Circulating Interleukin-8 Dynamics Parallels Disease Course and Is Linked to Clinical Outcomes in Severe COVID-19.

Severe COVID-19 frequently features a systemic deluge of cytokines. Circulating cytokines that can stratify risks are useful for more effective triage and management. Here, we ran a machine-learning algorithm on a dataset of 36 plasma cytokines in a cohort of severe COVID-19 to identify cytokine/s useful for describing the dynamic clinical state in multiple regression analysis. We performed RNA-sequencing of circulating blood cells collected at different time-points. From a Bayesian Information Criterion analysis, a combination of interleukin-8 (IL-8), Eotaxin, and Interferon-γ (IFNγ) was found to be significantly linked to blood oxygenation over seven days. Individually testing the cytokines in receiver operator characteristics analyses identified IL-8 as a strong stratifier for clinical outcomes. Circulating IL-8 dynamics paralleled disease course. We also revealed key transitions in immune transcriptome in patients stratified for circulating IL-8 at three time-points. The study identifies plasma IL-8 as a key pathogenic cytokine linking systemic hyper-inflammation to the clinical outcomes in COVID-19.

In silico strategies to identify protein-protein interaction modulator in cell-to-cell transmission of SARS CoV2.

RNA sequence data from SARS CoV2 patients helps to construct a gene network related to this disease. A detailed analysis of the human host response to SARS CoV2 with expression profiling by high-throughput sequencing has been accomplished with primary human lung epithelial cell lines. Using this data, the clustered gene annotation and gene network construction are performed with the help of the String database. Among the four clusters identified, only 1 with 44 genes could be annotated. Interestingly, this corresponded to basal cells with p = 1.37e - 05, which is relevant for respiratory tract infection. Functional enrichment analysis of genes present in the gene network has been completed using the String database and the Network Analyst tool. Among three types of cell-cell communication, only the anchoring junction between the basal cell membrane and the basal lamina in the host cell is involved in the virus transmission. In this junction point, a hemidesmosome structure plays a vital role in virus spread from one cell to basal lamina in the respiratory tract. In this protein complex structure, different integrin protein molecules of the host cell are used to promote the spread of virus infection into the extracellular matrix. So, small molecular blockers of different anchoring junction proteins, such as integrin alpha 3, integrin beta 1, can provide efficient protection against this deadly viral disease. ORF8 from SARS CoV2 virus can interact with both integrin proteins of human host. By using molecular docking technique, a ternary complex of these three proteins is modelled. Several oligopeptides are predicted as modulators for this ternary complex. In silico analysis of these modulators is very important to develop novel therapeutics for the treatment of SARS CoV2.

Deep variational graph autoencoders for novel host-directed therapy options against COVID-19.

The COVID-19 pandemic has been keeping asking urgent questions with respect to therapeutic options. Existing drugs that can be repurposed promise rapid implementation in practice because of their prior approval. Conceivably, there is still room for substantial improvement, because most advanced artificial intelligence techniques for screening drug repositories have not been exploited so far. We construct a comprehensive network by combining year-long curated drug-protein/protein-protein interaction data on the one hand, and most recent SARS-CoV-2 protein interaction data on the other hand. We learn the structure of the resulting encompassing molecular interaction network and predict missing links using variational graph autoencoders (VGAEs), as a most advanced deep learning technique that has not been explored so far. We focus on hitherto unknown links between drugs and human proteins that play key roles in the replication cycle of SARS-CoV-2. Thereby, we establish novel host-directed therapy (HDT) options whose utmost plausibility is confirmed by realistic simulations. As a consequence, many of the predicted links are likely to be crucial for the virus to thrive on the one hand, and can be targeted with existing drugs on the other hand.

A Deep Integrated Framework for Predicting SARS-CoV2–Human Protein-Protein Interaction

Drug repurposing for COVID-19 is now an actively developing area of research. Unavailability of a proper set of interactions between SARS-CoV2 and human host proteins limits the set of possible drug-targets. Toward this, we propose a deep learning based methodology for high confidence interaction prediction between SARS-CoV2 and human host proteins. First, our technique leverage the landmark advantage of Node2Vec to produce a low dimensional embedding from a compiled interaction network that puts SARS-CoV2 proteins, target human host proteins (CoV-host), and the whole human interactome into an encompassing context. Second, we can able to combine information from protein sequence, gene ontology terms and physical interactions information in the prediction task. Third, we proposed a way to rank the host proteins that are potential candidate for target by SARS-CoV2 proteins. Last but not least the method supports a meaningful connection between the predicted proteins and different repurposable drugs to use against COVID-19.

JID 'Long run effects of Covid-19 on developing countries' Special Issue Editorial Introduction.

The developing world has experienced unprecedented effects of the Covid-19 pandemic. The devastating effects of this major crisis are felt in all possible spheres of the developing world and with a serious impact on social and economic development in developing countries. The spread of Covid-19, which has brought the world to a near standstill, has given rise to the question on the socioeconomic effects of the pandemic. The special issue on Covid-19 at JID aims to bring together contemporary research on several aspects of how the devastating effects of the pandemic have panned out in different spheres of life, particularly, in the developing world. This special issue has 10 papers with a particular emphasis on evidence of the impact of the Covid-19 pandemic in Sub-Saharan Africa and South America. The volume documents studies on the effects of the pandemic at the macro-level, for economy wide effects, the impact of the pandemic on firms and on its effects on households.

Topological Analysis on Multi-scenario Graphs: Applications Toward Discerning Variability in SARS-CoV-2 and Topic Similarity in Research

A network is often an obvious choice for modeling real-life interconnected systems, where the nodes represent interacting objects and the edges represent their associations. There has been immense progress in complex network analysis with methods and tools that can provide important insights into the respective scenario. In the advancement of information technology and globalization, the amount of data is increasing day by day, and it is indeed incomprehensible without the help of network science. This work highlights how we can model multiple interaction scenarios under a single umbrella to uncover novel insights. We show that a varying scenario gets reflected by the change of topological patterns in interaction networks. We construct multi-scenario graphs, a novel framework proposed by us, from real-life environments followed by topological analysis. We focus on two different application areas: analyzing geographical variations in SARS-CoV-2 and studying topic similarity in citation patterns.