Biology subjects

Yin, D.

Publications and source records attributed to Yin, D..

At least 19 recordsLinked to original sources

bioRxiv2026

AutoScreen: AI Co-Scientist System for Target Discovery in Functional Genomics

Target discovery in functional genomics remains largely manual and time-consuming, lacking systematic tools for efficient and reproducible gene-level hypothesis generation. We introduce AutoScreen, an AI co-scientist system supporting target discovery through both Pre-screen Design, which constructs perturbation libraries de novo from free-text research descriptions, and Post-screen Analysis, which re-ranks experimental screen hits by integrating statistical scores with biological context. AutoScreen leverages the complementary strengths of multiple specialist agents that perform deep research into multi-modal evidence, information restructuring, parallel searches across >26 biomedical databases, evidence synthesis, and target review to provide transparent, explainable gene prioritization with pipeline provenance. Across 320 genome-scale CRISPR screens as expert-curated benchmarks, AutoScreen achieved a ~19% increase in validated-hit recovery among its top 100 predictions, relative to the strongest agent baseline, and required a 1.2-fold smaller library to recover the same number of hits at the top-500 reference point. AutoScreen reached mean average precision more than two orders-of-magnitude above random baseline. Further, we validated AutoScreen in cancer immune-evasion case studies focusing on natural killer (NK) and T-cell therapeutics. AutoScreen recovered NK-resistance genes that were initially lower-ranked in a leukemia screen, moving MUC1, PDPN, and LRRC15 from raw ranks of 118, 81, 1384 to 5, 44, 659, respectively. In follow-up tumor killing assay with primary human NK cells, individual perturbation validated all three hits successfully. Next, in prospective benchmarking across two cytotoxic T-cell-killing screens, AutoScreen recovered 77.1% of ground-truth hits called by the consensus of two gold-standard analysis pipelines (FDR<0.1), achieving 8% improvement over the strongest general-purpose LLM agent baseline. Finally, we re-analyzed >500 public datasets to construct the AutoScreen Resource Hub, a growing knowledge base of pre-computed, annotated reports for CRISPR screens, RNA-seq differential expression, and gene-level UK Biobank genome-wide association studies. This agentic AI approach enables real-time genomics benchmark construction that are continuously updated, expanded, for evaluating frontier AI co-scientists. Overall, AutoScreen enables AI-powered target discovery to be more auditable, scalable, extensible, and reproducible.

bioengineering↗

bioRxiv2026

Autoreactive antibody production by intrarenal B cells in mouse kidney allograft rejection

Antibody-mediated rejection (AMR) is a major cause of kidney allograft failure, with donor HLA-specific antibodies (DSA) recognized as primary drivers of AMR pathology. However, a significant proportion of AMR diagnoses lack detectable DSA, thus implicating DSA-independent mechanisms. In support, we previously reported on the accumulation of autoreactive B cells in rejecting renal biopsies, which raised the possibility that autoreactive IgG produced within the allograft contributes to graft pathology. In this study, we used mouse models to show that rejecting kidney allografts preferentially promoted a breach in autoreactive B cell tolerance, leading to the local production of autoreactive antibodies. Notably, autoreactive IgG responses were uncoupled from DSA production, indicative of distinct regulation. Organoid cultures confirmed that autoantibody production was observed in the rejecting kidney, whereas DSAs were produced in both the lymph node and graft. Intrarenal B cells expressing Nur77 were enriched for autoreactivity, consistent with in situ antigen recognition. Finally, autoreactive antibodies are pathogenic, since inhibiting autoantibody production with transient anti-IL-15 and CTLA-4Ig led to preserved kidney allografts. These unique features of in situ autoantibody responses may be relevant to diverse diseases with chronic tissue inflammation beyond transplantation.

immunology↗

bioRxiv2026

Context-dependent functional diversity of dorsomedial posterior parietal neurons revealed by single-unit fMRI mapping during naturalistic viewing

The dorsomedial posterior parietal cortex (dmPPC) plays an important role in episodic processing by integrating sensory, cognitive, and motor information across distributed brain systems. However, how individual dmPPC neurons participate in large-scale functional organization during naturalistic experience remains poorly understood. To address this question, we combined single-unit electrophysiology and awake fMRI in separate cohorts of rhesus macaques viewing identical naturalistic video stimuli. Using single-unit fMRI mapping, we generated whole-brain neuron-BOLD functional maps by correlating individual neuronal activity with voxel-wise fMRI signals across the brain. We found that neuron-BOLD functional maps exhibited strong context-dependent organization, with neurons recorded during the same video context showing substantially greater similarity than neurons recorded during different video conditions. Compared with neuronal spiking activity or critical fMRI frames alone, neuron-BOLD functional maps more robustly capture contextual structure. Despite this shared large-scale organization, neighboring neurons recorded simultaneously from the same electrode often displayed markedly distinct whole-brain association patterns, revealing substantial local functional heterogeneity within the dmPPC. In addition, distributed cortical and medial temporal regions exhibited highly context-dependent neuron-BOLD association patterns during naturalistic viewing. Together, these findings demonstrate that dmPPC neurons participate in dynamic and heterogeneous large-scale functional organization during naturalistic episodic processing. More broadly, this study establishes single-unit fMRI mapping as a framework for linking single-neuron activity to distributed whole-brain dynamics across contextual conditions. Significance StatementUsing single-unit fMRI mapping, this study examined how individual dorsomedial posterior parietal cortex (dmPPC) neurons relate to large-scale brain activity during naturalistic video viewing in macaque monkeys. We found that neuron-BOLD functional maps exhibit strong context-dependent organization and capture contextual structure more robustly than neuronal spiking activity or fMRI frames alone. Despite this shared organization, neighboring dmPPC neurons often displayed markedly distinct whole-brain association patterns, revealing substantial local functional heterogeneity. These findings provide insight into how local neuronal populations participate in distributed cortical organization during naturalistic episodic processing.

neuroscience↗

bioRxiv2026

The 10 bits/s bottleneck as error-correcting redundancy: an information-theoretic theory of cognitive reserve

Among individuals with equivalent Alzheimers pathology, cognitive outcomes can diverge by decades, a phenomenon termed cognitive reserve that remains descriptive after thirty years of research. We propose that the [~]109-to-10 bits/s gap between sensory input and behavioral output functions as error-correcting redundancy in the sense of Shannons channel coding theorem. Progressive neuronal loss maps to symbol erasure in a redundant code, and the critical damage fraction at which cognition fails is dc = 1 - k/n, where k {approx} 10 bits/s is the behavioral channel requirement and n is the effective number of coding units. We evaluate this threshold across three channel models (binary erasure, Gaussian, and Erd[o]s-Renyi percolation) and show that all produce a sharp phase transition from reliable to unreliable decoding. The framework makes four testable predictions: (i) dc scales with the measurable redundancy ratio{rho} = n/k, which accounts for clinical heterogeneity; (ii) information-theoretic redundancy from resting-state fMRI should predict time-to-conversion beyond structural atrophy; (iii) the decline trajectory near dc is sharp, consistent with the "cognitive cliff"; and (iv) motor circuits, operating at higher bandwidth, have lower reserve than cognitive circuits. Significance StatementCognitive reserve (why some brains resist dementia pathology better than others) has been described for thirty years but never given a quantitative, information-theoretic foundation. We propose that the roughly hundred-million-fold gap between sensory input ([~]109 bits/s) and behavioral output ([~]10 bits/s) functions as error-correcting redundancy in the Shannon coding-theoretic sense. This yields a closed-form critical damage threshold, dc = 1 - k/n, below which cognitive function is preserved and above which it collapses; this is consistent with the clinically observed plateau-then-cliff pattern of dementia. The framework unifies cognitive reserve with channel coding theory, accounts for individual heterogeneity in disease onset, and generates falsifiable predictions that link information-theoretic redundancy measures to time-to-clinical-conversion.

neuroscience↗

bioRxiv2026

Hybrid eTFCE-GRF: Exact Cluster-Size Retrieval with Analytical p-Values for Voxel-Based Morphometry

Threshold-free cluster enhancement (TFCE) improves sensitivity in voxel-wise neuroimaging inference by integrating cluster extent across all thresholds, but its reliance on permutation testing makes it prohibitively slow for large datasets. Probabilistic TFCE (pTFCE) replaces permutations with analytical Gaussian random field (GRF) p-values, which reduces runtime by more than an order of magnitude, yet relies on a fixed threshold grid that introduces discretisation error. Exact TFCE (eTFCE) eliminates this discretisation by computing the integral exactly via a union-find data structure, but still requires permutations for inference. We propose a hybrid method that combines eTFCEs union-find data structure for exact cluster-size retrieval with pTFCEs analytical GRF inference. The union-find builds the full cluster hierarchy in a single pass over sorted voxels and enables exact cluster-size queries at any threshold in near-constant time; GRF theory then converts these sizes into analytical p-values without permutations. We validate the method through a six-experiment Monte Carlo study on synthetic phantoms (643, 80 subjects): null family-wise error rate is controlled at the nominal level (0/200 rejections, 95% CI [0.0%, 1.9%]); power curves match baseline pTFCE (Dice [≥] 0.999 at sufficient signal); smoothness estimation error is below 1%; and cross-variant concordance exceeds r = 0.99. On real brain data from UK Biobank (N = 500, within-vendor) and IXI (N = 563, cross-vendor), the method detects biologically plausible scanner, age, and sex effects; on IXI, significance maps form strict subsets of the reference R pTFCE output, which supports conservative family-wise error control. Both methods are implemented in pytfce, a pure-Python package with no R or FSL dependencies, available on PyPI. The baseline reimplementation completes whole-brain voxel-based morphometry in [~] 5 s (75 x faster than R pTFCE), while the hybrid variant completes in [~] 85 s (4.6 x faster) with the advantage of exact cluster-size retrieval; both are more than three orders of magnitude faster than permutation-based TFCE. HighlightsO_LIHybrid eTFCE-GRF: exact cluster-size retrieval with analytical inference C_LIO_LIFWER controlled at nominal level in Monte Carlo validation C_LIO_LIpytfce package 4.6 x (hybrid) to 75 x (baseline) faster than R pTFCE C_LIO_LIValidated on UK Biobank (N = 500) and IXI (N = 563) C_LIO_LIOpen-source Python package: pip install pytfce C_LI

neuroscience↗

bioRxiv2026

Histone neutralization protects the ischemic brain against stroke-associated pneumonia

Bacterial pneumonia aggravates ischemic stroke via mechanisms that still remain to be determined. In ischemic stroke patients and mice exposed to middle cerebral artery occlusion, we show that stroke-associated pneumonia markedly worsens clinical stroke outcome. In mice, pneumonia induced 3 days after stroke impaired neurological recovery and increased brain neutrophil infiltrates, blood-brain barrier breakdown, cerebral microvascular thrombosis, and progressive brain atrophy. The antibiotic amoxicillin only partially ameliorated pneumonia-associated neurological deficits and neutrophil infiltrates. Neutrophils were critical mediators of pneumonia-induced blood-brain barrier breakdown and microvascular thrombosis. Notably, administration of a neutralizing anti-histone antibody during pneumonia--unlike degradation or blockade of neutrophil extracellular trap formation or myeloperoxidase inhibition--restored long-term neurological recovery and prevented brain atrophy in stroke-associated pneumonia mice. This study identifies extracellular histones as key drivers of secondary inflammatory brain injury and establishes histone neutralization as a therapeutic strategy with an extended treatment window in the post-acute stroke phase. One Sentence SummaryNeutralizing extracellular histones reverses pneumonia-driven secondary brain injury and restores long-term recovery after ischemic stroke.

neuroscience↗

bioRxiv2026

UBE3C retrofits the proteasome to enforce degradation of ultra-stable folds

Neurodegeneration and proteinopathies arise from mutated or misfolded proteins that form ultra-stable, proteotoxic aggregates that evade proteasomal degradation despite ubiquitylation1-8. These conditions are exacerbated by the deubiquitylase USP14 that can prematurely rescue substrates9-11. To counter these obstacles to effective proteolysis, eukaryotes evolved the highly conserved HECT-type E3 ligase UBE3C--dysregulated in neurodegeneration12,13 and overexpressed in cancers14-17--to reprogram the 26S proteasome toward heightened activity via unknown mechanisms18-25. Here we visualized functional dynamics of the human UBE3C-retrofitted proteasome enforcing degradation of a re-engineered superfolder GFP that normally escapes proteolysis, using time-resolved cryo-electron microscopy. A continuum of non-equilibrium conformations, comprising fourteen proteasome conformers orthogonally combined with four UBE3C states or three USP14 states, reveals a cryptic UBE3C-receptor site in the proteasomal lid and captures key intermediates of ubiquitin-chain elongation and branching at four linkage-specific ubiquitin-binding sites. Remarkably, UBE3C creates an extreme shortcut for ubiquitin shuttling that bypasses USP14, promotes USP14 recycling and allosterically strengthens substrate-unfolding forces of the AAA-ATPase unfoldase to ultimately surmount the unfolding energy barrier of ultra-stable proteins. These findings define the complete functional cycle of the UBE3C-reprogrammed proteasome, illuminating how UBE3C simultaneously controls USP14 and the proteasome to enforce clearance of ultra-stable folds, and establish crucial mechanistic foundations for proteolysis-targeting therapeutic discovery.

biochemistry↗

bioRxiv2026

Differential higher-order superassembly of HECT-type UBE3 ligases controlled by calcium signals

The yeast E4 ligase Hul5 transiently associates with the proteasome during heat shock and proteotoxic stress to maintain cytosolic protein quality control. Two of its human orthologs, UBE3B and UBE3C, have specialized to safeguard mitochondria and inherit Hul5 function, respectively. We determined cryo-electron microscopy structures of full-length human UBE3B and UBE3C in complex with calmodulin at 2.9-3.5 [A] in calcium-free and calcium-saturated conditions. Calmodulin acts as an inter-protomer molecular glue clamping UBE3B into ring-shaped anti-parallel homodimers or asymmetric trimers, and remodels UBE3C into a conformation competent for proteasome association. Calcium binding to calmodulin promotes disassembly of these higher-order complexes, toggling UBE3B/C from E4 to E3 activity. These findings reveal how calmodulin regulates higher-order architectures of E3/E4 ligases to rewire the ubiquitin-proteasome system via calcium signaling.

biochemistry↗

bioRxiv2025

De novo design of protein binders that target DELE1 to inhibit the mitochondrial stress response

Mitochondrial stress activates the integrated stress response (ISR) through the mitochondrial protein DELE1, which relays stress signals to the cytosolic kinase HRI to induce ATF4. Dysregulation of DELE1-mediated signaling has been implicated in pathological conditions, yet molecular strategies to modulate DELE1 remain unavailable. Here, we report de novo designed proteins that bind DELE1, block its oligomerization, and inhibit DELE1-mediated ISR activation. Several designs form stable complexes with DELE1 and disrupt its oligomerization in vitro while preserving DELE1s ability to bind HRI. In cells, these designs suppress ATF4 induction during mitochondrial stress and impair the recovery of elongated mitochondrial morphology following transient insult. Crystal structure of a representative binder, together with structural modeling and targeted mutagenesis, confirm that the designed proteins engage a critical interface required for DELE1 oligomerization. These findings establish DELE1 as a druggable target and demonstrate that de novo designed proteins offer precise tools to modulate this pathway, providing a foundation for future therapeutic exploration. SIGNIFICANCEMitochondrial stress activates the integrated stress response through the signaling protein DELE1, but no molecular tools have been available to directly target DELE1 and selectively modulate this pathway. We developed de novo designed protein binders that recognize a critical oligomerization interface in DELE1, disrupt its assembly, and suppress mitochondrial stress-induced ISR activation in cells. These binders also impair recovery of mitochondrial network morphology following transient stress, linking DELE1 assembly to adaptive remodeling. Our study establishes DELE1 as a tractable and druggable target in mitochondrial stress signaling and demonstrates that de novo protein design can generate precise modulators of intracellular stress-response pathways.

biochemistry↗

bioRxiv2025

Immunogenicity and Protective Efficacy of Toxoplasma gondii SRS67 and SRS20A Proteins in Mice

Toxoplasma gondii, a medically significant zoonotic protozoan, relies on surface SAG-family antigens for host adhesion and immune evasion. SRS67 and SRS20A, two such antigens, are hypothesized to be highly immunogenic. Here, we comprehensively evaluated their immunological properties and protective efficacy in mice. Recombinant SRS67 and SRS20A, produced via prokaryotic expression, induced high-titer, specific polyclonal antisera in BALB/c mice and rabbits. Western blotting confirmed these antisera recognized native RH strain tachyzoite antigens, validating strong immunogenicity. qPCR showed SRS67 transcription was higher in PRU (chronic) than RH (acute) strains, while SRS20A expression was comparable. Immunofluorescence, using GAP45 as a membrane marker, localized both proteins as puncta around RH tachyzoite membranes. In vitro phenotypic assays indicated that the corresponding polyclonal antibodies significantly inhibited the invasion of host cells by T. gondii and subsequent intracellular proliferation. Challenge studies demonstrated protective efficacy: in acute RH infection, immunized mice (single or combined antigens) showed 60% higher survival, with bivalent groups delaying mortality by 1-2 days. In chronic PRU infection, all immunized groups had >85% fewer brain cysts, and bivalents better attenuated neuropathology (e.g., meningeal thickening). Mechanistically, SRS67 upregulated Th1 cytokines (TNF-, IL-18) in acute infection, while both proteins downregulated IFN-{gamma} to limit immune-mediated damage; GSH upregulation indicated antioxidative involvement. In chronic infection, co-immunization synergistically enhanced SOD activity to counter oxidative stress, with IFN-{gamma} downregulation maintaining immune homeostasis. These findings support SRS67 and SRS20A as promising T. gondii vaccine candidates, acting via coordinated Th1 immunity and antioxidative pathways.

zoology↗

bioRxiv2025

SpaTranslator: A deep generative framework for universal spatial multi-omics cross-modality translation

Recent advances in spatial omics technologies have enabled the simultaneous analysis of multiple molecular patterns in tissue sections, offering unprecedented insights into cellular microenvironments. However, the high cost of measurements and the sparsity of data restrict the availability of paired spatial multi-omics datasets. Here, we present SpaTranslator, a deep generative framework that integrates graph neural networks with an adversarial variational generative model to fully capture spatial characteristics and enable effective cross-modality translation of spatial omics data, enabling simulation of paired spatial multi-omics data from single-omics measurements. Extensive experiments demonstrate that SpaTranslator consistently outperforms baseline methods in both clustering accuracy and biological coherence across various real-world scenarios, including spatial transcriptomics-epigenomics and spatial transcriptomics-proteomics translation tasks. Furthermore, SpaTranslator provides biologically meaningful insights through marker genes and proteins recovery, motif enrichment analysis, and gene regulation inference. Our work offers an effective and adaptable solution for spatial multi-omics cross-modality translation, supporting a broad range of biological and biomedical research.

bioinformatics↗

bioRxiv2025

Can large language models reliably extract human disease genes from full-text scientific literature?

Manual extraction of high-fidelity gene-disease-phenotype information from human genetics literature is a labor-intensive task that requires trained human genetics researchers to read through many primary research papers. This presents a major challenge for maintaining up-to-date human disease genetic databases. Recent exploration into large language models (LLMs) opens new directions in automating this manual process. However, most approaches depend on pre-training, finetuning, or specialized generative artificial intelligence (GenAI) tools, but there is a lack of empirical evidence to show whether commercially-available LLMs can be directly used to reliably extract gene-disease-phenotype for human genetic diseases. Herein, we perform a benchmark of the use of three zero-shot prompted LLMs, namely GPT-4, DeepSeek and Claude, without task-specific fine-tuning, to extract human genetic information directly from full text of scientific papers. Using known congenital heart diseases (CHD) genes found in the open access CHDgene database (https://chdgene.victorchang.edu.au/) as the benchmark data set, GPT-4o achieved overall 88.8% extraction accuracy across 23 gene entries containing over 57 references, with 100% accuracy in gene name, 78.3% and 76.7% in disease and phenotype fields respectively. This work introduces a lightweight, easy-to-deploy, and yet robust LLM-based agent named GeneAgent, analyze sources of disagreement, and highlight the feasibility of integrating powerful LLM into genetic evidence synthesis workflows. Highlight- First systematic benchmark of LLMs for extracting human gene-disease-phenotype relationships from full-text biomedical articles - GeneAgent: a lightweight, highly accurate prompt-only LLM agent - New domain task-specific evaluation framework

bioinformatics↗

bioRxiv2025

AtlasAgent: Vision language model and Agent-guided Framework for Evaluation of Atlas-scale Single-cell Integration

As single-cell omics transitions into the era of AI-virtual cells (AIVC), where large-scale single-cell data integration becomes prevalent, the computational demands of integration evaluation emerge as critical scalability bottlenecks. Traditional integration evaluation pipelines, requiring metrics like k-nearest-neighbor batch effect test (kBET) and Local Inverse Simpsons Index (iLISI) employed by state-of-the-art scIB method, often demand large computational resources and long runtimes, making them infeasible for large scale integration studies. Herein, we present AtlasAgent, the first vision-language model (VLM)-powered and AI agent framework to accelerate atlas-scale integration evaluation at unprecedented speed and scale. We systematically evaluate batch correction quality, biological signal preservation and overcorrection risks using chain-of-thought reasoning in conjunction with few-shot and zero-shot prompting strategies. AtlasAgent completes evaluation within 32 seconds, in contrast to scIBs runtime of 5.55 hours in GPU, while identifying the scIB-determint best integration methods within the top-3 in 88.3% of the time, lowering evaluation time from hours to seconds while preserving alignment with domain expert reasoning. AtlasAgent pioneers the use of VLMs to realize scalable and rapid integration evaluation at atlas scale.

bioinformatics↗

bioRxiv2025

Comparative ScRNA-Seq Profiling of Antigen-Specific CD4+ T cells in Semi-Allogeneic Transplantation and Pregnancy Reveals Intersecting Signatures of Rejection and Tolerance

Transplantation of allogeneic organs requires lifelong immunosuppression to prevent rejection. Prior sensitization and resultant memory T cells are barriers to achieving successful transplant tolerance. In reproductive immunology by contrast, pregnancy represents a spontaneous model of tolerance where the semi-allogeneic fetus evades rejection even in multiparous or rejection-sensitized mothers. CD8+ T cell phenotypes of tolerance and rejection have been previously reported in transplant and pregnancy, but the transcriptional states of donor and fetus-specific CD4+ T cells remain poorly defined. Here, we performed Single-cell RNA-sequencing on endogenous, antigen-specific CD4+ T cells across models of allogeneic heart transplants and naive or paternal skin-sensitized pregnancy. We identified expanded T follicular helper (Tfh) and non-follicular effectors in transplant rejection absent in tolerance. Naive pregnancy resulted in a modest expansion of effector clusters with transcriptional quiescence that mirrored virgin mice. Successful sensitized pregnancy resulted in expanded Tfh clusters consistent with increased fetal-specific antibodies and limited non-Tfh effector responses. Most striking were the extensive changes imposed on donor-specific Foxp3pos regulatory T cells (Tregs) resulting in the co-clustering together with Foxp3neg T conventional cells (Tconvs) in transplant tolerance and the emergence of a Foxp3neg Type I Regulatory cluster observed in pregnancy of sensitized dams. Finally, we showed that these transcriptomes were relevant and enriched in human datasets of health and disease respectively. Thus, the context-dependent signatures of antigen-specific CD4+ T cells provide new insights into their divergent responses to allogeneic conflict at the intersection of transplant and reproductive immunology. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=178 HEIGHT=200 SRC="FIGDIR/small/663404v2_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@1b30a86org.highwire.dtl.DTLVardef@17820cforg.highwire.dtl.DTLVardef@1b1fbeaorg.highwire.dtl.DTLVardef@11ff17_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

Rapaprotin is Activated by an Endopeptidase to Disassemble 26S Proteasome

The 19S regulatory particle (RP) associates with the 20S core particle (CP) to form the 26S proteasome, an evolutionarily conserved holoenzyme that plays key roles in both physiological and pathological processes. Proteasome inhibitors that target the catalytic subunits within the 20S have proven to be valuable research tools and therapeutics for various cancers. Herein we report the discovery of rapaprotin, a 26S proteasome assembly inhibitor from our natural product-inspired hybrid macrocycle rapafucin library. Rapaprotin induces apoptosis in both myeloma and leukemia cell lines. Genome-wide CRISPR-Cas9 screen identified a cytosolic enzyme, prolyl endopeptidase (PREP) that is required for the pro-apoptotic activity of rapaprotin. Further mechanistic studies revealed that rapaprotin acts as a molecular transformer, changing from an inactive cyclic form into an active linear form, rapaprotin-L, upon PREP cleavage, to block 26S proteasome activity. Time-resolved cryogenic electron microscopy (cryo-EM) revealed that rapaprotin-L induces dissociation of the 19S RP from the 26S holoenzyme, which was verified in cells. Furthermore, rapaprotin exhibits a marked synergistic effect with FDA-approved proteasome inhibitors and resensitizes drug-resistant multiple myeloma cells from patients to bortezomib. Taken together, these results suggest that rapaprotin is a new chemical tool to probe the dynamics of the 26S proteasome assembly and a promising anticancer drug lead.

pharmacology and toxicology↗

bioRxiv2024

Efficacy and limitations of combined A2.CAR Treg and anti-CD154 therapy in a mouse model of haplo-mismatched heart transplantation

Successful allograft specific tolerance induction would eliminate the need for daily immunosuppression and improve post-transplant quality of life. Adoptive cell therapy with regulatory T cells expressing donor-specific Chimeric Antigen Receptors (CAR-Tregs) is a promising strategy, but as monotherapy, cannot prolong the survival with allografts with multiple MHC mismatches. Using an HLA-A2-transgenic haplo-mismatched heart transplantation model in immunocompetent C57Bl/6 recipients, we show that HLA-A2-specific (A2) CAR Tregs was able to synergize with low dose of anti-CD154 to enhance graft survival. Using haplo-mismatched grafts expressing the 2W-OVA transgene and tetramer-based tracking of 2W- and OVA-specific T cells, we showed that in mice with accepted grafts, A2.CAR Tregs inhibited endogenous non-A2 donor- specific T cell, B cell and antibody responses, and promoted a significant increase in endogenous FoxP3+Tregs with indirect donor-specificity. By contrast, in mice where A2.CAR Tregs failed to prolong graft survival, FoxP3neg A2.CAR T cells preferentially accumulated in rejecting allografts and endogenous donor-specific responses were not controlled. This study therefore provides the first evidence for synergy between A2.CAR Tregs and CD154 blockade to promote infectious tolerance in immunocompetent recipients of haplo-mismatched heart grafts and defines features of A2.CAR Tregs when they fail to reshape host immunity towards allograft tolerance.

immunology↗

bioRxiv2024

CRISPR-GPT: An LLM Agent for Automated Design of Gene-Editing Experiments

Performing effective gene-editing experiments requires a deep understanding of both the CRISPR technology and the biological system involved. Meanwhile, despite their versatility and promise, Large Language Models (LLMs) often lack domain-specific knowledge and struggle to accurately solve biological design problems. We present CRISPR-GPT, an LLM agent system to automate and enhance CRISPR-based gene-editing design and data analysis. CRISPR-GPT leverages the reasoning capabilities of LLMs for complex task decomposition, decision-making, and interactive human-artificial intelligence (AI) collaboration. This system incorporates domain expertise, retrieval techniques, external tools, and a specialized LLM fine-tuned with open-forum discussions among scientists. CRISPR-GPT assists users in selecting CRISPR systems, experiment planning, designing gRNAs, choosing delivery methods, drafting protocols, designing assays, and analyzing data. We showcase the potential of CRISPR-GPT by knocking-out four genes with CRISPR-Cas12a in a human lung adenocarcinoma cell line and epigenetically activating two genes using CRISPR-dCas9 in human melanoma cell line. CRISPR-GPT enables fully AI-guided gene-editing experiment design and analysis across different modalities, validating its effectiveness as an AI co-pilot in genome engineering.

bioinformatics↗

bioRxiv2023

Differential developmental blueprints of organ-intrinsic nervous systems

The organ-intrinsic nervous system is a major interface between visceral organs and the brain, mediating important sensory and regulatory functions in the body-brain axis and serving as critical local processors for organ homeostasis. Molecularly, anatomically, and functionally, organ-intrinsic neurons are highly specialized for their host organs. However, the underlying mechanism that drives this specialization is largely unknown. Here, we describe the differential strategies utilized to achieve organ-specific organization between the enteric nervous system (ENS)1 and the intrinsic cardiac nervous system (ICNS)2, a neuronal network essential for heart performance but poorly characterized. Integrating high-resolution whole-embryo imaging, single-cell genomics, spatial transcriptomics, proteomics, and bioinformatics, we uncover that unlike the ENS which is highly mobile and colonizes the entire gastrointestinal (GI) tract, the ICNS uses a rich set of extracellular matrix (ECM) genes that match with surrounding heart cells and an intermediate dedicated neuronal progenitor state to stabilize itself for a beads-on-the-necklace organization on heart atria. While ICNS- and ENS-precursors are genetically similar, their differentiation paths are influenced by their host-organs, leading to distinct mature neuron types. Co-culturing ENS-precursors with heart cells shifts their identity towards the ICNS and induces the expression of heart-matching ECM genes. Our cross-organ study thus reveals fundamental principles for the maturation and specialization of organ-intrinsic neurons.

neuroscience↗