scholarly journals Honey bee (Apis mellifera) larval pheromones may regulate gene expression related to foraging task specialization

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Rong Ma ◽  
Juliana Rangel ◽  
Christina M. Grozinger
2019 ◽  
Author(s):  
R Ma ◽  
J Rangel ◽  
CM Grozinger

AbstractBackgroundForaging behavior in honey bees (Apis mellifera) is a complex phenotype which is regulated by physiological state and social signals. How these factors are integrated at the molecular level to modulate foraging behavior has not been well-characterized. The transition of worker bees from nursing to foraging behavior is mediated by large-scale changes in brain gene expression, which are influenced by pheromones produced by the queen and larvae. Larval pheromones can also stimulate foragers to leave the colony to collect pollen, but the mechanisms underpinning this rapid behavioral plasticity are unknown. Furthermore, the mechanisms through which foragers specialize on collecting nectar or pollen, and how larval pheromones impact these different behavioral states, remains to be determined. Here, we investigated the patterns of gene expression related to rapid behavioral plasticity and task allocation among honey bee foragers exposed to two larval pheromones, brood pheromone (BP) and (E)-beta-ocimene (EBO).ResultsWe hypothesized that both pheromones would alter expression of genes in the brain related to foraging and would differentially impact expression of genes in the brains of pollen compared to nectar foragers. Combining data reduction, clustering, and network analysis methods, we found that foraging preference (nectar vs. pollen) and pheromone exposure are each associated with specific brain gene expression profiles. Furthermore, pheromone exposure has a strong transcriptional effect on genes that are preferentially expressed in nectar foragers. Representation factor analysis between our study and previous landmark honey bee transcriptome studies revealed significant overlaps for both pheromone communication and foraging task specialization.ConclusionsSocial signals (i.e. pheromones) may invoke foraging-related genes to upregulate pollen foraging at both long and short time scales. These results provide new insights into how social signals integrate with task specialization at the molecular level and highlights the important role that brain gene expression plays in behavioral plasticity across time scales.


1992 ◽  
Vol 66 (1) ◽  
pp. 95-105 ◽  
Author(s):  
A M Colberg-Poley ◽  
L D Santomenna ◽  
P P Harlow ◽  
P A Benfield ◽  
D J Tenney

2019 ◽  
Vol 70 (19) ◽  
pp. 5355-5374 ◽  
Author(s):  
Dandan Zang ◽  
Jingxin Wang ◽  
Xin Zhang ◽  
Zhujun Liu ◽  
Yucheng Wang

Abstract Plant heat shock transcription factors (HSFs) are involved in heat and other abiotic stress responses. However, their functions in salt tolerance are little known. In this study, we characterized the function of a HSF from Arabidopsis, AtHSFA7b, in salt tolerance. AtHSFA7b is a nuclear protein with transactivation activity. ChIP-seq combined with an RNA-seq assay indicated that AtHSFA7b preferentially binds to a novel cis-acting element, termed the E-box-like motif, to regulate gene expression; it also binds to the heat shock element motif. Under salt conditions, AtHSFA7b regulates its target genes to mediate serial physiological changes, including maintaining cellular ion homeostasis, reducing water loss rate, decreasing reactive oxygen species accumulation, and adjusting osmotic potential, which ultimately leads to improved salt tolerance. Additionally, most cellulose synthase-like (CSL) and cellulose synthase (CESA) family genes were inhibited by AtHSFA7b; some of them were randomly selected for salt tolerance characterization, and they were mainly found to negatively modulate salt tolerance. By contrast, some transcription factors (TFs) were induced by AtHSFA7b; among them, we randomly identified six TFs that positively regulate salt tolerance. Thus, AtHSFA7b serves as a transactivator that positively mediates salinity tolerance mainly through binding to the E-box-like motif to regulate gene expression.


2006 ◽  
Vol 3 (2) ◽  
pp. 109-122 ◽  
Author(s):  
◽  
Christopher H. Bryant ◽  
Graham J.L. Kemp ◽  
Marija Cvijovic

Summary We have taken a first step towards learning which upstream Open Reading Frames (uORFs) regulate gene expression (i.e., which uORFs are functional) in the yeast Saccharomyces cerevisiae. We do this by integrating data from several resources and combining a bioinformatics tool, ORF Finder, with a machine learning technique, inductive logic programming (ILP). Here, we report the challenge of using ILP as part of this integrative system, in order to automatically generate a model that identifies functional uORFs. Our method makes searching for novel functional uORFs more efficient than random sampling. An attempt has been made to predict novel functional uORFs using our method. Some preliminary evidence that our model may be biologically meaningful is presented.


Nature ◽  
2008 ◽  
Vol 453 (7194) ◽  
pp. 534-538 ◽  
Author(s):  
Oliver H. Tam ◽  
Alexei A. Aravin ◽  
Paula Stein ◽  
Angelique Girard ◽  
Elizabeth P. Murchison ◽  
...  

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