Genomic diversity and CRISPR-Cas systems in the cyanobacterium Nostoc in the High Arctic.

Nostoc (Nostocales, Cyanobacteria) has a global distribution in the Polar Regions. However, the genomic diversity of Nostoc is little known and there are no genomes available for polar Nostoc. Here we carried out the first genomic analysis of the Nostoc commune morphotype with a recent sample from the High Arctic and a herbarium specimen collected during the British Arctic Expedition (1875-76). Comparisons of the polar genomes with 26 present-day non-polar members of the Nostocales family highlighted that there are pronounced genetic variations among Nostoc strains and species. Osmoprotection and other stress genes were found in all Nostoc strains, but the two Arctic strains had markedly higher numbers of biosynthetic gene clusters for uncharacterised non-ribosomal peptide synthetases, suggesting a high diversity of secondary metabolites. Since viral-host interactions contribute to microbial diversity, we analysed the CRISPR-Cas systems in the Arctic and two temperate Nostoc species. There were a large number of unique repeat-spacer arrays in each genome, indicating diverse histories of viral attack. All Nostoc strains had a subtype I-D system, but the polar specimens also showed evidence of a subtype I-B system that has not been previously reported in cyanobacteria, suggesting diverse cyanobacteria-virus interactions in the Arctic.

Predicting Ion Mobility Collision Cross-Sections Using a Deep Neural Network: DeepCCS.

Untargeted metabolomic measurements using mass spectrometry are a powerful tool for uncovering new small molecules with environmental and biological importance. The small molecule identification step, however, still remains an enormous challenge due to fragmentation difficulties or unspecific fragment ion information. Current methods to address this challenge are often dependent on databases or require the use of nuclear magnetic resonance (NMR), which have their own difficulties. The use of the gas-phase collision cross section (CCS) values obtained from ion mobility spectrometry (IMS) measurements were recently demonstrated to reduce the number of false positive metabolite identifications. While promising, the amount of empirical CCS information currently available is limited, thus predictive CCS methods need to be developed. In this article, we expand upon current experimental IMS capabilities by predicting the CCS values using a deep learning algorithm. We successfully developed and trained a prediction model for CCS values requiring only information about a compound's SMILES notation and ion type. The use of data from five different laboratories using different instruments allowed the algorithm to be trained and tested on more than 2400 molecules. The resulting CCS predictions were found to achieve a coefficient of determination of 0.97 and median relative error of 2.7% for a wide range of molecules. Furthermore, the method requires only a small amount of processing power to predict CCS values. Considering the performance, time, and resources necessary, as well as its applicability to a variety of molecules, this model was able to outperform all currently available CCS prediction algorithms.