The Coronavirus Network Explorer: mining a large-scale knowledge graph for effects of SARS-CoV-2 on host cell function

Unveiling the Role of Endoplasmic Reticulum Stress Pathways in Canine Demodicosis

Canine demodicosis is a prevalent skin disease caused by overpopulation of a commensal species of Demodex mite, yet its precise cause remains unknown. Research suggests that T-cell exhaustion, increased immunosuppressive cytokines, induction of regulatory T cells and increased expression of immune checkpoint inhibitors may contribute to its pathogenesis. This study aimed to gain a deeper understanding of the molecular changes occurring in canine demodicosis using mass spectrometry and pathway enrichment analysis. The results indicate that endoplasmic reticulum stress promotes canine demodicosis through regulation of three linked signalling pathways: eIF2, mTOR, and eIF4 and p70S6K. These pathways are involved in the modulation of Toll-like receptors, most notably TLR2, and have been shown to play a role in the pathogenesis of skin diseases in both dogs and humans. Moreover, these pathways are also implicated in the promotion of immunosuppressive M2 phenotype macrophages. Immunohistochemical analysis, utilising common markers of dendritic cells and macrophages, verified the presence of M2 macrophages in canine demodicosis. The proteomic analysis also identified immunological disease, organismal injury and abnormalities and inflammatory response as the most significant underlying diseases and disorders associated with canine demodicosis. This study demonstrates that Demodex mites, through ER stress, unfolded protein response and M2 macrophages contribute to an immunosuppressive microenvironment, thereby assisting in their proliferation.

Healthcare knowledge graph construction: A systematic review of the state-of-the-art, open issues, and opportunities

The incorporation of data analytics in the healthcare industry has made significant progress, driven by the demand for efficient and effective big data analytics solutions. Knowledge graphs (KGs) have proven utility in this arena and are rooted in a number of healthcare applications to furnish better data representation and knowledge inference. However, in conjunction with a lack of a representative KG construction taxonomy, several existing approaches in this designated domain are inadequate and inferior. This paper is the first to provide a comprehensive taxonomy and a bird's eye view of healthcare KG construction. Additionally, a thorough examination of the current state-of-the-art techniques drawn from academic works relevant to various healthcare contexts is carried out. These techniques are critically evaluated in terms of methods used for knowledge extraction, types of the knowledge base and sources, and the incorporated evaluation protocols. Finally, several research findings and existing issues in the literature are reported and discussed, opening horizons for future research in this vibrant area.

Extensive blood transcriptome analysis reveals cellular signaling networks activated by circulating glycocalyx components reflecting vascular injury in COVID-19

Background

Degradation of the endothelial protective glycocalyx layer during COVID-19 infection leads to shedding of major glycocalyx components. These circulating proteins and their degradation products may feedback on immune and endothelial cells and activate molecular signaling cascades in COVID-19 associated microvascular injury. To test this hypothesis, we measured plasma glycocalyx components in patients with SARS-CoV-2 infection of variable disease severity and identified molecular signaling networks activated by glycocalyx components in immune and endothelial cells.

Methods

We studied patients with RT-PCR confirmed COVID-19 pneumonia, patients with COVID-19 Acute Respiratory Distress Syndrome (ARDS) and healthy controls (wildtype, n=20 in each group) and measured syndecan-1, heparan sulfate and hyaluronic acid. The in-silico construction of signaling networks was based on RNA sequencing (RNAseq) of mRNA transcripts derived from blood cells and of miRNAs isolated from extracellular vesicles from the identical cohort. Differentially regulated RNAs between groups were identified by gene expression analysis. Both RNAseq data sets were used for network construction of circulating glycosaminoglycans focusing on immune and endothelial cells.

Results

Plasma concentrations of glycocalyx components were highest in COVID-19 ARDS. Hyaluronic acid plasma levels in patients admitted with COVID-19 pneumonia who later developed ARDS during hospital treatment (n=8) were significantly higher at hospital admission than in patients with an early recovery. RNAseq identified hyaluronic acid as an upregulator of TLR4 in pneumonia and ARDS. In COVID-19 ARDS, syndecan-1 increased IL-6, which was significantly higher than in pneumonia. In ARDS, hyaluronic acid activated NRP1, a co-receptor of activated VEGFA, which is associated with pulmonary vascular hyperpermeability and interacted with VCAN (upregulated), a proteoglycan important for chemokine communication.

Conclusions

Circulating glycocalyx components in COVID-19 have distinct biologic feedback effects on immune and endothelial cells and result in upregulation of key regulatory transcripts leading to further immune activation and more severe systemic inflammation. These consequences are most pronounced during the early hospital phase of COVID-19 before pulmonary failure develops. Elevated levels of circulating glycocalyx components may early identify patients at risk for microvascular injury and ARDS. The timely inhibition of glycocalyx degradation could provide a novel therapeutic approach to prevent the development of ARDS in COVID-19.

Mining hidden knowledge: embedding models of cause-effect relationships curated from the biomedical literature

Motivation

We explore the use of literature-curated signed causal gene expression and gene-function relationships to construct unsupervised embeddings of genes, biological functions and diseases. Our goal is to prioritize and predict activating and inhibiting functional associations of genes and to discover hidden relationships between functions. As an application, we are particularly interested in the automatic construction of networks that capture relevant biology in a given disease context.

Results

We evaluated several unsupervised gene embedding models leveraging literature-curated signed causal gene expression findings. Using linear regression, we show that, based on these gene embeddings, gene-function relationships can be predicted with about 95% precision for the highest scoring genes. Function embedding vectors, derived from parameters of the linear regression model, allow inference of relationships between different functions or diseases. We show for several diseases that gene and function embeddings can be used to recover key drivers of pathogenesis, as well as underlying cellular and physiological processes. These results are presented as disease-centric networks of genes and functions. To illustrate the applicability of our approach to other machine learning tasks, we also computed embeddings for drug molecules, which were then tested using a simple neural network to predict drug-disease associations.

Availability and implementation

Python implementations of the gene and function embedding algorithms operating on a subset of our literature-curated content as well as other code used for this paper are made available as part of the Supplementary data.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Integrative COVID-19 biological network inference with probabilistic core decomposition

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for millions of deaths around the world. To help contribute to the understanding of crucial knowledge and to further generate new hypotheses relevant to SARS-CoV-2 and human protein interactions, we make use of the information abundant Biomine probabilistic database and extend the experimentally identified SARS-CoV-2-human protein-protein interaction (PPI) network in silico. We generate an extended network by integrating information from the Biomine database, the PPI network and other experimentally validated results. To generate novel hypotheses, we focus on the high-connectivity sub-communities that overlap most with the integrated experimentally validated results in the extended network. Therefore, we propose a new data analysis pipeline that can efficiently compute core decomposition on the extended network and identify dense subgraphs. We then evaluate the identified dense subgraph and the generated hypotheses in three contexts: literature validation for uncovered virus targeting genes and proteins, gene function enrichment analysis on subgraphs and literature support on drug repurposing for identified tissues and diseases related to COVID-19. The major types of the generated hypotheses are proteins with their encoding genes and we rank them by sorting their connections to the integrated experimentally validated nodes. In addition, we compile a comprehensive list of novel genes, and proteins potentially related to COVID-19, as well as novel diseases which might be comorbidities. Together with the generated hypotheses, our results provide novel knowledge relevant to COVID-19 for further validation.

Molecular Mechanisms of Palmitic Acid Augmentation in COVID-19 Pathologies

The coronavirus disease 2019 (COVID-19) pandemic has claimed over 2.7 million lives globally. Obesity has been associated with increased severity and mortality of COVID-19. However, the molecular mechanisms by which obesity exacerbates COVID-19 pathologies are not well-defined. The levels of free fatty acids (FFAs) are elevated in obese subjects. This study was therefore designed to examine how excess levels of different FFAs may affect the progression of COVID-19. Biological molecules associated with palmitic acid (PA) and COVID-19 were retrieved from QIAGEN Knowledge Base, and Ingenuity Pathway Analysis tools were used to analyze these datasets and explore the potential pathways affected by different FFAs. Our study found that one of the top 10 canonical pathways affected by PA was the coronavirus pathogenesis pathway, mediated by key inflammatory mediators, including PTGS2; cytokines, including IL1β and IL6; chemokines, including CCL2 and CCL5; transcription factors, including NFκB; translation regulators, including EEF1A1; and apoptotic mediators, including BAX. In contrast, n-3 fatty acids may attenuate PA's activation of the coronavirus pathogenesis pathway by inhibiting the activity of such mediators as IL1β, CCL2, PTGS2, and BAX. Furthermore, PA may modulate the expression of ACE2, the main cell surface receptor for the SARS-CoV-2 spike protein.