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Biology subjects

Veytsman, B.

Publications and source records attributed to Veytsman, B..

4 recordsLinked to original sources

Environment-driven active transport of influenza A virus

Biological media such as airway mucus and extracellular matrix are usually viewed as transport barriers that particles cross by passive diffusion or with internal engines. We show instead that a particle can move actively by modifying the landscape it traverses, creating environmental memory and directional cues in two and three dimensions. Influenza A virus (IAV) realizes this principle through its envelope proteins hemagglutinin (HA) and neuraminidase (NA), which bind and cleave sialylated glycan receptors, respectively. Combining theory, simulations and single-virus tracking, we connect bind--cleave kinetics and HA--NA organization to macroscopic transport. Cleavage dissipates chemical free energy, biases rebinding to the edited landscape and leaves a trail that shapes future encounters. In heterogeneous receptor landscapes, multivalent binding biases motion toward higher receptor density, while receptor destruction by NA can amplify this bias by sharpening the contrast sampled by HA. Experiments on reconstituted glycan membranes show that IAV steps are biased up local receptor gradients, as predicted. The theory suggests that virion-to-virion variability can distribute transport functions across a population, providing a physical hedge against complex receptor environments. Together, these results establish environment-driven active matter as a mechanism for motorless transport powered and guided by chemical modification of the environment.

biophysics↗

The kinetics and optimality of influenza A virus locomotion

Influenza A viruses (IAVs) must navigate through a dense extracellular mucus to infect airway epithelial cells. The mucous layer, composed of glycosylated biopolymers (mucins), presents sialic acid that binds to ligands on the viral envelope and can be irreversibly cleaved by viral enzymes. It was recently discovered that filamentous IAVs exhibit directed persistent motion along their long axis on sialic acid-coated surfaces. This study demonstrates through stochastic simulations and mean-field theory, how IAVs harness a burnt-bridge Brownian ratchet mechanism for directed persistent translational motion. Importantly, our analysis reveals that equilibrium features of the system primarily control the dynamics, even out-of-equilibrium, and that ligand asymmetry allows for more robust directed transport. We show viruses occupy the optimal parameter range ( Goldilocks zone) for efficient mucous transport, possibly due to the evolutionary adaptation of enzyme kinetics. Our findings suggest novel therapeutic targets and provide insight into possible mechanisms of zoonotic transmission.

biophysics↗

Configurational entropy is an intrinsic driver of tissue structural heterogeneity

Tissues comprise ordered arrangements of cells that can be surprisingly disordered in their details. How the properties of single cells and their microenvironment contribute to the balance between order and disorder at the tissue-scale remains poorly understood. Here, we address this question using the self-organization of human mammary organoids as a model. We find that organoids behave like a dynamic structural ensemble at the steady state. We apply a maximum entropy formalism to derive the ensemble distribution from three measurable parameters - the degeneracy of structural states, interfacial energy, and tissue activity (the energy associated with positional fluctuations). We link these parameters with the molecular and microenvironmental factors that control them to precisely engineer the ensemble across multiple conditions. Our analysis reveals that the entropy associated with structural degeneracy sets a theoretical limit to tissue order and provides new insight for tissue engineering, development, and our understanding of disease progression.

bioengineering↗

The impact of preprint servers in the formation of novel ideas

We study whether novel ideas in biomedical literature appear first in preprints or traditional journals. We develop a Bayesian method to estimate the time of appearance for a phrase in the literature, and apply it to a number of phrases, both automatically extracted and suggested by experts. We see that presently most phrases appear first in the traditional journals, but there is a number of phrases with the first appearance on preprint servers. A comparison of the general composition of texts from bioRxiv and traditional journals shows a growing trend of bioRxiv being predictive of traditional journals. We discuss the application of the method for related problems.

scientific communication and education↗