, knowledge of honey bee biology lags behind many other livestock species. In this study we have used scRNA-Seq to map cell types to different developmental stages of the worker honey bee (prepupa at day 11 and pupa at day 15), and sought to determine their gene signatures and thereby provide potential functional annotations for as yet poorly characterized genes. To identify cell type populations we examined the cell-to-cell network based on the similarity of the single-cells’ transcriptomic profiles. Grouping similar cells together we identified 63 different cell clusters of which 15 clusters were identifiable at both stages. To determine genes associated with specific cell populations or with a particular biological process involved in honey bee development, we used gene co-expression analysis. We combined this analysis with literature mining, the honey bee protein atlas and Gene Ontology analysis to determine cell cluster identity. Of the cell clusters identified, 9 were related to the nervous system, 7 to the fat body, 14 to the cuticle, 5 to muscle, 4 to compound eye, 2 to midgut, 2 to hemocytes and 1 to malpighian tubule/pericardial nephrocyte. To our knowledge, this is the first whole single cell atlas of honey bees at any stage of development and demonstrates the potential for further work to investigate their biology of at the cellular level.
2023-07-18 | E-MTAB-13158 | biostudies-arrayexpress
Project description:The microsporidia Nosema ceranae are intracellular parasites that proliferate in the midgut epithelial cells of honey bees (Apis mellifera). To analyze the pathological effects of those microsporidia, we orally infected honey bee workers 7 days after their emergence. Bees were flash frozen 15 days after the infection. Then, the effects on the gut ventriculi were analyzed and compared to non-infected (control) bees.
2012-05-23 | GSE25455 | GEO
Project description:Transcriptome sequencing has become the main methodology for analyzing the relationship between genes and characteristics of interests, particularly those associated with diseases and economic traits. Because of its functional superiority, commercial royal jelly (RJ) and its production are major areas of focus in the field of apiculture. Multiple lines of evidence have demonstrated that many factors affect RJ output by activating or inhibiting various target genes and signaling pathways to augment their efficient replication. The coding sequences made available by the Honey Bee Genome Sequencing Consortium have permitted a pathway-based approach for investigating the development of the hypopharyngeal glands (HGs). In the present study, 3573941, 3562730, 3551541, 3524453, and 3615558 clean reads were obtained from the HGs of five full-sister honey bee samples using Solexa RNA sequencing technology. These reads were then assembled into 18378, 17785, 17065, 17105, and 17995 unigenes, respectively, and aligned to the DFCI Honey Bee Gene Index database. The differentially expressed genes (DEGs) data were also correlated with detailed morphological data for HGs acini. The results identify areas that warrant further study, including those that can be used to improve honey bee breeding techniques and help ensure stable yields of RJ with high quality traits.
2014-05-20 | GSE47136 | GEO
Project description:In honey bees, Vitellogenin (Vg) is hypothesized to be a major factor affecting hormone signaling, food-related behavior, immunity, stress resistance and lifespan. Likewise microRNAs play important roles in posttranscriptional gene regulation and affect many biological processes thereby showing many parallels to Vg functions. The molecular basis of Vg and microRNA interactions is largely unknown. Here, we exploited the well-established RNA interference (RNAi) protocol for Vg knockdown to investigate its effects on microRNA population in honey bee forager’s brain and fat body tissue. To identify microRNAs that are differentially expressed between tissues in control and knockdown foragers, we used µParaflo® microfluidic oligonucleotide microRNA microarrays. Our results show 76 and 74 miRNAs were expressed in the brain of control and knockdown foragers whereas 66 and 69 miRNAs were expressed in the fat body of control and knockdown foragers respectively. Target prediction identified potential seed matches for differentially expressed subset of microRNAs affected by Vg knockdown. These candidate genes are involved in a broad range of biological processes including insulin signaling, juvenile hormone (JH) and ecdysteroid signaling previously shown to affect foraging behavior. Thus, here we demonstrate a causal link between Vg expression-variation and variation in the abundance of microRNAs in different tissues with possible consequences for regulation of foraging behavior.
2013-03-07 | GSE44915 | GEO
Project description:Purpose: Parts of Europe and the United States have witnessed dramatic losses in commercially managed honey bees over the past decade to what is considered an unsustainable extent. The large-scale loss of honey bees has considerable implications for the agricultural economy because honey bees are one of the leading pollinators of numerous crops. Honey bee declines have been associated with several interactive factors. Poor nutrition and viral infection are two environmental stressors that pose heightened dangers to honey bee health. Methods: We used RNA-sequencing to examine how monofloral diets (Rockrose and Chestnut) and Israeli acute paralysis virus inoculation influence gene expression patterns in honey bees. Results: We found a considerable nutritional response, with almost 2,000 transcripts changing with diet quality. The majority of these genes were over-represented for nutrient signaling (insulin resistance) and immune response (Notch signaling and JaK-STAT pathways). Somewhat unexpectedly, the transcriptomic response to viral infection was fairly limited. We only found 43 transcripts to be differentially expressed, some with known immune functions (argonaute-2), transcriptional regulation, and muscle contraction. We created contrasts to determine if any protective mechanisms of good diet were due to direct effects on immune function (resistance) or indirect effects on energy availability (tolerance). A similar number of resistance and tolerance candidate differentially expressed genes were found, suggesting both processes may play significant roles in dietary buffering from pathogen infection. We also compared the virus main effect in our study (polyandrous colonies) to that obtained in a previous study (single-drone colonies) and verified significant overlap in differential expression despite visualization methods showing differences in the noisiness levels between these two datasets. Conclusions: Through transcriptional contrasts and functional enrichment analysis, we add to evidence of feedbacks between diet and disease in honey bees. We also show that comparing results derived from polyandrous colonies (which are typically more natural) and single-drone colonies (which usually yield more signal) may allow researchers to identify transcriptomic patterns in honey bees that are concurrently less artificial and less noisy. Altogether, we hope this work underlines possible merits of using data visualization techniques and multiple datasets when interpreting RNA-sequencing studies.
2018-10-30 | GSE121885 | GEO