Biology subjects

Fyta, M.

Publications and source records attributed to Fyta, M..

4 recordsLinked to original sources

bioRxiv2026

Benchmarking Imputation Methods for Single-Cell RNA Sequencing Data Using Peripheral Blood Mononuclear Cells from Acute Myocardial Infarction Patients

Acute myocardial infarction (AMI) remains one of the leading causes of mortality worldwide, and the following post-effects, such as post-AMI inflammation and tissue repair, involve peripheral blood mononuclear cells playing a critical role. The influence of imputation methods in biological data is assessed with respect to high-resolution single-cell RNA sequencing (scRNAseq) data relevant to these cells. Still scRNAseq data often encounter a lot of dropout events, leading to sparse and noisy datasets, hampering downstream results. To assess the influence of the missingness in the data, we artificially impose different levels of dropout in available scRNAseq data by leveraging various imputation techniques. Specifically, we introduce artificial missingness at 10%, 20%, and 30% levels under a missing completely at random (MCAR) framework, repeated across 10 independent runs. We benchmarked six imputation strategies - MAGIC, IterativeImputer, KNNImputer, Mean Imputation, SoftImpute, and a Generative adversarial network (GAN) - based approaches using multiple evaluation metrics: marker gene preservation, clustering consistency (Adjusted Rand Index - ARI), gene-wise correlation with ground truth, and structural separation (silhouette scores). The results clearly underline that no single imputation method dominated across all metrics. Overall, Mean and KNN imputers showed limited recovery across all benchmarks. GAN excelled in global transcriptional recovery and SoftImpute in preserving biologically meaningful cell-type signals. Our results highlight the importance of selecting the imputation methods as part of the pre-processing step towards the downstream biological questions related to transcriptome recovery, detection of marker genes, or maintaining cell-type-specific resolution.

bioinformatics↗

bioRxiv2026

Nanopore event detection in a simple and adaptive way

Nanopore read-out, that is the current signals measured across nanometer-sized openings in dielectric membranes or through natural protein channels, enables the detection, identification and sequencing of individual molecules. The detection can take place by analyzing the events of single biomolecules interacting with the pore. The accuracy in the detection of these single events is key for identification of physicochemical properties of analyte molecules. To this end, we further develop a very simple, fast, almost parameter-free, and adaptable cluster-based event detection (CBED) algorithm that clusters the nanopore signals prior to detecting nanopore events. The algorithm is validated against two other event detection schemes with respect to simplicity and efficiency. For this, nanopore data from four different experiments stemming from different laboratories that vary in the nanopore type, size, and analyte are considered. The comparison is made on the basis of the number of events detected, their quality, and the most important features extracted from nanopore events. Our results underline the higher efficiency and less noise of the CBED detected events for biological nanopore data and the need for an on-the-fly adaptivity of the baseline current for a class of solid-state nanopore data.

bioinformatics↗

bioRxiv2026

Graph-based learning and read-out of nanopore translocation event signals

Nanopores enable single-molecule analysis by measuring current signals through nanoscale pores in either biological or solid-state membranes. Accurate detection of analyte fingerprints within the pore environment is essential for reading-out the analyte type. We develop a framework for robust and label-free detection of the molecular nanopore events using a graph representation of the measured signals. To this end, we build a graph-based two-stage workflow based on a convolutional and graph neural networks that first perform a fast screening of the nanopore events, followed by a deep validation of these. The learned model can thus efficiently and in an unsupervised manner select possible molecular signatures (the current blockades) in the full signal, denoise, validate, reconstruct these, and predict the morphology of unseen molecular events. We could show that the learned model can efficiently predict the correct event morphology for the same analyte within a 2.4-fold range of transmembrane voltage values not included in the training. The developed graph-based workflow is modular, generalizable, and provided that it is trained on a huge amount of different nanopore experiments has the potential to become a blueprint model for nanopore read-out. Such a read-out model would be able to identify subtle differences in molecules like proteins, as well as their conformational or folding states. The proposed framework is developed using experimental signals from DNA translocation through an aerolysin pore and demonstrates a unified approach linking unsupervised feature learning to raw-signal inference for single-molecule sensing.

bioengineering↗

bioRxiv2026

AlphaFold 3 captures oligomeric states and interaction dynamics of MLO ion channels

Mildew resistance Locus O (MLO) proteins have been originally identified as susceptibility factors for the fungal powdery mildew disease. Beyond immunity, they function in polarized secretion, including root and root hair elongation, trichome development, and fertilization. Moreover, MLO proteins mediate Ca{superscript 2} influx, either indirectly by recruiting Ca{superscript 2}-permeable channels to the plasma membrane or by acting as ion channels themselves. The latter raises the question of whether MLO proteins oligomerize to mediate ion transport across membranes. Here, we present an AlphaFold 3-based modeling pipeline for the reproducible assessment of MLO-containing protein complexes using AlphaFolds built-in confidence metrics together with structural and dynamic analyses. The resulting predictions for homo-oligomers of the prototypic barley Mlo support dimeric and trimeric assemblies, with the trimer forming a central membrane-spanning pore. Notably, AlphaFold 3 captured discrete conformational states of this trimer, as reflected by the clustering of confidence metrics. Computational structural analyses indicated that higher-confidence models adopt a closed pore conformation, whereas lower-confidence predictions reflect progressively expanding pore diameters. Molecular dynamics simulations further showed Ca{superscript 2} permeability of the putative open models. Our pipeline similarly predicts trimeric assemblies for MLO variants from Arabidopsis thaliana and Marchantia polymorpha, suggesting a conserved MLO structural scaffold within the land plant lineage. Additional Molecular Dynamics simulations revealed that closed models of barley Mlo and A. thaliana MLO2 open under simulated membrane tension, supporting the notion that MLO proteins are mechanosensitive ion channels. Moreover, predictions of MLO proteins with its known interactors, EF-hand proteins and exocyst complex subunit EXO70 proteins, suggest a mechanism for feedback inhibition of MLO-mediated ion flux and provide comprehensive experimental support for AlphaFold 3-predicted protein interfaces. Altogether, our results provide a structural framework for MLO channel architecture and regulation, while our prediction, modeling, and simulation pipeline should be useful beyond the study of this specific protein family. One-sentence summaryThis article describes AlphaFold 3-based analyses of MLO proteins, revealing the predicted structure of MLO membrane pores, their dynamic opening and closing, and their association with interacting proteins, including calmodulin and calmodulin-like calcium sensor proteins and exocyst complex subunit EXO70 proteins.

plant biology↗