Biology subjects

Zhang, C.

Publications and source records attributed to Zhang, C..

At least 19 recordsLinked to original sources

bioRxiv2026

N-Acetylglucosamine ameliorates knee osteoarthritis by enhancing UDP-GlcNAc conversion and chondrocyte DNA repair capacity

Background: Knee osteoarthritis (KOA) is a degenerative joint disease characterized by progressive cartilage degradation and synovial inflammation, while current therapies offer only symptomatic relief. This study aimed to determine whether N-acetylglucosamine (GlcNAc) is superior to glucosamine hydrochloride (GlcN{middle dot}HCl) in slowing the progression of KOA and to elucidate the underlying mechanisms involved. Methods: Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was employed to assess serum concentrations of UDP-GlcNAc to conduct a comprehensive evaluation of the pharmacokinetics and pharmacodynamics associated with both GlcNAc and GlcN{middle dot}HCl. Using a destabilization of the medial meniscus (DMM) rat model, we performed micro-computed tomography and histopathological assessments to evaluate structural changes in the joint tissues. Additionally, we conducted bulk RNA sequencing, flow cytometry and ELISA to validate the efficacy of GlcNAc in anti-inflammatory immunoregulation and DNA repair in knee cartilage. Results: GlcNAc achieved higher peak serum concentrations and more efficient conversion to UDP-GlcNAc than GlcN{middle dot}HCl, with UDP-GlcNAc levels 4.57 times higher at 9 hours after administration. GlcNAc significantly attenuated KOA-like pathological changes, downregulated catabolic markers Mmp3 and Adamts4, decreased pain-related neural markers Pgp9.5 and Ngf, and alleviated pain sensitivity. RNA-seq identified 4,395 downregulated DEGs in DMM rats, of which GlcNAc upregulated 2,808 compared to only 391 by GlcN{middle dot}HCl. GlcNAc promoted DNA repair and adaptive immune cell differentiation pathways, and decreased serum IL-1{beta}, IL-6, and IL-17. Four candidate DNA repair genes, Hist1h2an, Poli, Ube2t, and Hells, were identified as significantly upregulated by GlcNAc, with GSEA confirming homologous recombination repair pathway enrichment. Conclusion: This study shows GlcNAc outperforms GlcN{middle dot}HCl in rat DMM knee osteoarthritis via multiple mechanisms, including 4.57-fold higher UDP-GlcNAc conversion, reduced cartilage degeneration, immune rebalancing, and DNA repair activation, supporting GlcNAc as a promising KOA candidate warranting further clinical study.

pharmacology and toxicology↗

bioRxiv2026

Urban planning leaves an audible legacy in a megacity's bird soundscape

Cities are planned for people, yet planning also determines where wildlife persists and where residents hear it. How can this acoustic footprint guide planning and monitoring? We combined AI-powered passive acoustic monitoring at 31 stations with detection-corrected models and spatial validation to map Asian Koel activity across Shenzhen. Koel acoustic opportunity declined with tree and built cover and increased with starling and myna opportunity; host information improved prediction at unmonitored sites. Weekly opportunity expanded in spring and varied across the city; sampled-minute exposure concentrated around dawn. Supported predictions intersected planning-sensitive places, while 51.5% of the city grid remained a monitoring gap. We translated these boundaries into monitoring priorities for underrepresented environments and uncertain spring predictions. This reproducible workflow reveals the acoustic imprint of the present-day urban landscape and connects ecological diagnosis, prediction boundaries and adaptive monitoring to inform urban coexistence.

ecology↗

bioRxiv2026

SlMYB21 and SlMYB24 cooperatively regulate petal coloration and movement in Solanum lycopersicum

In seed plants, reproductive success depends on the precise coordination of floral organ development. Here, we identify and characterize the Solanum lycopersicum transcription factor (TF) SlMYB24 as a member of the R2R3-MYB subgroup 19. While Slmyb24 mutants exhibited no obvious phenotypic alterations, the previously characterized Slmyb21 mutant displayed defects in floral development that were further exacerbated in the Slmyb21 Slmyb24 double mutant, resulting in a severe phenotype characterized by female sterility and whitish petals that failed to open. Petals of Slmyb21 Slmyb24 exhibited reduced xanthophyll content and elevated abscisic acid (ABA) levels, accompanied by reduced water content and decreased levels of primary sugars. Integration of transcriptome (RNA-sequencing) and cistrome (DNA affinity purification sequencing, DAP-seq) data identified genes involved in carotenoid and ABA biosynthesis and water transport as candidate regulatory targets of SlMYB21 and SlMYB24. These TFs activated the promoters of PHYTOENE SYNTHASE2 (PSY2), LYCOPENE {beta}-CYCLASE (LCY-B) and the petal-expressed aquaporin TONOPLAST INTRINSIC PROTEIN1.1 (TIP1.1), whereas they repressed 9-cis-EPOXYCAROTENOID DIOXYGENASE1 (NCED1) promoter activity. In addition, SlMYB21 bound to the SlMYB24 promoter and activated its expression. Promoter studies and electrophoretic mobility shift assays revealed that SlMYB21 and SlMYB24 bind the AC-rich DNA-binding motif GTTAGGT, which is to be found in the respective promoters and is consistent with the extensive overlap in their binding profiles. Together, these results reveal that the functions of SlMYB21 and SlMYB24 extend beyond fertility to coordinate petal pigmentation and opening.

plant biology↗

bioRxiv2026

Human brain white matter charts in international populations

Brain charts provide normative references for identifying neurodevelopmental milestones and clinically meaningful deviations. Existing white matter (WM) charts are derived predominantly from European and North American populations, limiting generalizability. Here, we established the first set of population-specific WM charts of 198 morphological and microstructural phenotypes for both Chinese (N=19,215) and Western (N=15,584) populations across the human lifespan. Developmental trajectories and spatial organization of WM maturation and degeneration were broadly conserved across the populations, whereas maturational timing varied across pathways and metrics. The Chinese population showed earlier maturation in several projection and sensorimotor pathways, but more protracted maturation in higher-order association and limbic pathways. Chinese population-matched charts more accurately predicted WM phenotypes and detected more extreme disease-related deviations in brain health in the Chinese population than Western-derived models. Individual deviation patterns further discriminated patients from healthy controls and predicted cognitive performance and plasma biomarkers. Our findings reveal population-specific WM maturation and show that reference-population mismatch can reduce the accuracy and clinical sensitivity of normative brain charts.

neuroscience↗

bioRxiv2026

MCT4-Associated Lactate Export Defines Myeloid Metabolic Reprogramming during Head and Neck Cancer Progression

Head and neck squamous cell carcinoma (HNSCC) develops within a metabolically remodeled tumor microenvironment, yet how metabolic programs evolve across specific cell populations during tumor progression remains poorly understood. We integrated single-cell RNA-sequencing data from five HNSCC cohorts comprising 645,351 cells from 114 patients and inferred metabolic flux using single-cell Flux Estimation Analysis (scFEA). Myeloid cells showed the highest and most consistent metabolic allocation to lactate export among major cell types. In ten patients with paired normal mucosa and carcinoma, myeloid lactate-export allocation increased from 1.72% to 3.31% of total inferred metabolic flux, with an increase in every patient. SPP1+ macrophages showed the highest lactate-export allocation in carcinoma. Lactate export was predominantly associated with MCT4/SLC16A3. In carcinoma, myeloid SLC16A3 expression exceeded SLC16A1 by 17-fold, and CPTAC-HNSCC confirmed tumor enrichment of MCT4 protein. Together, these findings identify increased MCT4-associated lactate export as a prominent feature of myeloid metabolic reprogramming during HNSCC progression.

cancer biology↗

bioRxiv2026

Self-extending DNA recording with stochastic and programmed writing

Recording cellular histories requires new information to accumulate without erasing earlier records. Here we introduce Infinite Printer, a CRISPR recording architecture that couples self-extension to regeneration of a writable terminal context. A self-targeting prime-editing guide RNA (stpegRNA) directs repeat extension, while base editing generates diversity in situ without a separate insertion template for each character state. Earlier sequence remains upstream of the renewed terminus, linking substrate growth with record retention. Auxiliary pegRNAs specify payloads within a write-renew-write architecture. After three rounds of payload input, 4,436 distinct complete CCA-based records contained identities from all three pools. Longer AC/AT records retained shared upstream marks, and full-length sequence analysis resolved additional endpoint distinctions. Joint single-cell readout recovered 1,549 distinct records and 7,669 combinations across 12,302 transcriptome-linked cell labels from a single stpegRNA design. Spatial-transcriptomic readout recovered spacer records at 11,330 capture spots, linking record content to tissue coordinates and gene-expression profiles. Infinite Printer unites substrate renewal, sequence diversification and programmed writing with cell- and tissue-linked readout, providing a foundation for future lineage recording across large cell populations during mammalian development.

synthetic biology↗

bioRxiv2026

HighFold-Lasso: Topology-Guided Prediction of Lasso Peptide Structures Using AlphaFold3

Lasso peptides are a class of ribosomally synthesized and post-translationally modified peptides (RiPPs) characterized by a mechanically interlocked topology, in which the C-terminal tail is threaded through an N-terminal macrolactam ring formed by an isopeptide bond. This unique topology often confers high thermal stability and resistance to proteolytic degradation. However, the isopeptide linkage, threaded topology, and class-specific disulfide connectivity make lasso peptide structures difficult to reconstruct reliably using general-purpose structure prediction models. Here, we developed HighFold-Lasso, which combines custom covalent connectivity with lasso scaffold-guided adaptive low-noise initialization to enable conditional structure generation guided by lasso conformational priors. On a test set of 52 experimentally determined structures, HighFold-Lasso achieved a 100% lasso topology success rate, with a mean RMSD C-alpha of 1.942 Angstrom for the ring-loop region and 2.375 Angstrom for the full peptide. The framework supports modeling of the four classical lasso peptide classes (I-IV) and can represent noncanonical amino acids, providing computational support for structure-function studies and molecular engineering of lasso peptides.

bioinformatics↗

bioRxiv2026

Alpha Spatial: Language-guided prediction of single-cell spatial gene expression from histology

Routine histology preserves tissue architecture but leaves its molecular organization largely unmeasured. Recovering this information requires learning across heterogeneous gene panels and constraining a biologically ill-posed morphology-to-expression mapping. Here we present Alpha Spatial (AS), a language-conditioned framework that predicts cell-level gene expression from histology while jointly learning from different tissues and panels. Matched biological context improved prediction over omitted context, whereas incompatible context reduced accuracy. Multi-tissue learning further improved breast-tissue prediction, and AS outperformed the evaluated methods while preserving molecular-state organization. In breast cancer, AS recovered POSTN-associated stromal programmes and HER2-associated molecular and spatial differences. Exploratory patient-level analyses identified a CXCL14-associated spatial state whose abundance was associated with non-response to trastuzumab-containing treatment and larger residual invasive tumours. These findings establish a framework for reusing heterogeneous molecular observations to investigate tissue organization and treatment-associated microenvironments in archived histology.

bioinformatics↗

bioRxiv2026

Structural Basis for the Rational Design of UAMC-1110-Based FAP Radiopharmaceuticals

Radioligands targeting fibroblast activation protein alpha (FAP) are promising tools for cancer diagnosis and therapy. However, the molecular binding mode of clinically established UAMC-1110-derived ligands such as FAPI-46 remains insufficiently characterized. To simplify synthesis while preserving key binding properties, we designed two novel derivatives - FAP-DD-101 (short aminoalkyl linker) and FAP-DD-102 (extended aminohexanoic acid spacer) - and evaluated them alongside FAPI-46 using molecular docking and all-atom molecular dynamics (MD) simulations. Across all derivatives, we identified a highly stable, energetically favorable charge-anchored binding mode, in which the 2-cyano-4,4-difluoropyrrolidine head anchors in a negatively charged subregion formed by Glu202, Glu203, and Glu204, mimicking the binding architecture observed in the FAP-linagliptin complex. MD trajectories confirmed that FAPI-46 and FAP-DD-101 maintain exceptional stability in this pose, with several non-anchored FAPI-46 trajectories spontaneously evolving toward engagement with this electrostatic subregion. For FAP-DD-102, linker elongation slightly reduced pose robustness, yet initial charge-anchored states still yielded superior average binding energies and vector stability compared to non-anchored poses. Overall, these findings elucidate the key structural determinants of FAP recognition, establishing a rational framework for designing synthetically accessible FAPI radiotracers with retained binding stability.

biophysics↗

bioRxiv2026

Establishing genetic tractability in Jeongeupia sacculi HS-3: disruption of rpoS hinders biofilm formation

Jeongeupia sacculi (HS-3) is a novel {beta}-proteobacterium with a unique multicellular life cycle. The genetic tractability and mechanisms underlying differentiation are unknown. Here, we adapt a method of homologous recombination for genetic modification in HS-3 and generate a disruption mutant of rpoS, which encodes the alternative sigma factor{sigma} S commonly associated with general stress response or major behavioral switches. We then characterize phenotypes associated with rpoS disruption. Under low-nutrient conditions, rpoS disruption reduced Congo red binding, consistent with rpoS acting as a regulator of low-nutrient response by upregulating production of biofilm-associated materials. Surprisingly, rpoS disruption did not significantly alter survival in response to other stressors in general stress response assays tested. Our work establishes a method for genetic manipulation in HS-3 and enables future investigation into the life cycle of HS-3, including whether rpoS has a role in differentiation triggered by low-nutrient response.

microbiology↗

bioRxiv2026

Network fixation expansion microscopy

Expansion microscopy (ExM) physically magnifies preserved biological specimens by using a specimen-permeating hydrogel to pull biomolecules, such as proteins, apart from each other. ExM protocols exhibit a tension between the need to initially fix proteins to each other during sample preservation, to retain proteins and their nanoscale organization, and the need to separate proteins from each other isotropically, to achieve physical magnification. We here propose to address this tension by fixing proteins directly to the hydrogel network itself, which then serves as a common substrate for both initial fixation and final separation. We equip proteins with chemical handles, then form a specimen-permeating hydrogel that binds the handles, in a ~15-minute chemical process conducted entirely at -20oC. This network fixation ExM (nfExM) procedure enables excellent epitope retention and nanoscale organization preservation, in cultured cells. nfExM may help support ultrastructural investigations of protein complexes and interactions.

molecular biology↗

bioRxiv2026

Conservation of gene co-expression networks and neuropsychiatric-neurological disease gene programs between mouse and human frontal cortex at single-cell resolution

Mouse models are widely used in translational neuroscience, but whether a gene has the same partners in mouse and human prefrontal cortex (PFC) is unknown. The genes whose expression rises and falls with a given gene across samples (its co-expression partners) indicate the molecular program to which it belongs. We compared the co-expression of 13,214 genes between mouse medial PFC and human dorsolateral PFC in 246,211 single-nucleus RNA-sequencing profiles. These two regions are not homologous, so part of any measured difference may reflect region and not species. The study has five main findings. First, the similarity of partners for single genes was modest (median 0.169; 0, unrelated; 1, identical). This median was below that of every comparison among human datasets but within the range of comparisons among mouse datasets, and correction for measurement noise raised it to 0.349 at most. Second, groups of co-expressed genes (modules) were strongly preserved. Third, neuropsychiatric disease genes were modestly more conserved than other genes, but no more than control genes matched for mutational constraint and expression, consistent with general gene properties and with how these genes are discovered. Fourth, no disease gene was shown to have different partners in the two species. Fifth, four of five human modules recurred in an independent human dataset. We also identified two sources of error: analyzing only the most variable genes excludes most disease genes, and networks built from few samples, as for glial cells, underestimate the similarity between species. Scores and code are released for cross-species studies.

genomics↗

bioRxiv2026

Long-form hibernation-promoting factor coordinates 100S assembly and translational repression

Ribosome hibernation mediated by long-form hibernation-promoting factor (HPF) requires coordination of ribosome dimerization and translational repression, but how these functions are integrated remains unclear. Here, we present cryo-EM structures of native 100S from a deep-sea Bacillus isolate, revealing a conformationally heterogeneous 100S population. Beyond the established roles of the HPF N-terminal domain (NTD) in decoding-center occlusion and the C-terminal domain (CTD) in dimerization, our analyses reveal additional contributions from the Linker and a charged CTD surface near the 16S rRNA 3' terminus containing the anti-Shine-Dalgarno (aSD) sequence. Linker substitutions differentially affect translational repression while retaining 100S formation, with distinct effects of the two Linker segments. Mutation of the charged CTD surface enhances translational repression while retaining robust 100S formation, suggesting an additional role beyond CTD-mediated ribosome dimerization. Functional analyses in an E. coli cell-free translation system further show that the inhibitory activity of individual HPF regions varies with translational context. Together, these findings support a modular model in which the NTD, Linker, and CTD make distinct but interconnected contributions to 100S assembly and translational repression.

biochemistry↗

bioRxiv2026

A kinetic-aware approach to infer metabolic variations and flux using transcriptomics and metabolomics data

Assessing metabolic variations and flux quantities enable systematic understandings of metabolic shifts, reprogramming, adaptation and interactions in human diseases. However, omics-based estimation of metabolic flux and its variation remains challenging due to several fundamental limitations: the need for disease and tissue context specific metabolic model; nonlinear enzyme kinetic model that links enzyme and substrate changes to reaction flux; partial, unpaired, and snap-shot measurements across omics modalities; and uncertainty in computational prediction. Here, we present Michaelis-Menten model-based Flux Estimation Analysis (mmFEA), a Monte Carlo framework for estimating condition-specific flux changes by integrating paired or unpaired metabolomics and transcriptomics (or proteomics) data. mmFEA separates each reaction-rate change into enzyme- and substrate-associated components and assess reaction rate using Michaelis-Menten kinetics equation. A baseline metabolite saturation rate is introduced by integrating protein language model predicted kinetic parameters and human baseline level metabolic concentration to enable kinetic-aware integration of unpaired substrate and enzyme level measurements. Distribution of metabolic flux and variations between conditions are further computed using MCMC sampling by treating Michaelis-Menten-derived marginal flux distribution as prior and coherency in flux balance as likelihood. To benchmark mmFEA, we generated an in-house multi-omics data set including transcriptomics, metabolomics, metabolic activity functional assay, and CRISPR screening data using pancreatic cancer cell line system treated by APEX1 inhibitors. We demonstrated that mmFEA could accurately capture experimentally observed metabolic changes and achieved a better performance than all baseline methods. Our analysis revealed the necessity in using both substrate and enzyme modality and kinetic aware model in metabolic flux assessment. Further analysis using independent pancreatic cancer cohorts further validated the robustness of mmFEA, supporting integration of condition-linked unpaired data. Pan-cancer and spatial multi-omic applications demonstrated the use of mmFEA for resolving context-dependent metabolic variation when direct flux measurements are unavailable. Together, mmFEA provides a mechanistically grounded framework for estimating relative metabolic flux changes and their uncertainty from heterogeneous omics data.

systems biology↗

bioRxiv2026

Evidence Accumulation in Synaptic Efficacy: Activity-Silent Decision-Making with Short-Term Plasticity

Decision-making requires accumulation of evidence over time, yet exactly how neural circuits implement this computation remains debated. Classical models posit that evidence accumulation is realized by ramping neural activity, whereas recent experimental findings suggest an alternative mechanism in which neural activity remains largely silent before abruptly transitioning into an active decision state. This mechanism leaves a fundamental question unanswered: how is evidence accumulated during the neural activity-silent period? To address this question, we trained excitatory-inhibitory recurrent neural networks (RNNs) with short-term synaptic plasticity (STP) on a reaction-time decision task. We found that after training, the networks learned to leverage STP to make decisions in an activity-silent manner. Choice-tuning analysis revealed a clear functional organization of the network, and connection-pruning analysis uncovered the coupling between choice-selective excitatory and inhibitory neural groups. Guided by the architecture of trained RNNs, we constructed a minimal neural circuit model, in which evidence accumulates in facilitated synaptic efficacy that progressively reshapes the circuit's phase portrait, eventually driving the network state across the stability boundary and triggering a rapid transition to a choice state. We further compared activity before a choice between the theoretical STP and Static models to examine the potential energy benefit of activity-silent decision-making. Together, our results suggest STP as a mechanism for evidence accumulation in activity-silent decision-making and offer insights into the design of energy-efficient artificial neural networks.

neuroscience↗

bioRxiv2026

Sequence-specific and programmable catalytic degradation of extracellular microRNAs by membrane-anchored DNA nanomachines

Extracellular microRNAs (miRNAs) contribute to metastasis through communication between tumour cells and their microenvironment, yet their sustained, sequence-selective depletion remains challenging. Here we report Programmable RNA-Intercepting Molecular Eraser (PRIME), a membrane-anchored DNA nanomachine that organizes miRNA recognition and catalytic degradation at the cell surface. A double-tetrahedral DNA scaffold positions a programmable recognition hairpin beside RNase H. Target binding generates an RNA/DNA hybrid for RNase H-mediated cleavage, and product release resets the hairpin for subsequent degradation cycles. Membrane-anchored PRIME depleted matched extracellular miRNAs by 70.6-89.4% across lung, breast and cervical cancer cell models. Multi-target PRIME (M-PRIME) inhibited migration by 71.3-96.0% in Transwell assays across these models. To evaluate its therapeutic potential, we established an orthotopic lung-tumour model and an early-intervention breast-cancer lung-colonization model in mice. Aptamer-directed M-PRIME combined with gemcitabine reduced pulmonary tumour burden by 74.5% in established lung tumours and 93.2% in the lung-colonization model. These effects were consistent with PRIME-mediated extracellular miRNA degradation, with circulating target miRNA levels reduced by 77.5-98.7% relative to gemcitabine alone. Together, these findings establish a programmable material strategy for sustained extracellular RNA depletion and support its potential to enhance chemotherapy in preclinical tumour models.

biochemistry↗

bioRxiv2026

RNA-guided contrastive learning enhances patient-level prediction from histology

Predicting molecular receptor status, including estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), directly from routine hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) could reduce reliance on costly immunohistochemistry, but image-only models have no access to the transcriptomic programs that define these subtypes. Here we show that coupling a whole-slide vision transformer (GigaPath) with bulk RNA-sequencing profiles through a training-time-only contrastive objective improves receptor-status classification from WSIs alone, without requiring any RNA-seq data at inference. Rather than training a dedicated bulk-transcriptomics encoder, we purpose the gene-sentence encoding and pretrained text encoder from BioBERT1 and apply them to bulk RNA-seq profiles from The Cancer Genome Atlas breast cancer cohort (TCGA-BRCA). Under five-fold cross-validation, this RNA-guided pre-training consistently improved AUROC for ER, PR, and HER2 relative to a contrastive-pre-training-free baseline and reproducibly introduced receptor-relevant structure into the learned slide representation. However, evaluation on independent external cohorts revealed a striking, receptor-dependent divergence: gains for ER and PR, both governed by broad, multi-gene luminal transcriptional programs, generalized robustly, whereas the apparent AUROC improvement for HER2, driven by focal ERBB2 amplification rather than a coordinated transcriptional signature, masked a severe loss of classification sensitivity at the operating threshold. These findings establish training-time transcriptomic supervision as a practical route to enhancing histology-only molecular subtyping, while showing that gene-selection strategy, and not model architecture alone, determines which molecular alterations such supervision can teach a vision model to recognize.

pathology↗

bioRxiv2026

When label-free morphology is sufficient for targeted cellular measurements

Label-free morphology captures cellular responses, but its ability to substitute for targeted cellular measurements depends on the intended use. We assess measurement sufficiency using nearly 10 million paired phase-reporter observations across 1,000 gene knockouts, 52 fluorescent reporters and 73 A549 screens. Ten predictors differed in recovery across held-out fields, genes and screens. Same-cell pairing controls identified reporter-state information beyond perturbation averages and measured covariates. Perturbation ranking often persisted despite reduced variation in predicted response magnitudes. Predictions of held-out knockout responses retained a median 79% of the strongest hits, whereas functional-term retention varied. Atlas-derived criteria identified 30 quantitative and 7 ranking proxies for a fixed ensemble, with reference reproducibility constraining interpretation. In independent primary-hepatocyte data, brightfield predictions prioritized 10 of the 11 compounds with the largest measured metabolic-activity losses among 217 held-out compounds. Measurement sufficiency therefore depends on target, predictor, context and scientific task, beyond predictive correlation alone.

bioinformatics↗