Biology subjects

Huang, R.

Publications and source records attributed to Huang, R..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Integrated single-cell and bulk transcriptomic analysis leverages liver metastasis-related genes to develop a prognostic model for colorectal cancer patients

PurposeDifferentially expressed genes (DEGs) between colorectal cancer liver metastasis (CRLM) epithelium and primary colorectal cancer (CRC) epithelium (LMR DEGs) identified based on single-cell RNA sequencing (scRNA-seq) data may become new biomarkers for CRC prognosis. MethodsAn scRNA-seq dataset was used to describe the cellular landscape of primary CRC and CRLM and identify LMR DEGs. Prognostic LMR DEGs were identified in the bulk RNA-seq dataset. Based on the prognostic LMR DEGs, multiple machine learning algorithm combinations were compared in terms of their C-index, and the best model was selected for the construction of the LMR score. ResultsAmong the 2070 LMR DEGs, 426 prognostic LMR DEGs were ultimately obtained. The combination of the randomized survival forest (RSF) model and ridge regression had the highest C-index and was therefore used to construct a 15-gene scoring system (LMR score). In the external validation set, the 1- and 5-year AUCs of the LMR score were greater than those of the AJCC stage and other scoring systems constructed with a similar dataset. In addition, the LMR score was closely associated with factors that influence CRC outcomes, such as immune infiltration. ConclusionThe LMR score may be a reliable new biomarker for predicting the prognosis of patients with CRC.

cancer biology↗

bioRxiv2026

Biophysical trade-offs in antibody evolution are resolved by conformation-mediated epistasis

Protein evolution is constrained by multidimensional biophysical factors, in which mutations that enhance one property often compromise another. Antibodies represent an extreme case: they evolve rapidly to bind diverse antigens, yet mutations that improve affinity can disrupt folding, reduce cell-surface trafficking, or promote self-reactivity, and are typically selected against during affinity maturation. Though biophysical characterization of individual antibodies suggests that such trade-offs are pervasive, their impact on antibody evolutionary trajectories remains unclear, in part because existing high-throughput biophysical methods rely on heterologous systems that are often poorly suited for human proteins. Here, we develop a high-throughput platform to quantify multiple biophysical parameters of large libraries of full-length proteins that are natively synthesized, processed, and displayed on human cells. We apply this approach to a human antibody lineage that matures to recognize divergent SARS-CoV-2 variants by measuring the surface expression, antigen affinity, and self-reactivity for all 213 possible evolutionary intermediates between the unmutated and mature sequences. These measurements reveal that mutations differentially affect these biophysical properties - in some cases, improving one property at the expense of another. We leverage these data to compute the likelihood of all possible evolutionary paths, finding that very few paths can navigate these multidimensional requirements. The few accessible paths acquire mutations in a specific order that either circumvent trade-offs between biophysical properties or offset deleterious effects on one property with beneficial effects on another. By determining the structures of the ancestral and evolved antibodies, we find that these coordinated mutational effects arise from a conformational rearrangement that alleviates steric clashes and reshapes the biophysical landscape, enabling otherwise inaccessible mutational paths. Together, this work defines the multidimensional biophysical constraints and structural mechanisms that govern antibody evolution and establishes a general framework for mapping and predicting the biophysical effects of mutations in human proteins.

evolutionary biology↗

bioRxiv2026

Functional activation reveals a repressor-gated fourth type VI secretion system in clinical Pseudomonas aeruginosa

Pseudomonas aeruginosa is a major opportunistic pathogen in which the type VI secretion system (T6SS) contributes to interbacterial competition and virulence. While most strains encode three T6SSs, we here functionally characterize a fourth system (H4-T6SS) in a clinical P. aeruginosa isolate. The H4-T6SS gene cluster is transcriptionally silent under laboratory conditions. By promoter rewiring, we demonstrate that the H4-T6SS assembles a contractile machine and mediates effector secretion and robust inter- and intraspecific antagonism. We identify a dominant pore-forming effector and its secretion pathway and a silent yet conserved nuclease effector. Genetic and biochemical analyses show that H4-T6SS expression is activated by an H4-encoded transcriptional regulator and repressed by H-NS family proteins. Analysis of over 1,200 clinical isolates reveals that the H4-T6SS is distributed across lineages and geographic regions. Finally, we observe functional crosstalk between H4- and H2-T6SSs. Together, these findings establish H4-T6SS as a functional and repressor-gated secretion system that expands the competitive arsenal of clinical P. aeruginosa.

microbiology↗

bioRxiv2025

A Comprehensive Treatment-Induced Resistance Atlas of Glioblastoma Reveals a Fibrotic Niche Shielding the Tumor from Immunotherapy

Therapeutic resistance in IDH-wildtype glioblastoma (GBM) is driven by an intricate interplay between cellular plasticity and protective microenvironmental niches. By constructing the GRIT-Atlas--a massive transcriptomic compendium encompassing nearly one million high-quality single cells across 17 cohorts--and cross-analyzing it with 48 independent Visium spatial transcriptomics sections, we discover a recurrent "Spatial Resistance Triad" comprising mesenchymal-like (MES-like) malignant cells, myeloid-derived suppressor cells (MDSCs), and collagen-secreting cancer-associated fibroblasts (CAFs). This large-scale spatial integration reveals that the triad physically fortifies microvascular proliferation (MVP) and pseudopalisading necrosis (PAN) niches. To substantiate these findings at true single-cell resolution, we deploy high-plex spatial molecular imaging (CosMx SMI) to map over 400,000 single cells across a clinical validation cohort. This high-resolution architecture firmly validates the triads geography and, critically, provides structural gradient evidence unmasking a spatiotemporal continuum wherein PAN emerges as a direct functional consequence of MVP advancement and subsequent vascular collapse. Mechanistically, stromal CAF-derived COL6A1 engages CD44 receptors on MES-like cells to accelerate a potent neural stemness cascade within these protective domains. Utilizing a blood-brain barrier (BBB)-penetrant multi-library compound screen, we identify Lacidipine as a multi-modal stromal disruptor that successfully silences CAF activation and halts matrix secretion. Incorporating Lacidipine into the standard chemo-immunotherapy backbone (TMZ + anti-CSF1R) completely dismantles the protective desmoplastic matrix niche, forcing a profound collapse of intracranial tumor burden and dramatically extending overall survival in orthotopic models. Collectively, our study leverages unprecedented single-cell and spatial scale to provide a definitive blueprint of GBM therapeutic evasion, establishing matrix-targeted intervention as a mandatory prerequisite for successful glioblastoma eradication.

cancer biology↗

bioRxiv2025

The Bacillus subtilis class A penicillin-binding protein 4 (PBP4) requires an accessory protein RpdA

Peptidoglycan (PG) synthesis is essential to maintain cell integrity during bacterial growth and division. In Bacillus subtilis, PG synthesis involves class A PBPs that act independently and class B PBPs that function in complexes for cell elongation (the elongasome) and division (the divisome). Here, we identify RpdA (formerly YufK) as a specific regulator of PBP4. Inactivation of either vegetative class A PBP (PBP1 or PBP4) by the {beta}-lactam antibiotic cefuroxime (CEF) is toxic and their loss confers CEF resistance. Similarly, loss of RpdA increases CEF resistance and genetic epistasis studies reveal that RpdA functions in a pathway with PBP4. In the absence of RpdA, PBP4 is no longer membrane localized. Analysis of a predicted RpdA-PBP4 protein complex suggests that RpdA has a second function in addition to serving as a membrane scaffold. Induction of RpdA reduces sensitivity to an undecaprenyl-phosphate binding antibiotic, consistent with a role in recycling of this important lipid carrier. We conclude that RpdA is a PBP4 accessory protein critical for its localization and activity.

microbiology↗

bioRxiv2025

Multi-modal, multi-species, and multi-task latent-space model for decoding level of consciousness

Assessing the degree and characteristics of consciousness is central to caring for patients with Disorders of Consciousness (DoC), yet current standard of care is a bedside questionnaire. Using a laminar neural probe we introduce a self-supervised, multi-modal representation learning approach that constructs an interpretable 2-D latent space of brain state from continuous neural recordings. We jointly encode local field potentials (LFPs), single-unit firing rates, and a movement proxy into a time-aware autoregressive VAE (TN-VAE). In rats, pigs and humans undergoing controlled depth anesthesia experiments, the learned latent trajectories nonlinearly, but smoothly, separate four expert-defined states (awake, light, moderate, deep) at 2s resolution, exceeding the temporal granularity of behavioral scoring (15m). The latent space supports linear readout of both coarse state and individual behavioral components, and its axes align with known physiology: delta/alpha power differentiates unconscious sub-states, while gamma and unit firing distinguish wakefulness. To enable cross-species use and incomplete modality sets, we add lightweight, species-specific stitching layers. This model pretrained on tri-modal rat data and fine-tuned with unimodal (LFP) pig data and unimodal human intraoperative data, successfully separates awake versus anesthetized states in new unseen human subjects, demonstrating zero-shot multi-subject transfer without per-patient calibration. The model further generalizes in a multi-task manner to predict the results of additional stimuli. These results highlight a path toward a foundation model for DoC that generalizes across sessions, subjects, species, and stimuli to enable scalable, continuous brain-state monitoring and clinical decision support.

neuroscience↗

bioRxiv2025

Rapid robust high-fidelity 3D neuronal extraction from multi-view projections

Recent developments in imaging facilitate large-scale three-dimensional (3D) neuronal recording. While these huge amounts of data shed new light on population-level neural coding, it is much harder to extract neuronal calcium dynamics from 3D volumes than 2D images with the existence of noise and scattering. Here, we presented DeepWonder3D, a general end-to- end pipeline for rapid and robust 3D neuronal extraction with high fidelity. Instead of processing voxel by voxel, DeepWonder3D works on the multi-view projections of 3D imaging data obtained either digitally or optically through specific point spread functions (PSFs) for general applicability to diverse techniques, including point-scanning microscopy, light- field microscopy, and two-photon synthetic aperture microscopy. Integrating denoising, resolution registration, background removal, neuronal extraction, and multi-view fusion into a unified pipeline tailored for large-scale high-resolution datasets contaminated by noise and scattering, DeepWonder3D outperforms state-of-the-art methods in 3D localization accuracy with a 10-fold reduction in computational costs, validated by numerical simulations and a hybrid two-photon/light-field imaging system. With the RUSH3D mesoscope, DeepWonder3D achieves rapid high-fidelity 3D calcium extraction of tens of thousands of neurons across the mouse cortex within hours.

neuroscience↗

bioRxiv2025

Canonical Hidden Markov Model Networks for Studying M/EEG

Dynamic brain networks identified in magneto/encephalography (M/EEG) recordings provide new insights into human brain activity. One established method uses Hidden Markov Models (HMMs) and has been shown to infer reproducible, fast-switching brain networks in a variety of cognitive and disease conditions. Often these studies are done on small bespoke (boutique) datasets (N < 100) and analysed in isolation of other M/EEG datasets. Instead of training a new model for each boutique study, which is computationally expensive, we propose the use of a canonical HMM. This provides a common reference through which different studies can be described using the same set of networks. We provide HMMs for a range of model orders (4-16 states) in parcellated source space and sensor space. These HMMs were trained on 1849 MEG recordings (N = 621, 18-88 years old, 194 hours), capturing population variability in both rest and task data. We illustrate applications of this canonical HMM approach in parcellated source space using boutique MEG and EEG datasets. Applying the canonical HMM in parcel space requires the boutique dataset to be preprocessed and source reconstructed in the same way as the canonical HMM training data. Applying the canonical HMM in the sensor space requires the same sensor layout and preprocessing as the canonical HMM training data. The canonical HMMs have been made publicly available as an open-access resource, providing sets of canonical brain networks that can be used to compare individuals within and across a range of datasets. HighlightsO_LIInstead of training a new HMM for every study, which is computationally expensive and can lead to less reliable results, we propose a canonical HMM instead. C_LIO_LIThe canonical HMM provides a common reference through which different studies can be described using the same set of networks. C_LIO_LIThe canonical HMM was learnt from 1849 MEG recordings (194 hours of data; resting state and task) in participants aged 18-88 years. C_LIO_LIWe demonstrate applications of the canonical HMM on three parcellated source space boutique studies: an Alzheimers resting-state MEG dataset, a working memory task MEG dataset, and a resting-state EEG dataset. C_LIO_LIWe transfer the parcel-level canonical HMM to sensor space, avoiding the need for source reconstruction. C_LIO_LIWe release the canonical HMM (for varying model orders) as an open-access resource for M/EEG research. C_LI

neuroscience↗

bioRxiv2025

SELEX-HTCFQ Platform: Developing DNA Enhancers of ADAR1 to Suppress ZBP1-Dependent Immunopathology

Excessive immune activation drives pathological inflammation through dysregulated ZBP1 signaling, yet this sensor remains crucial for immune surveillance, necessitating targeted therapies that selectively inhibit pathology while preserving protective functions. Here, we developed an innovative SELEX-HTCFQ platform that combines Systematic Evolution of Ligands by Exponential Enrichment (SELEX) with high-throughput competitive fluorescence quenching (HTCFQ) to identify ADAR1-specific enhancers. Capitalizing on ADAR1s natural ability to suppress ZBP1 via competitive Z-nucleic acid binding, this platforms dual assessment of affinity and selectivity identified aptamer A4, a highly specific ADAR1 enhancers demonstrating over 40-fold selectivity over ZBP1. A4 allosterically modulates ADAR1s activity, thereby potentiating its inhibitory effect on ZBP1, which not only mitigates the excessive inflammatory response but also maintains the delicate balance of the immune system. These ADAR1 enhancers represent precision molecular tools for reprogramming Z-nucleic acid sensing, offering a promising therapeutic paradigm for cytokine storm syndromes and necroptosis-driven disorders through selective pathway modulation.

biochemistry↗

bioRxiv2025

Spatial learning in multi-scale environments: Roles of hippocampus, orbitofrontal cortex, and retrosplenial cortex

Navigation cognition involves the learning of multi-scale environments and the formation of cognitive maps. How do humans build cognitive maps in multi-scale environments? Cognitive maps are thought to be organized hierarchically, with local representations for subareas and global representations for the entire environment. However, it remains unclear how spatial learning influences the representations of multi-scale environments and their underlying neural mechanisms across different levels of representation. In the current study, we built a virtual environment (VE) with four orthogonally positioned rectangular rooms, each containing eight objects at the corners. Twenty-three healthy subjects completed a four-session spatial memory experiment conducted over two weeks. We measured their brain activity by using BOLD-fMRI at two stages: pre-learning stage and post-learning, when they were judging the relative direction between the objects within the VE. We found that with the progression of learning, the subjects shifted from relying on local, directional cues to using more global representations of the environment. At the neural level, the hippocampus (HIP), retrosplenial cortex (RSC), and orbitofrontal cortex (OFC) played distinct roles in encoding spatial information across the two learning stages. Specifically, after learning, the HIP shifted from local to global representations, while the RSC and OFC supported the integration of spatial information across these representational levels. In addition, the anterior cingulate cortex was involved in forming global representations, facilitating efficient spatial processing as learning advanced. These findings revealed how spatial learning leads to adaptive shifts in brain activity, contributing to the formation of cognitive maps in complex, multi-scale environments. HighlightsO_LIUsing fMRI to study neural mechanisms of cognitive map formation in a multi-scale environment. C_LIO_LIDetected that local and global spatial representations coexist and evolve during spatial learning. C_LIO_LIFound that learning progress is associated with activation changes in the entorhinal cortex, hippocampus, and retrosplenial cortex. C_LIO_LIRevealed the complementary roles of hippocampus and orbitofrontal cortex in representing the environment. C_LIO_LIShowed that the retrosplenial cortex improves both local and global representations, supporting the integration of multi-scale spatial information. C_LI

neuroscience↗

bioRxiv2025

Modelling Discrete States and Long-Term Dynamics in Functional Brain Networks

Functional brain network dynamics underlie fundamental aspects of human cognition and behaviour, including memory, ageing, and a range of clinical disorders. It has been shown that ongoing brain network dynamics can be reliably inferred at fast, sub-second timescales from electrophysiological data using unsupervised machine learning. However, these methods often struggle with inherent trade-offs. For example, Hidden Markov Models (HMMs) have been used to infer categorical brain network states that provide good interpretability but do not model long-range temporal structure. Recently, deep learning approaches using recurrent neural networks (e.g., Dynamic Network Modes) have been proposed to model long-range temporal dependencies, but at the expense of interpretability. In this paper, we introduce Dynamic Network States (DyNeStE) to address this problem. This new model employs amortised Bayesian inference with recurrent neural networks to model long-range temporal structure and uses a Gumbel-Softmax distribution to enforce categorical states for greater interpretability. In both simulations and real resting-state magnetoencephalography data, DyNeStE was able to recover plausible dynamic brain network states and showed superior performance over the HMM in capturing long-range temporal dependencies in network dynamics. These dynamic networks were reproducible across independent data splits and build on established HMM-based findings. Together, these results highlight DyNeStE as an interpretable and temporally informative framework, capable of representing large-scale neural activity as discrete state transitions while capturing transient and long-range brain network dynamics.

neuroscience↗

bioRxiv2025

TLR4 promotes liver fibrosis in metabolic dysfunction-associated steatohepatitis by a mechanism independent of hepatocytes and inflammatory cells

In metabolic dysfunction-associated steatotic liver disease (MASLD), liver fibrosis is the most important prognostic factor, and fibrosis can lead to hepatic cirrhosis and cancer. In the liver toll-like receptor 4 (TLR4) signaling promotes fibrosis. To investigate the role of TLR4 in the development of MASLD, we used a mouse model of MASLD, and we deleted the Tlr4 gene either in the whole body or selectively in inflammatory cells or hepatocytes. Mice with a whole-body deletion of Tlr4 developed MASH with steatosis, hepatocellular injury and inflammation; however, Tlr4 deletion prevented the deposition of fibrosis. Tlr4 deletion on immune cells or in hepatocytes did not prevent fibrosis. These results suggest that activation of TLR4 in other hepatic cells, possibly hepatic stellate cells, or in a combination of cells promotes liver fibrosis in MASLD.

pathology↗

bioRxiv2025

Hugin-AstA circuitry is a novel central energy sensor that directly regulates sweet sensation in Drosophila and mouse

Taste sensation plays a crucial role in shaping feeding behavior and is intricately influenced by internal states like hunger or satiety. Despite the identification of numerous neural substrates regulating feeding behavior, the central neural substrate that linked energy-sensing and taste sensation remained elusive. Here, we identified a novel neural circuitry that could directly sense internal energy state and modulate sweet sensation in the Drosophila brain. Specifically, a subset of neuropeptidergic neurons expressing hugin directly detected elevated levels of circulating glucose via glucose transporter Glut1 and ATP-sensitive potassium channel. Upon activation, these neurons released hugin peptide and activated downstream Allatostatin A (AstA)+ neurons via its cognate receptor PK2-R1. Subsequently, the activation of AstA+ neurons then directly inhibited sweet sensation via AstA peptide and its cognate receptor AstA-R1 expressed in sweet-sensing Gr5a+ neurons. We also showed that neuromedin U (NMU), the mammalian homolog of fly hugin, served as an energy sensor to suppress sweet sensation. Therefore, these data identify hugin+ neuron as a glucose-responsive central energy-sensing module that modulates sweet sensation across species.

neuroscience↗

bioRxiv2025

Cyclin-dependent kinase 9 inhibitors as oncogene signaling modulators in combination with targeted therapy for the treatment of colorectal cancer

BackgroundColorectal cancer (CRC) is the second deadliest cancer worldwide and new treatment options are urgently needed. Cyclin dependent kinase 9 (CDK9) promotes aberrant RNA transcription in cancer and is a promising target for cancer therapies. MethodsUsing CRC cell lines as well as newly established patient-derived organoid models of CRC, we studied the clinically promising CDK9 inhibitors (AZD4573, BAY1125152/VIP152/enitociclib, and NVP2) to determine their therapeutic potential. We investigated the efficacy and mechanisms of action through cell growth and cytotoxicity assays, RNAseq, immunoblotting, and IHC. ResultsWe found CDK9 inhibitors to be highly potent against CRC, through suppression of proliferation and induction of apoptosis. Our results demonstrated CDK9 inhibitors to be broadly active against a set of CRC models derived from a diverse patient population. Our mechanistic studies showed significant suppression of the Mitogen-active protein kinase (MAPK) signaling pathway due to CDK9 inhibitor treatment, suggesting that CDK9 inhibitor efficacy could be enhanced when combined with MAPK pathway inhibitors. As proof-of-concept, we found that CDK9 inhibitors and MEK inhibitors could be combined to synergistically suppress CRC growth and survival. ConclusionsCDK9 inhibitors show promising activity against patient-derived models of CRC. MAPK signaling is particularly suppressed by CDK9 inhibitors. Combining CDK9 inhibitors and targeted therapy against MAPK signaling pathway may be a viable strategy worthy of further investigation preclinically and clinically. State of translational relevanceNovel therapies for chemotherapy-refractory CRC remain urgently needed. Rapid validation of efficacy in appropriate translational models can provide the necessary rationale to test new therapies in clinical trials. Using newly established patient-derived organoids, we validate the preclinical efficacy of transcriptional CDK9 inhibitors against CRC. This is a therapeutic class that has been scarcely evaluated for efficacy against CRC. We found that CDK9 inhibitors potently inhibit patient-derived organoid models of CRC. Our mechanistic work also reveals that clinically achievable concentrations of CDK9 inhibitor treatment results in suppression of MAPK signaling. As proof-of-concept of the potential actionability of our findings, we demonstrate that CDK9 inhibitors can be combined with MEK inhibitors to synergistically suppress CRC growth. Our study provides new patient-derived CRC models as a resource available to others for therapeutic testing, as well as a roadmap for novel hypotheses about synergistic combination treatments using CDK9 inhibitors.

cancer biology↗

bioRxiv2025

Inhibition of Clostridioides difficile-specific DNA adenine methyltransferase CamA by analogs of S-adenosyl-L-methionine

Epigenetically-targeted therapies, especially those inhibiting S-adenosyl-L-methionine (SAM)- dependent methylations of DNA, mRNA and histones, have advanced rapidly in cancer treatment. However, these therapies remain underexplored for antibiotic development, despite the growing threat of antimicrobial resistance. Here, we screened a focused library of SAM analogs against the DNA adenine methyltransferase CamA specific to the enteric pathogen Clostridioides difficile. At the same time, we examined six other adenine methyltransferases, including two bacterial DNA methyltransferases, and four human RNA methyltransferases having distinct RNA substrates. Compound 113 selectively inhibited CamA (IC50 = 0.15 M). In addition, compound 67 inhibited Caulobacter crescentus CcrM (IC50 = 1.8 M), which has orthologs present in pathogens such as Brucella; while compounds 77 and 37 inhibited the human DNA methyltransferase complexes MettL3-MettL14 and MettL5-Trm112, respectively, at 7-8 M concentrations. These results provide chemical probes for exploring the role of CamA in sporulation and colonization, with potential as antivirulence agents against C. difficile infection. Our study also introduces the first chemical probes for inhibiting bacterial CcrM and human MettL5, each of which plays key roles in their respective hosts. Table of Contents Graphic (TOC) O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=163 SRC="FIGDIR/small/670177v1_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@f7d56org.highwire.dtl.DTLVardef@edbd09org.highwire.dtl.DTLVardef@15abc9org.highwire.dtl.DTLVardef@c12ca7_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2025

Evolution of Insulin, Insulin-like Growth Factor, and Their Cognate Receptors in Vertebrates, Invertebrates, and Viruses

The insulin and insulin-like growth factor (IGF) system regulates essential biological functions such as growth, metabolism, and development. While its physiological roles are well characterized, the evolutionary origins and molecular diversification of its ligands and receptors remain incompletely defined. Here, we present the most comprehensive phylogenetic and sequence conservation analysis of this system to date, using over 1,000 sequences from vertebrates, invertebrates, and viruses. Our analyses reveal that insulin, IGF-1, and IGF-2 form distinct monophyletic clades that diverged after the emergence of vertebrates, with IGF-1 being the most conserved ligand. We show that IGF1R-binding residues, especially in the A- and B- domains of IGF-1, are highly conserved across vertebrates, while insulins Site 2 residues, which overlap with its dimerization and hexamerization surface, are more variable--correlating with the loss of hexamer formation in hystricomorphs, reptiles, and jawless fish. Unexpectedly, we identify a 12-amino acid insert in the insulin receptor (IR) of turtles and tortoises, previously thought to be unique to mammalian IR-B isoform, suggesting an earlier evolutionary origin of isoform diversity. We also show that marsupials and monotremes retain ancestral receptor domain features shared with reptiles and birds, and that avian insulins, particularly A-chain residues, are unusually conserved. Viral insulin/IGF-like peptides (VILPs) fall into two distinct clades that resemble either IGFs or insulin. Together, these findings illuminate the evolutionary architecture of the insulin/IGF system, highlight unexpected lineage-specific adaptations, and provide a framework for understanding hormone-receptor function across biology and therapeutic design.

evolutionary biology↗

bioRxiv2025

A pre-menopausal single-cell atlas for ovarian drug discovery

Multi-tissue single-cell atlas efforts have transformed our understanding of cellular diversity across the human body and led to the creation of harmonized resources to advance research and therapeutic development. Ovarian biology, however, often remains underrepresented in these resources and is rarely analyzed with menopausal status as a biological variable. Here, we present the Menopause Cell Map (MenoMap), an integrated single-cell resource comprising more than 2 million cells from 13 healthy human tissues, including the ovary, from pre- and post-menopausal age donors. This harmonized atlas leverages curated samples from healthy female donors and enables transcriptomic comparisons across cell types, tissues, and organs while preserving menopausal status based on age as an interpretable variable. Using this resource, we show that menopause-associated gene expression changes are highly context dependent, with prominent remodeling in ovarian stromal, endothelial, immune, and reproductive cell populations. Within the ovary, post-menopausal remodeling rewired intercellular communication and shifted reproductive and steroidogenic programs toward collagen-integrin signaling, endothelial-to-mesenchymal transition, and senescence concentrated in the endothelial and stromal compartments. Cross-species comparison with young and aged mouse ovarian single-cell data showed these endothelial and stromal changes were conserved with age, most prominently in tumor necrosis factor-nuclear factor-kappa-beta signaling. Finally, we apply Human Protein Atlas-inspired specificity rules and fertility phenotype annotations to evaluate how menopausal age status affects tissue- and cell-type-specific gene classification and to prioritize ovary-enriched genes for downstream biological and translational investigation. Through this analysis, we find that the tissue specificity classification of several genes involved in reproductive-specific programs change in the pre- to post-menopausal transition, highlighting the need for age-aware and healthy donors in multi-tissue single-cell atlas efforts. Together, the MenoMap provides a human single-cell framework that leverages existing and standardized single-cell datasets curated for studying ovarian biology across reproductive aging, evaluating the influence of menopausal status on gene expression and tissue specificity, and nominating candidate genes for future investigation in reproductive biology, fertility, and target discovery.

bioinformatics↗

bioRxiv2025

Xcr1+ type 1 conventional dendritic cells are essential mediators for atherosclerosis progression

BackgroundAtherosclerosis is characterized by lipid accumulation within plaques, leading to foam cell formation and an inflammatory response within the aortic lesions. Lipid disorders have been extensively investigated, however the cellular and molecular mechanisms that trigger the inflammatory response in atherosclerotic plaques remain far from being fully understood. Xcr1+ cDC1 cells are newly identified antigen-presenting cells in activating immune cells. However, the role of cDC1 cells in the development of atherosclerosis remains highly controversial. ResultsWe first confirmed the presence of cDC1 within human atherosclerotic plaques and discovered a significant association between the increasing cDC1 numbers and atherosclerosis progression in mice. Subsequently, we established Xcr1Cre-Gfp Rosa26LSL-DTA ApoE-/- mice, a novel and complex genetic model, in which cDC1 was constitutively depleted in vivo during atherosclerosis development. Intriguingly, we observed a notable reduction in atherosclerotic lesions in hyperlipidemic mice, alongside suppressed T cell activation of both CD4+ and CD8+ subsets in the aortic plaques. Notably, aortic macrophages and serum lipid levels were not significantly changed in the cDC1-depleted mice. Single-cell RNA sequencing revealed heterogeneity of Xcr1+ cDC1 cells across the aorta and lymphoid organs under hyperlipidemic conditions. As Xcr1 is the sole receptor for Xcl1, we next explored to target Xcr1+ cDC1 cells via Xcl1 by establishing Xcl1 and ApoE deficient mice. Xcl1 knockout mice exhibited decreased atherosclerotic plaque formation and reduced aortic cDC1 accumulation, indicating that Xcl1 contributes to cDC1-mediated atherosclerotic lesion development. ConclusionsOur results reveal crucial roles of cDC1 in atherosclerosis progression and provide insights into the development of immunotherapies by targeting cDC1 through Xcl1.

immunology↗