Biology subjects

Peng, Q.

Publications and source records attributed to Peng, Q..

At least 55 records · Page 3Linked to original sources

bioRxiv2024

Cryo-EM structures of human cone visual pigments

Trichromatic color vision in humans constitutes a pivotal evolutionary adaptation, endowing individuals with the capacity to discern and discriminate a diverse spectrum of colors. This unique visual capability confers a selective advantage crucial for successful adaptation, survival, and reproductive success in the natural environment. Color vision in humans is facilitated by the red, green, and blue cone visual pigments within cone photoreceptor cells. These pigments consist of a G-protein-coupled receptor opsin apoprotein and a chromophore covalently linked to opsins. Despite the elucidated structure of rhodopsin, the structures of cone visual pigments have yet to be determined. Here, we present the cryo-EM structures of three human cone visual pigments in complex with G proteins. Our structural analysis reveals detailed interactions between cone opsins, all-trans-retinal, and G proteins, indicating their active state. We also provide a concise summary and analysis of mutations in human cone opsins, elucidating potential relationships between residue substitutions and spectral tuning. Notably, S1162.67Y, A2335.52S, Y2776.44F were found to induce a blue shift in the absorption spectrum of the red-pigment, while the substitutions W2816.48Y and K3127.43A resulted in the absence of the absorption spectrum. The structural elucidation of human cone visual pigments significantly contributes to our understanding of how distinct types of cone cells perceive light across varying wavelengths. Furthermore, it provides a deeper insight into the functioning of the human trichromatic vision system, probing the mechanisms enabling humans to perceive a broad spectrum of colors.

biophysics↗

bioRxiv2024

Cryo-EM structure and biochemical analysis of human chemokine receptor CCR8

The C-C motif chemokine receptor 8 (CCR8) is a class A G-protein coupled receptor that has emerged as a promising therapeutic target in cancer and autoimmune diseases. Although the structures of human CCR8 in complex an antagonist antibody Fab1 and an endogenous agonist ligand CCL1 have been solved, the structure of ligand-free CCR8 remains to be determined. Here, we solved the cryo-electron microscopy (cryo-EM) structure of the human CCR8-Gi complex in the absence of a ligand. Structural analysis and comparison revealed that CCR8 in our apo structure undergoes some conformational change and is similar to that in the CCL1-CCR8 complex structure, indicating an active state. In addition, the key residues of CCR8 involved in the recognition of LMD-009, a potent nonpeptidic agonist, were investigated by mutating CCR8 and testing the calcium flux induced by LMD-009-CCR8 interaction. Two mutants of CCR8, Y1133.32A, and E2867.39A, showed a dramatically decreased ability in mediating calcium mobilization, indicating their key interaction with LMD-009. These structural and biochemical analyses provided molecular insights into the agonism and activation of CCR8 and will also facilitate CCR8-targeted therapy.

biophysics↗

bioRxiv2023

KGRACDA: A Model Based on Knowledge Graph from Recursion and Attention Aggregation for CircRNA-disease Association Prediction

CircRNA has been found to exhibit with human diseases, rendering the investigation of the circRNA-disease association (CDA) of significant importance. Deep learning provides a novel avenue for predicting CDA, however, due to the limited availability of comprehensive CDA datasets, most existing methods operate by directly reasoning over triplets of embedded entities and relations, neglecting the explicit capture local depth information. In this study, we propose a novel approach called Knowledge Graph-Enhanced Recursive Aggregation for CDA (KGRACDA), which enables the querying of explicit local structures surrounding triplets, thereby serving as crucial evidence for predicting CDA. First, we construct a comprehensive CDA knowledge graph utilizing a large-scale, multi-source heterogeneous dataset. Subsequently, we develop a graph neural network using recursive techniques and employ attention aggregation to generate CDA prediction scores. Experimental results demonstrate that KGRACDA surpasses other state-of-the-art methods by explicitly capturing local depth information related to CDA. To improve the convenience of CDA prediction, we have updated an interactive web platform named HNRBase v2.0, which provides rich visualization of circRNA data, and allows users to easily download data and predict CDA using KGRACDA. The code and datasets are publicly available at https://github.com/maoyuanma/KGRACDA.

bioinformatics↗

bioRxiv2023

Targeting endothelial FOXO1 protects diabetic β-cells and improves wound healing

The forkhead box O1 (FOXO1) transcription factor plays critical roles in regulating not only metabolic activity but also angiogenesis in the vascular endothelium1-4. Our previous studies show that epsin endocytic adaptors can regulate both angiogenesis and lymphangiogenesis5-7. Endothelial cells (ECs) lining the inside of blood vessels are continuously exposed to circulating insulin and insulin-like growth factors (IGFs). Emerging evidences suggest that ECs can affect {beta}-cell function8-11. Excessive IGF2, especially elevated local IGF2 levels in islets, may represent a risk factor for developing diabetes12-15; however, the underlying molecular mechanisms by which aberrant angiogenesis and endothelium-derived factors regulate pancreatic {beta}-cell function in diabetes remain unclear. Here, we report that the pancreas of diabetic patients as well as the pancreas, skin, and plasma of streptozotocin/high fat diet (STZ/HFD)-induced diabetic mice and db/db mice contains excess IGF2, which can lead to {beta}-cell dysfunction and apoptosis. Single-cell transcriptomics combined with mass spectrometry analysis reveal that endothelial-specific knockout of FOXO1 increases circulating soluble and cell-membrane or intracellular expression levels of IGF type 2 receptor (IGF2R) and CCCTC-binding factor (CTCF), while decreasing IGF2 levels in diabetes. Both IGFR215-17 and CTCF18-21 can reduce IGF2 levels and may ameliorate {beta}-cell decline associated with excess IGF2 in diabetes. Furthermore, depletion of FOXO1, epsins, or knockdown of ULK1 inhibits autophagy formation in ECs, preventing degradation of vascular endothelial growth factor receptor 2 (VEGFR2) to promote angiogenesis and improve wound healing in diabetes. Our findings reveal that endothelial FOXO1 regulates epsin-dependent angiogenesis and affects {beta}-cell function and fate through CTCF and IGF2-IGF2R, providing a potential strategy for ameliorating diabetes and accelerating cutaneous wound healing.

cell biology↗

bioRxiv2023

Adaptive Functions of Structural Variants in Human Brain Development

Quantifying the structural variants (SVs) in nonhuman primates could provide a niche to clarify the genetic backgrounds underlying human-specific traits, but such resource is largely lacking. Here, we report an accurate SV atlas in a population of 562 rhesus macaques, verified by two public SV benchmarks, an inhouse benchmark of eight macaque genomes with long-read sequencing and another inhouse benchmark of one macaque genome with whole-genome assembly. This accurate, quantitative SV map indicates stronger purifying selection on inversions, one type of poorly-clarified SVs to date, especially for those located on regulatory regions, suggesting a strategy for prioritizing inversions with the most important functions. Based on the distribution and the evolutionary features of these inversions in macaque population, we then identified 75 human-specific inversions, clarified their functional effects and prioritized them. Notably, the top-ranked inversions have substantially shaped the human transcriptome, through their dual-effects of reconfiguring the ancestral genomic architecture and introducing regional mutation hotspots at the inverted regions. As a proof-of-concept, we linked APCDD1, located on one of these inversions with the highest rank score and downregulated in human brains, to neuronal maturation. The accumulation of human-specific mutations on its promoter region, accelerated by the formation of the inversion, contributed to the decreased expression in humans. Notably, the overexpression of APCDD1 could accelerate the neuronal maturation, while its depletion in mice delays the neuronal maturation. This study thus highlights the contribution of SVs, especially the inversions, to the distinct features in human brain development.

evolutionary biology↗

bioRxiv2023

Commensal microbiome dysbiosis elicits IL-8 signaling to drive fibrotic skin disease

Commensal bacteria are core players in wound healing whose function in the opposite pathophysiological process-scarring is presently unclear. Here, we document the association between bacteria and a specific skin fibrotic disease-keloid, which might offer a promising avenue for translational practice. ABSTRACTWound healing is an intensely studied topic involved in many relevant pathophysiological processes, including fibrosis. Despite the large interest in fibrosis, the network that related to commensal microbiota and skin fibrosis remain mysterious. Here, we pay attention to keloid, a classical yet intractable skin fibrotic disease to establish the association between commensal microbiota to scaring tissue. Our histological data reveal the presence of microbiota in the keloids. 16S rRNA sequencing characterize microbial composition and divergence between the pathological and normal skin tissue. Moreover, the data show elevation of interleukin-8 both in the circulation and keloid tissue, which elicited the collagen accumulation and migratory program of dermal fibroblasts via CXCR1/2 receptor. Our research provides insights into the pathology of human fibrotic diseases, advocating commensal bacteria and IL-8 signaling as useful targets in future interventions of recurrent keloid disease.

microbiology↗

bioRxiv2023

Drosophila Amus and Bin3 methylases functionally replace mammalian MePCE for capping and the stabilization of U6 and 7SK snRNAs

U6 and 7SK snRNAs process a 5 cap, believed to be essential for their stability and maintained by mammalian MePCE or Drosophila Bin3 enzymes. Although loss of either protein results in 7SK instability, loss of neither is associated with U6 reduction. Their yeast homolog is also not required for U6 stability, casting further doubts on the function of capping U6. Here we show that the Drosophila Amus protein, homologous to both Bin3 and MePCE, is essential for U6 but not 7SK stability. A full function of Amus is required for Drosophila development, and that rests primarily on Amuss methylase activity. Remarkably, the loss of U6 is rescued by the expression of an Amus-MePCE hybrid protein harboring the methyltransferase domain from MePCE, highlighting the conserved function of the two proteins as the U6 capping enzyme. Our new investigations in human cells establish a dependence of both U6 and 7SK stability on MePCE, resolving a long-standing uncertainty. While uncovering an interesting division of labor of Bin3/MePCE/Amus proteins, we discovered a "Bin3-Box" domain present only in enzymes associated with 7SK regulation. Targeted mutagenesis in Drosophila confirmed its importance for Bin3 function, revealing a possible conserved element in 7SK but not U6 biology.

genetics↗

bioRxiv2023

Storm: Incorporating transient dynamics to infer the RNA velocity with metabolic labeling information

AO_SCPLOWBSTRACTC_SCPLOWThe time-resolved scRNA-seq (tscRNA-seq) provides the possibility to infer physically meaningful kinetic parameters, e.g., the transcription, splicing or RNA degradation rate constants with correct magnitudes, and RNA velocities by incorporating temporal information. Previous approaches utilizing the deterministic dynamics and steady-state assumption on gene expression states are insufficient to achieve favorable results for the data involving transient process. We present a dynamical approach, Storm (Stochastic models of RNA metabolic-labeling), to overcome these limitations by solving stochastic differential equations of gene expression dynamics. The derivation reveals that the new mRNA sequencing data obeys different types of cell-specific Poisson distributions when jointly considering both biological and cell-specific technical noise. Storm deals with measured counts data directly and extends the RNA velocity methodology based on metabolic labeling scRNA-seq data to transient stochastic systems. Furthermore, we relax the constant parameter assumption over genes/cells to obtain gene-cell-specific transcription/splicing rates and gene-specific degradation rates, thus revealing time-dependent and cell-state specific transcriptional regulations. Storm will facilitate the study of the statistical properties of tscRNA-seq data, eventually advancing our understanding of the dynamic transcription regulation during development and disease.

systems biology↗

bioRxiv2023

BertNDA: a Model Based on Graph-Bert and Multi-scale Information Fusion for ncRNA-disease Association Prediction

Non-coding RNAs (ncRNAs) are a class of RNA molecules that lack the ability to encode proteins in human cells, yet play crucial roles in various biological process. Understanding these relationships and how different ncRNAs interact with each other to affect diseases can vastly contribute to their diagnosis, prevention, and treatment. However, predicting tertiary interactions between ncRNA-disease associations by utilizing structural information across multiple scales remains a challenging task. It should be noted that research on predicting tertiary interaction between trinary ncRNA-disease associations is scarce, highlighting the need for further studies in this area. In this work, we propose a predictive framework, called BertNDA, which aims to predict association between miRNA, lncRNA and disease. The framework employs Laplace transform of graph structure and WL (Weisfeiler-Lehman) absolute role coding to extract global information. Local information is identified by the connectionless subgraph which aggregates neighbor feature. Moreover, an EMLP (Element-wise MLP) structure is designed to fuse the multi-scale feature representation of nodes. Furtherly, feature representation is encoded by using a Transformer-encoder structure, the prediction-layer outputs the final correlation between miRNA, lncRNA and diseases. The 5-fold cross-validation result furtherly demonstrates that BertNDA outperforms the state-of-the-art method in predicting assignment. Furthermore, an online prediction platform that embeds our prediction model is designed for users to experience. Overall, our model provides an efficient, accurate, and comprehensive tool for predicting ncRNA-disease associations. The code of our method is available in: https://github.com/zhiweining/BertNDA-main.

bioinformatics↗

bioRxiv2023

Development of Synthetic Modulator Enabling Long-Term Propagation and Neurogenesis of Human-Derived Neural Progenitor Cells

Neural progenitor cells (NPCs) are important cells for in vitro drug screening and the cell-based therapy for brain-related disorders, which requires well-defined and reproducible culture systems. Current strategy the use of protein growth factors presents challenges in terms of reproducibility and cost. In this study, we have developed a novel DNA-based modulator to regulate FGFR signaling of NPCs, enabling maintenance of the stemness over 50 passages and neurogenesis towards neurons. The DNA-based FGFR-agonist effectively promoted FGFR1 phosphorylation and activated the downstream ERK signaling pathway in FGFR1-positive cells. Using human embryonic stem cell lines, we differentiated them into NPCs and replaced basic fibroblast growth factor (bFGF) in the regulator culture medium with DNA-based FGFR-agonist for artificially elicited FGFR signaling. The results demonstrated that the FGFR-agonist could promote NPCs proliferation and neurosphere formation, recapitulating the function of bFGF. Notably, transcriptomic analysis revealed that FGFR-agonist could customize the stemness-associated transcription program, while decouples the neuronal differentiation program, highly resembling that the native ligand, bFGF. Moreover, our culture condition facilitated the successful propagation of NPCs for over 50 passages, while retaining their ability to efficiently differentiate into neurons. Overall, our approach provides a highly effective method for expanding NPCs, offering new opportunities for disease-in-dish research and drug screening for neural degeneration.

neuroscience↗

bioRxiv2023

Vacuolar H+-ATPase subunit a was identified as the target protein of the oomycete inhibitor fluopicolide

Approximately 240 fungicides are currently in use. However, only a few direct targets have been identified, which limits the development of fungicides and rapid resistance monitoring. Fluopicolide, which is an excellent oomycete inhibitor, is classified as delocalization of spectrin-like proteins inhibitors by FRAC. In the current study, a Pc-actinin knockout had no effect on the sensitivity of Phytophthora capsici to fluopicolide. The vacuolar H+-ATPase subunit a (PcVHA-a) was identified using a BSA-seq and DARTS assay. Four kinds of point mutations in PcVHA-a that cause fluopicolide resistance in P. capsici were confirmed using site-directed mutagenesis. The results of MST, molecular docking, and a DARTS assay indicated that PcVHA-a could bind fluopicolide. Sequence analysis and a molecular docking assay proved the specificity of fluopicolide to oomycetes or fish. Our results suggest that PcVHA-a is the target of fluopicolide, and H+-ATPase could be used as a novel target for the development of new fungicides.

pharmacology and toxicology↗

bioRxiv2023

KGETCDA: an efficient representation learning framework based on knowledge graph encoder from transformer for predicting circRNA-disease associations

Recent studies have demonstrated the significant role that circRNA plays in the progression of human diseases. Identifying circRNA-disease associations (CDA) in an efficient manner can offer crucial insights into disease diagnosis. While traditional biological experiments can be time-consuming and labor-intensive, computational methods have emerged as a viable alternative in recent years. However, these methods are often limited by data sparsity and their inability to explore high-order information. In this paper, we introduce a novel method named Knowledge Graph Encoder from Transformer for predicting CDA (KGETCDA). Specifically, KGETCDA first integrates more than 10 databases to construct a large heterogeneous non-coding RNA dataset, which contains multiple relationships between circRNA, miRNA, lncRNA and disease. Then, a biological knowledge graph is created based on this dataset and Transformer-based knowledge representation learning and attentive propagation layers are applied to obtain high-quality embeddings with accurately captured high-order interaction information. Finally, multilayer perceptron is utilized to predict the matching scores of CDA based on their embeddings. Our empirical results demonstrate that KGETCDA significantly outperforms other state-of-the-art models. To enhance user experience, we have developed an interactive web-based platform named HNRBase that allows users to visualize, download data and make predictions using KGETCDA with ease.

bioinformatics↗

bioRxiv2023

6mA-Sniper: Quantifying 6mA Sites in Eukaryotes at Single-Nucleotide Resolution

While N6-methyldeoxyadenine (6mA) modification has been linked to fundamental regulatory processes in prokaryotes, its prevalence and functional implications in eukaryotes are controversial. Here, we report 6mA-Sniper to quantify 6mA sites in eukaryotes at single-nucleotide resolution. With 6mA-Sniper, we delineated an accurate 6mA profile in C. elegans with 2,034 sites, significantly enriched on sequences of [GC]GAG motif. Twenty-six of 39 6mA events with MnlI restriction endonuclease sites were experimentally verified, demonstrating the feasibility of this method. Notably, the enrichment of these 6mA sites on a specific sequence motif, their within-population conservation and the combinatorial patterns, and the selective constrains on them jointly support an active model for the shaping of the profile by some undiscovered methyltransferases. In a joint study (Cell Research, in revision), Ma et al. reported METL-9 as a new methyltransferase in shaping the basal and stress-dependent 6mA profile in C. elegans. Notably, with the 6mA profile identified by 6mA-Sniper at single-nucleotide resolution, we found that the levels of 6mAs are significantly decreased in strains with the removal of METL-9 (METL-9 KO-OP50), while generally increased after P. aeruginosa infection, further verified the efficiency of 6mA-Sniper in accurately pinpointing 6mA sites. Moreover, for the regions marked by 998 6mA sites emerged specifically after the infection, we identified an enrichment of the upregulated genes after the infection. The gene upregulations are likely mediated through a mutual exclusive crosstalk between 6mA and H3K27me3 modification, as supported by their co-occurrence, and the signal of increased H3K27me3 at regions marked by 6mAs depleted in METL-9 KO-OP50 strains. Notably, in different C. elegans strains, the cross-strain genetic variants removing 6mA sites are associated with the decreased expression of their host genes, and the removal of two randomly-selected 6mA events with genome editing directly decreased the expression of their host genes. We thus highlight 6mA regulation as a previously-neglected regulator of transcriptome in eukaryotes.

genomics↗

bioRxiv2023

CircRNA-disease inference using deep ensemble model based on triple association

Accumulating evidence indicates more and more circular RNAs (i.e. circRNAs) have played a vital role in regulating gene expression and are related to diseases through different biological procedures. Predicting circRNA-disease associations helps to conjecture possible disease related circRNA and facilitate human disease diagnosis and downstream treatment. Nevertheless, little effort was made to uncover the interaction between various diseases and circRNAs. In our work, human circRNA-disease association network is first generated using known miRNA-circRNA interactions and disease related miRNA (microRNA) information. Then we further integrated this information to compute similarity scores between human diseases and circRNAs. Here, we proposed one deep ensemble model called DeepInteract, which first used two stacked auto-encoders to explore hidden features utilizing similarity information, and adopted a 3-layer neuron network to predict the final association. Our method is capable of capturing more complex non-linear features comparing to other approaches. Our results indicate the proposed method is superior to other previous competitors. Many prediction results have been validated by some biological experiments using our model.

bioinformatics↗

bioRxiv2022

Spatial-temporal evolution characteristics of land use and its driving factor analysis during the period of 2000-2020 in Putian City, Fujian Province

Exploring the spatial and temporal evolution characteristics of land use and its driving force can provide scientific basis for the construction of regional ecological civilization. Based on the remote sensing cloud computing platform of Google Earth Engine (GEE), the study took the remote sensing images of the three periods of 2000, 2010 and 2020 as the basic data and interpreted them to obtain the basic land use data of Putian city and its five districts and counties. The land-use change characteristics of Putian city in those periods were systematically analyzed by using the methods of single land-use dynamics and Geo-informatic Tupu, so as to reveal the Tupu characteristics of land-use transfer and the spatial-temporal evolution pattern in the past 20 years. Then social and economic development indicators were selected to further explore the key driving force of land-use evolution in the study area by canonical correspondence analysis. The results showed that: 1) the land use structure of Putian city was mainly composed of cultivated land and forest land, and the other land types were embedded in them and the built-up land continued to expand outward. 2) The spatial distribution of Tupu units of land use transfer in Putian city was significantly different, change types of land use were diversified and the areas of cultivated land and grassland transferred out were the most obvious. Different degrees of cultivated land abandonment had occurred in various regions. Putian city was facing great challenges in ensuring food security and curbing the non-agricultural and non grain of cultivated land. 3) The macro-economic development in a specific period, especially the urban expansion and the development of the secondary industry caused by the merger of cities and counties, were the key factors driving the spatial and temporal evolution of land use and the differential distribution pattern in Putian city. The author suggested that land resources should be used efficiently in addition to optimizing the layout of land use and carrying out the renovation of abandoned land. Therefore, the study proposed to strengthen the scientific planning and effective regulation of land use, combine spatial and temporal resources of cultivated land with agriculture, culture and tourism, and build ecological barriers and ecological corridors to ensure the coordinated and sustainable development of regional ecology and economy.

ecology↗

bioRxiv2022

A Purinergic Mechanism Underlying Metformin Regulation of Hyperglycemia

Metformin, created in 1922, has been the first-line medication treating type 2 diabetes mellitus for almost 70 years; however, its mechanism of action has been heavily debated, partly because most prior studies used supratherapeutic concentrations exceeding 1mM. Here we report that at a clinically relevant concentration of 10 M, metformin blocks high glucose-stimulated ATP secretion from hepatocytes mediating its antihyperglycemic action. Following glucose administration, mice demonstrate increased circulating ATP concentrations, which are prevented by metformin. Extracellular ATP through P2Y2 receptors (P2Y2R) compromises insulin-induced AKT activation and increases hepatic glucose production. In addition, metformin-dependent improvement in glucose tolerance is abolished in P2Y2R-null mice. Thus, removing the target of extracellular ATP, P2Y2R, mimics the effects of metformin, revealing a novel purinergic antidiabetic mechanism for metformin. One-Sentence SummaryMetformin abolishes high glucose-stimulated ATP secretion, preventing purinergic mechanism-mediated hepatic insulin resistance and glucose production.

pharmacology and toxicology↗

bioRxiv2022

LncDLSM: Identification of Long Non-coding RNAs with Deep Learning-based Sequence Model

Long non-coding RNAs (LncRNAs) serve a vital role in regulating gene expressions and other biological processes. Differentiation of lncRNAs from protein-coding transcripts helps researchers dig into the mechanism of lncRNA formation and its downstream regulations related to various diseases. Previous works have been proposed to identify lncRNAs, including traditional bio-sequencing and machine learning approaches. Considering the tedious work of biological characteristic-based feature extraction procedures and inevitable artifacts during bio-sequencing processes, those lncRNA detection methods are not always satisfactory. Hence, in this work, we presented lncDLSM, a deep learning-based framework differentiating lncRNA from other protein-coding transcripts without dependencies on prior biological knowledge. lncDLSM is a helpful tool for identifying lncRNAs compared with other biological feature-based machine learning methods and can be applied to other species by transfer learning achieving satisfactory results. Further experiments showed that different species display distinct boundaries among distributions corresponding to the homology and the specificity among species, respectively. An online web server is provided to the community for easy use and efficient identification of lncRNA, available at http://39.106.16.168/lncDLSM.

bioinformatics↗

bioRxiv2022

Targeting Epsins by nanotherapy regulates lipid metabolism and promotes 1 ABCG1-mediated cholesterol efflux to fortify atheroma regression

BACKGROUNDExcess cholesterol accumulation in lesional macrophages elicits complex responses in atherosclerosis. Epsins, a family of endocytic adaptors, fuel the progression of atherosclerosis; however, the underlying mechanism and therapeutic potential of targeting Epsins remains unknown. In this study, we determined the role of Epsins in macrophage-mediated metabolic regulation. We then developed an innovative method to therapeutically-target macrophage Epsins with specially-designed S2P-conjugated lipid nanoparticles (NPs), which encapsulate small interfering RNAs to suppress Epsins. METHODSWe used single cell RNA sequencing (scRNA-seq) with our newly developed algorithm MEBOCOST to study cell-cell communications mediated by metabolites from sender cells and sensor proteins on receiver cells. Biomedical, cellular and molecular approaches were utilized to investigate the role of macrophage Epsins in regulating lipid metabolism and transport. We performed this study using myeloid-specific Epsin double knockout (LysM-DKO) mice and mice with a genetic reduction of ABCG1 (LysM-DKO-ABCG1fl/+). The NPs targeting lesional macrophages were developed to encapsulate interfering RNAs to treat atherosclerosis. RESULTSWe revealed that Epsins regulate lipid metabolism and transport in atherosclerotic macrophages. Inhibiting Epsins by nanotherapy halts inflammation and accelerates atheroma resolution. Harnessing lesional macrophage-specific NP delivery of Epsin siRNAs, we showed that silencing of macrophage Epsins markedly diminished atherosclerotic plaque size and promoted plaque regression. Mechanistically, we demonstrated that Epsins bound to CD36 to facilitate lipid uptake by enhancing CD36 endocytosis and recycling. Conversely, Epsins promoted ABCG1 degradation via lysosomes and hampered ABCG1-mediated cholesterol efflux and reverse cholesterol transport. In a LysM-DKO-ABCG1fl/+ mouse model, enhanced cholesterol efflux and reverse transport due to Epsin deficiency was suppressed by the reduction of ABCG1. CONCLUSIONSOur findings suggest that targeting Epsins in lesional macrophages may offer therapeutic benefits for advanced atherosclerosis by reducing CD36-mediated lipid uptake and increasing ABCG1-mediated cholesterol efflux. Novelty and SignificanceO_ST_ABSWHAT IS KNOWN?C_ST_ABSO_LIEpsin endocytic adaptor proteins are upregulated in human and mouse atherosclerotic lesions C_LIO_LILesional macrophages internalize lipids primarily through scavenger receptor-mediated endocytosis such as CD36 and SR-A C_LIO_LIMacrophage-mediated cholesterol efflux and reverse cholesterol transport is crucial to atheroma resolution C_LI WHAT NEW INFORMATION DOES THIS ARTICLE CONTRIBUTE?O_LIScRNA-seq combined with the newly-developed algorithm MEBOCOST reveals that Epsins are involved in macrophage-mediated lipid metabolic regulation C_LIO_LIMacrophage epsins promote lipid uptake by targeting CD36 endocytosis and membrane recycling via the Epsin ENTH domain C_LIO_LIEpsins bind ubiquitinated ABCG1--resulting in the endocytosis and lysosomal degradation of this cholesterol transporter, which reduces cholesterol efflux. C_LIO_LIMacrophage-specific, nanoparticle-mediated RNAi delivery exhibits a therapeutic benefit for the treatment of atherosclerosis C_LIO_LIAtherosclerotic plaque regression using this nanoparticle delivery platform represents a clinically-relevant approach for the treatment of advance atherosclerosis C_LI BRIEF SUMMARYDespite the development of new cholesterol-lowering therapies, including the recently approved PCSK9 small interfering RNA (siRNA) antagonists, patients still face a tremendous risk of developing major acute cardiovascular events resulting from chronic inflammation in the plaque. We employed a novel nanomedicine platform containing a stabilin-2 targeting peptide (S2P) to deliver Epsin-specific siRNAs to lesional macrophages. We discovered that inhibition of these adaptor proteins in lesional macrophages significantly diminished plaque size and necrotic core area, increased fibrous cap thickness, and promoted plaque regression.

cell biology↗