Biology subjects

Karlicki, M.

Publications and source records attributed to Karlicki, M..

4 recordsLinked to original sources

bioRxiv2025

Divergent biofilm and free-living microbial communities on Phragmites australis and in plankton across an eutrophication gradient in the Great Masurian Lakes (Poland)

Biofilms, consisting of bacteria and protists, are key components of freshwater microbial communities and contribute to ecosystem functioning. Although biofilm and planktonic communities may share taxa, the extent of their overlap and the drivers of differentiation are not fully understood. We used 16S and 18S rRNA gene metabarcoding to examine bacterial and protist communities in Phragmites australis stem biofilms and planktonic fractions across lakes with differing trophic states in the Great Masurian Lakes (NE Poland). Biofilm communities on reed stems were compositionally distinct from planktonic ones, sharing only a limited core microbiome. The biofilm core was larger than that of planktonic communities and included taxa identified by machine learning as characteristic of biofilm assemblages. Planktonic communities varied among lakes, whereas P. australis-associated biofilms were comparatively similar, which may reflect host association and lifestyle-related buffering. Additionally, biofilm suspensions showed higher physiological activity per inoculum volume and broader substrate use under the assay conditions than free-living communities. These results highlight the distinct nature of freshwater biofilms and underscore the importance of jointly examining biofilm and planktonic fractions of prokaryotic and eukaryotic communities in studies of microbial diversity and function in lake ecosystems.

ecology↗

bioRxiv2025

From short to long reads: enhanced protist diversity profiling via Nanopore metabarcoding

In the last decades environmental metabarcoding has revolutionised biodiversity research, particularly for microbial organisms such as protists, enabling large-scale assessments of diversity and ecological patterns across time and space. With the advent of long-read sequencing, Nanopore-based metabarcoding represents a promising alternative to short-read approaches. Due to the limited number of available studies, the effectiveness of Nanopore sequencing - alone or in combination with short-read data - for assessing the biodiversity and ecological patterns of protists in different ecosystems is not yet sufficiently explored. Here we present BaNaNA (Barcoding Nanopore Neat Annotator), a pipeline designed to generate high-quality OTUs and abundance estimates from Nanopore sequencing data. The performance of the pipeline was evaluated using a mock community as well as on marine and freshwater environmental samples to demonstrate its relevance for protist biodiversity and ecological studies. Our results show that BaNaNA generates high-quality full-length 18S rDNA OTUs from Nanopore long reads that are directly comparable to short-read V4-18S rDNA ASVs, supporting their synergistic use in long-term biodiversity studies. While both approaches reveal similar overall community diversity, long-read OTUs provide greater taxonomic resolution, richer phylogenetic information enabling the discovery of new clades, and yield fewer false positives. These advantages make long-read Nanopore metabarcoding not only a powerful complement but also a reliable replacement to short-read methods. By providing a pipeline for processing Nanopore data, BaNaNA paves the way for a broader application of long-read Nanopore sequencing in protist ecology and biodiversity research.

ecology↗

bioRxiv2024

Seasonal dynamics and drivers of microbial communities in a temperate dimictic lake: insights from metabarcoding and machine learning

Microbial communities, consisting of prokaryotes and protists, play a central role in ecological processes in aquatic environments. To understand these communities, metabarcoding provides a powerful tool to assess their taxonomic composition and to track spatio-temporal dynamics in both marine and freshwater environments. While previous research has primarily focused on marine ecosystems, it is important to study microbial communities in freshwater environments, which are characterised by high diversity and susceptibility to rapid environmental change. In temperate lakes, despite extensive research on temporal changes in physico-chemical factors and microscopic studies of plankton, there is a notable research gap regarding their eukaryotic microbial communities. Our study fills this gap by investigating the diversity and seasonal changes of prokaryotic and eukaryotic communities in Lake Ro[s] (Poland), a representative temperate lake characterised by two mixing episodes in spring and autumn and pronounced stratification in summer. Our metabarcoding analysis revealed that both the bacterial and protist communities exhibit distinct seasonal patterns that are not necessarily shaped by dominant taxa. To decipher the drivers of the seasonal communities, we used machine learning and statistical methods and identified crucial amplicon sequence variants (ASVs) specific to each season. In addition, we identified a distinct community in the anoxic hypolimnion. We have also shown that the key factors shaping the community composition in Lake Ro[s] are temperature, oxygen and silicon concentration. Understanding these community structures and the underlying factors is crucial in the context of climate change, which might affect mixing patterns and lead to prolonged stratification. Given the pronounced seasonal shifts observed in these communities, we can anticipate that climate change will profoundly impact the functioning of temperate dimictic lakes.

ecology↗

bioRxiv2021

Tiara: Deep learning-based classification system for eukaryotic sequences

MotivationWith a large number of metagenomic datasets becoming available, the eukaryotic metagenomics emerged as a new challenge. The proper classification of eukaryotic nuclear and organellar genomes is an essential step towards the better understanding of eukaryotic diversity. ResultsWe developed Tiara, a deep-learning-based approach for identification of eukaryotic sequences in the metagenomic data sets. Its two-step classification process enables the classification of nuclear and organellar eukaryotic fractions and subsequently divides organellar sequences to plastidial and mitochondrial. Using test dataset, we have shown that Tiara performs similarly to EukRep for prokaryotes classification and outperformed it for eukaryotes classification with lower calculation time. Tiara is also the only available tool correctly classifying organellar sequences. Availability and implementationTiara is implemented in python 3.8, available at https://github.com/ibe-uw/tiara and tested on Unix-based systems. It is released under an open-source MIT license and documentation is available at https://ibe-uw.github.io/tiara. Version 1.0.1 of Tiara has been used for all benchmarks.

bioinformatics↗