Biology subjects

Szavits-Nossan, J.

Publications and source records attributed to Szavits-Nossan, J..

7 recordsLinked to original sources

bioRxiv2025

High-Throughput Site-Specific N-Glycosylation Profiling of Human Fibrinogen in Atrial Fibrillation

Fibrinogen is a major plasma glycoprotein involved in blood coagulation and inflammatory responses. Alterations in its glycosylation have been implicated in various pathological conditions, yet its site-specific N-glycosylation profile remains largely unexplored in a clinical context. Here, we present a high-throughput LC-MS workflow for site-specific analysis of fibrinogen N-glycosylation using a cost-effective ethanol precipitation enrichment method. The method demonstrated good intra- and inter-plate repeatability (CV: 5% and 12%, respectively) and was validated through the first assessment of intraindividual temporal stability in healthy individuals, revealing consistent glycosylation patterns within individuals. Application to 181 atrial fibrillation (AF) patients and 52 healthy controls identified three gamma chain glycoforms significantly associated with AF. Most notably, increased levels of the asialylated N4H5, known to enhance fibrin bundle thickness and promote clot formation, suggest a potential mechanism linking glycosylation changes to the prothrombotic state in AF. Furthermore, fibrinogen sialylation showed strong associations with cardiovascular risk factors, including triglycerides, BMI, and glucose levels. Longitudinal analysis of 108 AF patients six months post-catheter ablation showed stability in the AF-associated glycan profile. Our findings establish fibrinogen glycosylation as a potential biomarker for cardiovascular conditions and demonstrate the utility of site-specific glycosylation analysis for clinical applications.

molecular biology↗

bioRxiv2024

Joint distribution of nuclear and cytoplasmic mRNA levels in stochastic models of gene expression: analytical results and parameter inference

Common stochastic models of gene expression predict the analytical distribution of the total mRNA level per cell but not at subcellular resolution. Here, for a wide class of models of transcription initiation, we obtain an exact steady-state solution for the joint distribution of nuclear and cytoplasmic mRNA levels per cell. Correcting the solution for extrinsic noise and fitting to single human cell data, we precisely quantify the extent of bursty expression in thousands of genes and associate it with their biological functions.

biophysics↗

bioRxiv2023

Transient power-law behaviour following induction distinguishes between competing models of stochastic gene expression

What features of transcription can be learnt by fitting mathematical models of gene expression to mRNA count data? Given a suite of models, fitting to data selects an optimal one, thus identifying a probable transcriptional mechanism. Whilst attractive, the utility of this methodology remains unclear. Here, we sample steady-state, single-cell mRNA count distributions from parameters in the physiological range, and show they cannot be used to confidently estimate the number of inactive gene states, i.e. the number of rate-limiting steps in transcriptional initiation. Distributions from over 99% of the parameter space generated using models with 2, 3, or 4 inactive states can be well fit by one with a single inactive state. However, we show that for many minutes following induction, eukaryotic cells show an increase in the mean mRNA count that obeys a power law whose exponent equals the sum of the number of states visited from the initial inactive to the active state and the number of rate-limiting post-transcriptional processing steps. Our study shows that estimation of the exponent from eukaryotic data can be sufficient to determine a lower bound on the total number of regulatory steps in transcription initiation, splicing, and nuclear export.

systems biology↗

bioRxiv2022

Predicting variability in nascent RNA from transcription initiation kinetics

Fluctuations in the number of nascent RNA accurately reflect transcriptional activity. However, mathematical models predicting their distributions are difficult to solve analytically due to their non-Markovian nature stemming from transcriptional elongation. Here we circumvent this problem by deriving an exact relationship between the steady-state distribution of nascent RNA and the distribution of initiation times, which can be computed for any general initiation mechanism described by a set of first-order reactions. We test our theory using simulations and live cell imaging data.

biophysics↗

bioRxiv2021

Mean-field theory accurately captures the variation of copy number distributions across the mRNA's life cycle

We consider a stochastic model where a gene switches between two states, an mRNA transcript is released in the active state and subsequently it undergoes an arbitrary number of sequential unimolecular steps before being degraded. The reactions effectively describe various stages of the mRNA life cycle such as initiation, elongation, termination, splicing, export and degradation. We construct a novel mean-field approach that leads to closed-form steady-state distributions for the number of transcript molecules at each stage of the mRNA life cycle. By comparison with stochastic simulations, we show that the approximation is highly accurate over all of parameter space, independent of the type of expression (constitutive or bursty) and of the shape of the distribution (unimodal, bimodal and nearly bimodal). The theory predicts that in a population of identical cells, any bimodality is gradually washed away as the mRNA progresses through its life cycle.

systems biology↗

bioRxiv2019

Accurate measures of translation efficiency and traffic using ribosome profiling

One of the main goals of ribosome profiling is to quantify the rate of protein synthesis at the level of translation. Here, we develop a method for inferring translation elongation kinetics from ribosome profiling data using recent advances in the mathematical modelling of mRNA translation. Our method distinguishes between the elongation rate intrinsic to the ribosomes stepping cycle and the actual elongation rate that takes into account ribosome interference. This distinction allows us to quantify the extent of ribosomal collisions along the transcript and identify individual codons where ribosomal collisions are likely. When examining ribosome profiling in yeast, we observe that translation initiation and elongation are close to their optima, and traffic is minimised at the beginning of the transcript to favour ribosome recruitment. However, we find many individual sites of congestion along the mRNAs where the probability of ribosome interference can reach 50%. Our work provides new measures of translation initiation and elongation efficiencies, emphasising the importance of rating these two stages of translation separately.

systems biology↗

bioRxiv2019

Power series method for solving TASEP-based models of mRNA translation

We develop a method for solving mathematical models of messenger RNA (mRNA) translation based on the totally asymmetric simple exclusion process (TASEP). Our main goal is to demonstrate that the method is versatile and applicable to realistic models of translation. To this end we consider the TASEP with codon-dependent elongation rates, premature termination due to ribosome drop-off and translation reinitiation due to circularisation of the mRNA. We apply the method to the model organism Saccharomyces cerevisiae under physiological conditions and find excellent agreements with the results of stochastic simulations. Our findings suggest that the common view on translation as being rate-limited by initiation is oversimplistic. Instead we find theoretical evidence for ribosome interference and also theoretical support for the ramp hypothesis which argues that codons at the beginning of genes have slower elongation rates in order to reduce ribosome density and jamming.

biophysics↗