Stratification of COVID-19 Severity Using SeptiCyte RAPID, a Novel Host Immune Response Test.

SeptiCyte® RAPID is a gene expression assay measuring the relative expression levels of host response genes PLA2G7 and PLAC8, indicative of a dysregulated immune response during sepsis. As severe forms of COVID-19 may be considered viral sepsis, we evaluated SeptiCyte RAPID in a series of 94 patients admitted to Foch Hospital (Suresnes, France) with proven SARS-CoV-2 infection. EDTA blood was collected in the emergency department (ED) in 67 cases, in the intensive care unit (ICU) in 23 cases and in conventional units in 4 cases. SeptiScore (0-15 scale) increased with COVID-19 severity. Patients in ICU had the highest SeptiScores, producing values comparable to 8 patients with culture-confirmed bacterial sepsis. Receiver operating characteristic (ROC) curve analysis had an area under the curve (AUC) of 0.81 for discriminating patients requiring ICU admission from patients who were immediately discharged or from patients requiring hospitalization in conventional units. SeptiScores increased with the extent of the lung injury. For 68 patients, a chest computed tomography (CT) scan was performed within 24 h of COVID-19 diagnosis. SeptiScore >7 suggested lung injury ≥50% (AUC = 0.86). SeptiCyte RAPID was compared to other biomarkers for discriminating Critical + Severe COVID-19 in ICU, versus Moderate + Mild COVID-19 not in ICU. The mean AUC for SeptiCyte RAPID was superior to that of any individual biomarker or combination thereof. In contrast to C-reactive protein (CRP), correlation of SeptiScore with lung injury was not impacted by treatment with anti-inflammatory agents. SeptiCyte RAPID can be a useful tool to identify patients with severe forms of COVID-19 in ED, as well as during follow-up.

Serum Protein Changes in Pediatric Sepsis Patients Identified With an Aptamer-Based Multiplexed Proteomic Approach.

OBJECTIVES: Sepsis, a life-threatening organ dysfunction caused by a dysregulated host response to infection, is a leading cause of death and disability among children worldwide. Identifying sepsis in pediatric patients is difficult and can lead to treatment delay. Our objective was to assess the host proteomic response to infection utilizing an aptamer-based multiplexed proteomics approach to identify novel serum protein changes that might help distinguish between pediatric sepsis and infection-negative systemic inflammation and hence can potentially improve sensitivity and specificity of the diagnosis of sepsis over current clinical criteria approaches. DESIGN: Retrospective, observational cohort study. SETTING: PICU and cardiac ICU, Seattle Children's Hospital, Seattle, WA. PATIENTS: A cohort of 40 children with clinically overt sepsis and 30 children immediately postcardiopulmonary bypass surgery (infection-negative systemic inflammation control subjects) was recruited. Children with sepsis had a confirmed or suspected infection, two or more systemic inflammatory response syndrome criteria, and at least cardiovascular and/or pulmonary organ dysfunction. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Serum samples from 35 of the sepsis and 28 of the bypass surgery subjects were available for screening with an aptamer-based proteomic platform that measures 1,305 proteins to search for large-scale serum protein expression pattern changes in sepsis. A total of 111 proteins were significantly differentially expressed between the sepsis and control groups, using the linear models for microarray data (linear modeling) and Boruta (decision trees) R packages, with 55 being previously identified in sepsis patients. Weighted gene correlation network analysis helped identify 76 proteins that correlated highly with clinical sepsis traits, 27 of which had not been previously reported in sepsis. CONCLUSIONS: The serum protein changes identified with the aptamer-based multiplexed proteomics approach used in this study can be useful to distinguish between sepsis and noninfectious systemic inflammation.

Peptide based diagnostics: are random-sequence peptides more useful than tiling proteome sequences?

Diagnostics using peptide ligands have been available for decades. However, their adoption in diagnostics has been limited, not because of poor sensitivity but in many cases due to diminished specificity. Numerous reports suggest that protein-based rather than peptide-based disease detection is more specific. We examined two different approaches to peptide-based diagnostics using Coccidioides (aka Valley Fever) as the disease model. Although the pathogen was discovered more than a century ago, a highly sensitive diagnostic remains unavailable. We present a case study where two different approaches to diagnosing Valley Fever were used: first, overlapping Valley Fever epitopes representing immunodominant Coccidioides antigens were tiled using a microarray format of presynthesized peptides. Second, a set of random sequence peptides identified using a 10,000 peptide immunosignaturing microarray was compared for sensitivity and specificity. The scientific hypothesis tested was that actual epitope peptides from Coccidioides would provide sufficient sensitivity and specificity as a diagnostic. Results demonstrated that random sequence peptides exhibited higher accuracy when classifying different stages of Valley Fever infection vs. epitope peptides. The epitope peptide array did provide better performance than the existing immunodiffusion array, but when directly compared to the random sequence peptides, reported lower overall accuracy. This study suggests that there are competing aspects of antibody recognition that involve conservation of pathogen sequence and aspects of mimotope recognition and amino acid substitutions. These factors may prove critical when developing the next generation of high-performance immunodiagnostics.

GuiTope: an application for mapping random-sequence peptides to protein sequences.

BACKGROUND: Random-sequence peptide libraries are a commonly used tool to identify novel ligands for binding antibodies, other proteins, and small molecules. It is often of interest to compare the selected peptide sequences to the natural protein binding partners to infer the exact binding site or the importance of particular residues. The ability to search a set of sequences for similarity to a set of peptides may sometimes enable the prediction of an antibody epitope or a novel binding partner. We have developed a software application designed specifically for this task. RESULTS: GuiTope provides a graphical user interface for aligning peptide sequences to protein sequences. All alignment parameters are accessible to the user including the ability to specify the amino acid frequency in the peptide library; these frequencies often differ significantly from those assumed by popular alignment programs. It also includes a novel feature to align di-peptide inversions, which we have found improves the accuracy of antibody epitope prediction from peptide microarray data and shows utility in analyzing phage display datasets. Finally, GuiTope can randomly select peptides from a given library to estimate a null distribution of scores and calculate statistical significance. CONCLUSIONS: GuiTope provides a convenient method for comparing selected peptide sequences to protein sequences, including flexible alignment parameters, novel alignment features, ability to search a database, and statistical significance of results. The software is available as an executable (for PC) at http://www.immunosignature.com/software and ongoing updates and source code will be available at sourceforge.net.