Plasmidome of an environmental Acinetobacter lwoffii strain originating from a former gold and arsenic mine

Emerging important Acinetobacter strains commonly accommodate a plethora of mobile elements including plasmids of different size. Plasmids, apart from encoding modules enabling their self-replication and/or transmission, can carry a diverse number of genes, allowing the host cell to survive in an environment that would otherwise be lethal or restrictive for growth. The present study characterizes the plasmidome generated from an arsenic-resistant strain named ZS207, classified as Acinetobacter lwoffii . Sequencing effort revealed the presence of nine plasmids in the size between 4.3 and 38.4 kb as well as one 186.6 kb megaplasmid. All plasmids, except the megaplasmid, do apparently not confer distinguishing phenotypic features . In contrast, the megaplasmid carries arsenic and heavy metals resistance regions similar to those found in permafrost A. lwoffii strains. In-depth in silico analyses have shown a significant similarity between the regions from these plasmids, especially concerning multiple transposable elements, transfer and mobilization genes, and toxin-antitoxin systems. Since ars genes encode proteins of major significance in terms of potential use in bioremediation, arsenic resistance level of ZS207 was determined and the functionality of selected ars genes was examined . Additionally, we checked the functionality of plasmid-encoded toxin-antitoxin systems and their impact on the formation of persister cells

Lead shielding efficiency from the gamma background measurements in the salt cavern of the Polkowice–Sieroszowice copper mine

The studies of lead shielding efficiency from the gamma background measurements were performed in the salt cavern of the copper mine - a site considered for an underground laboratory. Within the energy range of 50–2700 keV, the measured gamma-ray count rates normalized to the mass of the high-purity detectors germanium crystal are 5.93 and 6.32 s−1kg−1 for the used low-background and portable spectrometers, respectively. The gamma-ray flux of 0.124 (2) cm−2s−1 connected with the natural radioisotopes was observed by the portable HPGe, including 0.026 (1) cm−2s−1 contribution of radon decay products, whereas the photon flux at the spectrum continuum was 0.18 (5) cm−2s−1

Bio-On-Magnetic-Beads (BOMB): Open platform for high-throughput nucleic acid extraction and manipulation

Current molecular biology laboratories rely heavily on the purification and manipulation of nucleic acids. Yet, commonly used centrifuge- and column-based protocols require specialised equipment, often use toxic reagents and are not economically scalable or practical to use in a high-throughput manner. Although it has been known for some time that magnetic beads can provide an elegant answer to these issues, the development of open-source protocols based on beads has been limited. In this article, we provide step-by-step instructions for an easy synthesis of functionalised magnetic beads, and detailed protocols for their use in the high-throughput purification of plasmids, genomic DNA and total RNA from different sources, as well as environmental TNA and PCR amplicons. We also provide a bead-based protocol for bisulfite conversion, and size selection of DNA and RNA fragments. Comparison to other methods highlights the capability, versatility and extreme cost-effectiveness of using magnetic beads. These open source protocols and the associated webpage (https://bomb.bio) can serve as a platform for further protocol customisation and community engagement

Studies on water transport in quasi two-dimensional porous systems using neutron radiography

The spontaneous wetting and drying of flat porous samples of linen, cotton and synthetic textiles were studied using dynamic neutron radiography (DNR). The progress of the wetting process of the media was delineated from the obtained neutron dynamical radiography images. The results of the investigation reveal a non-classical behaviour of kinetics of wicking of these materials. The character of the wetting kinetics is discussed in terms of the fractal character of the tortuosity of fabric capillaries

Consistent metagenomic biomarker detection via robust PCA

BACKGROUND: Recent developments of high throughput sequencing technologies allow the characterization of the microbial communities inhabiting our world. Various metagenomic studies have suggested using microbial taxa as potential biomarkers for certain diseases. In practice, the number of available samples varies from experiment to experiment. Therefore, a robust biomarker detection algorithm is needed to provide a set of potential markers irrespective of the number of available samples. Consistent performance is essential to derive solid biological conclusions and to transfer these findings into clinical applications. Surprisingly, the consistency of a metagenomic biomarker detection algorithm with respect to the variation in the experiment size has not been addressed by the current state-of-art algorithms. RESULTS: We propose a consistency-classification framework that enables the assessment of consistency and classification performance of a biomarker discovery algorithm. This evaluation protocol is based on random resampling to mimic the variation in the experiment size. Moreover, we model the metagenomic data matrix as a superposition of two matrices. The first matrix is a low-rank matrix that models the abundance levels of the irrelevant bacteria. The second matrix is a sparse matrix that captures the abundance levels of the bacteria that are differentially abundant between different phenotypes. Then, we propose a novel Robust Principal Component Analysis (RPCA) based biomarker discovery algorithm to recover the sparse matrix. RPCA belongs to the class of multivariate feature selection methods which treat the features collectively rather than individually. This provides the proposed algorithm with an inherent ability to handle the complex microbial interactions. Comprehensive comparisons of RPCA with the state-of-the-art algorithms on two realistic datasets are conducted. Results show that RPCA consistently outperforms the other algorithms in terms of classification accuracy and reproducibility performance. CONCLUSIONS: The RPCA-based biomarker detection algorithm provides a high reproducibility performance irrespective of the complexity of the dataset or the number of selected biomarkers. Also, RPCA selects biomarkers with quite high discriminative accuracy. Thus, RPCA is a consistent and accurate tool for selecting taxanomical biomarkers for different microbial populations. REVIEWERS: This article was reviewed by Masanori Arita and Zoltan Gaspari. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13062-017-0175-4) contains supplementary material, which is available to authorized users