Structure and function of gene regulatory networks associated with worker sterility in honeybees

Abstract

A characteristic of eusocial bees is a reproductive division of labor in which one or a few queens monopolize reproduction, while her worker daughters take on reproductively altruistic roles within the colony. The evolution of worker reproductive altruism involves indirect selection for the coordinated expression of genes that regulate personal reproduction, but evidence for this type of selection remains elusive. In this study, we tested whether genes coexpressed under queen‐induced worker sterility show evidence of adaptive organization within a model brain transcriptional regulatory network (TRN). If so, this structured pattern would imply that indirect selection on nonreproductive workers has influenced the functional organization of genes within the network, specifically to regulate the expression of sterility. We found that literature‐curated sets of candidate genes for sterility, ranging in size from 18 to 267, show strong evidence of clustering within the three‐dimensional space of the TRN. This finding suggests that our candidate sets of genes for sterility form functional modules within the living bee brain's TRN. Moreover, these same gene sets colocate to a single, albeit large, region of the TRN's topology. This spatially organized and convergent pattern contrasts with a null expectation for functionally unrelated genes to be haphazardly distributed throughout the network. Our meta‐genomic analysis therefore provides first evidence for a truly “social transcriptome” that may regulate the conditional expression of honeybee worker sterility.

From gene list to gene network: Recognizing functional connections that regulate behavioral traits

“…Likewise, specific network gene clusters have been implicated in worker temperament (Alaux et al, ), worker ovary activation (Thompson et al, ), nurse‐to‐forager maturation (Ament et al, ), and other behavioral traits that characterize social reproduction (Amdam, Page, Fondrk, & Brent, ; Ament, Corona, Pollock, & Robinson, ; Hunt et al, ; Wang et al, ). Akin to the “sterility cluster” deduced by Sobotka et al (), these studies describe networked gene clusters underlying variation in other social traits.…”

“…Building on the cocitation analyses of genes underlying honeybee worker sterility, Sobotka, Daley, Chandrasekaran, Rubin, and Thompson () took a different approach. They took an independently derived 2,382‐gene model transcriptional regulatory network (TRN) not related to sterility (cf Chandrasekaran et al, ) and used it as a template over which to plot compiled lists of “sterility” genes.…”

“…(a) The 361‐gene subcluster of a model honeybee TRN that is enriched for genes associated with worker sterility on a microarray (red). The figure is redrawn from Sobotka et al () and highlights the two most‐connected hub genes (yellow), which are the nuclear hormone receptor fushi tarazu ( ftz‐f1 ; AM10191) with n = 73 connections and fruitless ( fru ; AM09450) with n = 53 connections. This network has a short average path length (2.815), which tends to be disassortative (−0.31) and is strongly scale free, as indicated by the power‐law decay in degree distribution (b).…”

“…As evidenced by hypergeometric tests against a null distribution, Sobotka et al reported that three of the four sterility-gene sets they examined (ranging in size from 18 to 267 genes) did cluster This spatially convergent organization pattern describes how genes "communicate" within a network and rejects a null expectation of functionally disconnected genes to be haphazardly distributed throughout the TRN. The network analysis of Sobotka et al (2016) thus cultivated the prospect of a truly "social transcriptome" that regulates the conditional expression of honeybee sterility. The gene-regulatory effect on worker sterility is considered social because it confers no direct fitness to the worker and thus must have evolved by indirect selection through a worker's reproducing relatives.…”

Investigating the evolution and development of biological complexity under the framework of epigenetics

“…The epigenotype can integrate information from external sources and influence development to produce a cohesive organism that will be, to some extent, acclimate to its environment (Jaenisch & Bird, 2003;Figure 2a). For a given organism, while the genotype is static, the epigenotype can be modified during an organism's life and can incorporate changes in phenotypic interactions and the environment including community structure (Sobotka, Daley, Chandrasekaran, Rubin, & Thompson, 2016;Yan et al, 2014; Figure 2d). By incorporating environmental and developmental signals, different interactions might be created or dismantled during different stages of development or in response to environmental cues (Angers, Castonguay, & Massicotte, 2010;Gilbert & Epel, 2009).…”