Proteomic Analysis Identifies Molecular Players and Biological Processes Specific to SARS-CoV-2 Exposure in Endothelial Cells.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been responsible for the severe pandemic of acute respiratory disease, coronavirus disease 2019 (COVID-19), experienced in the 21st century. The clinical manifestations range from mild symptoms to abnormal blood coagulation and severe respiratory failure. In severe cases, COVID-19 manifests as a thromboinflammatory disease. Damage to the vascular compartment caused by SARS-CoV-2 has been linked to thrombosis, triggered by an enhanced immune response. The molecular mechanisms underlying endothelial activation have not been fully elucidated. We aimed to identify the proteins correlated to the molecular response of human umbilical vein endothelial cells (HUVECs) after exposure to SARS-CoV-2, which might help to unravel the molecular mechanisms of endothelium activation in COVID-19. In this direction, we exposed HUVECs to SARS-CoV-2 and analyzed the expression of specific cellular receptors, and changes in the proteome of HUVECs at different time points. We identified that HUVECs exhibit non-productive infection without cytopathic effects, in addition to the lack of expression of specific cell receptors known to be essential for SARS-CoV-2 entry into cells. We highlighted the enrichment of the protein SUMOylation pathway and the increase in SUMO2, which was confirmed by orthogonal assays. In conclusion, proteomic analysis revealed that the exposure to SARS-CoV-2 induced oxidative stress and changes in protein abundance and pathways enrichment that resembled endothelial dysfunction.

Chemometrics and analytical blank on the at-line monitoring of Zika-VLP production using near-infrared spectroscopy.

The Zika disease caused by the Zika virus was declared a Public Health Emergency by the World Health Union (WHO), with microcephaly as the most critical consequence. Aiming to reduce the spread of the virus, biopharmaceutical organizations invest in vaccine research and production, based on multiple platforms. A crescent vaccine production approach is based on virus-like particles (VLP), for not having genetic material in its composition, hypoallergenic and non-mutant character. For bioprocess, it is essential to have means of real-time monitoring, which can be assessed using process analysis techniques such as Near-infrared (NIR) spectroscopy, that can be combined with chemometric methods, like Partial-Least Squares (PLS) and Artificial Neural Networks (ANN) for prediction of biochemical variables. This work proposes a biochemical Zika VLP upstream production at-line monitoring model using NIR spectroscopy comparing sampling conditions (with or without cells), analytical blank (air, ultrapure water), and spectra pre-processing approaches. Seven experiments in a benchtop bioreactor using recombinant baculovirus/Sf9 insect cell platform in serum-free medium were performed to obtain biochemical and spectral data for chemometrics modeling (PLS and ANN), composed by a random data split (80 % calibration, 20 % validation) for cross-validation of the PLS models and 70 % training, 15 % testing, 15 % validation for ANN. The best models generated in the present work presented an average absolute error of 1.59 × 105 cell/mL for density of viable cells, 2.37 % for cell viability, 0.25 g/L for glucose, 0.007 g/L for lactate, 0.138 g/L for glutamine, 0.18 g/L for glutamate, 0,003 g/L for ammonium, and 0.014 g/L for potassium.

A multivariate calibration procedure for UV/VIS spectrometric monitoring of BHK-21 cell metabolism and growth.

Monitoring mammalian cell culture with UV­vis spectroscopy has not been widely explored. The aim of this work was to calibrate Partial Least Squares (PLS) models from off-line UV­vis spectral data in order to predict some nutrients and metabolites, as well as viable cell concentrations for mammalian cell bioprocess using phenol red in culture medium. The BHK-21 cell line was used as a mammalian cell model. Spectra of samples taken from batches performed at different dissolved oxygen concentrations (10, 30, 50, and 70% air saturation), in two bioreactor configurations and with two strategies to control pH were used to calibrate and validate PLS models. Glutamine, glutamate, glucose, and lactate concentrations were suitably predicted by means of this strategy. Especially for glutamine and glucose concentrations, the prediction error averages were lower than 0.5060.10 mM and 2.2160.16 mM, respectively. These values are comparable with those previously reported using near infrared and Raman spectroscopy in conjunction with PLS. However, viable cell concentration models need to be improved. The present work allows for UV­vis at-line sensor development, decrease cost related to nutrients and metabolite quantifications and establishment of fed-batch feeding schemes.