TY - JOUR A1 - Hurd, Paul J. A1 - Grübel, Kornelia A1 - Wojciechowski, Marek A1 - Maleszka, Ryszard A1 - Rössler, Wolfgang T1 - Novel structure in the nuclei of honey bee brain neurons revealed by immunostaining JF - Scientific Reports N2 - 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. KW - mushroom body calyx KW - synaptic complexes KW - bodies KW - insect KW - plasticity KW - insights KW - genome KW - model KW - proteins KW - methylation KW - biological techniques KW - cell biology KW - developmental biology KW - molecular biology KW - neuroscience Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-260059 VL - 11 ER - TY - JOUR A1 - Lindgreen, Stinus A1 - Umu, Sinan Uğur A1 - Lai, Alicia Sook-Wei A1 - Eldai, Hisham A1 - Liu, Wenting A1 - McGimpsey, Stephanie A1 - Wheeler, Nicole E. A1 - Biggs, Patrick J. A1 - Thomson, Nick R. A1 - Barquist, Lars A1 - Poole, Anthony M. A1 - Gardner, Paul P. T1 - Robust Identification of Noncoding RNA from Transcriptomes Requires Phylogenetically-Informed Sampling JF - PLOS Computational Biology N2 - 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. KW - protein families database KW - small nucleolar RNAs KW - bacterial genomes KW - comparative genomics KW - dark-matter KW - homology search KW - archaea KW - sequence KW - alignment KW - insights Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-115259 VL - 10 IS - 10 ER -