Looking Beyond Respiratory Cultures: Microbiome-Cytokine Signatures of Bacterial Pneumonia and Tracheobronchitis in Lung Transplant Recipients

Abstract

Bacterial pneumonia and tracheobronchitis are diagnosed frequently following lung transplantation. The diseases share clinical signs of inflammation and are often difficult to differentiate based on culture results. Microbiome and host immune-response signatures that distinguish between pneumonia and tracheobronchitis are undefined. Using a retrospective study design, we selected 49 bronchoalveolar lavage fluid samples from 16 lung transplant recipients associated with pneumonia (n = 8), tracheobronchitis (n = 12) or colonization without respiratory infection (n = 29). We ensured an even distribution of Pseudomonas aeruginosa or Staphylococcus aureus culture-positive samples across the groups. Bayesian regression analysis identified non-culture-based signatures comprising 16S ribosomal RNA microbiome profiles, cytokine levels and clinical variables that characterized the three diagnoses. Relative to samples associated with colonization, those from pneumonia had significantly lower microbial diversity, decreased levels of several bacterial genera and prominent multifunctional cytokine responses. In contrast, tracheobronchitis was characterized by high microbial diversity and multifunctional cytokine responses that differed from those of pneumoniacolonization comparisons. The dissimilar microbiomes and cytokine responses underlying bacterial pneumonia and tracheobronchitis following lung transplantation suggest that the diseases result from different pathogenic processes. Microbiomes and cytokine responses had complementary features, suggesting that they are closely interconnected in the pathogenesis of both diseases.

The endotracheal tube microbiome associated with Pseudomonas aeruginosa or Staphylococcus epidermidis

“…Loss of microbial diversity has been previously identified as a consistent marker of a diseased microbiome3334. In our study, while the incidence of VAP was indeed lower in the S. epidermidis colonized group, the patients who did develop VAP in this group did not show any loss of microbial diversity compared to others in this group (p = 0.464).…”

Insights into study design and statistical analyses in translational microbiome studies

“…For example, in a study examining the role of the microbiome in the development of pneumonia, it could be beneficial to focus on the most common type of pneumonia in the patient population rather than trying to profile all of the different etiological types. This can be achieved by specifying welldefined inclusion and exclusion criteria based on respiratory cultures such that only patients meeting the culture criteria for a specific etiology are included in the study (23). Such a targeted recruitment of patients increases the power of the study and is a judicious use of limited resources.…”

“…A good control population in this case includes patients with a clinical phenotype that is a clear contrast to either of the two conditions of interest. For example, when studying pneumonia and tracheobronchitis which are two closely related and almost indistinguishable culture-positive conditions that occur in patients admitted to the intensive care unit or in lung transplant recipients, the control population could comprise patients with asymptomatic colonization confirmed by culture (23). When the microbiome is utilized for monitoring the severity or progression of a disease or the efficacy of a treatment process, patient subsets with either less severe disease or lower treatment-response could be used as controls.…”

“…In the years following the completion of the National Institutes of Health's human microbiome project (1), there has been a large volume of research identifying and cataloging the microbiome in physiological and pathological states including metabolic disorders (2) [obesity (3,4), diabetes (5), metabolic syndrome (6)], autoimmune disorders (7,8) [inflammatory bowel diseases (9,10), multiple sclerosis (11), rheumatic disease (12)], infectious diseases (13) [upper and lower respiratory tract infections (14), candidiasis (15,16), urinary tract infections (17), secondary infections following cystic fibrosis (18) and HIV infections (19)], cancer (20,21) and cancer-related infections (22), lung and stem-cell (23,24) transplant-related infections and long-term effects of antibiotic-associated dysbiosis (25) [Clostridium difficile infections (26)]. …”

“…The microbiome can serve as a diagnostic adjunct to traditional clinical and laboratory measures (23,(27)(28)(29), a determinant of treatment-response (30), a window into the side-effects of exposure to antibiotics (25), a baseline measurement prior to the initiation of therapy (31), and a signature of immune processes such as inflammation (23,32). Furthermore, it can be used to guide nutrition and preventive interventions (33), monitor the severity, progression or recovery from disease (34), track treatment-outcomes (35), and identify small-molecule drugs (36) and mechanisms of pathogenesis which can then be targeted for therapeutic interventions (37).…”