Chronic Inflammation Permanently Reshapes Tissue-Resident Immunity in Celiac Disease.

Tissue-resident lymphocytes play a key role in immune surveillance, but it remains unclear how these inherently stable cell populations respond to chronic inflammation. In the setting of celiac disease (CeD), where exposure to dietary antigen can be controlled, gluten-induced inflammation triggered a profound depletion of naturally occurring Vγ4+/Vδ1+ intraepithelial lymphocytes (IELs) with innate cytolytic properties and specificity for the butyrophilin-like (BTNL) molecules BTNL3/BTNL8. Creation of a new niche with reduced expression of BTNL8 and loss of Vγ4+/Vδ1+ IELs was accompanied by the expansion of gluten-sensitive, interferon-γ-producing Vδ1+ IELs bearing T cell receptors (TCRs) with a shared non-germline-encoded motif that failed to recognize BTNL3/BTNL8. Exclusion of dietary gluten restored BTNL8 expression but was insufficient to reconstitute the physiological Vγ4+/Vδ1+ subset among TCRγδ+ IELs. Collectively, these data show that chronic inflammation permanently reconfigures the tissue-resident TCRγδ+ IEL compartment in CeD. VIDEO ABSTRACT.

A Conserved Bicycle Model for Circadian Clock Control of Membrane Excitability.

Circadian clocks regulate membrane excitability in master pacemaker neurons to control daily rhythms of sleep and wake. Here, we find that two distinctly timed electrical drives collaborate to impose rhythmicity on Drosophila clock neurons. In the morning, a voltage-independent sodium conductance via the NA/NALCN ion channel depolarizes these neurons. This current is driven by the rhythmic expression of NCA localization factor-1, linking the molecular clock to ion channel function. In the evening, basal potassium currents peak to silence clock neurons. Remarkably, daily antiphase cycles of sodium and potassium currents also drive mouse clock neuron rhythms. Thus, we reveal an evolutionarily ancient strategy for the neural mechanisms that govern daily sleep and wake.

Single-cell analysis defines the divergence between the innate lymphoid cell lineage and lymphoid tissue-inducer cell lineage.

The precise lineage relationship between innate lymphoid cells (ILCs) and lymphoid tissue-inducer (LTi) cells is poorly understood. Using single-cell multiplex transcriptional analysis of 100 lymphoid genes and single-cell cultures of fetal liver precursor cells, we identified the common proximal precursor to these lineages and found that its bifurcation was marked by differential induction of the transcription factors PLZF and TCF1. Acquisition of individual effector programs specific to the ILC subsets ILC1, ILC2 and ILC3 was initiated later, at the common ILC precursor stage, by transient expression of mixed ILC1, ILC2 and ILC3 transcriptional patterns, whereas, in contrast, the development of LTi cells did not go through multilineage priming. Our findings provide insight into the divergent mechanisms of the differentiation of the ILC lineage and LTi cell lineage and establish a high-resolution 'blueprint' of their development.

A unified model for the dynamics of ATP-independent ultrafast contraction.

In nature, several ciliated protists possess the remarkable ability to execute ultrafast motions using protein assemblies called myonemes, which contract in response to Ca2+ ions. Existing theories, such as actomyosin contractility and macroscopic biomechanical latches, do not adequately describe these systems, necessitating development of models to understand their mechanisms. In this study, we image and quantitatively analyze the contractile kinematics observed in two ciliated protists (Vorticella sp. and Spirostomum sp.), and, based on the mechanochemistry of these organisms, we propose a minimal mathematical model that reproduces our observations as well as those published previously. Analyzing the model reveals three distinct dynamic regimes, differentiated by the rate of chemical driving and the importance of inertia. We characterize their unique scaling behaviors and kinematic signatures. Besides providing insights into Ca2+-powered myoneme contraction in protists, our work may also inform the rational design of ultrafast bioengineered systems such as active synthetic cells.

Cooperative recruitment of Yan via a high-affinity ETS supersite organizes repression to confer specificity and robustness to cardiac cell fate specification.

Cis-regulatory modules (CRMs) are defined by unique combinations of transcription factor-binding sites. Emerging evidence suggests that the number, affinity, and organization of sites play important roles in regulating enhancer output and, ultimately, gene expression. Here, we investigate how the cis-regulatory logic of a tissue-specific CRM responsible for even-skipped (eve) induction during cardiogenesis organizes the competing inputs of two E-twenty-six (ETS) members: the activator Pointed (Pnt) and the repressor Yan. Using a combination of reporter gene assays and CRISPR-Cas9 gene editing, we suggest that Yan and Pnt have distinct syntax preferences. Not only does Yan prefer high-affinity sites, but an overlapping pair of such sites is necessary and sufficient for Yan to tune Eve expression levels in newly specified cardioblasts and block ectopic Eve induction and cell fate specification in surrounding progenitors. Mechanistically, the efficient Yan recruitment promoted by this high-affinity ETS supersite not only biases Yan-Pnt competition at the specific CRM but also organizes Yan-repressive complexes in three dimensions across the eve locus. Taken together, our results uncover a novel mechanism by which differential interpretation of CRM syntax by a competing repressor-activator pair can confer both specificity and robustness to developmental transitions.

KidA, a multi-PAS domain protein, tunes the period of the cyanobacterial circadian oscillator.

The cyanobacterial clock presents a unique opportunity to understand the biochemical basis of circadian rhythms. The core oscillator, composed of the KaiA, KaiB, and KaiC proteins, has been extensively studied, but a complete picture of its connection to the physiology of the cell is lacking. To identify previously unknown components of the clock, we used KaiB locked in its active fold as bait in an immunoprecipitation/mass spectrometry approach. We found that the most abundant interactor, other than KaiC, was a putative diguanylate cyclase protein predicted to contain multiple Per-Arnt-Sim (PAS) domains, which we propose to name KidA. Here we show that KidA directly binds to the fold-switched active form of KaiB through its N-terminal PAS domains. We found that KidA shortens the period of the circadian clock both in vivo and in vitro and alters the ability of the clock to entrain to light-dark cycles. The dose-dependent effect of KidA on the clock period could be quantitatively recapitulated by a mathematical model in which KidA stabilizes the fold-switched form of KaiB, favoring rebinding to KaiC. Put together, our results show that the period and amplitude of the clock can be modulated by regulating the access of KaiB to the fold-switched form.

A self-reinforcing regulatory network triggered by limiting IL-7 activates pre-BCR signaling and differentiation.

The molecular crosstalk between the interleukin 7 receptor (IL-7R) and the precursor to the B cell antigen receptor (pre-BCR) in B lymphopoiesis has not been elucidated. Here we demonstrate that in pre-B cells, the IL-7R but not the pre-BCR was coupled to phosphatidylinositol-3-OH kinase (PI(3)K) and the kinase Akt; signaling by this pathway inhibited expression of recombination-activating gene 1 (Rag1) and Rag2. Attenuation of IL-7 signaling resulted in upregulation of the transcription factors Foxo1 and Pax5, which coactivated many pre-B cell genes, including Rag1, Rag2 and Blnk. Induction of Blnk (which encodes the signaling adaptor BLNK) enabled pre-BCR signaling via the signaling molecule Syk and promoted immunoglobulin light-chain rearrangement. BLNK expression also antagonized Akt activation, thereby augmenting the accumulation of Foxo1 and Pax5. This self-reinforcing molecular circuit seemed to sense limiting concentrations of IL-7 and functioned to constrain the proliferation of pre-B cells and trigger their differentiation.

Pervasive, conserved secondary structure in highly charged protein regions.

Understanding how protein sequences confer function remains a defining challenge in molecular biology. Two approaches have yielded enormous insight yet are often pursued separately: structure-based, where sequence-encoded structures mediate function, and disorder-based, where sequences dictate physicochemical and dynamical properties which determine function in the absence of stable structure. Here we study highly charged protein regions (>40% charged residues), which are routinely presumed to be disordered. Using recent advances in structure prediction and experimental structures, we show that roughly 40% of these regions form well-structured helices. Features often used to predict disorder-high charge density, low hydrophobicity, low sequence complexity, and evolutionarily varying length-are also compatible with solvated, variable-length helices. We show that a simple composition classifier predicts the existence of structure far better than well-established heuristics based on charge and hydropathy. We show that helical structure is more prevalent than previously appreciated in highly charged regions of diverse proteomes and characterize the conservation of highly charged regions. Our results underscore the importance of integrating, rather than choosing between, structure- and disorder-based approaches.

Motor crosslinking augments elasticity in active nematics.

In active materials, uncoordinated internal stresses lead to emergent long-range flows. An understanding of how the behavior of active materials depends on mesoscopic (hydrodynamic) parameters is developing, but there remains a gap in knowledge concerning how hydrodynamic parameters depend on the properties of microscopic elements. In this work, we combine experiments and multiscale modeling to relate the structure and dynamics of active nematics composed of biopolymer filaments and molecular motors to their microscopic properties, in particular motor processivity, speed, and valency. We show that crosslinking of filaments by both motors and passive crosslinkers not only augments the contributions to nematic elasticity from excluded volume effects but dominates them. By altering motor kinetics we show that a competition between motor speed and crosslinking results in a nonmonotonic dependence of nematic flow on motor speed. By modulating passive filament crosslinking we show that energy transfer into nematic flow is in large part dictated by crosslinking. Thus motor proteins both generate activity and contribute to nematic elasticity. Our results provide new insights for rationally engineering active materials.

Accurate estimates of dynamical statistics using memory.

Many chemical reactions and molecular processes occur on time scales that are significantly longer than those accessible by direct simulations. One successful approach to estimating dynamical statistics for such processes is to use many short time series of observations of the system to construct a Markov state model, which approximates the dynamics of the system as memoryless transitions between a set of discrete states. The dynamical Galerkin approximation (DGA) is a closely related framework for estimating dynamical statistics, such as committors and mean first passage times, by approximating solutions to their equations with a projection onto a basis. Because the projected dynamics are generally not memoryless, the Markov approximation can result in significant systematic errors. Inspired by quasi-Markov state models, which employ the generalized master equation to encode memory resulting from the projection, we reformulate DGA to account for memory and analyze its performance on two systems: a two-dimensional triple well and the AIB9 peptide. We demonstrate that our method is robust to the choice of basis and can decrease the time series length required to obtain accurate kinetics by an order of magnitude.

A strong nonequilibrium bound for sorting of cross-linkers on growing biopolymers.

Understanding the role of nonequilibrium driving in self-organization is crucial for developing a predictive description of biological systems, yet it is impeded by their complexity. The actin cytoskeleton serves as a paradigm for how equilibrium and nonequilibrium forces combine to give rise to self-organization. Motivated by recent experiments that show that actin filament growth rates can tune the morphology of a growing actin bundle cross-linked by two competing types of actin-binding proteins [S. L. Freedman et al., Proc. Natl. Acad. Sci. U.S.A. 116, 16192-16197 (2019)], we construct a minimal model for such a system and show that the dynamics of a growing actin bundle are subject to a set of thermodynamic constraints that relate its nonequilibrium driving, morphology, and molecular fluxes. The thermodynamic constraints reveal the importance of correlations between these molecular fluxes and offer a route to estimating microscopic driving forces from microscopy experiments.

Epigenetic repression of the Igk locus by STAT5-mediated recruitment of the histone methyltransferase Ezh2.

During B lymphopoiesis, recombination of the locus encoding the immunoglobulin κ-chain complex (Igk) requires expression of the precursor to the B cell antigen receptor (pre-BCR) and escape from signaling via the interleukin 7 receptor (IL-7R). By activating the transcription factor STAT5, IL-7R signaling maintains proliferation and represses Igk germline transcription by unknown mechanisms. We demonstrate that a STAT5 tetramer bound the Igk intronic enhancer (E(κi)), which led to recruitment of the histone methyltransferase Ezh2. Ezh2 marked trimethylation of histone H3 at Lys27 (H3K27me3) throughout the κ-chain joining region (J(κ)) to the κ-chain constant region (C(κ)). In the absence of Ezh2, IL-7 failed to repress Igk germline transcription. H3K27me3 modifications were lost after termination of IL-7R-STAT5 signaling, and the transcription factor E2A bound E(κi), which resulted in acquisition of H3K4me1 and acetylated histone H4 (H4Ac). Genome-wide analyses showed a STAT5 tetrameric binding motif associated with transcriptional repression. Our data demonstrate how IL-7R signaling represses Igk germline transcription and provide a general model for STAT5-mediated epigenetic transcriptional repression.

Simulating structured fluids with tensorial viscoelasticity.

We consider an immersed elastic body that is actively driven through a structured fluid by a motor or an external force. The behavior of such a system generally cannot be solved analytically, necessitating the use of numerical methods. However, current numerical methods omit important details of the microscopic structure and dynamics of the fluid, which can modulate the magnitudes and directions of viscoelastic restoring forces. To address this issue, we develop a simulation platform for modeling viscoelastic media with tensorial elasticity. We build on the lattice Boltzmann algorithm and incorporate viscoelastic forces, elastic immersed objects, a microscopic orientation field, and coupling between viscoelasticity and the orientation field. We demonstrate our method by characterizing how the viscoelastic restoring force on a driven immersed object depends on various key parameters as well as the tensorial character of the elastic response. We find that the restoring force depends non-monotonically on the rate of diffusion of the stress and the size of the object. We further show how the restoring force depends on the relative orientation of the microscopic structure and the pulling direction. These results imply that accounting for previously neglected physical features, such as stress diffusion and the microscopic orientation field, can improve the realism of viscoelastic simulations. We discuss possible applications and extensions to the method.

Inexact iterative numerical linear algebra for neural network-based spectral estimation and rare-event prediction.

Understanding dynamics in complex systems is challenging because there are many degrees of freedom, and those that are most important for describing events of interest are often not obvious. The leading eigenfunctions of the transition operator are useful for visualization, and they can provide an efficient basis for computing statistics, such as the likelihood and average time of events (predictions). Here, we develop inexact iterative linear algebra methods for computing these eigenfunctions (spectral estimation) and making predictions from a dataset of short trajectories sampled at finite intervals. We demonstrate the methods on a low-dimensional model that facilitates visualization and a high-dimensional model of a biomolecular system. Implications for the prediction problem in reinforcement learning are discussed.

Understanding the sources of error in MBAR through asymptotic analysis.

Many sampling strategies commonly used in molecular dynamics, such as umbrella sampling and alchemical free energy methods, involve sampling from multiple states. The Multistate Bennett Acceptance Ratio (MBAR) formalism is a widely used way of recombining the resulting data. However, the error of the MBAR estimator is not well-understood: previous error analyses of MBAR assumed independent samples. In this work, we derive a central limit theorem for MBAR estimates in the presence of correlated data, further justifying the use of MBAR in practical applications. Moreover, our central limit theorem yields an estimate of the error that can be decomposed into contributions from the individual Markov chains used to sample the states. This gives additional insight into how sampling in each state affects the overall error. We demonstrate our error estimator on an umbrella sampling calculation of the free energy of isomerization of the alanine dipeptide and an alchemical calculation of the hydration free energy of methane. Our numerical results demonstrate that the time required for the Markov chain to decorrelate in individual states can contribute considerably to the total MBAR error, highlighting the importance of accurately addressing the effect of sample correlation.

Spatiotemporal control of liquid crystal structure and dynamics through activity patterning.

Active materials are capable of converting free energy into mechanical work to produce autonomous motion, and exhibit striking collective dynamics that biology relies on for essential functions. Controlling those dynamics and transport in synthetic systems has been particularly challenging. Here, we introduce the concept of spatially structured activity as a means of controlling and manipulating transport in active nematic liquid crystals consisting of actin filaments and light-sensitive myosin motors. Simulations and experiments are used to demonstrate that topological defects can be generated at will and then constrained to move along specified trajectories by inducing local stresses in an otherwise passive material. These results provide a foundation for the design of autonomous and reconfigurable microfluidic systems where transport is controlled by modulating activity with light.

Thermodynamic Control of Activity Patterns in Cytoskeletal Networks.

Biological materials, such as the actin cytoskeleton, exhibit remarkable structural adaptability to various external stimuli by consuming different amounts of energy. In this Letter, we use methods from large deviation theory to identify a thermodynamic control principle for structural transitions in a model cytoskeletal network. Specifically, we demonstrate that biasing the dynamics with respect to the work done by nonequilibrium components effectively renormalizes the interaction strength between such components, which can eventually result in a morphological transition. Our work demonstrates how a thermodynamic quantity can be used to renormalize effective interactions, which in turn can tune structure in a predictable manner, suggesting a thermodynamic principle for the control of cytoskeletal structure and dynamics.

Transcriptional regulation of germinal center B and plasma cell fates by dynamical control of IRF4.

The transcription factor IRF4 regulates immunoglobulin class switch recombination and plasma cell differentiation. Its differing concentrations appear to regulate mutually antagonistic programs of B and plasma cell gene expression. We show IRF4 to be also required for generation of germinal center (GC) B cells. Its transient expression in vivo induced the expression of key GC genes including Bcl6 and Aicda. In contrast, sustained and higher concentrations of IRF4 promoted the generation of plasma cells while antagonizing the GC fate. IRF4 cobound with the transcription factors PU.1 or BATF to Ets or AP-1 composite motifs, associated with genes involved in B cell activation and the GC response. At higher concentrations, IRF4 binding shifted to interferon sequence response motifs; these enriched for genes involved in plasma cell differentiation. Our results support a model of "kinetic control" in which signaling-induced dynamics of IRF4 in activated B cells control their cell-fate outcomes.

Computing transition path theory quantities with trajectory stratification.

Transition path theory computes statistics from ensembles of reactive trajectories. A common strategy for sampling reactive trajectories is to control the branching and pruning of trajectories so as to enhance the sampling of low probability segments. However, it can be challenging to apply transition path theory to data from such methods because determining whether configurations and trajectory segments are part of reactive trajectories requires looking backward and forward in time. Here, we show how this issue can be overcome efficiently by introducing simple data structures. We illustrate the approach in the context of nonequilibrium umbrella sampling, but the strategy is general and can be used to obtain transition path theory statistics from other methods that sample segments of unbiased trajectories.

Augmented transition path theory for sequences of events.

Transition path theory provides a statistical description of the dynamics of a reaction in terms of local spatial quantities. In its original formulation, it is limited to reactions that consist of trajectories flowing from a reactant set A to a product set B. We extend the basic concepts and principles of transition path theory to reactions in which trajectories exhibit a specified sequence of events and illustrate the utility of this generalization on examples.