Predicting COVID-19 Severity with a Specific Nucleocapsid Antibody plus Disease Risk Factor Score
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ABSTRACT:
PROVIDER: PRJNA723367 | ENA |
REPOSITORIES: ENA
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Project description:Effective methods for predicting COVID-19 disease trajectories are urgently needed. Here, enzyme-linked immunosorbent assay (ELISA) and coronavirus antigen microarray (COVAM) analysis mapped antibody epitopes in the plasma of COVID-19 patients (n = 86) experiencing a wide range of disease states. The experiments identified antibodies to a 21-residue epitope from nucleocapsid (termed Ep9) associated with severe disease, including admission to the intensive care unit (ICU), requirement for ventilators, or death. Importantly, anti-Ep9 antibodies can be detected within 6 days post-symptom onset and sometimes within 1 day. Furthermore, anti-Ep9 antibodies correlate with various comorbidities and hallmarks of immune hyperactivity. We introduce a simple-to-calculate, disease risk factor score to quantitate each patient’s comorbidities and age. For patients with anti-Ep9 antibodies, scores above 3.0 predict more severe disease outcomes with a 13.42 likelihood ratio (96.7% specificity). The results lay the groundwork for a new type of COVID-19 prognostic to allow early identification and triage of high-risk patients. Such information could guide more effective therapeutic intervention.
2021-04-21 | GSE172471 | GEO
Project description:Effective methods for predicting COVID-19 disease trajectories are urgently needed. Here, enzyme-linked immunosorbent assay (ELISA) and coronavirus antigen microarray (COVAM) analysis mapped antibody epitopes in the plasma of COVID-19 patients (n = 86) experiencing a wide range of disease states. The experiments identified antibodies to a 21-residue epitope from nucleocapsid (termed Ep9) associated with severe disease, including admission to the intensive care unit (ICU), requirement for ventilators, or death. Importantly, anti-Ep9 antibodies can be detected within 6 days post-symptom onset and sometimes within 1 day. Furthermore, anti-Ep9 antibodies correlate with various comorbidities and hallmarks of immune hyperactivity. We introduce a simple-to-calculate, disease risk factor score to quantitate each patient's comorbidities and age. For patients with anti-Ep9 antibodies, scores above 3.0 predict more severe disease outcomes with a 13.42 likelihood ratio (96.7% specificity). The results lay the groundwork for a new type of COVID-19 prognostic to allow early identification and triage of high-risk patients. Such information could guide more effective therapeutic intervention.IMPORTANCE The COVID-19 pandemic has resulted in over two million deaths worldwide. Despite efforts to fight the virus, the disease continues to overwhelm hospitals with severely ill patients. Diagnosis of COVID-19 is readily accomplished through a multitude of reliable testing platforms; however, prognostic prediction remains elusive. To this end, we identified a short epitope from the SARS-CoV-2 nucleocapsid protein and also a disease risk factor score based upon comorbidities and age. The presence of antibodies specifically binding to this epitope plus a score cutoff can predict severe COVID-19 outcomes with 96.7% specificity.
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Project description:nasosinus polyposis (=PNS) is a chronic inflammatory disease of the nasal cavity and sinus cavity with a prevalence of 2 to 4% in the general population. The functional impact of PNS has a major impact on one’s quality of life.Medical treatment, nasal irrigations and local glucocorticoids as well as short treatment of oral glucocorticoids have shown a short-term benefit versus placebo. Surgery improves the efficacy of local treatments, but 40% of patients have a recurrence of polyps at 18 months and 20% of patients require a surgery recovery at 5 years. The development of monoclonal antibodies directly targeting the inflammatory way is a real public health issue. Bachert C. et al recently demonstrated the efficacy of Dupilumab (anti-IL-4/13 antibody) injected subcutaneously on the overall symptomatology of PNS. The marketing authorization (AMM) for the first biotherapy to be available soon includes PNS’s severe nature and resistance to treatment, although there is no consensus definition or score to characterize this severity. This study aims at developping a score for assessing severity in PNS. We suggest that this assessment strategy could be applied to the PNS. The originality and the innovative character of this project is the statistical modeling behind the creation of the score. Thus, the importance of symptoms, anatomical and biological factors, the estimation of the quality of life of the patients, the level of care use and the number of surgeries all become measurable indicators that are a direct reflexion of the severity and the control of PNS, called latent variables because they cannot be directly measured. To consider this detail, modeling using structural equations seems optimal to develop a severity score (PSI score) of PNS in order to personalize the therapeutic care of patients. Main Objective: To develop a severity score for PNS using latent variable modeling: Polyps Severity Index (PSI)
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Project description:Physiological, anatomical, and clinical laboratory analytic scoring systems (APACHE, Injury Severity Score (ISS)) have been utilized, with limited success, to predict outcome following injury. We hypothesized that a peripheral blood leukocyte gene expression score could predict outcome, including multiple organ failure, following severe blunt trauma. Contributor: The Inflammation and the Host Response to Injury Large Scale Collaborative Research Program Keywords: expression profiles
2009-04-13 | GSE11375 | GEO
Project description:Crosslinking immunoprecipitation and sequencing was used to characterize nucleocapsid-RNA interactions in Rift Valley fever virus infection. This data set includes illumina HiSeq paired-end reads of Rift Valley fever virus infected HEK293 cells. The sequencing libraries were generated from nucleocapsid-bound RNAs.
2021-10-22 | GSE186067 | GEO
Project description:Physiological, anatomical, and clinical laboratory analytic scoring systems (APACHE, Injury Severity Score (ISS)) have been utilized, with limited success, to predict outcome following injury. We hypothesized that a peripheral blood leukocyte gene expression score could predict outcome, including multiple organ failure, following severe blunt trauma. Contributor: The Inflammation and the Host Response to Injury Large Scale Collaborative Research Program Keywords: expression profiles cRNA derived from whole blood leukocytes obtained within 12 hours of hospital admission provided gene expression data for the entire genome that were used to create a gene expression score for each patient. Expression profiles from healthy volunteers were averaged to create a reference gene expression profile which was used to compute a difference from reference (DFR) score for each patient. This score described the overall genomic response of patients within the first 12 hours following severe blunt trauma. Regression models were used to compare the association of the DFR, APACHE and ISS scores with outcome.
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