Until now, all reports state that during eversion of the drone endophallus, two substances are ejected, viz. semen and mucus, and that the mating sign of a queen bee consists of the bulb of the endophallus filled with mucus. I examined substances ejected during eversion of drone endophalli, as well as substances present in the mating sign. In the fully everted endophallus, creamy semen was found near the chitinized plates, amorphous white mucus was located further distally and a transparent-whitish condensed substance appeared at the end of the everted endophallus. In mating signs, white mucus was found near the bursa copulatrix of the queens and a transparent-whitish condensed substance in the distal part of the sign. Microscopic examinations showed that the transparent-whitish substance consisted of fragments of epithelial membranes sloughed from mucus glands. Thus, not two substances, but three, viz. semen, mucus and epithelial membranes, are ejected during endophallus eversion and natural mating with queen bees.

The term “metagenomics” represents a combination of molecular and bioinformatic tools used to assess the genetic information of a community without prior cultivation of the individual species. It is valuable for the study of microorganisms of which only a minor fraction is yet culturable. The collective genomes present in an environmental sample or in an enrichment of target cells are extracted and subject to sequence-based or functional analyses. The field of metagenomics is evolving very rapidly, especially due to newly developed high-throughput sequencing technologies and increased computational power. Metatranscriptome and – proteome analyses are increasingly combined with metagenomic studies in order to assess not only the genetic potential of a microbial community, but also the genes expressed in a particular environment. The present chapter gives a short historical overview of the early years of metagenome analyses, and of possible applications. Challenges regarding the molecular and bioinformatic part of metagenome analyses are discussed. The molecular section includes strategies to access a metagenome. Methods to enrich for cells or the DNA of certain subpopulations prior to metagenome analysis, as well as to extract, purify and amplify DNA are given. The construction and sequence-based screening of small and large insert metagenomic libraries as well as a library-independent metagenomic approach using a new high throughput sequencing technology are described. The bioinformatic section provides an overview of assembly and binning tools, gene prediction programs, and annotation systems. This section also addresses the problem of metagenomic studies on habitats with high microbial diversity. Moreover, approaches to analyse phylogenetic and functional diversity within a dataset are discussed. The aim of the chapter is to provide the reader with basic information on both the molecular and bioinformatic aspects of metagenome analysis, to give hints to further reading and, therefore, to enable the reader to use this valuable method in an appropriate way in his or her studies.

Nestmate recognition in Apis cerana and Apis mellifera was studied by introducing sealed queen cells heterospecifically between queenless colonies. No A. cerana queens were accepted by queenless A. mellifera; but A. mellifera queens were accepted in queenless A. cerana colonies. A. mellifera queens oviposited in queenless A. cerana colonies, but A. cerana workers removed most eggs. In time, egg removals declined, and some A. mellifera larvae that hatched from these eggs reached adulthood, and eventually about half of the workers were newly emerged A. mellifera. Eventually, the colonies consisted only of A. mellifera after A. cerana workers died by attrition. A. mellifera workers are more sensitive to nestmate recognition and killed the A. cerana virgin queens. In mixed-species colonies, after newly emerged A. mellifera workers matured, they removed eggs laid by the A. cerana queens until there were no workers to replace the old ones.

The columnar epithelial cells of the mucus gland begin to synthesize secretory material in the late pupal stage, and this material gradually accumulates in the lumen, beginning soon after emergence of the adult drones. Histochemical tests demonstrated secretory activity in the epithelial cells and revealed the biochemical nature of the secretions as a mixture of proteins, carbohydrates and lipids. Total proteins, lipids and carbohydrates were detected in concentrations of 333.2 ± 13.883, 208.60 ± 11.69 and 44.82 ± 2.94 μg/mg, respectively, showing that proteins form the major constituents of the mucus gland secretory material. SDS-PAGE of mucus gland secretory material revealed about 15 proteins of molecular weight ranging from 2.5 to 151.2 kDa. Three proteins of 45, 43 and 37 kDa were stained intensely and can be considered as the major class of mucus proteins.

Pyrrolysine followed selenocysteine in order of discovery. While both atypical amino acids are encoded by canonical stop codons, the mechanisms by which they are inserted into protein are very different. Pyrrolysine is carried to the ribosome by tRNA^Pyl (encoded by pylT) whose unusual structure possesses the CUA anticodon needed to decode UAG. A pyrrolysyl-tRNA synthetase (product of pylS) ligates pyrrolysine to tRNA^Pyl. Pyrrolysine is made by the products of the pylBCD genes without the need for tRNA^Pyl, contrasting with selenocysteine synthesis on tRNA^Sec. Isolated examples of the pylTSBCD genes, often in a single cluster, have been found in genomes of methanogenic Archaea, G+ Bacteria, and δ-proteobacteria. Escherichia coli transformed with pyl genes translates UAG as endogenously synthesized pyrrolysine. The ease of the lateral transfer of the genetic encoding of pyrrolysine is now being exploited for tailoring recombinant proteins. Pyrrolysine incorporation appears to occur to some extent by amber suppression on a genome-wide basis in methanogenic Archaea. With some methylamine methyltransferase transcripts, a putative pyrrolysine insertion sequence (PYLIS) forms an in-frame stem-loop 3^′ to the translated UAG, analogous to such loops required in Bacteria for translation of UGA as selenocysteine. PYLIS sequences are not found in all types of methylamine methyltransferases. Unlike the precedent of selenocysteine, after deletion of PYLIS, significant UAG translation remains with a marked increase in UAG-directed termination, suggesting some part of the PYLIS sequence functions in enhancing amber suppression. Some methanogen genomes encode additional homologs of elongation and release factors, however, their limited distribution suggests at best a nonessential role in enhancing UAG translation as pyrrolysine.

In this study, we investigated whether differences in the reproductive biology of honey bee (Apis mellifera) queens and laying workers are reflected in their eggs. We first tested the capacity of queen- and worker-laid male eggs to withstand dry conditions, by incubating samples at 30.0, 74.9, and 98.7% relative humidity. We found that worker-laid eggs were more sensitive to desiccation. Secondly, we measured the weight and quantities of vitellin, total protein, lipid, glycogen, and free carbohydrate in queen- and workerlaid eggs. Although worker-laid eggs were found to be heavier than queen-laid eggs in two of the four replicates, no systematic differences were found regarding nutrient content. Finally, we compared the duration of embryo development in the two egg types. Worker-laid eggs developed more slowly than queen-laid eggs in two out of three replicates, suggesting that they may only be partly mature at the moment they are laid. Possible causes and consequences of the observed differences are discussed.

Social insect colonies display a remarkable ability to adjust investment in reproduction (i.e., production of sexuals) in accordance with environmental conditions such as season and food availability. How this feat is accomplished by the colony’s queen(s) and workers remains a puzzle. Here, I review what we have learned about this subject in the European honeybee (Apis mellifera), specifically with regard to a colony’s production of males (drones). I identify five environmental conditions that influence colony-level patterns of drone production and then define five stages of drone rearing that are accomplished by the queen and workers. Using this framework, I detail our current understanding of how the queen or workers adjust their actions at each stage of drone rearing in response to each of the environmental conditions. Future investigations of this topic in honeybees and other social insect societies will lead to a better understanding of how colonies manage to flexibly and efficiently allocate their resources under changing environmental conditions.

Key Points

The wealth of genome data for host-adapted bacteria have made it possible to re-examine and quantify the level of integration between bacteria and their hosts.

For genera with multiple hosts there seems to be a correlation between host range, host population size and the repertoire of bacterial outer surface proteins.

Shifts in bacterial lifestyle towards greater interaction with the host are mediated by mechanisms such as gain of new functions through horizontal gene transfer and duplication and functional divergence of existing genes.

Bacteria from many different phyla have established relationships with animals, plants and invertebrates. Mutualistic relationships are mostly seen in invertebrates, suggesting that plants and animals present barriers to obligate mutualism that are difficult to overcome.

Secretion systems in bacteria are essential for the interaction with their eukaryotic hosts. Horizontal gene transfer events, functional diversification and innovation through gene duplication of these systems have made it possible for bacteria to adapt to their host in various ways, giving rise to pathogenic or mutualistic relationships.

Obligate mutualistic bacteria with highly eroded genomes have survived owing to selective pressure on the essential function they contribute to eukaryotic fitness. This contribution has resulted in intimate relationships, in which some bacteria have transferred their genes to the host nuclear genome or established consortia with other co-habiting symbionts for the benefit of the host.

Polyandry, the multiple mating of females with more than one male, is a behavioural pattern found throughout the animal kingdom. In eusocial Hymenoptera (the bees, wasps and ants) polyandry is present in many taxa, and also the species with the highest degrees of polyandry known so far are found in this group. Polyandry has evolved multiple times and the genera with polyandrous species provide excellent test systems to study the evolution of multiple mating and its consequences for natural selection. Polyandrous colonies are composed of many paternal subfamilies (= patrilines), creating conflict potential on the one hand, and a template for variability among the colony members on the other hand.

Background

Establishing the relationship between an organism's genome sequence and its phenotype is a fundamental challenge that remains largely unsolved. Accurately predicting microbial phenotypes solely based on genomic features will allow us to infer relevant phenotypic characteristics when the availability of a genome sequence precedes experimental characterization, a scenario that is favored by the advent of novel high-throughput and single cell sequencing techniques.

Results

We present a novel approach to predict the phenotype of prokaryotes directly from their protein domain frequencies. Our discriminative machine learning approach provides high prediction accuracy of relevant phenotypes such as motility, oxygen requirement or spore formation. Moreover, the set of discriminative domains provides biological insight into the underlying phenotype-genotype relationship and enables deriving hypotheses on the possible functions of uncharacterized domains.

Conclusions

Fast and accurate prediction of microbial phenotypes based on genomic protein domain content is feasible and has the potential to provide novel biological insights. First results of a systematic check for annotation errors indicate that our approach may also be applied to semi-automatic correction and completion of the existing phenotype annotation.