Biology subjects

Wang, J.

Publications and source records attributed to Wang, J..

At least 127 records · Page 7Linked to original sources

bioRxiv2026

Multi-kingdom microbial diversity and interaction landscapes in mosquitoes revealed by 5,163 individual meta-transcriptomes

Mosquitoes are pathogen vectors embedded within diverse microbial ecosystems. However, the nature and interactions among their multi-kingdom microbiome remain poorly understood. We conducted a nationwide single-mosquito meta-transcriptomic survey of 5,163 mosquitoes representing 100 species across China, integrating viral discovery with marker-gene profiling of bacteria, archaea, fungi, and other eukaryotic microbes. From this, we identified 1,606 microbial species-level taxa, including extensive novel diversity, and revealed pronounced host species-specific organization of mosquito-associated communities. We detected 34 pathogens or potential pathogens of human or animal relevance, whose prevalence, abundance, host range, and geographic distribution defined distinct epidemiological patterns. Network analysis uncovered pervasive cross-kingdom microbial associations, including candidate antiviral relationships involving Wolbachia and other microbial taxa. Our study establishes a detailed view of the full-spectrum microbiome and provides a resource and conceptual framework for studying vector competence, pathogen emergence, and microbiome-informed mosquito-borne disease control.

microbiology↗

bioRxiv2026

Multi-parametric NIR-II fluorescence perfusion imaging towards quantitative stroke evaluation

Preclinical stroke research is severely hampered by the lack of high-throughput, quantitative perfusion imaging tools capable of bridging the gap between functional hemodynamics and structural injury. The emerging second near-infrared (NIR-II) fluorescence imaging offers superior spatial resolution and tissue penetration, but its utility remains restricted by a lack of standardized analysis protocols and inability to resolve in-depth infarct regions. Here, we introduce FIMPA, an integrated analytical framework for standardized, multi-parametric stroke evaluation based on NIR-II fluorescence perfusion imaging. FIMPA systematically processes the image sequence through motion correction and atlas registration to generate an anatomically aligned library of 125 vascular and hemodynamic parameters. Statistical screening identifies 67 stroke-correlated features, enabling objective, data-driven quantification of perfusion deficits. To resolve spatial infarct boundaries, we developed FIMPA-SI (FIMPA Stroke Index), a deep learning-based index utilizing a ViT-UNETR framework. By leveraging self-supervised pre-training with MRI data, FIMPA-SI suppresses dominant vascular signals to translate multi-parametric dynamics into anatomically precise stroke severity maps. Validated in tMCAO mice, FIMPA-SI achieves high spatial concordance with MRI and significantly outperforms conventional laser speckle contrast imaging and single-parameter metrics. FIMPA provides a robust solution for accelerating preclinical therapeutics and advancing optical neuroimaging.

bioengineering↗

bioRxiv2026

High-Resolution Subtyping of Pediatric Low-Grade Glioma Using an Integrated Meta-Clustering Framework

Pediatric low-grade glioma (pLGG) is the most common type of brain tumor in children, accounting for approximately 30% of all central nervous system tumors in children. pLGG has multiple molecular subtypes that differ in disease progression, recurrence patterns, and treatment responses. Conventional wet lab approaches including molecular profiling and histopathological studies for pLGG characterization are time consuming, costly, and laborious. Recently, methods based on artificial intelligence (AI) or machine learning (ML) have been widely used for pLGG molecular categorization, but most of them can only identify two or three pLGG subtypes. To more comprehensively characterize the molecular subtypes of pLGG and their potential biological and therapeutic significance, we develop an integrated meta-clustering approach, namely Meta-pLGG, that can explore high resolution molecular subtypes and their transcriptional heterogeneity for pLGG. Specifically, we first performed multiple rounds of random projection (RP) to generate dimension-reduced feature vectors from pLGG transcriptomics data, each of which was subsequently clustered by different clustering algorithms including hierarchical clustering, K-means, Self-Organizing Maps (SOM), Non-negative Matrix Factorization (NMF), Gaussian Mixture Model (GMM), and Spectral Clustering, as base clustering methods. Then, to yield robust clustering performance, we integrated the clustering results of these RP based individual clustering algorithms by adopting a weighted meta-clustering (wMetaC) approach. Results based on 532 pLGG patients suggested that our proposed approach demonstrated superior stability and discriminative powers for higher resolution pLGG subtyping compared to conventional approaches. Based on consensus matrix analysis, we identified two major pLGG mega-subtypes, with one further subdivided into three subgroups and the other into two. Then, we performed cluster specific differential gene expression analysis, molecular pathway analysis, and gene-drug-disease association analysis. The results showed that the identified five subgroups exhibited significant subtype-specific transcriptomic heterogeneity. In summary, our meta-clustering approach demonstrated much higher performance and robustness in identifying higher resolution molecular subtypes of pLGG, revealing the molecular heterogeneity within pLGG and potentially providing new insights for more precise molecular subtyping and precision therapy.

bioinformatics↗

bioRxiv2026

Detection of Frustration-related Operant Behavior in Rats via Machine Learning Methods

Despite its strong link to neuropsychiatric conditions, frustration remains critically understudied in humans and animals alike. Therefore, there is an urgent need to develop tools to understand and therapeutically target frustration-related functions. Interestingly, humans and rats respond similarly during frustrative nonreward by increasing barpress durations. We previously validated barpress duration in rat operant tasks as a reliable measure of frustration-related behavior; however, it is wellknown that in addition to duration of responding, emotional states such as frustration alter other aspects of responding such as force of pressing. One-dimensional, static measures such as maximum force could miss rich information contained within operant data. Thus, the objective of this study is to apply machine learning (ML) to force/time profiles to discriminate frustration-related barpresses from non-frustration-related barpresses. Results showed an AUROC for FR1 (i.e., non-frustrated) vs. extinction (frustrated condition) for individual barpresses of 0.65 that improved to 0.84 with a chunk size of 10. The model generalized well to progressive ratio responding, a different kind of frustration procedure. We conclude that force/time profiling does provide utility beyond one dimensional measures of duration or force separately, meaning that we can indeed infer the internal state of frustration from behavior using ML techniques. Importantly, this project will also serve as proof-of-concept for applying ML to predict other internal states from barpress data.

animal behavior and cognition↗

bioRxiv2026

Dissecting the TMEM132A-EGFR Dependency to Unlock Translational Therapeutic Opportunities for Pan-Solid Tumor

Solid tumors remain refractory to conventional treatments, yet cell surface proteins, by virtue of their extracellular accessibility and critical roles in tumor signaling, represent an attractive class of targets for precision-targeted therapy. Here, we report that TMEM132A is an essential and previously unrecognized pan-cancer target. TMEM132A interacts directly with EGFR and stabilizes its expression, thereby tethering EGFR at the plasma membrane and sustaining constitutive activation of lipid synthesis. Mechanistically, the TMEM132A-EGFR axis promotes lipogenesis by facilitating SREBP nuclear translocation, which in turn upregulates ACLY and ACSS2 expression to drive acetyl-CoA production and downstream lipid biosynthesis, ultimately disrupting lipid droplet homeostasis. To therapeutically target this axis, we developed a nanobody, LFNanoT132A#3, which effectively blocks the TMEM132A-EGFR interaction, abrogates downstream signaling activation, and potently inhibits proliferation across multiple solid tumor types. Notably, LFNanoT132A also exerts robust antitumor activity against H1975 xenografts, a model resistant to first- and second- generation EGFR inhibitors, underscoring its potential to overcome conventional drug resistance. Our findings establish TMEM132A#3 as a critical node in membrane-tethered oncogenic signaling and metabolic rewiring, and position LFNanoT132A#3 as a promising therapeutic candidate for precision cancer therapy.

cancer biology↗

bioRxiv2026

TomatoPGFM: A graph-conditioned foundation model for tomato pangenomes

Most genomic foundation models are pretrained on independent linear assemblies and therefore do not explicitly represent population-level segment sharing or local graph connectivity. We developed TomatoPGFM, a graph-conditioned model pretrained on 54.65 Gb of sequence from 66 tomato (Solanum spp.) accessions. Sequence tokens were conditioned on pangenome node attributes and local adjacency, and the model was optimised using masked language modelling and graph-feature reconstruction. To evaluate model responses to graph-conditioned input, we compared aligned, shuffled and disabled graph inputs in 25,000 windows from the training panel. Sequence-aligned graph input produced lower masked language modelling loss than graph-off at all five curriculum stages in both training-panel strata, while the shuffled perturbation generally yielded intermediate losses. We then assessed sequence-only transfer in Solanum sitiens LA1974 and S. lycopersicum MicroTom, neither of which was used for graph construction or pretraining. Frozen-probe AUROC values for gene-versus-intergenic and coding-sequence-versus-intergenic classification ranged from 0.8489 to 0.9593. TomatoPGFM produced higher AUROC point estimates than DNABERT-2 in all four comparisons. Enabling the zero-feature GraphAdapter pathway with adjacency messaging disabled changed throughput by less than 1% at 512-2,048 positions under the tested configuration. Together, these results show that TomatoPGFM responds consistently to sequence-aligned pangenome context in training-panel sequences and provides informative sequence representations for genic-region classification in accessions excluded from graph construction and pretraining.

bioinformatics↗

bioRxiv2026

Endogenous insulin mediates dynamic coordination between pancreatic cancer and systemic metabolism

Pancreatic ductal adenocarcinoma (PDAC) is commonly associated with obesity, diabetes, and cachexia. The pancreatic anabolic hormone, insulin, is implicated in each of these metabolic diseases, but how insulin levels affect tumor growth and relevant host physiological factors, was unclear. To address this, we transplanted orthotopic PDAC patient-derived organoids into mice consuming a hyperinsulinemia-inducing high-fat diet (HFD). We found that insulin concentrations were higher in tumors compared to circulation. Genetically reducing insulin levels reduced PDAC growth, particularly in HFD-fed mature male mice. In turn, we found the presence of pancreatic tumors increased glucose clearance and limited the expected dietary-induced gains in insulin, weight and fat mass. Interestingly, tumor growth in normal-chow-fed mice, but not HFD-fed mice, was associated with declining circulating insulin levels, as well as declines in fat and muscle mass. Together, these data provide new insights into the complex, insulin-centred interplay between PDAC and the hosts systemic metabolism that underlie cancer-associated metabolic dysfunction.

cancer biology↗

bioRxiv2026

Spatial profiling of pooled mRNA-LNP delivery in vivo with NanoSTAMP

Existing pooled lipid nanoparticle (LNP) screens lack spatial information on formulation localization, cellular uptake, and associated multicellular tissue responses. Here we introduce NanoSTAMP, a spatially resolved, pooled in vivo screening platform for barcoded LNP libraries that uses fluorescence in situ hybridization (FISH)-based barcode readout and is compatible with spatial omics. NanoSTAMP links LNP formulation to cell-type specific uptake, cargo expression, and nearby multicellular neighborhoods within intact tissue, which enables spatially-informed design of RNA delivery, establishing tissue architecture as a dimension of LNP performance.

bioengineering↗

bioRxiv2026

Programmable Recruitment of RNA-Binding Proteins Enables Small Molecule-Directed Destabilization of Nuclear Pre-mRNA

Chemically induced proximity has not been systematically applied to control RNA fate. Here, a programmable platform was developed to identify RNA-binding proteins (RBPs) that can be recruited by small molecules to destabilize RNA. Using microtubule-associated protein Tau (MAPT) pre-mRNA as a model target, a heterobifunctional molecule was designed to bind both a ligandable structure in MAPT pre-mRNA and FKBP12F36V-tagged RBPs. Screening of a library of tagged RBPs identified several proteins that reduced MAPT RNA levels, including zinc finger protein 36 (ZFP36) and nanos C2HC-type zinc finger 3 (NANOS3). The approach was then extended from engineered proteins to an endogenous RBP. Using small molecule ligandability maps, a cysteine-reactive ligand for ZFP36 was identified. When this ligand was linked to the MAPT-binding small molecule, endogenous ZFP36 was recruited to MAPT mRNA, reducing its abundance in cells. Genetic and chemical controls demonstrated that activity was dependent on both RNA binding and ZFP36 recruitment, supporting an induced-proximity mechanism. These studies establish a general strategy for identifying new recruitable RBP effectors and should advance ribonuclease-targeting chimera (RiboTAC) technology by expanding the repertoire of effector proteins that can be harnessed for RNA degradation. More broadly, new effectors can be discovered through model reporter-based screens and translated to endogenous systems by mining known protein binders and ligandability maps, providing a systematic path to develop small molecules that control RNA stability, including RNAs targeted through structured regions of nuclear pre-mRNAs.

biochemistry↗

bioRxiv2026

Addressing technical variations in ATAC-seq data and improving motif accessibility analyses

Tagmentation-based methods such as ATAC-seq and Cut&Tag have provided easy ways to profile the epigenome in low-input samples and even single cells. In this contribution, we discuss forms of bias (i.e. technical variations) in tagmentation-based data, in particular ATAC-seq, and introduce three R/bioconductor packages to facilitate bulk and single-cell epigenomic data analysis, with a special focus on motif accessibility analysis. The weightedMotifAccess package uses weight models to enable motif accessibility analysis, including transcription factor footprint information. The betterChromVAR package provides a novel, analytical re-implementation of the popular chromVAR method that offers substantial speed improvements, eliminates stochasticity, and offers additional features. Based on this, we also propose a method, CVnorm, that outperforms alternatives in normalizing technical bias in peak count data. The computational efficiency of these tools further enables a new framework for systematically investigating synergistic and antagonistic interactions between transcription factor motifs. Finally, the epiwraps package streamlines the visualization, normalization, and summarization of epigenomic data.

bioinformatics↗

bioRxiv2026

RevPert: ranking candidate drivers of transcriptomic state transitions via gallery-native reverse perturbation

Cellular state transitions underlie adaptation, ageing and disease, yet prioritizing catalogued genetic perturbations whose expression signatures match an observed transcriptomic shift remains difficult. Most models predict phenotype from a nominated intervention, whereas genetic inverse benchmarks are largely restricted to within-screen identity recovery. Here we introduce RevPert, a gallery-native reverse perturbation model that ranks a fixed genetic catalog for a query contrast {Delta}Y* = YB - YA by combining signed Pearson connectivity with a learned residual. Across Replogle Essential Perturb-seq (four lines) and LINCS-KO screens (ten lines), RevPert recovered held-out interventions at leading performance relative to matched baselines. Applied to public drug-resistance contrasts in HCC and CML, dual-arm ranking placed pre-specified disease anchors far higher on the expected arms than ranking the same signatures by differential-expression magnitude alone (Essential residual model for HCC; a transductive GWPS residual for CML). RevPert therefore couples within-screen reverse ranking to a screen-external signed-geometry check; the latter calibrates literature anchors and is not claimed as held-out recovery.

bioinformatics↗

bioRxiv2026

Structural-functional calibration corrects single-neuron identity errors in volumetric calcium imaging

Volumetric calcium imaging is increasingly used to capture larger neuronal populations at higher throughput, but high-speed axial sampling can compromise single-neuron identity. Here we identify cross-plane identity duplication as a structured error in volumetric imaging: anisotropic axial blurring and plane-wise functional segmentation can repeatedly detect the same neuron across adjacent planes, creating duplicate functional nodes that inflate neuronal counts and distort network phenotypes. We developed Comprehensive Label-Guided (CLG) volumetric imaging, a structural-functional calibration framework that uses nuclear labels as stable three-dimensional identity anchors for calcium signals. CLG combines nuclear labeling, deep-learning-based 3D segmentation, anatomical registration and identity-guided trace reassignment. In larval zebrafish whole-brain recordings, CLG resolved ~30,000 redundant detections and reduced estimated neuronal counts by 37-46%. In mouse visual cortex, CLG consolidated ~40% of putative duplicates and recovered over 2,000 active neurons missed by calcium-only analysis. Across baseline and perturbed conditions, calibration stabilized graph-derived measurements of hub organization, long-range correlations and network resilience. CLG therefore defines an anatomy-constrained identity-calibration layer for reliable single-neuron-resolved volumetric imaging.

neuroscience↗

bioRxiv2026

Working memory retention by medial entorhinal cortex low-dimensional neural dynamics

The ability to retain information over tens of seconds is essential for carrying out many routine tasks. Recurrently connected circuits are thought to support memory over these timescales, yet how they maintain and represent remembered information remains unclear. We asked whether the medial entorhinal cortex (mEC), where recurrent attractor dynamics generate grid-cell firing patterns and single toroidal attractor manifolds in two-dimensional environments, also supports low-dimensional neural activity patterns during memory retention. During a working-memory task, delay-period mEC dynamics did not show key features of grid firing in simple environments. Instead, broader mEC populations, not limited to grid cells, expressed multiple sequences and recurring activity patterns. Periodically repeating delay-period sequences retained task-relevant information, including past locations and future turn directions. These findings suggest that the mEC network generates low-dimensional dynamics more broadly, with grid-cell activity representing one manifestation and more complex manifold topologies supporting task-related representations during working memory.

neuroscience↗

bioRxiv2026

Membrane Anisotropy Reshapes Scale-Free Correlations and Directional Mechanical Susceptibility in Transmembrane Proteins

Long-range correlated motions couple distant regions of a protein, providing a physical basis for allosteric communication, cooperative conformational change, and the balance between structural stability and sensitivity to perturbations. Yet membrane proteins operate within a strongly anisotropic lipid bilayer, and how this environment reshapes such system-spanning coordination remains unclear. Using an implicit-membrane anisotropic network model, we perform a proteome-wide analysis of more than 3,000 human transmembrane proteins. We find that long-range correlations remain scale free under membrane constraints but become strongly direction dependent. Across protein sizes and topologies, their correlation lengths continue to scale with the corresponding molecular dimensions, while increasing membrane anisotropy extends in-plane correlations and shortens those along the membrane normal. Because spontaneous correlations and perturbation responses arise from the same underlying mechanics, we further resolve residue-level responses into in-plane and normal components. The resulting directional mechanical susceptibility provides new predictions of mutation-sensitive sites in GPCRs beyond those captured by conventional scalar flexibility measures. Together, these results show how environmental symmetry breaking can organize protein mechanics across scales, linking collective dynamics to the functional sensitivity of individual residues and connecting a general physical mechanism to experimentally measurable protein function.

biophysics↗

bioRxiv2026

Juvenile AAV-Mediated MEF2C Gene Replacement Ameliorates Selected Phenotypes in Mef2c-Haploinsufficient Mice

MEF2C haploinsufficiency syndrome is a severe neurodevelopmental disorder for which no disease-directed treatment is available. We investigated whether neuron-directed adeno- associated virus (AAV) delivery of a functional MEF2C coding sequence during the juvenile period could modify disease-relevant phenotypes in mice heterozygous for a Mef2c exon 4 deletion. Transcript-level analysis identified a brain-enriched MEF2C isoform containing the 1 and {beta} regions (nMEF2C) and a skeletal-muscle-enriched isoform containing 2 but lacking {beta} (mMEF2C). Separate human-synapsin-driven AAV vectors encoding either isoform were administered at postnatal day 28. Control-treated Mef2c heterozygous mice retained baseline sociability but lacked social-novelty preference. Mice treated with either nMEF2C or mMEF2C displayed social-novelty preference and improved selected responses to a new social partner, whereas open-field effects were limited. nMEF2C replacement also corrected dark-phase wakefulness and non-rapid eye movement sleep abnormalities and modified selected state- dependent electroencephalographic ratios, without broadly changing absolute band amplitudes or social-contact electroencephalographic activity. Atlas-based whole-brain mapping revealed region-selective reductions in parvalbumin-immunoreactive profiles; direct statistical evidence of cellular rescue was confined to the secondary motor area after nMEF2C treatment. These findings show that selected MEF2C-dependent phenotypes remain modifiable during the juvenile period and support further optimization of MEF2C gene replacement with respect to isoform, dose, expression control, and cellular targeting.

neuroscience↗

bioRxiv2026

NACraft: Programmatic nucleic-acid aptamer design via all-atom structure-model feedback

Protein-nucleic-acid interactions underpin diverse biological processes and provide a basis for molecular sensing, regulation and therapeutic intervention. However, the coupled dependence of aptamer function on nucleotide sequence, three-dimensional folding and target binding makes rational RNA and DNA binder design challenging. Here we present NACraft, a training-free and programmatic framework for all-atom nucleic-acid aptamer design based on backpropagation through structure-model feedback. By composing binding, sequence-similarity and anti-binding constraints, NACraft supports de novo generation, similarity-guided sampling and target-selective design within a unified optimization framework, without task-specific training or fine-tuning. Computational experiments showed that NACraft generated high-confidence candidates de novo across diverse protein targets, with further improvements achieved through similarity-guided design for both RNA and DNA complexes. Its target-selective design capability was further validated in silico, with 69.44% of paired candidates generated to favour the positive target EGFR over the off-target HER2. Under matched independent AlphaFold3 evaluation, NACraft achieved better performance than ODesign in 10 of 11 NA-12 targets and 17 of 20 protein target-length settings. Together, these results demonstrate the effectiveness and versatility of NACraft and extend structure-model hallucination toward programmatic nucleic-acid aptamer design. Codehttps://github.com/OTEAM-AI4S/NACraft

bioinformatics↗

bioRxiv2026

TM6SF2 binds cholesterol, interacts with apolipoprotein B, and promotes hepatic lipid secretion

A missense variant in TM6SF2 (transmembrane 6 superfamily member 2, TM6SF2E167K) is a major risk factor for steatotic liver disease1, while protecting against coronary artery disease2. TM6SF2 is a polytopic resident protein of the smooth endoplasmic reticulum (ER) and ER-Golgi intermediate compartment that promotes lipidation of hepatic ApoB-containing lipoproteins before secretion into the circulation. Here, we used cryo-electron microscopy (cryo-EM) to determine the structures of TM6SF2 and TM6SF2E167K at 3.64 [A] and 3.58 [A] resolution, respectively. TM6SF2 comprises 10 transmembrane helices that bind a single cholesterol molecule within a transmembrane cavity. The protein assembles into homodimers and homotetramers that interact with ApoB. Structural and biochemical analyses show that the E167K substitution reduces cholesterol binding and ApoB interaction without disrupting overall protein structure. Expression of wild-type, but not mutant, TM6SF2 restores hepatic triglyceride secretion in TM6SF2-deficient hepatocytes. Together, these findings establish the first structural framework for the bulk lipidation step in hepatic lipoprotein biogenesis, the principal pathway for hepatic triglyceride and cholesterol export into the circulation.

cell biology↗

bioRxiv2026

Oxytocin promotes advantageous inequity preference by modulating the fairness norm enforcement system

Aversion to inequity is innate in humans and constitutes a major contributor to social tension. Such aversion can be triggered either when one receives more (advantageous inequity-AI) or less (disadvantageous inequity-DI) than others. However, it remains unclear whether oxytocin can modulate how humans respond to inequity. The present pre-registered study combined behavioral measures, computational modelling, and fMRI in a novel monetary allocation evaluation paradigm to examine oxytocins modulation of behavioral and neural responses to inequity. Results showed that oxytocin increased perceived fairness and AI allocation choices via enhancing AI preference independent of gain vs. loss contexts, which was further confirmed by model-based results of oxytocin decreasing AI aversion. In parallel at the neural level, oxytocin increased fairness rating-related activity in regions within fairness-related norm enforcement systems involved in norm violation detection, salience processing, and cognitive control. Multivariate-based representational similarity analyses provided further evidence that oxytocin induced distinct representational patterns of inequity in these core regions. These results suggest that oxytocin promoted AI preference possibly by strengthening regulatory control to support the pursuit of economic self-interest, while simultaneously enhancing the salience of AI allocations. Our study provides new insights into behavioral and neural mechanisms through which oxytocin modulates fairness-related behavior.

neuroscience↗