Glaciers have recently been recognized as ecosystems comprised of several distinct habitats: a sunlit and oxygenated glacial surface, glacial ice, and a dark, mostly anoxic glacial bed. Surface meltwaters annually flood the subglacial sediments by means of drainage channels. Glacial surfaces host aquatic microhabitats called cryoconite holes, regarded as “hot spots” of microbial abundance and activity, largely contributing to the meltwaters’ bacterial diversity. This study presents an investigation of cryoconite hole anaerobes and discusses their possible impact on subglacial microbial communities, combining 16S rRNA gene fragment amplicon sequencing and the traditional enrichment culture technique. Cryoconite hole sediment harbored bacteria belonging mainly to the Proteobacteria (21%), Bacteroidetes (16%), Actinobacteria (14%), and Planctomycetes (6%) phyla. An 8-week incubation of those sediments in Postgate C medium for sulfate reducers in airtight bottles, emulating subglacial conditions, eliminated a great majority of dominant taxa, leading to enrichment of the Firmicutes (62%), Proteobacteria (14%), and Bacteroidetes (13%), which consisted of anaerobic genera like Clostridium, Psychrosinus, Paludibacter, and Acetobacterium. Enrichment of Pseudomonas spp. also occurred, suggesting it played a role as a dominant oxygen scavenger, providing a possible scenario for anaerobic niche establishment in subglacial habitats. To our knowledge, this is the first paper to provide insight into the diversity of the anaerobic part of the cryoconite hole microbial community and its potential to contribute to matter turnover in anoxic, subglacial sites.

QA-RecombineIt provides a web interface to assess the quality of protein 3D structure models and to improve the accuracy of models by merging fragments of multiple input models. QA-RecombineIt has been developed for protein modelers who are working on difficult problems, have a set of different homology models and/or de novo models (from methods such as I-TASSER or ROSETTA) and would like to obtain one consensus model that incorporates the best parts into one structure that is internally coherent. An advanced mode is also available, in which one can modify the operation of the fragment recombination algorithm by manually identifying individual fragments or entire models to recombine. Our method produces up to 100 models that are expected to be on the average more accurate than the starting models. Therefore, our server may be useful for crystallographic protein structure determination, where protein models are used for Molecular Replacement to solve the phase problem. To address the latter possibility, a special feature was added to the QA-RecombineIt server. The QA-RecombineIt server can be freely accessed at http://iimcb.genesilico.pl/qarecombineit/.

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Kae1 is a subunit of the highly evolutionarily conserved KEOPS/EKC complex, which is involved in universal (t6A37) tRNA modification. Several reports have discussed the participation of this complex in transcription regulation in yeast and human cells, including our previous observations of KaeA, an Aspergillus nidulans homologue of Kae1p. The aim of this project was to confirm the role of KaeA in transcription, employing high-throughput transcriptomic (RNA-Seq and ChIP-Seq) and proteomic (LC-MS) analysis. We confirmed that KaeA is a subunit of the KEOPS complex in A. nidulans. An analysis of kaeA19 and kaeA25 mutants showed that, although the (t6A37) tRNA modification is unaffected in both mutants, they reveal significantly altered transcriptomes compared to the wild type. The finding that KaeA is localized in chromatin and identifying its protein partners allows us to postulate an additional nuclear function for the protein. Our data shed light on the universal bi-functional role of this factor and proves that the activity of this protein is not limited to tRNA modification in cytoplasm, but also affects the transcriptional activity of a number of nuclear genes. Data are available via the NCBI’s GEO database under identifiers GSE206830 (RNA-Seq) and GSE206874 (ChIP-Seq), and via ProteomeXchange with identifier PXD034554 (proteomic).

Biodiversity analysis of metagenomic and metatranscriptomic data acquired from nextgeneration sequencing (NGS) requires following multiple analytic steps, often independent from each other with exception of passing output files of previous step as input for the following. If parameterization of steps following one after another is independent from one another, they may be pipelined. There are three most popular pipelines used for NGS analyses: QIIME, mothur and MetAMOS. In this work we describe our extensions to the latter. One is supplementing MetAMOS' default modes with taxonomic and metabolic biodiversity using metagenomics and metatranscriptomics data and the other provides a web-based interface to run predefined analyses that is easy to integrate with laboratory information management systems.PeerJ PrePrints | https://doi.org/10.7287/peerj.preprints.1706v1 | CC-BY 4.0 Open Access | rec: