COVID-19 Knowledge Graph from semantic integration of biomedical literature and databases.

SUMMARY: The global response to the COVID-19 pandemic has led to a rapid increase of scientific literature on this deadly disease. Extracting knowledge from biomedical literature and integrating it with relevant information from curated biological databases is essential to gain insight into COVID-19 etiology, diagnosis and treatment. We used Semantic Web technology RDF to integrate COVID-19 knowledge mined from literature by iTextMine, PubTator and SemRep with relevant biological databases and formalized the knowledge in a standardized and computable COVID-19 Knowledge Graph (KG). We published the COVID-19 KG via a SPARQL endpoint to support federated queries on the Semantic Web and developed a knowledge portal with browsing and searching interfaces. We also developed a RESTful API to support programmatic access and provided RDF dumps for download. AVAILABILITY AND IMPLEMENTATION: The COVID-19 Knowledge Graph is publicly available under CC-BY 4.0 license at https://research.bioinformatics.udel.edu/covid19kg/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

piNET: a versatile web platform for downstream analysis and visualization of proteomics data.

Rapid progress in proteomics and large-scale profiling of biological systems at the protein level necessitates the continued development of efficient computational tools for the analysis and interpretation of proteomics data. Here, we present the piNET server that facilitates integrated annotation, analysis and visualization of quantitative proteomics data, with emphasis on PTM networks and integration with the LINCS library of chemical and genetic perturbation signatures in order to provide further mechanistic and functional insights. The primary input for the server consists of a set of peptides or proteins, optionally with PTM sites, and their corresponding abundance values. Several interconnected workflows can be used to generate: (i) interactive graphs and tables providing comprehensive annotation and mapping between peptides and proteins with PTM sites; (ii) high resolution and interactive visualization for enzyme-substrate networks, including kinases and their phospho-peptide targets; (iii) mapping and visualization of LINCS signature connectivity for chemical inhibitors or genetic knockdown of enzymes upstream of their target PTM sites. piNET has been built using a modular Spring-Boot JAVA platform as a fast, versatile and easy to use tool. The Apache Lucene indexing is used for fast mapping of peptides into UniProt entries for the human, mouse and other commonly used model organism proteomes. PTM-centric network analyses combine PhosphoSitePlus, iPTMnet and SIGNOR databases of validated enzyme-substrate relationships, for kinase networks augmented by DeepPhos predictions and sequence-based mapping of PhosphoSitePlus consensus motifs. Concordant LINCS signatures are mapped using iLINCS. For each workflow, a RESTful API counterpart can be used to generate the results programmatically in the json format. The server is available at http://pinet-server.org, and it is free and open to all users without login requirement.

UniRule: a unified rule resource for automatic annotation in the UniProt Knowledgebase.

MOTIVATION: The number of protein records in the UniProt Knowledgebase (UniProtKB: https://www.uniprot.org) continues to grow rapidly as a result of genome sequencing and the prediction of protein-coding genes. Providing functional annotation for these proteins presents a significant and continuing challenge. RESULTS: In response to this challenge, UniProt has developed a method of annotation, known as UniRule, based on expertly curated rules, which integrates related systems (RuleBase, HAMAP, PIRSR, PIRNR) developed by the members of the UniProt consortium. UniRule uses protein family signatures from InterPro, combined with taxonomic and other constraints, to select sets of reviewed proteins which have common functional properties supported by experimental evidence. This annotation is propagated to unreviewed records in UniProtKB that meet the same selection criteria, most of which do not have (and are never likely to have) experimentally verified functional annotation. Release 2020_01 of UniProtKB contains 6496 UniRule rules which provide annotation for 53 million proteins, accounting for 30% of the 178 million records in UniProtKB. UniRule provides scalable enrichment of annotation in UniProtKB. AVAILABILITY AND IMPLEMENTATION: UniRule rules are integrated into UniProtKB and can be viewed at https://www.uniprot.org/unirule/. UniRule rules and the code required to run the rules, are publicly available for researchers who wish to annotate their own sequences. The implementation used to run the rules is known as UniFIRE and is available at https://gitlab.ebi.ac.uk/uniprot-public/unifire.

Pulmonary fibrosis and its related factors in discharged patients with new corona virus pneumonia: a cohort study.

BACKGROUND: Thousands of Coronavirus Disease 2019 (COVID-19) patients have been discharged from hospitals Persistent follow-up studies are required to evaluate the prevalence of post-COVID-19 fibrosis. METHODS: This study involves 462 laboratory-confirmed patients with COVID-19 who were admitted to Shenzhen Third People's Hospital from January 11, 2020 to April 26, 2020. A total of 457 patients underwent thin-section chest CT scans during the hospitalization or after discharge to identify the pulmonary lesion. A total of 287 patients were followed up from 90 to 150 days after the onset of the disease, and lung function tests were conducted about three months after the onset. The risk factors affecting the persistence of pulmonary fibrosis were identified through regression analysis and the prediction model of the persistence of pulmonary fibrosis was established. RESULTS: Parenchymal bands, irregular interfaces, reticulation and traction bronchiectasis were the most common CT features in all COVID-19 patients. During the 0-30, 31-60, 61-90, 91-120 and > 120 days after onset, 86.87%, 74.40%, 79.56%, 68.12% and 62.03% patients developed with pulmonary fibrosis and 4.53%, 19.61%, 18.02%, 38.30% and 48.98% patients reversed pulmonary fibrosis, respectively. It was observed that Age, BMI, Fever, and Highest PCT were predictive factors for sustaining fibrosis even after 90 days from onset. A predictive model of the persistence with pulmonary fibrosis was developed based-on the Logistic Regression method with an accuracy, PPV, NPV, Sensitivity and Specificity of the model of 76%, 71%, 79%, 67%, and 82%, respectively. More than half of the COVID-19 patients revealed abnormal conditions in lung function after 90 days from onset, and the ratio of abnormal lung function did not differ on a statistically significant level between the fibrotic and non-fibrotic groups. CONCLUSIONS: Persistent pulmonary fibrosis was more likely to develop in patients with older age, higher BMI, severe/critical condition, fever, a longer viral clearance time, pre-existing disease and delayed hospitalization. Fibrosis developed in COVID-19 patients could be reversed in about a third of the patients after 120 days from onset. The pulmonary function of less than half of COVID-19 patients could turn to normal condition after three months from onset. An effective prediction model with an average area under the curve (AUC) of 0.84 was established to predict the persistence of pulmonary fibrosis in COVID-19 patients for early diagnosis.

The novel coronavirus (COVID-19) pneumonia with negative detection of viral ribonucleic acid from nasopharyngeal swabs: a case report.

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) outbreak started in Wuhan, Hubei, China since Dec 2019 and cases of infection have been continuously reported in various countries. It is now clear that the SARS-COV-2 coronavirus is transmissible from human to human. Nucleic acid detection is considered as the gold standard for the diagnosis of COVID-19. In this case report, we describe our experience in detection of SARS-COV-2 from a confirmed patient using nucleic acid test of bronchoalveolar-lavage fluid (BALF) samples but not nasopharyngeal swabs. CASE PRESENTATION: We present a case of severely ill SARS-COV-2 infected 46-year-old man with fever, coughing and chest tightness. We performed viral detection using his BALF samples and imaging method (CT) for confirmation. The patient received combination of interferonalfa-1b and ribavirin, lopinavir and ritonavir for antiviral treatment at different stages. Other medication was also given to him in combination for anti-inflammation, intestinal microbial regulation, phlegm elimination, liver protection and pulmonary fibrosis prevention purposes. We provided oxygen supply to him using BIPAP ventilator and high-flow humidification oxygen therapy instrument to facilitate respiration. The patient was cured and discharged. CONCLUSION: This case report described an effective supportive medication scheme to treat SARS-COV-2 infected patient and emphasized the necessity of detection of the viral genome using BALF samples and its significance in the diagnosis and prognosis of the disease.

Epidemiological trends and virological traits of hepatitis B virus infection in pregnant women and neonates.

In infants, hepatitis B virus (HBV) infections are mainly acquired by mother-to-child transmission (MTCT). Current tests for the presence of HBV markers at birth can neither confirm nor exclude MTCT. The aim of this study was to find an early diagnostic marker of HBV MTCT. From 2011 to 2016, we studied a total of 5999 pregnant women who gave birth at our hospital in Shenzhen City, China. HBsAg-positive mothers and their offspring (n=386 pairs) were tested at birth for HBV markers, and 207 infants were followed up at 7-12 months after birth. The HBsAg-seropositive rate of the pregnant women was 12.5%. Additionally, 28.0%, 36.0%, 98.5% and 6.6% of umbilical cord (UC) blood samples of neonates were found to be positive for HBsAg, HBeAg, anti-HBc and HBV-DNA, respectively, whereas for neonatal femoral venous (FV) blood, the percentages were 16.2%, 38.0%, 98.8% and 2.6%, respectively. Mothers with high HBV DNA loads and those who were HBeAg positive were the most likely to have HBV-positive offspring. Immunoprophylaxis failed in five infants: the difference in median HBV DNA titer between UC blood from infants with and without HBV MTCT was statistically significant, and there was no significant difference in HBV DNA titer between UC blood and in peripheral blood of infants with HBV MTCT. In conclusion, we found that HBeAg positivity and high HBV loads are strong risk factors for MTCT of HBV and that the HBV DNA titer in the UC is a good predictor for HBV MTCT.

Protein Ontology (PRO): enhancing and scaling up the representation of protein entities.

The Protein Ontology (PRO; http://purl.obolibrary.org/obo/pr) formally defines and describes taxon-specific and taxon-neutral protein-related entities in three major areas: proteins related by evolution; proteins produced from a given gene; and protein-containing complexes. PRO thus serves as a tool for referencing protein entities at any level of specificity. To enhance this ability, and to facilitate the comparison of such entities described in different resources, we developed a standardized representation of proteoforms using UniProtKB as a sequence reference and PSI-MOD as a post-translational modification reference. We illustrate its use in facilitating an alignment between PRO and Reactome protein entities. We also address issues of scalability, describing our first steps into the use of text mining to identify protein-related entities, the large-scale import of proteoform information from expert curated resources, and our ability to dynamically generate PRO terms. Web views for individual terms are now more informative about closely-related terms, including for example an interactive multiple sequence alignment. Finally, we describe recent improvement in semantic utility, with PRO now represented in OWL and as a SPARQL endpoint. These developments will further support the anticipated growth of PRO and facilitate discoverability of and allow aggregation of data relating to protein entities.

Transcriptional profiling of liver during the critical embryo-to-hatchling transition period in the chicken (Gallus gallus).

BACKGROUND: Although hatching is perhaps the most abrupt and profound metabolic challenge that a chicken must undergo; there have been no attempts to functionally map the metabolic pathways induced in liver during the embryo-to-hatchling transition. Furthermore, we know very little about the metabolic and regulatory factors that regulate lipid metabolism in late embryos or newly-hatched chicks. In the present study, we examined hepatic transcriptomes of 12 embryos and 12 hatchling chicks during the peri-hatch period-or the metabolic switch from chorioallantoic to pulmonary respiration. RESULTS: Initial hierarchical clustering revealed two distinct, albeit opposing, patterns of hepatic gene expression. Cluster A genes are largely lipolytic and highly expressed in embryos. While, Cluster B genes are lipogenic/thermogenic and mainly controlled by the lipogenic transcription factor THRSPA. Using pairwise comparisons of embryo and hatchling ages, we found 1272 genes that were differentially expressed between embryos and hatchling chicks, including 24 transcription factors and 284 genes that regulate lipid metabolism. The three most differentially-expressed transcripts found in liver of embryos were MOGAT1, DIO3 and PDK4, whereas THRSPA, FASN and DIO2 were highest in hatchlings. An unusual finding was the "ectopic" and extremely high differentially expression of seven feather keratin transcripts in liver of 16 day embryos, which coincides with engorgement of liver with yolk lipids. Gene interaction networks show several transcription factors, transcriptional co-activators/co-inhibitors and their downstream genes that exert a 'ying-yang' action on lipid metabolism during the embryo-to-hatching transition. These upstream regulators include ligand-activated transcription factors, sirtuins and Kruppel-like factors. CONCLUSIONS: Our genome-wide transcriptional analysis has greatly expanded the hepatic repertoire of regulatory and metabolic genes involved in the embryo-to-hatchling transition. New knowledge was gained on interactive transcriptional networks and metabolic pathways that enable the abrupt switch from ectothermy (embryo) to endothermy (hatchling) in the chicken. Several transcription factors and their coactivators/co-inhibitors appear to exert opposing actions on lipid metabolism, leading to the predominance of lipolysis in embryos and lipogenesis in hatchlings. Our analysis of hepatic transcriptomes has enabled discovery of opposing, interconnected and interdependent transcriptional regulators that provide precise ying-yang or homeorhetic regulation of lipid metabolism during the critical embryo-to-hatchling transition.

Computational clustering for viral reference proteomes.

MOTIVATION: The enormous number of redundant sequenced genomes has hindered efforts to analyze and functionally annotate proteins. As the taxonomy of viruses is not uniformly defined, viral proteomes pose special challenges in this regard. Grouping viruses based on the similarity of their proteins at proteome scale can normalize against potential taxonomic nomenclature anomalies. RESULTS: We present Viral Reference Proteomes (Viral RPs), which are computed from complete virus proteomes within UniProtKB. Viral RPs based on 95, 75, 55, 35 and 15% co-membership in proteome similarity based clusters are provided. Comparison of our computational Viral RPs with UniProt's curator-selected Reference Proteomes indicates that the two sets are consistent and complementary. Furthermore, each Viral RP represents a cluster of virus proteomes that was consistent with virus or host taxonomy. We provide BLASTP search and FTP download of Viral RP protein sequences, and a browser to facilitate the visualization of Viral RPs. AVAILABILITY AND IMPLEMENTATION: http://proteininformationresource.org/rps/viruses/ CONTACT: chenc@udel.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Elevated FGF21 secretion, PGC-1α and ketogenic enzyme expression are hallmarks of iron-sulfur cluster depletion in human skeletal muscle.

Iron-sulfur (Fe-S) clusters are ancient enzyme cofactors found in virtually all life forms. We evaluated the physiological effects of chronic Fe-S cluster deficiency in human skeletal muscle, a tissue that relies heavily on Fe-S cluster-mediated aerobic energy metabolism. Despite greatly decreased oxidative capacity, muscle tissue from patients deficient in the Fe-S cluster scaffold protein ISCU showed a predominance of type I oxidative muscle fibers and higher capillary density, enhanced expression of transcriptional co-activator PGC-1α and increased mitochondrial fatty acid oxidation genes. These Fe-S cluster-deficient muscles showed a dramatic up-regulation of the ketogenic enzyme HMGCS2 and the secreted protein FGF21 (fibroblast growth factor 21). Enhanced muscle FGF21 expression was reflected by elevated circulating FGF21 levels in the patients, and robust FGF21 secretion could be recapitulated by respiratory chain inhibition in cultured myotubes. Our findings reveal that mitochondrial energy starvation elicits a coordinated response in Fe-S-deficient skeletal muscle that is reflected systemically by increased plasma FGF21 levels.

A fast Peptide Match service for UniProt Knowledgebase.

SUMMARY: We have developed a new web application for peptide matching using Apache Lucene-based search engine. The Peptide Match service is designed to quickly retrieve all occurrences of a given query peptide from UniProt Knowledgebase (UniProtKB) with isoforms. The matched proteins are shown in summary tables with rich annotations, including matched sequence region(s) and links to corresponding proteins in a number of proteomic/peptide spectral databases. The results are grouped by taxonomy and can be browsed by organism, taxonomic group or taxonomy tree. The service supports queries where isobaric leucine and isoleucine are treated equivalent, and an option for searching UniRef100 representative sequences, as well as dynamic queries to major proteomic databases. In addition to the web interface, we also provide RESTful web services. The underlying data are updated every 4 weeks in accordance with the UniProt releases. AVAILABILITY: http://proteininformationresource.org/peptide.shtml. CONTACT: chenc@udel.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

KSFinder-a knowledge graph model for link prediction of novel phosphorylated substrates of kinases.

Background: Aberrant protein kinase regulation leading to abnormal substrate phosphorylation is associated with several human diseases. Despite the promise of therapies targeting kinases, many human kinases remain understudied. Most existing computational tools predicting phosphorylation cover less than 50% of known human kinases. They utilize local feature selection based on protein sequences, motifs, domains, structures, and/or functions, and do not consider the heterogeneous relationships of the proteins. In this work, we present KSFinder, a tool that predicts kinase-substrate links by capturing the inherent association of proteins in a network comprising 85% of the known human kinases. We also postulate the potential role of two understudied kinases based on their substrate predictions from KSFinder. Methods: KSFinder learns the semantic relationships in a phosphoproteome knowledge graph using a knowledge graph embedding algorithm and represents the nodes in low-dimensional vectors. A multilayer perceptron (MLP) classifier is trained to discern kinase-substrate links using the embedded vectors. KSFinder uses a strategic negative generation approach that eliminates biases in entity representation and combines data from experimentally validated non-interacting protein pairs, proteins from different subcellular locations, and random sampling. We assess KSFinder's generalization capability on four different datasets and compare its performance with other state-of-the-art prediction models. We employ KSFinder to predict substrates of 68 "dark" kinases considered understudied by the Illuminating the Druggable Genome program and use our text-mining tool, RLIMS-P along with manual curation, to search for literature evidence for the predictions. In a case study, we performed functional enrichment analysis for two dark kinases - HIPK3 and CAMKK1 using their predicted substrates. Results: KSFinder shows improved performance over other kinase-substrate prediction models and generalized prediction ability on different datasets. We identified literature evidence for 17 novel predictions involving an understudied kinase. All of these 17 predictions had a probability score ≥0.7 (nine at >0.9, six at 0.8-0.9, and two at 0.7-0.8). The evaluation of 93,593 negative predictions (probability ≤0.3) identified four false negatives. The top enriched biological processes of HIPK3 substrates relate to the regulation of extracellular matrix and epigenetic gene expression, while CAMKK1 substrates include lipid storage regulation and glucose homeostasis. Conclusions: KSFinder outperforms the current kinase-substrate prediction tools with higher kinase coverage. The strategically developed negatives provide a superior generalization ability for KSFinder. We predicted substrates of 432 kinases, 68 of which are understudied, and hypothesized the potential functions of two dark kinases using their predicted substrates.

Analysis of multiple organ function damage in patients with severe COVID-19 pneumonia.

Background: This study aims to analyze the changes and significance of organ function indices in patients with severe Coronavirus Disease 2019 (COVID-19) pneumonia for prediction of major organ damages and guiding treatment schemes. Methods: 63 patients with severe COVID-19 pneumonia were selected as the severe group and 73 patients with mild syndromes were selected as the mild group. SAS9.4 software was used for statistical analysis of the data. Results: Levels of ALT, AST, cTnI, Cr, PT, APTT and Ddimer of the severe group were significantly higher while PLT was lower than those of the mild group. The data of all quantitative variables were converted into categorical variables. Significantly higher levels of AST, ALB, D-dimer and higher proportion of bilateral lung involvement were observed from the severe group comparing to those in the mild group, while the difference in the other indices between the two groups was insignificant in statistical perspective. Conclusions: There are significant differences in the levels of multiple organ function indices between the severe group and the mild group of patients with COVID-19 pneumonia infection. Through examining the relevant indices, conditions of patients' multiple organ function damage could be predicted and used as guidance of treatment.

A knowledge graph representation learning approach to predict novel kinase-substrate interactions.

The human proteome contains a vast network of interacting kinases and substrates. Even though some kinases have proven to be immensely useful as therapeutic targets, a majority are still understudied. In this work, we present a novel knowledge graph representation learning approach to predict novel interaction partners for understudied kinases. Our approach uses a phosphoproteomic knowledge graph constructed by integrating data from iPTMnet, protein ontology, gene ontology and BioKG. The representations of kinases and substrates in this knowledge graph are learned by performing directed random walks on triples coupled with a modified SkipGram or CBOW model. These representations are then used as an input to a supervised classification model to predict novel interactions for understudied kinases. We also present a post-predictive analysis of the predicted interactions and an ablation study of the phosphoproteomic knowledge graph to gain an insight into the biology of the understudied kinases.

iPTMnet RESTful API for Post-translational Modification Network Analysis.

iPTMnet is a resource that combines rich information about protein post-translational modifications (PTM) from curated databases as well as text mining tools. Researchers can use the iPTMnet website to query, analyze and download the PTM data. In this chapter we describe the iPTMnet RESTful API which provides a way to streamline the integration of iPTMnet data into an automated data analysis workflow. In the first section, we give an overview of the architecture of the API. In the second section, we describe various function defined by the API and provide detailed examples of using these functions.

Low-to-Moderate Dosage and Short-Term Use of Corticosteroids Benefit Patients With Severe COVID-19 Infections.

Although the FDA has given emergency use authorization (EUA) for some antiviral drugs for the treatment of COVID-19, no direct antiviral drugs have been identified for the treatment of critically ill patients, the most important treatment is suppression of the hyperinflammation. The purpose of this study was to evaluate the role of corticosteroids in hospitalized severe or critical patients positive for COVID-19. This is a retrospective single-center descriptive study. Patients classified as having severe or critical COVID-19 infections with acute respiratory dysfunction syndrome in Shenzhen Third People's Hospital were enrolled from January 11th to March 30th, 2020. Ninety patients were classified as having severe or critical COVID-19 infections. The patients were treated with methylprednisolone with a low-to-moderate dosage and short duration. The days from the symptom onset to methylprednisolone were about 8 days. Eighteen patients were treated with invasive ventilation and intensive care unit (ICU) care. All the patients in the severe group and ten in the critical group recovered and were discharged. Three critical cases with invasive ventilation died. Although cases were much more severe in the corticosteroid-treated group, the mortality was not significantly increased. Early use of low-to-moderate dosage and short duration of corticosteroid may be the more accurate immune-modulatory treatment and brings more benefits to severe patients with COVID-19.

Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients: A Multi-Center Retrospective Study in China.

Coronavirus disease 2019 (COVID-19) has become a worldwide pandemic. Hospitalized patients of COVID-19 suffer from a high mortality rate, motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk patients. Here, we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital, Wuhan, China (development cohort) and externally validated with data from two other centers: 141 inpatients from Jinyintan Hospital, Wuhan, China (validation cohort 1) and 432 inpatients from The Third People's Hospital of Shenzhen, Shenzhen, China (validation cohort 2). The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of death. The risk score can predict the mortality of individual patients more than 12 d in advance with more than 90% accuracy across all cohorts. Moreover, the Kaplan-Meier score shows that patients can be clearly differentiated upon admission as low, intermediate, or high risk, with an area under the curve (AUC) score of 0.9551. In summary, a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); it has also been validated in independent cohorts.

NLRX1 can counteract innate immune response induced by an external stimulus favoring HBV infection by competitive inhibition of MAVS-RLRs signaling in HepG2-NTCP cells.

INTRODUCTION: This study is aimed at the determination of the effect of the immune-regulatory factor NLRX1 on the antiviral activity of hepatocytes against an external stimuli favoring hepatitis B virus infection, and to explore its mechanism of action. METHODS: A HepG2-NTCP model was established using the LV003 lentivirus. Cells were transfected using an overexpression vector and NLRX1 siRNA to achieve overexpression and interference of NLRX1 expression (OV-NLRX1, si-NLRX1). Levels of HBsAg and HBcAg were determined using Western blotting analysis and immunohistochemical analysis. The levels of hepatitis B virus DNA and hepatitis B virus cccDNA were determined by real-time quantitative polymerase chain reaction. The expression and transcriptional activity of IFN-α, IFN-ß, and IL-6 were measured using real-time quantitative polymerase chain reaction, enzyme-linked immunosorbent assay, and promoter-luciferase reporter plasmids. Co-immunoprecipitation was used to determine the effect of NLRX1 on the interaction between MAVS and RIG-1. Western blotting was used to obtain the phosphorylation of essential proteins in the MAVS-RLRs signaling pathways. RESULTS: NLRX1 promoted HepG2-NTCP cell hepatitis B virus infection. Compared to the control group, the levels of HBsAg, HBcAg, hepatitis B virus cccDNA, and hepatitis B virus DNA increased in the OV-NLRX1 group and decreased in the si-NLRX1. Co-immunoprecipitation results showed that NLRX1 competitively inhibited the interaction between MAVS and RIG-1, and inhibited the phosphorylation of p65, IRF3, and IRF7. Additionally, NLRX1 reduced the transcription activity and expression levels of the final products: IFN-α, IFN-ß, and IL-6. CONCLUSIONS: NLRX1 can counteract innate immune response induced by an external stimuli favoring hepatitis B virus infection by competitive inhibition of MAVS-RLRs signaling in HepG2-NTCP cells. Inhibition of the MAVS-RLR-mediated signaling pathways leads to a decline in the expression levels of I-IFN and IL-6.

RESTful API for iPTMnet: a resource for protein post-translational modification network discovery.

iPTMnet is a bioinformatics resource that integrates protein post-translational modification (PTM) data from text mining and curated databases and ontologies to aid in knowledge discovery and scientific study. The current iPTMnet website can be used for querying and browsing rich PTM information but does not support automated iPTMnet data integration with other tools. Hence, we have developed a RESTful API utilizing the latest developments in cloud technologies to facilitate the integration of iPTMnet into existing tools and pipelines. We have packaged iPTMnet API software in Docker containers and published it on DockerHub for easy redistribution. We have also developed Python and R packages that allow users to integrate iPTMnet for scientific discovery, as demonstrated in a use case that connects PTM sites to kinase signaling pathways.