Plasma proteomics identify potential severity biomarkers from COVID-19 associated network.

PURPOSE: Coronavirus disease 2019 (COVID-19) continues to threaten public health globally. Severe acute respiratory coronavirus type 2 (SARS-CoV-2) infection-dependent alterations in the host cell signaling network may unveil potential target proteins and pathways for therapeutic strategies. In this study, we aim to define early severity biomarkers and monitor altered pathways in the course of SARS-CoV-2 infection. EXPERIMENTAL DESIGN: We systematically analyzed plasma proteomes of COVID-19 patients from Turkey by using mass spectrometry. Different severity grades (moderate, severe, and critical) and periods of disease (early, inflammatory, and recovery) are monitored. Significant alterations in protein expressions are used to reconstruct the COVID-19 associated network that was further extended to connect viral and host proteins. RESULTS: Across all COVID-19 patients, 111 differentially expressed proteins were found, of which 28 proteins were unique to our study mainly enriching in immunoglobulin production. By monitoring different severity grades and periods of disease, CLEC3B, MST1, and ITIH2 were identified as potential early predictors of COVID-19 severity. Most importantly, we extended the COVID-19 associated network with viral proteins and showed the connectedness of viral proteins with human proteins. The most connected viral protein ORF8, which has a role in immune evasion, targets many host proteins tightly connected to the deregulated human plasma proteins. CONCLUSIONS AND CLINICAL RELEVANCE: Plasma proteomes from critical patients are intrinsically clustered in a distinct group than severe and moderate patients. Importantly, we did not recover any grouping based on the infection period, suggesting their distinct proteome even in the recovery phase. The new potential early severity markers can be further studied for their value in the clinics to monitor COVID-19 prognosis. Beyond the list of plasma proteins, our disease-associated network unravels altered pathways, and the possible therapeutic targets in SARS-CoV-2 infection by connecting human and viral proteins. Follow-up studies on the disease associated network that we propose here will be useful to determine molecular details of viral perturbation and to address how the infection affects human physiology.

Solubility Prediction of Drugs in Binary Solvent Mixtures at Various Temperatures Using a Minimum Number of Experimental Data Points.

This study aimed to provide a rational experimental design to collect a minimum number of experimental data points for a drug dissolved in a given binary solvent mixture at various temperatures, and to describe a computational procedure to predict the solubility of the drugs in any solvent composition and temperature of interest. We gathered available solubility data sets from papers published from 2012 to 2016 (56 data sets, 3488 data points totally). The mean percentage deviations (MPD) used to check the accuracy of predictions was calculated by Eq. 10. Fifty-six datasets were analyzed using 8 training data points which the overall MPD was calculated to be 15.5% ± 15.1%, and for 52 datasets after excluding 5 outlier sets was 12.1% ± 8.9%. The paired t test was conducted to compare the MPD values obtained from the models trained by 7 and 8 training data points and the reduction in prediction overall MPD (from 17.7% to 15.5%) was statistically significant (p < 0.04). To further reduction in MPD values, the computations were also conducted using 9 training data points, which did not reveal any significant difference comparing to the predictions using 8 training data points (p > 0.88). This observation revealed that the model adequately trained using 8 data points and could be used as a practical strategy for predicting the solubility of drugs in binary solvent mixtures at various temperatures with acceptable prediction error and using minimum experimental efforts. These sorts of predictions are highly in demand in the pharmaceutical industry.