Targeting regulator of G protein signaling 1 in tumor-specific T cells enhances their trafficking to breast cancer.

Reduced infiltration of anti-tumor lymphocytes remains a major cause of tumor immune evasion and is correlated with poor cancer survival. Here, we found that upregulation of regulator of G protein signaling (RGS)1 in helper TH1 cells and cytotoxic T lymphocytes (CTLs) reduced their trafficking to and survival in tumors and was associated with shorter survival of patients with breast and lung cancer. RGS1 was upregulated by type II interferon (IFN)-signal transducer and activator of transcription (STAT)1 signaling and impaired trafficking of circulating T cells to tumors by inhibiting calcium influx and suppressing activation of the kinases ERK and AKT. RGS1 knockdown in adoptively transferred tumor-specific CTLs significantly increased their infiltration and survival in breast and lung tumor grafts and effectively inhibited tumor growth in vivo, which was further improved when combined with programmed death ligand (PD-L)1 checkpoint inhibition. Our findings reveal RGS1 is important for tumor immune evasion and suggest that targeting RGS1 may provide a new strategy for tumor immunotherapy.

Tumour circular RNAs elicit anti-tumour immunity by encoding cryptic peptides.

Emerging data have shown that previously defined noncoding genomes might encode peptides that bind human leukocyte antigen (HLA) as cryptic antigens to stimulate adaptive immunity1,2. However, the significance and mechanisms of action of cryptic antigens in anti-tumour immunity remain unclear. Here mass spectrometry of the HLA class I (HLA-I) peptidome coupled with ribosome sequencing of human breast cancer samples identified HLA-I-binding cryptic antigenic peptides that were noncanonically translated by a tumour-specific circular RNA (circRNA): circFAM53B. The cryptic peptides efficiently primed naive CD4+ and CD8+ T cells in an antigen-specific manner and induced anti-tumour immunity. Clinically, the expression of circFAM53B and its encoded peptides was associated with substantial infiltration of antigen-specific CD8+ T cells and better survival in patients with breast cancer and patients with melanoma. Mechanistically, circFAM53B-encoded peptides had strong binding affinity to both HLA-I and HLA-II molecules. In vivo, administration of vaccines consisting of tumour-specific circRNA or its encoded peptides in mice bearing breast cancer tumours or melanoma induced enhanced infiltration of tumour-antigen-specific cytotoxic T cells, which led to effective tumour control. Overall, our findings reveal that noncanonical translation of circRNAs can drive efficient anti-tumour immunity, which suggests that vaccination exploiting tumour-specific circRNAs may serve as an immunotherapeutic strategy against malignant tumours.

Large-scale foundation model on single-cell transcriptomics.

Large pretrained models have become foundation models leading to breakthroughs in natural language processing and related fields. Developing foundation models for deciphering the 'languages' of cells and facilitating biomedical research is promising yet challenging. Here we developed a large pretrained model scFoundation, also named 'xTrimoscFoundationα', with 100 million parameters covering about 20,000 genes, pretrained on over 50 million human single-cell transcriptomic profiles. scFoundation is a large-scale model in terms of the size of trainable parameters, dimensionality of genes and volume of training data. Its asymmetric transformer-like architecture and pretraining task design empower effectively capturing complex context relations among genes in a variety of cell types and states. Experiments showed its merit as a foundation model that achieved state-of-the-art performances in a diverse array of single-cell analysis tasks such as gene expression enhancement, tissue drug response prediction, single-cell drug response classification, single-cell perturbation prediction, cell type annotation and gene module inference.

Discovery of metal-binding proteins by thermal proteome profiling.

Metal-binding proteins (MBPs) have various and important biological roles in all living species and many human diseases are intricately linked to dysfunctional MBPs. Here, we report a chemoproteomic method named 'metal extraction-triggered agitation logged by thermal proteome profiling' (METAL-TPP) to globally profile MBPs in proteomes. The method involves the extraction of metals from MBPs using chelators and monitoring the resulting protein stability changes through thermal proteome profiling. Applying METAL-TPP to the human proteome with a broad-spectrum chelator, EDTA, revealed a group of proteins with reduced thermal stability that contained both previously known MBPs and currently unannotated MBP candidates. Biochemical characterization of one potential target, glutamine-fructose-6-phosphate transaminase 2 (GFPT2), showed that zinc bound the protein, inhibited its enzymatic activity and modulated the hexosamine biosynthesis pathway. METAL-TPP profiling with another chelator, TPEN, uncovered additional MBPs in proteomes. Collectively, this study developed a robust tool for proteomic discovery of MBPs and provides a rich resource for functional studies of metals in cell biology.

Single-Cell Transcriptomes and Immune Repertoires Reveal the Cell State and Molecular Changes in Pemphigus Vulgaris.

The etiology and pathogenesis of pemphigus vulgaris (PV) entail intricate interactions between immune cells and epithelial cells. However, the specific subtypes of immune cells involved in PV, along with their respective roles, remain elusive. Likewise, the precise functions and mechanisms by which glucocorticoids affect cell types within the disease context require further elucidation. To address these knowledge gaps, we performed 5' single-cell RNA sequencing, combined with V(D)J enrichment on buccal mucosal lesions and peripheral blood samples from treatment-naive patients with PV, in conjunction with post-treatment peripheral blood samples obtained after oral prednisone treatment. Our findings suggest that the IL-1α signaling pathway, myeloid APCs, inflammatory CD8+ resident memory T cells, and dysfunctional CD4+ regulatory T cells are involved in the pathogenesis of PV. Part of these findings were validated by immunohistochemical assays and multiplex immunofluorescence assays. Furthermore, our results highlight the significant impact of prednisone treatment on monocytes and mucosal-associated invariant T cells while revealing a limited effect on CD4+ regulatory T cells. Additionally, we present the CDR3 amino acid sequence of BCR related to PV disease and investigate the characteristics of TCR/BCR clonotypes. In conclusion, our study provides a comprehensive understanding of PV, particularly focusing on the mucosal-dominant type, and sheds light on the effects of glucocorticoids within the PV context. These insights hold promise for the development of new therapeutic strategies in this autoimmune disorder.

Ameliorating parkinsonian motor dysfunction by targeting histamine receptors in entopeduncular nucleus-thalamus circuitry.

In Parkinson's disease (PD), reduced dopamine levels in the basal ganglia have been associated with altered neuronal firing and motor dysfunction. It remains unclear whether the altered firing rate or pattern of basal ganglia neurons leads to parkinsonism-associated motor dysfunction. In the present study, we show that increased histaminergic innervation of the entopeduncular nucleus (EPN) in the mouse model of PD leads to activation of EPN parvalbumin (PV) neurons projecting to the thalamic motor nucleus via hyperpolarization-activated cyclic nucleotide-gated (HCN) channels coupled to postsynaptic H2R. Simultaneously, this effect is negatively regulated by presynaptic H3R activation in subthalamic nucleus (STN) glutamatergic neurons projecting to the EPN. Notably, the activation of both types of receptors ameliorates parkinsonism-associated motor dysfunction. Pharmacological activation of H2R or genetic upregulation of HCN2 in EPNPV neurons, which reduce neuronal burst firing, ameliorates parkinsonism-associated motor dysfunction independent of changes in the neuronal firing rate. In addition, optogenetic inhibition of EPNPV neurons and pharmacological activation or genetic upregulation of H3R in EPN-projecting STNGlu neurons ameliorate parkinsonism-associated motor dysfunction by reducing the firing rate rather than altering the firing pattern of EPNPV neurons. Thus, although a reduced firing rate and more regular firing pattern of EPNPV neurons correlate with amelioration in parkinsonism-associated motor dysfunction, the firing pattern appears to be more critical in this context. These results also confirm that targeting H2R and its downstream HCN2 channel in EPNPV neurons and H3R in EPN-projecting STNGlu neurons may represent potential therapeutic strategies for the clinical treatment of parkinsonism-associated motor dysfunction.

Co-evolution-based prediction of metal-binding sites in proteomes by machine learning.

Metal ions have various important biological roles in proteins, including structural maintenance, molecular recognition and catalysis. Previous methods of predicting metal-binding sites in proteomes were based on either sequence or structural motifs. Here we developed a co-evolution-based pipeline named 'MetalNet' to systematically predict metal-binding sites in proteomes. We applied MetalNet to proteomes of four representative prokaryotic species and predicted 4,849 potential metalloproteins, which substantially expands the currently annotated metalloproteomes. We biochemically and structurally validated previously unannotated metal-binding sites in several proteins, including apo-citrate lyase phosphoribosyl-dephospho-CoA transferase citX, an Escherichia coli enzyme lacking structural or sequence homology to any known metalloprotein (Protein Data Bank (PDB) codes: 7DCM and 7DCN ). MetalNet also successfully recapitulated all known zinc-binding sites from the human spliceosome complex. The pipeline of MetalNet provides a unique and enabling tool for interrogating the hidden metalloproteome and studying metal biology.

Role of HCN channels in the functions of basal ganglia and Parkinson's disease.

Parkinson's disease (PD) is a motor disorder resulting from dopaminergic neuron degeneration in the substantia nigra caused by age, genetics, and environment. The disease severely impacts a patient's quality of life and can even be life-threatening. The hyperpolarization-activated cyclic nucleotide-gated (HCN) channel is a member of the HCN1-4 gene family and is widely expressed in basal ganglia nuclei. The hyperpolarization-activated current mediated by the HCN channel has a distinct impact on neuronal excitability and rhythmic activity associated with PD pathogenesis, as it affects the firing activity, including both firing rate and firing pattern, of neurons in the basal ganglia nuclei. This review aims to comprehensively understand the characteristics of HCN channels by summarizing their regulatory role in neuronal firing activity of the basal ganglia nuclei. Furthermore, the distribution and characteristics of HCN channels in each nucleus of the basal ganglia group and their effect on PD symptoms through modulating neuronal electrical activity are discussed. Since the roles of the substantia nigra pars compacta and reticulata, as well as globus pallidus externus and internus, are distinct in the basal ganglia circuit, they are individually described. Lastly, this investigation briefly highlights that the HCN channel expressed on microglia plays a role in the pathological process of PD by affecting the neuroinflammatory response.

MvGraphDTA: multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs.

BACKGROUND: Accurately identifying drug-target affinity (DTA) plays a pivotal role in drug screening, design, and repurposing in pharmaceutical industry. It not only reduces the time, labor, and economic costs associated with biological experiments but also expedites drug development process. However, achieving the desired level of computational accuracy for DTA identification methods remains a significant challenge. RESULTS: We proposed a novel multi-view-based graph deep model known as MvGraphDTA for DTA prediction. MvGraphDTA employed a graph convolutional network (GCN) to extract the structural features from original graphs of drugs and targets, respectively. It went a step further by constructing line graphs with edges as vertices based on original graphs of drugs and targets. GCN was also used to extract the relationship features within their line graphs. To enhance the complementarity between the extracted features from original graphs and line graphs, MvGraphDTA fused the extracted multi-view features of drugs and targets, respectively. Finally, these fused features were concatenated and passed through a fully connected (FC) network to predict DTA. CONCLUSIONS: During the experiments, we performed data augmentation on all the training sets used. Experimental results showed that MvGraphDTA outperformed the competitive state-of-the-art methods on benchmark datasets for DTA prediction. Additionally, we evaluated the universality and generalization performance of MvGraphDTA on additional datasets. Experimental outcomes revealed that MvGraphDTA exhibited good universality and generalization capability, making it a reliable tool for drug-target interaction prediction.

Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning.

BACKGROUND: Accurately identifying drug-target interaction (DTI), affinity (DTA), and binding sites (DTS) is crucial for drug screening, repositioning, and design, as well as for understanding the functions of target. Although there are a few online platforms based on deep learning for drug-target interaction, affinity, and binding sites identification, there is currently no integrated online platforms for all three aspects. RESULTS: Our solution, the novel integrated online platform Drug-Online, has been developed to facilitate drug screening, target identification, and understanding the functions of target in a progressive manner of "interaction-affinity-binding sites". Drug-Online platform consists of three parts: the first part uses the drug-target interaction identification method MGraphDTA, based on graph neural networks (GNN) and convolutional neural networks (CNN), to identify whether there is a drug-target interaction. If an interaction is identified, the second part employs the drug-target affinity identification method MMDTA, also based on GNN and CNN, to calculate the strength of drug-target interaction, i.e., affinity. Finally, the third part identifies drug-target binding sites, i.e., pockets. The method pt-lm-gnn used in this part is also based on GNN. CONCLUSIONS: Drug-Online is a reliable online platform that integrates drug-target interaction, affinity, and binding sites identification. It is freely available via the Internet at http://39.106.7.26:8000/Drug-Online/ .

GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs.

Most proteins exert their functions by interacting with other proteins, making the identification of protein-protein interactions (PPI) crucial for understanding biological activities, pathological mechanisms, and clinical therapies. Developing effective and reliable computational methods for predicting PPI can significantly reduce the time-consuming and labor-intensive associated traditional biological experiments. However, accurately identifying the specific categories of protein-protein interactions and improving the prediction accuracy of the computational methods remain dual challenges. To tackle these challenges, we proposed a novel graph neural network method called GNNGL-PPI for multi-category prediction of PPI based on global graphs and local subgraphs. GNNGL-PPI consisted of two main components: using Graph Isomorphism Network (GIN) to extract global graph features from PPI network graph, and employing GIN As Kernel (GIN-AK) to extract local subgraph features from the subgraphs of protein vertices. Additionally, considering the imbalanced distribution of samples in each category within the benchmark datasets, we introduced an Asymmetric Loss (ASL) function to further enhance the predictive performance of the method. Through evaluations on six benchmark test sets formed by three different dataset partitioning algorithms (Random, BFS, DFS), GNNGL-PPI outperformed the state-of-the-art multi-category prediction methods of PPI, as measured by the comprehensive performance evaluation metric F1-measure. Furthermore, interpretability analysis confirmed the effectiveness of GNNGL-PPI as a reliable multi-category prediction method for predicting protein-protein interactions.

Copper Difluorocarbene Enables Catalytic Difluoromethylation.

Despite the synthetic versatility of difluorocarbene, its high reactivity severely regulates widespread applications of difluorocarbene in organic synthesis. Here, we report a copper difluorocarbene-involved catalytic coupling, representing a new mode of the difluoromethylation reaction. This method allows difluoromethylation of a wide range of readily available allyl/propargyl electrophiles with NaBH3CN and low-cost difluorocarbene precursor BrCF2CO2K, featuring high cost-efficiency, high stereo- and regioselectivities, and high functional group tolerance, even with complex drug-like molecules. Applying the method led to the efficient synthesis of deuterated difluoromethylated compounds of medicinal interest. The resulting difluoromethylated allyl and allenyl products can serve as versatile synthons for diverse transformations, rendering the approach attractive for synthesizing complex fluorinated structures. Experimental mechanistic studies and computational calculations reveal that the formation of a difluoromethylcopper(I) intermediate through the nucleophilic attack of boron hydride on the copper(I) difluorocarbene is the key step in the reaction.

DRP1 inhibition-mediated mitochondrial elongation abolishes cancer stemness, enhances glutaminolysis, and drives ferroptosis in oral squamous cell carcinoma.

BACKGROUND: Mitochondrial dynamics play a fundamental role in determining stem cell fate. However, the underlying mechanisms of mitochondrial dynamics in the stemness acquisition of cancer cells are incompletely understood. METHODS: Metabolomic profiling of cells were analyzed by MS/MS. The genomic distribution of H3K27me3 was measured by CUT&Tag. Oral squamous cell carcinoma (OSCC) cells depended on glucose or glutamine fueling TCA cycle were monitored by 13C-isotope tracing. Organoids and tumors from patients and mice were treated with DRP1 inhibitors mdivi-1, ferroptosis inducer erastin, or combination with mdivi-1 and erastin to evaluate treatment effects. RESULTS: Mitochondria of OSCC stem cells own fragment mitochondrial network and DRP1 is required for maintenance of their globular morphology. Imbalanced mitochondrial dynamics induced by DRP1 knockdown suppressed stemness of OSCC cells. Elongated mitochondria increased α-ketoglutarate levels and enhanced glutaminolysis to fuel the TCA cycle by increasing glutamine transporter ASCT2 expression. α-KG promoted the demethylation of histone H3K27me3, resulting in downregulation of SNAI2 associated with stemness and EMT. Significantly, suppressing DRP1 enhanced the anticancer effects of ferroptosis. CONCLUSION: Our study reveals a novel mechanism underlying mitochondrial dynamics mediated cancer stemness acquisition and highlights the therapeutic potential of mitochondria elongation to increase the susceptibility of cancer cells to ferroptosis.

The clinical implication and translational research of OSCC differentiation.

BACKGROUND: The clinical value and molecular characteristics of tumor differentiation in oral squamous cell carcinoma (OSCC) remain unclear. There is a lack of a related molecular classification prediction system based on pathological images for precision medicine. METHODS: Integration of epidemiology, genomics, experiments, and deep learning to clarify the clinical value and molecular characteristics, and develop a novel OSCC molecular classification prediction system. RESULTS: Large-scale epidemiology data (n = 118,817) demonstrated OSCC differentiation was a significant prognosis indicator (p < 0.001), and well-differentiated OSCC was more chemo-resistant than poorly differentiated OSCC. These results were confirmed in the TCGA database and in vitro. Furthermore, we found chemo-resistant related pathways and cell cycle-related pathways were up-regulated in well- and poorly differentiated OSCC, respectively. Based on the characteristics of OSCC differentiation, a molecular grade of OSCC was obtained and combined with pathological images to establish a novel prediction system through deep learning, named ShuffleNetV2-based Molecular Grade of OSCC (SMGO). Importantly, our independent multi-center cohort of OSCC (n = 340) confirmed the high accuracy of SMGO. CONCLUSIONS: OSCC differentiation was a significant indicator of prognosis and chemotherapy selection. Importantly, SMGO could be an indispensable reference for OSCC differentiation and assist the decision-making of chemotherapy.

GNN-DDAS: Drug discovery for identifying anti-schistosome small molecules based on graph neural network.

Schistosomiasis is a tropical disease that poses a significant risk to hundreds of millions of people, yet often goes unnoticed. While praziquantel, a widely used anti-schistosome drug, has a low cost and a high cure rate, it has several drawbacks. These include ineffectiveness against schistosome larvae, reduced efficacy in young children, and emerging drug resistance. Discovering new and active anti-schistosome small molecules is therefore critical, but this process presents the challenge of low accuracy in computer-aided methods. To address this issue, we proposed GNN-DDAS, a novel deep learning framework based on graph neural networks (GNN), designed for drug discovery to identify active anti-schistosome (DDAS) small molecules. Initially, a multi-layer perceptron was used to derive sequence features from various representations of small molecule SMILES. Next, GNN was employed to extract structural features from molecular graphs. Finally, the extracted sequence and structural features were then concatenated and fed into a fully connected network to predict active anti-schistosome small molecules. Experimental results showed that GNN-DDAS exhibited superior performance compared to the benchmark methods on both benchmark and real-world application datasets. Additionally, the use of GNNExplainer model allowed us to analyze the key substructure features of small molecules, providing insight into the effectiveness of GNN-DDAS. Overall, GNN-DDAS provided a promising solution for discovering new and active anti-schistosome small molecules.

Mass cytometry and transcriptomic profiling reveal PD1 blockade induced alterations in oral carcinogenesis.

Oral squamous cell carcinoma is the predominant subtype of head and neck squamous cell carcinoma, characterized by a challenging prognosis. In this study, we established a murine model of oral carcinogenesis using 4-nitroquinoline-1-oxide (4-NQO) induction to investigate the impact of immunotherapy on microenvironmental alterations. Mice in the precancerous condition were randomly divided into two groups: one receiving programmed death-1 (PD1) monoclonal antibody treatment and the other, control immunoglobulin G. Our observations showed that while PD1 blockade effectively delayed the progression of carcinogenesis, it did not completely impede or reverse it. To unravel the underlying reasons for the limited effectiveness of PD1 blockade, we collected tongue lesions and applied mass cytometry (CyTOF) and RNA sequencing (RNA-seq) to characterize the microenvironment. CyTOF analysis revealed an increased macrophage subset (expressing high levels of IFNγ and iNOS) alongside a diminished Th1-like subset (exhibiting low expression of TCF7) and three myeloid-derived suppressor cell subsets (displaying low expression of MHC Class II or IFNγ) following anti-PD1 treatment. Notably, we observed an increased presence of cancer-associated fibroblasts (CAFs) expressing collagen-related genes after PD1 blockade. Furthermore, we found a negative correlation between the infiltration levels of CAFs and CD8+ T cells. These findings were validated in murine tongue tissue slides, and publicly available multi-omics datasets. Our results suggest that CAFs may impair the therapeutic efficacy of PD1 blockade in oral carcinogenesis by the remodeling of the extracellular matrix.

Synergistic effect of mesenchymal stem cell-derived extracellular vesicle and miR-137 alleviates autism-like behaviors by modulating the NF-κB pathway.

Autism spectrum disorder (ASD) is a multifaceted neurodevelopmental disorder predominant in childhood. Despite existing treatments, the benefits are still limited. This study explored the effectiveness of mesenchymal stem cell-derived extracellular vesicles (MSC-EVs) loaded with miR-137 in enhancing autism-like behaviors and mitigating neuroinflammation. Utilizing BTBR mice as an autism model, the study demonstrated that intranasal administration of MSC-miR137-EVs ameliorates autism-like behaviors and inhibits pro-inflammatory factors via the TLR4/NF-κB pathway. In vitro evaluation of LPS-activated BV2 cells revealed that MSC-miR137-EVs target the TLR4/NF-κB pathway through miR-137 inhibits proinflammatory M1 microglia. Moreover, bioinformatics analysis identified that MSC-EVs are rich in miR-146a-5p, which targets the TRAF6/NF-κB signaling pathway. In summary, the findings suggest that the integration of MSC-EVs with miR-137 may be a promising therapeutic strategy for ASD, which is worthy of clinical adoption.

20 Watt single-frequency 509 nm laser by single-pass second harmonic generation in an LBO crystal.

High power 509 nm continuous-wave (CW) lasers have important applications in science and communication. Here we demonstrate a robust high-power single-frequency 509 nm laser system based on nonlinear phase demodulation technique and single-pass second harmonic generation (SHG) configuration. In experiments, the single-frequency fundamental wave at 1018 nm was linewidth-broadened by an electro-optical modulator and then amplified to 207 W in a ytterbium-doped fiber amplifier. In subsequent single-pass SHG stage, over 20 W CW single-frequency 509 nm laser was generated in a LiB3O5 crystal with a SHG efficiency of 9.7%. To the best of our knowledge, this is the highest reported power for CW single-frequency 509 nm laser, which could be used for advanced underwater optical communication and preparation of cesium Rydberg state.

Exogenous PD-L1 binds to PD-1 to alleviate and prevent autism-like behaviors in maternal immune activation-induced male offspring mice.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder caused by the interaction of multiple pathogenic factors. Epidemiological studies and animal experiments indicate that maternal immune activation (MIA) is closely related to the development of ASD in offspring. A large number of pro-inflammatory cytokines are transferred from the placenta to the fetal brain during MIA, which impedes fetal neurodevelopment and is accompanied by activation of immune cells and microglia. Programmed cell death protein 1 (PD-1) can be highly expressed on the surface of various activated immune cells, when combined with programmed cell death-ligand 1 (PD-L1), it can activate the PD-1/PD-L1 pathway and exert powerful immunosuppressive effects, suggesting that this immune checkpoint may have the potential to treat MIA-induced ASD. This study combined bioinformatics analysis and experimental validation to explore the efficacy of Fc-fused PD-L1 (PD-L1-Fc) in treating MIA-induced ASD. Bioinformatics analysis results showed that in human placental inflammation, IL-6 was upregulated, T cells proliferated significantly, and the PD-1/PD-L1 pathway was significantly enriched. The experimental results showed that intraperitoneal injection of poly(I:C) induced MIA in pregnant mice resulted in significant expression of IL-6 in their serum, placenta, and fetal brain. At the same time, the expression of PD-1 and PD-L1 in the placenta and fetal brain increased, CD4+ T cells in the spleen were significantly activated, and PD-1 expression increased. Their offspring mice exhibited typical ASD-like behaviors. In vitro experiments on primary microglia of offspring mice have confirmed that the expression of IL-6, PD-1, and PD-L1 is significantly increased, and PD-L1-Fc effectively reduced their expression levels. In the prefrontal cortex of MIA offspring mice, there was an increase in the expression of IL-6, PD-1, and PD-L1; activation of microglial cells, and colocalization with PD-1. Then we administered brain stereotaxic injections of PD-L1-Fc to MIA offspring mice and intraperitoneal injections to MIA pregnant mice. The results indicated that PD-L1-Fc effectively suppressed neuroinflammation in the frontal cortex of offspring mice and partially ameliorated ASD-like behaviors; MIA in pregnant mice was significantly alleviated, and the offspring mice they produced did not exhibit neuroinflammation or ASD-like behaviors. In summary, we have demonstrated the therapeutic ability of PD-L1-Fc for MIA-induced ASD, aiming to provide new strategies and insights for the treatment of ASD.

Lifestyle behaviors and risk of cardiovascular disease and prognosis among individuals with cardiovascular disease: a systematic review and meta-analysis of 71 prospective cohort studies.

BACKGROUND: Healthy lifestyle behaviors (LBs) have been widely recommended for the prevention and management of cardiovascular disease (CVD). Despite a large number of studies exploring the association between combined LBs and CVD, a notable gap exists in integration of relevant literatures. We conducted a systematic review and meta-analysis of prospective cohort studies to analyze the correlation between combined LBs and the occurrence of CVD, as well as to estimate the risk of various health complications in individuals already diagnosed with CVD. METHODS: Articles published up to February 10, 2023 were sourced through PubMed, EMBASE and Web of Science. Eligible prospective cohort studies that reported the relations of combined LBs with pre-determined outcomes were included. Summary relative risks (RRs) and 95% confidence intervals (CIs) were estimated using either a fixed or random-effects model. Subgroup analysis, meta-regression, publication bias, and sensitivity analysis were as well performed. RESULTS: In the general population, individuals with the healthiest combination of LBs exhibited a significant risk reduction of 58% for CVD and 55% for CVD mortality. For individuals diagnosed with CVD, adherence to the healthiest combination of LBs corresponded to a significant risk reduction of 62% for CVD recurrence and 67% for all-cause mortality, when compared to those with the least-healthy combination of LBs. In the analysis of dose-response relationship, for each increment of 1 healthy LB, there was a corresponding decrease in risk of 17% for CVD and 19% for CVD mortality within the general population. Similarly, among individuals diagnosed with CVD, each additional healthy LB was associated with a risk reduction of 27% for CVD recurrence and 27% for all-cause mortality. CONCLUSIONS: Adopting healthy LBs is associated with substantial risk reduction in CVD, CVD mortality, and adverse outcomes among individuals diagnosed with CVD. Rather than focusing solely on individual healthy LB, it is advisable to advocate for the adoption of multiple LBs for the prevention and management of CVD. TRIAL REGISTRATION: PROSPERO: CRD42023431731.