The CTSA University of Texas Health Science Center (UTHSC) Northeast—Tyler and Rio Grande Valley Success Story: How Rural, Underserved Academic Communities Rapidly Built a Robust Engine for Collaborative COVID-19 Clinical Research

In 2018, The University of Texas Health Science Center–Tyler and University of Texas Rio Grande Valley were invited to develop clinical research units for an existing Clinical and Translational Science Award (CTSA) consortium with the objective to equip medically underserved, economically disadvantaged communities and subsequently to deploy COVID-19 clinical trials in response to a public health emergency.

Interferon-γ Preferentially Promotes Necroptosis of Lung Epithelial Cells by Upregulating MLKL.

Necroptosis, a form of programmed lytic cell death, has emerged as a driving factor in the pathogenesis of acute lung injury (ALI). As ALI is often associated with a cytokine storm, we determined whether pro-inflammatory cytokines modulate the susceptibility of lung cells to necroptosis and which mediators dominate to control necroptosis. In this study, we pretreated/primed mouse primary lung epithelial and endothelial cells with various inflammatory mediators and assessed cell type-dependent responses to different necroptosis inducers and their underlying mechanisms. We found that interferon-γ (IFNγ) as low as 1 ng/mL preferentially promoted necroptosis and accelerated the release of damage-associated molecular patterns from primary alveolar and airway epithelial cells but not lung microvascular endothelial cells. Type-I IFNα was about fifty-fold less effective than IFNγ. Conversely, TNFα or agonists of Toll-like receptor-3 (TLR3), TLR4, TLR7 and TLR9 had a minor effect. The enhanced necroptosis in IFNγ-activated lung epithelial cells was dependent on IFNγ signaling and receptor-interacting protein kinase-3. We further showed that necroptosis effector mixed lineage kinase domain-like protein (MLKL) was predominantly induced by IFNγ, contributing to the enhanced necroptosis in lung epithelial cells. Collectively, our findings indicate that IFNγ is a potent enhancer of lung epithelial cell susceptibility to necroptosis.

The CTSA University of Texas Health Science Center (UTHSC) Northeast-Tyler and Rio Grande Valley Success Story: How Rural, Underserved Academic Communities Rapidly Built a Robust Engine for Collaborative COVID-19 Clinical Research.

In 2018, The University of Texas Health Science Center-Tyler and University of Texas Rio Grande Valley were invited to develop clinical research units for an existing Clinical and Translational Science Award (CTSA) consortium with the objective to equip medically underserved, economically disadvantaged communities and subsequently to deploy COVID-19 clinical trials in response to a public health emergency.

Phage Display-Derived Peptide for the Specific Binding of SARS-CoV-2.

Beginning from the end of 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic swept all over the world and is still afflicting the whole global population. Given that the vaccine-manufacturing ability is limited and the virus can evolve quickly, vaccination alone may not be able to end the pandemic, thus developing fast and accurate diagnoses and effective therapeutics will always be unmet needs. Phage display peptide library has been used in screening antigen-specific peptides for the invention of novel mimic receptors/ligands. Here, we report that a 12-mer phage display peptide library has been screened against the SARS-CoV-2 receptor-binding domain (RBD), and five of the screened peptides show binding ability with the RBD protein by the enzyme-linked immune sorbent assay. The surface plasmon resonance assay further demonstrates that peptide no. 1 can specifically bind to SARS-CoV-2 RBD with a binding affinity constant (K d) of 5.8 µM. Transmission electron microscopy coupled with a magnetic bead assay further confirms that the screened peptide can specifically bind the inactivated SARS-CoV-2 virus. This SARS-CoV-2-specific peptide holds great promise as a new bioreceptor/ligand for the rapid and accurate detection of SARS-CoV-2.

Associations of D-Dimer on Admission and Clinical Features of COVID-19 Patients: A Systematic Review, Meta-Analysis, and Meta-Regression.

Background: Dynamic D-dimer level is a key biomarker for the severity and mortality of COVID-19 (coronavirus disease 2019). How aberrant fibrinolysis influences the clinical progression of COVID-19 presents a clinicopathological dilemma challenging intensivists. Methods: We performed meta-analysis and meta regression to analyze the associations of plasma D-dimer with 106 clinical variables to identify a panoramic view of the derangements of fibrinolysis in 14,862 patients of 42 studies. There were no limitations of age, gender, race, and country. Raw data of each group were extracted separately by two investigators. Individual data of case series, median and interquartile range, and ranges of median or mean were converted to SDM (standard deviation of mean). Findings: The weighted mean difference of D-dimer was 0.97 µg/mL (95% CI 0.65, 1.29) between mild and severe groups, as shown by meta-analysis. Publication bias was significant. Meta-regression identified 58 of 106 clinical variables were associated with plasma D-dimer levels. Of these, 11 readouts were negatively related to the level of plasma D-dimer. Further, age and gender were confounding factors. There were 22 variables independently correlated with the D-dimer level, including respiratory rate, dyspnea plasma K+, glucose, SpO2, BUN (blood urea nitrogen), bilirubin, ALT (alanine aminotransferase), AST (aspartate aminotransferase), systolic blood pressure, and CK (creatine kinase). Interpretation: These findings support elevated D-dimer as an independent predictor for both mortality and complications. The identified D-dimer-associated clinical variables draw a landscape integrating the aggregate effects of systemically suppressive and pulmonary hyperactive derangements of fibrinolysis, and the D-dimer-associated clinical biomarkers, and conceptually parameters could be combined for risk stratification, potentially for tracking thrombolytic therapy or alternative interventions.