Biology subjects

Kisand, V.

Publications and source records attributed to Kisand, V..

3 recordsLinked to original sources

bioRxiv2020

Drivers of change and ecosystem status in a temperate lake over the last Post-Glacial period from 14.5 kyr

Understanding the long-term dynamics of ecological communities on the centuries-to-millennia scale is important for explaining present-day biodiversity patterns. Placing these patterns in a historical context could yield reliable tools for predicting possible future scenarios. Paleoarchives of macro+ and micro-fossil remains, and most importantly biomarkers such as fossil pigments and ancient DNA present in various sedimentary deposits, allow long term changes in ecological communities to be analysed. We use recent compilations of data including fossil pigments, metabarcoding of sedimentary ancient DNA and microfossils together with data analysis to understand the impact of gradual versus abrupt climate changes on a lake`s ecosystem status over the last 14.5 kyr. We give examples of hypotheses that need long-term data, which can be addressed using well-established paleoproxy variables. These variables describe the climate, together with vegetation change and the appearance of anthropogenic forcing, either as a gradual change or an abrupt event. We were able to detect abrupt changes in the lake ecosystem during the stable period of the Holocene Thermal Maximum and we highlight the increased frequency and degree of perturbation in lakes due to non-immediate human activity over a larger region. Both observations demonstrate an impaired relationship between a gradual external driver and ecosystem response and apply to future scenarios of climate warming and increased human impact in north-eastern Europe.

ecology↗

bioRxiv2020

Selective antibiofilm properties and biocompatibility of nano-ZnO and nano-ZnO/Ag coated surfaces

Spread of pathogenic microbes and antibiotic-resistant bacteria in health-care settings and public spaces is a serious public health challenge. Materials that prevent solid surface colonization or impede touch-transfer of viable microbes could provide means to decrease pathogen transfer from high-touch surfaces in critical applications. ZnO and Ag nanoparticles have shown great potential in antimicrobial applications. Less is known about nano-enabled surfaces. Here we demonstrate that surfaces coated with nano-ZnO or nano-ZnO/Ag composites are not cytotoxic to human keratinocytes and possess species-selective medium-dependent antibiofilm activity against Escherichia coli, Staphylococcus aureus and Candida albicans. Colonization of nano-ZnO and nano-ZnO/Ag surfaces by E. coli and S. aureus was decreased in static oligotrophic conditions (no planktonic growth). Moderate to no effect was observed for bacterial biofilms in growth medium (supporting exponential growth). Inversely, nano-ZnO surfaces enhanced biofilm formation by C. albicans in oligotrophic conditions. However, enhanced C. albicans biofilm formation on nano-ZnO surfaces was effectively counteracted by the addition of Ag. Possible selective enhancement of biofilm formation by the yeast C. albicans on Zn-enabled surfaces should be taken into account in antimicrobial surface development. Our results also indicated the importance of the use of application-appropriate test conditions and exposure medium in antimicrobial surface testing.

microbiology↗

bioRxiv2018

A k-mer-based method for the identification of phenotype-associated genomic biomarkers and predicting phenotypes of sequenced bacteria.

We have developed an easy-to-use and memory-efficient method called PhenotypeSeeker that (a) generates a k-mer-based statistical model for predicting a given phenotype and (b) predicts the phenotype from the sequencing data of a given bacterial isolate. The method was validated on 167 Klebsiella pneumoniae isolates (virulence), 200 Pseudomonas aeruginosa isolates (ciprofloxacin resistance) and 460 Clostridium difficile isolates (azithromycin resistance). The phenotype prediction models trained from these datasets performed with 88% accuracy on the K. pneumoniae test set, 88% on the P. aeruginosa test set and 96.5% on the C. difficile test set. Prediction accuracy was the same for assembled sequences and raw sequencing data; however, building the model from assembled genomes is significantly faster. On these datasets, the model building on a mid-range Linux server takes approximately 3 to 5 hours per phenotype if assembled genomes are used and 10 hours per phenotype if raw sequencing data are used. The phenotype prediction from assembled genomes takes less than one second per isolate. Thus, PhenotypeSeeker should be well-suited for predicting phenotypes from large sequencing datasets.\n\nPhenotypeSeeker is implemented in Python programming language, is open-source software and is available at GitHub (https://github.com/bioinfo-ut/PhenotypeSeeker/).\n\nSummaryPredicting phenotypic properties of bacterial isolates from their genomic sequences has numerous potential applications. A good example would be prediction of antimicrobial resistance and virulence phenotypes for use in medical diagnostics. We have developed a method that is able to predict phenotypes of interest from the genomic sequence of the isolate within seconds. The method uses statistical model that can be trained automatically on isolates with known phenotype. The method is implemented in Python programming language and can be run on low-end Linux server and/or on laptop computers.

bioinformatics↗