Biology subjects

Kardar, M.

Publications and source records attributed to Kardar, M..

6 recordsLinked to original sources

bioRxiv2025

A unifying model of LAT condensates in reconstitution experiments

The formation of condensates by the Linker for the Activation of T-cells (LAT) is a key signal gating and amplification step in the T-cell receptor signaling pathway. LAT condensation is challenging to study in-vivo and is therefore often investigated using reconstitution experiments. While these experiments recapitulate key aspects of LAT condensation, they also exhibit some puzzling features. Here, we describe the mechanisms underlying these observations using two complementary models. First, we employ a Smoluchowski aggregation model to show that the delay time before condensation is observed arises from a low effective binding probability between LAT monomers. Second, we propose a field-theoretic model that reproduces all condensate morphologies observed in experiments, showing that they can arise from common underlying dynamics modulated by variations in experimental conditions. This result unifies different experimental observations reported previously. While this article addresses open questions regarding the formation of LAT condensates, our results also provide a common framework for understanding condensation of other multivalent membrane proteins such as EGFR, FGFR2, and nephrin.

biophysics↗

bioRxiv2024

Active RNA synthesis patterns nuclear condensates

Biomolecular condensates are membraneless compartments that organize biochemical processes in cells. In contrast to well-understood mechanisms describing how condensates form and dissolve, the principles underlying condensate patterning - including their size, number and spacing in the cell - remain largely unknown. We hypothesized that RNA, a key regulator of condensate formation and dissolution, influences condensate patterning. Using nucleolar fibrillar centers (FCs) as a model condensate, we found that inhibiting ribosomal RNA synthesis significantly alters the patterning of FCs. Physical theory and experimental observations support a model whereby active RNA synthesis generates a non-equilibrium state that arrests condensate coarsening and thus contributes to condensate patterning. Altering FC condensate patterning by expression of the FC component TCOF1 impairs ribosomal RNA processing, linking condensate patterning to biological function. These results reveal how non-equilibrium states driven by active chemical processes regulate condensate patterning, which is important for cellular biochemistry and function.

biophysics↗

bioRxiv2023

How persistent infection overcomes peripheral tolerance mechanisms to cause T cell-mediated autoimmune disease

T cells help orchestrate immune responses to pathogens, and their aberrant regulation can trigger autoimmunity. Recent studies highlight that a threshold number of T cells (a quorum) must be activated in a tissue to mount a functional immune response. These collective effects allow the T cell repertoire to respond to pathogens while suppressing autoimmunity due to circulating autoreactive T cells. Our computational studies show that increasing numbers of pathogenic peptides targeted by T cells during persistent or severe viral infections increase the probability of activating T cells that are weakly reactive to self-antigens (molecular mimicry). These T cells are easily re-activated by the self-antigens and contribute to exceeding the quorum threshold required to mount autoimmune responses. Rare peptides that activate many T cells are sampled more readily during severe/persistent infections than in acute infections, which amplifies these effects. Experiments in mice to test predictions from these mechanistic insights are suggested.

immunology↗

bioRxiv2022

Polymer folding through active processes recreates features of genome organization

From proteins to chromosomes, polymers fold into specific conformations that control their biological function. Polymer folding has long been studied with equilibrium thermodynamics, yet intracellular organization and regulation involve energy-consuming, active processes. Signatures of activity have been measured in the context of chromatin motion, which shows spatial correlations and enhanced subdiffusion only in the presence of adenosine triphosphate (ATP). Moreover, chromatin motion varies with genomic coordinate, pointing towards a heterogeneous pattern of active processes along the sequence. How do such patterns of activity affect the conformation of a polymer such as chromatin? We address this question by combining analytical theory and simulations to study a polymer subjected to sequence-dependent correlated active forces. Our analysis shows that a local increase in activity (larger active forces) can cause the polymer backbone to bend and expand, while less active segments straighten out and condense. Our simulations further predict that modest activity differences can drive compartmentalization of the polymer consistent with the patterns observed in chromosome conformation capture experiments. Moreover, segments of the polymer that show correlated active (sub)diffusion attract each other through effective long-ranged harmonic interactions, whereas anticorrelations lead to effective repulsions. Thus, our theory offers non-equilibrium mechanisms for forming genomic compartments, which cannot be distinguished from affinity-based folding using structural data alone. As a first step toward disentangling active and passive mechanisms of folding, we discuss a data-driven approach to discern if and how active processes affect genome organization.

biophysics↗

bioRxiv2021

Inferring the intrinsic mutational fitness landscape of influenza-like evolving antigens from temporally ordered sequence data

There still are no effective long-term protective vaccines against viruses that continuously evolve under immune pressure such as seasonal influenza, which has caused, and can cause, devastating epidemics in the human population. For finding such a broadly protective immunization strategy it is useful to know how easily the virus can escape via mutation from specific antibody responses. This information is encoded in the fitness landscape of the viral proteins (i.e., knowledge of the viral fitness as a function of sequence). Here we present a computational method to infer the intrinsic mutational fitness landscape of influenza-like evolving antigens from yearly sequence data. We test inference performance with computer-generated sequence data that are based on stochastic simulations mimicking basic features of immune-driven viral evolution. Although the numerically simulated model does create a phylogeny based on the allowed mutations, the inference scheme does not use this information. This provides a contrast to other methods that rely on reconstruction of phylogenetic trees. Our method just needs a sufficient number of samples over multiple years. With our method we are able to infer single-as well as pairwise mutational fitness effects from the simulated sequence time series for short antigenic proteins. Our fitness inference approach may have potential future use for design of immunization protocols by identifying intrinsically vulnerable immune target combinations on antigens that evolve under immune-driven selection. This approach may in the future be applied to influenza and other novel viruses such as SARS-CoV-2, which evolves and, like influenza, might continue to escape the natural and vaccine-mediated immune pressures.

evolutionary biology↗

bioRxiv2019

Mean-field computational approach to HIV dynamics on a fitness landscape

During acute infection by the human immunodeficiency virus (HIV), the intra-host viral population accumulates mutations due to selection by host T cell responses. The timescales at which HIV mutations emerge vary widely, correlating with their diversity in the global population of hosts, and with the relative strengths at which different regions of the HIV sequence are targeted by the host. In recent years, \"fitness landscapes\" of HIV proteins have been estimated from this global diversity, capturing residue-specific fitness costs and epistatic interactions between residues, and have been shown to predict the locations and relative timescales of escape mutations arising in hosts with known T cell responses. However, existing computational approaches to HIV dynamics that make use of realistic fitness landscapes are limited to fixed-population-size stochastic simulations, and extending other dynamical approaches to capture full sequence-level detail is computationally nontrivial. In this paper, we introduce and study an alternative approach for simulating HIV dynamics given a fitness landscape, which we designate the evolutionary mean-field (EMF) method. EMF is the high-recombination-rate limit of a model of HIV replication and mutation, which we justify using methods from statistical physics. EMF outputs a set of time-dependent \"effective fitnesses\" that crucially depend on epistatic interactions and the sequence background, as well as predictions of the frequencies of mutation at each HIV residue over time. As a proof of principle, we apply this method to the dynamics of the p24 gag protein infecting a host whose T cell responses are known. Specifically, we show how fitness costs and epistatic interactions in the fitness landscape, the relative strengths of T cell responses, and the HIV sequence background, impact the locations and time course of HIV escape mutations, consistent with previous work. We also describe features of longer-term dynamics, specifically reversions, in terms of the effective fitnesses yielded by EMF, and quantify the mean fitness and entropy of the intra-host population over time. Finally, we develop a stochastic population dynamics version of EMF, extending prior stochastic approaches to a time-varying population size that crucially depends on the fitness of strains existing in the intra-host population at each time. The EMF approach offers a framework for understanding features of HIV dynamics in terms of effective fitnesses, and allows for a more detailed study of how the fitness landscape and sequence background impact both the evolutionary and population dynamics of HIV, in a computationally tractable way.\n\nAuthor summaryAs fitness landscapes of HIV proteins become more accurately known, it may become possible to more faithfully predict the locations and timescales of HIV mutations arising in a host with known immune responses, which may inform the design of vaccine immunogens. However, existing approaches for computing HIV dynamics given a fitness landscape are limited to fixed-population-size stochastic simulations. Here, we present an alternative approach that we designate the evolutionary mean-field (EMF) method. It takes as input an HIV fitness landscape and the locations and strengths of host immune responses, and outputs a set of time-dependent \"effective fitnesses\" and frequencies of mutation at each HIV residue over time. EMF is the high-recombination-rate limit of a model of HIV replication and mutation, which we derive using methods from statistical physics. We apply EMF on an example to show how fitness costs and epistatic interactions in the fitness landscape, the relative strengths of host immune responses, and the HIV sequence background, impact the locations and time course of HIV mutations. We also develop a stochastic population dynamics version of EMF where population size changes crucially depend on the fitness of strains existing in the population at each time. EMF makes quick predictions of the dynamics of HIV mutations through the effective fitnesses, crucially taking into account the fitness landscape and sequence background, and enables more detailed studies of how these affect the evolutionary and population dynamics of HIV, in a computationally tractable way.

evolutionary biology↗