Biology subjects

Chen, B.

Publications and source records attributed to Chen, B..

At least 127 records · Page 7Linked to original sources

bioRxiv2024

Light-guided actin polymerization drives directed motility in protocells

Motility is a hallmark of lifes dynamic processes, enabling cells to actively chase prey, repair wounds, and shape organs. Recreating these intricate behaviors using well-defined molecules remains a major challenge at the intersection of biology, physics, and molecular engineering. Although the polymerization force of the actin cytoskeleton is characterized as a primary driver of cell motility, building a minimal platform to test this process in protocellular systems has proven elusive. The difficulty lies in the daunting task of distilling key components from motile cells and integrating them into model membranes in a physiologically relevant manner. To address this, we developed a method to optically control actin polymerization with high spatiotemporal precision within cell-mimetic lipid vesicles known as giant unilamellar vesicles (GUVs). Within these active protocells, the reorganization of actin networks triggered outward membrane extensions as well as the unidirectional movement of GUVs at speeds of up to 0.43 {micro}m/min, within the range of adherent mammalian cells. Notably, our findings reveal the requirements of both branched and linear actin networks for efficient membrane protrusions. This approach offers a powerful platform for unraveling the intricacies of cell migration, designing synthetic cells with active morphodynamics, and advancing bioengineering applications, such as self-propelled delivery systems and autonomous tissue-like materials.

synthetic biology↗

bioRxiv2024

Hypomorphic NOTCH1 Expression Alters Cardiomyocyte Cellular Architecture in Hypoplastic Left Heart Syndrome

NOTCH1 is a protein involved in cardiac development and mutations in NOTCH1 are implicated in left sided congenital heart disease, including hypoplastic left heart syndrome (HLHS). Therapeutic advances for HLHS patients have been hampered by the absence of suitable experimental models. Here, using human induced pluripotent stem cells (hiPSCs), we have generated a series of CRISPR-edited complex heterozygous hypomorphic mutations in NOTCH1 that recapitulate mutations seen in HLHS patients. These cells demonstrate the downregulation of genes associated with mitochondria, the actin cytoskeleton, and cardiomyocyte development. In addition, these hypomorphic NOTCH1 hiPSCs cells showed an increased propensity to differentiate towards non-cardiomyocyte lineages such as fibroblasts and smooth muscle cells, a finding confirmed in HLHS patient-derived myocardial tissue. Abnormalities in sarcomeric and mitochondrial architecture contributed to decreased ATP production, abnormal calcium transients, and reduced contractility. Using a split nano-luciferase system as a NOTCH1 intracellular reporter, we screened a library of FDA-approved compounds. Auranofin, an agent currently employed for rheumatoid arthritis, was identified as a candidate drug that could rescue impaired differentiation and contractility seen in the hypomorphic NOTCH1 model. These findings point to cell-autonomous abnormalities in hypomorphic NOTCH1 pre-cardiomyocyte cells that can be leveraged to identify potential therapeutic strategies for patients with severe congenital cardiac abnormalities. HighlightsO_LICRISPR edited and patient-derived iPSCs with hypomorphic NOTCH1 expression were used to characterize a cell-based model of hypoplastic left heart syndrome. C_LIO_LIHypomorphic NOTCH1 iPSCs have abnormalities in pathways associated with mitochondrial function, actin cytoskeleton, and cardiomyocyte development. C_LIO_LIHypomorphic NOTCH1 iPSCs have less pluripotency and tend to skew differentiation away from cardiomyocytes and towards fibroblasts and smooth muscle cells. C_LIO_LINOTCH1 is required for cardiac cytoskeletal and mitochondrial architecture, and to preserve contractility and ATP production. C_LIO_LIA high-throughput drug screen identified auranofin as an agent that may benefit patients with hypoplastic left heart syndrome. C_LI

cell biology↗

bioRxiv2024

Mechanism of NPC1L1 mediated 27-hydroxycholesterol metabolisms in the occurrence and development of osteoporosis

Cholesterol metabolism is closely related to the occurrence and development of osteoporosis, but the exact mechanism remains unclear. Niemann-Pick C1-like 1 (NPC1L1) is one of the key cholesterol transporter proteins, however, there are few reports on the functions of NPC1L1 besides regulating cholesterol transport, let alone bone homeostasis. Our preliminary research indicated that NPC1L1 may play a negative regulatory role in osteogenic differentiation. In this study, using in vitro osteogenic differentiation experiment and mouse osteoporosis model, we showed here that NPC1L1 expression was downregulated during osteogenesis, and NPC1L1 knockdown significantly enhanced osteogenic differentiation ability of osteoblasts and delayed progress of osteoporosis. Mechanistically, through RNA sequencing, NPC1L1 was found regulate cholesterol metabolism rather than transportation. It increased 27-Hydroxycholesterol (27-OHC) level through activating 27-hydroxylase (Cyp27a1), resulting in 27-OHC accumulation in osteoblasts and inhibition of osteogenesis. Moreover, C/EBP was proved mediated NPC1L1 promotes production of 27-OHC by Cyp27a1. These findings reveal that NPC1L1 inhibits osteogenesis and promotes osteoporosis via regulate cholesterol metabolism. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/615783v1_ufig1.gif" ALT="Figure 1"> View larger version (59K): org.highwire.dtl.DTLVardef@11a62bforg.highwire.dtl.DTLVardef@148fb9aorg.highwire.dtl.DTLVardef@7e486eorg.highwire.dtl.DTLVardef@c53116_HPS_FORMAT_FIGEXP M_FIG NPC1L1 inhibits osteogenic differentiation and promotes the progression of OP through the C/EBP/Cyp27a1/27-OHC axis. C_FIG

molecular biology↗

bioRxiv2024

Caveolin-1 regulates context-dependent signaling and survival in Ewing Sarcoma.

Plasticity is a hallmark function of cancer cells, yet the mechanisms that enable dynamic switching between survival states remain incompletely understood. Here, we identify Caveolin-1, a membrane-domain scaffolding protein, as a context-dependent regulator of survival signaling in Ewing sarcoma (EwS). Single-cell analyses reveal a distinct subpopulation of EwS cells marked by high CD99 and elevated Caveolin-1 expression. These CD99High cells exhibit unique morphology, transcriptional programs, and markedly enhanced survival both under chemotherapeutic challenge and in vivo. Importantly, CD99High and CD99Low states are reversible, providing EwS cells with a flexible route to survival-oriented plasticity. Mechanistically, we show that Caveolin-1 in CD99High cells orchestrates PI3K/AKT survival signaling by modulating the spatial organization of PI3K activity on the plasma membrane. We propose that the CD99High state establishes a Caveolin-1-driven signaling architecture that supports survival through mechanisms distinct from those used by CD99Low cells. These findings uncover a dynamic state transition in EwS cells and position Caveolin-1 as a key driver of context-specific survival signaling.

cancer biology↗

bioRxiv2024

STANCE: a unified statistical model to detect cell-type-specific spatially variable genes in spatial transcriptomics

A significant challenge in analyzing spatial transcriptomics data is the effective and efficient detection of spatially variable genes (SVGs), whose expression exhibits non-random spatial patterns in tissues. Many SVGs show spatial variation in expression that is highly correlated with cell type categories or compositions, leading to the concept of cell type-specific spatially variable genes (ctSVGs). Existing statistical methods for detecting ctSVGs treat cell type-specific spatial effects as fixed effects when modeling, resulting in a critical issue: the testing results are not invariant to the rotation of spatial coordinates. Additionally, an SVG may display random spatial patterns within a cell type, and a ctSVG may exhibit random spatial patterns from a general perspective, indicating that an SVG does not necessarily have to be a ctSVG, and vice versa. This poses challenges in real analysis when detecting SVGs or ctSVGs. To address these problems, we propose STANCE, a unified statistical model developed to detect both SVG and ctSVGs in spatial transcriptomics. By integrating gene expression, spatial location, and cell type composition through a linear mixed-effect model, STANCE enables the identification of both SVGs and ctSVGs in an initial stage, followed by a second stage test dedicated to ctSVG detection. Its design ensures robustness in complex scenarios and the results are spatial rotation invariant. We demonstrated the performance of STANCE through comprehensive simulations and analyses of three public datasets. The downstream analyses based on ctSVGs detected by STANCE suggest promising future applications of the model in spatial transcriptomics and various areas of genome biology. A software implementation of STANCE is available at https://github.com/Cui-STT-Lab/STANCE.

bioinformatics↗

bioRxiv2024

Imputing abundance of over 2500 surface proteins from single-cell transcriptomes with context-agnostic zero-shot deep ensembles

Cell surface proteins serve as primary drug targets and cell identity markers. The emergence of techniques like CITE-seq has enabled simultaneous quantification of surface protein abundance and transcript expression for multimodal data analysis within individual cells. The published data have been utilized to train machine learning models for predicting surface protein abundance based solely from transcript expression. However, the small scale of proteins predicted and the poor generalization ability for these computational approaches across diverse contexts, such as different tissues or disease states, impede their widespread adoption. Here we propose SPIDER (surface protein prediction using deep ensembles from single-cell RNA-seq), a context-agnostic zero-shot deep ensemble model, which enables the large-scale prediction of cell surface protein abundance and generalizes better to various contexts. Comprehensive benchmarking shows that SPIDER outperforms other state-of-the-art methods. Using the predicted surface abundance of >2500 proteins from single-cell transcriptomes, we demonstrate the broad applications of SPIDER including cell type annotation, biomarker/target identification, and cell-cell interaction analysis in hepatocellular carcinoma and colorectal cancer.

bioinformatics↗

bioRxiv2024

Fibrillarin modulates fetal hemoglobin silencing

Decoding the molecular mechanisms underlying human fetal ({gamma}) globin gene silencing impacts therapeutic strategies for {beta}-thalassemia and sickle cell disease. Here, we identified a nucleolar protein, fibrillarin (FBL), which mediates the methylation of glutamine104 in histone H2A and functions as a repressor of the {gamma}-globin gene in cultured erythroid cells, including those from {beta}-thalassemia patients. Conditional Fbl depletion in adult {beta}-YAC transgenic mice or in {beta}IVS-2-654-thalassemic mice reactivated the human {gamma}-globin gene or murine embryonic globin expression, respectively, which corrects hematologic and pathologic defects in {beta}-thalassemic mice. We showed that FBL plays a dual role in activating BCL11A expression and repressing {gamma}-globin gene expression, which is dependent on its histone methyltransferase activity. Our study may provide an alternative strategy for therapeutic targeted treatment of {beta}-hemoglobinopathies.

molecular biology↗

bioRxiv2024

MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative Pre-Training

Multiple Sequence Alignment (MSA) plays a pivotal role in unveiling the evolutionary trajectories of protein families. The accuracy of protein structure predictions is often compromised for protein sequences that lack sufficient homologous information to construct high-quality MSA. Although various methods have been proposed to generate virtual MSA under these conditions, they fall short in comprehensively capturing the intricate co-evolutionary patterns within MSA or require guidance from external oracle models. Here we introduce MSAGPT, a novel approach to prompt protein structure predictions via MSA generative pre-training in the low-MSA regime. MSAGPT employs a simple yet effective 2D evolutionary positional encoding scheme to model the complex evolutionary patterns. Endowed by this, its flexible 1D MSA decoding framework facilitates zero-or few-shot learning. More-over, we demonstrate that leveraging the feedback from AlphaFold2 can further enhance the models capacity via Rejective Fine-tuning (RFT) and Reinforcement Learning from AF2 Feedback (RLAF). Extensive experiments confirm the efficacy of MSAGPT in generating faithful virtual MSA to enhance the structure prediction accuracy (up to +8.5% TM-Score on few-shot scenarios). The transfer learning capabilities also highlight its great potential for facilitating other protein tasks.

bioinformatics↗

bioRxiv2024

Training Compute-Optimal Protein Language Models

We explore optimally training protein language models, an area of significant interest in biological research where guidance on best practices is limited. Most models are trained with extensive compute resources until performance gains plateau, focusing primarily on increasing model sizes rather than optimizing the efficient compute frontier that balances performance and compute budgets. Our investigation is grounded in a massive dataset consisting of 939 million protein sequences. We trained over 300 models ranging from 3.5 million to 10.7 billion parameters on 5 to 200 billion unique tokens, to investigate the relations between model sizes, training token numbers, and objectives. First, we observed the effect of diminishing returns for the Causal Language Model (CLM) and that of overfitting for the Masked Language Model (MLM) when repeating the commonly used Uniref database. To address this, we included metagenomic protein sequences in the training set to increase the diversity and avoid the plateau or overfitting effects. Second, we obtained the scaling laws of CLM and MLM on Transformer, tailored to the specific characteristics of protein sequence data. Third, we observe a transfer scaling phenomenon from CLM to MLM, further demonstrating the effectiveness of transfer through scaling behaviors based on estimated Effectively Transferred Tokens. Finally, to validate our scaling laws, we compare the large-scale versions of ESM-2 and PROGEN2 on downstream tasks, encompassing evaluations of protein generation as well as structure- and function-related tasks, all within less or equivalent pre-training compute budgets.

bioinformatics↗

bioRxiv2024

Leveraging protein language and structural modelsfor early prediction of antibodies with fast clearance

Monoclonal antibodies (mAbs) with long systemic persistence are widely used as therapeutics. However, antibodies with atypically fast clearance require more dosing, limiting their clinical usefulness. Deep learning can facilitate using sequence-based modeling to predict potential pharmacokinetic (PK) liabilities before antibody generation. Assembling a dataset of 103 mAbs with measured nonspecific clearance in cynomolgus monkeys (cyno), and using transfer learning from large protein language models, we developed multiple machine learning models to predict mAb clearance as fast/slow clearing. Focusing on minimizing misclassification of potentially promising molecules as fast clearing, our results show that using physicochemical properties yielded up to 73.1+/-1.1% classification accuracy on hold-out test data (precision 65.2+/-2.3%). Using only sequence-based features from deep learning protein language models yielded a comparable performance of 71+/-1.4% (precision 65.5+/-2.5%). Combining structural and deep learning derived features yielded a similar accuracy of 73.9+/-1.1%, and slightly improved precision (68.3+/-2.4%). Features important for classifying fast/slow clearance point to charge, moment, and surface area properties at pH 7.4 as well as deep learning derived features. These results suggest that the protein language models provide comparable information and predictive performance of clearance as physicochemical features. This work provides a foundation for in silico prediction of protein pharmacokinetics to inform antibody candidate generation and early deprioritization of designs with high risk of fast clearance. More generally, it illustrates the value of transfer learning-based application of protein language models to address characteristics of importance for protein therapeutics.

pharmacology and toxicology↗

bioRxiv2024

Genomic and Genetic Insights into Mendel's Pea Genes

Pea, Pisum sativum, is an excellent model system through which Gregor Mendel established the foundational principles of inheritance. Surprisingly, till today, the molecular nature of the genetic differences underlying the seven pairs of contrasting traits that Mendel studied in detail remains partially understood. Here, we present a genomic and phenotypic variation map, coupled with haplotype-phenotype association analyses across a wide range of traits in a global Pisum diversity panel. We focus on a genomics-enabled genetic dissection of each of the seven traits Mendel studied, revealing many previously undescribed alleles for the four characterized genes, R, Le, I and A, and elucidating the gene identities and mutations for the remaining three uncharacterized traits. Notably, we identify: (1) a ca. 100kb deletion upstream of the Chlorophyll synthase (ChlG) gene, which generates aberrant transcripts and confers the yellow pod phenotype of gp mutants; (2) an in-frame premature stop codon mutation in a Dodeca-CLE41/44 signalling peptide which explains the parchmentless mutant phenotype corresponding to p; and (3) a 5bp in-frame deletion in a CIK-like receptor kinase gene corresponding to the fasciated stem phenotype fa, which Mendel described in terms of flower position, and we postulate the existence of a Modifier of fa (Mfa) locus that masks this meristem defect. Mendel noted the pleiotropy of the a mutation, including inhibition of axil ring anthocyanin pigmentation, a trait we found to be controlled by allelic variants of the gene D within an R2R3-MYB gene cluster. Furthermore, we characterize and validate natural variation of a quantitative genetic locus governing both pod width and seed weight, characters that Mendel deemed were not sufficiently demarcated for his analyses. This study establishes a cornerstone for fundamental research, education in biology and genetics, and pea breeding practices.

genetics↗

bioRxiv2024

The theory of massively repeated evolution and full identifications of Cancer Driving Nucleotides (CDNs)

Tumorigenesis, like most complex genetic traits, is driven by the joint actions of many mutations. At the nucleotide level, such mutations are Cancer Driving Nucleotides (CDNs). The full sets of CDNs are necessary, and perhaps even sufficient, for the understanding and treatment of each cancer patient. Currently, only a small fraction of CDNs is known as most mutations accrued in tumors are not drivers. We now develop the theory of CDNs on the basis that cancer evolution is massively repeated in millions of individuals. Hence, any advantageous mutation should recur frequently and, conversely, any mutation that does not is either a passenger or deleterious mutation. In the TCGA cancer database (sample size n = 300 - 1000), point mutations may recur in i out of n patients. This study explores a wide range of mutation characteristics to determine the limit of recurrences (i*) driven solely by neutral evolution. Since no neutral mutation can reach i* = 3, all mutations recurring at i [≥] 3 are CDNs. The theory shows the feasibility of identifying almost all CDNs if n increases to 100,000 for each cancer type. At present, only < 10% of CDNs have been identified. When the full sets of CDNs are identified, the evolutionary mechanism of tumorigenesis in each case can be known and, importantly, gene targeted therapy will be far more effective in treatment and robust against drug resistance.

evolutionary biology↗

bioRxiv2024

On the discovered Cancer Driving Nucleotides (CDNs)-Distributions across genes, cancer types and patients

A central goal of cancer genomics is to identify, in each patient, all the cancer driving mutations. Among them, point mutations are referred to as Cancer Driving Nucleotides (CDNs), which recur in cancers. The companion study shows that the probability of i recurrent hits in n patients would decrease exponentially with i; hence, any mutation with i [≥] 3 hits in the TCGA database is a high-probability CDN. This study characterizes the 50[~]150 CDNs identifiable for each cancer type of TCGA (while anticipating 10 times more undiscovered ones) as follows: i) CDNs tend to code for amino acids of divergent chemical properties. ii) At the genic level, far more CDNs (>5-fold) fall on non-canonical than canonical cancer driving genes (CDGs). Most undiscovered CDNs are expected to be on unknown CDGs. iii) CDNs tend to be more widely shared among cancer types than canonical CDGs, mainly because of the higher resolution at the nucleotide than the whole-gene level. iv) Most important, among the 50[~]100 coding region mutations carried by a cancer patient, 5[~]8 CDNs are expected but only 0[~]2 CDNs have been identified at present. This low level of identification has hampered functional test and gene targeted therapy. We show that, by expanding the sample size to 105, most CDNs can be identified. Full CDN identification will then facilitate the design of patient-specific targeting against multiple CDN-harboring genes.

evolutionary biology↗

bioRxiv2024

Behavioral impairments are linked to neuroinflammation in mice with Cerebral Cavernous Malformation disease

BackgroundCerebral Cavernous Malformations (CCMs) are neurovascular abnormalities in the central nervous system (CNS) caused by loss of function mutations in KRIT1 (CCM1), CCM2, or PDCD10 (CCM3) genes. One of the most common symptoms in CCM patients is associated with motor disability, weakness, seizures, stress, and anxiety, and the extent of the symptom or symptoms may be due to the location of the lesion within the CNS or whether multiple lesions are present. Previous studies have primarily focused on understanding the pathology of CCM using animal models. However, more research has yet to explore the potential impact of CCM lesions on behavioral deficits in animal models, including effects on short-term and long-term memory, motor coordination, and function. MethodsWe used the accelerating RotaRod test to assess motor and coordination deficits. We also used the open field test to assess locomotor activity and pathology-related behavior and Pavlovian fear conditioning to assess short--and long-term memory deficits. Our behavioral studies were complemented by proteomics, histology, immunofluorescence, and imaging techniques. We found that neuroinflammation is crucial in behavioral deficits in male and female mice with neurovascular CCM lesions (Slco1c1-iCreERT2; Pdcd10fl/fl; Pdcd10BECKO). ResultsFunctional behavior tests in male and female Pdcd10BECKO mice revealed that CCM lesions cause sudden motor coordination deficits associated with the manifestation of profound neuroinflammatory lesions. Our findings indicate that maturation of CCM lesions in Pdcd10BECKO mice also experienced a significant change in short- and long-term memory compared to their littermate controls, Pdcd10fl/fl mice. Proteomic experiments reveal that as CCM lesions mature, there is an increase in pathways associated with inflammation, coagulation, and angiogenesis, and a decrease in pathways associated with learning and plasticity. Therefore, our study shows that Pdcd10BECKO mice display a wide range of behavioral deficits due to significant lesion formation in their central nervous system and that signaling pathways associated with neuroinflammation and learning impact behavioral outcomes. ConclusionsOur study found that CCM animal models exhibited behavioral impairments such as decreased motor coordination and amnesia. These impairments were associated with the maturation of CCM lesions that displayed a neuroinflammatory pattern.

animal behavior and cognition↗

bioRxiv2024

Amd1-skp2 cascade regulates hepatocyte proliferation during liver growth and hepatocellular carcinoma development in zebrafish

Rapid hepatocyte proliferation is a common characteristic of liver growth in early liver development and hepatocellular carcinoma (HCC). Clarifying the mechanism how liver growth is regulated in development would benefit studying HCC progress. Although many genes have been reported to be involved in liver growth during early development, the mechanism underlying liver growth is far from being elucidated. Here we found that the liver undergoes rapid growth from 2dpf to 5dpf in zebrafish. Comparing the transcriptome for different staged hepatocytes identified 813 hepatocyte enriched genes at 3 dpf. Among them, S-adenosylmethionine decarboxylase proenzyme (AMD1) had not been previously reported to be involved in liver development. Our study further confirmed that amd1 was highly enriched in hepatocytes during liver rapid growth and that amd1 mutation inhibited liver growth mainly by repressing hepatocyte proliferation. Mechanistically, amd1 loss of function downregulated the expression of skp2, and skp2 is required for amd1 to regulate hepatocyte proliferation. Furthermore, the role of Amd1-Skp2 cascade in regulating hepatocyte proliferation was also conserved in a zebrafish HCC model. In conclusion, our study systematically identified some uncharacterized genes possibly being involved in regulating liver growth during zebrafish development, and revealed the role of amd1-skp2 cascade in regulating hepatocyte proliferation during liver growth and HCC progression. This work also provided a database which would benefit elucidating the mechanism how hepatocyte proliferation is regulated during liver growth and HCC progress.

developmental biology↗

bioRxiv2024

Endothelial cannabinoid CB1 receptor deficiency reduces arterial inflammation and lipid uptake in response to atheroprone shear stress

BackgroundPeripherally restricted cannabinoid CB1 receptor antagonists without central side effects hold promise for treating metabolic disorders including diabetes and obesity. In atherosclerosis, the specific effects of peripheral CB1 signaling in vascular endothelial cells (ECs) remain incompletely understood. Methods and resultsEndothelial expression of the CB1 encoding gene CNR1 was detectable in human plaque single-cell RNA sequencing data. In situ hybridization of Cnr1 in murine aortas revealed a significantly increased endothelial expression within atheroprone compared to atheroresistant regions. In vitro, CNR1 was upregulated by oscillatory shear stress (OSS) in human aortic endothelial cells (HAoECs). Endothelial CB1 deficiency (Cnr1EC-KO) in female mice on atherogenic background resulted in pronounced endothelial phenotypic changes, with reduced vascular inflammation and permeability. This resulted in attenuated plaque development with reduced lipid content in female mice, while reduced white and brown adipose tissue mass and liver steatosis were observed in both males and females. Ex vivo imaging of carotid arteries via two-photon microscopy revealed less labeled low density lipoprotein (Dil-LDL) uptake in Cnr1EC-KO. This was accompanied by a significant reduction of aortic endothelial caveolin-1 (CAV1) expression, a key structural protein involved in lipid transcytosis, in female Cnr1EC-KO mice. In vitro, pharmacological blocking with CB1 antagonist AM281 reproduced the inhibition of CAV1 expression and Dil-LDL uptake in response to atheroprone OSS in HAoECs, which was dependent on cAMP-mediated PKA activation. Conversely, the CB1 agonist ACEA increased Dil-LDL uptake and CAV1 expression in HAoECs. Finally, treatment of atherosclerotic mice with the peripheral CB1 antagonist JD-5037 reduced plaque progression, CAV1 and endothelial adhesion molecule expression in female mice. ConclusionsThese results confirm an essential role of endothelial CB1 to the pathogenesis of atherosclerosis. Peripheral CB1 antagonists may hold promise as an effective therapeutic strategy for treating atherosclerosis and related metabolic disorders. NOVELTY AND SIGNIFICANCEWhat is known? O_LIEnhanced endocannabinoid-cannabinoid CB1 receptor signaling has been implicated in metabolic disorders, atherosclerosis, and hypertension, but the cell-specific role of endothelial CB1 in atherosclerosis is not well understood. C_LIO_LIGlobal CB1 antagonists improve metabolic function and inhibit atherosclerotic plaque development in mouse models, but have failed in the clinic due to centrally mediated psychiatric side effects. C_LI What is new? O_LIBased on human single-cell RNA sequencing data, CB1 is expressed in human plaque ECs. C_LIO_LIUsing transgenic mouse models and human primary aortic endothelial cells, we provide evidence for a key role of CB1 in endothelial shear stress response, inflammatory gene expression, and LDL uptake. C_LIO_LIThe underlying signaling pathway of CB1-induced endothelial LDL uptake involves a cAMP-PKA-dependent regulation of caveolin 1 (CAV1) expression, a structural protein of the shear stress sensitive signaling domains of the plasma membrane. C_LIO_LIBy limiting endothelial CAV1 and VCAM1 expression, peripherally restricted CB1 antagonists confer atheroprotection in mice. C_LI Our findings reveal that endothelial CB1 expression is induced by atheroprone shear stress responses and contributes to impaired vascular barrier function, inflammation, and lipid uptake, thereby promoting atherosclerotic lesion formation and progression. By elucidating the transcriptomic pathways regulated by endothelial CB1 and its broad influence on lipid uptake and metabolism in arteries, liver, and brown and white adipose tissue, our study provides insights into novel pathways and potential interventions for the treatment of atherosclerosis and metabolic disorders. The use of peripherally restricted CB1 antagonists that specifically target vascular inflammation and tissue lipid storage could be a complementary and safe therapeutic avenue to treat cardiovascular and metabolic disease comorbidities without altering CB1 signaling in the brain.

immunology↗

bioRxiv2024

Connection Building Method: High-Accuracy MicroRNA Target Prediction Using Disease Semantic Strings

MicroRNAs (miRNAs) regulate gene expression by binding to mRNAs, inhibiting translation, or promoting mRNA degradation. miRNAs are of great importance in the development of various diseases. Currently, numerous sequence-based miRNA target prediction tools are available, however, only 1% of their predictions have been experimentally validated. In this study, we propose a novel approach that leverages disease similarity degree between miRNAs and genes as a key feature to further refine human sequence-based predicted miRNA target interactions (MTIs). To quantify the similarity degree of diseases, we fine-tuned the Sentence-BERT model. Our method achieved an F1 score of 0.88 in accurately distinguishing human protein-level experimentally validated MTIs (functional MTIs, validated through western blot or reporter assay) and predicted MTIs. Moreover, this method exhibits exceptional generalizability across different databases. We applied the proposed method to analyze 1,220,904 human MTIs sourced from miRTarbase, miRDB, and miRWalk, encompassing 6,085 genes and 1,261 pre-miRNAs. Our model was trained in miRTarBase 2022. However, we accurately identified 90% (518/574) of the updated functional MTIs in miRTarbase 2025. This study has the potential to provide valuable insights into the understanding of miRNA-gene regulatory networks and to promote advancements in disease diagnosis, treatment, and drug development.

bioinformatics↗

bioRxiv2024

Quantitative intra-Golgi transport and organization data suggest the stable compartment nature of the Golgi

How the intra-Golgi secretory transport works remains a mystery. The cisternal progression and the stable compartment models have been proposed and are under debate. Classic cisternal progression model posits that both the intra-Golgi transport and Golgi exit of secretory cargos should occur at a constant velocity dictated by the cisternal progression; furthermore, COPI-mediated intra-Golgi retrograde transport is essential for maintaining the Golgi organization. Leveraging our recently developed Golgi imaging tools in nocodazole-induced Golgi ministacks, we found that the intra-Golgi transport velocity of a secretory cargo decreases during their transition from the cis to the trans-side of the Golgi, and different cargos exhibit distinct velocities even within the same cisternae. We observed a vast variation in the Golgi residence times of different cargos. Remarkably, truncation of the luminal domain causes the Golgi residence time of Tac -- a standard transmembrane secretory cargo without intra-Golgi recycling signals -- to extend from 16 min to a notable 3.4 h. Additionally, when COPI-mediated intra-Golgi retrograde transport was inhibited by brefeldin A, we found that nocodazole-induced Golgi can remain stacked for over 30 - 60 minutes. Therefore, our findings challenge the classical cisternal progression model and suggest the stable compartment nature of the Golgi.

cell biology↗