bioRxiv : the preprint server for biology最新文献

Lung size control and cell type specification are key unresolved issues. In this study, we have engineered mosaic patterns of Hippo signaling to reveal the developmental potential of SOX9+ progenitors at the distal lung buds. Our results show that the distal SOX9+ subdomain is sufficient to direct lung outgrowth through bifurcation, providing a mechanism for lung size control. Through single-cell analyses, we identify transitional cell states and candidates for promoting cell fates. Moreover, genetic analysis reveals that Hippo signaling induces distinct cell fates at different SOX9+ subdomains to produce the conducting airways and the alveolar epithelium. These results provide the first extensive map of the developmental paths of lung cells. Some of the developmental paths of transitional cell states in mice correspond to those in human lungs. Together, these studies provide mechanistic insight into how Hippo signaling controls the sequential expansion and differentiation of SOX9+ progenitors to construct the mammalian lungs.

As the most abundant human cell and the foundation of transfusion medicine, red blood cells (RBCs) offer a unique readout of systemic health, yet they have never been characterized at population scale. We generated a proteome atlas of 13,091 blood donors with multi-omics longitudinal phenotyping, characterizing the influence of demographics and genetic variation on the reproducibility of RBC proteomes across donations. Elastic-net aging clocks captured biological aging with high accuracy and uncovered genetic regulators of ΔAge at FN1, C4/IKZF1, CRAT, PFAS, TRIM58. Across independent cohorts, ΔAge was accelerated in G6PD deficiency, sickle cell trait/disease, and iron deficiency, reversed by iron repletion, and slowed in high-frequency donors, linking molecular aging to brain iron/myelin and cognitive performance. Molecular aging signatures predicted storage, osmotic, and oxidative hemolysis, hemoglobin increments after transfusion, and long-term donor activity over 12-years. These results establish RBC proteomics as a scalable biomarker of aging, donor healthspan, and transfusion outcomes.

Abstract figure:

Highlights: RBC proteome atlas of 13,091 donors reveals demographic and genetic programsGenetically encoded RBC aging clocks identify regulators of molecular ΔageMolecular aging features predict hemolysis and transfusion response across cohortsRBC molecular age forecasts long-term donor activity over a 12-year follow-up.

Fasting enhances small intestinal regeneration after radiation but the contribution of the gut microbiome to this process remains uncharacterized. We identify Akkermansia muciniphila ( AKK ) as a key mediator of this response. AKK was enriched in fasted mice and its antibiotic depletion abrogated radioprotection whereas reintroduction restored both organismal survival and intestinal integrity. Fasting elevated propionic acid, consistent with AKK 's metabolic output. AKK -conditioned medium and propionate induced histone H3 acetylation in intestinal stem cell cultures while in vivo fasting induced AKK -dependent H3K27ac and H3K9ac, remodeling promoter-enhancer landscapes in crypt epithelial cells. Epigenetic profiling revealed a rewired core regulatory program enriched for pioneer transcription factors (Foxa, Gata, Klf), architectural organizers (Ctcf, Boris), and lineage-defining and metabolic regulators (Cdx2, Hnf4). This program supports expansion of a population of persister stem cells characterized by open chromatin accessibility at key stem and regenerative-associated loci including Clu , Olfm4 , Lgr5, Ascl2, Lrig1, Sox9, Rnf43, and Axin2. These findings define a fasting-induced microbiome-metabolite-chromatin axis that epigenetically primes highly plastic persister stem cells for rapid regeneration of the intestinal epithelium following radiation-induced injury.

Significance statement: Fasting changes the gut microbiome, but how these changes help the body recover from damage is not well understood. We found that fasting increases a helpful bacterium, Akkermansia muciniphila , which produces propionate, which drives epigenetic changes by modifying histones and regulating gene activity. These changes promote the expansion of persister stem cells that help the intestine recover after radiation. This study shows how fasting and gut bacteria work together to protect healthy tissue and suggests that diet or microbial treatments could help reduce side effects of cancer radiotherapy.

Photoactivated adenylyl cyclases (PACs) convert ATP to cyclic AMP (cAMP) through long-range photoinduced allosteric communication between a BLUF domain and a distant adenylyl cyclase (AC) domain. Although photoactivation of the BLUF domain induces only minimal structural changes, it activates a chemical reaction about 4-5 nm away. Here, we combine molecular dynamics simulations, electronic structure calculations, network theory, and machine-learning approaches to investigate photoinduced allostery in the PAC from Beggiatoa sp. (bPAC). We observed that the photoexcitation enables electron transfer from a conserved tyrosine (Tyr7) to the flavin isoalloxazine ring, while the free energy of the electron transfer remains similar across active and inactive mutants. Therefore, photoinduced allosteric activity arises from conformational effects rather than changes in the electronic parameters. Using network theory and eigenvector centrality analysis, we identified residues relevant to allosteric pathways linking the BLUF and AC domains. Furthermore, we used machine-learning (ML) models to distinguish active and inactive conformational states without prior knowledge of functional residues. Remarkably, the ML models identified key regions known from network analysis. Together, these results provide a generalizable frame-work for understanding allosteric pathways in blue-light-sensitive proteins.

The circadian rhythm orchestrates gene expression and critical physiological processes but becomes disrupted with aging, contributing to disease. How this disruption interacts with cellular senescence-a key driver of aging pathology-remains poorly defined. We studied renal gene expression at four timepoints over 24hrs in 6- and 24-month-old genetically diverse UM-HET3 mice of both sexes and performed complementary analyses in synchronized fibroblasts sampled at seven timepoints. Aging dysregulated core clock relationships, including loss of the canonical anti-phase expression between Bmal1 and Per2 . Senescence-associated genes were not static but exhibited pronounced oscillations, with senescence phenotypes varying by sex and time of day. Differential expression analysis revealed immune activation, metabolic rewiring, and epigenetic changes that were sex- and time-dependent. Variance analysis uncovered increased transcriptional noise in aging, particularly in circadian-regulated pathways such as RNA splicing, ribosome biogenesis, and TOR signaling. Single-nucleus RNA-Seq identified two cell populations lacking the normal Bmal1 - Cdkn1a expression relationship: one senescent-like and another profibrotic, revealing distinct cell states linked to circadian dysregulation. Fibroblasts recapitulated key age-related circadian changes seen in the kidneys, including phase shifts in mTOR and oxidative phosphorylation. Together, this work demonstrates that senescence phenotypes are dynamic, sex-specific, and time-of-day dependent, and introduces a new framework for detecting senescent cells based on circadian gene relationships. These findings underscore the need to integrate temporal context into aging research and therapeutic strategies.

RNA structure plays a crucial role in diverse biological processes beyond the translation of genetic information. Therefore, the development of reliable methods for RNA structure prediction is essential for understanding RNA structure-related functions, however accurate and comprehensive RNA structure prediction remains challenging. Here, we focus on secondary structure prediction of transfer RNA (tRNA) using structure probing coupled with next-generation sequencing (tRNA Structure-seq). In silico prediction of Saccharomyces cerevisiae tRNA secondary structures achieves only 56.9% accuracy on average. Incorporation of dimethyl sulfate (DMS) probing data improve prediction accuracy to 87.4%, which is still not sufficient for practical tRNA structure prediction. To overcome this, we optimized the tRNA Structure-seq analysis pipeline by explicitly incorporating natural tRNA modifications detected in tRNA sequencing data and by refining pseudo-free energy parameters specifically optimized for tRNA structure prediction. Using this optimized pipeline, the average prediction accuracy is remarkably improved to 94%. Furthermore, analysis of multiple structural conformations predicted from DMS probing data indicates that S. cerevisiae tRNAs predominantly adopt the canonical cloverleaf secondary structure under in vivo conditions. Finally, we examined tRNA structures under mild stress conditions, including heat stress, osmotic stress, and antibiotic stress. These perturbations had minimal effects on in vivo tRNA secondary structure, demonstrating that S. cerevisiae tRNAs maintain structural stability under physiologically relevant stress conditions. In summary, our results establish an optimized tRNA Structure-seq analysis that enables highly accurate tRNA secondary structure prediction and reveals the intrinsic robustness of tRNA structures in living cells.

Motivation: Long-read metagenomic sequencing improves assembly contiguity and enables genome-resolved analysis of complex microbial communities, but accurate taxonomic classification of long reads and assembled contigs remains challenging. Highly scalable k-mer-based classifiers such as Kraken2 frequently over-assign fine-rank taxonomic labels when applied to long-read data, producing high false positive classification rates driven by sparse or localized k-mer matches, particularly in microbiomes with extensive taxonomic novelty.

Results: We present Perseus , a lineage-aware confidence estimation framework for taxonomic classification that models the spatial distribution and hierarchical consistency of k-mer evidence along sequences. This formulation reframes taxonomic classification as a hierarchical confidence estimation problem rather than a single-rank prediction task. Perseus refines k-mer-level taxonomic signals from Kraken2 using a multi-headed convolutional neural network that estimates calibrated confidence scores for taxonomic correctness at each canonical rank. Using these estimates, Perseus confirms assignments, backs off to higher taxonomic ranks, or abstains when evidence is insufficient, prioritizing correctness and lineage consistency over overly specific assignments. Across simulations of taxonomic novelty and real-world metagenomic datasets, Perseus consistently and substantially reduces the false assignment rate while improving precision and lineage-consistent accuracy. These improvements are most pronounced for long reads and assembled contigs, where spatial context enables reliable discrimination between consistent taxonomic signal and spurious matches.

Availability and implementation: Perseus integrates with existing Kraken2 workflows and is available at https://github.com/matnguyen/perseus .

Contact: mnguye99@jh.edu , mschatz@cs.jhu.edu.

Supplementary information: Supplementary data are available online.

Significance: Standard methods to characterize peripheral blood mononuclear cells (PBMCs) are often destructive, lack metabolic information, or do not provide single-cell resolution. Label-free tools that non-destructively measure single-cell metabolism within PBMCs can provide new layers of information to characterize disease state and cell therapy potential.

Aim: Determine whether non-destructive fluorescence lifetime imaging microscopy (FLIM) of endogenous metabolic co-factors NAD(P)H and FAD, or optical metabolic imaging (OMI), can identify immune cell subsets and activation state within heterogeneous PBMC cultures.

Approach: OMI measured single-cell metabolism of PBMCs from 3 different human donors in the quiescent or activated (phorbol 12-myristate 13-acetate and ionomycin) state. Fluorescent antibodies were used as ground truth labels for single-cell classifiers of immune cell subtypes.

Results: OMI identified quiescent vs. activated PBMCs with 93% accuracy at only 2 hours post-stimulation, identified monocytes within quiescent and activated PBMCs with 96% and 88% accuracy, respectively, and identified NK cells within quiescent and activated PBMCs with 74% accuracy.

Conclusion: OMI identifies activation state and immune cell subpopulations within PBMCs, enabling single-cell and label-free measurements of metabolic heterogeneity within complex PBMC samples. Therefore, OMI could enhance PBMC immunophenotyping for diagnostic and therapeutic applications.

Statement of discovery: We demonstrate that autofluorescence lifetime imaging can resolve functional and phenotypic metabolic subpopulations within a mixed culture of immune cells from human blood. This provides a new technique to characterize metabolic activity within immune cells from the peripheral blood of patients, which could improve disease diagnostics and the production of cell therapies.

Biological data is prone to both intrinsic and extrinsic noise and variability between experimental replicas. That same stochasticity and heterogeneity can carry information about underlying biochemical mechanisms but, if not incorporated in modeling and probabilistic inference, can also bias parameter estimates and misguide predictions and, subsequently, experiment design. Mechanistic inference typically requires lengthy simulations (e.g., the Stochastic Simulation Algorithm (SSA)); approximations to chemical master equation (CME) solutions that lack rigorous error tracking; or deterministic averaging that lacks the complexity necessary to reflect the data. We introduce the Stochastic System Identification Toolkit (SSIT) - a fast, flexible, and open-source software package available on GitHub that makes use of MATLAB's efficient and diverse computational architecture. The SSIT is designed for building, simulating, and solving chemical reaction models using ODEs, moments, SSA, Finite State Projection truncations of the CME, or hybrid methods; sensitivity analysis and Fisher information quantification; parameter fitting using likelihood- or Bayesian-based methods; handling of experimental noise and measurement errors using probabilistic distortion operators; and sequential experiment design that empowers users to save time and resources while gaining the most information possible out of their data. The SSIT also offers advanced modeling tools, including model reduction methods for increased efficiency and joint fitting of models and datasets with overlapping reactions/parameters. To facilitate the ease and speed of use, the SSIT provides a graphical user interface and ready-made, adaptable pipelines that can be run in the background from commandline or high-performance computing clusters. We demonstrate features of the SSIT on two experimental datasets: the first consists of published mRNA count data that reflect Saccharomyces cerevisiae yeast cell response to osmotic shock using single-cell single-molecule fluorescence in situ hybridization; the second consists of single-cell RNA sequencing measurements of 151 activating genes in breast cancer cells following treatment with dexamethasone.

Fear extinction requires dynamic updating of cortical representations, yet the neural mechanisms underlying successful extinction remain poorly understood. Some psychoactive substances induce structural plasticity in medial prefrontal cortex (mPFC), possibly underlying their therapeutic potential. Here we investigated whether MDMA, which enhances fear extinction, induces prefrontal structural and functional plasticity, and measured its effects on ensemble representations during extinction. Longitudinal two-photon microscopy revealed that MDMA increased spine density and spinogenesis across prefrontal subregions. Miniscope Ca²⁺ imaging in infralimbic cortex (IL) during fear extinction revealed that IL became more correlated with the suppression of freezing behavior, consistent with a strengthening of its role in extinction. Longitudinal cell registration demonstrated accelerated representational drift across days in MDMA-treated mice; this effect was strongest in a functionally defined subpopulation of neurons that showed suppression of activity to conditioned cues. These findings demonstrate that MDMA facilitates structural and functional neuroplasticity, potentially underlying its enhancement of extinction learning.