At the blood-tissue interface, vasculature luminal surface is critical for molecular transport, signaling transduction, and cell extravasation. Here, we present a method for proteomic profiling of the vasculature luminal surface in vivo, broadly applicable to any vertebrate. Quantitative mass spectrometry revealed the luminal surface proteome of the mouse brain vasculature and its temporal evolution from development to aging. In vivo genetic perturbation found that the arginine transporter SLC7A1 and the nitric oxide synthase NOS3 are needed for blood-brain barrier integrity in neonatal but not adult mice, whereas the hyaluronan degradation enzyme HYAL2 safeguards the barrier throughout the lifespan. By characterizing the proteomic dynamics of the vasculature luminal surface, the study links the metabolism of nitric oxide and hyaluronan to blood-brain barrier integrity.

OBJECTIVE: A recent genome-wide association study (GWAS) reported a significant genetic association between rs34330 of cyclin-dependent kinase inhibitor 1B (CDKN1B) and risk of systemic lupus erythematosus (SLE) in Han Chinese. This study aims to validate the reported association and elucidate the biochemical mechanisms underlying the variant's effect.

METHODS: We performed allelic association with SLE followed by meta-analysis across 11 independent cohorts (n=28,872). We applied in silico bioinformatics and experimental validation in SLE-relevant cell lines to determine the functional consequences of rs34330.

RESULTS: We replicated genetic association between SLE and rs34330 (P =5.29x10 , OR (95% CI)=0.84 (0.81-0.87)). Follow-up bioinformatics and eQTL analysis suggest that rs34330 is located in active chromatin and potentially regulates several target genes. Using luciferase and ChIP-qPCR, we demonstrated substantial allele-specific promoter and enhancer activity, and allele-specific binding of three histone marks (H3K27ac, H3K4me3, H3K4me1), RNA pol II, CTCF, and a critical immune transcription factor (IRF-1). Chromosome conformation capture (3C) detected long-range chromatin interactions between rs34330 and the promoters of neighboring genes APOLD1 and DDX47, and effects on CDKN1B and the other target genes were directly validated by CRISPR-based genome editing. Finally, CRISPR-dCas9-based epigenetic activation/silencing confirmed these results. Gene-edited cell lines also showed higher levels of proliferation and apoptosis.

CONCLUSION: Collectively, these findings suggest a mechanism whereby the rs34330 risk allele (C) influences the presence of histone marks, RNA pol II, and the IRF-1 transcription factor to regulate expression of several target genes linked to proliferation and apoptosis, which potentially underlie the association of rs34330 with SLE.

One-third of the mammalian proteome is comprised of transmembrane and secretory proteins that are synthesized on endoplasmic reticulum (ER). Here, we investigate the spatial distribution and regulation of mRNAs encoding these membrane and secretory proteins (termed "secretome" mRNAs) through live cell, single molecule tracking to directly monitor the position and translation states of secretome mRNAs on ER and their relationship to other organelles. Notably, translation of secretome mRNAs occurred preferentially near lysosomes on ER marked by the ER junction-associated protein, Lunapark. Knockdown of Lunapark reduced the extent of secretome mRNA translation without affecting translation of other mRNAs. Less secretome mRNA translation also occurred when lysosome function was perturbed by raising lysosomal pH or inhibiting lysosomal proteases. Secretome mRNA translation near lysosomes was enhanced during amino acid deprivation. Addition of the integrated stress response inhibitor, ISRIB, reversed the translation inhibition seen in Lunapark knockdown cells, implying an eIF2 dependency. Altogether, these findings uncover a novel coordination between ER and lysosomes, in which local release of amino acids and other factors from ER-associated lysosomes patterns and regulates translation of mRNAs encoding secretory and membrane proteins.

Epigenome is sensitive to metabolic inputs and crucial for aging. Lysosomes emerge as a signaling hub to sense metabolic cues and regulate longevity. We unveil that lysosomal metabolic pathways signal through the epigenome to regulate transgenerational longevity in Caenorhabditis elegans. We discovered that the induction of lysosomal lipid signaling and lysosomal AMP-activated protein kinase (AMPK), or the reduction of lysosomal mechanistic-target-of-rapamycin (mTOR) signaling, increases the expression of histone H3.3 variant and elevates H3K79 methylation, leading to lifespan extension across multiple generations. This transgenerational pro-longevity effect requires intestine-to-germline transportation of H3.3 and a germline-specific H3K79 methyltransferase, and can be recapitulated by overexpressing H3.3 or the H3K79 methyltransferase. This work uncovers a lysosome-epigenome signaling axis linking soma and germline to mediate the transgenerational inheritance of longevity.

bioRxiv preprint: https://www.biorxiv.org/content/early/2025/05/23/2025.05.21.652954

Epigenome is sensitive to metabolic inputs and crucial for aging. Lysosomes emerge as a signaling hub to sense metabolic cues and regulate longevity. We unveil that lysosomal metabolic pathways signal through the epigenome to regulate transgenerational longevity in Caenorhabditis elegans. We discovered that the induction of lysosomal lipid signaling and lysosomal AMP-activated protein kinase (AMPK), or the reduction of lysosomal mechanistic-target-of-rapamycin (mTOR) signaling, increases the expression of histone H3.3 variant and elevates H3K79 methylation, leading to lifespan extension across multiple generations. This transgenerational pro-longevity effect requires intestine-to-germline transportation of H3.3 and a germline-specific H3K79 methyltransferase, and can be recapitulated by overexpressing H3.3 or the H3K79 methyltransferase. This work uncovers a lysosome-epigenome signaling axis linking soma and germline to mediate the transgenerational inheritance of longevity.Competing Interest StatementThe authors have declared no competing interest.National Institutes of Health, RF1AG074540, DP1DK113644Howard Hughes Medical Institute, https://ror.org/006w34k90

Using phase-diversity wavefront sensing, machine learning-based deformable mirror control, and neural representations of the unknown sample and phase aberration, we demonstrate precise reconstruction of sample structures in fluorescence microscopy, even under severe, high-order aberrations.Full-text article not available; see video presentation

We aim to improve segmentation through the use of machine learning tools during region agglomeration. We propose an active learning approach for performing hierarchical agglomerative segmentation from superpixels. Our method combines multiple features at all scales of the agglomerative process, works for data with an arbitrary number of dimensions, and scales to very large datasets. We advocate the use of variation of information to measure segmentation accuracy, particularly in 3D electron microscopy (EM) images of neural tissue, and using this metric demonstrate an improvement over competing algorithms in EM and natural images.

We present an approach to solving computer vision problems in which the goal is to produce a high-dimensional, pixel-based interpretation of some aspect of the underlying structure of an image. Such tasks have traditionally been categorized as “low-level vision” problems, and examples include image denoising, boundary detection, and motion estimation. Our approach is characterized by two main elements, both of which represent a departure from previous work. The first is a focus on convolutional networks, a machine learning strategy that operates directly on an input image with no use of hand-designed features and employs many thousands of free parameters that are learned from data. Previous work in low-level vision has been largely focused on completely handdesigned algorithms or learning methods with a hand-designed feature space. We demonstrate that a learning approach with high model complexity, but zero prior knowledge about any specific image domain, can outperform existing techniques even in the challenging area of natural image processing. We also present results that establish how convolutional networks are closely related to Markov random fields (MRFs), a popular probabilistic approach to image analysis, but can in practice can achieve significantly greater model complexity. The second aspect of our approach is the use of domain specific cost functions and learning algorithms that reflect the structured nature of certain prediction problems in image analysis. In particular, we show how concepts from digital topology can be used in the context of boundary detection to both evaluate and optimize the high-order property of topological accuracy. We demonstrate that these techniques can significantly improve the machine learning approach and outperform state of the art boundary detection and segmentation methods. Throughout our work we maintain a special interest and focus on application of our methods to connectomics, an emerging scientific discipline that seeks highthroughput methods for recovering neural connectivity data from brains. This application requires solving low-level image analysis problems on a tera-voxel or peta-voxel scale, and therefore represents an extremely challenging and exciting arena for the development of computer vision methods.

Single-walled carbon nanotubes (SWCNTs) functionalized with single-stranded DNAs can function as near-infrared nanosensors for molecular analytes. However, predicting which analytes elicit strong optical responses for specific nanosensors remains challenging. We developed machine learning (ML) models to predict analyte-induced fluorescence changes in a DNA–SWCNT dopamine nanosensor. Using a data set of 63 small molecules sampling chemical space around dopamine, we encoded analytes with RDKit fingerprints, with or without HOMO and LUMO energies, and applied principal component analysis to identify structural motifs associated with optical response strength. We trained support vector regression and classification models using two strategies: ensembles of 200 models and cross-validation. Regression models achieved mean R2 values of 0.2–0.4, with cross-validation outperforming ensembles, while classifiers reached mean F1 scores of ∼0.8. Cross-validation performed best for predictions on a blind set of 21 molecules. These findings show that ML can capture structure–response patterns in modest data sets and guide in silico DNA–SWCNT nanosensor design.