Cytotoxic T lymphocytes (CTLs) and natural killer cells kill virus-infected and tumor cells through the polarized release of perforin and granzymes. Perforin is a pore-forming toxin that creates a lesion in the plasma membrane of the target cell through which granzymes enter the cytosol and initiate apoptosis. Endosomal sorting complexes required for transport (ESCRT) proteins are involved in the repair of small membrane wounds. We found that ESCRT proteins were precisely recruited in target cells to sites of CTL engagement immediately after perforin release. Inhibition of ESCRT machinery in cancer-derived cells enhanced their susceptibility to CTL-mediated killing. Thus, repair of perforin pores by ESCRT machinery limits granzyme entry into the cytosol, potentially enabling target cells to resist cytolytic attack.

SignificanceInteractions between the cell nucleus and cytoskeleton regulate cell mechanics and are facilitated by the interplay between the nuclear lamina and linker of nucleoskeleton and cytoskeleton (LINC) complexes. To date, the specific contribution of the four lamin isoforms to nucleocytoskeletal connectivity and whole-cell mechanics remains unknown. We discover that A- and B-type lamins distinctively interact with LINC complexes that bind F-actin and vimentin filaments to differentially modulate cortical stiffness, cytoplasmic stiffness, and contractility of mouse embryonic fibroblasts (MEFs). We propose and experimentally verify an integrated lamin-LINC complex-cytoskeleton model that explains cellular mechanical phenotypes in lamin-deficient MEFs. Our findings uncover potential mechanisms for cellular defects in human laminopathies and many cancers associated with mutations or modifications in lamin isoforms.

In this work, we find that CD8 T cells expressing inhibitory killer cell immunoglobulin-like receptors (KIRs) are the human equivalent of Ly49CD8 regulatory T cells in mice and are increased in the blood and inflamed tissues of patients with a variety of autoimmune diseases. Moreover, these CD8 T cells efficiently eliminated pathogenic gliadin-specific CD4 T cells from the leukocytes of celiac disease patients in vitro. We also find elevated levels of KIRCD8 T cells, but not CD4 regulatory T cells, in COVID-19 patients, correlating with disease severity and vasculitis. Selective ablation of Ly49CD8 T cells in virus-infected mice led to autoimmunity after infection. Our results indicate that in both species, these regulatory CD8 T cells act specifically to suppress pathogenic T cells in autoimmune and infectious diseases.

Animal wings deform during flight in ways that can enhance lift, facilitate flight control, and mitigate damage. Monitoring the structural and aerodynamic state of the wing is challenging because deformations are passive, and the flow fields are unsteady; it requires distributed mechanosensors that respond to local airflow and strain on the wing. Without a complete map of the sensor arrays, it is impossible to model control strategies underpinned by them. Here, we present the first systematic characterization of mechanosensors on the dragonfly's wings: morphology, distribution, and wiring. By combining a cross-species survey of sensor distribution with quantitative neuroanatomy and a high-fidelity finite element analysis, we show that the mechanosensors are well placed to perceive features of the wing dynamics relevant to flight. This work describes the wing sensory apparatus in its entirety and advances our understanding of the sensorimotor loop that facilitates exquisite flight control in animals with highly deformable wings.

Mammalian chromosomes are organized into megabase-sized compartments that are further subdivided into topologically associating domains (TADs). While the formation of TADs is dependent on cohesin, the mechanism behind compartmentalization remains enigmatic. Here, we show that the bromodomain and extraterminal (BET) family scaffold protein BRD2 promotes spatial mixing and compartmentalization of active chromatin after cohesin loss. This activity is independent of transcription but requires BRD2 to recognize acetylated targets through its double bromodomain and interact with binding partners with its low-complexity domain. Notably, genome compartmentalization mediated by BRD2 is antagonized on the one hand by cohesin and on the other hand by the BET homolog protein BRD4, both of which inhibit BRD2 binding to chromatin. Polymer simulation of our data supports a BRD2-cohesin interplay model of nuclear topology, in which genome compartmentalization results from a competition between loop extrusion and chromatin-state-specific affinity interactions.

Motor systems must continuously adapt their output to maintain a desired trajectory. While the spinal circuits underlying rhythmic locomotion are well described, little is known about how the network modulates its output strength. A major challenge has been the difficulty of recording from spinal neurons during behavior. Here, we use voltage imaging to map the membrane potential of large populations of glutamatergic neurons throughout the spinal cord of the larval zebrafish during fictive swimming in a virtual environment. We characterized a previously undescribed subpopulation of tonic-spiking ventral V3 neurons whose spike rate correlated with swimming strength and bout length. Optogenetic activation of V3 neurons led to stronger swimming and longer bouts but did not affect tail beat frequency. Genetic ablation of V3 neurons led to reduced locomotor adaptation. The power of voltage imaging allowed us to identify V3 neurons as a critical driver of locomotor adaptation in zebrafish.

Manifold attractors are a key framework for understanding how continuous variables, such as position or head direction, are encoded in the brain. In this framework, the variable is represented along a continuum of persistent neuronal states which forms a manifold attactor. Neural networks with symmetric synaptic connectivity that can implement manifold attractors have become the dominant model in this framework. In addition to a symmetric connectome, these networks imply homogeneity of individual-neuron tuning curves and symmetry of the representational space; these features are largely inconsistent with neurobiological data. Here, we developed a theory for computations based on manifold attractors in trained neural networks and show how these manifolds can cope with diverse neuronal responses, imperfections in the geometry of the manifold and a high level of synaptic heterogeneity. In such heterogeneous trained networks, a continuous representational space emerges from a small set of stimuli used for training. Furthermore, we find that the network response to external inputs depends on the geometry of the representation and on the level of synaptic heterogeneity in an analytically tractable and interpretable way. Finally, we show that a too complex geometry of the neuronal representation impairs the attractiveness of the manifold and may lead to its destabilization. Our framework reveals that continuous features can be represented in the recurrent dynamics of heterogeneous networks without assuming unrealistic symmetry. It suggests that the representational space of putative manifold attractors in the brain dictates the dynamics in their vicinity.

Daily temporal organisation offers a fitness advantage and is determined by an interplay between environmental rhythms and circadian clocks. While light:dark cycles robustly synchronise circadian clocks, it is not clear how animals experiencing only weak environmental cues deal with this problem. Like humans, Drosophila originate in sub-Saharan Africa and spread North up to the polar circle, experiencing long summer days or even constant light (LL). LL disrupts clock function, due to constant activation of CRYPTOCHROME, which induces degradation of the clock protein TIMELESS (TIM), but temperature cycles are able to overcome these deleterious effects of LL. We show here that for this to occur a recently evolved natural timeless allele (ls-tim) is required, encoding the less light-sensitive L-TIM in addition to S-TIM, the only form encoded by the ancient s-tim allele. We show that only ls-tim flies can synchronise their behaviour to semi-natural conditions typical for Northern European summers, suggesting that this functional gain is driving the Northward ls-tim spread.

The availability of both anatomical connectivity and brain-wide neural activity measurements in C. elegans make the worm a promising system for learning detailed, mechanistic models of an entire nervous system in a data-driven way. However, one faces several challenges when constructing such a model. We often do not have direct experimental access to important modeling details such as single-neuron dynamics and the signs and strengths of the synaptic connectivity. Further, neural activity can only be measured in a subset of neurons, often indirectly via calcium imaging, and significant trial-to-trial variability has been observed. To address these challenges, we introduce a connectome-constrained latent variable model (CC-LVM) of the unobserved voltage dynamics of the entire C. elegans nervous system and the observed calcium signals. We used the framework of variational autoencoders to fit parameters of the mechanistic simulation constituting the generative model of the LVM to calcium imaging observations. A variational approximate posterior distribution over latent voltage traces for all neurons is efficiently inferred using an inference network, and constrained by a prior distribution given by the biophysical simulation of neural dynamics. We applied this model to an experimental whole-brain dataset, and found that connectomic constraints enable our LVM to predict the activity of neurons whose activity were withheld significantly better than models unconstrained by a connectome. We explored models with different degrees of biophysical detail, and found that models with realistic conductance-based synapses provide markedly better predictions than current-based synapses for this system.

The reconstruction of neural circuits from serial section electron microscopy (ssEM) images is being accelerated by automatic image segmentation methods. Segmentation accuracy is often limited by the preceding step of aligning 2D section images to create a 3D image stack. Precise and robust alignment in the presence of image artifacts is challenging, especially as datasets are attaining the petascale. We present a computational pipeline for aligning ssEM images with several key elements. Self-supervised convolutional nets are trained via metric learning to encode and align image pairs, and they are used to initialize iterative fine-tuning of alignment. A procedure called vector voting increases robustness to image artifacts or missing image data. For speedup the series is divided into blocks that are distributed to computational workers for alignment. The blocks are aligned to each other by composing transformations with decay, which achieves a global alignment without resorting to a time-consuming global optimization. We apply our pipeline to a whole fly brain dataset, and show improved accuracy relative to prior state of the art. We also demonstrate that our pipeline scales to a cubic millimeter of mouse visual cortex. Our pipeline is publicly available through two open source Python packages.