@article{HurdGruebelWojciechowskietal.2021,
  author    = {Hurd, Paul J. and Gr{\"u}bel, Kornelia and Wojciechowski, Marek and Maleszka, Ryszard and R{\"o}ssler, Wolfgang},
  title     = {Novel structure in the nuclei of honey bee brain neurons revealed by immunostaining},
  series = {Scientific Reports},
  volume    = {11},
  journal   = {Scientific Reports},
  doi       = {10.1038/s41598-021-86078-5},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-260059},
  pages     = {6852},
  year      = {2021},
  abstract  = {In the course of a screen designed to produce antibodies (ABs) with affinity to proteins in the honey bee brain we found an interesting AB that detects a highly specific epitope predominantly in the nuclei of Kenyon cells (KCs). The observed staining pattern is unique, and its unfamiliarity indicates a novel previously unseen nuclear structure that does not colocalize with the cytoskeletal protein f-actin. A single rod-like assembly, 3.7-4.1 mu m long, is present in each nucleus of KCs in adult brains of worker bees and drones with the strongest immuno-labelling found in foraging bees. In brains of young queens, the labelling is more sporadic, and the rod-like structure appears to be shorter (similar to 2.1 mu m). No immunostaining is detectable in worker larvae. In pupal stage 5 during a peak of brain development only some occasional staining was identified. Although the cellular function of this unexpected structure has not been determined, the unusual distinctiveness of the revealed pattern suggests an unknown and potentially important protein assembly. One possibility is that this nuclear assembly is part of the KCs plasticity underlying the brain maturation in adult honey bees. Because no labelling with this AB is detectable in brains of the fly Drosophila melanogaster and the ant Camponotus floridanus, we tentatively named this antibody AmBNSab (Apis mellifera Brain Neurons Specific antibody). Here we report our results to make them accessible to a broader community and invite further research to unravel the biological role of this curious nuclear structure in the honey bee central brain.},
  language  = {en}
}
@article{LindgreenUmuLaietal.2014,
  author    = {Lindgreen, Stinus and Umu, Sinan Uğur and Lai, Alicia Sook-Wei and Eldai, Hisham and Liu, Wenting and McGimpsey, Stephanie and Wheeler, Nicole E. and Biggs, Patrick J. and Thomson, Nick R. and Barquist, Lars and Poole, Anthony M. and Gardner, Paul P.},
  title     = {Robust Identification of Noncoding RNA from Transcriptomes Requires Phylogenetically-Informed Sampling},
  series = {PLOS Computational Biology},
  volume    = {10},
  journal   = {PLOS Computational Biology},
  number    = {10},
  doi       = {10.1371/journal.pcbi.1003907},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-115259},
  pages     = {e1003907},
  year      = {2014},
  abstract  = {Noncoding RNAs are integral to a wide range of biological processes, including translation, gene regulation, host-pathogen interactions and environmental sensing. While genomics is now a mature field, our capacity to identify noncoding RNA elements in bacterial and archaeal genomes is hampered by the difficulty of de novo identification. The emergence of new technologies for characterizing transcriptome outputs, notably RNA-seq, are improving noncoding RNA identification and expression quantification. However, a major challenge is to robustly distinguish functional outputs from transcriptional noise. To establish whether annotation of existing transcriptome data has effectively captured all functional outputs, we analysed over 400 publicly available RNA-seq datasets spanning 37 different Archaea and Bacteria. Using comparative tools, we identify close to a thousand highly-expressed candidate noncoding RNAs. However, our analyses reveal that capacity to identify noncoding RNA outputs is strongly dependent on phylogenetic sampling. Surprisingly, and in stark contrast to protein-coding genes, the phylogenetic window for effective use of comparative methods is perversely narrow: aggregating public datasets only produced one phylogenetic cluster where these tools could be used to robustly separate unannotated noncoding RNAs from a null hypothesis of transcriptional noise. Our results show that for the full potential of transcriptomics data to be realized, a change in experimental design is paramount: effective transcriptomics requires phylogeny-aware sampling.},
  language  = {en}
}