Biology subjects

Tashev, S. A.

Publications and source records attributed to Tashev, S. A..

4 recordsLinked to original sources

bioRxiv2026

Stochastic colonization and host-to-host transmission shape gut bacterial variability

Understanding the kinetic processes that govern bacterial population dynamics within hosts is critical in developing effective strategies to control microbiota. However, inferring population dynamics is challenging due to large host-to-host bacterial population variability stemming from stochastic colonization events, as well as the inability to continuously monitor the bacterial population without disturbing the host. Using C. elegans fed E. coli under different diets, we show that early colonization acts as a stochastic bottleneck that drives substantial divergence in host-level bacterial loads, and that the spreading of bacteria from colonized worms to sterile ones regulates this variability by altering effective colonization pressure. These conclusions are reinforced using a simulation-based inference framework that quantifies stochastic within-host population dynamics from discrete snapshot data, enabling inference of effective colonization and growth rates across heterogeneous hosts with variable carrying capacities. Applying this framework, we further demonstrate that the bacterial predator B. bacteriovorus reduces average gut bacterial loads by two orders of magnitude, primarily by suppressing environmental recolonization and subsequent host-to-host transmission rather than eliminating established intra-host populations. Together, these results reveal that host-associated microbial population dynamics are strongly impacted by environmental colonization processes that modulate stochastic entry events.

microbiology↗

bioRxiv2025

REPOP: bacterial population quantification from platecounts

Bacterial counts from native environments, such as soil or the animal gut, often show substantial variability across replicate samples. This heterogeneity is typically attributed to genetic or environmental factors. A common approach to estimating bacterial populations involves successive dilution and plating, followed by multiplying colony counts by dilution factors. This method, however, overestimates the heterogeneity in bacterial population because it conflates the inherent uncertainty in drawing a subsample from the total population with the uncertainty in the sample arising from biological origins. In other words, this approach may obscure features that may otherwise be present in the data hinting at the presence of genuine subpopulations. For example, in plate counting applied to C. elegans gut microbiota, observed multimodality is often interpreted as large host-to-host variance, while the randomness introduced by measurement is frequently ignored. To explicitly account for the uncertainty introduced by dilution and plating randomness, we introduce REPOP, a PyTorch-based library to REconstruct POpulations from Plates within a Bayesian framework. Beyond simple cases, REPOP addresses more complex scenarios, including multimodal populations and correcting the mathematically subtle, but experimentally relevant, bias introduced by excluding plates deemed too crowded to distinguish individual colonies. We demonstrate REPOPs ability to resolve distinct population peaks otherwise obscured by standard multiplication methods. Applications to both simulated and experimental datasets, including bacterial samples of different concentrations and ones from the gut microbiota of C. elegans, show that REPOP accurately recovers the underlying multimodality by properly accounting for error propagation, where naive multiplication fails. REPOP is available on GitHub: https://github.com/LabPresse/REPOP.

microbiology↗

bioRxiv2025

Bayesian Inference of Binding Kinetics from Fluorescence Time Series

The study of binding kinetics via the analysis of fluorescence time traces is often con-founded by measurement noise and photophysics. Although photoblinking can be mitigated by using labels less likely to photoswitch, photobleaching generally cannot be eliminated. Current methods for measuring binding and unbinding rates are therefore limited by concurrent photobleaching events. Here, we propose a method to infer binding and unbinding rates alongside photobleaching rates using fluorescence intensity traces. Our approach is a two-stage process involving analyzing individual regions of interest (ROIs) with a Hidden Markov Model to infer the fluorescence intensity levels of each trace. We then use the inferred intensity level state trajectory from all ROIs to infer kinetic rates. Our method has several advantages, including the ability to analyze noisy traces, account for the presence of photobleaching events, and provide uncertainties associated with the inferred binding kinetics. We demonstrate the effectiveness and reliability of our method through simulations and data from DNA origami binding experiments.

biochemistry↗

bioRxiv2023

A General Method to Accurately Count Molecular Complexes and Determine the Degree of Labelling in Cells Using Protein Tags

Determining the label to target ratio, also known as degree of labelling (DOL), is crucial for quantitative fluorescence microscopy and a high DOL with minimal unspecific labelling is beneficial for fluorescence microscopy in general. Yet, robust, versatile, and easy-to-use tools for measuring cell-specific labelling efficiencies are not available. This study presents a novel DOL determination technique named Protein-tag DOL (ProDOL), which enables fast DOL measurements and optimisation of protein-tag labelling. With ProDOL various factors affecting labelling efficiency, including substrate type, incubation time, and concentration, as well as sample fixation and cell type can be easily assessed. We applied ProDOL to investigate how HIV-1 pathogenesis factor Nef modulates CD4 T cell activation measuring total and activated copy numbers of the adaptor protein SLP-76 in signalling microclusters. ProDOL proved to be a versatile and robust tool for labelling calibration, enabling determination of labelling efficiencies, optimisation of strategies, and quantification of protein stoichiometry.

cell biology↗