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Sidorczuk, K.

Publications and source records attributed to Sidorczuk, K..

2 recordsLinked to original sources

Dating the photosynthetic organelle evolution in Archaeplastida, Paulinella and secondary-plastid bearing lineages

Photosynthetic eukaryotes have shaped the Earths biosphere by producing O2 and converting light into organic compounds in specialized organelles called plastids. Plastids evolved from free-living cyanobacteria engulfed by heterotrophic unicellular eukaryotes in processes called cyanobacterial endosymbioses. Two independent such processes have been reported so far. The first gave rise to primary plastids and three Archaeplastida lineages: glaucophytes, red algae and green algae with land plants, whereas the second resulted in chromatophores in the rhizarian amoeba Paulinella. Importantly, archaeplastidans donated their plastids to many protist groups, thereby further spreading photosynthesis across the tree of life. To reveal the complex plastid evolution, we performed comprehensive phylogenetic and multi-clock analyses based on new fossil calibration points and the greatest number yet of plastid-encoded proteins from 108 taxa, representing a large diversity of photosynthetic organisms. Our results indicate that primary plastids evolved prior to 2.1 - 1.8 Bya, i.e. before glaucophytes diverged from the other archaeplastidans. Like the primary plastids before, Paulinella chromatophores evolved in low salinity habitats and possibly before 292 - 266 Mya. Red and green algae were engulfed by cryptophyte and chlorarachniophyte ancestors between 1.7 - 1.4 Bya, and 1.1 - 1.0 Bya, respectively; the former subsequently triggered plastid transfers to other eukaryotes. The diversification rate of the photosynthetic organisms increased with temperature and CO2 but decreased with O2 and volcanic activity. We also studied the impact of various molecular clocks and calibration sets on the age estimation and clearly indicate that the clocks are the source of greater differences. Significance StatementCyanobacteria and eukaryote endosymbioses created a multitude of photosynthetic organelles called plastids that feed most life on our planet. For decades scientists have been trying to untangle the puzzle of plastid evolution, i.e. when and how plastids were acquired and spread throughout the eukaryotic tree of life. To answer these questions we applied phylogenetic and multi-clock methods combined with new fossil calibration points on large data sets. Our results push back in the Earths history most key events concerning plastid evolution compared to previous reports. Additionally, we discovered a significant impact of climatic and atmospheric parameters on the diversification rate of plastid lineages. The estimated divergence times enabled us to reinterpret taxonomic classification of controversial fossils.

evolutionary biology↗

The impact of negative data sampling on antimicrobial peptide prediction

Antimicrobial peptides (AMPs) are a heterogeneous group of short polypeptides that target microorganisms but also viruses and cancer cells. Due to their lower selection for resistance compared to traditional antibiotics, AMPs have been attracting the ever-growing attention from researchers, including bioinformaticians. Machine learning represents the most cost-effective method for novel AMP discovery and consequently many computational tools for AMP prediction have been recently developed. In this article, we investigate the impact of negative data sampling on model performance and benchmarking. We generated 660 predictive models using 12 machine learning architectures, a single positive data set and 11 negative data sampling methods; the architectures and methods were defined on the basis of published AMP prediction software. Our results clearly indicate that similar training and benchmark data set, i.e. produced by the same or a similar negative data sampling method, positively affect model performance. Consequently, all the benchmark analyses that have been performed for AMP prediction models are significantly biased and, moreover, we do not know which model is the most accurate. To provide researchers with reliable information about the performance of AMP predictors, we also created a web server AMPBenchmark for fair model benchmarking. AMPBenchmark is available at http://BioGenies.info/AMPBenchmark.

bioinformatics↗