Exploration on the potential mechanism of baicalein on Porcine Deltacoronavirus infection based on network pharmacology

This study aimed to explore the protective mechanism of baicalein against porcine deltacoronavirus (PDCoV) infection. The targets of baicalein were obtained through Pharmamapper, Pubchem, STITCH, TCMSP and Swiss Targer Prediction databases, and the targets of PDCoV infection were obtained according to the proteomics data from our previous study. The targets of baicalein-PDCoV interaction were obtained and analyzed by STRING database and Cytoscape 3.8.2 software to construct a network diagram of "baicalein-PDCoV-targets". The CytoNCA was used to analyze network topology and core network construction. Metascape database was used for GO and KEGG analysis of core network genes. The expression levels of genes in the predicted signaling pathways were detected in vitro. A total of 268 potential targets of baicalein were screened out. There were 75 potential targets of baicalein-PDCoV infection. GO enrichment results showed that baicalein was mainly involved in the formations of membrane raft, spindle and mitochondrial membrane, cell cycle and MAPK signaling pathways. A total of 277 signaling pathways (P < 0.01) were screened out by KEGG enrichment. The PI3K-Akt, Ras and MAPK signaling pathways were the main pathways that involved in the protective effects of baicalein against PDCoV infection. The results showed that compared with the cellular control groups, the mRNA expressions of PI3K, AKT and NF-B significantly increased in the PDCoV infection group. Compared with the PDCoV group, treatment of baicalein significantly decreased the mRNA expressions of PI3K, AKT and NF-B (P < 0.05). The effect of baicalein on PDCoV infection has the characteristics of multi-targets and multi-pathways, through the intervention of AKT1, HSP90AA1, SRC, EGFR, CASP3, MAPK, STAT3 and other core genes in regulating PI3K-Akt signaling pathway, Ras signaling pathway and MAPK signaling pathway, apoptosis, and virus infection. These results suggested that baicalein could be a potential therapeutic drug against PDCoV infection for further study.

SperoPredictor: An Integrated Machine Learning and Molecular Docking-Based Drug Repurposing Framework With Use Case of COVID-19.

The global spread of the SARS coronavirus 2 (SARS-CoV-2), its manifestation in human hosts as a contagious disease, and its variants have induced a pandemic resulting in the deaths of over 6,000,000 people. Extensive efforts have been devoted to drug research to cure and refrain the spread of COVID-19, but only one drug has received FDA approval yet. Traditional drug discovery is inefficient, costly, and unable to react to pandemic threats. Drug repurposing represents an effective strategy for drug discovery and reduces the time and cost compared to de novo drug discovery. In this study, a generic drug repurposing framework (SperoPredictor) has been developed which systematically integrates the various types of drugs and disease data and takes the advantage of machine learning (Random Forest, Tree Ensemble, and Gradient Boosted Trees) to repurpose potential drug candidates against any disease of interest. Drug and disease data for FDA-approved drugs (n = 2,865), containing four drug features and three disease features, were collected from chemical and biological databases and integrated with the form of drug-disease association tables. The resulting dataset was split into 70% for training, 15% for testing, and the remaining 15% for validation. The testing and validation accuracies of the models were 99.3% for Random Forest and 99.03% for Tree Ensemble. In practice, SperoPredictor identified 25 potential drug candidates against 6 human host-target proteomes identified from a systematic review of journals. Literature-based validation indicated 12 of 25 predicted drugs (48%) have been already used for COVID-19 followed by molecular docking and re-docking which indicated 4 of 13 drugs (30%) as potential candidates against COVID-19 to be pre-clinically and clinically validated. Finally, SperoPredictor results illustrated the ability of the platform to be rapidly deployed to repurpose the drugs as a rapid response to emergent situations (like COVID-19 and other pandemics).

Insights on the evolution of Coronavirinae in general, and SARS-CoV-2 in particular, through innovative biocomputational resources.

The structural proteins of coronaviruses portray critical information to address issues of classification, assembly constraints, and evolutionary pathways involving host shifts. We compiled 173 complete protein sequences from isolates belonging to the four genera of the subfamily Coronavirinae. We calculate a single matrix of viral distance as a linear combination of protein distances. The minimum spanning tree (MST) connecting the individuals captures the structure of their similarities. The MST re-capitulates the known phylogeny of Coronovirinae. Hosts were mapped onto the MST and we found a non-trivial concordance between host phylogeny and viral proteomic distance. We also study the chimerism in our dataset through computational simulations. We found evidence that structural units coming from loosely related hosts hardly give rise to feasible chimeras in nature. This work offers a fresh way to analyze features of SARS-CoV-2 and related viruses.

Understanding the Interaction between Type 2 Diabetes and COVID-via Extracellular Vesicle Proteomic and Phosphoproteomic Signatures

Evidence supporting the involvement of EVs in the pathogenesis/severity of SARS-CoV-2 infection is starting to accumulate. However, little is known about their specific associations in the context of COVID-and type 2 diabetes interaction. Our study included 48 plasma samples (N=12/group) obtained from COVID-patients with and without diabetes and from patients with non-COVID-acute respiratory diagnosis (RSP) with and without diabetes. Participants were identified from a set of 494 patients hospitalized at AdventHealth in June-August 2020. Important efforts were made to ensure the homogeneity of the study cohort. Patients with type 1 diabetes, or pregnant, or that went directly into the ICU were excluded, and 4 balanced groups were identified after 10,000 random cohorts were generated and differences in age, gender, race, and ethnicity statistically assessed. EVs were isolated using EVTRAP (Tymora) . Mass spectrometry-based methods were used to detect the global EV proteome and phosphoproteome. Differentially expressed features, enriched pathways, and enriched tissue-specific protein sets were identified. Multidimensional scaling of all EV proteomic and phosphoproteomic data and unsupervised clustering of differentially expressed (absolute fold change ≥ 2, P < 0.05, FDR < 0.05) EV proteins and phosphoproteins successfully distinguished the 4 study groups with close to 100% accuracy. Importantly, we detected enriched pathway networks that suggest the potential therapeutic utility of PKC inhibitors such as bisindolylmaleimide IX, sotrastaurin, and enzastaumn, and inhibitors of ROCK1 such the isoquinoline derivative Fasudil. In conclusion, we characterized the proteomic landscape of the interaction between type 2 diabetes and COVID-and defined disease-specific EV proteomic signatures that provide insight into the disease pathobiology and druggable targets with potential clinical utility.

Comprehensive machine learning analysis on the phenotypes of COVID-19 patients using transcriptome data. (Special issue.)

Purpose: Evolving technologies allow us to measure human molecular data in a wide reach. Those data are extensively used by researchers in many studies and help in advancements of medical field. Transcriptome, proteome, metabolome, and epigenome are few such molecular data. This study utilizes the transcriptome data of COVID-19 patients to uncover the dysregulated genes in the SARS-COV-2. Method: Selected genes are used in machine learning models to predict various phenotypes of those patients. Ten different phenotypes are studied here such as time since onset, COVID-19 status, connection between age and COVID-19, hospitalization status and ICU status, using classification models. Further, this study compares molecular characterization of COVID-19 patients with other respiratory diseases.

Single Cell Proteomic Analysis of COVID-19 Patient Samples by Mass Cytometry

Introduction: The devastating outbreak of SARS-CoV2 and the associated COVID-19 has had a severe impact on the global community. While recent advances in vaccination have given some hope of respite the proven ability of the virus to mutate and potentially generate vaccine resistant strains indicates that there can be no relaxation of our urgent efforts to better understand the effects of this disease. A critical part of this effort is to understand the changes to the immune system of infected patients since both viral clearance and most symptoms of the disease are mediated by the immune system. Recent advances in single cell technologies, such as mass cytometry (also known as CyTOF) have revealed high levels of heterogeneity among immune cells. Methods: Mass cytometry was used to assess the single cell proteome of millions of cells from the blood of patients with COVID-19. Results: Wide ranging changes to a variety of cell populations occur. Simultaneous assessment of changes to both common populations and such as classical monocytes, and rare sub-populations of FOXP3 expressing regulatory T-cells and Tfollicular helper cells was observed Conclusion: Simultaneous assessment of wide ranging cell populations may indicate how they interact during COVID-19 and suggest that differences between regulatory T-cell subsets among moderate, severe, and critical patient groups may be a factor in pathogenesis.