Biology subjects

Yao, J.

Publications and source records attributed to Yao, J..

At least 91 records · Page 5Linked to original sources

bioRxiv2024

Spatial-DC: a robust deep learning-based method for deconvolution of spatial proteomics

Spatial proteomics studies the spatial distribution of proteins within cells or tissues, providing a new perspective for comprehending cellular processes and disease mechanisms. One important challenge of current spatial proteomics technologies is low resolution, resulting in multiple cells in each spatial proteomics spot. While methods have been proposed to infer the composition of potential cell types in such spots for spatial transcriptomics, the depressed correlation and divergent quantification between transcriptome and proteome limits their capability in spatial proteomics. To enhance the utility of spatial proteomics data, we propose Spatial-DC (Spatial Digital Cytometry), a deep learning-based framework that infers cell-type composition in each spot and reconstructs spatially and cell-type resolved proteomic profiles. We achieve this by utilizing transfer learning and self-supervised learning with graph convolutional networks (GCN), which enables the incorporation of target spatial proteomics with reference single-cell or single-cell-type proteomics data. Through extensive simulations of spatial proteomics data, we demonstrate that Spatial-DC outperforms eight state-of-the-art methods in estimating cell-type composition and provides meaningful reconstructions of proteomic profiles for individual cell types. We apply Spatial-DC to different tissues measured using both multiplexed antibody-based and mass spectrometry (MS)-based spatial proteomics technologies. The results showcase Spatial-DCs superior sensitivity in providing more refined cell-type distribution maps compared to cell-type-specific marker-based distributions and its feasibility in reconstructing spatially and cell-type resolved proteomic profiles from real-world spatial proteomics data. Moreover, we validate Spatial-DC with a newly self-collected pancreatic cancer spatial proteomics data characterized by complex tumor microenvironment (TME) and identify the signaling direction and strength at both spatial and cell-type levels within the TME, suggesting cell-type-specific and spatially-resolved interactions linked to tumor outcomes. Our results highlight Spatial-DC as a versatile tool for the deconvolution of spatial proteomics data across various tissue slices, providing a reliable foundation for downstream analysis.

bioinformatics↗

bioRxiv2024

Sub-clustering, marker identification and multi-omics integration influenced by protoplasting in plant scRNA -seq data analysis

Single-cell RNA sequencing (scRNA-seq) technology is a powerful tool for exploring cell heterogeneity and lineage dynamics, revealing complex mechanisms driving tissue function and development. However, plant cells rigid cell walls require a protoplasing step, an enzymatic process that can introduce biases. The impact of protoplasting on plant scRNA-seq data remains poorly understood. In this study, we analyzed gene expression patterns from bulk RNA-seq of various plant tissues before and after protoplasting. Cell-type specific protoplasting effects were discovered in different plant tissues. Protoplasting-related biases were found to distort clustering, marker gene identification, and multi-omics integration. By calculating protoplasted scores based on gene expression, we identified and mitigated protoplasting effects, enabling the accurate clustering and annotation of tobacco BY-2 cells and identification of cell-cycle-related genes. Our findings underscore the importance of assessing and correcting for protoplasting biases in plant scRNA-seq data analysis, offering new insights for more accurate data interpretation and biological discovery.

bioinformatics↗

bioRxiv2024

PTTG1-Mediated Pericyte Dysfunction Drives Diabetes-Induced Microvascular Dysfunction

BackgroundPericytes are crucial for the development, stabilization, and functional regulation of microvasculature, especially in the retina. In diabetic retinopathy (DR), early loss of pericytes is a key event that drives microvascular dysfunction. Despite their critical role, the mechanisms underlying the functional heterogeneity of pericytes in DR remain poorly understood, impeding the development of effective therapeutic strategies. MethodsWe employed single-cell RNA sequencing to construct a comprehensive single- cell atlas of non-diabetic and diabetic retinas. Using bioinformatic clustering and subcluster analysis, we identified a specific pericyte subcluster associated with diabetic microvascular complications. Differential gene expression analysis and immunofluorescence validation highlighted PTTG1 as a potential key regulator of pericyte dysfunction. To investigate its functional role, we emplyed CRISPR/Cas9 and adenoviral vectors to modulate PTTG1 expression in vitro and in vivo. Combined transcriptomic and metabolomic approaches were used to explore the mechanistic pathways through which PTTG1 influences pericyte biology and vascular function. ResultsWe identified a novel pericyte subcluster characterized by elevated expression of PTTG1, which was strongly correlated with diabetic microvascular dysfunction. Silencing PTTG1 using CRISPR/Cas9 and siRNA in vitro mitigated pericyte dysfunction under high- glucose conditions. Targeted knockdown of PTTG1 using viral vectors improved retinal vascular integrity and reduced neovascularization in diabetic mice. Transcriptomic and untargeted metabolomic analyses revealed that PTTG1 knockdown reprogrammed pericyte energy metabolism by modulating glycolysis pathway genes, reducing oxidative stress, and restoring pericyte function, ultimately alleviating microvascular dysfunction in DR. ConclusionsPTTG1 plays a critical role in regulating pericyte dysfunction and maintaining vascular homeostasis in diabetic retinopathy. By modulating key metabolic pathways and pericyte phenotypes, PTTG1 represents a promising therapeutic target for treating diabetic microvascular complications. These insights offer a novel molecular framework for developing targeted therapies aimed at restoring retinal vascular health in diabetic patients.

molecular biology↗

bioRxiv2024

Evaluation of T Cell Receptor Construction Methods from scRNA-Seq Data

T cell receptors (TCRs) serve pivotal roles in the adaptive immune system by enabling recognition and response to pathogens and irregular cells. Various methods exist for TCR construction from single-cell RNA sequencing (scRNA-seq) datasets, each with its unique characteristics regarding accuracy, sensitivity, adaptability, usability, time, and memory consumption. Yet, a comprehensive understanding of their relative strengths and weaknesses for different applications remains elusive. In our research, we implemented a benchmark analysis utilizing experimental single-cell immune profiling datasets encompassing paired scRNA-seq as input and scTCR-seq datasets as ground truth reference from human and mouse. Additionally, we introduced a novel simulator, YASIM-scTCR (Yet Another Simulator for single-cell TCR), capable of generating scTCR-seq reads containing a diverse array of TCR- derived sequences under different sequencing depths and read lengths. Our results consistently showed that TRUST4 outperformed others across multiple datasets, while MiXCR and DeRR also demonstrated considerable accuracy. We also discovered that the sequencing depth inherently imposes a critical constraint on successful TCR construction from scRNA-seq data. In summary, we present a benchmark study to aid researchers in choosing the most appropriate methods for reconstructing TCR from scRNA-seq data.

bioinformatics↗

bioRxiv2024

Phage CRISPR-like regulatory RNAs silence bacterial adaptive and innate immunity

CRISPR RNAs (crRNAs) encoded by CRISPR arrays direct Cas nucleases against viruses. Some CRISPR-Cas loci also contain mini-arrays encoding crRNA-like RNAs (crlRNAs), which modulate transcription of cas or associated toxin genes that safeguard CRISPR-Cas activity. Here, we report that phages hijack these host crlRNA regulatory circuits to suppress bacterial immunity. Viral crlRNAs compete with host crRNAs for Cas protein binding and reprogram Cas proteins to silence their own expression or suppress transcription of CRISPR-safeguarding toxins. These anti-defense crlRNAs are found across diverse phages and plasmids, frequently organized into multiplexed transcriptional units resembling CRISPR arrays, and often synergize with co-encoded anti-CRISPR proteins to disable hierarchical host immune responses. Furthermore, we demonstrate that these compact viral crlRNA architectures can be harnessed for efficient mammalian genome editing. Our findings uncover widespread viral hijacking of host Cas proteins via crlRNAs, highlighting the recurrent exaptation of CRISPR-Cas regulatory functions to counter multilayered bacterial immune defenses.

microbiology↗

bioRxiv2024

An adaptive autoregressive diffusion approach to design active humanized antibody and nanobody

Humanization is a critical process for designing efficiently specific antibodies and nanobodies prior to clinical trials. Developing widely recognized deep learning techniques or frameworks for humanizing conventional antibodies and nanobodies presents a valuable yet challenging task. Inspired by the effectiveness of diffusion models across various applications, we introduce HuDiff, an adaptive diffusion approach for humanizing antibodies and nanobodies from scratch, referred to as HuDiff-Ab and HuDiff-Nb, respectively. This approach begins the humanization process exclusively with complementarity-determining region (CDR) sequences, eliminating the need for humanized templates. On public benchmark datasets, the results of HuDiff-Abs humanized antibodies are more similar to experimentally humanized antibodies than to those of the Sapiens humanization model. Besides, humanized nanobodies produced by HuDiff-Nb exhibit a higher humanness score and greater nativeness than those generated by the Lammanade pipeline for humanization nanobody. We apply HuDiff to humanize a mouse antibody and an alpaca nanobody, both targeting the SARS-CoV-2 RBD, and validate the binding affinity of humanized sequences through Bio-Layer Interferometry (BLI) experiments. The results show the binding affinity of the best humanized antibody is nearly equal to that of the parental mouse antibody (0.15 nM vs. 0.12 nM). Remarkably, the top-performing humanized nanobody exhibits a significantly enhanced binding affinity compared to the parental alpaca nanobody (2.52 nM vs. 5.47 nM), representing a 54% increase. These findings indicate that our approach HuDiff is highly effective in enhancing the humanness of antibodies and nanobodies while maintaining or potentially improving the binding affinity of the designed humanized sequences. The code and checkpoints of HuDiff are available at https://github.com/TencentAI4S/HuDiff.

bioinformatics↗

bioRxiv2024

All-trans retinoic acid-mediated ADAR1 degradation synergizes with PD-1 blockade to suppress pancreatic cancer

As a double-stranded RNA-editing enzyme and an interferon-stimulated gene, double-stranded RNA-specific adenosine deaminase (ADAR1) suppresses interferon signaling and contributes to immunotherapy resistance. Suppression of ADAR1 overcomes immunotherapy resistance in preclinical models, but has not yet been translated to clinical settings. By conducting a screening of a subset of the FDA-approved drugs, we found that all-trans retinoic acid (ATRA, also known as tretinoin) caused ADAR1 protein degradation through ubiquitin-proteasome pathways and concomitantly increased PD-L1 expression in pancreatic and breast cancers. In addition, the combination of ATRA and PD-1 blockade reprogrammed the tumor microenvironment and unleashed antitumor immunity and thereby impeded tumor growth in pancreatic cancer mouse models. In a pilot clinical trial, a higher dose of ATRA plus the anti-PD-1 antibody nivolumab prolonged median overall survival in patients with chemotherapy-resistant pancreatic cancer compared to a lower dose of the same regimen. In this study, ATRA was the first drug to be found to cause ADAR1 degradation. We propose translation of a promising 2-pronged antitumor strategy using ATRA and nivolumab to convert immunologically "cold" into "hot" tumors susceptible to immune checkpoint blockade.

cancer biology↗

bioRxiv2024

Advanced deep-tissue imaging and manipulation enabled by biliverdin reductase knockout.

We developed near-infrared (NIR) photoacoustic and fluorescence probes, as well as optogenetic tools from bacteriophytochromes, and enhanced their performance using biliverdin reductase-A knock-out model (Blvra-/-). Blvra-/- elevates endogenous heme-derived biliverdin chromophore for bacteriophytochrome-derived NIR constructs. Consequently, light-controlled transcription with IsPadC-based optogenetic tool improved up to 25-fold compared to wild-type cells, with 100-fold activation in Blvra-/- neurons. In vivo, light-induced insulin production in Blvra-/- reduced blood glucose in diabetes by [~]60%, indicating high potential for optogenetic therapy. Using 3D photoacoustic, ultrasound, and two-photon fluorescence imaging, we overcame depth limitations of recording NIR probes. We achieved simultaneous photoacoustic imaging of DrBphP in neurons and super-resolution ultrasound localization microscopy of blood vessels [~]7 mm deep in the brain, with intact scalp and skull. Two-photon microscopy provided cell-level resolution of miRFP720-expressing neurons [~]2.2 mm deep. Blvra-/- significantly enhances efficacy of biliverdin-dependent NIR systems, making it promising platform for interrogation and manipulation of biological processes.

cell biology↗

bioRxiv2024

Brainstem neurons coordinate the bladder and urethra sphincter for urination

Urination, a vital and conserved process of emptying urine from the urinary bladder in mammals, requires precise coordination between the bladder and external urethral sphincter (EUS) that is tightly controlled by a complex neural network. However, the specific subpopulation of neurons that accounts for such coordination remains unidentified, limiting the development of target-specific therapies for certain urination disorders, e.g., detrusor-sphincter dyssynergia. Here, we find that cells expressing estrogen receptor 1 (ESR1+) in the pontine micturition center (PMC) initiate voiding when activated and suspend ongoing voiding when suppressed, each at 100% reliability. Transection of the pelvic nerve does not impair PMCESR1+ neurons control of the EUS via the pudendal nerve, whereas transection of the pudendal nerve does not impair their control of the bladder via the pelvic nerve. Anatomically, PMCESR1+ neurons consist of three distinct spinal-projection-based subpopulations: one targeting the sacral parasympathetic nucleus (SPN), one innervating the dorsal gray commissure (DGC), and a third that projects to both regions, thereby enforcing the coordination of bladder contraction and sphincter relaxation in a rigid temporal sequence. Thus, we identify a cell type in the brainstem that controls the bladder-urethra coordination for urination.

neuroscience↗

bioRxiv2024

Beyond Static Brain Atlases: AI-Powered Open Databasing and Dynamic Mining of Brain-Wide Neuron Morphometry

We introduce NeuroXiv (neuroxiv.org), a large-scale, AI-powered database that provides detailed 3D morphologies of individual neurons mapped to a standard brain atlas, designed to support a wide array of dynamic, interactive neuroscience applications. NeuroXiv offers a comprehensive collection of 175,149 atlas-oriented reconstructed morphologies of individual neurons derived from more than 518 mouse brains, classified into 292 distinct types and mapped into the Common Coordinate Framework Version 3 (CCFv3). Different from conventional static brain atlases that are often limited to data-browsing, NeuroXiv allows interactive analyses as well as uploading and databasing custom neuron morphologies, which are mapped to the brain atlas for objective comparisons. Powered by a cutting-edge AI engine (AIPOM), NeuroXiv enables dynamic, user-specific analysis and data mining. We specifically developed a mixture-of-experts algorithm to harness the capabilities of multiple large language models. We also developed a client program to achieve more than 10 times better performance compared to a typical server-side setup. We demonstrate NeuroXivs scalability, efficiency, flexibility, openness, and robustness through various applications.

bioinformatics↗

bioRxiv2024

IgGM: A Generative Model for Functional Antibody and Nanobody Design

AO_SCPLOWBSTRACTC_SCPLOWImmunoglobulins are crucial proteins produced by the immune system to identify and bind to foreign substances, playing an essential role in shielding organisms from infections and diseases. Designing specific antibodies opens new pathways for disease treatment. With the rise of deep learning, AI-driven drug design has become possible, leading to several methods for antibody design. However, many of these approaches require additional conditions that differ from real-world scenarios, making it challenging to incorporate them into existing antibody design processes. Here, we introduce IgGM, a generative model for the de novo design of immunoglobulins with functional specificity. IgGM produces antibody sequences and structures simultaneously for a given antigen, consisting of three core components: a pre-trained language model for extracting sequence features, a feature learning module for identifying pertinent features, and a prediction module that outputs designed antibody sequences and the predicted complete antibody-antigen complex structure. IgGM has demonstrated its effectiveness in not only predicting structures but also designing novel antibodies and nanobodies. This makes it highly applicable in a wide range of practical situations related to antibody and nanobody design. 1

bioinformatics↗

bioRxiv2024

Effect of fumarate and live yeast on methane emissions, rumen fermentation, blood metabolites, and lactation performance in dairy goats

The objective of this study was to evaluate the effects and combined effects of fumarate (32 g/d) and active dry yeast (ADY) (1.5 g/d) on methane emissions, rumen fermentation, microbial community and function, apparent total tract digestibility, blood metabolites, and lactation performance in 28 lactating goats using a randomized complete block design with a 2 x 2 factorial arrangement. Fumarate supplementation inhibited methane emission rates for 3 h post-feeding, while ADY increased the methane emission rates for 2 h pre-feeding, without combined effects between them. Fumarate increased the rumen pH and reduced total VFA concentration without changing the VFA profiles before feeding. Based on two custom dataset of 4,674 metagenome-assembled genomes and 7,758,615 nonredundant genes, neither fumarate nor live yeast influenced the rumen microbial community and function. The addition of fumarate reduced the concentrations of glucose, serum malondialdehyde (MDA), and ammonia in the serum, as well as the activity of insulin, while increasing the activity of serum malate dehydrogenase (MDH) and the levels of urea in both serum and milk. The addition of live yeast reduced serum glucose levels and increased serum MDA concentrations and MUN levels. There was a negative interaction between fumaric acid and live yeast on the levels of urea nitrogen in serum and milk. ConclusionsThe methane inhibition effect of fumaric acid persists for 3 h post-feeding, reflecting its retention or metabolism time in the rumen. Fumaric acid enhances the TCA cycle and the urea cycle in dairy goats, thereby improving energy utilization efficiency and reducing the risk of ammonia toxicity.

microbiology↗

bioRxiv2024

An automated microfluidic platform for toxicity testing based on Caenorhabditis elegans

Humans are frequently exposed to a multitude of chemicals daily, necessitating efficient methods for rapidly assessing toxicity and potential health risks. Microfluidics has shown promise as an intelligent tool for rapid compound testing, owing to its flexibility in integrating with automated devices. The article introduces an automated microfluidic platform, based on Caenorhabditis elegans (C. elegans), designed for chemical toxicity testing. This platform consists of three modules - worm culture, monitoring, and image analysis - which enable automated worm culturing, drug delivery, periodic monitoring, and automated phenotypic analysis. Researchers have designed a bridged microfluidic chip that permits worms to move freely during experiments and established an economical monitoring module for long-term tracking and periodic imaging. Furthermore, they have developed an automated image analysis algorithm to automatically determine worm bending frequency. The platform was subsequently utilized for long-term toxicological assessments of the organophosphate pesticide and environmental pollutants. Results indicated that the platform can effectively evaluate the general and developmental impacts of chemicals. The automated microfluidic worm analysis platform holds significant potential for applications in drug safety assessmentand drug screening research, contributing to human health and industry advancement.

bioengineering↗

bioRxiv2024

Characterization, enrichment, and computational modeling of cross-linked actin networks in trabecular meshwork cells

PurposeCross-linked actin networks (CLANs) are prevalent in the glaucomatous trabecular meshwork (TM), yet their role in ocular hypertension remains unclear. We used a human TM cell line that spontaneously forms fluorescently-labeled CLANs (GTM3L) to explore the origin of CLANs, developed techniques to increase CLAN incidence in GMT3L cells, and computationally studied the biomechanical properties of CLAN-containing cells. MethodsGTM3L cells were fluorescently sorted for viral copy number analysis. CLAN incidence was increased by (i) differential sorting of cells by adhesion, (ii) cell deswelling, and (iii) cell selection based on cell stiffness. GTM3L cells were also cultured on glass or soft hydrogel to determine substrate stiffness effects on CLAN incidence. Computational models were constructed to mimic and study the biomechanical properties of CLANs. ResultsAll GTM3L cells had an average of 1 viral copy per cell. LifeAct-GFP expression level did not affect CLAN incidence rate, but CLAN rate was increased from [~]0.28% to [~]50% by a combination of adhesion selection, cell deswelling, and cell stiffness-based sorting. Further, GTM3L cells formed more CLANs on a stiff vs. a soft substrate. Computational modeling predicted that CLANs contribute to higher cell stiffness, including increased resistance of the nucleus to tensile stress when CLANs are physically linked to the nucleus. ConclusionsIt is possible to greatly enhance CLAN incidence in GTM3L cells. CLANs are mechanosensitive structures that affect cell biomechanical properties. Further research is needed to determine the effect of CLANs on TM biomechanics and mechanobiology as well as the etiology of CLAN formation in the TM.

cell biology↗

bioRxiv2024

A single-molecule nanopore sequencing platform

Nanopore sequencing, a third-generation sequencing technology, has revolutionized the gene sequencing industry with its advantages of long reads, fast speed, real-time sequencing and analysis, and potential in detecting base modifications. This technology allows researchers to sequence longer DNA fragments in a single read, providing more comprehensive genomic information compared to previous methods. Nanopore sequencing operates on electrical signals generated by a nanopore embedded in a membrane separating two electrolyte-filled chambers. When single-stranded DNA (ssDNA) passes through the nanopore, it creates variations in the current that correspond to different DNA bases. By analyzing these current fluctuations with machine learning algorithms, the DNA sequence can be determined. In this study, we introduced several improvements to nanopore sequencing, including nanopore local chemistry sequencing, novel motor and pore proteins, chip design, and basecalling algorithms. Our new nanopore sequencing platform, CycloneSEQ, demonstrated long-duration sequencing (107 hours) on a single chip with high yield (>50 Gb). In human genomic DNA sequencing, CycloneSEQ was able to produce long reads with N50 33.6 kb and modal identity 97.0%. Preliminary findings on human whole-genome de novo assembly, variant calling, metagenomics sequencing, and single-cell RNA sequencing have further highlighted CycloneSEQs potential across different areas of genomics.

genomics↗

bioRxiv2024

A human induced pluripotent stem (hiPS) cell model for the holistic study of epithelial to mesenchymal transitions (EMTs)

The epithelial to mesenchymal transition (EMT) is a widely studied but poorly defined state change due to the variety of ways in which it has been characterized in cells. There is a need for reproducible cell model systems that enable the integration and comparison of different types of measured observations of cells across many distinct cellular contexts. We present human induced pluripotent stem (hiPS) cells as such a model system by demonstrating its utility through a comparative analysis of hiPS cell-EMT in 2D and 3D cell culture geometries. We developed live-imaging-based assays to directly compare examples of changes in cell function (via migration timing), molecular components (via expression of marker proteins), organization (via reorganization of cell junctions), and environment (via dynamics of basement membrane) in the same experimental system. The EMT-related changes we measured occurred earlier in 2D colonies than in 3D lumenoids, likely due to differences in the basement membrane environments associated with 2D vs. 3D initial hiPS cell culture geometries. We have made the 449 60-hour-long 3D time-lapse movies and the associated tools used for analysis and visualization open-source and easily accessible as a resource for future work in this field.

cell biology↗

bioRxiv2024

Therapeutic targeting of Syndecan-1 axis overcomes acquired resistance to KRAS-targeted therapy in gastrointestinal cancers

The therapeutic benefit of recently developed mutant KRAS (mKRAS) inhibitors has been limited by the rapid onset of resistance. Here, we aimed to delineate the mechanisms underlying acquired resistance to mKRAS inhibition and identify actionable targets for overcoming this clinical challenge. Previously, we identified Syndecan-1 (SDC1) as a key effector for pancreatic cancer progression whose surface expression is driven by mKRAS. By leveraging both pancreatic and colorectal cancer models, we found that surface SDC1 expression was initially diminished upon mKRAS inhibition, but recovered in tumor cells that bypass mKRAS dependency. Functional studies showed that these tumors depended on SDC1 for survival, further establishing SDC1 as a driver for the acquired resistance to mKRAS inhibition. Mechanistically, we revealed that the YAP1-SDC1 axis was the major driving force for bypassing mKRAS dependency to sustain nutrient salvage machinery and tumor maintenance. Specifically, YAP1 activation mediated the recovery of SDC1 localization on cell surface that sustained macropinocytosis and enhanced the activation of multiple RTKs, promoting resistance to KRAS-targeted therapy. Overall, our study has provided the rationale for targeting the YAP-SDC1 axis to overcome resistance to mKRAS inhibition, thereby revealing new therapeutic opportunities for improving the clinical outcome of patients with KRAS-mutated cancers.

cancer biology↗

bioRxiv2024

A Small Interfering Peptide Potentiates AMPA Receptor Diffusional Trapping and Prevents Social-Isolation-Induced Forgetting of Fear Memory

Synaptic trapping of AMPA receptors (AMPARs) is a key mechanism regulating excitatory synaptic transmission and activity-dependent plasticity underlying learning and memory. Destabilization of synaptic AMPARs is increasingly implicated in cognitive dysfunction across neurological and neuropsychiatric disorders, yet strategies to directly modulate this process in vivo remain limited, constraining both mechanistic insight and therapeutic development. Here we present a peptide-based strategy to enhance AMPAR synaptic trapping by targeting the interaction between transmembrane AMPA receptor regulatory proteins (TARPs) and activity-regulated cytoskeleton-associated protein (ARC/Arg3.1). We designed a 21-amino-acid TAT-fused peptide (TARP-pep) mimicking the TARP C-terminal motif that binds the ARC N-lobe. TARP-pep disrupted the TARP-ARC interaction and increased the stabilization of AMPARs at synaptic surfaces in cultured hippocampal neurons. In vivo, acute intrahippocampal infusion of TARP-pep enhanced perforant path-evoked synaptic transmission in the rat dentate gyrus (DG) and strengthened the interaction between TARPs and postsynaptic density protein 95 (PSD-95), a key mechanism underlying AMPAR anchoring. Consistent with a long-term potentiation (LTP)-like synaptic state, TARP-pep increased basal phosphorylation of Ca2+/calmodulin-dependent protein kinase II (CaMKII) and occluded further chemical LTP (cLTP)-induced increases in phosphorylated CaMKII (p-CaMKII). Notably, a 7-day regimen of daily bilateral DG injections of TARP-pep prevented social isolation (SI)-induced impairment of fear memory in mice. Together, these findings identify the TARP-ARC interaction as a druggable regulator of AMPAR diffusional trapping and highlight synaptic AMPAR stabilization as a promising therapeutic strategy for preserving cognitive function under conditions of circuit vulnerability.

neuroscience↗