Faculty Bibliography

Respiratory virus infections cause significant morbidity and mortality ranging from mild uncomplicated acute respiratory illness to severe complications, such as acute respiratory distress syndrome, multiple organ failure, and death during epidemics and pandemics. We present a protocol to systematically study patients with severe acute respiratory infection (SARI), including severe acute respiratory syndrome coronavirus 2, due to respiratory viral pathogens to evaluate the natural history, prognostic biomarkers, and characteristics, including hospital stress, associated with clinical outcomes and severity.

Li et al., demonstrate how Neisseria spp., thought to be a commensal, can play a pathogenic role in bronchiectasis. Here, we discuss how our thinking has evolved from the classical Koch's postulates to a complex network of microbial-host interactions and their role in disease pathogenesis.

The last decade of research has revolutionized our understanding of respiratory microbiology, revealing that the lungs and airways contain diverse and dynamic microbial communities in health and disease. This "respiratory ecosystem"-a densely interconnected environment of microbial and host interactions-represents a tremendous and under-appreciated source of biological and clinical heterogeneity across patients with acute and chronic lung disease. Unlike other major sources of heterogeneity, such as comorbidities and host genetics, the respiratory microbiome is readily modifiable by clinical interventions, and therefore represents an untapped opportunity for therapeutic manipulation. As a potential "treatable trait" in efforts to subphenotype patients and deliver precision medicine, the respiratory microbiome is a promising therapeutic target. In this Pulmonary Perspective, we identify and discuss multiple challenges, both conceptual and practical, that must be overcome before the respiratory microbiome can be effectively modulated as a therapeutic target. Barriers include: 1) the need to identify specific microbiologic and ecologic "targets" for therapeutic modulation; 2) the need for an improved understanding of the efficacy and persistence of response to respiratory microbiome-modulating interventions; 3) the need for clinicians to be able to access, understand and utilize microbiome data for sub-phenotyping patients, and 4) specific concerns in special populations (including children, patients with chronic lung disease, and critically ill patients). By delineating these barriers, we identify opportunities for prospective research to advance our understanding of the respiratory microbiome, its role in human respiratory disease, and its genuine potential as a therapeutic target.

Extracellular vesicles of endosomal origin, exosomes, mediate intercellular communication by transporting substrates with a variety of functions related to tissue homeostasis and disease. Their diagnostic and therapeutic potential has been recognized for diseases such as cancer in which signaling defects are prominent. However, it is unclear to what extent exosomes and their cargo inform the progression of infectious diseases. We recently defined a subset of exosomes termed defensosomes that are mobilized during bacterial infection in a manner dependent on autophagy proteins. Through incorporating protein receptors on their surface, defensosomes mediated host defense by binding and inhibiting pore-forming toxins secreted by bacterial pathogens. Given this capacity to serve as decoys that interfere with surface protein interactions, we investigated the role of defensosomes during infection by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the etiological agent of Coronavirus Disease 2019 (COVID-19). Consistent with a protective function, exosomes containing high levels of the viral receptor ACE2 in bronchoalveolar lavage fluid (BALF) from critically ill COVID-19 patients was associated with reduced intensive care unit (ICU) and hospitalization times. We found ACE2+ exosomes were induced by SARS-CoV-2 infection and activation of viral sensors in cell culture, which required the autophagy protein ATG16L1, defining these as defensosomes. We further demonstrate that ACE2+ defensosomes directly bind and block viral entry. These findings suggest that defensosomes may contribute to the antiviral response against SARS-CoV-2 and expand our knowledge on the regulation and effects of extracellular vesicles during infection.

Post Tuberculosis Lung Disease (PTLD) affects millions of tuberculosis survivors and is a global health burden. The immune mechanisms that drive PTLD are complex and have historically been under investigated. Here, we discuss two immune-mediated paradigms that could drive human PTLD. We review the characteristics of a fibrotic granuloma that favors the development of PTLD via an abundance of T-helper-2 and T-regulatory cells and an upregulation of TGF-β mediated collagen deposition. Next, we discuss the post-primary tuberculosis paradigm and the complex mixture of caseous pneumonia, cavity formation and fibrosis that can also lead to PTLD. We review the delicate balance between cellular subsets and cytokines of the innate and adaptive immune system in conjunction with host-derived proteases that can perpetuate the parenchymal lung damage seen in PTLD. Next, we discuss the role of novel host directed therapies (HDT) to limit the development of PTLD and in particular, the recent repurposing of established medications such as statins, metformin and doxycycline. Finally, we review the emerging role of novel imaging techniques as a non-invasive modality for the early recognition of PTLD. While access to computed tomography imaging is unlikely to be available widely in countries with a high TB burden, its use in research settings can help phenotype PTLD. Due to a lack of disease-specific biomarkers and controlled clinical trials, there are currently no evidence-based recommendations for the management of PTLD. It is likely that an integrated antifibrotic strategy that could simultaneously target inflammatory and pro-fibrotic pathways will probably emerge as a successful way to treat this complex condition. In a disease spectrum as wide as PTLD, a single immunologic or radiographic marker may not be sufficient and a combination is more likely to be a successful surrogate that could aid in the development of successful HDTs.

BACKGROUND:With the rapid accumulation of microbiome-wide association studies, a great amount of microbiome data are available to study the microbiome's role in human disease and advance the microbiome's potential use for disease prediction. However, the unique features of microbiome data hinder its utility for disease prediction. METHODS:Motivated from the polygenic risk score framework, we propose a microbial risk score (MRS) framework to aggregate the complicated microbial profile into a summarized risk score that can be used to measure and predict disease susceptibility. Specifically, the MRS algorithm involves two steps: (1) identifying a sub-community consisting of the signature microbial taxa associated with disease and (2) integrating the identified microbial taxa into a continuous score. The first step is carried out using the existing sophisticated microbial association tests and pruning and thresholding method in the discovery samples. The second step constructs a community-based MRS by calculating alpha diversity on the identified sub-community in the validation samples. Moreover, we propose a multi-omics data integration method by jointly modeling the proposed MRS and other risk scores constructed from other omics data in disease prediction. RESULTS:Through three comprehensive real-data analyses using the NYU Langone Health COVID-19 cohort, the gut microbiome health index (GMHI) multi-study cohort, and a large type 1 diabetes cohort separately, we exhibit and evaluate the utility of the proposed MRS framework for disease prediction and multi-omics data integration. In addition, the disease-specific MRSs for colorectal adenoma, colorectal cancer, Crohn's disease, and rheumatoid arthritis based on the relative abundances of 5, 6, 12, and 6 microbial taxa, respectively, are created and validated using the GMHI multi-study cohort. Especially, Crohn's disease MRS achieves AUCs of 0.88 (0.85-0.91) and 0.86 (0.78-0.95) in the discovery and validation cohorts, respectively. CONCLUSIONS:The proposed MRS framework sheds light on the utility of the microbiome data for disease prediction and multi-omics integration and provides a great potential in understanding the microbiome's role in disease diagnosis and prognosis. Video Abstract.

This report presents the proceedings from a workshop titled "Microbiome, Metabolism and Immunoregulation of Asthma" that was held virtually May 13 and 14, 2021. The workshop was jointly sponsored by the American Thoracic Society (Assembly on Allergy, Immunology, and Inflammation) and the National Institute of Allergy and Infectious Diseases. It convened an interdisciplinary group of experts with backgrounds in asthma immunology, microbiome science, metabolomics, computational biology, and translational pulmonary research. The main purpose was to identify key scientific gaps and needs to further advance research on microbial and metabolic mechanisms that may contribute to variable immune responses and disease heterogeneity in asthma. Discussions were structured around several topics, including 1) immune and microbial mechanisms of asthma pathogenesis in murine models, 2) the role of microbes in pediatric asthma exacerbations, 3) dysregulated metabolic pathways in asthma associated with obesity, 4) metabolism effects on macrophage function in adipose tissue and the lungs, 5) computational approaches to dissect microbiome-metabolite links, and 6) potential confounders of microbiome-disease associations in human studies. This report summarizes the major points of discussion, which included identification of specific knowledge gaps, challenges, and suggested directions for future research. These include questions surrounding mechanisms by which microbiota and metabolites shape host health versus an allergic or asthmatic state; direct and indirect influences of other biological factors, exposures, and comorbidities on these interactions; and ongoing technical and analytical gaps for clinical translation.

BACKGROUND/UNASSIGNED:With the rapid accumulation of microbiome-wide association studies, a great amount of microbiome data are available to study the microbiome's role in human disease and advance the microbiome's potential use for disease prediction. However, the unique features of microbiome data hinder its utility for disease prediction. METHODS/UNASSIGNED:Motivated from the polygenic risk score framework, we propose a microbial risk score (MRS) framework to aggregate the complicated microbial profile into a summarized risk score that can be used to measure and predict disease susceptibility. Specifically, the MRS algorithm involves two steps: 1) identifying a sub-community consisting of the signature microbial taxa associated with disease, and 2) integrating the identified microbial taxa into a continuous score. The first step is carried out using the existing sophisticated microbial association tests and pruning and thresholding method in the discovery samples. The second step constructs a community-based MRS by calculating alpha diversity on the identified sub-community in the validation samples. Moreover, we propose a multi-omics data integration method by jointly modeling the proposed MRS and other risk scores constructed from other omics data in disease prediction. RESULTS/UNASSIGNED:Through three comprehensive real data analyses using the NYU Langone Health COVID-19 cohort, the gut microbiome health index (GMHI) multi-study cohort, and a large type 1 diabetes cohort separately, we exhibit and evaluate the utility of the proposed MRS framework for disease prediction and multi-omics data integration. In addition, the disease-specific MRSs for colorectal adenoma, colorectal cancer, Crohn's disease, and rheumatoid arthritis based on the relative abundances of 5, 6, 12, and 6 microbial taxa respectively are created and validated using the GMHI multi-study cohort. Especially, Crohn's disease MRS achieves AUCs of 0.88 ([0.85-0.91]) and 0.86 ([0.78-0.95]) in the discovery and validation cohorts, respectively. CONCLUSIONS/UNASSIGNED:The proposed MRS framework sheds light on the utility of the microbiome data for disease prediction and multi-omics integration, and provides great potential in understanding the microbiome's role in disease diagnosis and prognosis.

In recent years, our understanding of the microbial world within us has been revolutionized by the use of culture-independent techniques. The use of multi-omic approaches can now not only comprehensively characterize the microbial environment but also evaluate its functional aspects and its relationship with the host immune response. Advances in bioinformatics have enabled high throughput and in-depth analyses of transcripts, proteins and metabolites and enormously expanded our understanding of the role of the human microbiome in different conditions. Such investigations of the lower airways have specific challenges but as the field develops, new approaches will be facilitated. In this review, we focus on how integrative multi-omics can advance our understanding of the microbial environment and its effects on the host immune tone in the lungs.