Biology subjects

Kubat, Z.

Publications and source records attributed to Kubat, Z..

5 recordsLinked to original sources

bioRxiv2026

Decoding pseudoexon activation by deep intronic variants using a high-throughput minigene assay in the NF1 gene

Deep intronic variants can cause aberrant splicing, often manifested as pseudoexon inclusion, which represents a significant challenge in clinical diagnostics. Pseudoexon activations have been repeatedly described in the NF1 gene, making it a promising genomic context for studying underlying mechanisms. Here, we developed a specialized minigene assay by combining high-throughput mutagenesis and next-generation sequencing to evaluate the splicing effect of 501 deep intronic variants within NF1 intron 3. We identified and validated eleven pseudoexon-activating variants, including seven novel ones, which clustered into four activation-prone hotspots. Additionally, we detected a rare, out-of-frame wild-type pseudoexon whose inclusion was significantly enhanced by three specific mutations. Using this experimentally validated dataset, we benchmarked SpliceAI performance and ESRseq scores to refine variant prioritization. This integrated high-throughput framework offers a robust starting point for the systematic functional prioritization of deep intronic variation, providing a scalable model for variant annotation in other disease-associated genes.

molecular biology↗

bioRxiv2026

Organic-acid-derived feather hydrolysate drives soil microbiome succession toward copiotrophic and putatively plant-beneficial taxa

As circular agricultural inputs are increasingly sought, feather hydrolysate (FH), derived from keratin-rich poultry waste, has emerged as a promising fertilizer and biostimulant. However, how FH shapes soil microbiome succession over time, and what functional potential this restructuring carries, remains poorly understood. Here, we used multi-marker high-throughput sequencing of 16S, 18S, and ITS rDNA to track soil microbial communities over 30 days following FH amendment, with sampling at days 0, 1, 4, 14, and 30. FH induced a rapid and reproducible microbial succession characterized by an early decline in alpha diversity and a pronounced day-1 bloom of copiotrophic taxa, as inferred from predicted rrn operon copy-number profiles. This initial response was accompanied by enrichment of bacterial genera previously associated with plant growth-promoting traits, including pathogen suppression, nutrient solubilization, siderophore production, phytohormone synthesis, and ACC deaminase activity. Although these groups gradually declined after the initial peak, several remained at significantly elevated abundances throughout the 30-day experimental period. In parallel, FH-treated soils showed a sustained reduction in the cumulative relative abundance of genera annotated as putative phytopathogens. By integrating temporal community profiling, rrn-based life-strategy inference, and literature-derived functional annotation, we show that FH acts as a strong ecological filter, shifting the soil microbiome toward copiotrophic and putatively plant-beneficial taxa. These findings provide a temporal framework for understanding FH-mediated microbiome restructuring and highlight the need for future studies linking inferred microbial functional potential to measured disease incidence, nutrient fluxes, and plant performance across diverse soil contexts.

microbiology↗

bioRxiv2026

Nowhere to Hide: The Effect of Centromere Architecture on LTR Retrotransposon Dynamics

Centromere architecture can determine where transposable elements persist, yet its effects on retrotransposon turnover remain poorly understood. Here we test a chromosome-level "nowhere-to-hide" model, in which holocentric chromosomes, owing to distributed centromere activity and reduced chromatin compartmentalization, provide fewer stable repeat-rich refugia than monocentric chromosomes. We combined genome-wide LTR retrotransposon annotation, spatial modelling and FISH across 40 holocentric plant species and 31 closely related monocentric relatives from Poales, Cuscuta, and Melanthiaceae. Overall LTR retrotransposon abundance and Ty1-copia/Ty3-gypsy composition were explained mainly by lineage history and chromosome size, rather than by holocentricity itself. By contrast, element persistence and removal showed dependence on centromere architecture. Intact LTR retrotransposons were younger in holocentric genomes, and holocentric chromosomes lacked the chromosome-size-dependent spatial clustering of element age observed in monocentrics. Solo-LTR profiles further revealed weaker spatial clustering of removal signatures in holocentric chromosomes, consistent with a more homogeneous chromosome-wide landscape of ectopic recombination. Epigenomic analyses of a matched Luzula-Juncus pair indicated that young elements can occur in centromeric chromatin, whereas solo LTRs are associated with more euchromatic contexts. These results support the nowhere-to-hide model, showing that centromere architecture does not shape LTR retrotransposons accumulation, but their persistence and removal efficiency.

evolutionary 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↗

bioRxiv2021

HiC-TE: a computational pipeline for Hi-C data analysis shows a possible role of repeat family interactions in the genome 3D organization

The role of repetitive DNA in the 3D organization of the interphase nucleus in plant cells is a subject of intensive study. High-throughput chromosome conformation capture (Hi-C) is a sequencing-based method detecting the proximity of DNA segments in nuclei. We combined Hi-C data, plant reference genome data and tools for the characterization of genomic repeats to build a Nextflow pipeline identifying and quantifying the contacts of specific repeats revealing the preferential homotypic interactions of ribosomal DNA, DNA transposons and some LTR retrotransposon families. We provide a novel way to analyze the organization of repetitive elements in the 3D nucleus.

bioinformatics↗