Biology subjects

Sakharova, H.

Publications and source records attributed to Sakharova, H..

3 recordsLinked to original sources

bioRxiv2026

Prefoldin function links meiotic chromosome segregation with cellular remodeling and reveals tubulin sensitivity of the meiotic spindle

Faithful chromosome segregation is essential for producing viable gametes during meiosis, a specialized type of cell division compared to mitosis. Unlike mitosis, meiosis involves two consecutive chromosome segregation events without an intervening round of DNA replication. Here we identify Gim3, a subunit of the ubiquitously expressed and conserved prefoldin complex, as a critical regulator of meiotic but not mitotic chromosome segregation in budding yeast. Loss of Gim3 causes profound defects in chromosome segregation and gamete viability through reduced tubulin protein levels, which are also associated with reduced spindle length. In mitosis, however, GIM3 deletion minimally affects spindle length and chromosome segregation, despite similarly reduced tubulin levels in both contexts, highlighting a previously unrecognized difference between the sensitivity of meiotic and mitotic spindles to tubulin abundance. In addition to chromosome segregation defects, gim3{Delta} cells exhibit aberrant meiotic cellular remodeling, including defects in exclusion of age-associated protein aggregates from newly forming gametes. Importantly, experimentally induced meiotic chromosome mis-segregation similarly disturbs cellular remodeling. Together, our findings identify Gim3 as a key factor required for maintaining chromosome segregation integrity during meiosis and reveal a previously unrecognized link between chromosome segregation and meiotic cellular remodeling.

cell biology↗

bioRxiv2025

Protein language models reveal evolutionary constraints on synonymous codon choice

Evolution has shaped the genetic code, with subtle pressures leading to preferences for some synonymous codons over others. Codons are translated at different speeds by the ribosome, imposing constraints on codon choice related to the process of translation. The structure and function of a protein may impose pressure to translate the associated mRNA at a particular speed in order to enable proper protein production, but the molecular basis and scope of these evolutionary constraints have remained elusive. Here, we show that information about codon constraints can be extracted from protein sequence alone. We leverage a protein language model to predict codon choice from amino acid sequence, combining implicit information about position and protein structure to learn subtle but generalizable constraints on codon choice in yeast. In parallel, we conduct a genome-wide screen of thousands of synonymous codon substitutions in endogenous loci in yeast, reliably identifying a small set of several hundred synonymous variants that increase or decrease fitness while showing that most positions have no measurable effect on growth. Our results suggest that cotranslational localization and translational accuracy, more than cotranslational protein folding, are major drivers of selective pressure on codon choice in eukaryotes. By considering both the small but wide-reaching effects of codon choice that can be learned from evolution and the strong but highly specific effects determined via experiment, we expose unappreciated biological constraints on codon choice.

genomics↗

bioRxiv2025

A generative language model decodes contextual constraints on codon choice for mRNA design

The genetic code allows multiple synonymous codons to encode the same amino acid, creating a vast sequence space for protein-coding regions. Codon choice can impact mRNA function and protein output, a consideration newly relevant with advances in mRNA technology. Genomes preferentially use some codons, but simple optimization methods that select preferred codons miss complex contextual patterns. We present Trias, an encoder-decoder language model trained on millions of eukaryotic coding sequences. Trias learns codon usage rules directly from sequence data, integrating local and global dependencies to generate species-specific codon sequences that align with biological constraints. Without explicit training on protein expression, Trias generates sequences and scores that correlate strongly with experimental measurements of mRNA stability, ribosome load, and protein output. The model outperforms commercial codon optimization tools in generating sequences resembling high-expression codon sequence variants. By modeling codon usage in context, Trias offers a data-driven framework for synthetic mRNA design and for understanding the molecular and evolutionary principles behind codon choice.

genomics↗