Biology subjects

Kratka, M.

Publications and source records attributed to Kratka, M..

3 recordsLinked to original sources

bioRxiv2025

Epigenetic reprogramming guides sexual dimorphism during floral development in Silene latifolia

Dioecy, the condition in which male and female individuals exist as separate plants, represents a fascinating and relatively rare reproductive strategy, offering unique opportunities to study the genetic and epigenetic regulation of sexual dimorphism. While sex determining genes underlying dioecy have already been described for several plant species, the role of epigenetic modifications in meristematic cell populations remains poorly understood. In this study we describe the spatio-temporal deposition of three epigenetic markers during early stages of floral development in model dioecious species Silene latifolia. We selected H3K4me1, H3K9me2 and active Ser2 phosphorylated form of RNA Polymerase II (Pol-IIS2ph), to assess levels of chromatin condensation and transcriptional activity of meristematic cells during key developmental stages. Utilizing the novel approach of an AI-assisted nuclei segmentation and high-content imaging we created a single-cell resolution atlas for male and female floral meristems. Our results show a relationship between transcription activity and sex determination during early meristem development. Moreover, our results suggest that H3K9me2 deposition in the developing meristem is linked to sex-specific chromatin reprogramming events, such as pollen mother cell formation during anther maturation. Overall, these results offer new insights into the role of chromatin regulation during floral meristem development and improves our understanding of sexual dimorphism in dioecious species.

plant biology↗

bioRxiv2024

Detection and classification of long terminal repeat sequences in plant LTR-retrotransposons and their analysis using explainable machine learning.

BackgroundLong terminal repeats (LTRs) represent important parts of LTR retrotransposons and retroviruses found in high copy numbers in a majority of eukaryotic genomes. LTRs contain regulatory sequences essential for the life cycle of the retrotransposon. Previous experimental and sequence studies have provided only limited information about LTR structure and composition, mostly from model systems. To enhance our understanding of these key compounds, we focused on the contrasts between LTRs of various retrotransposon families and other genomic regions. Furthermore, this approach can be utilized for the classification and prediction of LTRs. ResultsWe used machine learning methods suitable for DNA sequence classification and applied them to a large dataset of plant LTR retrotransposon sequences. We trained three machine learning models using (i) traditional model ensembles (Gradient Boosting - GBC), (ii) hybrid CNN-LSTM models, and (iii) a pre-trained transformer-based model (DNABERT) using k-mer sequence representation. All three approaches were successful in classifying and isolating LTRs in this data, as well as providing valuable insights into LTR sequence composition. The best classification (expressed as F1 score) achieved for LTR detection was 0.85 using the CNN-LSTM hybrid network model. The most accurate classification task was superfamily classification (F1=0.89) while the least accurate was family classification (F1=0.74). The trained models were subjected to explainability analysis. SHAP positional analysis identified a mixture of interesting features, many of which had a preferred absolute position within the LTR and/or were biologically relevant, such as a centrally positioned TATA-box, and TG..CA patterns around both LTR edges. ConclusionsOur results show that the models used here recognized biologically relevant motifs, such as core promoter elements in the LTR detection task, and a development and stress-related subclass of transcription factor binding sites in the family classification task. Explainability analysis also highlighted the importance of 5- and 3-edges in LTR identity and revealed need to analyze more than just dinucleotides at these ends. Our work shows the applicability of machine learning models to regulatory sequence analysis and classification, and demonstrates the important role of the identified motifs in LTR detection.

bioinformatics↗

bioRxiv2024

Repeat-based holocentromeres of the woodrush Luzula sylvatica reveal new insights into the evolutionary transition from mono- to holocentricity

Although the centromere is restricted to a single region of the chromosome in most studied eukaryotes, members of the rush family (Juncaceae) harbor either monocentric (Juncus) or holocentric (Luzula) chromosomes. This provides an opportunity to study the evolutionary mechanisms involved in the transition to holocentricity. Here by combining chromosome-scale genome assembly, epigenetic analyses, immuno-FISH, and super-resolution microscopy, we report the occurrence of repeat-based holocentromeres in L. sylvatica. We found an irregular distribution of genes, centromeric units, and most repeats along the chromosomes. We determined the centromere function predominantly associated with two satellite DNA repeats, Lusy1 and Lusy2 of 124- and 174-bp monomer length, respectively, while CENH3 also binds satellite-free gene-poor regions. Comparative repeat analysis revealed that Lusy1 is present in most Luzula species, suggesting a conserved centromere role of this repeat. Synteny between L. sylvatica (n = 6) and J. effusus (n = 21) genomes further evidenced a chromosome number reduction in Luzula derived from multiple chromosome fusions of ancestral J. effusus-like chromosomes. We propose that the transition to holocentricity in Luzula involves: (i) fusion of small chromosomes resembling Juncus-like centromeres; (ii) expansion of atypical centromeric units; and (iii) colonization of satellite DNA for centromere stabilization.

genomics↗