Bassat, Q. | bioRxiv Science

bioRxiv2021

Antibody Conversion rates to SARS-CoV-2 in Saliva from Children Attending Summer Schools in Barcelona, Spain

Surveillance tools to estimate infection rates in young populations are essential to guide recommendations for school reopening and management during viral epidemics. Ideally, field-deployable non-invasive, sensitive techniques are required to detect low viral load exposures among asymptomatic children. We determined SARS-CoV-2 antibody conversion by high-throughput Luminex assays in saliva samples collected weekly in 1,509 children and 396 adults in 22 Summer schools and 2 pre-schools in 27 venues in Barcelona, Spain, from June 29th to July 31st 2020, between the first and second COVID-19 pandemic waves. Saliva antibody conversion defined as [≥]4-fold increase in IgM, IgA and/or IgG levels to SARS-CoV-2 antigens between two visits over a 5-week period was 3.22% (49/1518), or 2.36% if accounting for potentially cross-reactive antibodies, six times higher than the cumulative infection rate (0.53%) by weekly saliva RT-PCR screening. IgG conversion was higher in adults (2.94%, 11/374) than children (1.31%, 15/1144) (p=0.035), IgG and IgA levels moderately increased with age, and antibodies were higher in females. Most antibody converters increased both IgG and IgA antibodies but some augmented either IgG or IgA, with a faster decay over time for IgA than IgG. Nucleocapsid rather than spike was the main antigen target. Anti-spike antibodies were significantly higher in individuals not reporting symptoms than symptomatic individuals, suggesting a protective role against COVID-19. To conclude, saliva antibody profiling including three isotypes and multiplexing antigens is a useful and more user-friendly tool for screening pediatric populations to determine SARS-CoV-2 exposure and guide public health policies during pandemics.

immunology↗

bioRxiv2020

Biomarkers to distinguish bacterial from viral pediatric clinical pneumonia in a malaria endemic setting

BACKGROUNDDifferentiating the etiology of acute febrile respiratory illness in children is a challenge in low-income, malaria-endemic settings because the main pathogens responsible (viruses, bacteria, and malaria parasites) overlap in clinical presentation and frequently occur together as mixed infections. The critical task is to rapidly identify bacterial pneumonia to enable appropriate antibiotic treatment, ideally at point of care. Current diagnostic tests are insufficient and there is a need for the discovery and development of new tools. Here we report the identification of a unique biomarker signature that can be identified in blood samples. METHODSBlood samples from 195 pediatric Mozambican patients with clinical pneumonia were analyzed with an aptamer-based high dynamic range assay to quantify [~]1200 proteins. For discovery of new biomarkers, we identified a training set of patient samples in which the underlying etiology of the pneumonia was established as bacterial, viral or malaria. Proteins whose abundances varied significantly between patients with verified etiologies (FDR<0.01) formed the basis for predictive diagnostic models that were created using machine learning techniques (Random Forest, Elastic Net). These models were validated on a dedicated test set of samples. RESULTS219 proteins had significantly different abundances between bacterial and viral infections, and 151 differed between bacterial infections and a mixed pool of viral and malaria infections. Predictive diagnostic models achieved >90% sensitivity and >80% specificity, regardless of whether one or two pathogen classes were present. Bacterial pneumonia was strongly associated with markers of neutrophil activity, in particular neutrophil degranulation. Degranulation markers included HP, LCN2, LTF, MPO, MMP8, PGLYRP1, RETN, SERPINA1, S100A9, and SLPI. CONCLUSIONBlood protein signatures highly associated with neutrophil biology reliably differentiated bacterial pneumonia from other causes. With appropriate technology, these markers could provide the basis for a rapid diagnostic for field-based triage for antibiotic treatment of pediatric pneumonia.

genomics↗