Genomic signatures underlying the oogenesis of the ectoparasitic mite Varroa destructor on its new host Apis mellifera

Introduction Host shifts of parasites can have devastating effects on novel hosts. One remarkable example is that of the ectoparasitic mite Varroa destructor, which has shifted hosts from Eastern honey bees (Apis cerana) to Western honey bees (Apis mellifera) and posed a major global threat to apiculture and wild honey bees. Objectives and methods To uncover the mechanisms underlying this rare successful host shift, we conducted a whole-genome analysis of host-shifted and nonshifted V. destructor mites and a cross-fostering infestation experiment. Results We found that oogenesis was upregulated in host-shifted mites on the new host A. mellifera relative to nonshifted mites. The transcriptomes of the host-shifted and nonshifted mites significantly differed as early as 1 h post-infestation of the new hosts. The differentially expressed genes were associated with nine genes carrying nonsynonymous high-FST SNPs, including mGluR2-like, Lamb2-like and Vitellogenin 6-like, which were also differentially expressed, and eIF4G, CG5800, Dap160 and Sas10, which were located in the center of the networks regulating the differentially expressed genes based on protein–protein interaction analysis. Conclusions The annotated functions of these genes were all associated with oogenesis. These genes appear to be the key genetic determinants of the oogenesis of host-shifted mites on the new host. Further study of these candidate genes will help elucidate the key mechanism underlying the success of host shifts of V. destructor

An overview of functional genomic tools in deciphering insecticide resistance

In this short review, we highlight three functional genomic technologies that have recently been contributing to the understanding of the molecular mechanisms underpinning insecticide resistance: the GAL4/UAS system, a molecular tool used to express genes of interest in a spatiotemporal controlled manner; the RNAi system, which is used to knock-down gene expression; and the most recently developed gene editing tool,CRISPR/Cas9, which can be used to knock-out and knock-in sequences of interest

Social signals and aversive learning in honey bee drones and workers

The dissemination of information is a basic element of group cohesion. In honey bees (Apis mellifera Linnaeus 1758), like in other social insects, the principal method for colony-wide information exchange is communication via pheromones. This medium of communication allows multiple individuals to conduct tasks critical to colony survival. Social signaling also establishes conflict at the level of the individual who must trade-off between attending to the immediate environment or the social demand. In this study we examined this conflict by challenging highly social worker honey bees, and less social male drone honey bees undergoing aversive training by presenting them with a social stress signal (isopentyl acetate, IPA). We utilized IPA exposure methods that caused lower learning performance in appetitive learning in workers. Exposure to isopentyl acetate (IPA) did not affect performance of drones and had a dose-specific effect on worker response, with positive effects diminishing at higher IPA doses. The IPA effects are specific because non-social cues, such as the odor cineole, improve learning performance in drones, and social homing signals (geraniol) did not have a discernible effect on drone or worker performance. We conclude that social signals do generate conflict and that response to them is dependent on signal relevance to the individual as well as the context. We discuss the effect of social signal on learning both related to its social role and potential evolutionary history.Peer reviewedPsycholog

Genome-wide methylation is modified by caloric restriction in<i> Daphnia magna</i>

Background The degradation of epigenetic control with age is associated with progressive diseases of ageing, including cancers, immunodeficiency and diabetes. Reduced caloric intake slows the effects of ageing and age-related disease in vertebrates and invertebrates, a process potentially mediated by the impact of caloric restriction on epigenetic factors such as DNA methylation. We used whole genome bisulphite sequencing to study how DNA methylation patterns change with diet in a small invertebrate, the crustacean Daphnia magna. Daphnia show the classic response of longer life under caloric restriction (CR), and they reproduce clonally, which permits the study of epigenetic changes in the absence of genetic variation. Results Global cytosine followed by guanine (CpG) methylation was 0.7–0.9%, and there was no difference in overall methylation levels between normal and calorie restricted replicates. However, 333 differentially methylated regions (DMRs) were evident between the normally fed and CR replicates post-filtering. Of these 65% were hypomethylated in the CR group, and 35% were hypermethylated in the CR group. Conclusions Our results demonstrate an effect of CR on the genome-wide methylation profile. This adds to a growing body of research in Daphnia magna that demonstrate an epigenomic response to environmental stimuli. Specifically, gene Ontology (GO) term enrichment of genes associated with hyper and hypo-methylated DMRs showed significant enrichment for methylation and acyl-CoA dehydrogenase activity, which are linked to current understanding of their roles in CR in invertebrate model organisms

Current technical approaches to brain energy metabolism

Neuroscience is a technology‐driven discipline and brain energy metabolism is no exception. Once satisfied with mapping metabolic pathways at organ level, we are now looking to learn what it is exactly that metabolic enzymes and transporters do and when, where do they reside, how are they regulated, and how do they relate to the specific functions of neurons, glial cells, and their subcellular domains and organelles, in different areas of the brain. Moreover, we aim to quantify the fluxes of metabolites within and between cells. Energy metabolism is not just a necessity for proper cell function and viability but plays specific roles in higher brain functions such as memory processing and behavior, whose mechanisms need to be understood at all hierarchical levels, from isolated proteins to whole subjects, in both health and disease. To this aim, the field takes advantage of diverse disciplines including anatomy, histology, physiology, biochemistry, bioenergetics, cellular biology, molecular biology, developmental biology, neurology, and mathematical modeling. This article presents a well‐referenced synopsis of the technical side of brain energy metabolism research. Detail and jargon are avoided whenever possible and emphasis is given to comparative strengths, limitations, and weaknesses, information that is often not available in regular articles.Fondecyt, Grant Number: 1160317; MINECO, Grant Numbers: SAF2016‐78114‐R and RTC‐2015‐3237‐1; CIBERFES, Grant Numer: CB16/10/00282; SP3‐People‐MC‐ITN program, Grant Number: 608381; EU BATCure, Grant Number: 666918; FEDER (European regional development fund); Investissement d'Avenir, Grant Number: ANR‐11‐INBS‐0011; French State in the context of the “Investments for the future” Program IdEx and the LabEx TRAIL, Grant Numbers: ANR‐10‐IDEX and ANR‐10‐LABX‐57; French–Swiss ANR‐FNS, Grant Numer: ANR‐15‐ CE37‐0012. University of Nottingham; BBSRC, Grant Numers: BB/L019396/1 and BB/K009192/1; MRC, Grant Number: MR/L020661/1; Deutsche Forschungsgemeinschaft, Grant Numers: DFG SPP 1757, SFB 894, and FOR 2289; European Commission, Grant Number: H2020‐FETPROACT 732344; Neurofibres, Grant Number: H2020‐MSCA‐ITN‐722053 EU‐GliaPhD; US National Institutes of Health, Grant Number: R01NS087611; Teva Pharmaceuticals; Agilent Technologies. IdEx, Grant Number: ANR‐10‐IDEX‐03‐02; French–Swiss ANR‐FNS, Grant number: 310030E‐164271; National Institutes of Neurologic Disease and Stroke at the National Institutes of Health, Grant Numer: R01 NS077773; University of Zurich and the Swiss National Science Foundation; Comisión Nacional de Investigación Científica y Tecnológica, Grant Numer: PB 01; Fondo Nacional de Desarrollo Científico y Tecnológico, Grant Numer: 1160317; Ministerio de Economía y Competitividad, Grant Numer: RTC‐2015‐3237‐1,SAF2016‐78114‐R; Agence Nationale de la Recherche, Grant Numers: ANR‐10‐IDEX, ANR‐10‐IDEX‐03‐02, ANR‐10‐LABX‐57, ANR‐11‐INBS‐0011, and ANR‐15‐ CE37‐0012; Biotechnology and Biological Sciences Research Council, Grant Numers: BB/L019396/1 and BB/K009192/1; Medical Research Council, Grant Numer: MR/L020661/1.Peer reviewe

Building a new research framework for social evolution: intralocus caste antagonism

The breeding and non‐breeding ‘castes’ of eusocial insects provide a striking example of role‐specific selection, where each caste maximises fitness through different morphological, behavioural and physiological trait values. Typically, queens are long‐lived egg‐layers, while workers are short‐lived, largely sterile foragers. Remarkably, the two castes are nevertheless produced by the same genome. The existence of inter‐caste genetic correlations is a neglected consequence of this shared genome, potentially hindering the evolution of caste dimorphism: alleles that increase the productivity of queens may decrease the productivity of workers and vice versa, such that each caste is prevented from reaching optimal trait values. A likely consequence of this ‘intralocus caste antagonism’ should be the maintenance of genetic variation for fitness and maladaptation within castes (termed ‘caste load’), analogous to the result of intralocus sexual antagonism. The aim of this review is to create a research framework for understanding caste antagonism, drawing in part upon conceptual similarities with sexual antagonism. By reviewing both the social insect and sexual antagonism literature, we highlight the current empirical evidence for caste antagonism, discuss social systems of interest, how antagonism might be resolved, and challenges for future research. We also introduce the idea that sexual and caste antagonism could interact, creating a three‐way antagonism over gene expression. This includes unpacking the implications of haplodiploidy for the outcome of this complex interaction