MSI-XGNN: an explainable GNN computational framework integrating transcription- and methylation-level biomarkers for microsatellite instability detection.

Microsatellite instability (MSI) is a hypermutator phenotype caused by DNA mismatch repair deficiency. MSI has been reported in various human cancers, particularly colorectal, gastric and endometrial cancers. MSI is a promising biomarker for cancer prognosis and immune checkpoint blockade immunotherapy. Several computational methods have been developed for MSI detection using DNA- or RNA-based approaches based on next-generation sequencing. Epigenetic mechanisms, such as DNA methylation, regulate gene expression and play critical roles in the development and progression of cancer. We here developed MSI-XGNN, a new computational framework for predicting MSI status using bulk RNA-sequencing and DNA methylation data. MSI-XGNN is an explainable deep learning model that combines a graph neural network (GNN) model to extract features from the gene-methylation probe network with a CatBoost model to classify MSI status. MSI-XGNN, which requires tumor-only samples, exhibited comparable performance with two well-known methods that require tumor-normal paired sequencing data, MSIsensor and MANTIS and better performance than several other tools. MSI-XGNN also showed good generalizability on independent validation datasets. MSI-XGNN identified six MSI markers consisting of four methylation probes (EPM2AIP1|MLH1:cg14598950, EPM2AIP1|MLH1:cg27331401, LNP1:cg05428436 and TSC22D2:cg15048832) and two genes (RPL22L1 and MSH4) constituting the optimal feature subset. All six markers were significantly associated with beneficial tumor microenvironment characteristics for immunotherapy, such as tumor mutation burden, neoantigens and immune checkpoint molecules such as programmed cell death-1 and cytotoxic T-lymphocyte antigen-4. Overall, our study provides a powerful and explainable deep learning model for predicting MSI status and identifying MSI markers that can potentially be used for clinical MSI evaluation.

microbiomeMarker: an R/Bioconductor package for microbiome marker identification and visualization.

SUMMARY: Characterizing biomarkers based on microbiome profiles has great potential for translational medicine and precision medicine. Here, we present microbiomeMarker, an R/Bioconductor package implementing commonly used normalization and differential analysis (DA) methods, and three supervised learning models to identify microbiome markers. microbiomeMarker also allows comparison of different methods of DA and confounder analysis. It uses standardized input and output formats, which renders it highly scalable and extensible, and allows it to seamlessly interface with other microbiome packages and tools. In addition, the package provides a set of functions to visualize and interpret the identified microbiome markers. AVAILABILITY AND IMPLEMENTATION: microbiomeMarker is freely available from Bioconductor (https://www.bioconductor.org/packages/microbiomeMarker). Source code is available and maintained at GitHub (https://github.com/yiluheihei/microbiomeMarker). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Snowball 2.0: Generic Material Data Parser for ChemDataExtractor.

The ever-growing amount of chemical data found in the scientific literature has led to the emergence of data-driven materials discovery. The first step in the pipeline, to automatically extract chemical information from plain text, has been driven by the development of software toolkits such as ChemDataExtractor. Such data extraction processes have created a demand for parsers that efficiently enable text mining. Here, we present Snowball 2.0, a sentence parser based on a semisupervised machine-learning algorithm. It can be used to extract any chemical property without additional training. We validate its precision, recall, and F-score by training and testing a model with sentences of semiconductor band gap information curated from journal articles. Snowball 2.0 builds on two previously developed Snowball algorithms. Evaluation of Snowball 2.0 shows a 15-20% increase in recall with marginally reduced precision over the previous version which has been incorporated into ChemDataExtractor 2.0, giving Snowball 2.0 better performance in most configurations. Snowball 2.0 offers more and better parsing options for ChemDataExtractor, and it is more capable in the pipeline of automated data extraction. Snowball 2.0 also features better generalizability, performance, learning efficiencies, and user-friendliness.

Dysregulated Response of Follicular Helper T Cells to Hepatitis B Surface Antigen Promotes HBV Persistence in Mice and Associates With Outcomes of Patients.

BACKGROUND & AIMS: Production of neutralizing antibodies against hepatitis B surface antigen (HBsAg) is dysregulated in patients with persistent hepatitis B virus (HBV) infection. We investigated mechanisms by which this immune response to the virus is disrupted and whether it can be restored to promote clearance of HBV. METHODS: Immune-competent C57BL/6N and C57BL/6J, as well as mice deficient in follicular helper T cells (Tfh-cell-deficient), B cells, or Foxp3+ T-regulatory cells (Treg cell deficient), were given hydrodynamic injections of pAAV/HBV1.2 plasmids. Some mice were given injections of sorted Tfh cells, pan-B cells, Treg cells, or a blocking antibody against CTLA4. Production of antibodies against HBsAg and clearance of HBV were assessed by flow cytometry, enzyme-linked immunosorbent assay, polymerase chain reaction, and immunohistochemical analyses. We obtained blood samples from patients with HBV infection and isolated Treg cells. We measured the ability of Treg cells to suppress production of interleukin 21 (IL21) in CD4+ T cells. RESULTS: Immune-competent C57BL/6N and C57BL/6J mice transfected with the plasmid encoding HBV had features of viral clearance and viral persistence observed in humans. A Tfh-cell response to HBsAg was required for clearance of HBV and was suppressed by Treg cells in mice with persistent HBV infection. Depletion of Treg cells or inhibition of Treg-cell function (with blocking antibody against CTLA4) restored the Tfh-cell response against HBsAg and clearance of HBV in mice. Impaired Tfh-cell response to HBsAg was observed in blood from patients with chronic HBV infection, responsiveness was restored by depletion of Treg cells or blocking antibody against CTLA4. CONCLUSIONS: In studies of HBV-infected mice and blood from patients with chronic HBV infection, we found a Tfh-cell response to HBsAg of to be required for HBV clearance, and that this response was blocked by Treg cells. Inhibiting Treg-cell activity using neutralizing antibody against CTLA4 restored the ability of Tfh cells to clear HBV infection; this approach might be developed for treatment of patients with chronic HBV infection.

The Myeloid LSECtin Is a DAP12-Coupled Receptor That Is Crucial for Inflammatory Response Induced by Ebola Virus Glycoprotein.

Fatal Ebola virus infection is characterized by a systemic inflammatory response similar to septic shock. Ebola glycoprotein (GP) is involved in this process through activating dendritic cells (DCs) and macrophages. However, the mechanism is unclear. Here, we showed that LSECtin (also known as CLEC4G) plays an important role in GP-mediated inflammatory responses in human DCs. Anti-LSECtin mAb engagement induced TNF-α and IL-6 production in DCs, whereas silencing of LSECtin abrogated this effect. Intriguingly, as a pathogen-derived ligand, Ebola GP could trigger TNF-α and IL-6 release by DCs through LSECtin. Mechanistic investigations revealed that LSECtin initiated signaling via association with a 12-kDa DNAX-activating protein (DAP12) and induced Syk activation. Mutation of key tyrosines in the DAP12 immunoreceptor tyrosine-based activation motif abrogated LSECtin-mediated signaling. Furthermore, Syk inhibitors significantly reduced the GP-triggered cytokine production in DCs. Therefore, our results demonstrate that LSECtin is required for the GP-induced inflammatory response, providing new insights into the EBOV-mediated inflammatory response.

Amplification-free detection of HBV DNA mediated by CRISPR-Cas12a using surface-enhanced Raman spectroscopy.

Nucleic acid markers have been widely used in the detection of various virus-related diseases, including hepatitis B virus (HBV), which is spreading worldwide. The trans-activated CRISPR-Cas system has shown excellent sensitivity and specificity in nucleic acid detection. However, nucleic acid testing usually requires amplification of the target nucleic acid for more accurate and specific detection; furthermore, current nucleic acid assays are time-consuming, costly, and are limited by non-specific cross-reactivity. We developed an amplification-free viral DNA biosensor-based diagnostic method that uses a clustered regularly interspaced short palindromic repeats-associated system (CRISPR/Cas)-based approach with surface enhanced Raman spectroscopy. This method can specifically identify the target site by changing the crRNA sequence. In addition, the incubation period and development of the disease can be determined by quantitative detection of viral DNA. This system could achieve rapid and highly sensitive detection of HBV DNA within 50 min and vast detection range from 0.1 pM to 1 nM. Therefore, a combined CRISPR/Cas12a-SERS-based assay would improve the sensitivity of detection in assays using multiple biomarkers. In conclusion, our CRISPR/Cas12a-based biosensor would enable rapid, simple, and sensitive detection of HBV nucleic acids.

Network pharmacology and experiment validation investigate the potential mechanism of triptolide in oral squamous cell carcinoma.

Objective: This study aimed to investigate the molecular mechanism of triptolide in the treatment of oral squamous cell carcinoma (OSCC) via network pharmacology and experimental validation. Methods: The network pharmacological method was used to predict the key targets, detect the signal pathways for the treatment of OSCC, and screen the critical components and targets for molecular docking. Predicted targets were validated in cellular and xenograft mouse model. Results: In this study, we predicted action on 17 relevant targets of OSCC by network pharmacology. PPI network demonstrated that Jun, MAPK8, TP53, STAT3, VEGFA, IL2, CXCR4, PTGS2, IL4 might be the critical targets of triptolide in the treatment of OSCC. These potential targets are mainly closely related to JAK-STAT and MAPK signaling pathways. The analysis of molecular docking showed that triptolide has high affinity with Jun, MAPK8 and TP53. Triptolide can suppress the growth of OSCC cells and xenograft mice tumor, and downregulate the expression of Jun, MAPK8, TP53, STAT3, VEGFA, IL2, CXCR4, PTGS2 to achieve the therapeutic effect of OSCC. Conclusion: Through network pharmacological methods and experimental studies, we predicted and validated the potential targets and related pathways of triptolide for OSCC treatment. The results suggest that triptolide can inhibit the growth of OSCC via several key targets.

Auto-generated database of semiconductor band gaps using ChemDataExtractor.

Large-scale databases of band gap information about semiconductors that are curated from the scientific literature have significant usefulness for computational databases and general semiconductor materials research. This work presents an auto-generated database of 100,236 semiconductor band gap records, extracted from 128,776 journal articles with their associated temperature information. The database was produced using ChemDataExtractor version 2.0, a 'chemistry-aware' software toolkit that uses Natural Language Processing (NLP) and machine-learning methods to extract chemical data from scientific documents. The modified Snowball algorithm of ChemDataExtractor has been extended to incorporate nested models, optimized by hyperparameter analysis, and used together with the default NLP parsers to achieve optimal quality of the database. Evaluation of the database shows a weighted precision of 84% and a weighted recall of 65%. To the best of our knowledge, this is the largest open-source non-computational band gap database to date. Database records are available in CSV, JSON, and MongoDB formats, which are machine readable and can assist data mining and semiconductor materials discovery.

Alterations in the gut microbiome and metabolic profile in rats acclimated to high environmental temperature.

Heat acclimation (HA) is the best strategy to improve heat stress tolerance by inducing positive physiological adaptations. Evidence indicates that the gut microbiome plays a fundamental role in the development of HA, and modulation of gut microbiota can improve tolerance to heat exposure and decrease the risks of heat illness. In this study, for the first time, we applied 16S rRNA gene sequencing and untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics to explore variations in the gut microbiome and faecal metabolic profiles in rats after HA. The gut microbiota of HA subjects exhibited higher diversity and richer microbes. HA altered the gut microbiota composition with significant increases in the genera Lactobacillus (a major probiotic) and Oscillospira alongside significant decreases in the genera Blautia and Allobaculum. The faecal metabolome was also significantly changed after HA, and among the 13 perturbed metabolites, (S)-AL 8810 and celastrol were increased. Moreover, the two increased genera were positively correlated with the two upregulated metabolites and negatively correlated with the other 11 downregulated metabolites, while the correlations between the two decreased genera and the upregulated/downregulated metabolites were completely contrary. In summary, both the structure of the gut microbiome community and the faecal metabolome were improved after 28 days of HA. These findings provide novel insights regarding the improvement of the gut microbiome and its functions as a potential mechanism by which HA confers protection against heat stress.

TIDB: a comprehensive database of trained immunity.

Trained immunity is a newly emerging concept that defines the ability of the innate immune system to form immune memory and provide long-lasting protection against previously encountered antigens. Accumulating evidence reveals that trained immunity not only has broad benefits to host defense but is also harmful to the host in chronic inflammatory diseases. However, all trained immunity-related information is scattered in the literature and thus is difficult to access. Here, we describe Trained Immunity DataBase (TIDB), a comprehensive database that provides well-studied trained immunity-related genes from human, rat and mouse as well as the related literature evidence. Moreover, TIDB also provides three modules to analyze the function of the trained-immunity-related genes of interest, including Reactome pathway over-representation analysis, Gene Ontology enrichment analysis and protein-protein interaction subnetwork reconstruction. We believe TIDB will help developing valuable strategies for vaccine design and immune-mediated disease therapy. Database URL: http://www.ieom-tm.com/tidb.

Detection of 12 Common Food-Borne Bacterial Pathogens by TaqMan Real-Time PCR Using a Single Set of Reaction Conditions.

Food safety has become an important public health issue worldwide. However, conventional methods for detection of food-borne pathogens are complicated, and labor-intensive. Moreover, the sensitivity is often low, and it is difficult to achieve high-throughput detection. This study developed a TaqMan real-time polymerase chain reaction (PCR) assay for the simultaneous detection and quantification of 12 common pathogens in a single reaction, including Escherichia coli O157:H7, Listeria monocytogenes/ivanovii, Salmonella enterica, Vibrio parahaemolyticus, ß-streptococcus hemolyticus, Yersinia enterocolitica, Enterococcus faecalis, Shigella spp., Proteus mirabilis, Vibrio fluvialis, Staphylococcus aureus, and Campylobacter jejuni in food and drinking water. Based on published sequence data, specific primers, and fluorescently-labeled hybridization probes were designed targeting based on the virulence genes of the 12 pathogens, and these primers and probes were optimized to achieve consistent reaction conditions. The assay was evaluated using 106 pure bacterial culture strains. There was no cross-reaction among the different pathogens. The analytical sensitivity was 1 copy/µL for E. coli O157:H7, L. monocytogenes/ivanovii, ß-streptococcus hemolyticus, Shigella spp., P. mirabilis, and V. fluvialis, 10 copies/µL for S. enterica, V. parahaemolyticus, Y. enterocolitica, E. faecalis, S. aureus, and C. jejuni, respectively. The limit of detection (LOD) was 296, 500, 177, 56, 960, 830, 625, 520, 573, 161, 875, and 495 CFU/mL for E. coli O157:H7, L. monocytogenes/ivanovii, S. enterica, V. parahaemolyticus, ß-streptococcus hemolyticus, Y. enterocolitica, E. faecalis, Shigella spp., P. mirabilis, V. fluvialis, S. aureus, and C. jejuni, respectively. The limit of detection for the assay in meat samples was 103 CFU/g for V. parahaemolyticus and 104 CFU/g for other 11 strains. Together, these results indicate that the optimized TaqMan real-time PCR assay will be useful for routine detection of pathogenic bacteria due to its rapid analysis, low cost, high-throughput, high specificity, and sensitivity.