Cheng Zhu

[This corrects the article DOI: 10.1063/5.0307361.].

Immunoreceptors experience forces that modulate their activities; however, demonstrating this in vivo has been limited by technical challenges. As a first step toward meeting this challenge, we adapted a synthetic Notch (SynNotch) receptor system to report force transmission through immunoreceptors in vivo by replacing the native ligand‐binding domain with a receptor‐specific antibody and rewiring Notch signaling to drive enhanced green fluorescent protein or luciferase expression. We expressed SynNotch on Jurkat cells targeting CD40 or T cell receptor (TCR) and characterized their activation in coculture with B or T cells, defining the requirements, optimal conditions, and kinetics of activation. Using complementary mechanobiology approaches, we quantified the exogenous force required for reporter activation and verified that activation depends on forces generated by receptor‐expressing sender cells rather than SynNotch‐expressing receiver cells. By implanting sensors and targets into immunocompromised mice, we visualized mechanically activated reporter expression on CD40 and TCR‐targeting SynNotch cells in vivo. Furthermore, CD40 and TCR signaling was amplified when the receptor bore force against mechanical support from immobilized ligand, indicating that force functions as a biologically relevant co‐stimulus. Together, our results establish mechanically activated SynNotch reporters as a useful strategy for detecting receptor‐associated mechanical signaling across two‐dimensional coculture, three‐dimensional organoid, and in vivo systems.

In a 2023 Small paper, the authors introduced a microfluidics‐based high‐throughput force spectroscopy assay to probe protein mechanics by measuring bead displacement under shear flow. Their analysis, however, appears to contain three issues that may have affected the accuracy of the results: (1) setting up the basic force balance equations incorrectly, (2) not considering the torque exerted on the bead by the hydrodynamic forces and not balancing the moments, and (3) assuming the linker anchoring the bead aligns with the bead's radius. Here, we demonstrate that these mistakes lead to erroneous force estimations on the bond. Using established formulations from Goldman, Tissot, Pierres, and others, we present the correct governing equations that balance both forces and moments. Applying this corrected model to the authors’ data shows that the forces experienced by single, double, and triple Biotin–NeutrAvidin bonds are 3.17‐, 5.54‐, and 29.30‐fold greater, respectively, than those reported by the authors, while the calculated bond lengths remain consistent. These findings highlight the importance of correctly setting up the basic force balance and incorporating torque in the moment balance in the calculation of force in the microfluidics‐based force spectroscopy.

Immunoreceptors experience forces that modulate their activities; however, demonstrating this in vivo has been limited by technical challenges. As a first step toward meeting this challenge, we adapted a synthetic Notch (SynNotch) receptor system to report forces on immunoreceptors in vivo by replacing the native ligand-binding domain with a receptor-specific antibody and rewiring Notch signaling to drive EGFP or luciferase expression. We expressed SynNotch on Jurkat cells targeting CD40 or T cell receptor (TCR) and characterized their activation in coculture with B or T cells, defining the requirements, optimal conditions, and kinetics of activation. Using complementary mechanobiology approaches, we quantified the exogenous force required for reporter activation and verified that activation depends on forces generated by receptor-expressing sender cells rather than SynNotch-expressing receiver cells. By implanting sensors and targets into immunocompromised mice, we visualized mechanically gated reporter activation on CD40 and TCR-targeting SynNotch cells in vivo. Furthermore, CD40 and TCR singaling was amplified when the receptor bore force against mechanical support from immobilized ligand, indicating that force functions as biologically relevant co-stimulus. Together, our results establish mechanically gated SynNotch reporters as a useful strategy for detecting receptor-associated mechanical signaling across 2D coculture, 3D organoid, and in vivo systems.

Background: T cells recognize antigens through T cell receptor (TCR) interactions with peptide-major histocompatibility complex (pMHC) assisted by the CD4 or CD8 coreceptors depending on the MHC class. Under force, TCR-pMHC interactions form catch or slip bonds whose profiles have been proposed to shape TCR triggering and ligand discrimination, but their predictive value, particularly in the presence of coreceptor engagement, remains incompletely characterized. Methods: Built upon our recent correlative analysis of TCR–pMHC bimolecular catch bonds with T cell function, we analyzed 27 paired TCR–pMHC datasets measured under conditions that either prevented or permitted CD8 or CD4 co-engagement, drawn from multiple laboratories, cell types and measurement techniques, supplemented with a set of new experiments, to correlate 2D and 3D biophysical parameters with antigen-induced T cell responses. Results: We found that metrics extracted from force-dependent bond lifetime curves (e.g. the optimal force and catch bond intensity) predict T cell activation and thymocyte selection better than force-free parameters measured either in situ at the T cell membrane or in fluid phase using purified ectodomain proteins, both when coreceptor contributions are absent and present. Moreover, CD8 co-engagement with pMHC systematically increases these metrics and improves TCR ligand discrimination, indicating coreceptor-mediated amplification of, or conversion to, catch-bonds. Conclusion: Our findings highlight the importance of force in antigen recognition by the TCR and reveal that parameters derived from the bond profile, especially in the presence of coreceptor, are more informative predictors of T cell activation compared to conventional affinity-based measurements. These results offer insights into the relation between catch bonds and CD8 co-engagement in TCR antigen recognition.

Proteins are dynamic molecular machines whose functions are determined by their structures. While static structures can offer initial insights or hypotheses about protein function, they are often insufficient for a detailed mechanistic understanding. Molecular dynamics (MD) simulations provide atomistic view of protein’s dynamic motion and conformational change, but the resulting high-dimensional data are challenging to interpret. Traditional summary statistics and dimensionality-reduction methods often focus on global motions and can overlook regional, yet functionally critical motions. Inspired by approaches from social network science, we introduce a novel perspective for analyzing MD simulations through dynamic community detection, where molecules are modeled as time-evolving graphs, and communities of residues or atoms that move coherently or exhibit functional coupling are identified. We present DynMoCo, a novel deep learning framework that integrates graph convolutional networks with recurrent models for end-to-end dynamic community detection on molecular graphs. Given a MD trajectory, DynMoCo identifies spatially grounded substructures, tracks their evolution over time, and can incorporate structural knowledge to ensure physically meaningful communities. We provide a library of custom-written scripts to allow users to extract and visualize these communites on the MD simulated molecules in motion. We demonstrate the method on force-ramp and force-clamp steered MD simulations of three integrin systems, revealing modular substructures within known domains and characterizing their conformational rearrangements during force-induced unbending. By reducing high-dimensional MD data into interpretable communities, this approach offers new insights into the intrinsic organization and dynamic function of complex biomolecular systems. SIGNIFICANCE: Proteins often perform their functions through dynamic, locally coordinated motions. Molecular dynamics simulations provide detailed views of these motions but produce high-dimensional data that are challenging to analyze and interpret. We present a novel deep learning model that analyzes molecular dynamics simulations data and identifies structurally coherent and potentially functionally related communities, while tracking their temporal evolution. This analysis tool provides a novel way to analyze MD data transforming it into interpretable representations of modular dynamic, enabling discovery of new mechanistic insights and advancing our understanding of how molecular motions drive biological function.

Immune cells rely on surface immunoreceptors to sense their environment. While the downstream signaling pathways of many immunoreceptors are well characterized, the initial molecular events that trigger signaling upon ligand engagement remain incompletely understood. Here, in this Review, we outline our current understanding of this immunoreceptor signal initiation problem, using the T cell antigen receptor (TCR) as a prototype. We synthesize decades of research on the TCR’s unique functional requirements and explore how these properties constrain potential triggering mechanisms. We evaluate prominent models of TCR signal initiation and highlight their respective strengths, limitations, complementary aspects and areas of ongoing debate. A central focus is the role of mechanical force in TCR triggering and antigen recognition for which we consider evidence for TCR–pMHC catch bonds, the capacity of T cells to generate endogenous forces and how these might modulate receptor–ligand kinetics and conformational changes to enhance antigen discrimination beyond classical kinetic proofreading models. By comparing TCR triggering with that of other immunoreceptors such as B cell receptors and Fc receptors, we discuss both shared principles and receptor-specific differences. This Review aims to consolidate current knowledge, reconcile conflicting findings and identify critical unanswered questions, in hopes of charting a path toward understanding how immunoreceptors convert ligand binding into cellular responses.

BACKGROUND: Thrombosis often starts with platelet adhesion via the interaction between VWF (von Willebrand factor) and its platelet receptor, the GPIb-IX (glycoprotein Ib-IX) complex. GPIb-IX also induces intracellular signals, stimulating VWF-, thrombin- and collagen-induced platelet activation. GPIb-IX signaling requires SFK (Src family kinase) Lyn. However, it remains unclear how Lyn mediates GPIb-IX signaling, whether Lyn directly binds to GPIb-IX, and if it is possible to target this signaling node for developing novel antithrombotics. METHODS: Direct binding of recombinant Lyn to glycoprotein Ibβ (GPIbβ) fragments was performed to map the Lyn-binding site. An inhibitory peptide mPLβ was then synthesized and tested for its interference with Lyn-GPIb-IX co-immunoprecipitation, platelet adhesion, ristocetin-, thrombin- or collagen-induced platelet aggregation/secretion, FeCl3-induced mouse carotid artery thrombosis and tail bleeding time. Biomembrane force probe was used to determine VWF-GPIb molecular bonding and consequent signaling. RESULTS: Lyn directly binds to GPIbβ (V144-A161) at the transmembrane/cytoplasmic domain interface, which is critical for not only the extracellular GPIb-IX ligand–induced intracellular signaling but also transmits outbound signals enhancing VWF-GPIb-IX interaction. The peptide mPLβ inhibited VWF-induced or α-thrombin-induced GPIbβ-Lyn interaction, Lyn/SFK activation, stable platelet adhesion and aggregation, and, notably, reduced GPIb-mediated platelet adhesion to VWF even in the presence of integrin inhibitors. BFP study further shows that mPLβ reduces the VWF-A1 domain-GPIb adhesion frequency and inhibits force-induced A1-mediated intraplatelet calcium elevation. Importantly, mPLβ formulated as a high-loading peptide nanoparticle inhibited platelet adhesion/aggregation induced by collagen and in vivo arterial thrombosis with a mild effect in prolonging bleeding time. CONCLUSIONS: Direct binding of Lyn to GPIbβ mediates 2-way GPIb-IX signaling to activate platelets and modulate VWF-GPIb interaction. Lyn-mediated GPIb-IX signaling is critical for platelet adhesion and aggregation induced by VWF and collagen and in arterial thrombosis. Targeting the Lyn-GPIbβ interaction has the potential for treating GPIb-IX-dependent thrombosis.

Background: T cells interact with peptide-major histocompatibility complex (pMHC) via the T cell receptor (TCR) and coreceptor CD4 or CD8 depending on the MHC class. These interactions form catch and slip bonds depending on the pMHC activity. Coreceptors and bond profiles impact TCR triggering and antigen discrimination. Methods: Built upon our recent correlative analysis of TCR–pMHC catch bond with T cell function, we analyzed 26 pairs of T-cell–pMHC interactions to compare the correlations of their biophysical metrics with antigen-induced T cell responses in two situations: when the coreceptor is prevented vs permitted to bind pMHC. Results: We found that the force-based metrics of TCR bond with pMHC perform better than parameters measured in the absence of force either in situ at the T cell membrane or in fluid phase using purified ectodomain proteins as predictors of T cell activation and thymocyte selection in both cases when the contributions of coreceptors are absent and present. Moreover, CD8 or CD4 co-engagement with pMHC systematically increases these metrics and increases TCR sensitivity and specificity, indicating coreceptor-mediated amplification of, or conversion to, catch-bonds that enhances mechanical tuning of TCR responses. Conclusion: Our findings highlight the importance of force in antigen recognition by the TCR and reveal that parameters derived from the bond profile, especially in the presence of coreceptor, are more informative predictors of T cell activation compared to conventional affinity-based measurements. These results offer mechanistic insights into the roles of catch bonds and coreceptors in TCR antigen recognition.