Biology subjects

Zheng, S.

Publications and source records attributed to Zheng, S..

At least 73 records · Page 4Linked to original sources

bioRxiv2025

DICAR-JP Regulates Ribosome Migration, a New Theory of Mitochondrial Protein Production

BackgroundDICAR plays a cardioprotection of diabetic models. DICAR-JP, a short sequence of DICAR, may represent DICARs active functional domain. NAC is necessary for the heart tissue development. Addtionally, OGDHL is a metabolism regulator. In this study, we examine a new mechanism of DICAR via NAC/OGDHL on regulating cardiomyocyte metabolism in DCM. We also sought to elucidate the function of DICAR/DICAR-JP in modulating NAC-dependent production of OGDHL nascent peptides and their mitochondrial translocation, and we also aimed to optimize the DICAR-JP sequence to be better therapy on DCM. MethodsSPR was to investigate the binding interaction between DICAR-JP and NAC. The function of DICAR-JP and DICAR-JP45 on OGDHL nascent peptide transfection was detected by in vitro translation approach. Biotin-DICAR-JP was transfected to study the function of DICAR-JP, NAC and OGDHL. Untargeted metabolomics was utilized to characterize the metabolic reprogramming of cardiomyocytes. AAV9-DICAR-JP and DICAR-JP45 were constructed and administered to db/db mice for 1-2 months to assess their cardioprotective effects detected by Echocardiography confirmed mouse cardiac function. ResultsOur findings demonstrated that DICAR-JP be the functional domain that interacts with NAC to regulate OGDHL nascent peptide expression. Disruption of OGDHL expression reversed the metabolic reprogramming observed in diabetic cardiomyocytes, highlighting its crucial role in maintaining cardiac metabolic homeostasis. DICAR-JP facilitated the translocation of OGDHL nascent peptides from the cytoplasm to the mitochondria, leading us to hypothesize that DICAR-JP plays a key role in regulating ribosomal migration from the endoplasmic reticulum to the mitochondria. We refer to this process as the Ribosome Migration. In our studies using AAV9-mediated DICAR-JP and DICAR-JP45 overexpression in heart tissue. DICAR-JP and DICAR-JP45 both exhibited significant cardioprotective effects against diabetic cardiomyopathy (DCM), comparable to those observed with Empagliflozin. ConclusionsIn a conclusion, we propose a new endogenous nucleic acid candidate drug library, and a new molecular framework, the Ribosome Migration, which implicates the DICAR-JP/NAC/OGDHL nascent peptide axis in metabolic reprogramming related to DCM. This theory constructs a new mitochondrion protein from nuclear original protein. Moreover, DICAR-JP45 shows strong potential as a nucleic acid-based therapeutic candidate for treating DCM. Novelty and SignificanceO_ST_ABSWhat Is Known?C_ST_ABSDICAR is a new protective circular noncoding RNA for diabetic cardiomyopathy. NAC is located in ribosome and regulates new peptide production, which is also a key factor in heart tissue development. OGDHL can regulate different energy metabolism in mitochondria. What New Information Does This Article Contribute?DICAR-JP is the functional domain that interacts with NAC to regulate OGDHL nascent peptide expression. There is an interaction between DICAR-JP and NAC. DICAR-JP sequence is protected by NAC and NAC function is mediated by DICAR-JP. We called this RNA functional domain. OGDHL expression reversed the metabolic reprogramming observed in diabetic cardiomyocytes, highlighting its crucial role in maintaining cardiac metabolic homeostasis. DICAR-JP takes part in tanscription and translocation of OGDHL nascent peptides from the cytoplasm to the mitochondria via plasma ribosome. We call this process the Ribosome Migration Theory. DICAR-JP and DICAR-JP45 both exhibited significant cardioprotective effects against diabetic cardiomyopathy (DCM), comparable to those observed with Dapagliflozin (DAPA). Our study revealed a novel molecular framework, the Ribosome Migration Theory, which implicates the DICAR-JP/NAC/OGDHL nascent peptide axis in metabolic reprogramming related to DCM. Moreover, DICAR-JP45 shows strong potential as a nucleic acid-based therapeutic candidate for treating DCM.

pharmacology and toxicology↗

bioRxiv2025

Capturing Natural Evolution in Function-guided RNA Design via Genomic Foundation Models

RNAs play critical roles in gene expression regulation, catalysis, genetic information transmission, and immune response. However, current RNA engineering approaches face limitations in designing RNA for efficient in vivo folding and functional execution. Here, we present a novel, zero-shot strategy that integrates genomic large language models (LLMs) with inverse folding models (IFMs) to generate RNA sequences that mirror natural evolutionary patterns while preserving the structural integrity of critical functional regions. Benchmarking 9 state-of-the-art genomic foundation models, we show that their unsupervised log-likelihood scores correlate strongly with RNA fitness, enabling zero-shot sequence design. Applied to fluorescent RNA aptamers, our approach yielded 20 Broccoli variants, over half of which achieved up to 155% in fluorescence and a twofold increase in binding affinity, with robust performance both in vitro and in vivo. For Pepper, two rounds of optimization produced 40 variants, 16 of which demonstrated up to 2.6 fold in fluorescence and a threefold boost in binding affinity, including five with superior in vivo brightness.This approach offers a scalable, efficient, and resource-light method for RNA design, with significant implications for RNA-based therapeutics and diagnostics. By leveraging general foundation models to facilitate RNA evolution in a zero-shot fashion, we believe our work represents a major advancement in the field of RNA engineering.

evolutionary biology↗

bioRxiv2025

Strengthening Antisense Oligonucleotide-Mediated Anti-Tumor Immunity via Metal-Organic Framework Nanoparticles

Overexpression of checkpoint proteins, such as programmed death ligand one (PD-L1), prevents immune recognition and enables cancer growth. Current monoclonal antibodies that block PD-L1 tend to be fragile, unable to penetrate tumors, and target cancer at later stages, thus leading to inconsistent patient outcomes. Antisense oligonucleotides (ASOs) provide an alternative to decrease PD-L1 expression, but require frequent high dosing due to fast degradation, rapid clearance, and poor cell uptake. To overcome these issues, we harnessed biocompatible metal-organic framework (MOF) nanoparticles, porous nanomaterials comprising metal nodes and organic linkers, to deliver ASOs. Encapsulating ASOs into MOFs enhances their stability and protection during intracellular delivery, leading to reduced PD-L1 expression and downstream immune recognition. Herein, we synthesized three distinct PD-L1-specific ASOs and loaded them individually into zirconium-based nano-sized NU-1000 MOFs, averaging [~]80% encapsulation efficiency. Release of encapsulated ASOs was sustained up to 7 days ex cellulo. MOF encapsulation increased ASO potency and reduced PD-L1 expression[~] 3-fold and 2-fold in triple negative breast cancer EMT6 and melanoma B16-F10 cells, respectively. We evaluated the impact of MOF-delivered ASOs on PD-L1-expressing immune cells, where we observed ca. 12-fold increases in dendritic cell co-stimulatory marker expression, and amplified T cell activation and proliferation compared to untreated cells (4-fold and 10-fold, respectively). Notably, these changes drove a 3-fold increase in tumor caspase-3 expression, a key mediator for apoptosis. This research highlights how MOFs can be harnessed to bypass ASO limitations without requiring sequence modifications, and offers a broadly applicable platform for improved oligonucleotide delivery for various genes of interest.

bioengineering↗

bioRxiv2025

Cortactin regulates metastatic dormancy of circulating tumor cells by suppressing mTOR-dependent senescence

Metastatic dormancy often refers to the stable cell cycle arrest of disseminated tumor cells (DTCs) at distant sites. However, whether circulating tumor cells (CTCs) in the blood microenvironment can enter a dormant state prior to extravasation and becoming DTCs remains unclear. Using patient-derived melanoma CTC lines and animal explant models (CDX), we identified a previously unrecognized role of the cytoskeletal regulator cortactin (encoded by CTTN) in controlling mTOR/p53-dependent senescence and metastatic dormancy. Cortactin was localized to Rab7-postive endosomes and engaged in late endosomal tethering and homeostasis. The depletion of cortactin resulted in the accumulation of aberrantly enlarged late endosomal aggregates that were positive for Rab7 and mTOR. The mTOR protein complex was accumulated and activated within these abnormal vesicular structures, leading to robust p53 activation through phosphorylation at serine (S) 15 and S33 sites. Consequently, melanoma CTCs underwent G0/G1 cell cycle arrest and entered cellular senescence. This unusual oncogene-induced senescence (OIS) mechanism was characterized by SASP upregulation, {beta}-galactosidase activity, depletion of Ki-67 and Lamin B1, and elevated mitochondrial ROS (mtROS) levels. Notably, a positive feedback loop between p53 and mtROS was essential for maintaining stable senescence in CTCs. In preclinical CDX mouse models, we developed a sequential therapeutic strategy combining cortactin depletion with anti-Bcl-xL senolytic drugs. Such "One-two punch" treatment strategy effectively eliminated viable CTCs and suppressed metastatic tumor growth in vivo. Thus, targeting cortactin to induce CTC senescence, followed by senolytic therapy, may represent a promising strategy to block CTC-mediated metastatic progression.

cancer biology↗

bioRxiv2025

AreTomoLive: Automated reconstruction of comprehensively-corrected and denoised cryo-electron tomograms in real-time and at high throughput

A high throughput processing pipeline that performs comprehensive corrections is needed to realize the full potential of cryo-electron tomography and subtomogram averaging. The fields fragmented software landscape remains a significant hurdle to this end. Here we present AreTomoLive, an automated real-time pipeline composed of two GPU-accelerated packages. The first, AreTomo3, streamlines tomographic alignment and reconstruction, with new features to fully account for sample geometry, locally correct the contrast transfer function, and curate data for downstream tasks. The second package, DenoisET, is a new implementation of the machine learning algorithm Noise2Noise and runs in parallel with AreTomo3 to perform contrast enhancement. To reduce barriers to routine use, AreTomoLive prioritizes automation: AreTomo3 autonomously pauses and reactivates processing depending on the status of data collection, while DenoisET algorithmically determines when to transition from training to inference. AreTomoLive endeavors to advance cryoET for in situ structural analysis with its comprehensive corrections and full automation.

cell biology↗

bioRxiv2025

Deconvolution to restore cryo-EM maps with anisotropic resolution

With technological advancements in recent years, single particle cryogenic electron microscopy (cryo-EM) has become a major methodology for structural biology. Structure determination by single particle cryo-EM is premised on randomly orientated particles embedded in a thin layer of vitreous ice to resolve high-resolution structure in all directions. In practice, preferentially distributed particle orientations and/or other imperfections in imaging and data processing deteriorate quality of obtained cryo-EM map. Here we present a deconvolution approach, named AR-Decon, that computationally improves the quality of cryo-EM maps. We tested and validated the procedure, compared its performance with that of machine learning based density modification method, and benchmarked its performance with a wide range of deposited maps. Our results show that AR-Decon is robust and is a generally applicable post-processing procedure for single particle cryo-EM.

biophysics↗

bioRxiv2025

Population-scale gene expression analysis reveals the contribution of expression diversity to the modern wheat improvement

Changes in gene expression are crucial for crop breeding, yet population genomics has primarily focused on sequence polymorphisms rather than gene expression diversity. The strategy of using single genome reference for RNA-seq analysis could not handle introgression bias, especially for hexaploidy wheat. Here, we conducted RNA-seq for 328 wheat lines, including representative diverse landraces and elite cultivars from China and the United States, to investigate the role of gene expression variation in shaping agronomic traits. Using pan-genome resources, we identified 20,615 more transcripts than using the Chinese Spring reference genome alone. We constructed a pan-gene atlas regulatory map through eQTL analysis, demonstrating that genes introgressed from wild relatives were under tight genetic control. Genes responding to environmental stress show higher activity after introgressed into the wheat genome, demonstrating how long-term breeding selection impacted the gene expression regulation of targeted introgression. Multi-omics modeling identified 231 high-confidence candidate genes for 34 field agronomic traits and the seedling resistance phenotypes of 8 powdery mildew isolates. More than one fifth of those candidates have no homolog in Chinese Spring reference genome. By utilizing the indexed KN9204 EMS library, 80% candidates showed significant trait difference between wild type and mutant lines. Furthermore, directional shifts in genes of which expression were changed by breeding improvement demonstrated distinct adaptations to local environments. Our study constructed a pan-gene atlas to correct the reference bias of reads mapping in RNA-seq studies and revealed the expression patterns of introgressed genes within the wheat genome and their regulatory mechanisms, which highlighted the impact of breeding selection on gene expression of the worlds most important crop.

plant biology↗

bioRxiv2025

Composition and RNA binding specificity of metazoan RNase MRP

Ribonuclease (RNase) MRP is a conserved RNA-based enzyme that is essential for maturation of ribosomal RNA (rRNA) in eukaryotes. However, the composition and RNA substrate specificity of this multisubunit ribonucleoprotein complex in higher eukaryotes remain a mystery. Here, we identify NEPRO and C18ORF21 as constitutive subunits of metazoan RNase MRP. Both proteins are specific to RNase MRP and are the only ones distinguishing this enzyme from the closely related RNase P, which selectively cleaves transfer RNA-like substrates. We find that NEPRO and C18ORF21 each form a complex with all other subunits of RNase MRP, stabilize its catalytic RNA, and are required for rRNA maturation and cell proliferation. We harness our discovery to identify a full suite of in vivo RNA targets of each enzyme, including positions of potential cleavage sites at nucleotide resolution. These findings resolve the general composition of metazoan RNase MRP, illuminate its RNA binding specificity, and provide valuable assets for functional exploration of this essential eukaryotic enzyme.

molecular biology↗

bioRxiv2025

GerNA-Bind: Geometric-enhanced RNA-ligand Binding Specificity Prediction with Deep Learning

RNA molecules are essential regulators of biological processes and promising therapeutic targets for various diseases. Discovering small molecules that selectively bind to specific RNA conformations remains challenging due to RNAs structural complexity and the limited availability of high-resolution data. Herein, we introduce GerNA-Bind, a geometric deep learning framework to predict RNA-ligand binding specificity by integrating multi-state RNA-ligand representations and interactions. GerNA-Bind achieves state-of-the-art performance on multiple benchmark datasets and excels in predicting interactions for low-homology RNA-ligand pairs. It achieves a 20.8% improvement in precision for binding site prediction compared to AlphaFold3. Furthermore, it offers informative, well-calibrated predictions with built-in uncertainty quantification. In a large-scale virtual screening application, GerNA-Bind identified 18 structurally diverse compounds targeting the oncogenic MALAT1 RNA, with experimentally confirmed submicromolar affinities. Among them, one lead compound selectively binds the MALAT1 triple-helix, reduces its transcript levels, and inhibits cancer cell migration. These findings highlight GerNA-Binds potential as a powerful tool for RNA-focused drug discovery, offering both accuracy and biological insight.

bioinformatics↗

bioRxiv2025

Generalization but not strengthening of negative memories drives the development of depression-like behaviors

Individuals with depression exhibit intensified and generalized negative memories. However, how prior memories actually influence onset and/or progression of depression, particularly whether the strengthening or the generalization of prior memories play a key role, is poorly understood. Here, we introduced behavioral paradigms to differentiate between memory strengthening and generalization, and found that negative experiences that produced memory overgeneralization, but not strengthening, induced depression-like behaviors. Furthermore, we identified that the medial prefrontal cortex (mPFC) to the bed nucleus of the stria terminals (BNST) projection functionally linked memory generalization to depression, together with single-cell calcium imaging which revealed a significant overlap in the mPFCBNST neuronal ensemble encoding depression-like behaviors with that encoding generalized memory, but not memory strength. Circuit-based transcriptomic analysis and chromophore-assisted light inactivation demonstrated that triggering actin remodeling in the mPFCBNST neurons, a memory consolidation mechanism enhanced generalization while reducing memory strength, also induced depression-like behaviors. Collectively, these findings suggest generalization of negative memories as a primary factor that drives depression-like behaviors, highlighting the importance of early identification of individuals at risk for depression who exhibit overgeneralized negative memories, and targeting overgeneralized negative memories in the treatment of depression.

animal behavior and cognition↗

bioRxiv2025

Multi-omic Characterization of HIV Effects at Single Cell Level across Human Brain Regions

HIV infection exerts profound and long-lasting neurodegenerative effects on the central nervous system (CNS) that can persist despite antiretroviral therapy (ART). Here, we used single-nucleus multiome sequencing to map the transcriptomic and epigenetic landscapes of postmortem human brains from 13 healthy individuals and 20 individuals with HIV who have a history of treatment with ART. Our study spanned three distinct regions--the prefrontal cortex, insular cortex, and ventral striatum--enabling a comprehensive exploration of region-specific and cross-regional perturbations. We found widespread and persistent HIV-associated transcriptional and epigenetic alterations across multiple cell types. Detailed analyses of microglia revealed state changes marked by immune activation and metabolic dysregulation, while integrative multiomic profiling of astrocytes identified multiple subpopulations, including a reactive subpopulation unique to HIV-infected brains. These findings suggest that cells from people with HIV exhibit molecular shifts that may underlie ongoing neuroinflammation and CNS dysfunction. Furthermore, cell-cell communication analyses uncovered dysregulated and pro-inflammatory interactions among glial populations, underscoring the multifaceted and enduring impact of HIV on the brain milieu. Collectively, our comprehensive atlas of HIV-associated brain changes reveals distinct glial cell states with signatures of pro-inflammatory signaling and metabolic dysregulation, providing a framework for developing targeted therapies for HIV-associated neurological dysfunction.

bioinformatics↗

bioRxiv2025

Integrate and generate single-cell proteomics from transcriptomics with cross-attention

MotivationSingle-cell RNA sequencing (scRNA-seq) and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) have experienced rapid advancements in recent years, accompanied by the development of numerous methods for analyzing scRNA-seq and CITE-seq data. These innovations have enabled deeper insights into cellular heterogeneity and functional phenotypes. However, analyzing scRNA-seq and CITE-seq data within a unified framework remains a significant challenge in the field of single-cell analysis. Specifically, this challenge centers on two primary objectives: aligning scRNA-seq and CITE-seq cells within an integrated representation space and generating antibody-derived tag (ADT) measurements for scRNA-seq cells. ResultsBy incorporating interrelationships between cells into a deep generative model with cross-attention, we introduced scProca to integrate and generate single-cell proteomics from transcriptomics. scProca delivers state-of-the-art performance in both integration and generation tasks across benchmark datasets. Furthermore, scProca can accommodate cells across experimental batches, showcasing its flexibility in complex experimental contexts. AvailabilityThe code of scProca is available at https://github.com/xiongbiolab/scProca, and replication for this study is available at https://github.com/ZzzsHuqiaAao/scProca-reproducibility.

bioinformatics↗

bioRxiv2025

Glycan-Mediated Mechanosensing Regulates Megakaryocyte-Biased Hematopoietic Stem Cell Subsets.

Definitive hematopoietic stem and progenitor cells (HSPCs) development relies on intrinsic and extrinsic programs to meet homeostatic and stress-related demands. The comprehensive mechanisms governing HSPCs fate remain poorly understood. Our study identifies B4GALT1, a glycosyltransferase essential for N-glycosylation, as a key modulator of HSPC lineage decisions. We demonstrate that B4GALT1 deficiency disrupts glycosylation patterns within the bone marrow (BM) niche, resulting in oncogenic glycan signatures and altered expression of Mucin13 in HSPCs. Loss of B4GALT1 expands HSPC pools and promotes megakaryocyte priming in HSPCs through transcriptional and chromatin modifications, enhancing the Wnt-Mucin13 axis. Mucin13, an oncogene characterized by aberrant glycosylation, underscores the critical role of B4GALT1 in sustaining BM glycosylation and mechanosensing, thereby regulating HSPC fate through functional, transcriptional, and chromatin dynamics. These observations provide insights into the impact of glycan structures on HSPC function, lineage reprogramming, and malignant transformation.

cell biology↗

bioRxiv2024

A universal single-cell transcriptomics atlas of human lung decodes multiple pulmonary diseases

Human lung is a complex organ susceptible to various diseases. Single-cell transcriptomic studies provide rich data to targeting specific research questions. Here, we present uniLUNG, the largest lung transcriptomic cell atlas, comprising over 10 million cells across 20 disease states and healthy controls. We ensembled a universal hierarchical annotation framework and conducted a full benchmarking of data integration to define a standardized nomenclature and marker genes for lung cell types. Using uniLUNG, we identified Lym-monocyte and T-like B cells, new cell types in specific lung diseases, confirming their existence by comparing with external single-cell atlases. Additionally, we discovered the NSCLC-like SCLC subpopulation, a transitional malignant cell population associated with the transition from NSCLC to SCLC, which was validated and further characterized in spatial dimensions, revealing its complex role in tumour progression. Overall, uniLUNG represents a comprehensive range of human lung cell diversity, providing valuable data resources and a reliable foundation for lung single-cell research. HIGHLIGHTSO_LIThe largest scRNA atlas for human lung covers 10 million cells from 20 lung states. C_LIO_LIA four-level universal cell annotation framework encompasses 120 lung cell types. C_LIO_LIComprehensive benchmarking on 18 strategies guides data integration. C_LIO_LISpecific distribution of Lym-monocytes and T-like B cells in specific lung diseases. C_LIO_LIThe NSCLC-like SCLC subpopulation in transitional events of malignant cells from NSCLC to SCLC. C_LI

bioinformatics↗

bioRxiv2024

A Systematic Comparison of Single-Cell Perturbation Response Prediction Models

Predicting single-cell transcriptional responses to perturbations is central to dissecting gene regulation and accelerating therapeutic design, yet the field lacks a rigorous, task-spanning assessment of model behavior. We present a large-scale benchmark of 13 representative methods and baselines across 25 datasets spanning diverse perturbation modalities and species, including two primary immune-cell drug-response resources. We evaluated three core tasks--generalization to unseen single-gene perturbations, prediction of combinatorial interactions, and transfer across cell types--using 24 metrics covering expression-level accuracy, relative changes, differential expression recovery, and distributional similarity. Across tasks, performance depended strongly on perturbation effect size and evaluation perspective: expression-level agreement was highest for small-effect perturbations resembling controls, whereas delta- and DE-based metrics improved with larger effects, providing clearer signals. Models shared a conservative bias, with fine-tuned foundation models compressing variance and underestimating synergistic effects in combinations. PerturbNet showed superior recovery of DE signatures in Tasks 1 and 2, while no method consistently generalized across cell types in Task 3, where biological consistency dominated outcomes. This benchmark establishes current methodological limits, clarifies that different metrics probe distinct biological signals rather than redundant summaries of the same prediction problem, and provides a foundation for developing virtual-cell models that more faithfully capture heterogeneous perturbation responses. Teaser: A large-scale benchmark reveals strengths and limits of models predicting single-cell perturbation responses.

bioinformatics↗

bioRxiv2024

LINC00599 Promotes Pulmonary Hypertension via LLPS with G3BP1 and MYH9

BACKGROUNDPulmonary hypertension (PH) represents a significant cardiovascular disorder marked by both functional and structural alterations within the pulmonary vasculature. Long non-coding RNAs (lncRNAs) have been closely associated with the pathogenesis and progression of PH. Nonetheless, the precise mechanisms by which lncRNAs interact with its downstream target molecules to modulate the disease remain inadequately elucidated. METHODSThe expression levels of LINC00599 were quantified in the lung tissues of mice and pulmonary arterial smooth muscle cells (PASMCs) under hypoxic conditions. The involvement of LINC00599 in the progression of PH and vascular remodeling was evaluated through in vivo studies. To investigate the mechanisms by which LINC00599 influences the proliferation of human PASMCs, small interfering RNA and overexpression plasmids were employed. RESUITSThe expression of LINC00599 is upregulated in the medial layer of pulmonary arteries in experimental models of PH and in hypoxic PASMCs. Administration of a single dose of lentivirus-mediated shRNA targeting LINC00599 effectively reverses hypoxic PH in murine models. LINC00599 plays a critical role in PASMC proliferation by modulating stress granule formation through N6-methyladenosine (m6A) modification and promoting proliferation via liquid-liquid phase separation with myosin heavy chain 9. Furthermore, the expression of LINC00599 is regulated by a super-enhancer, which is mediated by the transcription factor ZNF263. CONCLUSIONSThese findings demonstrate for the first time that LINC00599, involved in liquid-liquid phase separation, facilitates the progression of pulmonary hypertension by enhancing the proliferation of pulmonary artery smooth muscle cells. GRAPHIC ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=158 SRC="FIGDIR/small/629439v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@7ac192org.highwire.dtl.DTLVardef@13a88b3org.highwire.dtl.DTLVardef@1e4c761org.highwire.dtl.DTLVardef@ddd105_HPS_FORMAT_FIGEXP M_FIG C_FIG What Is New?This study identified LINC00599 as a critical regulator of liquid-liquid phase separation through m6A modification and as a pivotal target in promoting the progression of PH. What Is Relevant?LncRNAs are intricately associated with the pathogenesis and progression of PH through the modulation of downstream target molecules. However, it remains unclear whether lncRNAs contribute to PH by forming specific subcellular structures with these target molecules. Furthermore, some studies have suggested that the increased presence of stress granules in PASMCs is implicated in the pathogenesis of pulmonary arterial hypertension. In this study, we provide evidence that LINC00599 promotes the development of PH by forming stress granules with its target proteins. Our findings indicate that the binding of LINC00599, modified with m6A, to G3BP1 and MYH9 in PASMCs suggests that the regulation of liquid-liquid phase separation by lncRNA m6A modification may represent a significant pathological mechanism underlying PH. What Are the Pathophysiological Implications?We found that LINC00599 levels were elevated in hypoxic PASMCs and experimental PH models. Knockout LINC00599 effectively prevented PH, indicating its potential as both a bomarker and therapeutic target for PH.

physiology↗

bioRxiv2024

Natural monoclonal autoantibodies against HERV-K102 Envelope-TM from SLE patients selectively eliminate autoreactive immune cells and cancer cells

In sharp contrast to immune dysfunction in cancer, autoimmune diseases such as systemic lupus erythematosus (SLE) exhibit excessive immune reactions characterized by high titers of autoantibodies to HERV-K102-envelope (HERV-K102-Env) neoantigens, which are frequently found in cancer patients but seldom elicit a strong immune response. Here we aim to address whether anti-HERV-K102-Env autoantibodies in SLE can effectively eliminate autoreactive immune cells and cancer cells expressing HERV-K102-Env neoantigens. We established world-first fully human autoantibody phage display library with 3.67x108 cfu using peripheral blood mononuclear cells (PBMCs) from SLE patients. Through high-throughput screening, we identified nineteen monoclonal autoantibodies (mAbs) targeting the conserved HERV-K102 Env-TM subunit. The EC50 values of these autoantibodies binding to the HERV-K102 Env-TM subunit ranged from 0.002436 g/ml to 1.798 g/ml. Remarkably, eleven of these mAbs not only recognized the HERV-K102 Env-TM glycoprotein on the cell surface but also effectively eliminated autoreactive B, T, and natural killer (NK) cells in SLE, as well as cancer cells. Our findings provide a conceptually new immunotherapy target HERV-K102 Env-TM subunit and open the era of using natural human autoantibodies to treat autoimmune disease and cancers. Several of these monoclonal autoantibodies show promise as potential diagnostic and therapeutic agents, paving the way for innovative approaches to treating SLE and various malignancies.

immunology↗

bioRxiv2024

EnzymeCAGE: A Geometric Foundation Model for Enzyme Retrieval with Evolutionary Insights

Enzyme catalysis is fundamental to life, driving the chemical transformations that sustain biological processes and support industrial applications. However, unraveling the intertwined relationships between enzymes and their catalytic reactions remains a significant challenge. Here, we present EnzymeCAGE, a catalytic-specific geometric foundation model trained on approximately 1 million structure-informed enzyme-reaction pairs, spanning over 2,000 species and encompassing an extensive diversity of genomic and metabolic information. EnzymeCAGE features a geometry-aware multi-modal architecture coupled with an evolutionary information integration module, enabling it to effectively model the nuanced relationships between enzyme structure, catalytic function, and reaction specificity. EnzymeCAGE supports both experimental and predicted enzyme structures and is applicable across diverse enzyme families, accommodating a broad range of metabolites and reaction types. Extensive evaluations demonstrate EnzymeCAGEs state-of-the-art performance in enzyme function prediction, reaction de-orphaning, catalytic site identification, and biosynthetic pathway reconstruction. These results highlight its potential as a transformative foundation model for understanding enzyme catalysis and accelerating the discovery of novel biocatalysts.

synthetic biology↗