Biology subjects

Gresova, K.

Publications and source records attributed to Gresova, K..

11 recordsLinked to original sources

bioRxiv2026

Argonaute 1 associates with nucleolar snoRNP machinery and regulates pre-rRNA levels and cell growth

Argonaute proteins are best known as cytoplasmic effectors of small-RNA-mediated gene silencing, although their compartment-specific functions remain incompletely understood. Here, we identify a previously unrecognized nucleolar role for Drosophila Argonaute 1 (AGO1). We observed stress-induced redistribution of AGO1 into cytoplasmic and nuclear ribonucleoprotein assemblies, whereas a distinct AGO1 pool consistently localized to Fibrillarin-positive nucleoli. Co-immunoprecipitation further showed that AGO1 associates with Fibrillarin-containing complexes in vivo. Re-analysis of AGO1-associated small-RNA sequencing data revealed snoRNAs, particularly C/D box snoRNAs, among AGO1-bound RNA species and increased snoRNA representation following AGO1 overexpression. Structural modelling demonstrated that simultaneous molecular interaction of AGO1 and the Fibrillarin-containing snoRNP machinery with the same snoRNA is stereochemically feasible, providing a structural framework for their potential association within a nucleolar snoRNP assembly. Functionally, AGO1 depletion reduced pre-rRNA levels and decreased both nucleolar and cell size in the follicular epithelium. Depletion of Dicer-1 did not reproduce these growth defects, arguing against a general consequence of impaired miRNA biogenesis. Together, these findings identify AGO1 as a nucleolar regulator that associates with snoRNP machinery and links small-RNA biology to pre-rRNA homeostasis and cell growth.

cell biology↗

bioRxiv2026

miRBind2 enables sequence-only prediction of miRNA binding and transcript repression

MotivationMicroRNAs (miRNAs) regulate gene expression by guiding Argonaute proteins to partially complementary sites on target RNAs. While classical prediction methods rely on engineered features such as seed match categories, evolutionary conservation, and site context, recent advances in deep learning offer the potential to learn targeting rules directly from sequence. We developed a sequence-based deep learning model that improves miRNA target site prediction, and further validated the learned target site representations by extending the model to gene-level functional repression prediction. ResultsWe introduce miRBind2, a deep learning method for miRNA target site prediction that incorporates a novel pairwise nucleotide representation capturing all possible miRNA-target nucleotide interactions, with a CNN-based architecture. miRBind2 outperforms previous SotA models across four independent datasets from the debiased miRBench benchmark, while using 92% fewer parameters. We show that the convolutional features and weights learned by miRBind2 can be transferred to transcript-level prediction by extending the miRBind2 architecture and fine-tuning it on miRNA perturbation experiments. This miRBind2-3UTR model predicts gene repression from sequence alone. On a dataset of 50,549 miRNA-gene pairs, miRBind2-3UTR significantly outperforms TargetScan. These results show that deep models pretrained on target site data can capture regulatory signals and predict functional repression without requiring conventional engineered biological features. AvailabilityModels and source code are freely available via GitHub (https://github.com/BioGeMT/miRBind_2.0). A publicly available web-tool for novel predictions and visualization is available at : (https://huggingface.co/spaces/dimostzim/BioGeMT-miRBind2) Contactpanagiotis.alexiou@um.edu.mt

bioinformatics↗

bioRxiv2026

AlphaFind v2: Similarity Search in AlphaFold DB and TED Domains across Structural Contexts

The availability of large-scale protein structure collections enables structure-based analysis of their function and evolution beyond what is possible from sequence alone. However, applying three-dimensional structure comparison at scale remains computationally demanding and limits practical exploration of large experimental and predicted collections. This creates a need for fast, structure-based search methods that retain biological relevance while enabling large-scale exploration. In this paper, we present AlphaFind v2, an application for finding structurally similar proteins in the AlphaFold Database (https://alphafold.ebi.ac.uk/) of predicted structures. AlphaFind v2 uses fast pre-filtering via state-of-the-art protein embeddings that preserve structural information, followed by refinement with US-align. The application presents multiple complementary search modes, including (i) search over full protein chains, (ii) search aware of the AlphaFold pLDDT metric, restricting similarity computation to the most stable and structurally relevant regions, (iii) search over protein domains from the TED database (https://ted.cathdb.info/), and (iv) a multidomain search mode, combining multiple chain-level domain matches within a single score and alignment. The application accepts protein identifiers and returns similar proteins with metrics, rich metadata, and interactive superpositions. AlphaFind v2 additionally allows searching within an organism or CATH label and matches the proteins with experimental structures. AlphaFind v2 is accessible at https://alphafind.ics.muni.cz/. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/710735v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@5e7ce7org.highwire.dtl.DTLVardef@15a3458org.highwire.dtl.DTLVardef@12299ccorg.highwire.dtl.DTLVardef@9f21af_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

SenCat: Cataloging human cell senescence through multiomic profiling of multiple senescent primary cell types

There is an urgent need to comprehensively catalog senescence markers across cell types in an organism in order to characterize senotypes and senescent cell heterogeneity. Here, we profiled the transcriptomes and proteomes in 14 different primary human cell types undergoing over 30 senescence paradigms to create a senescence catalog we termed SenCat. We found that, while senescent cells from all primary tissue types did not share a single unique marker, they did activate shared specific metabolic and damage-response pathways implicated in tissue repair. Machine learning analysis of the SenCat transcriptomic and proteomic datasets successfully identified independent sets of senescent human cells, and senescent-like cells in mouse lung and kidney. In sum, SenCat represents a much-needed resource to identify senescent cells across tissues in the body. HIGHLIGHTSO_LIIdentifying senescent cells in organisms in vivo remains a challenge C_LIO_LIWe created SenCat: a catalog transcriptomes and proteomes of senescent primary cells C_LIO_LIMachine learning (ML) analysis of SenCat identified robust senescence scores C_LIO_LIML-derived senescence scores uncovered senescent-like cell dynamics in vivo C_LI

molecular biology↗

bioRxiv2025

Viral Infection Induces Alzheimer's Disease-Related Pathways and Senescence in iPSC-Derived Neuronal Models

Structured AbstractO_ST_ABSINTRODUCTIONC_ST_ABSThe Pathogen Infection Hypothesis proposes that {beta}-Amyloid (A{beta}) functions as an antimicrobial peptide, with pathogen-induced aggregation potentially contributing to Alzheimers disease (AD) pathology. METHODSWe used human iPSC-derived 2D neurons and 3D cerebral organoids from wild-type and familial AD (PSEN1/2 mutant) lines to model acute infections with HSV-1 and TBEV and A{beta} aggregation. Transcriptomic and proteomic analyses were conducted to assess molecular responses. RESULTSHSV-1, but not TBEV, induced robust A{beta} clustering, which was, however, dependent on extracellular amyloid peptides. Transcriptomic profiling revealed widespread HSV-1-induced changes, including activation of neurodegeneration-related pathways. Proteomic profiling confirmed enrichment of neurodegeneration- and senescence-associated secretome signatures. PSEN1/2 mutations did not alter the acute infection response. Reanalysis of independent datasets confirmed our findings and revealed a limited protective effect of acyclovir. DISCUSSIONResults directly support the Pathogen Infection Hypothesis and suggest that preventing viral infections via vaccinations may represent a feasible approach to reducing AD risk.

neuroscience↗

bioRxiv2025

Cell-specific RNA isoform remodeling in the aging mouse brain

Aging is associated with increases in risk of multiple chronic diseases and at the cellular level, with disruptions in RNA homeostasis. Single-cell transcriptomic studies have revealed RNA abundance heterogeneity across brain cell types but the more complex changes in RNA processing such as alternative splicing, isoform usage, transcription start and polyadenylation site selection remain uncharacterized. Here, we combine single-cell analysis with long-read nanopore sequencing to capture full-length RNA isoforms and uncover temporal changes in RNA transcription, processing, and alternative splicing in the aging mouse cortex and hippocampus. By profiling transcriptomes from young adult to very old mice, we identify non-linear, cell-type-specific isoform expression changes and isoform usage shifts, primarily driven by transcription start site selection. These aging-associated isoform changes alter the coding potential and poly(A) site position of genes. Our data also reveal a high proportion of senescence in immune cells, far exceeding that of other cell types. We also identify isoform markers that, when applied to a machine learning model, distinguish senescent from normal immune cells. This study provides a full-length RNA isoform-based atlas of the aging mouse brain, offering insights into RNA metabolism remodeling across brain cell types throughout the lifespan.

genomics↗

bioRxiv2024

miRBench: A Comprehensive microRNA Binding Site Prediction Training and Benchmarking Dataset

MotivationMicroRNAs (miRNAs) are crucial regulators of gene expression, but the precise mechanisms governing their binding to target sites remain unclear. A major contributing factor to this is the lack of unbiased experimental datasets for training accurate prediction models. While recent experimental advances have provided numerous miRNA-target interactions, these are solely positive interactions. Generating negative examples in silico is challenging and prone to introducing biases, such as the miRNA frequency class bias identified in this work. Biases within datasets can compromise model generalization, leading models to learn dataset-specific artifacts rather than true biological patterns. ResultsWe introduce a novel methodology for negative sample generation that effectively mitigates the miRNA frequency class bias. Using this methodology, we curate several new, extensive datasets and benchmark several state-of-the-art methods on them. We find that a simple convolutional neural network model, retrained on some of these datasets, is able to outperform state-of-the-art methods. This highlights the potential for leveraging unbiased datasets to achieve improved performance in miRNA binding site prediction. To facilitate further research and lower the barrier to entry for machine learning researchers, we provide an easily accessible Python package, miRBench, for dataset retrieval, sequence encoding, and the execution of state-of-the-art models. AvailabilityThe miRBench Python Package is accessible at https://github.com/katarinagresova/miRBench/releases/tag/v1.0.0

bioinformatics↗

bioRxiv2024

AlphaFind: Discover structure similarity across the entire known proteome

AlphaFind is a web-based search engine that provides fast structure-based retrieval in the entire set of AlphaFold DB structures. Unlike other protein processing tools, AlphaFind is focused entirely on tertiary structure, automatically extracting the main 3D features of each protein chain and using a machine learning model to find the most similar structures. This indexing approach and the 3D feature extraction method used by AlphaFind have both demonstrated remarkable scalability to large datasets as well as to large protein structures. The web application itself has been designed with a focus on clarity and ease of use. The searcher accepts any valid Uniprot ID, PDB ID or gene symbol as input, and returns a set of similar protein chains from AlphaFold DB, including various similarity metrics between the query and each of the retrieved results. In addition to the main search functionality, the application provides 3D visualizations of protein structure superpositions in order to allow researchers to instantly analyze the structural similarity of the retrieved results. The AlphaFind web application is available online for free and without any registration at https://alphafind.fi.muni.cz. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=82 SRC="FIGDIR/small/580465v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1bf5ae8org.highwire.dtl.DTLVardef@1e975d7org.highwire.dtl.DTLVardef@378598org.highwire.dtl.DTLVardef@123e439_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

Beyond microRNAs: Analysis of chimeric reads characterises the diverse targetome of AGO2-mediated regulation.

Argonaute proteins are instrumental in regulating RNA stability and translation. AGO2, the major mammalian Argonaute protein, is known to primarily associate with microRNAs, a family of small RNA driver sequences, and identifies its targets primarily via a seed mediated partial complementarity process Despite numerous studies, a definitive experimental dataset of AGO2 driver-target interactions remains elusive. Our study employs two experimental methods - AGO2 CLASH and AGO2 eCLIP, to generate thousands of AGO2 target sites verified by chimeric reads. These chimeric reads contain both the AGO2 loaded small RNA driver and the target sequence, providing a robust resource for modeling AGO2 binding preferences. Our novel analysis pipeline reveals thousands of AGO2 target sites driven by microRNAs and a significant number of AGO2 drivers derived from fragments of other small RNAs such as tRNAs, YRNAs, snoRNAs, rRNAs, and more. We utilize convolutional neural networks to train machine learning models that accurately predict the binding potential for each driver class and experimentally validate several interactions. In conclusion, our comprehensive analysis of the AGO2 targetome broadens our understanding of its driver repertoire and potential function in development and disease. Moreover, we offer practical bioinformatic tools for future experiments and the prediction of AGO2 targets. All data and code from this study are freely available at https://github.com/ML-Bioinfo-CEITEC/HybriDetector/ Contactpanagiotis.alexiou@um.edu.mt, stepanka.vanacova@ceitec.muni.cz

molecular biology↗

bioRxiv2022

Genomic Benchmarks: A Collection of Datasets for Genomic Sequence Classification

In this paper, we propose a collection of curated and easily accessible sequence classification datasets in the field of genomics. The proposed collection is based on a combination of novel datasets constructed from the mining of publicly available databases and existing datasets obtained from published articles. The main aim of this effort is to create a repository for shared datasets that will make machine learning for genomics more comparable and reproducible while reducing the over-head of researchers that want to enter the field. The collection currently contains eight datasets that focus on regulatory elements (promoters, enhancers, open chromatin region) from three model organisms: human, mouse, and roundworm. A simple convolution neural network is also included in a repository and can be used as a baseline model. Benchmarks and the baseline model are distributed as the Python package genomic-benchmarks, and the code is available at https://github.com/ML-Bioinfo-CEITEC/genomic_benchmarks.

bioinformatics↗

bioRxiv2022

Fine-Tuning Transformers For Genomic Tasks

Transformers are a type of neural network architecture that has been successfully used to achieve state-of-the-art performance in numerous natural language processing tasks. However, what about DNA, the language life written in the four-letter alphabet? In this paper, we review the current state of Transformers usage in genomics and molecular biology in general, introduce a collection of benchmark datasets for the classification of genomic sequences, and compare the performance of several model architectures on those benchmarks, including a BERT-like model for DNA sequences DNABERT as implemented in HuggingFace (armheb/DNA_bert_6 model). In particular, we explore the effect of pre-training on a large DNA corpus vs training from scratch (with randomized weights). The results presented here can be used for identification of functional elements in human and other genomes.

bioinformatics↗