Cystic Fibrosis Research and Translation Center
Cystic Fibrosis Research and Translation Center
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Past Pilots

Pilot 1: Macrophage and Dendritic Cell Pathways in Cystic Fibrsosis

P.I.:    Lev Becker, PhD
           Assistant Professor
           Department of Pediatrics
           University of Chicago

Abstract: The clinical hallmark of cystic fibrosis (CF) is greatly increased susceptibility to infection with pathogenic bacteria like P. aeruginosa. Cystic fibrosis is primarily a disorder of electrolyte transport that results due to mutations in the CF transmembrane conductance regulator (CFTR) gene. One major unresolved issue in CF pathogenesis is how chloride dysregulation leads to the observed clinical manifestations. While it is well established that the lung epithelium contributes to this process, relatively little is known about the role of CFTR deficient alveolar macrophages (MΦ) and dendritic cells (DC).

We hypothesize that CFTR deficiency in MΦs and DCs influences their immune function thereby contributing to sustained bacterial infection and inflammation in CF. We intend to approach this unresolved issue from two angles. First, we will use a proteomics-based approach to identify plasma membrane-associated markers of differentially activated MΦs and DCs in humans and then use these markers to phenotype macrophages in normal and CF lungs. Second, by interrogating the plasma membrane proteomes of monocyte-derived MΦs and DCs from CF patients we will simultaneously address the effect of CFTR deficiency on cell differentiation and/or activation pathways and identify phenotype-specific effects of CFTR deficiency on protein expression. The functional significance of any CFTR-dependent changes in protein expression in MΦs/DCs will be determined by elucidating their effect on neutrophil function.

Collectively, the proposed studies should provide important insights into how CF monocytederived immune cells contribute to the pathogenesis of cystic fibrosis.

Pilot 2: Describing the CF gut microbiome in infancy and early childhood

P.I.:    Lucas Hoffman, MD, PhD
           Assistant Professor
           Department of Pediatrics
           Lab Web Page

Abstract: Disease of the lungs and intestinal tract are responsible for the majority of the morbidity and mortality of cystic fibrosis (CF). The manifestations of CF intestinal disease during early life primarily involve pancreatic exocrine insufficiency that causes nutrient malabsorption and, bulky stools that can result in intestinal obstruction. In people without CF, a complex relationship between gut microbiota, nutritional intake, nutrient absorption, and other measures of health has been demonstrated. GI tract microbes are known to be an important contributor to human nutrient metabolism. In CF, GI tract microbes may represent an important determinant of CF nutritional outcomes, which in turn can significantly impact severity of lung disease and overall longevity. The microbial constituents of the CF intestine, nor their relationship with clinical outcomes, have been well studied. Furthermore, some work has suggested a correlation between colonization of the airway and intestine in individual CF patients with a single microbial species (P. aeruginosa). Therefore the intestine may represent an important reservoir for airway infection. We hypothesize that the constituency of gut microbiota among children with CF (1) differs from that of children without CF, and (2) correlates with severity of malabsorption, vitamin deficiency, and nutritional state. To test these hypotheses, we propose a pilot study to establish methodology for studying gut microbiomes (as reflected by stool microbial content, defined with ultra high-throughput, cultureindependent molecular methods) among 10 infants and children with CF and age-matched non-CF controls. We will collect four stool samples per subject over a year to determine the within-subject variability of microbial characteristics and their relationship with the presence or absence of CF, with nutritional and clinical parameters (including weight, height, body mass index, growth rate of each of these parameters among both subject groups, and among CF subjects, serum vitamin levels, GI symptoms, and supplemental pancreatic enzyme dosage). Microbial species and gene content in stool samples will be defined using massively parallel next-generation DNA sequencing methods with which we have extensive experience. Estimates of the metabolic capability of the microbiome will be determined and correlated with these clinical parameters. Our goal in this pilot project is to apply these methods to collect sufficient preliminary data for a multicenter study of CF GI tract microbial species and gene content and their clinical relevance.

Pilot 4: Computational Tools For Identifying Compositional Shifts In The CF Gut Microbiome

P.I.:    Elhanan Borenstein, PhD
          Assistant Professor of Genome Sciences
          Adjunct Assistant Professor of Computer Science
          External Professor, The Santa Fe Institute

Abstract: Cystic fibrosis (CF) is often associated with diseases of the intestinal tract, ultimately leading to malnutrition and poor growth. This persistent growth failure resists nutrient and enzyme replacement therapy, suggesting that factors other than inadequate nutrient intake or malabsorption may contribute to malnutrition in children with CF. Specifically, it has been suggested that the CF gut microbiome may influence growth and clinical outcomes through its effects on host metabolism, nutrition, and immune function.

To examine this hypothesis, the Cystic Fibrosis Research Translation Center at UW is currently characterizing the gut microbiomes of children with and without CF, using massively parallel next-generation sequencing methods, with additional efforts underway. These initiatives are generating exciting metagenomic data, mapping, for the first time, the previously uncharted composition of the CF gut microbiome. However, considering the numerous factors affecting the composition of the gut microbiome and the overall functional uniformity across samples, standard comparative analysis may fail to detect significant patterns. Advanced computational methods are therefore required to sift through this ultra-high-throughput data and pinpoint potential functional capacities of the microbiome that may be associated with CF and with clinical outcomes.

In this pilot project we will therefore develop a suite of novel computational methods for identifying associations between the composition of the microbiome and specific host phenotypes such as CF status and clinical parameters. These methods are especially tailored to identify subtle differences in highly-multidimensional data with a relatively small sample size a common setting in comparative metagenomic analysis. We will focus on three complementary methods: First, we will develop a "Gene Set" based method for improved identification of over- and under-represented functional categories in the microbiome, inspired by microarray analysis. Second, we will develop a computational framework for co-occurrence-based grouping of genes in the microbiome and for dimension reduction, offering a more natural alternative to pathway-based grouping and accounting for inter-gene dependencies. Finally, we will develop a computational framework for simultaneously analyzing species and gene abundances, and for harnessing these two data sources in an integrated manner. Each of these methods aims to address specific weaknesses of standard comparative metagenomic analysis and to enhance the statistical power of such studies. We will further make the methods developed in this pilot project available to the research community both as an open source and as a web-application for wide accessibility.  

Applying these tools to study the microbiomes of children with and without CF will allow us to obtain novel insights into functional shifts in the CF microbiome and their role in CF-related growth failure. The methods developed in this pilot study and their application to CF would lay the foundation for a large-scale study aimed at developing a comprehensive computational framework for improved comparative metagenomic analysis for studying the microbiome's contribution to CF and to other human diseases.

Pilot 7: Acute Effects of Kalydeco in People with CF and the G551D Mutation

    Lucas Hoffman, MD, PhD
          Associate Professor
          Department of Pediatrics
          Adjunct Associate Professor,
          Department of Microbiology

In this study, we propose to test the hypothesis that the microbiota in respiratory samples from people with CF  ill change within 1 week with Kalydeco treatment, a period during which sweat chloride values decreased by more than 50% on average (P. Singh, pers. comm.).

Aim: Define the CF sputum microbiome before and after beginning Kalydeco.
We will extract DNA from the sputum samples taken before and at days 2 and 7 after beginning treatment with Kalydeco from all 12 study subjects (36 total samples). A portion of each sample will be treated with a reagent that will exclude DNA from dead cells (propidium monoazide11), limiting the proposed analysis to only live cells (as time and funds allow, sample aliquots that were not PMA treated will also be sequenced and the results compared). These treated samples will then be studied using an existing analytical pipeline (Illumina HiSeq shotgun sequencing followed by phylogenetic analysis using the MetaPhlAn computational approach12), developed through an existing collaboration between the Hoffman and Miller laboratories, to define the identities and relative abundances of bacteria, and computational metagenomic analysis to define the content of specific gene families, such as adhesins or metabolic pathways that may be selected within the CF airway. Total and individual viable bacterial loads will be determined using quantitative PCR.


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