Cytoplasmic tail of transmembrane Dscam controls antibacterial responses by regulating cell proliferation-related genes in hemocytes of Chinese mitten crab (Eriocheir sinensis).

In arthropods, the involvement of Dscam (Down syndrome cell adhesion molecule) in innate immunity has been extensively demonstrated. Its cytoplasmic tail contains multiple conserved functional sites, which indicates its involvement in different intracellular signaling pathways. In this study, we focused on the role of the cytoplasmic tail of Dscam in the Chinese mitten crab (Eriocheir sinensis) immune defense. In the group with cytoplasmic tail knockdown (the site was located on constant exons 37 and 38), 3885 differentially expressed genes (DEGs) were identified. The DEGs were enriched in small molecule binding, protein-containing complex binding, and immunity-related pathways. The expression of selected genes were validated using quantitative real-time reverse transcription PCR. We identified key Cell cycle, Janus kinase (JAK)-signal transducer, activator of transcription (STAT) and mitogen-activated protein kinase (MAPK) signaling pathway genes, the results indicated that the cytoplasmic tail of Dscam controls antibacterial responses by regulating cell proliferation-related genes in hemocytes.

The Social Inequality of Music: University Students from a Higher Social Class Are More Likely to Build Relationships and Feel Happiness Through Music.

The relationship between young people's music use and well-being has gained extensive interest in recent years. The relationship-building function of music is one of its most important functions. While many studies have documented the positive effects of this function, there is a lack of research discussing this topic from the perspective of social stratification. This study sampled 691(63.8% male, M age = 19.43, SD = 1.42) Chinese university students to examine the social class differences among university students in acquiring well-being through the relationship-building function of music. The results revealed that university students from a higher social class are more likely to acquire well-being through the relationship-building function of music. In addition, interdependent self-construal plays a moderating role in the mediating model. The mediating effect was only significant when university students have a higher level of interdependent self-construal. These results indicated social class differences among university students in the building of relationships with music, underscoring the need for future research and interventions to address social inequality in the context of music's functions.

Biological factors driving colorectal cancer metastasis.

Approximately 20% of colorectal cancer (CRC) patients present with metastasis at diagnosis. Among Stage I-III CRC patients who undergo surgical resection, 18% typically suffer from distal metastasis within the first three years following initial treatment. The median survival duration after the diagnosis of metastatic CRC (mCRC) is only 9 mo. mCRC is traditionally considered to be an advanced stage malignancy or is thought to be caused by incomplete resection of tumor tissue, allowing cancer cells to spread from primary to distant organs; however, increasing evidence suggests that the mCRC process can begin early in tumor development. CRC patients present with high heterogeneity and diverse cancer phenotypes that are classified on the basis of molecular and morphological alterations. Different genomic and nongenomic events can induce subclone diversity, which leads to cancer and metastasis. Throughout the course of mCRC, metastatic cascades are associated with invasive cancer cell migration through the circulatory system, extravasation, distal seeding, dormancy, and reactivation, with each step requiring specific molecular functions. However, cancer cells presenting neoantigens can be recognized and eliminated by the immune system. In this review, we explain the biological factors that drive CRC metastasis, namely, genomic instability, epigenetic instability, the metastatic cascade, the cancer-immunity cycle, and external lifestyle factors. Despite remarkable progress in CRC research, the role of molecular classification in therapeutic intervention remains unclear. This review shows the driving factors of mCRC which may help in identifying potential candidate biomarkers that can improve the diagnosis and early detection of mCRC cases.

GMFGRN: a matrix factorization and graph neural network approach for gene regulatory network inference.

The recent advances of single-cell RNA sequencing (scRNA-seq) have enabled reliable profiling of gene expression at the single-cell level, providing opportunities for accurate inference of gene regulatory networks (GRNs) on scRNA-seq data. Most methods for inferring GRNs suffer from the inability to eliminate transitive interactions or necessitate expensive computational resources. To address these, we present a novel method, termed GMFGRN, for accurate graph neural network (GNN)-based GRN inference from scRNA-seq data. GMFGRN employs GNN for matrix factorization and learns representative embeddings for genes. For transcription factor-gene pairs, it utilizes the learned embeddings to determine whether they interact with each other. The extensive suite of benchmarking experiments encompassing eight static scRNA-seq datasets alongside several state-of-the-art methods demonstrated mean improvements of 1.9 and 2.5% over the runner-up in area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). In addition, across four time-series datasets, maximum enhancements of 2.4 and 1.3% in AUROC and AUPRC were observed in comparison to the runner-up. Moreover, GMFGRN requires significantly less training time and memory consumption, with time and memory consumed <10% compared to the second-best method. These findings underscore the substantial potential of GMFGRN in the inference of GRNs. It is publicly available at https://github.com/Lishuoyy/GMFGRN.

How does economic inequality shape conspiracy theories? Empirical evidence from China.

Conspiracy theories tend to be prevalent, particularly in societies with high economic inequality. However, few studies have examined the relationship between economic inequality and belief in conspiracy theories. We propose that economic inequality leads people to believe conspiracy theories about economically advantaged groups (i.e., upwards conspiracy theories) and that moral evaluations of those groups mediate this relationship. Study 1 (N = 300) found support for these ideas in a survey among Chinese residents. Study 2 (N = 160) manipulated participants' perceptions of economic inequality in a virtual society. The manipulation shaped moral evaluations of economically advantaged groups, and conspiracy beliefs, in the predicted manner. In Study 3 (N = 191) and Study 4 (N = 210), we experimentally manipulated participants' perceptions of economic inequality in real Chinese society and replicated the results of Study 2. In addition, in Study 4, we find that economic inequality predicts belief in conspiracy theories about economically disadvantaged groups (i.e., downward conspiracy theories), which was mediated by anomie. We conclude that perceived economic inequality predicts conspiracy theories about economically advantaged groups and that moral evaluations account for this effect. Also, upward and downward conspiracy theory beliefs are associated with different psychological processes.

Recent advances in the biological depolymerization and upcycling of polyethylene terephthalate.

Polyethylene terephthalate (PET) is favored for its exceptional properties and widespread daily use. This review highlights recent advancements that enable the development of biological tools for PET decomposition, transforming PET into valuable platform chemicals and materials in upcycling processes. Enhancing PET hydrolases' catalytic activity and efficiency through protein engineering strategies is a priority, facilitating more effective PET waste management. Efforts to create novel PET hydrolases for large-scale PET depolymerization continue, but cost-effectiveness remains challenging. Hydrolyzed monomers must add additional value to make PET recycling economically attractive. Valorization of hydrolysis products through the upcycling process is expected to produce new compounds with different values and qualities from the initial polymer, making the decomposed monomers more appealing. Advances in synthetic biology and enzyme engineering hold promise for PET upcycling. While biological depolymerization offers environmental benefits, further research is needed to make PET upcycling sustainable and economically feasible.

A novel shared decision-making (SDM) tool for anticoagulation management in atrial fibrillation: protocol for a prospective, cluster randomized controlled trial.

BACKGROUND: Atrial fibrillation (AF) is a common arrhythmia that requires anticoagulation therapy to prevent stroke. However, there is still a significant under-/over-treatment in stroke prevention for patients with AF. The adherence and the risk of bleeding associated with oral anticoagulation therapy (OACs) are major concerns. Shared decision-making (SDM) is an approach that involves patients and healthcare providers in making decisions about treatment options. This study aims to assess the effectiveness of a novel SDM tool for anticoagulation management in AF. METHODS: The study will be a prospective, cluster randomized controlled trial involving 440 patients with AF in 8 community health service centers (clusters) in Shanghai, China. The SDM group will receive anticoagulation management through the novel SDM tool, while the control group will receive standard care. The follow-up period will be at least 2 years. The primary outcome will be any bleeding event, while secondary outcomes include the accordance of stroke prophylaxis for AF according to the current guidelines, time in therapeutic range (TTR), the occurrences of major bleeding and thrombosis events, and patient knowledge, adherence, and satisfaction. DISCUSSION: This study will provide evidence of the effectiveness of shared decision-making in improving the appropriateness of OAC use in Chinese AF patients. The findings may inform the development of guidelines and policies for the management of AF and anticoagulation therapy in China and other countries. TRIAL REGISTRATION: ChiCTR ChiCTR2200062123. Registered on 23 July 2022.

Integrating unsupervised language model with multi-view multiple sequence alignments for high-accuracy inter-chain contact prediction.

Accurate identification of inter-chain contacts in the protein complex is critical to determine the corresponding 3D structures and understand the biological functions. We proposed a new deep learning method, ICCPred, to deduce the inter-chain contacts from the amino acid sequences of the protein complex. This pipeline was built on the designed deep residual network architecture, integrating the pre-trained language model with three multiple sequence alignments (MSAs) from different biological views. Experimental results on 709 non-redundant benchmarking protein complexes showed that the proposed ICCPred significantly increased inter-chain contact prediction accuracy compared to the state-of-the-art approaches. Detailed data analyses showed that the significant advantage of ICCPred lies in the utilization of pre-trained transformer language models which can effectively extract the complementary co-evolution diversity from three MSAs. Meanwhile, the designed deep residual network enhances the correlation between the co-evolution diversity and the patterns of inter-chain contacts. These results demonstrated a new avenue for high-accuracy deep-learning inter-chain contact prediction that is applicable to large-scale protein-protein interaction annotations from sequence alone.

The Importance of Clinical Pharmacists in Improving Blood Glucose and Lipid Levels in Patients with Diabetes and Myocardial Infarction.

Purpose: The aim of this study was to evaluate whether intervention by clinical pharmacists can improve blood glucose and lipid levels in diabetic patients with complex medical conditions. Methods: The retrospective database included 138 patients with diabetes who had presented with acute myocardial infarction (AMI) between January 2019 and October 2021. Blood glucose and lipid levels were measured within 12 weeks and 78 weeks of follow-up. Propensity score matching (PSM) was used to balance the confounding effects of patients' characteristics. Results: A total of 138 eligible patients were assigned to either the intervention group (n = 47) or the usual care group (n = 91). After the intervention, there were significant improvements in blood glucose (glycosylated hemoglobin-HbA1C % from 9.0 to 8.3; fasting blood glucose-FBG mmol/L from 11.3 to 7.1; postprandial blood glucose-PBG mmol/L from 17.0 to 12.1; p < 0.001) and lipid levels (total cholesterol-TC from 4.9 to 3.5, low-density lipoprotein cholesterol-LDL-C from 3.0 to 1.8, p < 0.001, mmol/L) in both follow-up periods. The blood glucose effects were most pronounced in the PBG control rate (76.9% vs 54.0%) before PSM, while HbA1C% and PBG control rate after PSM were significantly higher in the intervention group (HbA1C% rate: 65.6% vs 38.5%; PBG rate: 79.2% vs 45.8%; p < 0.05, intervention vs non-intervention). Subgroup analysis further confirmed the improvement of blood glucose and lipid mainly in patients with higher baseline FBG (≧10mmol/L) and moderate follow-up duration (4-12 weeks). Conclusion: The intervention of clinical pharmacists in multidisciplinary team can significantly improve blood glucose and lipid levels in complex type 2 diabetic patients, especially those with high baseline FBG and moderate follow-up durations.

Electron Transfer Mechanism at the Interface of Multi-Heme Cytochromes and Metal Oxide.

Electroactive microbial cells have evolved unique extracellular electron transfer to conduct the reactions via redox outer-membrane (OM) proteins. However, the electron transfer mechanism at the interface of OM proteins and nanomaterial remains unclear. In this study, the mechanism for the electron transfer at biological/inorganic interface is investigated by integrating molecular modeling with electrochemical and spectroscopic measurements. For this purpose, a model system composed of OmcA, a typical OM protein, and the hexagonal tungsten trioxide (h-WO3 ) with good biocompatibility is selected. The interfacial electron transfer is dependent mainly on the special molecular configuration of OmcA and the microenvironment of the solvent exposed active center. Also, the apparent electron transfer rate can be tuned by site-directed mutagenesis at the axial ligand of the active center. Furthermore, the equilibrium state of the OmcA/h-WO3 systems suggests that their attachment is attributed to the limited number of residues. The electrochemical analysis of OmcA and its variants reveals that the wild type exhibits the fastest electron transfer rate, and the transient absorption spectroscopy further shows that the axial histidine plays an important role in the interfacial electron transfer process. This study provides a useful approach to promote the site-directed mutagenesis and nanomaterial design for bioelectrocatalytic applications.

Influence and mechanism of sodium-glucose cotransporter-2 inhibitors on the cardiac function: study protocol for a prospective cohort study.

Background: Acute myocardial infarction (AMI) poses a significant threat to cardiovascular diseases (CVDs), leading to a high risk of heart failure (HF) and cardiovascular death. Growing evidence has unveiled the potential of sodium-glucose cotransporter-2 (SGLT2) inhibitors to improve cardiovascular outcomes in patients with CVD regardless of diabetes, but there is limited evidence in AMI patients. Furthermore, it is controversial whether the effects can be ascribed to the amelioration of left ventricular (LV) function, which further complicates the understanding of their underlying mechanism. Methods: This study is a prospective, phase IV, open-label, parallel group, single-center trial conducted in a large tertiary teaching hospital in China. A total of 120 patients with AMI and type 2 diabetes mellitus (T2DM) will be included. Those who received SGLT2 inhibitors are considered as the experimental group, and those taking other antidiabetic agents are considered as the control group. The primary outcome is change in LV end-systolic volume index (LVESVi) measured by cardiac magnetic resonance (CMR) imaging from baseline during 1-year follow-up period. Secondary outcomes include other LV parameters such as LV mass, LV volume, and LV ejection fraction (EF); quality of life and functional capacity such as Kansas City Cardiomyopathy Questionnaire overall summary score (KCCQ-OS) and EuroQol-5 dimension (EQ-5D); biomarkers associated with diagnostic parameters of AMI and possible mechanisms on cardiovascular protection, such as creatine kinase, troponin T (TnT) level, troponin I (TnI) level, soluble suppression of tumorigenicity-2 (sST2), galectin-3 (Gal-3), fibroblast growth factor 21 (FGF21), and microRNA (miRNA) level. Discussion: This study aims to investigate whether SGLT2 inhibitors could improve LV function by measuring CMR, quality of life, and functional capacity in patients with AMI in real-world settings, providing evidence on the underlying mechanism of SGLT2 inhibitors on cardioprotection. Clinical trial registration: https://www.chictr.org.cn/showproj.html?proj=173672, identifier ChiCTR2200065792.

MTHFD2 reprograms macrophage polarization by inhibiting PTEN.

The one-carbon metabolism enzyme methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) is involved in the regulation of tumor oncogenesis and immune cell functions, but whether it can contribute to macrophage polarization remains elusive. Here, we show that MTHFD2 suppresses polarization of interferon-γ-activated macrophages (M(IFN-γ)) but enhances that of interleukin-4-activated macrophages (M(IL-4)) both in vitro and in vivo. Mechanistically, MTHFD2 interacts with phosphatase and tensin homolog (PTEN) to suppress PTEN's phosphatidylinositol 3,4,5-trisphosphate (PIP3) phosphatase activity and enhance downstream Akt activation, independent of the N-terminal mitochondria-targeting signal of MTHFD2. MTHFD2-PTEN interaction is promoted by IL-4 but not IFN-γ. Furthermore, amino acid residues (aa 215-225) of MTHFD2 directly target PTEN catalytic center (aa 118-141). Residue D168 of MTHFD2 is also critical for regulating PTEN's PIP3 phosphatase activity by affecting MTHFD2-PTEN interaction. Our study suggests a non-metabolic function of MTHFD2 by which MTHFD2 inhibits PTEN activity, orchestrates macrophage polarization, and alters macrophage-mediated immune responses.

Functionalization of OMVs for Biocatalytic Applications.

Outer membrane vesicles (OMVs) are miniature versions of gram-negative bacteria that contain almost the same content as their parent cells, particularly in terms of membrane composition. Using OMVs as biocatalysts is a promising approach due to their potential benefits, including their ability to be handled similarly to bacteria while lacking potentially pathogenic organisms. To employ OMVs as biocatalysts, they must be functionalized with immobilized enzymes to the OMV platform. Various enzyme immobilization techniques are available, including surface display and encapsulation, each with advantages and disadvantages depending on the objectives. This review provides a concise yet comprehensive overview of these immobilization techniques and their applications in utilizing OMVs as biocatalysts. Specifically, we discuss the use of OMVs in catalyzing the conversion of chemical compounds, their role in polymer degradation, and their performance in bioremediation.

Multi-omics blood atlas reveals unique features of immune and platelet responses to SARS-CoV-2 Omicron breakthrough infection.

Although host responses to the ancestral SARS-CoV-2 strain are well described, those to the new Omicron variants are less resolved. We profiled the clinical phenomes, transcriptomes, proteomes, metabolomes, and immune repertoires of >1,000 blood cell or plasma specimens from SARS-CoV-2 Omicron patients. Using in-depth integrated multi-omics, we dissected the host response dynamics during multiple disease phases to reveal the molecular and cellular landscapes in the blood. Specifically, we detected enhanced interferon-mediated antiviral signatures of platelets in Omicron-infected patients, and platelets preferentially formed widespread aggregates with leukocytes to modulate immune cell functions. In addition, patients who were re-tested positive for viral RNA showed marked reductions in B cell receptor clones, antibody generation, and neutralizing capacity against Omicron. Finally, we developed a machine learning model that accurately predicted the probability of re-positivity in Omicron patients. Our study may inspire a paradigm shift in studying systemic diseases and emerging public health concerns.

Early Changes of Serum Interleukin 14α Levels Predicts the Response to Anti-PD-1 Therapy in Cancer.

Background: Programmed cell death-1 (PD-1) blockade has been shown to confer clinical benefit in cancer patients. Here, we assessed the level of serum interleukin 14α (IL14α) in patients receiving anti-PD-1 treatment. Methods: This prospective study recruited 30 patients with advanced solid cancer who received pembrolizumab treatment in Northern Jiangsu People's Hospital between April 2016 and June 2018. The western blot analysis was used to assess the expression level of serum IL14α in patients at baseline and after 2 cycles of treatment. Interleukin 14α was performed using the unpaired 2-tailed Student test. The progression-free survival (PFS) and overall survival (OS) were calculated using the Kaplan-Meier method and compared by the log-rank test. Results: The early change of IL14α after 2 cycles of anti-PD-1 therapy was calculated as delta IL14α % change = (IL14α level after 2 cycles - IL14α level before treatment)/IL14α level before treatment × 100%. Receiver operating characteristic (ROC) was analyzed to get a cutoff point of delta IL14α % change as 2.46% (sensitivity = 85.71%, specificity = 62.5%; area under the ROC curve [AUC] = 0.7277, P = .034). Using this cutoff to subgroup the patients, an improved objective response rate was observed in patients with a delta IL14α change higher than 2.46% (P = .0072). A delta IL14α change over 2.46% was associated with a superior PFS (P = .0039). Conclusions: Early changes of serum IL14α levels may be a promising biomarker to predict outcomes in patients with solid cancer following anti-PD-1 treatment.

TripletCell: a deep metric learning framework for accurate annotation of cell types at the single-cell level.

Single-cell RNA sequencing (scRNA-seq) has significantly accelerated the experimental characterization of distinct cell lineages and types in complex tissues and organisms. Cell-type annotation is of great importance in most of the scRNA-seq analysis pipelines. However, manual cell-type annotation heavily relies on the quality of scRNA-seq data and marker genes, and therefore can be laborious and time-consuming. Furthermore, the heterogeneity of scRNA-seq datasets poses another challenge for accurate cell-type annotation, such as the batch effect induced by different scRNA-seq protocols and samples. To overcome these limitations, here we propose a novel pipeline, termed TripletCell, for cross-species, cross-protocol and cross-sample cell-type annotation. We developed a cell embedding and dimension-reduction module for the feature extraction (FE) in TripletCell, namely TripletCell-FE, to leverage the deep metric learning-based algorithm for the relationships between the reference gene expression matrix and the query cells. Our experimental studies on 21 datasets (covering nine scRNA-seq protocols, two species and three tissues) demonstrate that TripletCell outperformed state-of-the-art approaches for cell-type annotation. More importantly, regardless of protocols or species, TripletCell can deliver outstanding and robust performance in annotating different types of cells. TripletCell is freely available at https://github.com/liuyan3056/TripletCell. We believe that TripletCell is a reliable computational tool for accurately annotating various cell types using scRNA-seq data and will be instrumental in assisting the generation of novel biological hypotheses in cell biology.

Ammonia-induced oxidative stress triggered proinflammatory response and apoptosis in pig lungs.

Ammonia, a common toxic gas, is not only one of the main causes of haze, but also can enter respiratory tract and directly affect the health of humans and animals. Pig was used as an animal model for exploring the molecular mechanism and dose effect of ammonia toxicity to lung. In this study, the apoptosis of type II alveolar epithelial cells was observed in high ammonia exposure group using transmission electron microscopy. Gene and protein expression analysis using transcriptome sequencing and western blot showed that low ammonia exposure induced T-cell-involved proinflammatory response, but high ammonia exposure repressed the expression of DNA repair-related genes and affected ion transport. Moreover, high ammonia exposure significantly increased 8-hydroxy-2-deoxyguanosine (8-OHdG) level, meaning DNA oxidative damage occurred. In addition, both low and high ammonia exposure caused oxidative stress in pig lungs. Integrated analysis of transcriptome and metabolome revealed that the up-regulation of LDHB and ND2 took part in high ammonia exposure-affected pyruvate metabolism and oxidative phosphorylation progress, respectively. Inclusion, oxidative stress mediated ammonia-induced proinflammatory response and apoptosis of porcine lungs. These findings may provide new insights for understanding the ammonia toxicity to workers in livestock farms and chemical fertilizer plants.