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.

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.

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.

MAResNet: predicting transcription factor binding sites by combining multi-scale bottom-up and top-down attention and residual network.

Accurate identification of transcription factor binding sites is of great significance in understanding gene expression, biological development and drug design. Although a variety of methods based on deep-learning models and large-scale data have been developed to predict transcription factor binding sites in DNA sequences, there is room for further improvement in prediction performance. In addition, effective interpretation of deep-learning models is greatly desirable. Here we present MAResNet, a new deep-learning method, for predicting transcription factor binding sites on 690 ChIP-seq datasets. More specifically, MAResNet combines the bottom-up and top-down attention mechanisms and a state-of-the-art feed-forward network (ResNet), which is constructed by stacking attention modules that generate attention-aware features. In particular, the multi-scale attention mechanism is utilized at the first stage to extract rich and representative sequence features. We further discuss the attention-aware features learned from different attention modules in accordance with the changes as the layers go deeper. The features learned by MAResNet are also visualized through the TMAP tool to illustrate that the method can extract the unique characteristics of transcription factor binding sites. The performance of MAResNet is extensively tested on 690 test subsets with an average AUC of 0.927, which is higher than that of the current state-of-the-art methods. Overall, this study provides a new and useful framework for the prediction of transcription factor binding sites by combining the funnel attention modules with the residual network.

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.

Design, synthesis and biological evaluation of aloperine derivatives as potential anticancer agents.

Modifications at different positions on the aloperine molecule were performed to improve its anticancer activity and develop anticancer drugs. The in vitro anticancer activities of 44 synthesized compounds were evaluated. The effect of modification positions on anticancer activity was discussed and a structure-activity relationship analysis was established. A novel series of compounds with modifications at the N12 position showed much higher cytotoxicity than aloperine. Among them, compound 22 displayed promising in vitro anticancer activity against PC9 cells with a median inhibitory concentration (IC50) of 1.43 µM. The mechanism studies indicated that compound 22 induced cell apoptosis and cell cycle arrest in PC9 cells. These results demonstrate the potential of aloperine thiourea derivatives in anticancer activity.

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.

SAResNet: self-attention residual network for predicting DNA-protein binding.

Knowledge of the specificity of DNA-protein binding is crucial for understanding the mechanisms of gene expression, regulation and gene therapy. In recent years, deep-learning-based methods for predicting DNA-protein binding from sequence data have achieved significant success. Nevertheless, the current state-of-the-art computational methods have some drawbacks associated with the use of limited datasets with insufficient experimental data. To address this, we propose a novel transfer learning-based method, termed SAResNet, which combines the self-attention mechanism and residual network structure. More specifically, the attention-driven module captures the position information of the sequence, while the residual network structure guarantees that the high-level features of the binding site can be extracted. Meanwhile, the pre-training strategy used by SAResNet improves the learning ability of the network and accelerates the convergence speed of the network during transfer learning. The performance of SAResNet is extensively tested on 690 datasets from the ChIP-seq experiments with an average AUC of 92.0%, which is 4.4% higher than that of the best state-of-the-art method currently available. When tested on smaller datasets, the predictive performance is more clearly improved. Overall, we demonstrate that the superior performance of DNA-protein binding prediction on DNA sequences can be achieved by combining the attention mechanism and residual structure, and a novel pipeline is accordingly developed. The proposed methodology is generally applicable and can be used to address any other sequence classification problems.

Improving protein fold recognition using triplet network and ensemble deep learning.

Protein fold recognition is a critical step toward protein structure and function prediction, aiming at providing the most likely fold type of the query protein. In recent years, the development of deep learning (DL) technique has led to massive advances in this important field, and accordingly, the sensitivity of protein fold recognition has been dramatically improved. Most DL-based methods take an intermediate bottleneck layer as the feature representation of proteins with new fold types. However, this strategy is indirect, inefficient and conditional on the hypothesis that the bottleneck layer's representation is assumed as a good representation of proteins with new fold types. To address the above problem, in this work, we develop a new computational framework by combining triplet network and ensemble DL. We first train a DL-based model, termed FoldNet, which employs triplet loss to train the deep convolutional network. FoldNet directly optimizes the protein fold embedding itself, making the proteins with the same fold types be closer to each other than those with different fold types in the new protein embedding space. Subsequently, using the trained FoldNet, we implement a new residue-residue contact-assisted predictor, termed FoldTR, which improves protein fold recognition. Furthermore, we propose a new ensemble DL method, termed FSD_XGBoost, which combines protein fold embedding with the other two discriminative fold-specific features extracted by two DL-based methods SSAfold and DeepFR. The Top 1 sensitivity of FSD_XGBoost increases to 74.8% at the fold level, which is ~9% higher than that of the state-of-the-art method. Together, the results suggest that fold-specific features extracted by different DL methods complement with each other, and their combination can further improve fold recognition at the fold level. The implemented web server of FoldTR and benchmark datasets are publicly available at http://csbio.njust.edu.cn/bioinf/foldtr/.

Learning Cell Annotation under Multiple Reference Datasets by Multisource Domain Adaptation.

Accurate and efficient cell type annotation is essential for single-cell sequence analysis. Currently, cell type annotation using well-annotated reference datasets with powerful models has become increasingly popular. However, with the increasing amount of single-cell data, there is an urgent need to develop a novel annotation method that can integrate multiple reference datasets to improve cell type annotation performance. Since the unwanted batch effects between individual reference datasets, integrating multiple reference datasets is still an open challenge. To address this, we proposed scMDR and scMultiR, respectively, using multisource domain adaptation to learn cell type-specific information from multiple reference datasets and query cells. Based on the learned cell type-specific information, scMDR and scMultiR provide the most likely cell types for the query cells. Benchmark experiments demonstrated their state-of-the-art effectiveness for integrative single-cell assignment with multiple reference datasets.

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.

Improving the prediction of DNA-protein binding by integrating multi-scale dense convolutional network with fault-tolerant coding.

Accurate prediction of DNA-protein binding (DPB) is of great biological significance for studying the regulatory mechanism of gene expression. In recent years, with the rapid development of deep learning techniques, advanced deep neural networks have been introduced into the field and shown to significantly improve the prediction performance of DPB. However, these methods are primarily based on the DNA sequences measured by the ChIP-seq technology, failing to consider the possible partial variations of the motif sequences and errors of the sequencing technology itself. To address this, we propose a novel computational method, termed MSDenseNet, which combines a new fault-tolerant coding (FTC) scheme with the dense connectional deep neural networks. Three important factors can be attributed to the success of MSDenseNet: First, MSDenseNet utilizes a powerful feature representation approach, which transforms the raw DNA sequence into fusion coding using the fault-tolerant feature sequence; Second, in terms of network structure, MSDenseNet uses a multi-scale convolution within the dense layer and the multi-scale convolution preceding the dense block. This is shown to be able to significantly improve the network performance and accelerate the network convergence speed, and third, building upon the advanced deep neural network, MSDenseNet is capable of effectively mining the hidden complex relationship between the internal attributes of fusion sequence features to enhance the prediction of DPB. Benchmarking experiments on 690 ChIP-seq datasets show that MSDenseNet achieves an average AUC of 0.933 and outperforms the state-of-the-art method. The source code of MSDenseNet is available at https://github.com/csbio-njust-edu/msdensenet. The results show that MSDenseNet can effectively predict DPB. We anticipate that MSDenseNet will be exploited as a powerful tool to facilitate a more exhaustive understanding of DNA-binding proteins and help toward their functional characterization.

Chronic exposure to ammonia induces oxidative stress and enhanced glycolysis in lung of piglets.

Ammonia is one of the major environmental pollutants in the pig industry that seriously affects the airway health of pigs. In this study, we aimed to investigate the metabolic profiling changes of piglets' lung tissue after the exposure of 0 ppm (CG), 20 ppm (LG) and 50 ppm (HG) ammonia for 30 days. Compared with the control group, the obvious lung lesions were observed in HG, including interstitial thickening, inflammatory cell infiltration and focal hemorrhage. The significantly increased content of malondialdehyde in HG, combined with the significantly decreased mRNA expression of antioxidase and inflammatory-regulators in exposure groups, implied that ammonia exposure induced oxidative stress and diminished the anti-inflammatory response in lung tissues. Metabolomic analyses of lung tissues revealed 15 significantly altered metabolites among the three groups including multiple amino acids, carbohydrates and lipids. The accumulation of succinic acid, linoleic acid and phosphorylethanolamine and consumption of glucose, quinolinic acid and aspartic acid in ammonia exposure groups, indicated that energy supply from glucose aerobic oxidation was suppressed and the glycolysis and lipolysis were activated in lung tissues induced by chronic ammonia exposure.

High-dose sodium-glucose co-transporter-2 inhibitors are superior in type 2 diabetes: A meta-analysis of randomized clinical trials.

AIM: To determine the overall efficacy of high- versus low-dose sodium-glucose co-transporter-2 (SGLT2) inhibitors in patients with type 2 diabetes (T2D). MATERIAL AND METHODS: A literature search using MEDLINE, EMBASE and the Cochrane Library was performed from 1 January 2006 to 23 September 2020. Random effects models were used to calculate mean differences (MDs) and pooled relative risk (RR). Prespecified subgroup analyses for each SGLT2 inhibitor, follow-up and controls were performed. Leave-one-out sensitivity and meta-regression analyses were conducted. RESULTS: A total of 51 randomized controlled trials involving 23 989 participants (weighted mean age, 58.9 years; men, 58.8%) were eligible for our meta-analysis. For glycaemic regulation ability, a significant reduction in HbA1c (MD -0.080%, 95% confidence interval [CI] -0.100 to -0.060), fasting plasma glucose (MD -0.227 mmol/L, 95% CI -0.282 to -0.173) and postprandial plasma glucose (MD -0.834 mmol/L, 95% CI -1.268 to -0.400) levels was observed in the high-dose SGLT2 inhibitor group. Treatment with high-dose SGLT2 inhibitors enabled easier achievement of the target (HbA1c <7%) than low-dose SGLT2 inhibitors (RR 1.148, 95% CI 1.104 to 1.193). High-dose SGLT2 inhibitor-based treatment resulted in more efficient regulation of body weight and blood pressure (body weight: MD -0.346 kg, 95% CI -0.437 to -0.254; systolic blood pressure: MD -0.583 mmHg, 95% CI -0.903 to -0.263; diastolic blood pressure: MD -0.352 mmHg, 95% CI -0.563 to -0.142). The results were similar in sensitivity analyses. CONCLUSIONS: The overall efficacy of SGLT2 inhibitors, mainly canagliflozin, dapagliflozin and empagliflozin, was found to be dose dependent.

IgA immunoglobulin isotype of rheumatoid factor in primary Sjögren's syndrome.

Primary Sjögren's syndrome (pSS) is an autoimmune disease with autoantibodies overproduction, including rheumatoid factors (RF). RF-IgA, IgG immunoglobulin classes are suggested as potential biomarkers of pSS. We studied 76 patients with pSS (ACR/Eular 2017); laboratory tests included ESR, C-reactive protein, concentrations of gamma globulins, RF, Anti-SS-A/Ro, and anti-SS-B/La. Eye dryness and keratoconjunctivitis sicca were confirmed with Schirmer's test, the ocular staining score (OSS) using lissamine green, fluorescein staining and biopsy of minor salivary gland with the histopathological evaluation. Differences between groups were analyzed with U Mann-Whitney test. Correlations between quantitative variables were assessed with the Spearman correlation coefficient.. The best diagnostic values of immunoglobulin concentration for discriminating pSS patients and healthy individuals are for RF-IgA. With cut-off of 21.5 EU/mL, the sensitivity is 72% and specificity is 100%. Very high specificity (100%) is also obtained for RF-IgM concentration of 74.1 EU/mL. Sensitivity is, however, smaller than that for RF-IgA and amounted to 61%. The RF-IgG is the poorest indicator of pSS with 51% of sensitivity and 95% of specificity. To summarize RF-IgA strongly associate with anti-SS-A and anti-SS-B autoantibodies. Both RF-IgA and RF-IgM may be used as diagnostic tools for pSS. Conclusions: among the three studied rheumatoid factor subtypes, RF-IgA showed the best diagnostic accuracy for pSS. RF-IgA correlated with anti-SS-A/Ro and anti-SS-B antibodies even more closely than RF-IgM. The assessment of the RF-IgA serum concentration may be helpful in the process of establishing pSS diagnosis.

Risk of Major Gastrointestinal Bleeding With New vs Conventional Oral Anticoagulants: A Systematic Review and Meta-analysis.

BACKGROUND & AIMS: There is controversy over whether use of non-vitamin K antagonist oral anticoagulants (NOACs) associates with increased risk of major gastrointestinal bleeding (GIB) compared with conventional therapies (such as vitamin K antagonists or anti-platelet agents). We performed a systematic review and meta-analysis of data from randomized controlled trials and high-quality real-world studies. METHODS: We performed a systematic search of the MEDLINE, EMBASE, Cochrane Library, and ClinicalTrials.gov Website databases (through Oct 12, 2018) for randomized controlled trials and high-quality real-world studies that reported major GIB events in patients given NOACs or conventional therapy. Relative risks (RRs) for randomized controlled trials and adjusted hazard ratios (aHRs) for real-world studies were calculated separately using random-effects models. RESULTS: We analyzed data from 43 randomized controlled trials (183,752 patients) and 41 real-world studies (1,879,428 patients). The pooled major rates of GIB for patients on NOACs (1.19%) vs conventional treatment (0.92%) did not differ significantly (RR from randomized controlled trials, 1.09; 95% CI, 0.91-1.31 and aHR from real-world studies, 1.02; 95% CI, 0.94-1.10; Pinteraction=.52). Rivaroxaban, but not other NOACs, was associated with an increased risk for major GIB (RR from randomized controlled trials, 1.39; 95% CI, 1.17-1.65 and aHR from real-world studies, 1.14; 95% CI, 1.04-1.23; Pinteraction = .06). Analyses of subgroups, such as patients with different indications, dosage, or follow-up time, did not significantly affect results. Meta-regression analysis failed to detect any potential confounding to impact the primacy outcome. CONCLUSIONS: In a systematic review and meta-analysis of data from randomized controlled trials and real-world studies, we confirmed that there is no significant difference in risk of major GIB between patients receiving NOACs vs conventional treatment. Rivaroxaban users had a 39% increase in risk for major GIB.

Correction: Geospatial characteristics of measles transmission in China during 2005-2014.

[This corrects the article DOI: 10.1371/journal.pcbi.1005474.].

The origin of GSKIP, a multifaceted regulatory factor in the mammalian Wnt pathway.

GSK3ß interacting protein (GSKIP) is a naturally occurring negative regulator of GSK3ß and retains both the Protein Kinase A Regulatory subunit binding (PKA-RII) domain and GSK3ß interacting domain. Of these two domains, we found that PKA-RII is required for forming a working complex comprising PKA/GSKIP/GSK3ß/Drp1 to influence phosphorylation of Drp1 Ser637. In this study, bioinformatics and experimental explorations re-analyzing GSKIP's biofunctions suggest that the evolutionarily conserved Domain of Unknown Function (DUF727) is an ancestral prototype of GSKIP in prokaryotes, and acquired the C-terminal GSK3ß binding site (tail) in invertebrates except for Saccharomyces spp., after which the N-terminal PKA-RII binding region (head) evolved in vertebrates. These two regions mutually influence each other and modulate GSKIP binding to GSK3ß in yeast two-hybrid assays and co-immunoprecipitation. Molecular modeling showed that mammalian GSKIP could form a dimer through the L130 residue (GSK3ß binding site) rather than V41/L45 residues. In contrast, V41/L45P mutant facilitated a gain-of-function effect on GSKIP dimerization, further influencing binding behavior to GSK3ß compared to GSKIP wild-type (wt). The V41/L45 residues are not only responsible for PKA RII binding that controls GSK3ß activity, but also affect dimerization of GSKIP monomer, with net results of gain-of-function in GSKIP-GSK3ß interaction. In addition to its reported role in modulating Drp1, Ser637 phosphorylation caused mitochondrial elongation; we postulated that GSKIP might be involved in the Wnt signaling pathway as a scavenger to recruit GSK3ß away from the ß-catenin destruction complex and as a competitor to compete for GSK3ß binding, resulting in accumulation of S675 phosphorylated ß-catenin.