Faculty Bibliography

BACKGROUND:Platelets are effector cells of the innate and adaptive immune system, however understanding their role during inflammation-driven pathologies can be challenging due to several drawbacks associated with current platelet depletion methods. The generation of antisense oligonucleotides (ASO)s directed to thrombopoietin (Tpo) mRNA represents a novel method to reduce circulating platelet count. OBJECTIVE:To understand if Tpo-targeted ASO treatment represents a viable strategy to specifically reduce platelet count in mice. METHODS:Female and male mice were treated with TPO-targeted ASOs and platelet count and function assessed, in addition to circulating blood cell counts and hematopoietic stem and progenitor cells. The utility of the platelet-depletion strategy was assessed in a murine model of lower airway dysbiosis. RESULTS AND CONCLUSIONS/CONCLUSIONS:Herein, we describe how in mice, ASO-mediated silencing of hepatic TPO expression reduces platelet, megakaryocyte, and megakaryocyte progenitor count, without altering platelet activity. TPO ASO-mediated platelet depletion can be achieved acutely and sustained chronically in the absence of adverse bleeding. TPO ASO-mediated platelet depletion allows for the reintroduction of new platelets, an advantage over commonly used antibody-mediated depletion strategies. Using a murine model of lung inflammation, we demonstrate that platelet depletion, induced by either TPO ASO or anti-CD42b treatment, reduces the accumulation of inflammatory immune cells, including monocytes and macrophages, in the lung. Altogether, we characterize a new platelet depletion method that can be sustained chronically and allows for the reintroduction of new platelets highlighting the utility of the TPO ASO method to understand the role of platelets during chronic immune-driven pathologies.

The diverse microbial communities within our bodies produce metabolites that modulate host immune responses. Even the microbiome at distal sites has an important function in respiratory health. However, the clinical importance of the microbiome in tuberculosis, the biggest infectious cause of death worldwide, is only starting to be understood. Here, we critically review research on the microbiome's association with pulmonary tuberculosis. The research indicates five main points: (1) susceptibility to infection and progression to active tuberculosis is altered by gut Helicobacter co-infection, (2) aerosol Mycobacterium tuberculosis infection changes the gut microbiota, (3) oral anaerobes in the lung make metabolites that decrease pulmonary immunity and predict progression, (4) the increased susceptibility to reinfection of patients who have previously been treated for tuberculosis is likely due to the depletion of T-cell epitopes on commensal gut non-tuberculosis mycobacteria, and (5) the prolonged antibiotic treatment required for cure of tuberculosis has long-term detrimental effects on the microbiome. We highlight knowledge gaps, considerations for addressing these knowledge gaps, and describe potential targets for modifying the microbiome to control tuberculosis.

RATIONALE/BACKGROUND:Obstructive Sleep Apnea (OSA) is associated with recurrent obstruction, sub-epithelial edema, and airway inflammation. The resultant inflammation may influence or be influenced by the nasal microbiome. OBJECTIVES/OBJECTIVE:To evaluate whether the composition of the nasal microbiota is associated with obstructive sleep apnea and inflammatory biomarkers. METHODS:Two large cohorts were utilized: 1) a discovery cohort of 472 subjects from the WTCSNORE cohort; and 2) a validation cohort of 93 subjects from the Zaragoza Sleep cohort. Sleep apnea was diagnosed using home sleep tests. Nasal lavages were obtained from cohort subjects to measure: 1) microbiome composition (based on 16S rRNA gene sequencing); 2) biomarkers for inflammation (inflammatory cells, IL-8, and IL-6). Longitudinal 3 months samples were obtained in the validation cohort including post-CPAP treatment when indicated. RESULTS:In both cohorts, we identified that: 1) severity of OSA correlated with differences in microbiome diversity and composition; 2) the nasal microbiome of subjects with severe OSA were enriched with Streptococcus, Prevotella, and Veillonella; 3) the nasal microbiome differences were associated with inflammatory biomarkers. Network analysis identified clusters of co-occurring microbes that defined communities. Several common oral commensals (e.g., Streptococcus, Rothia, Veillonella, and Fusobacterium) correlated with apnea-hypopnea index. Three months of treatment with CPAP did not change the composition of the nasal microbiota. CONCLUSIONS:We demonstrate that the presence of an altered microbiome in severe OSA is associated with inflammatory markers. Further experimental approaches to explore causal links are needed.

Lung cancer remains the leading cause of cancer death worldwide1. With new treatment modalities, there has been a shift in focus to how we can predict who may respond to targeted treatments. Current data suggest that the human microbiome can affect lung cancer treatment through its effects on the systemic immune tone. Our group has shown that the lower airway microbiota of lung cancer patients is characterized by enrichment with oral commensals2 which triggers transcriptomic signatures (PI3K, MAPK) previously described in NSCLC 2,3. The impact of local lung dysbiosis on lung cancer progression and survival is unknown. Patients with suspicious nodules on imaging who underwent bronchoscopy were recruited. High-throughput sequencing of bacterial 16S rRNA-encoding gene amplicons was performed. Clustering was based on Dirichlet-Multinomial mixtures (DMM) modeling. RNAseq was performed on bronchial epithelial cells obtained by airway brushing. We focused our analysis on 83 NSCLC samples. Overall alpha-diversity showed that advanced stage (IIIb-VI) lower airway samples were more similar to buccal samples than local stage (I-IIIa), p<0.0001. In addition, worse 6-month and 1-year survival was associated with more similar alpha-diversity between lower airway and buccal samples (Figure 1A-D). Utilizing DMM two clusters were identified, Supraglottic-Predominant-Taxa (SPT) and Background-Predominant-Taxa (BPT). There was a significant increase in percentage of SPT in advance stage compared to local stage (p<0.008) Kaplan-Meir survival analysis shows worse survival in those with NSCLC who were clustered into the SPT group compared to BPT (p=0.0003, Figure 1E). With RNAseq, differentially expressed genes between advanced stage vs. local stage and 6-month vs. 1-year survival were not as pronounced as SPT vs. BPT (Figure 1F) suggesting that globally, transcriptomic changes between different stage and NSCLC survival were difficult to detect as compared to when airway microbiome were differentiated. In lung cancer, dysbiosis within the lower airway microenvironment, possibly by micro-aspiration, is associated with a worse 6-month and 1-year survival. This change is also associated with transcriptome changes in the local environment

Micro-aspiration commonly occurs in health and its prevalence is increased in many lung diseases. A dysbiotic signature identified by enrichment with oral microbes is associated with increased inflammation and distinct metabolic profile in the lung microenvironment. 16S rRNA gene sequencing is commonly used for taxonomic annotation and inferred functional capacity. However, it is desirable to actually measure the function of these bacteria. We therefore took an approach that combines marker gene amplification, whole genome shotgun sequencing (WGS), RNA sequencing (RNA) and measured metabolites. Upper (UA), lower airway (BAL), and background (BKG) samples were obtained via bronchoscopy from 19 healthy subjects. 16S rRNA gene sequencing was used to assess the microbiota and identify dysbiosis among lower airway samples. In parallel, functional microbial signatures were identified via WGS metagenome and RNA metatrascriptome sequencing. Dirichlet Multinomial Modelling identified two clusters from the 16S data where: 12 BAL samples clustered with BKG samples and were characterized as BAL. BPT (enriched with background characteristic taxa such as Acinetobacter, Pseudomonadales) and 7 clustered with most UA samples and were characterized as BAL. SPT (enriched with oral commensals such as Veillonella and Streptococcus). Using Gene-Set-Enrichment Analysis (GSEA), there is very little overlap between the three sequencing methods for taxonomic annotation (Figure 1A-F). For functional annotation, in samples clustered to BAL. SPT, withWGS, most upregulated pathways are associated with Ribosome while with RNA a number of pathways are upregulated including lipid metabolism and fatty acid metabolism. Comparing the differentially expressed pathways for BAL. SPT samples, between the three sequencing methods, some pathways share directionality and significance (Figure 1H,J,L). For example, fatty acid biosynthesis is significantly upregulated in all three sequencing methods. Short chain fatty acids (SCFAs) were therefore measured. UA and BAL samples had significantly higher levels of acetate and propionate when compared to BKG samples. However, only with RNA, KOs associated to these SCFAs are significantly elevated in both UA and BAL samples (Figure 1K), similar to measuring these SCFAs by mass spectrometry. In addition, taxa associated with these KOs in RNA are known upper airway commensals (Figure 1M). 16S rRNA gene sequencing does not provide accurate functional information in the lower airway microbiome. The addition of RNA metatranscriptomic characterization of the lower airway microbiota is feasible and provides functional insights that are consistent with metabolomic signatures identified in a dysbiotic signature. The addition of this technology to standard microbiome investigations may provide important insight into the interaction between microbiome and host

The use of next-generation sequencing and multiomic analysis reveals new insights on the identity of microbes in the lower airways blurring the lines between commensals and pathogens. Microbes are not found in isolation; rather they form complex metacommunities where microbe-host and microbe-microbe interactions play important roles on the host susceptibility to pathogens. In addition, the lower airway microbiota exert significant effects on host immune tone. Thus, this review highlights the roles that microbes in the respiratory tract play in the development of pneumonia.

BACKGROUND:In lung cancer, upregulation of the PI3K pathway is an early event that contributes to cell proliferation, survival, and tissue invasion. Upregulation of this pathway was recently described as associated with enrichment of the lower airways with bacteria identified as oral commensals. We hypothesize that host-microbe interactions in the lower airways of subjects with lung cancer affect known cancer pathways. METHODS:Airway brushes were collected prospectively from subjects with lung nodules at time of diagnostic bronchoscopy, including 39 subjects with final lung cancer diagnoses and 36 subjects with non-cancer diagnosis. Additionally, samples from 10 healthy control subjects were included. 16S rRNA gene amplicon sequencing and paired transcriptome sequencing (RNAseq) were performed on all airway samples. In addition, an in vitro model with airway epithelial cells exposed to bacteria/bacterial products was performed. RESULTS:The composition of the lower airway transcriptome in the cancer patients was significantly different from the controls, which included upregulation of ERK and PI3K signaling pathways. The lower airways of lung cancer patients were enriched for oral taxa (Streptococcus and Veillonella), which was associated with upregulation of the ERK and PI3K signaling pathways. In vitro exposure of airway epithelial cells to Veillonella, Prevotella, and Streptococcus led to upregulation of these same signaling pathways. CONCLUSIONS:The data presented here shows that several transcriptomic signatures previously identified as relevant to lung cancer pathogenesis are associated with enrichment of the lower airway microbiota with oral commensals.

Background: Aspiration is associated with non-tuberculous mycobacterial (NTM) pulmonary disease and airway dysbiosis is associated with increased inflammation. We examined whether NTM disease was associated with a distinct airway microbiota and immune profile.Methods: 297 oral wash and induced sputum samples were collected from 106 participants with respiratory symptoms and imaging abnormalities compatible with NTM. Lower airway samples were obtained in 20 participants undergoing bronchoscopy. 16S rRNA gene and a nested mycobacteriome sequencing approaches characterised microbiota composition. Inflammatory profiles of lower airway samples were also examined.Results: The prevalence of NTM+ cultures was 58%. Few changes were noted in microbiota characteristic or composition in oral wash and sputum samples among groups. Among NTM+ samples, 27% of the lower airway samples were enriched with Mycobacterium A mycobacteriome approach identified Mycobacterium in a greater percentage of samples, including some non-pathogenic strains. In NTM+ lower airway samples, taxa identified as oral commensals were associated with increased inflammatory biomarkers.Conclusions: The 16S rRNA gene sequencing approach is not sensitive in identifying NTM among airway samples which are culture positive. However, associations between lower airway inflammation and microbiota signatures suggest a potential role for these microbes in the inflammatory process in NTM disease.

In pediatric patients with chronic cough, respiratory culture techniques commonly yield negative results. Studies using culture-independent methods have found a high relative abundance of oral microbes in the lower airways, suggesting that the topographical continuity, and dynamics of the intraluminal contents of the aerodigestive system likely influence the lower airway microbiota. We hypothesize that in subjects with chronic cough, clinical diagnosis will correlate with distinct microbial signatures detected using culture-independent methods.