Biology subjects

Li, Y.

Publications and source records attributed to Li, Y..

At least 487 records · Page 27Linked to original sources

bioRxiv2026

Diet-Driven Gut Microbiome Differences Between Cohabiting Han and Yi Ethnic Groups in Liangshan, China

BackgroundWhile ethnic variations in gut microbiota are well-documented, their persistence in genetically distinct populations sharing the same geographic environment remains poorly understood. This study investigates the Han and Yi ethnic groups cohabiting rural Southwest China, where contrasting dietary traditions persist under identical environmental conditions. MethodsWe analyzed fecal samples from 100 healthy adults (50 Yi/50 Han) using 16S rRNA gene sequencing (V3-V4 region). Dietary intake was quantified via three 24-hour recalls validated with plasma biomarkers. Multivariate analyses (DESeq2, PERMANOVA) controlled for age, sex, and BMI covariates. ResultsYi individuals exhibited significantly higher Prevotellaceae (log2FC=1.82, padj=0.028) and Succinivibrionaceae (log2FC=2.15, padj=0.013) abundances, corresponding to their buckwheat-rich diet (58.5 {+/-} 12.3% vs Han 3.3{+/-}1.8%, p<0.001) Han microbiota showed enriched Bacteroidaceae (log2FC=1.24, padj=0.041), associated with higher animal fat intake (23.6% vs Yi 1.8%, p<0.001) Dietary factors (buckwheat/animal fat intake) explained greater microbiota variation than ethnic genetic ancestry (PERMANOVA R{superscript 2}=0.10 vs 0.07, p < 0.05) ConclusionCohabiting Han and Yi populations maintain distinct gut microbiota compositions primarily driven by dietary differences. These findings highlight diet as a stronger determinant than genetic ancestry in shaping microbial communities under shared environments. Future studies should validate these observations with metagenomic sequencing.

microbiology↗

bioRxiv2026

Uncompetitive Allosteric Inhibitor of Mitochondrial Creatine Kinase Prevents Binding and Release of Creatine by Stabilization of Loop Closure

Mitochondrial creatine kinase (MtCK) is a key enzyme in energy buffering and homeostasis in cells. It catalyzes transfer of phosphoryl group from ATP to creatine. Overexpression of MtCK occurs in many cancer cells to meet elevated energy demands, which is associated with poor prognosis. This suggests that MtCK may be a promising target for cancer therapeutics. We sought to discover first-in-class selective inhibitors of MtCK with diverse mechanisms of action using high-throughput screening, biochemical characterization and cryo-EM studies. Through these studies, we identified diverse types of compounds that modulate activity of MtCK in vitro, including fast-equilibrium and time-dependent orthosteric and allosteric inhibitors. Select hits were subjected to in vitro enzymatic and binding assays to assess MtCK inhibition and binding. A subset of inhibitors was advanced into structural studies using cryo-EM resulting in the molecular structure of MtCK with and without bound substrates in complex with an allosteric uncompetitive inhibitor that we discovered. These studies identified compounds unique binding pocket on MtCK and established the molecular steps manifesting in the apparent uncompetitive mode of inhibition. We demonstrate that the compound stabilizes an active site loop in a closed conformation restricting access of the creatine substrate to and the release of phosphocreatine product from its binding pocket. These findings establish chemical tools suitable for validation of MtCK as a promising breast cancer target with high therapeutic potential and build a foundation for future structure-guided optimization of the hit compounds of MtCK we identified and de novo rational design of novel MtCK inhibitors. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=160 SRC="FIGDIR/small/696683v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@1fd9870org.highwire.dtl.DTLVardef@230e31org.highwire.dtl.DTLVardef@178d8fdorg.highwire.dtl.DTLVardef@edeca6_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2026

TSTScope Unifies Single-Cell Multi-Omics to Identify Functional T Cell States Predictive of Immunotherapy Response

Immune checkpoint blockade (ICB) can produce durable responses in cancer, but reliable predictors of benefit are still lacking. CD8 tumor-specific T cells are essential for ICB efficacy, yet it remains unclear which functional states of these cells determine therapeutic success. To address this, we developed TSTScope, an interpretable deep learning framework that integrates single-cell transcriptomic and T-cell receptor sequencing data to generate unified representations of CD8 T-cell identity. By applying TSTScope to non-small cell lung cancer (NSCLC) datasets, we characterized the gene programs defining tumor specificity and computationally inferred a population of potential TSTs. Crucially, we demonstrate that clinical response is not a product of TST abundance, but is instead governed by their functional state. We derived the MPR score, a metric capturing this functional potential, which proved to be a robust predictor of treatment outcomes. In an independent validation cohort, the MPR score significantly outperformed established biomarkers. Collectively, TSTScope identifies a distinct functional state of tumor-specific T cells as a primary determinant of ICB efficacy, providing both a mechanistic framework and a potent tool for precision immunotherapy.

bioinformatics↗

bioRxiv2026

Exploring DNA methylation profiles in the pathogenesis of human osteoporosis via whole-genome bisulfite sequencing

BackgroundOsteoporosis is a prevalent bone metabolic disorder characterized by reduced bone mass, disruption of bone microarchitecture, and increased bone fragility, leading to a heightened risk of fracture. This condition significantly impairs patients quality of life and increases mortality risk. Emerging evidence suggests that DNA methylation may play a crucial role in regulating the expression of genes related to bone metabolism, thereby influencing the development of osteoporosis. However, the precise relationship between DNA methylation and osteoporosis remains unclear and warrants further investigation. ResultsOur study revealed significant differences in both the quantity and ratio of DNA methylation between individuals with osteoporosis and non-osteoporosis controls, with differences predominantly occurring in CpG islands. GO/KEGG enrichment analyses highlighted distinct osteoporosis-related gene pathways. Notably, we identified six genes, MSX1, HOXD4, AXIN2, WNT5A, TGFB1, and STAT3, respectively, that are potentially involved in the pathogenesis of osteoporosis and are broadly involved in various diseases and biological processes. ConclusionsThese findings indicate distinct methylation patterns between osteoporosis patients and healthy individuals, with differential methylation levels in genes associated with osteoporosis. This research offers new insights into the epigenetic mechanisms underlying osteoporosis.

genetics↗

bioRxiv2026

MR-μScope: a multi-scale and simultaneous MRI-miniaturized microscopy system for elucidating neurovascular coupling

Blood Oxygen Level Dependent functional MRI (BOLD-fMRI) revolutionized non-invasive brain mapping, yet the physiological origins of its signal, linking microscopic neurovascular activity to macroscopic hemodynamics, remain elusive. To bridge this gap, we developed MR-Scope: a simultaneous and multimodal platform that integrates preclinic high-field MRI (9.4T) and miniaturized fluorescence microscopy (Miniscope). Our system contains an MR-compatible Miniscope with electromagnetic shielding, a customized radiofrequency coil with a central optical window, and an adjustable animal cradle, enabling artifact-free acquisition of whole-brain fMRI alongside microscopic vascular dynamics. Phantom validation confirmed negligible cross-modal interference (fMRI SNR [≥] 15 dB; optical SNR [≥] 26 dB). MR-Scope captured stimulus-evoked microvascular dilation and blood flow velocity changes in the somatosensory cortex concurrent with BOLD signals, revealing vessel-size-dependent neurovascular coupling. MR-Scope provides an effective solution for deciphering multi-scale neurovascular interactions and offers unique potential for advancing research into brain function and disease mechanisms involving vascular pathology.

bioengineering↗

bioRxiv2026

Impacts of mutation accumulation and order on tumor initiation revealed by engineered murine colorectal cancer organoids

Tumorigenesis typically follows a multi-hit trajectory, driven by accumulating oncogenic mutations. Colorectal cancer (CRC) has long served as a paradigmatic model of multi-hit tumorigenesis, characterized by adenoma-carcinoma transition accompanied by acquisition of specific oncogene and tumor suppressor mutations. However, how the temporal order of early mutations influences CRC initiation remains poorly understood. To address this, we established a CRC tumorigenesis model using murine intestinal organoids. By introducing defined combinations of key CRC driver mutations (Kras, Apc, and Trp53) in distinct orders, we systematically investigated how the order of mutation accumulation affects tumor initiation. Our results reveal that the mutation accumulation confers growth advantages in both in vitro and in vivo models. Strikingly, mutation order also influenced the tumorigenic properties of the organoids. Whereas organoids with Trp53 loss before or after Apc loss similarly affected organoid phenotypes in vitro or tumorigenicity in immunodeficient mice, organoids with Trp53 loss preceding Apc inactivation exhibited reduced tumor-forming potential in immunocompetent mice, likely due to their distinct immunological features. Collectively, our study reveals a critical role of ordered mutation accumulation in CRC initiation, an insight that may hold clinical relevance.

cancer biology↗

bioRxiv2026

RodZ acts through MreBCD to activate the elongasome in Escherichia coli

Most bacteria are surrounded by a peptidoglycan (PG) matrix that maintains cell shape and provides protection against turgor pressure. In many rod-shaped bacteria, synthesis of PG along the cell cylinder is organized by the elongasome, also called the Rod complex, which consists of six highly conserved proteins, including the actin-like MreB, the PG synthase complex RodA-PBP2 and three regulatory membrane proteins, MreC, MreD and RodZ. However, how these proteins interact with each other to form the elongasome and synthesize lateral PG remains elusive. In this study, by characterizing MreC mutations affecting elongasome activity in Escherichia coli, we provide evidence that MreC alternates between an active and an inactive state and these mutations affect MreC interaction with PBP2. We also find that RodZ interacts with MreC via a periplasmic region and disruption of this interaction compromises elongasome function. Additionally, we show that the cytoplasmic region of RodZ, which interacts with MreB, work synergistically with activating MreC mutations to promote rod shape formation. These results indicate that RodZ activates the elongasome by initiating two signaling cascades, one in the periplasm via MreCD, and another in the cytoplasm through MreB. This role of RodZ in regulating elongasome activity is analogous to that of FtsN in activating the divisome, suggesting that the mechanisms governing the activation of PG synthesis in cell elongation and division are similar in E. coli. ImportanceThe elongasome, or Rod complex, mediates lateral peptidoglycan synthesis during cell elongation in many rod-shaped bacteria. It consists of the cytoskeletal protein MreB, the peptidoglycan synthase RodA-PBP2, and three regulatory proteins MreCD and RodZ. Although it has been extensively studied, how its activity is controlled remains incompletely understood. Here, we reveal the roles of MreCD and RodZ in regulating elongasome activity in Escherichia coli. Our results indicate that RodZ triggers lateral peptidoglycan synthesis by interacting with MreB in the cytoplasm and MreCD in the periplasm. These interactions likely switch MreBCD to the active state such that they can stimulate the activity of the RodA-PBP2 complex. This regulatory mechanism resembles the activation mechanism of the divisome that mediates septal peptidoglycan synthesis.

microbiology↗

bioRxiv2025

MicroGenomer: A Foundation Model for Transferable Microbial Genome Representations Enabling Multi-scale Genomic Understanding and Ecophysiological Trait Prediction

Microorganisms underpin global biogeochemical cycles and represent a vast, underexplored reservoir for sustainable biotechnology and human health. Their diversity, arising from the interplay of numerous genes within holistic genomic contexts, dictates complex ecophysiological traits across varied environments. To bridge the gap between complex genomic sequences and associated biological functions and phenotypic outcomes, we present MicroGenomer, a foundation model for transferable microbial genome representations enabling multi-scale genomic understanding and ecophysiological trait prediction. MicroGenomer leverages a hierarchical training strategy comprising pre-training on large-scale genomic sequences (234.5 billion base pairs), domain-specific mid-training using the GTDB-curated marker gene set, and task-specific post-training. Despite a streamlined architecture of 470 million parameters, MicroGenomer achieves performance on key tasks competitive with models nearly 85 times its size. Advancing beyond established gene-scale encoders, MicroGenomer generates robust embeddings at the genome scale for downstream modeling. Extensive evaluations demonstrate that MicroGenomer effectively captures phylogenetic structures in species space, excelling in geneand genome-scale understanding as well as ecophysiological trait prediction. The practical utility of these capabilities is further demonstrated by targeted wet-lab validation on newly isolated strains, indicating that MicroGenomers predictions provide reliable guidance for biological experiments. Collectively, MicroGenomer offers a high-performance, resource-efficient framework that transforms raw sequence data into actionable biological insights, providing a powerful foundation for microbiome research and biotechnology.

bioinformatics↗

bioRxiv2025

BNIP3-mTOR Signaling Mediates Resistance to MET Inhibition in Glioblastoma

Glioblastoma (GBM) is an aggressive primary brain malignancy with poor prognosis due to rapid progression, extensive invasiveness, and intrinsic resistance to standard therapies. Aberrant activation of receptor tyrosine kinases (RTKs), particularly MET, drives tumor proliferation, invasion, and therapy resistance. Here, we show that MET inhibition with crizotinib induces senescence and mitochondrial dysfunction in glioma-initiating cells (GICs), in part via downregulation of the mitochondrial protein BNIP3. However, BNIP3 downregulation activates mTOR signaling, enabling adaptive resistance. Targeting mTOR with everolimus in combination with crizotinib synergistically enhances anti-tumor effects, inducing apoptosis, senescence, and necroptosis, and significantly reducing cell viability and sphere-forming capacity. In orthotopic GBM xenograft models, this combination, particularly in a sequential regimen, markedly prolongs survival without overt toxicity. Our findings identify a BNIP3-mTOR signaling axis as a critical mediator of resistance to MET inhibition and provide a mechanistic rationale for combined MET and mTOR targeting as a promising therapeutic strategy in GBM. Statement of Translational RelevanceGlioblastoma (GBM) remains a highly aggressive and treatment-resistant brain tumor with limited therapeutic options. Our study identifies a BNIP3-mTOR signaling axis as a key mediator of resistance to MET inhibition. We show for the first time that combined MET and mTOR inhibition exhibits synergistic effects against GBM in vitro and in vivo. This combination prolongs survival without overt toxicity, providing a strong preclinical rationale for clinical evaluation in GBM patients with high MET expression and offering a promising strategy to overcome adaptive resistance.

cancer biology↗

bioRxiv2025

A Generative Foundation Model for Cryo-EM Densities

Single-particle cryo-electron microscopy (cryo-EM) enables structure determination of macromolecular complexes, yet reconstructions are often degraded by noise, anisotropic sampling and reconstruction artifacts. Correcting these effects is an ill-posed inverse problem that requires strong prior knowledge in addition to experimental data. Here we introduce cryoFM, a generative foundation model for cryo-EM densities that unifies data-driven structural priors with dataset-specific constraints within a Bayesian inference framework. CryoFM is trained in an unsupervised manner on thousands of high-quality cryo-EM maps using flow matching, learning a generalizable prior over macromolecular density distributions. Combined with explicit likelihood models describing experimental degradations, cryoFM enables flow posterior sampling, an inference-only procedure that performs denoising and restoration and refinement while remaining explicitly constrained by dataset-derived statistics. We show that this framework improves density reconstruction and refinement across diverse experimental settings, including preferred-orientation datasets and cases with strong spatial heterogeneity in signal-to-noise ratio, without introducing hallucinated features. In addition, cryoFM can be fine-tuned into conditional generative models for density post-processing, yielding maps with improved interpretability and fewer artifacts compared to existing supervised approaches. Together, cryoFM establishes generative foundation models as a principled and controllable framework for cryo-EM density reconstruction and modification.

biophysics↗

bioRxiv2025

OmniCell: Unified Foundation Modeling of Single-Cell and Spatial Transcriptomics for Cellular and Molecular Insights

A cells transcriptional programme is not fully defined by gene expression alone, but by the tissue context in which that programme is enacted. Single-cell RNA sequencing resolves molecular identity after dissociation, whereas spatial transcriptomics preserves tissue architecture but remains constrained by assay-specific sparsity and gene coverage. Here we present OmniCell, a tissue-contextual transcriptomic foundation model pretrained on 67 million dissociated and spatially resolved profiles. By integrating gene identity, expression magnitude and tissue context, OmniCell links transcriptional programmes to the cellular neighbourhoods and anatomical contexts in which they operate. OmniCell organised transcriptomes across molecular, cellular and tissue scales. It recovered cell-type-specific programmes and tissue-aligned gene modules, preserved robust cell-state structure across batches, species and rare populations, and improved the reconstruction of spatial cell identity, anatomical domains and cell-type composition. In human liver cancer Stereo-seq data, OmniCell resolved a tumour-margin transition zone characterised by immune infiltration, acute-phase inflammation, coagulation/complement activity and metallothionein-linked metal-ion detoxification. Contextual gene-embedding similarity analysis showed that gene relationships differed across tumour core, transition-zone and paratumour/adjacent non-malignant niches, indicating that OmniCell captures tissue-dependent gene function rather than expression similarity alone. In mouse brain development and macaque cortex, spatial virtual perturbations mapped regulatory genes onto stage- and region-specific anatomical programmes. Together, these results establish tissue context as a primary axis of transcriptomic representation and provide a framework for studying how cellular programmes acquire context-dependent biological meaning in intact tissues.

bioinformatics↗

bioRxiv2025

A novel dephosphorylation peptide inhibits 17β-HSD1 enzyme activity in ovarian granulosa cells and breast cancer cells

Estradiol (E2), a pivotal mammalian reproductive hormone, is closely associated with estrogen-dependent diseases. 17{beta}-hydroxysteroid dehydrogenase type 1 (17{beta}-HSD1), a critical E2 synthesis enzyme, has been understudied for its post-translational regulation-despite its potential as a therapeutic target, no clinically approved inhibitors are currently available. In our investigation of porcine follicular atresia mechanisms, we identified five differentially phosphorylated residues in 17{beta}-HSD1. Using in vitro site-directed mutagenesis and transgenic mouse models carrying point mutations, we established that Ser30 and Ser274 are critical phosphorylation sites that robustly modulate 17{beta}-HSD1 enzymatic activity. We further demonstrated that activin A and insulin-like growth factor 1 (IGF-1) enhance 17{beta}-HSD1 activity by promoting its phosphorylation at these two sites. To target these regulatory residues, we designed and synthesized cell-penetrating peptides (CPPs) that specifically suppress 17{beta}-HSD1 phosphorylation. Functional assays conclusively demonstrated that these CPPs significantly suppressed 17{beta}-HSD1 activity in vitro. Notably, they also inhibited the proliferation, migration and invasion of MCF-7 cells, as well as tumor growth in a mouse model of breast cancer. Our study provides novel mechanistic insights into the regulation of 17{beta}-HSD1 and E2 synthesis, addressing a critical gap in steroid hormone biology. The identification of Ser30/Ser274 as functional phosphorylation sites and the development of CPP-based inhibitors offer both theoretical advances and translational potential, opening new avenues for the treatment of estrogen-dependent breast cancer.

cell biology↗

bioRxiv2025

Dose-dependent NFI regulation of progenitor lifespan and output underlying human neocortical malformation

Nuclear Factor I (NFI) misexpressions in humans are associated with severe brain malformations, yet the underlying mechanisms remain poorly understood. Here we show that NFIs regulate the broad lineage progression and lifespan of radial glial progenitors (RGPs), and consequently neocortical development in a dose-dependent manner. Human cerebral organoids carrying patient-mimicking NFI mutations exhibit expression level-dependent bidirectional impairments in RGP temporal development, coinciding with patient phenotypes. In mouse models, selective removal of NFIs leads to a dramatic protraction of RGP lineage progression and lifespan, excessive progeny output, and neocortical overgrowth and abnormal folding, whereas overexpression of NFIs accelerates RGP lineage progression, resulting in developmental stage-dependent precocious productions of diverse neural progenies. Moreover, NFIs exhibit a positive auto-regulation and progressive increase in expression, and regulate distinct temporal-specific targets underlying RGP lineage progression. These results suggest that NFIs act as evolutionarily conserved key global temporal regulators of RGP lineage progression and neocortical development. HIGHLIGHTSO_LINFIs affect human RGP temporal development coinciding with patient phenotypes. C_LIO_LINFI removal protracts RGP lineage progression and lifespan with excessive progeny output. C_LIO_LINFI overexpression accelerates RGP lineage progression with precocious progeny output. C_LIO_LINFIs regulate distinct temporal-specific targets underlying RGP lineage progression. C_LI

neuroscience↗

bioRxiv2025

To Predict is to Design: Unlocking Generative Capabilities in All-Atom Structure Predictors via Geometric Score Distillation

Current protein binder design largely relies on a decoupled paradigm: generating backbones via unconditioned diffusion followed by sequence filling or refilling with inverse folding models. This separation prevents the design process from accessing the holistic validation metrics of structure predictors during generation, wasting rich physical priors. While recent works like BindCraft have successfully inverted AlphaFold2 for protein design1, extending this inversion to state-of-the-art all-atom diffusion predictors (e.g., AlphaFold3, Boltz-2) remains a formidable challenge, particularly for modalities requiring non-standard residues such as cyclic peptides. In this work, we present DREAM (Differentiable Refinement via Energy-Anchored Manifolds), a model-agnostic framework that turns the passive predictive trajectory of diffusion models into an active, lucid design process. DREAM repurposes Boltz-22--a leading open-source all-atom predictor--via Geometric Score Distillation (GSD), a technique enabling explicit gradient-based optimization directly through the frozen diffusion network. Unlike previous methods constrained by standard amino acids, DREAM directly unlocks the models latent chemical vocabulary, allowing gradients to autonomously select the optimal building blocks up to 55 residue types (including D-amino acids and post-translational modifications) to minimize energy. We demonstrate this capability by designing cyclic peptide binders for diverse targets, including PD-L1, B7-H3, and the human -Opioid Receptor (hMOR). Our results suggest that the programmable design of chemically complex modalities is not a distant goal, but a latent capability of current all-atom predictors, waiting to be inverted. Ultimately, to predict is to design.

biophysics↗

bioRxiv2025

MECP2 Duplication Uncouples Mitochondrial and Purine Metabolism During neuronal maturation

Mitochondria and nucleotide metabolism are critical for cellular and developmental homeostasis, yet their potential interdependence and role in neurodevelopmental disease remain unclear. In MECP2 Duplication Syndrome (MDS), we identify a conserved correlation between mitochondrial function and purine metabolism that is disrupted across human, organoid, and mouse models. Multiomics integration reveals Complex III as the focal point of mitochondrial collapse, leading to redox stress, DNA damage, and hyperactivation of the de novo purine biosynthesis via purinosome assembly. The breakdown of mitochondria-purinosome coupling compromises genome stability, impairs radial glia proliferation, and delays neuronal maturation. By linking a defined genetic dosage imbalance to metabolic network failure, our study positions the mitochondria-purinosome coordination as a fundamental control axis for neurodevelopment and a therapeutic entry point across metabolic and neurodevelopmental disorders. Metabolic control is fundamental to cellular function, influencing energy production, signaling, epigenetic regulation, and tissue homeostasis1. Nowhere is this more critical than in the brain, where tightly regulated metabolic networks sustain high energetic demands and support neuronal development, synaptic plasticity, and circuit formation2. Disruptions in these networks are increasingly implicated across a spectrum of neurodevelopmental disorders3-5, yet their precise metabolic signatures and mechanistic contributions remain poorly understood.

neuroscience↗

bioRxiv2025

Ensemble-DeepSets: an interpretable deep learning framework for single-cell resolution profiling of immunological aging

Immunosenescence increases susceptibility to infection and reduces vaccine responsiveness, yet bulk transcriptomic clocks obscure the cellular heterogeneity underlying this process. Here, we present IDEAL-Age, an interpretable deep learning framework that operates directly on single-cell PBMC transcriptomes. Benchmarking against 31 methods across independent cohorts demonstrates superior predictive performance. The frameworks interpretability uncovers linear and non-linear transcriptomic dynamics that reveal phase-specific physiological transitions, and identifies pro-youthful or pro-aging cellular contributions. Application to systemic lupus erythematosus (SLE) reveals accelerated immunological aging driven by interferon-associated monocyte shifts. IDEAL-Age establishes a high-resolution computational framework for deciphering systemic immune aging.

bioinformatics↗

bioRxiv2025

Screening and structural characterization of a nanobody targeting the thermostable green fluorescent protein

Nanobody-fluorescent protein pairs are powerful tools in imaging, protein purification, and structural biology studies. While thermostable GFP (TGP) offers improved characteristics over conventional GFP, nanobodies that specifically recognize TGP remain relatively limited. Here, we report the screening and identification of a synthetic nanobody, Sb32, that binds TGP with nanomolar affinity using the ribosome technique. The crystal structure of the Sb32-TGP complex, solved at 1.82 [A] resolution, revealed an unusual binding mode in which Complementarity Determining Region 2 (CDR2) provides the major contribution, rather than the typically dominant CDR3. Moreover, the interface is stabilized by an extensive hydration network, which compensates for relatively few direct contacts and may explain the fast association and dissociation kinetics observed for this complex. These findings expand the repertoire of TGP-specific nanobodies and highlight an alternative strategy by which nanobodies achieve high affinity. Sb32 provides a new reagent for TGP-based applications, with potential utility in membrane protein purification and structural studies.

biophysics↗

bioRxiv2025

Coordinated infraslow cortical oscillations of neuromodulators during NREM sleep

Neurotransmitters and neuromodulators regulate brain states through diverse mechanisms, yet how their activities are coordinated during sleep remains unresolved. Using in vivo fiber photometry in adult mice expressing genetically encoded fluorescent biosensors, combined with EEG/EMG recordings, we investigated the temporal organization of multiple neuromodulators during sleep in barrel cortex, with norepinephrine (NE) as a reference signal. All five neuromodulators examined, acetylcholine, serotonin, dopamine, histamine, and NE, exhibited synchronized infraslow cortical oscillations during NREM sleep. Optogenetic suppression of locus coeruleus (LC) neurons abolished NE oscillations and selectively reduced acetylcholine fluctuations in barrel cortex, whereas targeted inhibition of basal forebrain cholinergic neurons attenuated REM-associated acetylcholine elevations without disrupting NREM-related oscillations or NE dynamics. The synchronized infraslow cortical oscillations spanning multiple neuromodulators reveal a previously unrecognized mechanism for organizing sleep architecture.

neuroscience↗