Prognostic tools and candidate drugs based on plasma proteomics of patients with severe COVID-19 complications

COVID-19 complications still present a huge burden on healthcare systems and warrant predictive risk models to triage patients and inform early intervention. Here, we profile 893 plasma proteins from 50 severe and 50 mild-moderate COVID-19 patients, and 50 healthy controls, and show that 375 proteins are differentially expressed in the plasma of severe COVID-19 patients. These differentially expressed plasma proteins are implicated in the pathogenesis of COVID-19 and present targets for candidate drugs to prevent or treat severe complications. Based on the plasma proteomics and clinical lab tests, we also report a 12-plasma protein signature and a model of seven routine clinical tests that validate in an independent cohort as early risk predictors of COVID-19 severity and patient survival. The risk predictors and candidate drugs described in our study can be used and developed for personalized management of SARS-CoV-2 infected patients. 2022, The Author(s).The authors would like to thank all the patients, volunteers, and the healthcare co-workers from Allergy and Immunology Section-HMC, and Dr. Mohamed G.H. Mohamedali, Mr. Hassen Maatoug, and Mr. Ahmed Soliman from Hezm Mebairek General Hospital-HMC for developing disposable racks for samples transportation, tubes labeling, blood collection, and handling. We thank the support provided by Qatar University Biomedical Research Centre, Biosafety Level 3, and Associate Professor Hadi M. Yassine (M.Sc., Ph.D.). We also acknowledge the help of the Anti-Doping Lab-Qatar (ADLQ) and Qatar Red Crescent (QRC) for recruiting control samples. This work was supported by a grant fund from Hamad Medical Corporation (fund number MRC-05-003) and core funding from Qatar Biomedical Research Institute (QBRI).Scopu

Age, Disease Severity and Ethnicity Influence Humoral Responses in a Multi-Ethnic COVID-19 Cohort

The COVID-19 pandemic has affected all individuals across the globe in some way. Despite large numbers of reported seroprevalence studies, there remains a limited understanding of how the magnitude and epitope utilization of the humoral immune response to SARS-CoV-2 viral anti-gens varies within populations following natural infection. Here, we designed a quantitative, multi-epitope protein microarray comprising various nucleocapsid protein structural motifs, including two structural domains and three intrinsically disordered regions. Quantitative data from the microarray provided complete differentiation between cases and pre-pandemic controls (100% sensitivity and specificity) in a case-control cohort (n = 100). We then assessed the influence of disease severity, age, and ethnicity on the strength and breadth of the humoral response in a multi-ethnic cohort (n = 138). As expected, patients with severe disease showed significantly higher antibody titers and interestingly also had significantly broader epitope coverage. A significant increase in antibody titer and epitope coverage was observed with increasing age, in both mild and severe disease, which is promising for vaccine efficacy in older individuals. Additionally, we observed significant differences in the breadth and strength of the humoral immune response in relation to ethnicity, which may reflect differences in genetic and lifestyle factors. Furthermore, our data enabled localization of the immuno-dominant epitope to the C-terminal structural domain of the viral nucleocapsid protein in two independent cohorts. Overall, we have designed, validated, and tested an advanced serological assay that enables accurate quantitation of the humoral response post natural infection and that has revealed unexpected differences in the magnitude and epitope utilization within a population

DNAJB3 attenuates metabolic stress and promotes glucose uptake by eliciting Glut4 translocation

Failure of the heat shock response is a key event that leads to insulin resistance and type 2 diabetes. We recently showed that DNAJB3 co-chaperone is downregulated in obese and diabetic patients and that physical exercise restores its normal expression with a significant improvement of the clinical outcomes. In 3T3-L1 adipocytes, DNAJB3 has a role in improving the sensitivity to insulin and glucose uptake. In co-immunoprecipitation assays, DNAJB3 interacts with both JNK1 and IKKβ kinases. However, the functional impact of such interaction on their activities has not been investigated. Here, we assessed the effect of DNAJB3 on the respective activity of JNK1 and IKKβ in cell-based assays. Using JNK1- and IKKβ-dependent luciferase reporters, we show a marked decrease in luciferase activity by DNAJB3 in response to PMA and TNF-α that was consistent with a decrease in the translocation of p65/NF-κB to the nucleus in response to LPS. Furthermore, TNF-α-mediated IL-6 promoter activation and endogenous mRNA expression are significantly abrogated by DNAJB3 both in 3T3-L1 and C2C12 cells. The ability of DNAJB3 to mitigate ER stress and oxidative stress was also investigated and our data show a significant improvement of both forms of stress. Finally, we examined the effect of overexpressing and knocking down the expression of DNAJB3 on glucose uptake in C2C12 as well as the molecular determinants. Accordingly, we provide evidence for a role of DNAJB3 in promoting both basal and insulin-stimulated glucose uptake. Our finding reveals also a novel role of DNAJB3 in eliciting Glut4 translocation to the plasma membrane. These results suggest a physiological role of DNAJB3 in mitigating metabolic stress and improving glucose homeostasis and could therefore represent a novel therapeutic target for type 2 diabetes.Other Information Published in: Scientific Reports License: https://creativecommons.org/licenses/by/4.0See article on publisher's website: http://dx.doi.org/10.1038/s41598-019-41244-8</p

Blood-Based Proteomic Profiling Identifies Potential Biomarker Candidates and Pathogenic Pathways in Dementia

Dementia is a progressive and debilitating neurological disease that affects millions of people worldwide. Identifying the minimally invasive biomarkers associated with dementia that could provide insights into the disease pathogenesis, improve early diagnosis, and facilitate the development of effective treatments is pressing. Proteomic studies have emerged as a promising approach for identifying the protein biomarkers associated with dementia. This pilot study aimed to investigate the plasma proteome profile and identify a panel of various protein biomarkers for dementia. We used a high-throughput proximity extension immunoassay to quantify 1090 proteins in 122 participants (22 with dementia, 64 with mild cognitive impairment (MCI), and 36 controls with normal cognitive function). Limma-based differential expression analysis reported the dysregulation of 61 proteins in the plasma of those with dementia compared with controls, and machine learning algorithms identified 17 stable diagnostic biomarkers that differentiated individuals with AUC = 0.98 ± 0.02. There was also the dysregulation of 153 plasma proteins in individuals with dementia compared with those with MCI, and machine learning algorithms identified 8 biomarkers that classified dementia from MCI with an AUC of 0.87 ± 0.07. Moreover, multiple proteins selected in both diagnostic panels such as NEFL, IL17D, WNT9A, and PGF were negatively correlated with cognitive performance, with a correlation coefficient (r2) ≤ −0.47. Gene Ontology (GO) and pathway analysis of dementia-associated proteins implicated immune response, vascular injury, and extracellular matrix organization pathways in dementia pathogenesis. In conclusion, the combination of high-throughput proteomics and machine learning enabled us to identify a blood-based protein signature capable of potentially differentiating dementia from MCI and cognitively normal controls. Further research is required to validate these biomarkers and investigate the potential underlying mechanisms for the development of dementia

Prognostic tools and candidate drugs based on plasma proteomics of patients with severe COVID-19 complications

COVID-19 complications still present a huge burden on healthcare systems and warrant predictive risk models to triage patients and inform early intervention. Here, we profile 893 plasma proteins from 50 severe and 50 mild-moderate COVID-19 patients, and 50 healthy controls, and show that 375 proteins are differentially expressed in the plasma of severe COVID-19 patients. These differentially expressed plasma proteins are implicated in the pathogenesis of COVID-19 and present targets for candidate drugs to prevent or treat severe complications. Based on the plasma proteomics and clinical lab tests, we also report a 12-plasma protein signature and a model of seven routine clinical tests that validate in an independent cohort as early risk predictors of COVID-19 severity and patient survival. The risk predictors and candidate drugs described in our study can be used and developed for personalized management of SARS-CoV-2 infected patients.</div