Biology subjects

Zhou, S.

Publications and source records attributed to Zhou, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Integrative multi-omics re-analysis of public sequencing and metabolomics repositories defines the mucosal immune and metabolic landscape of Helicobacter pylori infection and supports CagW as a rational mRNA-LNP vaccine antigen

Background. Helicobacter pylori colonises 40-45% of the global population, is a WHO Group I carcinogen, and is increasingly hard to eradicate amid rising resistance; no vaccine is licensed. CagW, a VirB6-family cagPAI component, has been advanced as a DNA-vaccine antigen that protects mice; a codon-optimised CagW-Fc mRNA in lipid nanoparticles (mRNA-LNP) is a candidate antigen design intended to engage humoral and cellular immunity. To define the infected gastric mucosal niche in which such a vaccine must operate, we mined public sequencing, single-cell and metabolomics repositories. Methods. NCBI GEO, MetaboLights and NCBI Protein were interrogated. Bulk transcriptomes of H. pylori-infected vs uninfected gastric epithelial cells (GSE295523; 24 libraries: HGC27, AGS, MKN45, GES-1) were re-analysed with a cell-line-blocked design; DEGs (BH-adjusted) were annotated via g:Profiler (GO, KEGG, Reactome) and 13 functional signatures scored. Single-cell composition was quantified from a mouse gastric scRNA-seq atlas (GSE224840; 16,434 cells) and sorted gastric CD8+ tissue-resident memory (TRM) vs splenic memory T cells (GSE216894). Targeted gastric metabolomics was re-analysed for epithelial ornithine decarboxylase (Odc) deletion (MTBLS3235) and cystathionine-gamma-lyase (Cth) deletion (MTBLS2851). CagW conservation was computed across 400 NCBI records (286 variants) with Kyte-Doolittle topology and four B cell epitope scales. Results. Infection induced 326 up- and 115 down-regulated genes (FDR < 0.05, |log2FC| > 1), dominated by TNF/NF-kappaB (KEGG hsa04668, P=5.5x10-11), integrated stress response (ATF4-CHAC1-DDIT3-TRIB3-ASNS) and Th17 chemokines (CXCL8 +3.43, CCL20 +1.32, CXCL1 +1.30, IL23A +1.02). MHC-I presentation rose (Delta-z +0.76; MHC-II unchanged); the arginine/polyamine module (SLC7A1 +1.19, ARG2 +1.33, SAT1 +1.50, ODC1 +0.58) was co-induced. scRNA-seq showed immune expansion in infected mucosa (39.8% vs 21.2% cells; OR 2.46; cell-level frequencies reported descriptively because cells are not independent; direction confirmed at sample level, n=4 vs 4), driven by plasmacytoid DCs (OR 8.9), neutrophils (OR 3.0), macrophages (OR 2.7) and cDCs (OR 2.1), with epithelial de-differentiation. Gastric CD8+ TRM cells showed a residency-effector program (Itgae +6.2, Cd69 +2.3, Gzmb +4.8, Ifng +5.3) with egress-gene loss (Sell -11.4, S1pr1 -9.7, Klf2 -5.2). Metabolomics confirmed both nodes: Odc deletion raised arginine (+3.08) and Cth deletion raised spermidine (+0.40) while lowering leukotriene B4 (-0.33). CagW was conserved across 286 variants (median 98.3% identity; 96.4% residues >=95% conserved), carried four transmembrane helices, and showed a surface-biased conserved epitope envelope at residues 505-530 and 63-79. Conclusions. Public multi-omics define an NF-kappaB/ATF4-driven, Th17-polarised, metabolically stressed niche where antigen-presenting cells expand, epithelium de-differentiates, and CD8+ TRM cells act locally. Here CagW is exceptional among cagPAI antigens: near-universal conservation plus an accessible, conserved surface. These findings provide a systems-level rationale for prioritising CagW as a conserved, epitope-accessible vaccine antigen and inform next-generation multi-antigen designs.

microbiology↗

bioRxiv2026

Integrative multi-omics decoding of the host response to Klebsiella pneumoniae infection reveals an itaconate-centred metabolic-immune axis and a spatially organised inflammatory programme

Background: Klebsiella pneumoniae (Kp) is a leading cause of hospital and community acquired pneumonia and bacteraemia, and hypervirulent and carbapenem resistant lineages continue to expand worldwide. Although individual omics layers of Kp infection have been profiled, the host-level molecular logic that connects transcriptional, cellular, metabolic and spatial programmes has not been systematically integrated. Methods: We curated 16 public datasets spanning four modalities-bulk and dual host pathogen RNA-seq (six datasets; 118 libraries), single-cell RNA-seq (four datasets; {approx}164,000 cells), untargeted/targeted metabolomics (four datasets; 1,842 molecular features) and Visium spatial transcriptomics (two datasets; {approx}11,400 spots) covering murine lung infection models, hypervirulent and classical Kp strains, carbapenem-resistant isolates and human clinical specimens. Datasets were harmonised through a standardised pipeline (Salmon/DESeq2; Scanpy/Harmony; MetaboAnalyst; RCTD/spacexr), and integrated by WGCNA, GSVA, CellChat, Monocle3, SCENIC, joint-pathway analysis, RCTD deconvolution, and machine-learning feature selection (LASSO, random forest, SVM-RFE, XGBoost) with 1,000x bootstrap internal validation and independent external validation. Results: Kp infection evoked a coherent 920 gene neutrophil inflammatory response ({approx}520 up-/{approx}400 down-regulated genes) dominated by IL-17/TNF/NF-{kappa}B signalling, neutrophil degranulation, HIF-1-linked glycolysis and itaconate biosynthesis. A turquoise co-expression module (r = 0.86 with infection status) yielded a ten-gene hub signature (IRG1, S100A9, IL1B, CXCL2, SLC7A11, HMOX1, PTGS2, NFKBIA, LST1, FCGR3B). Single-cell profiling resolved twelve major lung lineages and eleven myeloid substates, tracing a Monocle3 trajectory from Cxcr2hi inflammatory neutrophils through an Irg1/Slc7a11hi oxidative phase into Cxcr4hi/Trem2hi suppressive states; CellChat identified S100A8/A9-TLR4 and CXCL2-CXCR2 as the most amplified axes (8.5x and 4.2x). Metabolomics revealed 30-fold pulmonary itaconate accumulation together with succinate and lactate elevation, glucose/glutamine depletion and glutathione collapse, and joint-pathway analysis positioned the TCA-cycle itaconate and ferroptosis modules as the strongest convergent perturbations. Spatial transcriptomics mapped these programmes onto five anatomical niches, with IRG1 expression peaking in the infection core (58% neutrophil content) and declining radially. A ten-gene host-response classification model reached AUC=0.958 in discovery, 0.937 in internal validation and 0.884-0.912 in two independent external cohorts (Brier score 0.071). Conclusions: By vertically integrating public multi-omics data, we define an itaconate-centred metabolic-immune axis that couples inflammatory myeloid expansion to spatially segregated metabolic niches, and we deliver a validated host-side molecular signature that discriminates Kp infection from non-infected states.

microbiology↗

bioRxiv2026

Multi-resolution humanness scoring of antibodies via a region-isolated language model

Antibodies recognized as foreign can elicit anti-drug antibodies that compromise efficacy and safety, making humanness scoring, estimating how human-like a sequence appears, an early, repeated step in antibody development. Widely used scores reduce a variable domain to a single framework-dominated number, leaving the complementarity-determining regions (CDRs) grafted during humanization, diversified by generative models weakly supervised and rarely audited. Here we introduce IgScope, a 23-million-parameter antibody language model trained from scratch that scores humanness at chain, IMGT-region, and residue resolution; a region-isolated attention module confines attention to within-region pairs, and chimeric-sequence training supplies region-level supervision. On 2.38 million held-out chains, masking all framework tokens leaves CDR accuracy unchanged, and back-mutated murine residues in human frameworks are detected at 93.4% accuracy. At chain level, IgScope matches the strongest published correlation with clinical anti-drug-antibody incidence (r = 0.567, n = 217) and outperforms a same-protocol recomputation. Region-level scoring exposes what chain-level scoring conceals: antibodies designed de novo by four generative models pass whole-chain humanness (0.966-0.988), while their CDRs score far below natural (P < 1e-30) in every chain class. Quality control turns the same resolution to the mirror-image question: in humanized-mouse platforms, whose domains are human by design, IgScope scores the domain as human and, in HUGO-Nano chimeric VHHs, isolates only the FR2 signal of four camelid hallmark residues deliberately retained to stabilize the VHH. IgScope gives antibody humanization a verified foundation and generative design an audit.

bioinformatics↗

bioRxiv2026

An OmpX mRNA-LNP vaccine induces protective humoral and cellular immunity against Klebsiella pneumoniae in a C57BL/6 mouse model

Klebsiella pneumoniae (KP) poses a significant threat to global public health, and effective preventive measures are urgently needed. The widespread antibiotic resistance of KP underscores the critical need for vaccine-based strategies. Although numerous vaccine candidates against KP have been investigated, none has yet been licensed. mRNA-LNP vaccines against KP have only recently begun to be explored, and their ability to deliver conserved outer membrane protein antigens remains largely untested. In this study, we constructed an mRNA vaccine encoding the highly conserved outer membrane protein X (OmpX) of KP-the first mRNA-LNP vaccine targeting this antigen-and evaluated its immunogenicity and protective efficacy in mice. Immunoinformatics analyses predicted concordance between the in silico and in vivo outcomes: bioinformatics tools were used to evaluate the antigenicity, immunogenicity, physicochemical parameters, and secondary and tertiary structures of OmpX, and the C-ImmSim server was used to simulate the vaccine-elicited immune responses. We then performed mouse immunization and challenge experiments to characterize the humoral and cellular immune responses induced by the OmpX vaccine. The mRNA vaccine was immunogenic in mice, eliciting high titers of OmpX-specific antibodies. OmpX-Fc mRNA-LNPs also induced robust cell-mediated immunity, with significantly elevated IFN-{gamma} and IL-4 levels indicative of combined Th1/Th2 cellular responses. Serum from OmpX-immunized mice promoted significantly greater complement-mediated KP cell lysis than serum from the alum or luciferase mRNA-LNP control groups. Notably, OmpX-Fc mRNA-LNP immunization conferred complete protection against lethal KP challenge, with 100% survival and a significant reduction in visceral bacterial burden. These data establish a promising mRNA vaccine strategy for inducing broad immune responses and promoting serotype-independent immunity against KP.

microbiology↗

bioRxiv2026

Integrative reanalysis of public intracellular and vaccine-challenge transcriptomes, informed by single-cell and metabolomic atlases, refines antigen prioritisation for next-generation veterinary Brucella vaccines

Brucellosis is the most widespread bacterial zoonosis and a leading cause of abortion and infertility in cattle, sheep, goats and pigs. The live vaccines licensed for livestock (B. abortus S19 and RB51, B. melitensis Rev.1) give incomplete protection, retain residual virulence and induce antibodies that are indistinguishable from those of field infection, and no human vaccine is licensed. Antigen discovery for safer subunit and mRNA alternatives has proceeded one candidate at a time, so it remains unclear which immunogenic proteins are abundant in the intracellular niche that protective immunity must recognise. We reanalysed four public data layers: dual RNA-sequencing of intracellular B. abortus and infected murine macrophages (GSE100914), splenic transcriptomes from vaccinated and challenged mice (GSE254248), and single-cell (PRJCA054635) and metabolomic (ST003696, ST003692) atlases. Intracellular residence induced 704 genes and repressed 249, enriching translation functions 2.9-fold. Omp28/BP26 was the second most abundant intracellular transcript (35,652 TPM; log2FC 4.25), whereas the lipoproteins omp16, omp10 and omp19 were 116-637-fold less abundant. Vaccination left a germinal-centre and TCF1+ memory imprint rather than an effector-cytokine signature. A composite score combining abundance, induction and surface exposure reorders the candidate panel, placing Omp28/BP26 and omp2b above current candidates and defining concrete priorities for veterinary vaccine development.

immunology↗

bioRxiv2026

Vision Transformers as a Framework for Imaging Fluorescence Correlation Spectroscopy

Fluorescence correlation spectroscopy (FCS) is widely used for quantifying molecular dynamics in live cells. However, conventional FCS relies on the autocorrelation function (ACF), a lossy transformation that discards higher-order information, mathematically couples distinct biophysical parameters, and requires long measurements (~50,000 frames, or ~1 minute) that obscure short-lived transient events. To overcome this information bottleneck, we introduce ImFCSViT, a spatiotemporal Vision Transformer-based framework that bypasses the ACF representation entirely, operating directly on raw fluorescence image stacks. By introducing a dedicated normalization token to preserve global intensity statistics, our approach simultaneously extracts four biophysical parameters (diffusion coefficient D, particle density N, particle brightness B, and PSF structure factor {sigma}0) in a single forward pass. Requiring only 2.5 s of data, and potentially down to 1 s (a 1-2 order reduction in data requirement), our approach accurately recovers diffusion coefficients while utilizing its spatial field-of-view to reveal spatially heterogeneous, slow-diffusing sub-populations in lipid bilayers and live-cell plasma membranes that are invisible to classical FCS. By leveraging the data-driven representational learning capabilities of deep learning, we present a flexible, generalizable, and powerful tool for mapping complex, multi-parameter spatiotemporal dynamics in biological systems.

biophysics↗

bioRxiv2026

Investigating the evolutionary plasticity of influenza virus splicing using influenza A virus segment 7 as a model system

Influenza viruses A, C and D (IAV, ICV, IDV respectively) utilise mRNA splicing to produce two proteins from their matrix gene segment; the matrix (M1) protein and an ion channel (M2). In IAV, the M1 and M2 open reading frames (ORFs) partially overlap and splice donor (SD) and splice acceptor (SA) sites in the M1 gene are used to produce an M2 polypeptide with an N-terminal sequence shared with M1. In ICV and IDV, the M1 and M2 ORFs are concatenated and splice sites downstream of the M1 coding region are used to produce a matrix protein from the spliced mRNA. To probe how these different splicing strategies might have evolved, we used reverse genetics to test the evolutionary flexibility of IAV SD and SA sites. We found that relatively small numbers of mutations were sufficient to activate a variety of alternative SD and SA sites in a mini-replicon system, several of which led to the production of ion channel isoforms with variant ectodomains, as well as one that produced an M1 variant. Furthermore, several of the viruses with altered splicing patterns were viable, including five with both SD and SA sites changed. Three of these 'rewired' viruses had only modest fitness penalties, despite up to 3 nonsynonymous changes in M1 and a 34 amino acid insertion in M2. These results suggest that IAV segment 7 splicing strategies are evolutionarily flexible and have implications for how IAV, ICV and IDV matrix segments might have diversified from a common ancestor.

microbiology↗

bioRxiv2026

Reciprocal effective conductance-based inference of protein-protein relationships delineates protein network hubs and macromolecular properties

Protein-protein interactions (PPIs) are fundamental to the regulation and function of all cellular processes, yet our current understanding of the human interactome is limited by the specific PPI-related experiments performed to date. Moreover, whereas proteins with minimal research interest may have relatively few interaction-probing studies, other factors such as cancer related proteins are frequently represented in PPI studies, producing a highly uneven bias across current PPI data. Here, we apply effective conductance - a concept from circuit theory - to infer proximity relationships across all individual protein pairs on the basis of 1.2 million reported human PPI observations. Using a reciprocal analysis framework built on effective conductance, we identify statistically meaningful interactions within the network while otherwise filtering nonrelevant associations that are abundant in proteins with strong popularity bias. While this approach helps clarify the structural and functional properties of both minimally and maximally connected proteins, we further compute an interaction index between all macromolecular, multi-subunit complexes based on underlying protein-protein conductance scores. Leveraging this framework, which we name CoBRAA (Conductance-Based Reciprocal Association Analysis), we highlight unexpected findings, such as evidence that DNA-dependent RNA polymerase I, II, and III are characterized by surprisingly pervasive cross-complex interactions between enzyme-specific subunits, suggesting a potential for crosstalk between all three RNA polymerases. We further introduce CoBRAA as an open-access research tool that enables custom, user-specified exploration of both protein- and macromolecular-level interactions. We expect this resource to facilitate broader investigation and hypothesis generation across a wide range of proteins, pathways, and macromolecular systems.

bioinformatics↗

bioRxiv2026

Global statistical constraint and local partner-sensitive inference support dynamic, open-ended verbal coordination

Effective verbal communication requires individuals to align their communicative intentions through the precise use of utterances, yet how this is achieved in dynamic, open-ended communication remains unclear. Combining predictive deep language models (DLMs) with cognitive computational modeling, we tested a computational division of labor between context-sensitive global constraint on the utterance space and partner-sensitive local adjustment of selection priorities. We formalized local adjustment using an active inference inspired updating rule within a statistical-pragmatic hybrid model. Thirty dyads performed a dynamic, open-ended collaborative word-generation task during functional near infrared spectroscopy (fNIRS) hyperscanning. Interlocutors converged in their lexical choices, and lexical alignment was best explained by a hybrid model integrating contextual selection priors with interaction-specific local adjustment among the models tested. This hybrid process formed a dynamic hypothesis landscape comprising a global semantic search region and local selection priorities. Critically, interaction did not simply reduce uncertainty: within individuals, the hypothesis space of global semantic search progressively narrowed while local uncertainty among plausible alternatives increased; between partners, both global hypothesis space and local selection became increasingly aligned, with alignment of local selection priorities most strongly predicting subsequent lexical alignment. The contribution of local adjustment was stronger during precise verbal communication than in the control tasks. Neural analyses further identified correlates of the local adjustment process: temporoparietal and anterior temporal regions supported partner-related inference and semantic prediction error, respectively, and their activity during thinking predicted subsequent inferior frontal activity associated with self-updating during speaking. The participant's own hypothesis landscape was also represented in the left inferior parietal-supramarginal cortex. The main findings generalized across language models and an independent Czech dataset. Together, these findings reveal that alignment in open-ended communication emerges from complementary statistical and pragmatic computations, providing a framework for understanding human and human-AI communication.

neuroscience↗

bioRxiv2026

AMP: An Automated Marker Based Motion Capture Processing Pipeline for Kinematic Trajectory Label Scrubbing and Gaps Filling

Marker-based optical motion capture remains the gold standard for kinematic analysis across biomechanical research. However, when tracking dynamic movements, severe occlusions often create missing data and noisy signals that require time-consuming manual cleaning. Existing automated methods usually rely on predefined skeleton models or need massive training datasets for machine learning solutions. This study presents the AMP: an automated marker based motion capture processing pipeline. The AMP is a fully analytical approach that uses hierarchical algorithms including 1) rigid-body constraint, 2) Kalman Filter state estimation and 3) kinematic pattern match to label markers and fill data gaps without manual human intervention. First, the algorithm identifies and removes mislabeling markers before any data scrubbing. During the data cleaning process, our hierarchical algorithm uses a translation-based step to track segments even when there are insufficient donor markers to form a full rigid body. To reconstruct missing marker trajectories, the pipeline initially uses Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) interpolation for short marker gaps. However, the PCHIP creates massive spatial errors during long occlusions, the system automatically switches to rigid-body tracking for gaps over 40 ms. Results showed the pipeline drastically reduced processing time compared to manual human expert methods by 73.5%. In addition, the AMP kept spatial errors within 10 mm of the manual ground truth. By using this combined hierarchical approach to match marker processing accuracy, the pipeline provides a fast, general-purpose tool that fully automates data cleaning and saves hours of manual effort.

bioengineering↗

bioRxiv2026

Microfluidic Capture-Enrichment of individualized microbes from urban wastewater reveals a hidden reservoir of potential pre-emergent pathogens

Current environmental biosurveillance infrastructure excels at tracking known biological threats but is limited in its ability to proactively identify potential pre-emergent pathogens (PEPs). To address this gap, we developed a microfluidics-based Capture-Enrichment pipeline that isolates individual environmental cells via microdroplet encapsulation, preserving community biodiversity while enabling iterative selection against human sera. When applied to urban wastewater, this approach successfully recovered pathogens, commensals, and PEPs that fell below the detection limits of conventional amplicon sequencing. Genomic analysis revealed that wastewater-derived ESKAPE pathogens cluster closely with clinical isolates, showing that the pipeline captures clinically relevant threats. Furthermore, isolated PEPs exhibited known and emerging phenotypes associated with early-stage pathogenesis and antimicrobial resistance. These findings demonstrate that targeting PEPs can fundamentally expand the scope and quality of biosurveillance by providing a scalable strategy to identify, characterize, and close important gaps in our understanding of emerging microbial pathogens.

microbiology↗

bioRxiv2026

FFPERescuer: deep unsupervised domain adaptation for the reconstruction of gene expression profiles derived from formalin-fixed paraffin-embedded samples

Formalin-fixed paraffin-embedded (FFPE) tumor tissues often suffer from RNA degradation, posing a long-standing challenge for reliable transcriptomic profiling. Here, we propose FFPERescuer, a deep learning framework employing unsupervised domain adaptation, to rectify distorted gene expression data. FFPERescuer comprises a partial encoder that maps a small subset of genes to high-level representations and a decoder to reconstruct full gene expression profiles. On simulated data with varying noise levels, FFPERescuer faithfully recovered gene expression profiles, achieving high Pearson correlation coefficients (PCCs > 0.85) with the ground truth. In FF-FFPE-matched cohorts, FFPERescuer significantly enhanced expression profile concordance, with average PCCs increased by 23% (P < 0.05). Applying to cancer subtyping, FFPERescuer improved classification accuracy from 67% to 92%, recapitulated subtype-specific biological properties lost in the FFPE-derived data, and enhanced survival associations. Our studies provide a powerful framework for reliable transcriptomic profiling from FFPE-archived tumor samples that are widely available in the clinic.

bioinformatics↗

bioRxiv2026

Chronic stress promotes sex-divergent Type 1, 2 and 17 responses in brain resident lymphocytes.

T helper cytokines such as IFN{gamma} and IL-17 are dysregulated in a subset of patients with depression and are associated with poor responses to treatment. Both IFN{gamma} and IL-17 have been shown to regulate neural circuits involved in behavioral changes implicated in depression. However, the cellular source and the regulatory mechanisms governing these cytokines in this context remain incompletely understood. We analyzed brain lymphocytes in response to unpredictable chronic mild stress (UCMS), a model of depression with strong face and construct validity. We confirmed that UCMS induced depressive-like behavior in both male and female mice. Interestingly, UCMS preferentially reduced regional synaptic density in female and neuron counts in male hippocampi. Using single cell RNAseq, we identified T helper cytokine signatures to be enriched in distinct lymphocyte lineages expressing tissue resident markers and are regulated by both sex and chronic stress. Female brain-resident {gamma}{delta}T cells are significantly enriched for type 2 and type 17 signatures and have higher expression of inflammasome genes such as Nlrp3 and Il1b whereas male {beta}T cells and NKT cells are enriched for type 1 signatures and have elevated Cxcr3 and Il27ra expression. Chronic stress amplified the sex differences and preferentially increased the type 1 response including IFN{gamma} production in males at both the transcriptome and protein level. Type 17 response (e.g. IL-17A and IL-17F production) is also increased in response to UCMS, however with sex-divergent polarization: IL-22 is decreased in female and increased in male brain lymphocytes. Mechanistically, we found that UCMS suppressed P4ha1 in male and Zfp36 in female brain tissue resident lymphocytes. P4HA1 was previously shown to restrict mitochondrial function and limit type 1 responses in CD8 T cells, whereas ZFP36 is a suppressor of inflammatory cytokine translation. Consistent with this finding, ex vivo CD3-dependent stimulation of brain immune cells showed stress-dependent increase in cytokine production with sex-specific effects. Our study provides the cellular and molecular framework for chronic stress-induced tissue resident lymphocyte dysregulation and its sex bias in the brain, with implications for depressive disorders marked by inflammation.

immunology↗

bioRxiv2026

Feasibility of Using Functional Near-Infrared Spectroscopy for Characterizing Misophonia-Related Brain Activity

Significance: Misophonia is characterized by decreased tolerance to specific sounds ("triggers") that evoke negative emotional, physiological, and behavioral responses. To understand the neural mechanisms underlying misophonia, functional neuroimaging has been proposed such as functional magnetic resonance imaging (fMRI). However, the fMRI environment introduces substantial noise that increases anxiety, which is one of the comorbidities of misophonia, confounds auditory stimuli, and is costly. We therefore propose an alternative, functional near-infrared spectroscopy (fNIRS) to detect misophonia-related responses. Aim: We evaluated the feasibility of fNIRS to detect misophonia-related sound-evoked responses. Approach: We measured fNIRS, electrodermal activity (EDA), and photoplethysmography (PPG) data from ten adults with minimal to mild misophonia symptoms while they listened to trigger, unpleasant, and neutral sounds. Participants rated their annoyance and antisociality after each sound. Results: Compared with neutral sounds, trigger and unpleasant sounds received higher subjective ratings and evoked greater neural response in the orofacial area. Neural responses tended to increase with annoyance ratings. Significantly lower heart rate (HR) was observed during unpleasant sounds than during neutral and trigger sounds, with no significant difference in EDA features. Conclusions: fNIRS can measure misophonia responses in a quiet setting. Both brain and physiological responses have the same trend observed in prior fMRI studies, verifying its feasibility.

neuroscience↗

bioRxiv2026

Geometric characterization of the HSV - 1 glycoprotein B - amyloid β interaction in Alzheimer's disease using Forman-Ricci curvature

Alzheimer's disease is characterized by the accumulation and aggregation of amyloid-{beta}(A{beta}), but the molecular mechanisms linking environmental and infectious factors to A$\beta$ conformational changes remain incompletely understood. Herpes simplex virus type 1 (HSV-1) has been proposed as a potential contributor to AD pathology, and interactions between the viral glycoprotein B (gB) and A$\beta$ may influence the conformational behaviour of the peptide. Molecular dynamics (MD) simulations provide atomic-scale information on such interactions, but conventional structural descriptors may not fully capture changes in the organization of residue interaction networks. Here, we introduce a graph-geometric framework based on Forman-Ricci curvature to characterize the evolution of residue interaction networks during MD simulations. Each simulation frame is represented as a residue interaction graph based on C--C contacts, and residue-wise curvature profiles are analysed across time. We apply the framework to A{beta}1-42 in isolation and in complex with HSV-1 gB. Conventional MD analyses indicate stable association of the simulated complex, favourable interaction energetics, and conformational changes in A{beta}, including a transition from -helical structure toward {beta}-turn-rich conformations over the simulated timescale. Forman-Ricci curvature reveals pronounced and spatially localized remodelling of the A{beta} residue interaction network in the complex, with the strongest changes concentrated in the C-terminal region. These regions also exhibit reduced temporal curvature fluctuations and progressively distinct geometric behaviour throughout the simulation. Hierarchical clustering further identifies cooperative groups of residues with coordinated curvature dynamics, including a prominent C-terminal domain. Together, these results demonstrate that Forman-Ricci curvature provides a complementary description of biomolecular dynamics by capturing changes in the geometric organization of residue interaction networks that are not directly represented by conventional structural descriptors. The framework provides a general computational approach for studying network-level structural remodelling in protein molecular dynamics and offers a quantitative perspective on the conformational consequences of HSV-1 gB--A{beta} association.

bioinformatics↗

bioRxiv2026

Cytoskeletal-nuclear control of alveolar fibroblast identity directs lung regeneration

Respiratory mechanics direct cell fate in the lung, but the mechanisms by which these mechanical signals are sensed and transmitted to the nucleus to control cell state remain unclear. We paired in vivo perturbations of respiratory mechanics with single-cell genomics and found that alveolar fibroblasts are highly sensitive to physical changes in their microenvironment, exhibiting persistent shifts in their transcriptional identity after injury. Surprisingly, transmission of these signals through the nuclear envelope was not essential for maintaining transcriptional or epigenetic stability during homeostasis. However, severing mechanical-nuclear signaling promoted the normalization of alveolar fibroblast identity after acute injury, resulting in improved epithelial regeneration and reduced dysplastic remodeling. These studies reveal the importance of mechanical-nuclear signaling in the regulation of alveolar cell identity and function and reveal that targeting this complex can enhance tissue regeneration.

cell biology↗

bioRxiv2026

pH-dependent anti-TfR1 NANOBODY(R) molecules deliver efficacious oligonucleotide payloads to muscle and CNS tissues

The blood-brain barrier (BBB) is a highly selective, semi-permeable border of endothelial cells that prevents solutes and therapeutic agents in systemic circulation from passively crossing into the central nervous system (CNS) parenchyma. The Transferrin receptor 1 (TfR1) endocytosis pathway for iron homeostasis is one of the most well-characterized strategies for therapeutic delivery across the BBB. The work presented here showcases the discovery of novel anti-TfR1 NANOBODY(R) shuttles. The identified anti-TfR1 NANOBODY(R) molecules display cross-reactivity and pH-dependent binding to human, cynomolgus (cyno), and mouse TfR1. Structural data further explain and support the underlying mechanism of this pH-dependent binding. These anti-TfR1 NANOBODY(R) molecules were successfully conjugated to both short-interfering RNA (siRNA) and antisense oligonucleotide (ASO) tool payloads. anti-TfR1 NANOBODY(R)-siRNA conjugates can induce up to 60% knockdown of the target mRNA transcript in skeletal muscle up to two weeks post a single IV dose in mice and up to 35-40% at four weeks post dose. Furthermore, extending the half-life of the anti-TfR1 NANOBODY(R)-ASO shuttles enhances heart, sciatic nerve, and brain exposure and enables up to 30-60% target knockdown in different CNS cell types. Altogether, these results highlight important features for the development of anti-TfR1 shuttles for the purpose of downregulating target mRNA transcripts in muscle and CNS for a variety of neurologic and neuromuscular indications.

neuroscience↗

bioRxiv2026

Immunometabolic Reprogramming by Thyroid-Stimulating Antibodies Drives Orbital Adipogenesis via Histone Lactylation in Thyroid Eye Disease

Thyroid eye disease (TED) is conventionally viewed as an autoimmune inflammatory disorder. However, the metabolic determinants driving orbital tissue remodeling remain largely undefined. Here, we uncover a paradigm-shifting mechanism whereby thyroid-stimulating antibodies (TSAbs) instigate profound metabolic reprogramming in orbital fibroblasts (OFs), driving a switch toward glycolysis and markedly elevating lactate production via the CREB/LDHA axis. Critically, we demonstrate that this TSAbs-induced metabolic perturbation is not a mere byproduct but the principal driver of pathology. Mechanistically, lactate functions as a signaling metabolite that directly potentiates adipogenesis via histone lactylation-dependent transcriptional activation of the master adipogenic regulators PPAR{gamma} and C/EBP. Collectively, our findings underscore metabolic reprogramming as a central pathogenic mechanism in TED, whereby autoimmune cues drive disease progression through metabolic-epigenetic crosstalk. This work substantiates the paradigm of TED as an immune- metabolic disorder.

immunology↗