Biology subjects

Gerasimov, E.

Publications and source records attributed to Gerasimov, E..

2 recordsLinked to original sources

bioRxiv2026

A p32 family RNA editing factor acts in mitochondrial ribosome biogenesis

Biogenesis of mitochondrial ribosomes (mitoribosomes) in the unicellular parasite Trypanosoma brucei requires an exceptionally large toolkit of assembly factors, identified in stable precursors of large and small mitoribosomal subunits (mtLSU and mtSSU) by cryoEM. Here, using genetic modifications and proteomic characterization of the immunoprecipitated assemblosome, the earliest characterized mtSSU precursor, we determined that a cap of its distinctive protrusion of hitherto unknown composition consists of a p22 homotrimer. This protein was previously implicated in the uridine-insertion editing of the cytochrome c oxidase subunit II transcript. Our functional analysis confirmed this role but revealed that its ablation also causes a loss of mtSSU and a systemic reduction in mitochondrial translation, phenocopying the depletion of established mitoribosomal assembly factors. Consequently, the oxidative phosphorylation system and mitochondrial function are compromised. The p22 protein belongs to the p32 family. We showed that five of its six trypanosomal members are involved in mtSSU biogenesis. Notably, p32 proteins are associated with mitoribosomes in two other distant eukaryotic lineages. A eukaryote-wide mapping of p32 proteins documented that their presence correlates with the retention of mitochondrial genomes. Together, our findings redefine trypanosomal p22 as a dual-function coordinator of mitochondrial gene expression and reveal that the ancestral role of the p32 family is associated with mitochondrial translation.

molecular biology↗

bioRxiv2026

NEuRT: A Transformer-Based Model for Explainable Neuronal Activity Analysis

The study of neuronal activity is essential for understanding brain function and its alterations in neurode-generative diseases. Advances in in vivo imaging have enabled real-time observation of neuronal dynamics, but classical statistical methods struggle to capture the complex, time-dependent interactions within neuronal networks. Machine learning offers promising solutions for analyzing high-dimensional neuronal data, yet their application in neuroscience remains limited. Here, we introduce NEuRT, a Bidirectional Encoder Representations from Transformers (BERT)-based model adapted for neuronal activity analysis. NEuRT leverages self-attention mechanisms to interpret complex neuronal interactions, providing insights into patterns that traditional methods may overlook. Pre-trained on the recently introduced large annotated dataset MICrONS for signal reconstruction, NeuRT demonstrates strong generalization, effectively reconstructing activity from both visual cortex two-photon and hippocampal miniature fluorescence microscopy. Built on the BERT architecture, the NEuRT model can be efficiently fine-tuned for a wide range of downstream tasks. We showcase its application in classifying wild-type and transgenic Alzheimers disease model mice, based on hippocampal activity, revealing group-specific features through attention map analysis. By reducing reliance on extensive labeled data, addressing a critical challenge in neuroscience, NEuRT bridges fundamental neuroscience and disease research, offering a robust framework for AI-driven and explainable neuronal activity analysis.

neuroscience↗