Metabolomic Signatures Differentiate Immune Responses in Avian Influenza Vaccine Recipients.

BACKGROUND: Avian influenza viruses pose significant risk to human health. Vaccines targeting the hemagglutinin of these viruses are poorly immunogenic without the use of adjuvants. METHODS: Twenty healthy men and women (18-49 years of age) were randomized to receive two doses of inactivated influenza A/H5N1 vaccine alone (IIV) or with AS03 adjuvant (IIV-AS03) one month apart. Urine and serum samples were collected on day 0 and on days 1, 3, and 7 following first vaccination and subjected to metabolomics analyses to identify metabolites, metabolic pathways, and metabolite clusters associated with immunization. RESULTS: Seventy-three differentially abundant (DA) serum and 88 urine metabolites were identified for any post-vaccination day comparison. Pathway analysis revealed enrichment of tryptophan, tyrosine and nicotinate metabolism in urine and serum among IIV-AS03 recipients. Increased urine abundance of 4-vinylphenol sulfate on Day 1 was associated with serologic response based on hemagglutination inhibition responses. In addition, 9 DA urine metabolites were identified in participants with malaise compared to those without. CONCLUSIONS: Our findings suggest that tryptophan, tyrosine, and nicotinate metabolism are upregulated among IIV-AS03 recipients compared with IIV alone. Metabolites within these pathways may serve as measures of immunogenicity and may provide mechanistic insights for adjuvanted vaccines.

Impact of respiratory viral infections on nasopharyngeal pneumococcal colonization dynamics in children.

PURPOSE OF REVIEW: Prevention of acute respiratory illnesses (ARI) in children is a global health priority, as these remain a leading cause of pediatric morbidity and mortality throughout the world. As new products and strategies to prevent respiratory infections caused by important pathogens such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza, respiratory syncytial virus and pneumococcus are advancing, increasing evidence suggests that these and other respiratory viruses and pneumococci may exhibit interactions that are associated with altered colonization and disease dynamics. We aim to review recent data evaluating interactions between respiratory viruses and pneumococci in the upper respiratory tract and their potential impact on pneumococcal colonization patterns and disease outcomes. RECENT FINDINGS: While interactions between influenza infection and subsequent increased susceptibility and transmissibility of colonizing pneumococci have been widely reported in the literature, emerging evidence suggests that human rhinovirus, SARS-CoV-2, and other viruses may also exhibit interactions with pneumococci and alter pneumococcal colonization patterns. Additionally, colonizing pneumococci may play a role in modifying outcomes associated with respiratory viral infections. Recent evidence suggests that vaccination with pneumococcal conjugate vaccines, and prevention of colonization with pneumococcal serotypes included in these vaccines, may be associated with reducing the risk of subsequent viral infection and the severity of the associated illnesses. SUMMARY: Understanding the direction and dynamics of viral-pneumococcal interactions may elucidate the potential effects of existing and emerging viral and bacterial vaccines and other preventive strategies on the health impact of these important respiratory pathogens.

Clinical prediction model: Multisystem inflammatory syndrome in children versus Kawasaki disease.

BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) is a rare but serious complication of severe acute respiratory syndrome coronavirus 2 infection. Features of MIS-C overlap with those of Kawasaki disease (KD). OBJECTIVE: The study objective was to develop a prediction model to assist with this diagnostic dilemma. METHODS: Data from a retrospective cohort of children hospitalized with KD before the coronavirus disease 2019 pandemic were compared to a prospective cohort of children hospitalized with MIS-C. A bootstrapped backwards selection process was used to develop a logistic regression model predicting the probability of MIS-C diagnosis. A nomogram was created for application to individual patients. RESULTS: Compared to children with incomplete and complete KD (N = 602), children with MIS-C (N = 105) were older and had longer hospitalizations; more frequent intensive care unit admissions and vasopressor use; lower white blood cell count, lymphocyte count, erythrocyte sedimentation rate, platelet count, sodium, and alanine aminotransferase; and higher hemoglobin and C-reactive protein (CRP) at admission. Left ventricular dysfunction was more frequent in patients with MIS-C, whereas coronary abnormalities were more common in those with KD. The final prediction model included age, sodium, platelet count, alanine aminotransferase, reduction in left ventricular ejection fraction, and CRP. The model exhibited good discrimination with AUC 0.96 (95% confidence interval: [0.94-0.98]) and was well calibrated (optimism-corrected intercept of -0.020 and slope of 0.99). CONCLUSIONS: A diagnostic prediction model utilizing admission information provides excellent discrimination between MIS-C and KD. This model may be useful for diagnosis of MIS-C but requires external validation.