Behavior-specific changes in transcriptional modules lead to distinct and predictable neurogenomic states
Behavior-specific changes in transcriptional modules lead to distinct and predictable neurogenomic states
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DOI：
10.1073/pnas.1114093108
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发表时间：
2011-11-01
影响因子：
11.1
通讯作者：
Robinson, Gene E.
中科院分区：
文献类型：
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作者：
Chandrasekaran, Sriram;Ament, Seth A.;Robinson, Gene E.
利用853只蜜蜂在自然环境下48种不同行为表型的脑转录组谱，我们报告了行为特异性神经基因组状态可以从转录因子（tf）及其预测靶基因的协调作用中推断出来。这些转录组谱的无监督分层聚类显示了三个集群对应于三个生态上重要的行为类别：攻击，成熟和觅食。为了探索可能调节这些行为特异性神经基因组状态的遗传影响，我们重建了一个脑转录调控网络（TRN）模型。这种脑TRN可以高精度地定量预测与行为相关的2000多个基因的基因表达变化，即使是对未经过训练的行为表型也是如此，这表明在蜜蜂大脑中存在一组核心的TFs来调节行为特异性基因表达，以及其他更特定于特定类别的TFs。在TRN中发挥关键作用的tf包括众所周知的神经和行为可塑性调节因子，例如Creb，以及在其他生物学背景下更为人所知的tf，例如。nf - κ B（免疫）。我们的结果揭示了关于基因和行为之间关系的三个见解。首先，不同的行为是由大脑中不同的神经基因组状态决定的。其次，不同行为背后的神经基因组状态依赖于共享的和不同的转录模块。第三，尽管大脑很复杂，但tf与其假定的靶基因之间的简单线性关系是潜在行为网络的一个令人惊讶的突出特征。
Using brain transcriptomic profiles from 853 individual honey bees exhibiting 48 distinct behavioral phenotypes in naturalistic contexts, we report that behavior-specific neurogenomic states can be inferred from the coordinated action of transcription factors (TFs) and their predicted target genes. Unsupervised hierarchical clustering of these transcriptomic profiles showed three clusters that correspond to three ecologically important behavioral categories: aggression, maturation, and foraging. To explore the genetic influences potentially regulating these behavior-specific neurogenomic states, we reconstructed a brain transcriptional regulatory network (TRN) model. This brain TRN quantitatively predicts with high accuracy gene expression changes of more than 2,000 genes involved in behavior, even for behavioral phenotypes on which it was not trained, suggesting that there is a core set of TFs that regulates behavior-specific gene expression in the bee brain, and other TFs more specific to particular categories. TFs playing key roles in the TRN include well-known regulators of neural and behavioral plasticity, e.g., Creb, as well as TFs better known in other biological contexts, e.g ., NF-kappa B (immunity). Our results reveal three insights concerning the relationship between genes and behavior. First, distinct behaviors are subserved by distinct neurogenomic states in the brain. Second, the neurogenomic states underlying different behaviors rely upon both shared and distinct transcriptional modules. Third, despite the complexity of the brain, simple linear relationships between TFs and their putative target genes are a surprisingly prominent feature of the networks underlying behavior.