RESUMO
The gut microbiota is widely implicated in host health and disease, inspiring translational efforts to implement our growing body of knowledge in clinical settings. However, the need to characterize gut microbiota by its genomic content limits the feasibility of rapid, point-of-care diagnostics. The microbiota produces a diverse array of xenobiotic metabolites that disseminate into tissues, including volatile organic compounds (VOCs) that may be excreted in breath. We hypothesize that breath contains gut microbe-derived VOCs that inform the composition and metabolic state of the microbiota. To explore this idea, we compared the breath volatilome and fecal gut microbiomes of 27 healthy children and found that breath VOC composition is correlated with gut microbiomes. To experimentally interrogate this finding, we devised a method for capturing exhaled breath from gnotobiotic mice. Breath volatiles are then profiled by gas-chromatography mass-spectrometry (GC-MS). Using this novel methodology, we found that the murine breath profile is markedly shaped by the composition of the gut microbiota. We also find that VOCs produced by gut microbes in pure culture can be identified in vivo in the breath of mice monocolonized with the same bacteria. Altogether, our studies identify microbe-derived VOCs excreted in breath and support a mechanism by which gut bacterial metabolism directly contributes to the mammalian breath VOC profiles.
RESUMO
Multisystem Inflammatory Syndrome in Childhood (MIS-C) follows SARS-CoV-2 infection and frequently leads to intensive care unit admission. The inability to rapidly discriminate MIS-C from similar febrile illnesses delays treatment and leads to misdiagnosis. To identify diagnostic discriminators at the time of emergency department presentation, we enrolled 104 children who met MIS-C screening criteria, 14 of whom were eventually diagnosed with MIS-C. Before treatment, we collected breath samples for volatiles and peripheral blood for measurement of plasma proteins and immune cell features. Clinical and laboratory features were used as inputs for a machine learning model to determine diagnostic importance. MIS-C was associated with significant changes in breath volatile organic compound (VOC) composition as well as increased plasma levels of secretory phospholipase A2 (PLA2G2A) and lipopolysaccharide binding protein (LBP). In an integrated model of all analytes, the proportion of TCRVß21.3+ non-naive CD4 T cells expressing Ki-67 had a high sensitivity and specificity for MIS-C, with diagnostic accuracy further enhanced by low sodium and high PLA2G2A. We anticipate that accurate diagnosis will become increasingly difficult as MIS-C becomes less common. Clinical validation and application of this diagnostic model may improve outcomes in children presenting with multisystem febrile illnesses.