The Airway Microbiota Signatures of Infection and Rejection in Lung Transplant RecipientsOriginal paper
Karen Pendergrass is a microbiome researcher specializing in microbiome-targeted interventions (MBTIs). She systematically analyzes scientific literature to identify microbial patterns, develop hypotheses, and validate interventions. As the founder of the Microbiome Signatures Database, she bridges microbiome research with clinical practice. In 2012, based on her own investigative research, she became the first documented case of FMT for Celiac Disease, four years before the first published case study.
Read MoreWhat was studied?
Researchers examined whether airway microbiota could distinguish infection from acute rejection in lung transplant recipients (LTRs). They analyzed 181 sputum samples from 59 LTRs, grouped as event-free (n=47), infection (n=103), or rejection (n=31).
How was it studied?
Sputum samples underwent 16S rRNA gene sequencing (V3-V4 region). Microbial diversity, composition, and differential taxa (via LEfSe) were compared across the three clinical groups, then combined with clinical markers (procalcitonin and T-lymphocyte levels) in random forest models to test predictive accuracy.
What did they find?
Airway microbial diversity and composition differed significantly among the three groups, most notably between rejection and the other two groups. LEfSe identified 19 differential taxa, including six genera, Actinomyces, Rothia, Abiotrophia, Neisseria, Prevotella, and Leptotrichia, enriched specifically in rejection. Combining these six genera with procalcitonin and T-lymphocyte levels yielded AUC values of 0.898 (event-free vs infection), 0.919 (event-free vs rejection), and 0.895 (infection vs rejection).
Why it matters
Distinguishing infection from rejection after lung transplant is a major clinical challenge because treatments differ and misdiagnosis can worsen outcomes. This cross-sectional study suggests airway microbiota, paired with routine clinical markers, could serve as an adjunct diagnostic indicator, though the authors note larger studies are needed to confirm the model.