Daniel Weaver

Finding the missing honey bee genes: Lessons learned from a genome upgrade

BackgroundThe first generation of genome sequence assemblies and annotations have had a significant impact upon our understanding of the biology of the sequenced species, the phylogenetic relationships among species, the study of populations within and across species, and have informed the biology of humans. As only a few Metazoan genomes are approaching finished quality (human, mouse, fly and worm), there is room for improvement of most genome assemblies. The honey bee (Apis mellifera) genome, published in 2006, was noted for its bimodal GC content distribution that affected the quality of the assembly in some regions and for fewer genes in the initial gene set (OGSv1.0) compared to what would be expected based on other sequenced insect genomes.ResultsHere, we report an improved honey bee genome assembly (Amel_4.5) with a new gene annotation set (OGSv3.2), and show that the honey bee genome contains a number of genes similar to that of other insect genomes, contrary to what was suggested in OGSv1.0. The new genome assembly is more contiguous and complete and the new gene set includes ~5000 more protein-coding genes, 50% more than previously reported. About 1/6 of the additional genes were due to improvements to the assembly, and the remaining were inferred based on new RNAseq and protein data.ConclusionsLessons learned from this genome upgrade have important implications for future genome sequencing projects. Furthermore, the improvements significantly enhance genomic resources for the honey bee, a key model for social behavior and essential to global ecology through pollination.

Gene expression patterns associated with queen honey bee longevity

The oxidative stress theory of aging proposes that accumulation of oxidative damage is the main proximate cause of aging and that lifespan is determined by the rate at which this damage occurs. Two predictions from this theory are that long-lived organisms produce fewer ROS or have increased antioxidant production. Based in these predictions, molecular mechanisms to promote longevity could include either changes in the regulation of mitochondrial genes that affect ROS production or elevated expression of antioxidant genes. We explored these possibilities in the honey bee, a good model for the study of aging because it has a caste system in which the same genome produces both a long-lived queen and a short-lived worker. We measured mRNA levels for genes encoding eight of the most prominent antioxidant enzymes and five mitochondrial proteins involved in respiration. The expression of antioxidant genes generally decreased with age in queens, but not in workers. Expression of most mitochondrial genes, in particular CytC, was higher in young queens, but these genes showed a faster age-related decline relative to workers. One exception to this trend was COX-I in thorax. This resulted in higher COX-I/CytC ratios in old queens compared to old workers, which suggests caste-specific differences in mitochondrial function that might be related to the caste-specific differences in longevity. Queen honey bee longevity appears to have evolved via mechanisms other than increased antioxidant gene expression.

BAYSIC: a Bayesian method for combining sets of genome variants with improved specificity and sensitivity

BackgroundAccurate genomic variant detection is an essential step in gleaning medically useful information from genome data. However, low concordance among variant-calling methods reduces confidence in the clinical validity of whole genome and exome sequence data, and confounds downstream analysis for applications in genome medicine.Here we describe BAYSIC (BAYeSian Integrated Caller), which combines SNP variant calls produced by different methods (e.g. GATK, FreeBayes, Atlas, SamTools, etc.) into a more accurate set of variant calls. BAYSIC differs from majority voting, consensus or other ad hoc intersection-based schemes for combining sets of genome variant calls. Unlike other classification methods, the underlying BAYSIC model does not require training using a “gold standard” of true positives. Rather, with each new dataset, BAYSIC performs an unsupervised, fully Bayesian latent class analysis to estimate false positive and false negative error rates for each input method. The user specifies a posterior probability threshold according to the user’s tolerance for false positive and false negative errors; lowering the posterior probability threshold allows the user to trade specificity for sensitivity while raising the threshold increases specificity in exchange for sensitivity.ResultsWe assessed the performance of BAYSIC in comparison to other variant detection methods using ten low coverage (~5X) samples from The 1000 Genomes Project, a tumor/normal exome pair (40X), and exome sequences (40X) from positive control samples previously identified to contain clinically relevant SNPs. We demonstrated BAYSIC’s superior variant-calling accuracy, both for somatic mutation detection and germline variant detection.ConclusionsBAYSIC provides a method for combining sets of SNP variant calls produced by different variant calling programs. The integrated set of SNP variant calls produced by BAYSIC improves the sensitivity and specificity of the variant calls used as input. In addition to combining sets of germline variants, BAYSIC can also be used to combine sets of somatic mutations detected in the context of tumor/normal sequencing experiments.

Population-genomic variation within RNA viruses of the Western honey bee, Apis mellifera, inferred from deep sequencing

BackgroundDeep sequencing of viruses isolated from infected hosts is an efficient way to measure population-genetic variation and can reveal patterns of dispersal and natural selection. In this study, we mined existing Illumina sequence reads to investigate single-nucleotide polymorphisms (SNPs) within two RNA viruses of the Western honey bee (Apis mellifera), deformed wing virus (DWV) and Israel acute paralysis virus (IAPV). All viral RNA was extracted from North American samples of honey bees or, in one case, the ectoparasitic mite Varroa destructor.ResultsCoverage depth was generally lower for IAPV than DWV, and marked gaps in coverage occurred in several narrow regions (< 50 bp) of IAPV. These coverage gaps occurred across sequencing runs and were virtually unchanged when reads were re-mapped with greater permissiveness (up to 8% divergence), suggesting a recurrent sequencing artifact rather than strain divergence. Consensus sequences of DWV for each sample showed little phylogenetic divergence, low nucleotide diversity, and strongly negative values of Fu and Li’s D statistic, suggesting a recent population bottleneck and/or purifying selection. The Kakugo strain of DWV fell outside of all other DWV sequences at 100% bootstrap support. IAPV consensus sequences supported the existence of multiple clades as had been previously reported, and Fu and Li’s D was closer to neutral expectation overall, although a sliding-window analysis identified a significantly positive D within the protease region, suggesting selection maintains diversity in that region. Within-sample mean diversity was comparable between the two viruses on average, although for both viruses there was substantial variation among samples in mean diversity at third codon positions and in the number of high-diversity sites. FST values were bimodal for DWV, likely reflecting neutral divergence in two low-diversity populations, whereas IAPV had several sites that were strong outliers with very low FST.ConclusionsThis initial survey of genetic variation within honey bee RNA viruses suggests future directions for studies examining the underlying causes of population-genetic structure in these economically important pathogens.

Sweetness and light: illuminating the honey bee genome

come this, Martin Beye supplied AT-rich DNA isolated fromis the first hymenopteran andthe fifth insect genome to be sequenced (Honey BeeGenome Sequencing Consortium, 2006) in what promisesto be a swarm of insect genome sequences expected toappear over the next few years (Table 1). The Honey BeeGenome Sequencing Project (HBGSP) was conceptualizedover a period from 1998 to 2001 by the community atcourses, conferences and workshops (Robinson, 1999;Maleszka, 2000; Pennisi, 2001). In addition, initial effortswere directed at physical and genetic maps of the genome(Estoup

Analysis of archived residual newborn screening blood spots after whole genome amplification.

BackgroundDeidentified newborn screening bloodspot samples (NBS) represent a valuable potential resource for genomic research if impediments to whole exome sequencing of NBS deoxyribonucleic acid (DNA), including the small amount of genomic DNA in NBS material, can be overcome. For instance, genomic analysis of NBS could be used to define allele frequencies of disease-associated variants in local populations, or to conduct prospective or retrospective studies relating genomic variation to disease emergence in pediatric populations over time. In this study, we compared the recovery of variant calls from exome sequences of amplified NBS genomic DNA to variant calls from exome sequencing of non-amplified NBS DNA from the same individuals.ResultsUsing a standard alignment-based Genome Analysis Toolkit (GATK), we find 62,000–76,000 additional variants in amplified samples. After application of a unique kmer enumeration and variant detection method (RUFUS), only 38,000–47,000 additional variants are observed in amplified gDNA. This result suggests that roughly half of the amplification-introduced variants identified using GATK may be the result of mapping errors and read misalignment.ConclusionsOur results show that it is possible to obtain informative, high-quality data from exome analysis of whole genome amplified NBS with the important caveat that different data generation and analysis methods can affect variant detection accuracy, and the concordance of variant calls in whole-genome amplified and non-amplified exomes.

Transcriptomic and functional resources for the small hive beetle Aethina tumida, a worldwide parasite of honey bees

The small hive beetle (SHB), Aethina tumida, is a major pest of managed honey bee (Apis mellifera) colonies in the United States and Australia, and an emergent threat in Europe. While strong honey bee colonies generally keep SHB populations in check, weak or stressed colonies can succumb to infestations. This parasite has spread from a sub-Saharan Africa to three continents, leading to immense management and regulatory costs. We performed a transcriptomic analysis involving deep sequencing of multiple life stages and both sexes of this species. The assembled transcriptome appears to be nearly complete, as judged by conserved insect orthologs and the ability to find plausible homologs for 11,952 proteins described from the genome of the red flour beetle. Expressed genes include each of the major metabolic, developmental and sensory groups, along with genes for proteins involved with immune defenses and insecticide resistance. We also present a total of 23,085 high-quality SNPs for the assembled contigs. We highlight potential differences between this beetle and its honey bee hosts, and suggest mechanisms of future research into the biology and control of this species. SNP resources will allow functional genetic analyses and analyses of dispersal for this invasive pest. All resources are posted as Supplemental Tables at https://data.nal.usda.gov/dataset/data-transcriptomic-and-functional-resources-small-hive-beetle-aethina-tumida-worldwide, and at NCBI under Bioproject PRJNA256171.

Beenome soon: honey bees as a model 'non- model' system for comparative genomics

While the explosion of genomic data and tools is fully apparent for model organisms, these tools are arguably changing paradigms most quickly in those species for which genetic studies are most challenging. One such species is the honey bee, Apis mellifera. New tools and resources for this species (e.g. [2,17]), an impending genome-sequencing project and new interdisciplinary teams will help bring the unique traits of honey bees into the world of comparative genomics. Several factors make honey bees a compelling choice for genomic studies. First, bees are outstanding experimental subjects for animal behaviour and learning, thanks to a well-known reward system [12], symbolic language [9,16] and phenomenal learning abilities [13]. Honey bees and other social insects also provide extreme examples of developmental switches, or polyphenisms — the generation of distinct phenotypes from an equivalent genetic background [5,6]. Associated with this switch are two traits that pique the interest of medical researchers — fertility and longevity. While workers are nearly sterile, queens lay hundreds of thousands of eggs each year, and live 10–20 times longer than workers. The causes and consequences of the queen–worker split, long known from the standpoint of nutrition, are ripe for genomic analyses. Honey bees also show promise as a unique disease model. Since their domestication several thousand years ago, it has been recognized that bees are targeted by many of the same pests that affect human health, viz. viruses, protozoa, bacteria, fungi and other arthropods. Given this range of pathogens and a living environment that resembles culture media in humidity, warmth and available nutrients, honey bee colonies remain remarkably refractory to disease. Genomic studies clarifying how honey bees tolerate and resist disease offer exciting parallels both with other insects, such as Drosophila [4,8] and Anopheles [3], and with mammalian systems. Bees and other social insects provide an important twist on the study of disease, by allowing investigations of social elements in both the transmission and progression of disease (e.g. in termites, where diseases can be slowed by an emergent ‘social immunity’ caused by contact between nestmates [15]). Bees also have direct effects on human health, and genetic studies are beginning to unravel the bases of both foraging behaviour [1,11] and aggressive behaviour [7], showing fascinating parallels with Drosophila and other model organisms. While the collective knowledge from thousands of years of bee breeding and research has given