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ARS Home Pacific West Area Tucson, Arizona Carl Hayden Bee Research Center Research Publications at this Location





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- Cold Storage Overview
- Why put colonies in cold storage to overwinter?
- When to put colonies in cold storage?
- Which colonies to put into cold storage?
- Cold Storage Overwintering Tool
- Overview
- Objective 1: Climate effects on bloom and floral rewards
- Objective 2. Landscape level nutrition models
- Objective 3: Determine pollinator number, diversity and pathogen load
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- Introduction
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Title:   Author  item
                                            AMENT, SETH - University Of Illinoisitem
                                            BLATTI, CHARLES - University Of Illinoisitem
                                            ALAUX, CEDRIC - University Of Illinoisitem
                                            WHEELER, MARSHA - University Of Illinoisitem
                                            TOTH, AMY - University Of Illinoisitem
                                            LECONTE, YVES - Institut National De La Recherche Agronomique (INRA)item
                                            HUNT, GREG - Purdue Universityitem
                                            GUZMAN-NOVOA, ERNESTO - University Of Guelphitem
                                            DeGrandi-Hoffman, Gloriaitem
                                            URIBE-RUBIO, JOSE LUIS - Instituto Nacional De Investigaciones Forestales Y Agropecuarias (INIFAP)item
                                            AMDAM, GRO - Arizona State Universityitem
                                            PAGE, ROBERT - Arizona State Universityitem
                                            RODRIGUEZ-ZAS, SANDRA - University Of Illinoisitem
                                            ROBINSON, GENE - University Of Illinoisitem
                                            SINHA, SAURABH - University Of Illinois
  Submitted to: Proceedings of the National Academy of Sciences (PNAS)
                                    Publication Type: Peer Reviewed Journal
                                    Publication Acceptance Date: 4/23/2012
                                    Publication  Date: N/A
                                    Citation: N/AInterpretive Summary: Honey bees progress through a series of complex behaviors as adults. Some are prompted by nutritional factors while others are under hormonal control. The age when a worker begins to forage is a major maturational milestone in bee life, and is determined by a variety of genetic  and environmental factors that induce differential expression of thousands of genes in the brain. This manuscript describes new statistical tools for identifying significant relationships between the expression patterns of single or combinations of genetic elements underlying behavior. The result of these statistical tools suggests that many different factors that affect behavior use the same elements for regulating gene expression. Though these elements exert common effects, they might be employed in distinct ways depending upon the factor and resulting behavior.
                                Technical Abstract: :  A  fundamental  problem  in  meta-analysis  is  how  to  systematically  combine  information  from  multiple  statistical  tests  to  rigorously  evaluate  a  single  overarching  hypothesis.  This occurs in systems biology when attempting to map genomic attributes to complex phenotypes such as behavior. Behavior and other  complex  phenotypes  are  influenced  by  intrinsic  and  environmental  determinants  that  act  on  the transcriptome,  but  little  is  known  about  how  these  determinants  interact  at  the  molecular  level.  We developed  an  informatic  technique  that  for  the  first  time  identifies  statistically  significant  meta- associations  between  gene  expression  patterns  and  transcription  factor  combinations.  Deploying this technique for brain transcriptome profiles from ca.  400  individual  bees,  we  show  that  diverse determinants  of  behavior  rely  on  specific,  shared  combinations  of  transcription  factors.  This regulatory code would have been missed by traditional gene co-expression or cis-regulatory analytic methods. We expect that our new meta-analysis tools will be useful for a broad array of problems in systems biology and other fields.


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