Biology subjects

Pantazis, Y.

Publications and source records attributed to Pantazis, Y..

4 recordsLinked to original sources

bioRxiv2026

Gene duplication shaped the origin and evolution of the vertebrate olfactory combinatorial code

Animals read their chemical environment through olfactory receptors, which form part of the class A GPCRs and are the largest gene family devoted to a single function in animals. Each odorant molecule activates a different combination of receptors, projects distinctly in the brain, and so yields a distinct odour percept. The principles of this coding are well described, and the evolution and regulation of these genes are actively researched. However, how the code was initially assembled and evolved has not been examined. Here we combine phylogenetics, receptor-ligand interactions predicted by paired protein and chemical language models, ancestral sequence reconstruction, and chemical-space analysis. Predicted activation profiles group odorants into six clusters -each enriched for its own set of odour descriptors- so that molecules read by similar receptor combinations tend to smell alike. Reconstructed ancestors place the origin of the code at the first duplication of the family, in the gnathostome ancestor, at the class I/class II split. The first duplicates then diverged asymmetrically into distinct chemical subspaces. One kept a third of the ancestral ligands; the other re-tuned almost completely and took up carboxylic acids -the polar chemistry long associated with class I. Aromatics were retained and enriched on the class II side. Combinatorial coding was not really invented: it arose once, at the first duplication, and was never lost.

evolutionary biology↗

bioRxiv2025

NEXT-FRET: A solution-based smFRET platform to resolve folding intermediates under native conditions

Folding intermediates are promising therapeutic targets in protein misfolding, bacterial virulence, and drug discovery. Yet, directly observing these transient, non-equilibrium states under physiological conditions remains challenging. Here, we introduce NEXT-FRET (Non-Equilibrium miXTure modeling of smFRET), a solution-based single-molecule FRET (smFRET) platform integrating accessible instrumentation and a time-variant Gaussian Mixture Model (tvGMM) to identify folding intermediates without microfluidics, surface tethering, or denaturants. Using maltose-binding protein (MBP) and its biologically relevant precursor (pre-MBP) for benchmarking and validation, we uncover previously elusive intermediates, including a long-hypothesized closed conformation undetected by existing single-molecule and ensemble methods. Furthermore, we demonstrate that chaperones modulate folding landscapes by stabilizing distinct intermediates, some effectively arresting folding progression; a mechanism increasingly recognized as a therapeutic strategy for targeted protein inactivation. Broadly applicable beyond folding, NEXT-FRET provides a generalizable, high-resolution analytical method for resolving transient biomolecular intermediates, offering a screening-compatible platform in biotechnology and elucidating proteostasis collapse in conformational diseases.

biophysics↗

bioRxiv2025

PROFET Predicts Continuous Gene Expression Dynamics from scRNA-seq Data to Elucidate Heterogeneity of Cancer Treatment Responses

Single-cell RNA sequencing profiles cellular heterogeneity but captures only static snapshots, limiting inference of gene expression dynamics. We developed PROFET (Particle-based Reconstruction Of generative Force-matched Expression Trajectories), a framework that reconstructs continuous, nonlinear single-cell trajectories from sparsely sampled scRNA-seq time series. PROFET combines a particle-based gradient-flow algorithm with simulation-free force matching to accurately infer cellular dynamics. Across mouse and human in vitro datasets and an in vivo axolotl regeneration dataset, PROFET achieved 2.6-12.5X lower prediction error than ten state-of-the-art trajectory inference methods. Applying PROFET to newly generated scRNA-seq data from a palbociclib-treated MCF7 cell line and three published breast cancer patient datasets, we reconstructed treatment-response trajectories and identified a resistant cell subpopulation exhibiting large phenotypic shifts and enrichment of the surface markers UNC5B, TLR3, PCDH19, PROCR, SLITRK6, and SEMA6B. PROFET provides a biologically grounded framework for reconstructing cell-state dynamics from static single-cell data across development, regeneration, and therapeutic response.

bioinformatics↗

bioRxiv2020

Latent Feature Representations for Human Gene Expression Data Improve Phenotypic Predictions

High-throughput technologies such as microarrays and RNA-sequencing (RNA-seq) allow to precisely quantify transcriptomic profiles, generating datasets that are inevitably high-dimensional. In this work, we investigate whether the whole human transcriptome can be represented in a compressed, low dimensional latent space without loosing relevant information. We thus constructed low-dimensional latent feature spaces of the human genome, by utilizing three dimensionality reduction approaches and a diverse set of curated datasets. We applied standard Principal Component Analysis (PCA), kernel PCA and Autoencoder Neural Networks on 1360 datasets from four different measurement technologies. The latent feature spaces are tested for their ability to (a) reconstruct the original data and (b) improve predictive performance on validation datasets not used during the creation of the feature space. While linear techniques show better reconstruction performance, nonlinear approaches, particularly, neural-based models seem to be able to capture non-additive interaction effects, and thus enjoy stronger predictive capabilities. Our results show that low dimensional representations of the human transcriptome can be achieved by integrating hundreds of datasets, despite the limited sample size of each dataset and the biological / technological heterogeneity across studies. The created space is two to three orders of magnitude smaller compared to the raw data, offering the ability of capturing a large portion of the original data variability and eventually reducing computational time for downstream analyses.

bioinformatics↗