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Small molecule binding within internal cavities provides a way to control protein function and structure, as exhibited in numerous natural and artificial settings. Unfortunately, most ways to identify suitable cavities require high-resolution structures a priori and may miss potential sites. Here we address this limitation via high-pressure solution NMR spectroscopy, taking advantage of the distinctive nonlinear pressure-induced chemical shift changes observed in proteins containing internal cavities and voids. We developed a method to rapidly characterize such nonlinearity among backbone ¹H and ¹⁵N amide signals without needing to have sequence-specific chemical shift assignments, taking advantage of routinely available ¹⁵N-labeled samples, instrumentation, and 2D ¹H/¹⁵N HSQC experiments. From such data, we find a strong correlation in the site-to-site variability in such nonlinearity with the total void volume within proteins, providing insights useful for prioritizing domains for ligand binding and indicating mode-of-action among such protein/ligand systems. We suggest that this experimental approach is a rapid and useful probe of otherwise hidden dynamic architectures of proteins, providing novel insights and opportunities into ligand binding and control.

Decades of publicly available molecular studies have generated millions of samples testing diverse interventions, yet these datasets were rarely analyzed for their effects on aging. Aging clocks now enable biological age estimation and life outcome prediction from molecular data, creating an opportunity to systematically mine this untapped resource. We developed ClockBase Agent, a publicly accessible platform that reanalyzes millions of human and mouse methylation and RNA-seq samples by integrating them with over 40 aging clock predictions. ClockBase Agent employs specialized AI agents that autonomously generate aging-focused hypotheses, evaluate intervention effects on biological age, conduct literature reviews, and produce scientific reports across all datasets. Reanalyzing 43,602 intervention-control comparisons through multiple aging biomarkers revealed thousands of age-modifying effects missed by original investigators, including over 500 interventions that significantly reduce biological age (e.g., ouabain, KMO inhibitor, fenofibrate, and NF1 knockout). Large-scale systematic analysis reveals fundamental patterns: significantly more interventions accelerate rather than decelerate aging, disease states predominantly accelerate biological age, and loss-of-function genetic approaches systematically outperform gain-of-function strategies in decelerating aging. As validation, we show that identified interventions converge on canonical longevity pathways and with strong concordance to independent lifespan databases. We further experimentally validated ouabain, a top-scoring AI-identified candidate, demonstrating reduced frailty progression, decreased neuroinflammation, and improved cardiac function in aged mice. ClockBase Agent establishes a paradigm where specialized AI agents systematically reanalyze all prior research to identify age-modifying interventions autonomously, transforming how we extract biological insights from existing data to advance human healthspan and longevity.

Despite growing recognition of the importance of cis -regulatory elements in vertebrate development, the mechanisms by which enhancers control gene expression during organogenesis remain incompletely understood. To address this gap, we investigated the regulation of the transcription factor dlx2b during zebrafish larval tooth formation. Using CRISPR/Cas9-mediated genome editing, we generated a GFP knock-in line that recapitulates dlx2b expression in developing tooth germs. Through targeted manipulation of enhancer sequences, we identified a minimal tooth enhancer (MTE), which is sufficient to drive most of the endogenous dlx2b tooth germ expression pattern in vivo . Functional dissection of the MTE revealed that four evolutionarily conserved transcription factor binding sites are essential for enhancer activity. Mutating these sites within a transgenic reporter abolishes enhancer-driven expression, while deletion of the same sequences at the endogenous dlx2b locus causes a dramatic shift in the gene's expression pattern. These findings suggest that loss of MTE function permits alternative cis -regulatory elements to gain control of the promoter, highlighting the dynamic nature of enhancer-promoter interactions during development. Together, these results uncover fundamental principles of enhancer function during vertebrate organogenesis and demonstrate the power of empirical dissection in decoding cis -regulatory architecture.

Studying proteins through the lens of evolution can reveal features such as conserved domains, lineage-specific variants, and co-occurring domain architectures in phylogenetic context across all superkingdoms. MolEvolvR enables researchers to conduct such evolution-focused studies to generate testable hypotheses about protein function and evolution. MolEvolvR is a novel web-app allowing researchers to visualize the molecular evolution of their proteins of interest in a phylogenetic context across the tree of life. It accepts multiple input formats - protein/domain sequences, homologous proteins, or domain scans - and, using a general-purpose computational workflow, returns detailed homolog data and dynamic graphical summaries (e.g., phylogenetic trees, multiple sequence alignments, domain architectures, domain proximity networks, phyletic spreads, co-occurrence patterns across lineages). MolEvolvR performs domain-centric searches to capture remote homologs that are missed by full-length searches, integrates domain architecture evolution with phyletic distribution analyses, and provides evolutionary context visualizations that reveal lineage-specific adaptations versus those that are broadly conserved. Thus, MolEvolvR is a powerful, easy-to-use web interface for computational protein characterization. The web-app can be accessed here: https://jravilab.org/molevolvr.

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.