bioRxiv : the preprint server for biology最新文献

During the course of infection, human immunodeficiency virus (HIV) maintains a stably integrated reservoir of replication-competent viruses within the host genome that are unaffected by antiretroviral therapy. Curative advancements rely heavily on targeting the anatomical reservoirs, though determinants of their evolutionary origins through phyloanatomic inference remain ill-supported through current sequencing and sequence analysis strategies. The vast replication-defective genomic landscape that comprises the HIV DNA population is often discarded in these evolutionary endeavors, despite key information regarding competent ancestry that can be gained from captured genomic regions outside the historically used viral envelope gene. Here, we describe the application of small-amplicon, single-cell DNA sequencing to blood and lymph node samples from a treatment-interrupted S[imian]IV-infected animal model and evaluate the contribution of genome coverage and inclusion on phylogenetic resolution and phyloanatomic inference. Findings from this study point to incomplete genomes as a significant source of phylogenetic information on movement of virus between tissue reservoirs during therapy.

Lungs exhibit strikingly diverse epithelial architectures - from the branched airways of mammals to the sac-like lungs of lizards and the looped airways of birds. Across lineages, the pulmonary mesenchyme gives rise to smooth muscle that interacts with and shapes the underlying pulmonary epithelium. In mammals and lizards, pulmonary smooth muscle forms early and drives epithelial branching, whereas in birds it appears only after morphogenesis is largely complete. The developmental basis for this delay has remained unclear. Using comparative single-cell RNA sequencing, ATAC-sequencing, and imaging of mouse, anole, and chicken embryos, we found that smooth muscle in the chicken lung is transcriptionally similar to vascular, rather than visceral, smooth muscle. Strikingly, imaging revealed smooth muscle cells extending between the pulmonary vasculature and the epithelium, and surgical removal of these vessels prevented the formation of smooth muscle around the airways. The vascular transcription factor PITX2 was highly expressed in these cells and its knockdown markedly reduced smooth muscle differentiation. Taken together, these findings identify vascular smooth muscle as the developmental source of pulmonary smooth muscle in birds and establish PITX2 as a key regulator of this lineage transition, revealing an unexpected developmental and evolutionary link between the circulatory and respiratory systems.

Interoceptors, sensory neurons that monitor internal organs and physiological states, are essential for regulating physiology, shaping behavior, and generating internal perceptions. Here, we present a comprehensive transcriptomic atlas of mouse lung interoceptors, identifying 10 molecular subtypes. These subtypes differ in developmental origin, sensory receptor repertoire, signaling molecules, anatomical receptive fields, terminal morphologies, and cell contacts. Activity recordings and functional interrogation of two Piezo2 ⁺ subtypes revealed distinct sensory properties and separate roles in breathing control: one regulates inspiratory time; the other regulates inspiratory flow. Together, these findings suggest that this pronounced cellular diversity of lung interoceptors enables the system to encode diverse and dynamic sensory information, mediate myriad local cellular interactions, and regulate respiratory physiology with precision.

Increasing the quantity and immunogenicity of neoantigens in tumors is essential for advancing immunotherapy. However, engineering neoantigens remains challenging due to the need for precise, tumor-specific antigen modification without affecting normal cells. To tackle this challenge, we developed Short Precise-Encodable ADAR Recruiting (SPEAR) ADAR-engagers, an approach that uses short guide RNAs to engage the endogenous RNA editor ADAR1 and direct it to regions of mRNA targets known to encode MHC-presented peptides. By precisely editing adenosine-to-inosine (A-to-I) in these contexts, we effectively mutate specific epitopes into neoepitopes (which we now term "editopes"). As proof of concept, we targeted the known antigen MART-1 (Melanoma-Associated Antigen Recognized by T cells-1), and demonstrated that guided ADAR1 editing can generate immunogenic epitopes that activate T cells and promote tumor cell elimination. Building on this concept, we developed a computational pipeline to identify tumor-specific somatic mutations suitable for SPEAR-mediated editing. This strategy enables selective neoantigen generation in cancer cells, effectively increasing their apparent tumor mutational burden and potentially enhancing their susceptibility to immunotherapy.

Single-cell RNA sequencing analysis centers on illuminating cell diversity and understanding the transcriptional mechanisms underlying cellular function. These datasets are large, noisy, and complex. Current analyses prioritize noise removal and dimensionality reduction to tackle these challenges and extract biological insight. We propose an alternative, physical approach to leverage the stochasticity, size, and multimodal nature of these data to explicitly distinguish their biological and technical facets while revealing the underlying regulatory processes. With the Python package Monod, we demonstrate how nascent and mature RNA counts, present in most published datasets, can be meaningfully "integrated" under biophysical models of transcription. By utilizing variation in these modalities, we can identify transcriptional modulation not discernible though changes in average gene expression, quantitatively compare mechanistic hypotheses of gene regulation, analyze transcriptional data from different technologies within a common framework, and minimize the use of opaque or distortive normalization and transformation techniques.

Deciphering the brain's structure-function relationship is key to understanding the neuronal mechanisms underlying perception and cognition. The cortical column, a vertical organization of neurons with similar functions, is a classic example of primate neocortex structure-function organization. While columns have been identified in primary sensory areas using parametric stimuli, their prevalence across higher-level cortex is debated, particularly regarding complex tuning in natural image space. However, a key hurdle in identifying columns is characterizing the complex, nonlinear tuning of neurons to high-dimensional sensory inputs. Building on prior findings of topological organization for features like color and orientation, we investigate functional clustering in macaque visual area V4 in non-parametric natural image space, using large-scale recordings and deep learning-based analysis. We combined linear probe recordings with deep learning methods to systematically characterize the tuning of >1,200 V4 neurons using in silico synthesis of most exciting images (MEIs), followed by in vivo verification. Single V4 neurons exhibited MEIs containing complex features, including textures and shapes, and even high-level attributes with eye-like appearance. Neurons recorded on the same silicon probe, inserted orthogonal to the cortical surface, often exhibited similarities in their spatial feature selectivity, suggesting a degree of functional organization along the cortical depth. We quantified MEI similarity using human psychophysics and distances in a contrastive learning-derived embedding space. Moreover, the selectivity of the V4 neuronal population showed evidence of clustering into functional groups of shared feature selectivity. These functional groups showed parallels with the feature maps of units in artificial vision systems, suggesting potential shared encoding strategies. These results demonstrate the feasibility and scalability of deep learning-based functional characterization of neuronal selectivity in naturalistic visual contexts, offering a framework for quantitatively mapping cortical organization across multiple levels of the visual hierarchy.

CD8+ T cells differentiate into diverse states that shape immune outcomes in cancer and chronic infection. To systematically define the transcription factors (TFs) driving these states, we built a comprehensive atlas integrating transcriptional and epigenetic data across nine CD8+ T cell states and inferred TF activity profiles. Our analysis catalogued TF activity fingerprints, uncovering regulatory mechanisms governing selective cell state differentiation. Leveraging this platform, we focused on two transcriptionally similar but functionally opposing states critical in tumor and viral contexts: terminally exhausted T cells (TEXterm), which are dysfunctional, and tissue-resident memory T cells (TRM), which are protective. Global TF community analysis revealed distinct biological pathways and TF-driven networks underlying protective versus dysfunctional states. Through in vivo CRISPR screening integrated with single-cell RNA sequencing (in vivo Perturb-seq), we delineated that TFs selectively govern TEXterm. We identified HIC1 and GFI1 as shared regulators of TEXterm and TRM differentiation and KLF6 as a unique regulator of TRM. Importantly, we discovered novel TEXterm single-state TFs, including ZSCAN20 and JDP2 with no prior known function in T cells. Targeted deletion of these TFs enhanced tumor control and synergized with immune checkpoint blockade. Consistently, their depletion in human T cells reduces the expression of inhibitory receptors and improves effector function. By decoupling exhaustion-selective from protective TRM programs, our platform enables more precise engineering of T cell states, advancing rational design of effective immunotherapies.

A key problem in learning is credit assignment. Biological systems lack a plausible mechanism to implement the backpropagation approach, a method that underlies much of the dramatic progress in artificial intelligence. Here, we use automated connectomic analysis to show that the synaptic architecture of songbird basal ganglia (Area X) supports local credit assignment using a variant of a node perturbation algorithm proposed in a model of reinforcement learning. Using two volume electron microscopy (vEM) datasets, we find that key predictions of the model hold true: axons that encode exploratory variability terminate predominantly on dendritic shafts, while axons that encode song timing (context) terminate predominantly on spines. Based on the detailed EM data, we then built a biophysical model of reinforcement learning that suggests that the synaptic dichotomy between variability and context encoding axons facilitates efficient learning. In combination, these findings provide strong evidence for a general, biologically plausible credit assignment model in vertebrate basal ganglia learning.

Neurons have an outsized metabolic demand, requiring continuous metabolic support from non-neuronal cells called glia. When this support fails, toxic metabolic byproducts accumulate, ultimately leading to excitotoxicity and neurodegeneration. Astrocytes, the primary synapse-associated glial cell type, are known to provide essential metabolites ( e.g. lactate) to sustain neuronal function. Here, we leverage the well-characterized Drosophila motor circuit to investigate another means of astrocyte-to-neuron metabolic support: activity-dependent trafficking of astrocyte mitochondria. Following optogenetic activation, motor neuron mitochondria migrate away from synapses. By contrast, astrocytic mitochondria accumulated peri-synaptically, and at times, were transferred into neighboring neurons. A genetic screen identified the mitochondrial adaptor protein Milton as a key regulator of this process. Astrocyte-specific milton knockdown disrupted regular mitochondrial trafficking, resulting in locomotor deficits, dysfunctional motor activity, and altered synapse number at the neuromuscular junction. These findings suggest that astrocytes dynamically redistribute mitochondria to buffer metabolic demand at synapses, highlighting a potential mechanism by which glia protect neural circuits from metabolic failure and neurodegeneration.

To understand why people avoid mental effort, it is crucial to reveal the mechanisms by which we learn and decide about mental effort costs. This study investigated whether mental effort cost learning aligns with temporal-difference (TD) learning or alternative mechanisms. Model-based fMRI analyses showed no correlation between cost prediction errors (CPEs) and activity in the dorsomedial frontal cortex/dorsal anterior cingulate cortex (dmFC/dACC) or striatum at the time of a fully informative effort cue about upcoming effort demands, contradicting the TD hypothesis. Instead, CPEs correlate with dmFC/dACC (positively) and caudate (negatively) activity at effort completion. Furthermore, only activity patterns at effort completion predict subsequent choices. These results show that decision policies are updated retrospectively at effort completion, updating expected costs with prediction error between experienced effort and prior expectations, demonstrating mental effort cost learning is retrospective, and imply that adaptive learning of mental effort cost does not follow canonical TD learning.