MALDI–MS Profiling to Address Honey Bee Health Status under Bacterial Challenge through Computational Modeling

Honey bees play a critical role in the maintenance of plant biodiversity and sustainability of food webs. In the past few decades, bees have been subjected to biotic and abiotic threats causing various colony disorders. Therefore, monitoring solutions to help beekeepers to improve bee health are necessary. Matrix‐assisted laser desorption ionization–mass spectrometry (MALDI–MS) profiling has emerged within this decade as a powerful tool to identify in routine micro‐organisms and is currently used in real‐time clinical diagnosis. MALDI BeeTyping is developed to monitor significant hemolymph molecular changes in honey bees upon infection with a series of entomopathogenic Gram‐positive and ‐negative bacteria. A Serratia marcescens strain isolated from one naturally infected honey bee collected from the field is also considered. A series of hemolymph molecular mass fingerprints is individually recorded and to the authors' knowledge, the first computational model harboring a predictive score of 97.92% and made of nine molecular signatures that discriminate and classify the honey bees’ systemic response to the bacteria is built. Hence, the model is challenged by classifying a training set of hemolymphs and an overall recognition of 91.93% is obtained. Through this work, a novel, time and cost saving high‐throughput strategy that addresses honey bee health on an individual scale is introduced.     

Flow Cytometry Bioinformatics

Flow cytometry bioinformatics is the application of bioinformatics to flow cytometry data, which involves storing, retrieving, organizing, and analyzing flow cytometry data using extensive computational resources and tools. Flow cytometry bioinformatics requires extensive use of and contributes to the development of techniques from computational statistics and machine learning. Flow cytometry and related methods allow the quantification of multiple independent biomarkers on large numbers of single cells. The rapid growth in the multidimensionality and throughput of flow cytometry data, particularly in the 2000s, has led to the creation of a variety of computational analysis methods, data standards, and public databases for the sharing of results. Computational methods exist to assist in the preprocessing of flow cytometry data, identifying cell populations within it, matching those cell populations across samples, and performing diagnosis and discovery using the results of previous steps. For preprocessing, this includes compensating for spectral overlap, transforming data onto scales conducive to visualization and analysis, assessing data for quality, and normalizing data across samples and experiments. For population identification, tools are available to aid traditional manual identification of populations in two-dimensional scatter plots (gating), to use dimensionality reduction to aid gating, and to find populations automatically in higher dimensional space in a variety of ways. It is also possible to characterize data in more comprehensive ways, such as the density-guided binary space partitioning technique known as probability binning, or by combinatorial gating. Finally, diagnosis using flow cytometry data can be aided by supervised learning techniques, and discovery of new cell types of biological importance by high-throughput statistical methods, as part of pipelines incorporating all of the aforementioned methods. Open standards, data, and software are also key parts of flow cytometry bioinformatics. Data standards include the widely adopted Flow Cytometry Standard (FCS) defining how data from cytometers should be stored, but also several new standards under development by the International Society for Advancement of Cytometry (ISAC) to aid in storing more detailed information about experimental design and analytical steps. Open data is slowly growing with the opening of the CytoBank database in 2010 and FlowRepository in 2012, both of which allow users to freely distribute their data, and the latter of which has been recommended as the preferred repository for MIFlowCyt-compliant data by ISAC. Open software is most widely available in the form of a suite of Bioconductor packages, but is also available for web execution on the GenePattern platform.

This is a “Topic Page” article for PLOS Computational Biology.

     

Hierarchy of Attractants for Honey Bee Swarms

Chemical signals influence the selection of potential nest cavities by honey bee reproductive swarms. Attractants for swarms include the odors of old dark honey bee brood combs, odors from noncomb hive materials and propolis, and Nasonov pheromone, the odor released from the Nasonov glands of worker bees. Based on crossover and choice test experiments, swarms were shown to prefer, among otherwise identical cavities, those cavities containing Nasonov pheromone over cavities with only comb or other hive odors, cavities containing old comb over those with only noncomb odors or propolis, and cavities containing noncomb odors or propolis over those without bee or hive odor. Synergy between odors was not observed; that is, comb and/or noncomb hive odors did not enhance the attractiveness of Nasonov pheromone. The data support a model based on a hierarchy of olfactory attractants used by honey bee swarms, in order of highest to lowest: Nasonov pheromone, comb odor, noncomb and propolis odors, and, finally, absence of bee- or hive-produced odor.     

Varroa-Virus Interaction in Collapsing Honey Bee Colonies

Varroa mites and viruses are the currently the high-profile suspects in collapsing bee colonies. Therefore, seasonal variation in varroa load and viruses (Acute-Kashmir-Israeli complex (AKI) and Deformed Wing Virus (DWV)) were monitored in a year-long study. We investigated the viral titres in honey bees and varroa mites from 23 colonies (15 apiaries) under three treatment conditions: Organic acids (11 colonies), pyrethroid (9 colonies) and untreated (3 colonies). Approximately 200 bees were sampled every month from April 2011 to October 2011, and April 2012. The 200 bees were split to 10 subsamples of 20 bees and analysed separately, which allows us to determine the prevalence of virus-infected bees. The treatment efficacy was often low for both treatments. In colonies where varroa treatment reduced the mite load, colonies overwintered successfully, allowing the mites and viruses to be carried over with the bees into the next season. In general, AKI and DWV titres did not show any notable response to the treatment and steadily increased over the season from April to October. In the untreated control group, titres increased most dramatically. Viral copies were correlated to number of varroa mites. Most colonies that collapsed over the winter had significantly higher AKI and DWV titres in October compared to survivors. Only treated colonies survived the winter. We discuss our results in relation to the varroa-virus model developed by Stephen Martin.     

Recipe for a Busy Bee: MicroRNAs in Honey Bee Caste Determination

Social caste determination in the honey bee is assumed to be determined by the dietary status of the young larvae and translated into physiological and epigenetic changes through nutrient-sensing pathways. We have employed Illumina/Solexa sequencing to examine the small RNA content in the bee larval food, and show that worker jelly is enriched in miRNA complexity and abundance relative to royal jelly. The miRNA levels in worker jelly were 7–215 fold higher than in royal jelly, and both jellies showed dynamic changes in miRNA content during the 4^th to 6^th day of larval development. Adding specific miRNAs to royal jelly elicited significant changes in queen larval mRNA expression and morphological characters of the emerging adult queen bee. We propose that miRNAs in the nurse bee secretions constitute an additional element in the regulatory control of caste determination in the honey bee.     

Hidden Diversity in Honey Bee Gut Symbionts Detected by Single-Cell Genomics

Microbial communities in animal guts are composed of diverse, specialized bacterial species, but little is known about how gut bacteria diversify to produce genetically and ecologically distinct entities. The gut microbiota of the honey bee, Apis mellifera, presents a useful model, because it consists of a small number of characteristic bacterial species, each showing signs of diversification. Here, we used single-cell genomics to study the variation within two species of the bee gut microbiota: Gilliamella apicola and Snodgrassella alvi. For both species, our analyses revealed extensive variation in intraspecific divergence of protein-coding genes but uniformly high levels of 16S rRNA similarity. In both species, the divergence of 16S rRNA loci appears to have been curtailed by frequent recombination within populations, while other genomic regions have continuously diverged. Furthermore, gene repertoires differ markedly among strains in both species, implying distinct metabolic capabilities. Our results show that, despite minimal divergence at 16S rRNA genes, in situ diversification occurs within gut communities and generates bacterial lineages with distinct ecological niches. Therefore, important dimensions of microbial diversity are not evident from analyses of 16S rRNA, and single cell genomics has potential to elucidate processes of bacterial diversification.     

流式细胞术研究细胞凋亡的方法与技术

细胞发生凋亡时,会伴随着一系列形态学、生物化学及分子生物学性质的变化,包括细胞皱缩,核染色质凝聚,细胞膜通透性改变,Caspases激活,线粒体跨膜电位降低,膜磷酯酰丝氨酸外化,胞质Ca2+浓度升高,DNA片段化及含量变化等特点.应用流式细胞术进行细胞凋亡的研究,对于探讨胚胎发育、衰老以及研究肿瘤的发生、发展和转化等病理生理过程和病毒感染及免疫等具有十分重要的意义.本文就细胞凋亡的特征、基于细胞膜功能的流式细胞术检测方法和基于细胞器功能的流式细胞术检测方法等关键性问题进行了阐述.     

IP-FCM: Immunoprecipitation Detected by Flow Cytometry

Immunoprecipitation detected by flow cytometry (IP-FCM) is an efficient method for detecting and quantifying protein-protein interactions. The basic principle extends that of sandwich ELISA, wherein the captured primary analyte can be detected together with other molecules physically associated within multiprotein complexes. The procedure involves covalent coupling of polystyrene latex microbeads with immunoprecipitating monoclonal antibodies (mAb) specific for a protein of interest, incubating these beads with cell lysates, probing captured protein complexes with fluorochrome-conjugated probes, and analyzing bead-associated fluorescence by flow cytometry. IP-FCM is extremely sensitive, allows analysis of proteins in their native (non-denatured) state, and is amenable to either semi-quantitative or quantitative analysis. As additional advantages, IP-FCM requires no genetic engineering or specialized equipment, other than a flow cytometer, and it can be readily adapted for high-throughput applications.Download video file.^{(71M, mov)}     

Learning and Task Interference by Corpse-removal Specialists in Honey Bee Colonies

Undertakers are considered to be among the most specialized of pre-foraging honey bee (Apis mellifera L.) workers. In this study we examined a possible benefit and a cost of the corpse-rem oval specialty, the improvement in performance with experience, and interference by individuals attempting to perform the same task in the same location, respectively. Experienced bees removed corpses significantly faster than less experienced bees and also were less likely to drop corpses while exiting the hive (5.5% vs. 14.3% of attempts). Superior performance by experienced undertakers might occur as a consequence of learning, or by greater ability from the outset. Because active undertakers (≥ 3 corpse removals) did not improve with experience over their own careers, learning was not demonstrated. An extreme specialist, Yellow 54, removed a total of 114 corpses (33.8% of experimentally introduced dead bees) from the hive over a 13-day period. This is the longest recorded tenure of undertaking to date and demonstrates how a few individuals can dominate this task in a honey bee colony-Yellow 54 removed corpses significantly faster than other active bees, but she demonstrated no obvious improvement in performance over her undertaking career. This suggests the possibility that active undertakers were more talented than less active undertakers, irrespective of learning. When two undertakers worked together to remove a corpse from the hive, they took longer to complete the task than did single individuals. When multiple undertakers flew together from the hive, they were less likely to clear a nearby obstruction than single undertakers and were more likely to drop the corpse within 1 m of the hive. Thus, mutual interference exacted a measurable cost as a result of the undertaking specialization while learning provided few benefits.     

The Grooming Invitation Dance of the Honey Bee

The grooming invitation dance is a striking behavior in honey bee colonies that has not been extensively studied. The objectives of this study were (1) to describe the dance through video analysis, (2) to test the functional hypothesis that it is a grooming solicitation signal, and (3) to analyze the stimuli that cause its production. A worker bee producing the grooming invitation dance stands stationary and vibrates her whole body from side‐to‐side at a frequency of 4.2 ± 0.2 Hz for 9.3 ± 1.0 s. Sometimes the bee mixes bouts of body vibration with brief bouts of self‐grooming (average duration = 1.4 s). Bees that perform the grooming invitation dance have a far higher probability of being quickly groomed by a nest mate than do bees that do not perform the dance. Bees that had chalk dust puffed onto the bases of their wings produced significantly more grooming invitation dances than did control bees that received only puffs of air. This shows that it may be the accumulation of small particles at the bases of the wings that normally triggers the dance. We suggest that the evolutionary origin of this signal is self‐grooming behavior.     

Non-Specific dsRNA-Mediated Antiviral Response in the Honey Bee

Honey bees are essential pollinators of numerous agricultural crops. Since 2006, honey bee populations have suffered considerable annual losses that are partially attributed to Colony Collapse Disorder (CCD). CCD is an unexplained phenomenon that correlates with elevated incidence of pathogens, including RNA viruses. Honey bees are eusocial insects that live in colonies of genetically related individuals that work in concert to gather and store nutrients. Their social organization provides numerous benefits, but also facilitates pathogen transmission between individuals. To investigate honey bee antiviral defense mechanisms, we developed an RNA virus infection model and discovered that administration of dsRNA, regardless of sequence, reduced virus infection. Our results suggest that dsRNA, a viral pathogen associated molecular pattern (PAMP), triggers an antiviral response that controls virus infection in honey bees.     

流式细胞术

流式细胞术是一种综合应用光学、机械学、流体力学、电子计算机、细胞生物学、分子免疫学等学科技术,对高速流动的细胞或亚细胞进行快速定量测定和分析的方法。它一秒钟能分析几千个细胞,并同时测定细胞的多个参数,广泛应用于生物医学的许多领域,如测定细胞的特征(形态、膜电位等)和细胞内pH,细胞DNA、蛋白质含量、表面受体、Ca2+等。对生物工程学来说,了解细胞的这些参数尤为重要,因为它们能比用传统技术测得的数据更好地描述细胞群体。从流式细胞仪对细胞多种参数的测定及原理,到它在生物工程学中的应用等方面进行了介绍,并讨论了流式细胞术的局限性和面临的挑战。