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Ren, W.

Publications and source records attributed to Ren, W..

At least 19 recordsLinked to original sources

Autophagy activator AA-20 engages CDK5 to promote lipophagy and improve neurodegeneration-associated phenotypes

Lipophagy, the selective degradation of lipid droplets (LDs) by autophagy, has the potential to ameliorate neurodegenerative diseases such as Parkinson's (PD) and Alzheimer's (AD), wherein LD accumulation has been linked to the exacerbation of the proteinopathy component of neurodegeneration. Pharmacological lipophagy inducers remain scarce. We previously identified AA-20 as a novel autophagy activator that acts independently of mTORC1 inhibition, thereby providing a means of stimulating autophagy while avoiding the immunosuppressive side effects associated with direct mTORC1 inhibition. Prior work also showed that AA-20 treatment reduces PolyQ aggregate levels in several C. elegans tissues. Here, we demonstrate that AA-20 enhances LD recruitment to autophagosomes and promotes LD degradation via lysosomal acid lipase activation in murine hippocampal neuronal cells, consistent with lipophagy activation. In human Swedish Amyloid Precursor Protein mutated cerebral organoids, two weeks of AA-20 treatment normalized aberrant lipidomic profiles by selectively reducing various disease-associated lipids, while also correcting the AD-associated electrical hyperexcitability phenotype. Moreover, we identified cyclin-dependent kinase 5 (CDK5), a neuronal kinase linked to AD hyperexcitability and PD pathology, as a target of AA-20. AA-20-induced autophagy and lipid clearance in HT22 cells require CDK5. In a neuronal seeding model of -synuclein pathology, AA-20 reduced pS129--synuclein levels. Collectively, these findings indicate AA-20 can promote lipophagic clearance of LDs and mitigate multiple cellular phenotypes associated with lipidopathy and proteinopathy in neurodegenerative disease models.

molecular biology↗

De novo protein design in an expanded chemical space

Generative protein design has made sequence and structure programmable, yet its chemical alphabet remains largely confined to the canonical twenty. Here, we establish CheMoDesign, an experimentally validated generative framework for designing proteins de novo around user-specified chemistry. CheMoDesign weakens pair-representation conditioning in a pretrained all-atom structure-prediction model while preserving chemical anchors, then refines generated backbones through sequence-conditioned denoising. By treating post-translational modifications, synthetic residues, and chemical modifications as user-defined building blocks, the framework generated proteins with chemistry-dependent recognition and reactivity in single-round experimental campaigns. Sulfotyrosine and nitrotyrosine enabled modification-dependent recognition of thrombin and HER2, respectively. Genetically encoded noncanonical residues enabled bioorthogonal affinity switching and light- or proximity-triggered covalent target capture. Post-expression chemical modifications yielded a light-activated covalent PD-L1 binder and an isoform-selective CA IX inhibitor (KD = 45 nM) designed to engage catalytic Zn2+;. CheMoDesign makes chemical mechanism a programmable dimension of de novo protein design.

biochemistry↗

MINT infers the latent single-cell spatial transcriptome from paired histology

Sequencing-based spatial transcriptomics captures transcriptome-wide molecular information, yet the corresponding cell-resolved tissue map remains incomplete. Histology records dense cellular morphology, tissue architecture and local context. Here, we present MINT (Morphological Integrative Network for Transcriptomics), which combines this latent cellular organisation with sample-specific spatial measurements to infer tissue-wide, cell-resolved transcriptomes without an external single-cell reference. MINT reconstructs expression from sparse Slide-tags profiles across unmatched adjacent sections; its shared nucleus-centred representation also resolves pooled Visium measurements into cell-level profiles. MINT improved Slide-tags alignment and expression recovery under controlled nuclear loss and cross-section perturbation. It also outperformed reference-free Visium disaggregation methods against Xenium ground truth and was validated with Visium HD sequencing. In human lung adenocarcinoma, MINT reconstructed the tumour immune microenvironment cell by cell and resolved tertiary lymphoid structures and their maturity. These results establish paired histology as a quantitative, sample-intrinsic complement to molecularly rich but cellularly incomplete spatial-transcriptomic assays.

bioinformatics↗

An N-Terminally Acetylated Degron Specifically Recognized by the CTLHMKLN1-FAM72A E3 Ubiquitin Ligase for Regulatory Protein Degradation

N-terminal acetylation of cellular proteins has been implicated in their proteasomal degradation through the Ac/N-degron pathway. Despite representing a major branch of the N-degron system, only a limited number of degrons and their motifs have been identified and characterized in this pathway, and the mechanism underpinning the recognition of N-terminal acetylation has remained largely elusive. Here, by interrogating FAM72A-mediated degradation of UNG2, we pinpointed the N-terminally acetylated sequence of UNG2 as a bona fide Ac/N-degron specifically recognized by the CTLHMKLN1-FAM72A E3 ubiquitin ligase. Rather than relying solely on a single acetylated N-terminal residue, this degron features a consensus Nt-AcM1-[ILV]2-G3 motif that governs both E3 targeting and the intracellular stability of the neo-substrates uncovered through proteomic screenings. Cryo-EM structure of the UNG2-FAM72A-MKLN1 complex further elucidated the molecular details supporting the high specificity of degron recognition. Notably, this degron pathway is subject to multilayered regulation, highlighting the characteristic conditionality of degron system. Our findings thus define a distinct AcM-[ILV]-G/N-degron pathway and provide mechanistic insights into degron recognition dictated by both N-terminal acetylation and sequence motif context.

biochemistry↗

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↗

Within-Subject Optogenetic Model Reveals Spatiotemporal Cortical Reorganization in Artificial Vision

Visual prostheses can elicit simple percepts such as letter forms through dynamically patterned stimulation, yet naturalistic dynamic vision remains out of reach despite hardware fully capable of high-resolution temporal control. This persistent gap suggests the bottleneck lies in how artificial input is represented by the cortex itself. A fundamental question remains unresolved: when artificial vision bypasses natural retinal encoding, what is lost in cortical representation? We address this question in this paper using a retinal optogenetic mouse model that isolates the consequence of bypassing retinal encoding while preserving downstream pathways. Using within-subject V1 cortex electrophysiology and CNN-based decoding under identical dynamic stimuli, we reveal dissociable, dimension-specific gaps. Temporally, artificial vision bypassed the frequency-dependent compression imposed by natural vision, extending stimulus-locked entrainment bandwidth. Spatially, direction-selective representations underwent systematic remapping, with disrupted phase organization and reduced encoding regularity. These findings show that bypassing natural retinal encoding reorganizes cortical representation along dissociable spatiotemporal dimensions, establishing a within-subject framework that renders such structural divergences easy to locate and quantifiable.

bioengineering↗

Virtual-cell verification enables self-auditing AI discovery for immune rejuvenation

Artificial-intelligence agents propose drug-discovery hypotheses faster than experiments can test them, yet their conclusions are rarely verified, against the underlying biology, the predicted perturbation, or the agent's own scoring logic. We close this verification gap with an agentic framework built on three verifiers. First, PACE, a phenotype verifier, resolves immune aging into ten directionally scored, cell-type-resolved gene-set modules, selected for cross-cohort stability across four PBMC cohorts, and outperforms five established aging clocks in an independent in-house aging cohort of 434 elderly donors. Second, CellQ, a virtual-cell verifier built with multi-modal LLM, compresses each single-cell transcriptome into eight discrete tokens aligned to a language model's vocabulary through residual vector quantization; it attains state-of-the-art perturbation prediction and uniquely resolves the weak, module-level shifts that differential-expression recovery misses. Third, an Analyzer-Planner-Auditor agent verifies its own scoring logic: screening 110 compounds in primary human PBMCs, it found aged-down modules more reversible than aged-up modules and revised its objective from an equal-weight mean to a balance-constrained minimum, a self-correction that generalized to an independent 13-compound T-cell assay. By verifying its predictions and its own objective against experiment, the framework points beyond hypothesis-generating AI toward self-correcting AI scientists whose objectives could continuously evolve.

bioinformatics↗

AI Analysis of a Copy Number Variant Database Identifies a Genetic Factor for a Murine Model of the Metabolic Syndrome

Copy number variants (CNVs) are a major source of genetic diversity and could contain some of the missing heritability for mouse models of human disease. However, mouse CNVs have not been comprehensively characterized because they are difficult to resolve in repeat-rich, segmentally duplicated or reference sequence-absent regions of the genome. Here we analyzed long range sequence (LRS) data for 40 inbred mouse strains and characterized CNVs using pangenome graph-based (and other) methods and a C57BL/6J telomere to telomere (T2T) genome reference sequence. We resolved 1,594 high-confidence CNVs that often overlap tandem repeats (60.3%), segmental duplications (44.8%) or pericentromeric regions (11.5%); and 131 CNVs were T2T sequence-specific. CNVs affected 384 protein-coding genes, which spanned a range of important functional classes. The 40-strain pangenome map expanded the genome sequence from 2.29 to 3.32 Gb, with the wild-derived strains accounting for the largest sequence increments. Two different AIs were sequentially used to analyze this database and identify a 29-kb deletion CNV within the Nlrp1b locus of KK mice that contributed to the metabolic syndrome they develop. Human NLRP1 alleles also were associated with metabolic syndrome features in human populations. Hence, AI analyses of this comprehensive T2T pangenome-based resource could uncover some of the missing heritability for mouse models of human diseases and biomedical traits.

genetics↗

Fast and Accurate Photon-Transport Modeling based on Foundation-Model-Encoded Implicit Neural Surrogate towards Optimized Near-Infrared Brain Stimulation

Clinical treatment planning for near-infrared (NIR) neuromodulation requires patient-specific dosimetry to optimize light fluence (LF) delivery to cortical targets. The gold-standard Monte Carlo photon-transport forward solver is accurate but computationally expensive and non-differentiable for personalized inverse design across subjects. Here, we present PhomiNeuro, a foundation-model-encoded differentiable surrogate for time-resolved LF modeling and pathology-guided inverse design. A pretrained 3D medical imaging foundation model (VISTA3D) was domain-adapted to 285 training head models annotated with optical properties, and then coupled to an implicit neural representation (INR) that predicts LF at arbitrary spatiotemporal coordinates. Regularized by a time-dependent diffusion-equation residual, it demonstrated superior fidelity in 84 held-out participants, and ablations identified VISTA3D-derived anatomical priors as dominant and physics regularization as complementary. PhomiNeuro enables fast and robust queries of LF and its gradients with respect to illumination parameters for targets guided by individual structural magnetic resonance imaging and amyloid positron emission tomography, achieving 466-fold per-iteration speedup and 167-fold end-to-end optimization speedup. Explanatory analyses of LF delivery variability, including sex differences and amyloid status, identified structural variations as the primary drivers. These results position PhomiNeuro as a highly extensible translational framework toward personalized treatment planning and digital twin development in precision neuromodulation.

bioengineering↗

Physics-Driven Zero-Shot Reconstruction of Isotropic 3D Fluorescence Microscopy under Undersampled Acquisition

Three-dimensional (3D) imaging represents the development of next generation of fluorescence microscopy. However, routine axial down-sampling makes isotropic resolution unrealistic. Here, we propose DeepUI, a physical zero-shot framework designed to achieve isotropic 3D fluorescence images from a low axial sampling rate. DeepUI fully leverages the intrinsic characteristics of 3D images through physics-guided degradation, which incorporates spatial-frequency joint learning to generate a scaled optical transfer function, combined with noise degradation and an up-sampling branch. Typically requiring just 5 minutes for training and 0.5 minutes for high-throughput and fast prediction, we demonstrate the superior performance of DeepUI to get isotropic results, and the exclusivity to axial down-sampling conditions, even in more challenging conditions, including defocused background, noise, and resolution blur.

bioinformatics↗

Interpreting Omics Data Analysis with Large Language Models for Disease Target and Drug Discovery

In biomedical scientific discovery, synthesizing prior knowledge from the literature is an essential component of interpreting numerical omics data analyses for disease target identification and drug discovery. Large language models (LLMs) alone can rapidly retrieve disease mechanisms from biomedical text, but text-only outputs are general and unreliable for target and drug prioritization without cohort-specific quantitative evidence. Herein, we propose a provenance-aware Text-to-Target framework that couples schema-constrained multi-model LLM retrieval with numeric omics data analysis. The key design is a modality-aware fusion step: candidates are partitioned into overlap-supported anchors, retrieval-only hidden hubs, and network-emergent novelty nodes, then propagated into staged hypothesis and strategy generation under topology constraints. We evaluate the model in Alzheimers disease (AD) and pancreatic ductal adenocarcinoma (PDAC). In PDAC, the workflow produced a balanced 75-gene candidate universe and a 23-strategy portfolio, with significant DepMap support at both target level and strategy level. In AD, stricter candidate controls yielded a compact 34-gene universe and 14 strategies; under an expanded CRISPRbrain registry, both target-level axes were significant, with strong strategy-level enrichment. Across both diseases, final strategies preserved full provenance closure to the candidate pool, enabling end-to-end auditability from retrieval artifacts to validation outputs. These results support a transferable discovery architecture in which omics evidence constrains biological activity, LLM retrieval expands mechanistic search space, and network-aware fusion preserves interpretability. The framework provides a reproducible basis for dual-disease target prioritization and motivates continuous literature-mechanism concordance with agentic evidence-refresh loops.

bioinformatics↗

Correlation, Regression, and Growth Curve Analyses of Body Weight and Body Measurements in Female Jiangyue Donkeys

ObjectiveThis study aimed to systematically analyze the correlation between body weight and body measurement traits and the growth pattern of female Jiangyue donkeys. MethodsA total of 484 female Jiangyue donkeys were selected to determine their body weight (Y) and 11 body measurement traits, including withers height (X1), body length (X2), chest circumference (X3), cannon circumference (X4), head length (X5), neck length (X6), chest width (X7), chest depth (X8), rump height (X9), rump length (X10) and rump width (X11). SPSS 27.0 statistical software was used to conduct descriptive statistics, correlation and regression analysis on body weight and body measurement traits, and the optimal regression equation between them was established by the stepwise method and validated. Furthermore, four growth curve models (Logistic, Gompertz, Brody and Von Bertalanffy) were used to fit the body weight of 241 female Jiangyue donkeys at different months of age. ResultsThe body weight of female Jiangyue donkeys was significantly positively correlated with all body measurement traits (p<0.01), with the highest correlation coefficient observed for chest circumference. The optimal regression equations for body weight established by stepwise regression yielded R{superscript 2} values of 0.918 and 0.844 for growing and adult donkeys, respectively (p<0.01). Among the four growth curve models, the Von Bertalanffy model exhibited the best fitting effect (R{superscript 2}=0.99992), with an estimated asymptotic body weight of 184.41 kg, which was close to the measured values of growing female donkeys. ConclusionThe regression models for body weight estimation and the Von Bertalanffy growth curve model for growth pattern evaluation established in this study can serve as effective tools and production management of female Jiangyue donkeys.

genetics↗

Clonal morphology-guided combination therapies overcome heterogeneity-driven drug tolerance

Tumor heterogeneity plays a critical role in tumor relapse and the development of drug resistance. Current personalized strategies, based on genetic profiling or bulk tumor drug sensitivity testing, offer limited clinical value as they overlook clonal heterogeneity. Here, through establishing primary tumor cell cultures from multiple metastatic sites, we observed that tumor cells exhibit high morphological plasticity and readily form distinct clonal morphologies during monoclonal expansion. Moreover, these heterogeneous clones displayed distinct tumorigenic and histological traits in orthotopic models, which may imply their differing clinical relevance in tumor progression. We also found that clonal morphological heterogeneity was exhibited by both established cell lines and primary lines derived from chemically induced tumor models. Subsequently, we analyzed the drug sensitivity profiles of tumor clones and identified clone-specific sensitive drugs. Despite limited in vitro synergy in pairwise combinations, both in vitro and in vivo assays confirmed their potent, clone-selective inhibition, suggesting rational drug combinations can precisely target distinct clonal populations. Taken together, Collectively, our findings propose that a heterogeneity-informed drug selection paradigm, enabled by rapid clonal morphology analysis of patient-derived cells, can be used to prevent recurrence and overcome resistance. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/703021v1_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@1db607dorg.highwire.dtl.DTLVardef@19bbcd4org.highwire.dtl.DTLVardef@18a3355org.highwire.dtl.DTLVardef@304794_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LITumor cells develop distinct morphologies during clonal expansion. C_LIO_LIDistinct morphologies serve as simple markers of tumor heterogeneity. C_LIO_LIDistinct clonal morphology defines a unique drug-response profile. C_LIO_LIClone-selective drug combinations combat heterogeneity-driven resistance. C_LI

cancer biology↗

Unifying constraints linking protein folding and native dynamics decoded from AlphaFold

The interplay between protein folding and native dynamics remains a central question in biophysics. Analyzing an extensive set of AlphaFold-predicted structures, we uncover a robust relationship between folding topology (contact order) and native dynamics (fluctuation entropy), showing that long-range contacts that slow folding also restrict conformational flexibility across protein sizes and taxonomic groups. Scaling analysis reveals that this relationship, together with its chain-length dependence, is consistent with power-law-like trends, reflecting common organizing constraints of protein architecture. Across species, increasing organismal complexity is associated with proteome-wide shifts toward lower contact order and higher fluctuation entropy. Together, evidence from folding, stability, and functional dynamics converges on unifying constraints, revealing an intrinsic physical organizing principle captured by AI models.

bioinformatics↗

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

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

bioengineering↗

Visuospatial attention exerts opposite modulatory effects on explicit and implicit audiovisual subjective synchrony via the frontoparietal network

Synchronization and integration constitute two essential subprocesses of audiovisual binding, commonly indexed by explicit subjective synchrony (ESS) and implicit subjective synchrony (ISS), respectively. Previous studies have shown that spontaneous fluctuations in visual attention are associated with ESS, and that ESS and ISS are negatively correlated, consistent with the temporal renormalization theory. However, it remains unknown whether controlled manipulations of visuospatial attention can causally modulate ESS and ISS in opposite directions via distinct neural mechanisms. To address this question, we conducted two complementary EEG experiments. In Experiment 1, ESS was assessed using a spatially cued beep-flash synchrony judgment task and the point of subjective simultaneity (PSS). In Experiment 2, ISS was measured using fusion and fission variants of the sound- induced flash illusion, with the peak illusion rate (PIR) as the behavioral index. This dual-paradigm design enabled direct comparison between explicit and implicit synchrony under matched attentional manipulations. Behaviorally, asymmetric allocation of visuospatial attention between auditory- and visual-leading conditions induced opposite shifts in PSS and PIR, specifically in the fusion variant. At the neural level, prestimulus frontal theta oscillations were associated with variations in PSS, whereas bilateral frontotemporal and left posterior beta oscillations were associated with variations in PIR. These results demonstrate that visuospatial attention exerts opposite modulatory effects on ESS and ISS through distinct oscillatory dynamics, providing causal evidence for the temporal renormalization theory and advancing understanding of how attentional control shapes multisensory temporal binding.

animal behavior and cognition↗

Light-orchestrated multi-step solid-phase picodroplet reactors

Picoliter droplet reactors, enabled by droplet microfluidics, are revolutionary tools for biochemical reactions with high efficiency, precision, and minimal reagent use. They excel in single-step reactions and reagent addition through droplet merging. Integrating reagent exchange and washing capabilities directly into these platforms may enable complex multi-step processes, such as de novo oligonucleotide synthesis and multiplex immunoassays. However, current picodroplet microfluidics, lacking such capabilities, remain critically deficient in executing multi-step processes. Here we introduce light-orchestrated solid-phase picodroplet reactors to overcome these limitations. Our platform employs optoelectronic tweezers to manipulate individual picodroplets and microbeads. Using this platform, we demonstrate an eight-step click chemistry-based DNA ligation synthesis cycle, with real-time in situ fluorescence detection of reaction products. This platform overcomes single-step limitations by achieving precise sequential encapsulation and decapsulation of beads with different reagent droplets, ensuring uniform reagent exposure and effective washing. Hence it mitigates reaction errors caused by nonuniform reagent exposure and trapped impurities in conventional bulk processing of microbeads. With minimal reagent consumption, real-time analysis, and programmable light- based control, the platform can potentially be scaled and fully automated into a universal and versatile picoliter-scale reagent handling robot for miniaturizing and streamlining workflows in synthetic biology, drug discovery, and beyond.

bioengineering↗

Towards Unified Neural Decoding with Brain Functional Network Modeling

Recent achievements in implantable brain-computer interfaces (iBCIs) have demonstrated the potential to decode cognitive and motor behaviors with intracranial brain recordings; however, individual physiological and electrode implantation heterogeneities have constrained current approaches to neural decoding within single individuals, rendering interindividual neural decoding elusive. Here, we present Multi-individual Brain Region-Aggregated Network (MIBRAIN), a neural decoding framework that constructs a whole functional brain network model by integrating intracranial neurophysiological recordings across multiple individuals. MIBRAIN leverages self-supervised learning to derive generalized neural prototypes and supports group-level analysis of brain-region interactions and inter-subject neural synchrony. To validate our framework, we recorded stereoelectroencephalography (sEEG) signals from a cohort of individuals performing Mandarin syllable articulation. Both real-time online and offline decoding experiments demonstrated significant improvements in both audible and silent articulation decoding, enhanced decoding accuracy with increased multi-subject data integration, and effective generalization to unseen subjects. Furthermore, neural predictions for regions without direct electrode coverage were validated against authentic neural data. Overall, this framework paves the way for robust neural decoding across individuals and offers insights for practical clinical applications.

neuroscience↗