Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.64898/2025.12.21.695865

A yeast model of 5-oxoproline accumulation reveals a general toleration to 5-oxoproline

Dubey, P.·BS, V.·Singh, P.·Sengupta, S.·Bachhawat, A.
View original source ↗Export metadata ↓

Abstract

5-oxoproline (5-OP) or pyroglutamic acid is an intermediate of the degradation arc of the glutathione cycle. It is metabolized into glutamate through the action of the 5-oxoprolinase enzyme, the only enzyme known to act on this metabolite. 5-OP has long been known to be relatively inert with a proposed role as an osomoprotectant. Recent studies on the 5-oxoprolinase enzyme in mammalian cells have, however, shown that knockdown or deletions of the 5-oxoprolinase make mice (and humans) prone to heart failure. This was ascribed to oxidative stress due to a two-fold elevation in 5-OP. To examine the consequences of 5-oxoproline accumulation more rigorously, we have created a yeast model for 5-oxoproline accumulation. We observed retardation of growth only when intracellular levels of 5-OP were increased 12-20 fold over the normal levels. A transcriptomics study was carried out under these conditions. We observed that while a large number of genes were regulated upto 2-fold, there were no single prominent pathways amongst them. Among the key genes upregulated were different efflux pumps. Using knockouts and also overexpression of selected genes, we could observe that many of the upregulated genes were involved in the cellular response to 5-OP accumulation. However, it did not appear that there was any significant oxidative stress response. The study suggests a need to reevaluate previous suppositions of the 5-OP induced oxidative stress response. Instead, we propose an alternative model to explain the possible consequences of 5-oxoprolinase deficiency based on these findings made with the yeast model.

Source connections

Explore related subjects

systems biologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Dubey, P., BS, V., Singh, P., Sengupta, S., Bachhawat, A.. 2025-12-23. A yeast model of 5-oxoproline accumulation reveals a general toleration to 5-oxoproline. https://doi.org/10.64898/2025.12.21.695865

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Interpretable machine learning coupled to gene regulatory networks uncovers subcircuits underlying cell fate decisions

Gene regulatory networks (GRNs) model causal linkages that control cell fate decisions and differentiation transitions. Prioritizing regulatory subnetworks underlying cell state differences is of critical importance, but current methods including those reliant on topological metrics introduce circularity as the metrics prioritizing TFs are computed from the same networks whose assumptions they inherit. Separately, interpretable machine learning methods can identify latent factors (LFs) that discriminate cellular states with formal statistical guarantees but do not model regulatory linkages. Here, we present FOCAL (Factor-Outcome Coupling for Assessment of Linkages), a paradigm to prioritize regulatory subnetworks by coupling state-specific and dynamic GRNs with outcome-supervised LFs learned using interpretable machine learning without reference to network topology. This shifts GRN focus from macroscopic TF nodes to state-specific and dynamic TF-gene linkages. In B and T cells, FOCAL identified GIFs (GRNs coupled to Interpretable latent Factors), prioritized regulatory subnetworks underlying established states as well as transient regulatory episodes preceding them. By coupling LFs learnt from perturbation experiments of lineage-defining TFs, FOCAL identified transcriptional predisposition to alternative fates within progenitor cell populations before overt differentiation. This uncovered a novel NFATC2-IRF8 interplay in activated B cells, that was validated by in-vitro and in-vivo genetic perturbations. The two transcription factors act cooperatively to restrain extrafollicular plasmablast differentiation and promote germinal center B cell fate.

systems biology↗
bioRxiv2026

Comprehensive in silico analysis reveals candidate regulatory mechanisms underlying selective cerebellar vulnerability in pontocerebellar hypoplasia

Pontocerebellar hypoplasia (PCH) is a group of ultrarare, neurodegenerative disorders characterized by cerebellar and pontine hypoplasia. Genetic analysis over the last two decades has revealed an increasing number of pathogenic variants in a wide range of broadly expressed genes functioning in RNA processing, tRNA metabolism, and translation. However, the mechanisms linking these ubiquitous processes to brain region-specific vulnerability are unknown. Here, we established a multi-level variant-to-function in silico framework to predict the molecular consequences of PCH-associated variants in TSEN complex genes. These variants were predicted to have heterogeneous effects on diverse protein properties, including stability, subcellular localization, and degradation, supporting variant-specific rather than uniform disease mechanisms. Complementary transcriptomic analyses showed that PCH-associated genes were not globally enriched in the prenatal cerebellum. Instead, their expression was coordinated in a stage- and cell type-specific manner during cerebellar development. We therefore hypothesize that multiple PCH-associated genes are regulated by a common set of transcription factors, providing an explanation of the selective vulnerability of the cerebellum and to the phenotypic convergence of genetically diverse PCH subtypes. In summary, this study prioritizes candidate variants for biochemical, cellular, and in vivo validation, and identifies regulatory programs, cell lineages, and developmental windows for targeted, mechanistically informed disease modelling.

systems biology↗
bioRxiv2026

Limit-pushing overexpression reveals constraints on protein abundance

Proteins are often classified as toxic or non-toxic without measuring the abundance reached, leaving constraints on tolerable protein abundance unresolved. We established a limit-pushing approach in Saccharomyces cerevisiae combining strong inducible expression with gTOW-mediated high-copy selection to counteract copy-number compensation while measuring protein abundance and growth. Nearly all of approximately 80 chromosome I proteins severely inhibited growth or reduced viability at sufficiently high abundance. We established IE50, the expression level associated with a 50% reduction in growth rate, to quantify their widely varying overexpression tolerance. IE50 was positively associated with predicted structural order and cytoplasmic localization propensity and negatively associated with sulphur content. Single-cell imaging linked higher tolerance to proteins remaining cytoplasmic without becoming aggregation-positive and revealed abundance-dependent changes in localization and organelle morphology. At extreme abundance, Fun12, Nup60, and Pex22 generated distinct large-scale intracellular states through specific sequence regions. These findings establish overexpression toxicity as a quantitative property linked to protein characteristics and reveal both constraints on tolerable abundance and sequence-dependent capacities for intracellular organization.

systems biology↗