Biology subjects

Milias-Argeitis, A.

Publications and source records attributed to Milias-Argeitis, A..

7 recordsLinked to original sources

bioRxiv2026

Cell cycle-dependent protein dynamics in budding yeast resolved by deconvolution of bulk proteomics

The cell division cycle is characterised by oscillatory dynamics in regulatory mechanisms and biosynthesis, coordinated with genome replication and segregation. To understand these dynamics, quantitative cell cycle-dependent protein concentration data is essential. Unfortunately, accurate resolution of cell cycle-dependent protein dynamics is challenging because single-cell proteomics is currently infeasible and bulk proteomics requires inherently imperfect cell synchronisation. Here, we developed a computational method to deconvolve cell cycle-dependent protein concentration dynamics and applied it to new budding yeast bulk proteome data. Key to this method was a yeast population model, parameterised with experimental cell cycle progression and volume growth data, for quantifying the desynchronisation in sampled populations. We performed deconvolution on 3373 proteins, using cross-validation to determine regularisation parameters, and identified 563 proteins with cell cycle-dependent dynamics. Many of these dynamics were consistent with known yeast biology and dynamic proteins were enriched for several metabolic process, extending previous observations and supporting the emerging picture of metabolic activity as varying substantially over cell cycle phases. We consider the generated cell cycle-resolved budding yeast proteome data a key resource.

systems biology↗

bioRxiv2025

OsTIR1F74G Expression Controls Basal and Auxin-Induced Degradation Rates of the AID System in Yeast

The auxin-inducible degron (AID) system has been widely used to conditionally and dynamically deplete proteins in yeast. In this system, the plant hormone auxin promotes OsTIR1-mediated degradation of proteins carrying an auxin-inducible degron tag. However, "basal" degradation of AID-tagged proteins in the absence of auxin has hampered work with essential proteins due to defective or non-viable strains. A second-generation AID system based on the OsTIRF47G mutant was recently introduced to overcome the limitations of basal degradation in budding yeast. However, it remained unclear to what extent the use of OsTIRF47G eliminates basal degradation and how it impacts auxin-induced degradation. Here, by performing a quantitative characterization of the basal and auxin-induced degradation dynamics using OsTIR1F74G in budding yeast, we find that basal degradation is still detectable and that it depends on OsTIR1F74G expression levels. We show that also the auxin-induced degradation rates and auxin-induced steady-state concentrations of AID-tagged proteins depend on OsTIR1F74G expression levels, in addition to the type of auxin used. Lastly, we showcase how increased basal degradation of AID-tagged functional proteins can impair cell growth and lead to unwanted phenotypes. We anticipate that our findings will guide future applications of the AID system in budding yeast by helping researchers select appropriate OsTIR1F74G expression levels, particularly in studies focused on precise control of degradation dynamics.

molecular biology↗

bioRxiv2024

Rapid and reversible regulation of cell cycle progression in budding yeast using optogenetics

The regulatory complexity of the eukaryotic cell cycle poses technical challenges in experiment design and data interpretation, leaving gaps in our understanding of how cells coordinate cell cycle-related processes. Traditional methods, such as knockouts and deletions are often ineffective to compensatory interactions in the cell cycle control network, while chemical agents that cause cell cycle arrest can have undesired pleiotropic effects. Synthetic inducible systems targeting specific cell cycle regulators offer potential solutions but are limited by the need for external inducers, which make fast reversibility technically challenging. To address these issues, we developed an optogenetic tool (OPTO-Cln2) that enables light-controlled and reversible regulation of G1 progression in budding yeast. Through extensive validation and benchmarking via time-lapse microscopy, we verify that OPTO-Cln2-carrying strains can rapidly toggle between normal and altered G1 progression. By integrating OPTO-Cln2 with a readout of nutrient-sensing pathways (TORC1 and PKA), we show that the oscillatory activity of these pathways is tightly coordinated with G1 progression. Finally, we demonstrate that the rapid reversibility of OPTO-Cln2 facilitates multiple cycles of synchronous arrest and release of liquid cell cultures. Our work provides a powerful new approach for studying cell cycle dynamics and the coordination of growth- with division-related processes.

synthetic biology↗

bioRxiv2024

Gradient matching accelerates mixed-effects inferencefor biochemical networks

Single-cell time series data frequently display considerable variability across a cell population. The current gold standard for inferring parameter distributions across cell populations is the Global Two Stage (GTS) approach for nonlinear mixed-effects (NLME) models. However, this method is computationally intensive, as it makes repeated use of non-convex optimization that in turn requires numerical integration of the underlying system. Here, we propose the Gradient Matching GTS (GMGTS) method as an efficient alternative to GTS. Gradient matching offers an integration-free approach to parameter estimation that is particularly powerful for dynamical systems that are linear in the unknown parameters, such as biochemical networks modeled by mass action kinetics. Here, we harness the power of gradient matching by integrating it into the GTS framework. To this end, we significantly expand the capabilities of gradient matching via uncertainty propagation calculations and the development of an iterative estimation scheme for partially observed systems. Through comparisons of GMGTS with GTS in different inference setups, we demonstrate that our method provides a significant computational advantage, thereby facilitating the use of complex NLME models in systems biology applications.

systems biology↗

bioRxiv2021

Vacuolar localization via the N-terminal domain of Sch9 is required for TORC1-dependent phosphorylation and downstream signal transduction

The budding yeast Sch9 kinase (functional ortholog of the mammalian S6 kinase) is a major effector of the Target of Rapamycin Complex 1 (TORC1) complex in the regulation of cell growth in response to nutrient availability and stress. In budding yeast, Sch9 is partially localized at the vacuolar surface, where it is phosphorylated by TORC1 under favorable growth conditions. Sch9 recruitment at the vacuole is mediated by direct interaction between PI(3,5)P2 on the vacuolar membrane and the region of Sch9 encompassing amino acid residues 1-390, which contains a C2 domain. Since many C2 domains mediate phospholipid binding, it had been suggested that the C2 domain of Sch9 mediates its vacuolar recruitment. However, the in vivo requirement of the C2 domain for Sch9 localization had not been demonstrated, and the phenotypic consequences of Sch9 delocalization remained unknown. Here, by examining cellular localization, phosphorylation state and growth phenotypes of Sch9 truncation mutants, we show that deletion of the N-terminal domain of Sch9 (aa 1-182), but not the C2 domain (aa 183-399), impairs vacuolar localization and TORC1-dependent phosphorylation of Sch9, while causing growth defects similar those observed in sch9{Delta} cells. Artificial tethering of an N-terminally truncated Sch9 mutant at the vacuolar membrane rescued TORC1-dependent phosphorylation and cell growth. Our study uncovers a key role for the N-terminal domain of Sch9 and demonstrates that recruitment of Sch9 at the vacuolar surface is necessary for TORC1-dependent phosphorylation and downstream signal transduction for the regulation of cell growth.

cell biology↗

bioRxiv2021

A photo-switchable yeast isocitrate dehydrogenase to control metabolic flux through the citric acid cycle

For various research questions in metabolism, it is highly desirable to have means available, with which the flux through specific pathways can be perturbed dynamically, in a reversible manner, and at a timescale that is consistent with the fast turnover rates of metabolism. Optogenetics, in principle, offers such possibility. Here, we developed an initial version of a photo-switchable isocitrate dehydrogenase (IDH) aimed at controlling the metabolic flux through the citric acid cycle in budding yeast. By inserting a protein-based light switch (LOV2) into computationally identified active/regulatory-coupled sites of IDH and by using in vivo screening in Saccharomyces cerevisiae, we obtained a number of IDH enzymes whose activity can be switched by light. Subsequent in-vivo characterization and optimization resulted in an initial version of photo-switchable (PS) IDH. While further improvements of the enzyme are necessary, our study demonstrates the efficacy of the overall approach from computational design, via in vivo screening and characterization. It also represents one of the first few examples, where optogenetics were used to control the activity of a metabolic enzyme.

synthetic biology↗

bioRxiv2017

Optogenetic Single-Cell Control Of Transcription Achieves Mrna Tunability And Reduced Variability

The study of gene expression at the single-cell level has exposed the importance of stochasticity for the behavior of cellular systems. Research on cellular variability has mostly relied on observing expression either in response to natural stimuli or to constant gene regulators. However, the ability to probe cells individually can lead to a deeper understanding of the underlying process. Here, we propose an experimental platform for optogenetic feedback control of individual cells. It consists of a digital micromirror device that, coupled to a microscope, can target light-responsive cells with individualized illumination profiles, thereby exploiting the good spatial resolution of optogenetic induction. Together with an automated software pipeline for segmentation, quantification and tracking of single cells, the platform enables independent and real-time control of numerous cells. We demonstrate our platform by regulating transcription in over a hundred yeast cells simultaneously, while achieving tunability of mRNA abundance. Using a novel technique to measure extrinsic variation, we further show that single cell feedback regulation of this highly stochastic process achieves a 10-fold reduction of extrinsic variation in nascent mRNA over population control, with superior control loop properties. Our platform establishes a new, flexible method for studying transcriptional dynamics in single cells.

synthetic biology↗