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.

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.

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.

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.

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.

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.

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.

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.

Protein ontology on the semantic web for knowledge discovery.

The Protein Ontology (PRO) provides an ontological representation of protein-related entities, ranging from protein families to proteoforms to complexes. Protein Ontology Linked Open Data (LOD) exposes, shares, and connects knowledge about protein-related entities on the Semantic Web using Resource Description Framework (RDF), thus enabling integration with other Linked Open Data for biological knowledge discovery. For example, proteins (or variants thereof) can be retrieved on the basis of specific disease associations. As a community resource, we strive to follow the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles, disseminate regular updates of our data, support multiple methods for accessing, querying and downloading data in various formats, and provide documentation both for scientists and programmers. PRO Linked Open Data can be browsed via faceted browser interface and queried using SPARQL via YASGUI. RDF data dumps are also available for download. Additionally, we developed RESTful APIs to support programmatic data access. We also provide W3C HCLS specification compliant metadata description for our data. The PRO Linked Open Data is available at https://lod.proconsortium.org/ .

Understanding ER+ Breast Cancer Dormancy Using Bioinspired Synthetic Matrices for Long-Term 3D Culture and Insights into Late Recurrence.

Late recurrences of breast cancer are hypothesized to originate from disseminated tumor cells that re-activate after a long period of dormancy, ≥5 years for estrogen-receptor positive (ER+) tumors. An outstanding question remains as to what the key microenvironment interactions are that regulate this complex process, and well-defined human model systems are needed for probing this. Here, a robust, bioinspired 3D ER+ dormancy culture model is established and utilized to probe the effects of matrix properties for common sites of late recurrence on breast cancer cell dormancy. Formation of dormant micrometastases over several weeks is examined for ER+ cells (T47D, BT474), where the timing of entry into dormancy versus persistent growth depends on matrix composition and cell type. In contrast, triple negative cells (MDA-MB-231), associated with early recurrence, are not observed to undergo long-term dormancy. Bioinformatic analyses quantitatively support an increased "dormancy score" gene signature for ER+ cells (T47D) and reveal differential expression of genes associated with different biological processes based on matrix composition. Further, these analyses support a link between dormancy and autophagy, a potential survival mechanism. This robust model system will allow systematic investigations of other cell-microenvironment interactions in dormancy and evaluation of therapeutics for preventing late recurrence.

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.

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.

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.

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.

Delayed peak of human infections and ongoing reassortment of H7N9 avian influenza virus in the newly affected western Chinese provinces during Wave Five.

OBJECTIVES: Eight additional provinces in western China reported human infections for the first time during the fifth wave of human H7N9 infections. The aim of this study was to analyze the epidemiological and virological characteristics of this outbreak. METHODS: The epidemiological data of H7N9 cases from the newly affected western Chinese provinces were collected and analyzed. Full-length genome sequences of H7N9 virus were downloaded from the GenBank and GISAID databases, and phylogenetic, genotyping, and genetic analyses were conducted. RESULTS: The peak of human infections in the newly affected western Chinese provinces was delayed by 4 months compared to the eastern Chinese provinces, and both low pathogenic (LP) and highly pathogenic (HP) H7N9-infected cases were found. The LP- and HP-H7N9 virus belonged to 10 different genotypes (including four new genotypes), of which G11 and G3 were the dominant genotypes, respectively. Almost all of these viruses originated from eastern and southern China and were most probably imported from neighboring provinces. Genetic characteristics of the circulating viruses were similar to those of the viruses from previously affected provinces during Wave Five. CONCLUSIONS: A delayed peak of human infections was observed in the newly affected western Chinese provinces, and reassortment has been ongoing since the introduction of H7N9 viruses. This study highlights the importance of continued surveillance of the circulation and evolution of H7N9 virus in western China.

Genome sequencing and transcript analysis of Hemileia vastatrix reveal expression dynamics of candidate effectors dependent on host compatibility.

Coffee leaf rust caused by the fungus Hemileia vastatrix is one of the most important leaf diseases of coffee plantations worldwide. Current knowledge of the H. vastatrix genome is limited and only a small fraction of the total fungal secretome has been identified. In order to obtain a more comprehensive understanding of its secretome, we aimed to sequence and assemble the entire H. vastatrix genome using two next-generation sequencing platforms and a hybrid assembly strategy. This resulted in a 547 Mb genome of H. vastatrix race XXXIII (Hv33), with 13,364 predicted genes that encode 13,034 putative proteins with transcriptomic support. Based on this proteome, 615 proteins contain putative secretion peptides, and lack transmembrane domains. From this putative secretome, 111 proteins were identified as candidate effectors (EHv33) unique to H. vastatrix, and a subset consisting of 17 EHv33 genes was selected for a temporal gene expression analysis during infection. Five genes were significantly induced early during an incompatible interaction, indicating their potential role as pre-haustorial effectors possibly recognized by the resistant coffee genotype. Another nine genes were significantly induced after haustorium formation in the compatible interaction. Overall, we suggest that this fungus is able to selectively mount its survival strategy with effectors that depend on the host genotype involved in the infection process.

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.