Background With numerous endemic subspecies representing four of its five evolutionary lineages, Europe holds a large fraction of Apis mellifera genetic diversity. This diversity and the natural distribution range have been altered by anthropogenic factors. The conservation of this natural heritage relies on the availability of accurate tools for subspecies diagnosis. Based on pool-sequence data from 2145 worker bees representing 22 populations sampled across Europe, we employed two highly discriminative approaches (PCA and FST) to select the most informative SNPs for ancestry inference. Results Using a supervised machine learning (ML) approach and a set of 3896 genotyped individuals, we could show that the 4094 selected single nucleotide polymorphisms (SNPs) provide an accurate prediction of ancestry inference in European honey bees. The best ML model was Linear Support Vector Classifier (Linear SVC) which correctly assigned most individuals to one of the 14 subspecies or different genetic origins with a mean accuracy of 96.2% ± 0.8 SD. A total of 3.8% of test individuals were misclassified, most probably due to limited differentiation between the subspecies caused by close geographical proximity, or human interference of genetic integrity of reference subspecies, or a combination thereof. Conclusions The diagnostic tool presented here will contribute to a sustainable conservation and support breeding activities in order to preserve the genetic heritage of European honey bees.

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Na+/K+-ATPase maintains electrochemical gradients of Na+ and K+ essential for a variety of cellular functions including neuronal activity. The α-subunit of the Na+/K+-ATPase exists in four different isoforms (α1–α4) encoded by different genes. With a view to future use of pig as an animal model in studies of human diseases caused by Na+/K+-ATPase mutations, we have determined the porcine coding sequences of the α1–α3 genes, ATP1A1, ATP1A2, and ATP1A3, their chromosomal localization, and expression patterns. Our ATP1A1 sequence accords with the sequences from several species at five positions where the amino acid residue of the previously published porcine ATP1A1 sequence differs. These corrections include replacement of glutamine 841 with arginine. Analysis of the functional consequences of substitution of the arginine revealed its importance for Na+ binding, which can be explained by interaction of the arginine with the C-terminus, stabilizing one of the Na+ sites. Quantitative real-time PCR expression analyses of porcine ATP1A1, ATP1A2, and ATP1A3 mRNA showed that all three transcripts are expressed in the embryonic brain as early as 60 days of gestation. Expression of α3 is confined to neuronal tissue. Generally, the expression patterns of ATP1A1, ATP1A2, and ATP1A3 transcripts were found similar to their human counterparts, except for lack of α3 expression in porcine heart. These expression patterns were confirmed at the protein level. We also report the sequence of the porcine ATP1A3 promoter, which was found to be closely homologous to its human counterpart. The function and specificity of the porcine ATP1A3 promoter was analyzed in transgenic zebrafish, demonstrating that it is active and drives expression in embryonic brain and spinal cord. The results of the present study provide a sound basis for employing the ATP1A3 promoter in attempts to generate transgenic porcine models of neurological diseases caused by ATP1A3 mutations.

Background: Whole-genome sequencing has become routine for population genetic studies. Sequencing of individuals provides maximal data but is rather expensive and fewer samples can be studied. In contrast, sequencing a pool of samples (pool-seq) can provide sufficient data, while presenting less of an economic challenge. Few studies have compared the two approaches to infer population genetic structure and diversity in real datasets. Here, we apply individual sequencing (ind-seq) and pool-seq to the study of Western honey bees (Apis mellifera). Methods: We collected honey bee workers that belonged to 14 populations, including 13 subspecies, totaling 1347 colonies, who were individually (139 individuals) and pool-sequenced (14 pools). We compared allele frequencies, genetic diversity estimates, and population structure as inferred by the two approaches. Results: Pool-seq and ind-seq revealed near identical population structure and genetic diversities, albeit at different costs. While pool-seq provides genome-wide polymorphism data at considerably lower costs, ind-seq can provide additional information, including the identification of population substructures, hybridization, or individual outliers. Conclusions: If costs are not the limiting factor, we recommend using ind-seq, as population genetic structure can be inferred similarly well, with the advantage gained from individual genetic information. Not least, it also significantly reduces the effort required for the collection of numerous samples and their further processing in the laboratory.

The transcriptome is the absolute set of transcripts in a tissue or cell at the time of sampling. In this study RNA-Seq is employed to enable the differential analysis of the transcriptome profile for ten porcine tissues in order to evaluate differences between the tissues at the gene and isoform expression level, together with an analysis of variation in transcription start sites, promoter usage, and splicing. Totally, 223 million RNA fragments were sequenced leading to the identification of 59,930 transcribed gene locations and 290,936 transcript variants using Cufflinks with similarity to approximately 13,899 annotated human genes. Pairwise analysis of tissues for differential expression at the gene level showed that the smallest differences were between tissues originating from the porcine brain. Interestingly, the relative level of differential expression at the isoform level did generally not vary between tissue contrasts. Furthermore, analysis of differential promoter usage between tissues, revealed a proportionally higher variation between cerebellum (CBE) versus frontal cortex and cerebellum versus hypothalamus (HYP) than in the remaining comparisons. In addition, the comparison of differential transcription start sites showed that the number of these sites is generally increased in comparisons including hypothalamus in contrast to other pairwise assessments. A comprehensive analysis of one of the tissue contrasts, i.e. cerebellum versus heart for differential variation at the gene, isoform, and transcription start site (TSS), and promoter level showed that several of the genes differed at all four levels. Interestingly, these genes were mainly annotated to the "electron transport chain" and neuronal differentiation, emphasizing that "tissue important" genes are regulated at several levels. Furthermore, our analysis shows that the "across tissue approach" has a promising potential when screening for possible explanations for variations, such as those observed at the gene expression levels.