Biology subjects

Jiang, Y.

Publications and source records attributed to Jiang, Y..

At least 145 records · Page 8Linked to original sources

bioRxiv2025

ProtLoc-GRPO: Cell line-specific subcellular localization prediction using a graph-based model and reinforcement learning

Subcellular localization prediction is crucial for understanding protein functions and cellular processes. Subcellular localization is dependent on tissue and cell lines derived from different cell types. Predicting cell line-specific subcellular localization using the information of protein-protein interactions (PPIs) offers deeper insights into dynamic cellular organization and molecular mechanisms. However, many existing PPI networks contain systematic errors that limit prediction accuracy. In this study, we propose a reinforcement learning approach, ProtLoc-GRPO, to enhance subcellular localization prediction by optimizing the structure of the underlying PPI network. ProtLoc-GRPO learns to rank and retain the most informative PPI edges to maximize the macro-F1 score for cell line-specific subcellular localization. Our approach yields a 7% improvement in macro-F1 score over the baseline. We further evaluate its robustness across various edge pruning rates and benchmark it against conventional pruning strategies. Results show that our proposed method consistently outperforms existing approaches. To our knowledge, this work represents the first study to predict cell line-specific protein subcellular localization and the first application of the Group Relative Policy Optimization (GRPO) framework to a graph-based model for bioinformatics tasks.

bioinformatics↗

bioRxiv2025

A Paracrine-to-Autocrine Shunt of GREM1 Fuels Colorectal Cancer Metastasis via ACVR1C

Tumor cells typically rely on paracrine stromal signals to guide malignant behaviors, but whether they acquire signaling autonomy to support metastasis remains unclear. We elucidate this in colorectal cancer (CRC) by uncovering a paracrine-to-autocrine shunt of Gremlin1 (GREM1), a canonical stromal-secreted antagonist of bone morphogenetic proteins (BMPs). We demonstrate that while GREM1 remains restricted to stromal cells in earlier-stage (I-III) CRC, its ectopic expression in tumor epithelium increases markedly in stage IV. Mechanistically, we identify ACVR1C as a novel, high-affinity epithelial receptor for GREM1. Their interaction activates SMAD2/3 signaling, which upregulates SNAI1 and GREM1, establishing a feedback loop that amplifies epithelial-mesenchymal transition (EMT). Disrupting this loop impairs CRC metastasis in vivo. Clinically, epithelial GREM1 or ACVR1C expression predicts metastasis and poor survival. These findings define a paradigm in which tumor cells hijack stromal GREM1 to establish a GREM1-ACVR1C autocrine loop that sustains EMT and metastasis, marking a shift toward signaling autonomy and revealing a targetable vulnerability in advanced CRC.

cancer biology↗

bioRxiv2025

Endothelial Kallikrein-Related Peptidase 8 Promotes Diabetic Nephropathy through a LIFR dependent mechanism

BackgroundDiabetic nephropathy (DN) is the primary microvascular complication of diabetes mellitus; however, the exact pathways in endothelial cells (ECs) linked to DN progress remain unclear. Tissue kallikrein-related peptidases (KLKs) participate in pathophysiological processes in ECs. We aimed to explore the roles of endothelial KLKs in DN and define the underlying mechanisms. Methods and resultsKLK8 was the most highly upregulated member of KLKs in renal tissues in streptozotocin (STZ)-induced diabetic mice and cultured glomerular ECs (GECs) upon high glucose (HG) treatment. Both global (Klk8-/-) and endothelial Klk8 knockout (Klk8{Delta}EC) mice displayed improved albuminuria, mesangial matrix expansion and glomerulosclerosis caused by STZ compared to Klk8f/f mice. Single-cell RNA seq (scRNA-seq) showed that many pathways associated with DN in ECs, mesangial cells (MCs) and tubule cells were reversed in Klk8{Delta}EC mice. Endothelial-to-mesenchymal transition (EndMT) was extensively improved in Klk8{Delta}EC mice, and by KLK8 siRNA in cultured GECs upon HG. Using proteome and other biochemical approaches, we revealed that KLK8 cleaved syndecan-4(SDC4), which contributed to loss of glycocalyx integrity in GECs in cultured cells and animal diabetic models. Furthermore, scRNA-seq showed that Lifr was one of the key genes linked to the disease progressed in ECs and MCs and regulated by endothelial Klk8. LIFR signaling contributed to HG-induced GEC dysfunction and MC activation, which was associated with endothelial Klk8. Knockdown of Lifr by lentivirus-Lifr shRNA ameliorated hallmark features of DN and improved EndMT and Sdc4 expression glomeruli of diabetic mice. LIFR was upregulated in GECs and MCs in DN patients. Circulatory levels of LIF, KLK8 and soluble SDC4 were increased in patients with DN, and KLK8 level was positively correlated with LIF, soluble SDC4 and creatinine levels. ConclusionEndothelial KLK8 promotes GECs dysfunction and abnormal crosstalk with MCs through a LIFR-dependent signaling in DN, which immediately highlights therapeutic targets for DN. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=171 SRC="FIGDIR/small/662673v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@dae3b1org.highwire.dtl.DTLVardef@9aab24org.highwire.dtl.DTLVardef@59eaa5org.highwire.dtl.DTLVardef@13c138a_HPS_FORMAT_FIGEXP M_FIG C_FIG

pathology↗

bioRxiv2025

An expanded reference catalog of translated open reading frames for biomedical research

Non-canonical (i.e., unannotated) open reading frames (ncORFs) have until recently been omitted from reference genome annotations, despite evidence of their translation, limiting their incorporation into biomedical research. To address this, in 2022, we initiated the TransCODE consortium and built the first community-driven consensus catalog of human ncORFs, which was openly distributed to the research community via Ensembl-GENCODE. While this catalog represented a starting point for reference ncORF annotation, major technical and scientific issues remained. In particular, this initial catalogue had no standardized framework to judge the evidence of translation for individual ncORFs. Here, we present an expanded and refined catalog of the human reference annotation of ncORFs. By incorporating more datasets and by lifting constraints on ORF length and start-codon, we define a comprehensive set of 28,359 ncORFs that is nearly four times the size of the previous catalog. Furthermore, to aid users who wish to work with ncORFs with the strongest and most reproducible signals of translation, we utilized a data-driven framework (i.e. translation signature scores) to assess the accumulated evidence for any individual ncORF. Using this approach, we derive a subset of 7,888 ncORFs with translation evidence on par with canonical protein-coding genes, which we refer to as the Primary set. This set can serve as a reliable reference for downstream analyses and validation, with a particular emphasis on high quality. Overall, this update reflects continual community-driven efforts to make ncORFs accessible and actionable to the broader research public and further iterations of the catalog will continue to expand and refine this resource.

genomics↗

bioRxiv2025

Single-Domain Antibody-Based Autophagosome-Targeting Chimera for Tau Clearance and Motor Function Restoration in Tauopathies

Tauopathies are neurodegenerative diseases characterized by pathological tau accumulation, leading to motor and neuropsychiatric symptoms. Effective tau-targeting therapies remain a major challenge. Here, we present 1D9-LIR{Delta}TP53INP2, a single-domain antibody (sdAb)-based protein degrader that facilitates tau clearance via the autophagy-lysosomal pathway. This engineered molecule combines the anti-tau sdAb 1D9 with an LC3-interacting region (LIR{Delta}TP53INP2) to promote autophagosomal recruitment, mimicking autophagy receptors by simultaneously binding tau and LC3. In frontotemporal dementia (FTD) patient-derived neurons and JNPL3 tauopathy mice, both harboring the P301L tau mutation, 1D9-LIR{Delta}TP53INP2 significantly reduced tau levels and improved motor function in mice. These findings underscore the therapeutic potential of sdAb-based protein degraders for tauopathies. Given the challenges of brain delivery for conventional antibodies, sdAbs with enhanced brain penetration and efficacy offer a promising strategy for treatment of neurodegenerative diseases.

neuroscience↗

bioRxiv2025

An Intrinsic-hoc Framework for Heterogeneous Cellular Senescence Elucidation Using Deep Graph Representation Learning and Experimental Validation

Cellular senescence is a primordial driver of tissue and organ aging, and the accumulation of senescent cells (SnCs) has been implicated in numerous age-related diseases. A major barrier to studying senescence is the rarity and heterogeneity of SnCs, which are not a uniform population but instead comprise diverse senotypes shaped by cell-of-origin and microenvironmental context. Such heterogeneity exceeds what classical senescence hallmarks can resolve at single-cell resolution, motivating the need for computational frameworks that can capture senotype-level diversity intrinsically. Here, we introduce DeepSAS, a deep graph representation learning framework that robustly identifies cell-type-specific SnCs and their senescence-associated genes (SnGs). DeepSAS incorporates a heterogeneous graph that integrates intracellular transcriptional states with intercellular communication cues, enabling the joint inference of senescent cells and senescence-linked genes through attention-based contrastive learning. Applied to public healthy eye and lung atlases, DeepSAS identified SnCs whose proportions positively correlate with aging. From in-house idiopathic pulmonary fibrosis (IPF) patient scRNA-seq data, DeepSAS detected 1,678 SnCs (out of 24,125 cells) and 263 SnGs across 26 cell types, including 43 SnGs that are uniquely associated with a single cell type. We generated high-resolution Xenium spatial transcriptomics data to further validate SnGs in IPF, revealing NFE2L2 as a SnG specifically enriched in CTHRC1+ fibroblasts. Notably, the ex vivo bleomycin-induced senescence in human precision-cut lung slice (hPCLS) samples similarly identified NFE2L2 as an SnG in CTHRC1+ fibroblasts, albeit with stronger transcriptional signals, suggesting mechanistic differences in senescence cells associated with chronic and acute injury. Overall, DeepSAS uncovers distinct senescence programs and infers cell-type-specific SnGs that are difficult to resolve using existing marker-based approaches. We believe it offers a generalizable and translationally relevant strategy for advancing senescence biology and therapeutic development.

bioinformatics↗

bioRxiv2025

Interaction of Wnt and SHH gradients synchronizes cell cycle 1 exit and differentiation

Classic models of the French flag problem depict sharp cell-type boundaries emerging from threshold responses to morphogen gradients. Yet, how such boundaries arise during dynamic cell-state transitions remains unclear. We use hair follicle dermal condensates to study a sharp cell-type transition in which proliferative progenitors synchronously undergo cell-cycle exit and molecular differentiation. Using genetic and genomic approaches, we show that Wnt and Hedgehog signaling interact to coordinate the timing of these processes. When their activities are temporally aligned, intermediate transitional states are compressed through cell-cycle exit, producing a sharp cell-type boundary; when misaligned, transitional states expand, yielding fuzzy borders. Mechanistically, elevated Wnt activity promotes cell-cycle exit by regulating chromatin binding of the Hedgehog mediator GLI3. Hedgehog signaling induces differentiation genes in a Wnt-dependent manner and simultaneously elevates Wnt activity, aligning arrest and differentiation in time. These findings reveal a mechanism wherein morphogen interactions coordinate transitions to generate precise cell-type boundaries.

developmental biology↗

bioRxiv2025

Medial preoptic area FoxO1 controls metabolic adaptation in a sexually dimorphic manner

The medial preoptic area (MPOA) of the hypothalamus is essential for metabolic adaptation to environmental challenges, though the molecular mechanisms underlying this process remain poorly understood. Here, we investigate the role of Forkhead transcription factor O1 (FoxO1), a key mediator of stress adaptation, in MPOA-dependent metabolic responses to temperature and nutritional changes. Our findings reveal sex-specific responses to both nutritional and temperature challenges. In female mice, but not males, a high-fat diet (HFD) challenge decreased FoxO1 expression in the MPOA. Specific deletion of FoxO1 in MPOA neurons (FoxO1-KOMPOA) had no effect on body weight under normal chow-fed conditions but protected females from HFD-induced obesity (DIO). These protected females exhibited increased lean mass, decreased fat mass, enhanced thermogenesis, increased energy expenditure, and reduced food intake under HFD conditions. They also showed enhanced cold-induced heat production at 6{degrees}C, though this effect vanished at thermoneutrality (30{degrees}C). The protection against DIO was abolished by ovariectomy (OVX) and was not restored by 17{beta}-estradiol supplementation, suggesting an estrogen-independent mechanism. Conversely, constitutive activation of FoxO1 in MPOA neurons (FoxO1-CAMPOA) increased DIO susceptibility in both sexes. Together, these findings demonstrate that FoxO1MPOA plays a crucial role in coordinating metabolic adaptation to nutritional and temperature challenges specifically in female mice.

physiology↗

bioRxiv2025

Cross-talk between RNA secondary and three-dimensional structure prediction: a comprehensive study

In recent years, various computational methods have been developed to predict the three-dimensional (3D) structures of RNAs. Due to its hierarchical folding property, RNA secondary (2D) structure is usually used as input for 3D structure prediction to improve accuracy and efficiency. However, the extent to which the accuracy of input 2D structure affects the performance of 3D structure prediction remains to be further investigated. Additionally, whether and how the input base-pairing interactions are modified during the 3D structure modeling process is another question worth exploring. To address these issues, here we comprehensively benchmark five representative 3D structure prediction models on extensive datasets, using 2D structures of varied accuracies as input. Our results indicate that there is a pervasive cross-talk between RNA 2D and 3D structure predictions, where the performance dependence of 3D structure prediction on the accuracy of input 2D structure is closely associated with the 3D models ability to modify the input base-pairing interactions during structure modeling. Furthermore, we also observed that RNA 3D structure prediction performance is more sensitive to the occurrence of false positive base pairs in the input 2D structure than to true positive base pairs, suggesting a worthy direction to further improve the model performance. Author summaryThree-dimensional (3D) structural modeling of RNAs with large sizes and complex topologies remains challenging despite the availability of (predicted) 2D structures as constraints. Exploring the potential cross-talk between 2D and 3D structure predictions is a worthy direction to improve the performance of RNA structure modeling. In this study, we found that all tested popular RNA 3D structure prediction models were able to modify the original base pairing interactions contained in the input 2D structure during the 3D modeling process. Especially, all of these models presented increased F1-score for the optimal combination of RNA 2D and 3D structure prediction models. The results suggest that a worthwhile direction to further improve the performance of RNA 3D structure prediction is to minimize the incidence of incorrectly predicted base pairing interactions during modeling process without compromising or even improving the presence of correct interactions in the input 2D structure.

biophysics↗

bioRxiv2025

An atlas of cell type specific regulatory effects in cattle

Understanding the genetic and molecular architecture of complex traits and artificial selection is crucial for advancing sustainable precision breeding in cattle and other livestock. Yet, how genetic variation affects cellular gene expression remains elusive in cattle. Here, by integrating 8,866 bulk RNA-seq samples and 999,192 single cells of 81 cell types in 22 bovine tissues, we presented a comprehensive atlas of regulatory variants at the cell type resolution in cattle. By colocalizing with bulk-tissue expression quantitative trait loci (beQTL), we detected 57,043 novel cell-type stratified eQTL and cell-type/state interaction eQTL in 18,153 genes, which also exhibited a stronger tissue/cell-type specificity than beQTL. By examining genome-wide associations (GWAS) of 44 complex traits, these cell-resolved eQTL were colocalized with 505 (24%) additional GWAS loci compared to beQTL. Through integrating this resource with selection signatures between dairy and beef cattle, we provided tissue/cell-specific regulatory insights into cattle breeding. Overall, the current atlas of cell-type-specific regulatory variants will serve as an invaluable resource for cattle genomics and selective breeding.

genetics↗

bioRxiv2025

Extended NGN2 Expression in iPSCs Dramatically Enhances Purity of Neuronal Cultures

iPSC-derived neuronal cultures can provide valuable insights into the pathogenesis of neurological disease. However, single-cell iPSC clones expressing NGN2 and mCherry exhibit spontaneous loss of mCherry fluorescence, raising questions about the homogeneity of neurons derived from what appear to be heterogeneous iPSCs. We find that mCherry silencing does not influence iNeurons with two lines of evidence. First, using single-cell proteomics, we found that spontaneous mCherry silencing does not drive heterogeneity in iPSCs. Second, bulk proteomics and immunofluorescence analysis indicated that iNeurons from iPSCs expressing or lacking mCherry both resemble cortical glutamatergic neurons. The primary confounding factor in iNeuron generation was that suboptimal neuronal conversion led to cell aggregates comprised of actively proliferating neuronal progenitor cells and astrocytes as the culture developed. Our results indicate that extended NGN2 dosage substantially improves neuron purity. HighlightsO_LIEF1-mCherry undergoes spontaneous silencing at the AAVS1 locus C_LIO_LISingle-cell proteomics reveals heterogeneity in edited iPSCs independent of mCherry silencing C_LIO_LImCherry silencing has minimal impact on NGN2-mediated neuronal differentiation. C_LIO_LIExtended NGN2 induction produces more homogenous neuronal cultures C_LI eTOC blurbExpression of a mCherry reporter under the EF1 promoter and integrated into the AAVS1 locus is silenced in undifferentiated iPSCs, whereas CAG-driven rtTA3G remains active for NGN2-induced neuronal differentiation. Optimizing NGN2 induction in iPSC cultures is crucial for generating homogeneous neuronal cultures, as suboptimal conditions result in heterogeneous populations enriched with neural progenitor cells (NPCs) and astrocytes, and a disrupted neuronal organization.

biochemistry↗

bioRxiv2025

Epigenetic Subtypes of High-Grade T1 Bladder Cancer Reveal Intra-Tumor Heterogeneity and Distinct Interactions with Tumor Microenvironment.

High-Grade T1 (HGT1) Non-Muscle Invasive Bladder Cancer (NMIBC) is a clinically heterogeneous disease, characterized by unpredictable treatment responses and limited tools for recurrence prediction. Although molecular classification efforts have been made, patient stratification still primarily relies on clinicopathological features, which offer limited clinical precision. In this study, we integrated chromatin profiling in bulk with single-nuclei(sn) RNA-seq, immunohistochemistry and spatial transcriptomics to define epigenetic subtypes of HGT1, characterize their heterogeneity, and investigate tumor-microenvironment interactions. Our findings reveal distinct chromatin profiles differentiating urothelial (URO) and micropapillary (MP) histological variants of high-risk HGT1. We identified three epigenetic states: two within the URO group, luminal-like inflammatory (LLI) and basal-like (BL), and a separate signature unique to MP tumors. Single-cell and spatial resolution approaches validated intratumoral heterogeneity and provided insight into subtype-specific microenvironmental contexts. Notably, [~]40% of URO tumors exhibited spatially distinct coexisting LLI and BL components. BL regions showed enrichment for angiogenesis and hypoxia pathways and were preferentially located near vascular stroma, while LLI regions showed to be located at the core of the tumor. MP tumors featured a markedly different microenvironment, characterized by diverse populations of cancer-associated fibroblasts (CAFs) and M2-polarized macrophages intermingled with tumor cells, suggesting a more immunosuppressive niche which could account for their worse clinical outcome. URO tumors, by comparison, showed a more immune-excluded phenotype. These findings provide a detailed molecular and spatial map of bladder cancer heterogeneity, highlighting how distinct epigenetic subtypes and histological variants are associated with tumor architecture and microenvironmental interactions. Our data underscore the need for subtype-specific therapeutic strategies to more effectively address the complexity already existing at HGT1 bladder cancer.

cancer biology↗

bioRxiv2025

Predicting Disease-Specific Histone Modifications and Functional Effects of Non-coding Variants by Leveraging DNA Language Models

BackgroundEpigenetic modifications play a vital role in the pathogenesis of human diseases, particularly neurodegenerative disorders such as Alzheimers disease (AD), where dysregulated histone modifications are strongly implicated in disease mechanisms. While recent advances underscore the importance of accurately identifying these modifications to elucidate their contribution to AD pathology, existing computational methods remain limited by their generic approaches that overlook disease-specific epigenetic signatures. ResultsTo bridge this gap, we developed a novel large language model (LLM)-based deep learning framework tailored for disease-contextual prediction of histone modifications and variant effects. Focusing on AD as a case study, we integrated epigenomic data from multiple patient samples to construct a comprehensive, disease-specific histone modification dataset, enabling our model to learn AD-associated molecular signatures. A key innovation of our approach is the incorporation of a Mixture of Experts (MoE) architecture, which effectively distinguishes between disease and healthy epigenetic states, allowing for precise identification of AD-relevant epigenetic modification patterns. Our model demonstrates robust performance in disease-specific histone modification prediction, achieving mean area under receiver-operating curves (AUROCs) ranging from 0.7863 to 0.9142, significantly outperforming existing state-of-the-art methods that lack disease context. Beyond accurate modification site prediction, our framework provides important biological insights by successfully prioritizing AD-associated genetic variants, which show significant enrichment in disease-relevant pathways, supporting their potential functional role in AD pathogenesis. These findings suggest that differential modification loci identified by our model may represent key regulatory elements in AD. ConclusionsOur framework establishes a powerful new paradigm for epigenetic research that can be extended to other complex diseases, offering both a valuable tool for variant effect interpretation and a promising strategy for uncovering novel disease mechanisms through epigenetic profiling.

bioinformatics↗

bioRxiv2025

The abscisic acid could reverse the cuproptosis in sepsis-associated acute kidney injury through the inhibition of ferredoxin-1

Sepsis-associated acute kidney injury (S-AKI) is a grave clinical condition with a poor prognosis. Prior studies have observed a potential correlation between copper (Cu) and S-AKI, though the underlying mechanisms remain to be fully elucidated. The present study aims to investigate the underlying mechanism between Cu and renal dysfunction under sepsis conditions. Utilizing a cecal ligation and puncture (CLP) murine model, we observed a substantial upregulation of inflammatory factors and deteriorated renal function (p<0.05), concomitant with an augmented mortality rate (p<0..05). Concurrently, the renal tissue exhibited a series of pathological manifestations, including fibrosis and cell death. Concurrently, the expression of the cuproptosis-regulating protein FDX-1 (p<0.05) was found to be elevated in CLP mice. Collectively, these data indicated the activation of cuproptosis in CLP-induced S-AKI. Intriguingly, the application of abscisic acid (ABA), a FDX-1 inhibitor, led to a substantial alleviation of mortality rate and inflammatory factor level (p<0.05). Furthermore, a significant alleviation was observed in renal pathology and biofunction (p<0.05). Collectively, these observations suggest that ABA exerts a protective effect on renal function during cuproptosis promoted S-AKI. Consequently, it can be concluded that ABA has the potential to reverse cuproptosis in S-AKI by inhibiting FDX-1.

molecular biology↗

bioRxiv2025

HEAL-KGGen: A Hierarchical Multi-Agent LLM Framework with Knowledge Graph Enhancement for Genetic Biomarker-Based Medical Diagnosis

The discovery and validation of genetic biomarkers across diverse diseases demand intelligent systems capable of integrating complex multi-omics data with clinical relevance. We introduce HEAL-KGGen, an end-to-end framework that enhances Large Language Models (LLMs) through a hierarchical multi-agent architecture and an automatically constructed medical knowledge graph. The system includes a General Practitioner (GP) agent for initial biomarker triage and specialist agents for genomics, transcriptomics, proteomics, and clinical interpretation. The core innovation of HEAL-KGGen lies in its dynamic knowledge graph pipeline, which combines entity extraction based on patterns and semantics, ontology-aligned normalization (using UMLS, MeSH, SNOMED CT) and the construction of multi-source relationships from biomedical databases and literature. Retrieved subgraphs are transformed into contextual prompts that guide LLM reasoning via structured, explainable pathways. Our experiments show that HEAL-KGGen significantly improves question-answering accuracy across multiple mainstream large language models, with the highest improvement observed on Claude 3.5 Sonnetachieving a 43.75% increase in accuracy., confirming the value of domain-specific graph knowledge in advancing LLM performance for genetic and molecular diagnostics.

bioinformatics↗

bioRxiv2025

In silico design and validation of high-affinity RNA aptamers for SARS-CoV-2 comparable to neutralizing antibodies

Nucleic acid aptamers hold promise for clinical applications, yet understanding their molecular binding mechanisms to target proteins and efficiently optimizing their binding affinities remain challenging. Here, we present CAAMO (Computer-Aided Aptamer Modeling and Optimization), which integrates in silico aptamer design with experimental validation to accelerate the development of aptamer-based RNA therapeutics. Starting from the sequence information of a reported RNA aptamer, Ta, for the SARS-CoV-2 spike protein, our CAAMO method first determines its binding mode with the spike proteins receptor binding domain (RBD) through a multi-strategy computational approach. We then optimize its binding affinity via structure-based rational design. Among the six designed candidates, five were experimentally verified and exhibited enhanced binding affinities compared to the original Ta sequence. Furthermore, we directly compared the binding properties of the RNA aptamers to neutralizing antibodies, and found that the designed aptamer TaG34C demonstrated a comparable binding affinity to the RBD compared to the representative neutralizing antibodies analyzed in this study. This highlights its potential as an alternative to existing COVID-19 antibodies. Our work provides a robust approach for the efficient design of a relatively large number of high-affinity aptamers with complicated topologies. This approach paves the way for the development of aptamer-based RNA diagnostics and therapeutics.

biophysics↗

bioRxiv2025

Single cell multiomic analysis of the impact of Delta-9-tetrahydrocannabinol on HIV infected CD4 T cells

Cannabis use is prevalent among individuals living with HIV in the United States, but the impact of cannabis exposure on the reservoir of latently infected cells that persists during antiretroviral therapy (ART) remains unclear. To address this gap, we analyzed the effect of {Delta}-9-tetrahydrocannabinol (THC) on primary CD4 T cells that were latently infected with HIV. We found that THC had no detectable effect on baseline or latency reversing agent (LRA) stimulated HIV expression, or on expression of an activation marker (CD38). However, using an integrated multiomic single-cell analysis of genome-wide chromatin accessibility and gene expression, we observed altered expression of several hundred genes in HIV infected CD4 T cells after THC exposure, including transcriptional downregulation of genes involved in protein translation and antiviral pathways, indicating that THC suppresses innate immune activation in infected cells. Additionally, chromatin accessibility analysis demonstrated upregulated chromatin binding activity for the transcriptional regulator CTCF, and reduced activity for members of the ETS transcription factor family in infected cells after THC exposure. These findings provide insights into the mechanisms by which cannabis use could influence the persistence of HIV within cellular reservoirs and the molecular phenotype of latently infected cells. Further elucidation of the underlying mechanisms involved in THC-mediated changes to HIV infected cells, will lead to an improved understanding of the impact of cannabis use on the HIV reservoir. ImportanceCannabis use is common among individuals living with HIV, but the long-term effects of cannabis use on the HIV reservoir are not yet studied completely. We employed advanced single-cell technologies to reveal how cannabis components, specifically THC, influence HIV-infected immune cells and their pattern of gene expression. We found that, while THC doesnt reactivate virus in latently infected cells, it alters the molecular characteristics of these infected immune cells. These findings are important because they underscore how cannabis could regulate persistent infection in people living with HIV. Understanding these cellular changes in response to THC could be helpful for successful treatment for people living with HIV.

molecular biology↗

bioRxiv2025

Biocompatible Multi-functional Polymeric Material for Mineralized Tissue Adhesion

This study developed a biocompatible multifunctional thiol-ene resin system for adhesion to dentin mineralized tissue. Adhesive resins maintain the strength and longevity of dental composite restorations through chemophysical bonding to exposed dentin surfaces after cavity preparations. Dental pulp cells are exposed to residual monomers transported through dentinal tubules. Monomers of conventional adhesive systems may result in inhomogeneous polymer networks and the release of residual monomers that cause cytotoxicity. In this study, we develop a one-step multi-functional polymeric resin system by incorporating trimethylolpropane triacrylate (TMPTA) and bis[2-(methacryloyloxy)ethyl] phosphate (BMEP) to enhance both mechanical properties and adhesion to dentin. Molecular dynamics simulations identified an optimal triacylate:trithiol ratio of 2.5:1, which was consistent with rheological and mechanical tests that yielded a storage modulus of ~30 MPa with or without BMEP. Shear bond tests demonstrated that the addition of BMEP significantly improved dentin adhesion, achieving a shear bond strength of 10.8 MPa, comparable to the commercial primer Clearfil SE Bond. Nanoindentation modulus mapping characterized the hybrid layer and mechanical gradient of the adhesive resin system. Further, the triacrylate-BMEP resin showed biocompatibility with fibroblasts in vitro. These findings suggest the triacrylate-trithiol crosslinking and chemophysical bonding of BMEP provide enhanced bond strength and biocompatibility for dental applications.

bioengineering↗