Longitudinal Analysis of Respiratory and Intestinal Microbiome in Cystic Fibrosis
Conditions: Cystic Fibrosis
Sponsor: University of Kiel

 
 
Bobis/Franke, Kiel 
1 
 
 
 
 
 
 
Longitudinal Analysis of Respiratory and 
Intestinal Microbiome in Cystic Fibrosis 
 
28.09.2017 
 
 
 

 
 
Bobis/Franke, Kiel 
2 
 
Title of study: 
Longitudinal Analysis of respiratory and intestinal microbiome in cystic 
fibrosis 
 
Clinical project leader: Dr. med. Ingrid Bobis, CF unit of the Department of 
Pneumology, Clinic for Internal Medicine I, University Hospital Schleswig-Holstein (UKSH), 
campus Kiel, Germany 
 
Scientific project leader: Prof. Dr. Andre Franke (Dipl.-Biol.), Institute of Clincial 
Molecular Biology (IKMB), Kiel University and UKSH, campus Kiel, Germany 
 
Project team: 
(in alphabetical order)  
 
Prof. Dr. Burkhard Bewig, Department of Pneumology, Clinic for Internal Medicine I, UKSH, 
campus Kiel 
Dr. Corinna Bang, IKMB, Kiel University and UKSH, campus Kiel 
Prof. Dr. Andreas Claass, CF unit, Pediatric Department, Städtisches Krankenhaus Kiel  
Prof. Dr. Andre Franke, IKMB, Kiel University and UKSH, campus Kiel 
Prof. Philipp Rosenstiel, IKMB, Kiel University and UKSH, campus Kiel 
Prof. Dr. Stefan Schreiber, Clinic for Internal Medicine I, UKSH, campus Kiel and IKMB, Kiel 
University and UKSH, campus Kiel 
Dr. Christian Timke, CF unit, Pediatric department, Städtisches Krankenhaus  
(all Kiel, Germany) 
 
 

 
 
Bobis/Franke, Kiel 
3 
 
1. Background 
1.1 Cystic fibrosis  
Cystic fibrosis is caused by mutations in the CFTR gene with an autosomal recessive 
inheritance. It is a rare disease with a prevalence of 1-9/100,000 in Europe. The CFTR 
mutations result in a dysfunctional chloride channel that affects secretion from all exocrine 
glands.  
The disease is chronic and generally progressive, with onset usually occurring during early 
childhood or, occasionally, at birth (meconium ileus). Virtually any internal organ may be 
involved but the principle manifestations concern the breathing apparatus (chronic 
bronchitis), pancreas (pancreatic insufficiency, adolescent diabetes and occasionally 
pancreatitis) and, more rarely, the intestine (stercoral obstruction) or liver (cirrhosis). The 
most common form of cystic fibrosis is associated with respiratory symptoms, digestive 
problems (steatorrhea and/or constipation) and staturoponderal growth anomalies. Mortality 
and morbidity depend on the extent of bronchopulmonary involvement. Male sterility is a 
constant feature. Late-onset forms, which are usually only mild or monosymptomatic, have 
also been reported. 
Diagnosis is suspected on the basis of sweat test results (chloride concentration above 
60 mmol/L) and is confirmed by identification of a CFTR mutation. Neonatal testing has been 
widely available since the end of 2002 and leads to diagnosis in 95% of cases. 
Classical treatment of cystic fibrosis is mainly symptomatic, revolving around bronchial 
drainage, antibiotics for respiratory infections, pancreatic analysis and administration of 
vitamins and calorific supplements for digestive and nutritional problems. These cost-
effective treatments have significantly improved the prognosis for cystic fibrosis patients: in 
the 1960's the majority of patients died before 5 years of age, whereas the current average 
life-span exceeds 35 years and life-expectancy is 40 years.  
1.2 Microbial involvement in CF 
The lung of CF patients is often affected by recurrent infections and colonized by 
Pseudomonas aeruginosa that form a viscous biofilm. Recent studies describe a complex 
microbiome in patients with cystic fibrosis, and common threads have emerged across 
studies in the relationships observed between features of the respiratory microbiome and 
clinical outcomes. CF airway microbial communities were shown to be relatively stable during 
periods of clinical stability, but differ substantially between patients [1]. See following 
Figure 1. Most studies to date have focused on bacterial members of the respiratory 

 
 
Bobis/Franke, Kiel 
4 
 
microbiome, but some recent investigations have also begun to characterize fungal and viral 
communities in CF [2, 3]. 
 
Taken from [1] 
 
Although studies of respiratory microbiota have dominated CF microbiome studies, 
gastrointestinal complications of CF are a significant cause of disease morbidity, and 
problems beyond pancreatic insufficiency are often difficult to manage. Thus recently, 
investigations have also begun to characterize and analyze relationships between 
gastrointestinal bacterial microbiota, clinical disease markers, and profiles of airway bacterial 
community composition ([4, 5]; reviewed in [6]). Our knowledge on the relation of the 
intestinal and respiratory microbiome in CF is still very limited, but has great potential for 
biomarker discovery.  
In CF, antibiotic therapy given in purpose to limit lung damage by bacterial colonization and 
infection, is a mainstay of therapy. Changes in respiratory and intestinal microbiome 
composition and diversity and the interrelationship between them are still to be elucidated. 
1.3 Modern CF therapy 
Since recently, medication is available that directly targets the impaired CFTR function. 
Depending on the type of mutation, the respective substances are called CFTR-potentiators 
or –correctors. In 2011, two large clinical studies (STRIVE und ENVISION) showed that the 
CFTR-potentiator Ivacaftor (trade name Kalydeco®) significantly improves clinical 

 
 
Bobis/Franke, Kiel 
5 
 
parameters in patients with the rare G551D mutation [7, 8], and with to date eight other rare 
CFTR mutations.  
Patients with the most common CFTR mutation Phe508del (= F508del) benefit from a 
therapy with a combination of the CFTR-corrector Lumacaftor with the CFTR-potentiator 
Ivacaftor, as shown in the TRANSPORT und TRAFFIC studies [9]. In Germany this 
combination is approved for CF since Nov 2015 (trade name Orkambi®). It improves CFTR 
function and thus influences secretion, e.g. in the lung and gut of patients. 
For Ivacaftor alone, a small study comprising three patients described a modified respiratory 
microbiome with a trend towards higher diversity [10]. Based on this, we hypothesize that the 
treatment with Kalydeco or Orkambi® will influence the composition of the CF microbiome 
in eligible patients towards a healthy and stable high-diversity state.  
2. Aims 
Our study targets the influence of standard CF therapy on the CF microbiome. The 
respiratory and intestinal microbiome and their interrelationship will be characterized in a 
longitudinal study design. The observed changes will be correlated to functional (FEV1, body 
weight, laboratory parameters) und quality of life (CFQ-R, Kiel-Q) parameters We aim at the 
detection of significant changes in microbial parameters in the respiratory tract and in the gut 
following medical therapy (e.g. antibiotics, CFTR modulators). The microbial composition will 
further be correlated to treatment response, in order to discover potential microbial 
biomarkers. 
3. Study design  
3.1 Participants and clinical data 
The study comprises analysis of clinical parameters and microbiome and 8 time points. 
Patients are recruited from the adult cf centre at the University Hospital of Schleswig 
Holstein, Campus Kiel, Innere Medizin I. Eligible patients are patients with a confirmed 
diagnosis of cystic fibrosis (sweat chloride >60mmol/l and /or 2 cf causing mutations in the 
CFTR gene) and the ability to give informed consent . 
All participants receive a questionnaire at each time point of sampling that will either be filled 
in by themselves or by a study nurse via a telephone interview. Clinical data is collected by 
the medical study team from standard health records and via a questionnaire. From clinical 
data CF-genotype, microbial status concerning chronic infection with Pseudomonas 

 
 
Bobis/Franke, Kiel 
6 
 
aeruginosa and other bacteria, height and weight, current medication, presence and 
frequency of exacerbations, lung function parameters (e.g.FEV1, FVC, MEF 25, MEF 75-25), 
full blood count, C reactive protein, liver function tests (AST, ALT, GGT, ALP, TBil), 
creatinkinase, calprotectin and elastase will be enrolled. The questionnaires contain i) basic 
items, like age and body weight, ii) items related to the CF phenotype and the clinical 
outcome, like quality-of-life, lung function and other medical complaints, and iii) items that 
relate to the microbiome, like food intake or other medication. 
3.2 Recruitment and biosampling  
Biosampling will be performed at each time point with a tolerance of three days. For stool 
sampling the participants will receive sampling tubes that will be sent back by mail. 
Respiratory samples will either be taken during the visit by the medical study team or by the 
participants at home. At each time point two stool samples, one sputum sample and one 
throat swab will be collected.  
Biosampling is performed in an anonymized fashion. Sample preparation and the generation 
of molecular profiles is conducted at the IKMB and will be managed by Dr. Corinna Bang. 
The IKMB staff receives samples and phenotypes without any information that allows the 
identification of individual study participants, so that data protection policies will be fulfilled 
within the entire study. 
3.3 Phases 
The study course is subdivided in two phases. The first 3 samples collected at timepoints “0”, 
“2” and “4” will generate a baseline for analysis of the comparison with following samples.  
 
 

 
 
Bobis/Franke, Kiel 
7 
 
 
4. Molecular profiling and data analysis 
4.1 Sample preparation 
All collected samples will be processed at the IKMB according to established protocols. DNA 
will be isolated from stool and respiratory samples and used for the generation of microbial 
profiles. In addition, the isolated DNA samples can be used for all established sequencing 
application. A second stool sample will be used to measure the intestinal inflammatory 
marker Calprotectin and Elastase-1, in order to document intestinal and pancreatic 
involvement. Both methods are applied in the routine clinical setting, and are thus 
standardized and well established. 
4.2 Microbial profiling by next generation sequencing (NGS) 
Both bacterial and fungal profiles will analyzed within the study. The characterization of 
bacterial profiles is conducted by sequencing the 16S rRNA gene, the so-called 16S DNA, 
which is in parts highly variable and present in all bacterial and archaeal organisms. The 16S 
analysis by NGS is well established allows microbial profiling down to the genus level at a 
qualitative and quantitative level. 
 
According to established protocols [11], the 
V1-V2 region of the 16S rDNA of the 
extracted DNA is amplified in a sequence-
specific PCR reaction. The PCR products 
are than normalized and a maximum of 384 
samples are than pooled for sequencing 
with the Illumina MiSeq system. 
For fungal profiling the highly variable ITS1 
and ITS2 gene regions will be amplified and 
processed 
as 
described 
for 
bacterial 
profiling. For both bacteria and fungi the 
identification of the corresponding microorganisms is conducted by the comparison of the 
obtained sequences to public sequence databases.  

 
 
Bobis/Franke, Kiel 
8 
 
4.3 Statistical analysis of microbial profiles and modeling of microbial 
changes 
The obtained sequences provide information of the composition of the microbial community 
in each sample at a qualitative and quantitative level. As the primary outcome the diversity 
within one sample (α-diversity) will be determined that can be expressed in various indices, 
e.g. Chao1 or Shannon index, and changes in α-diversity will be calculated (Δdiv). In 
addition, the specific composition of the microbiome profiles will the analyzed. Respiratory 
and intestinal profiles will first be investigated separately, and will be than be analyzed for a 
possible correlation. The following procedures and tests will be conducted in the given order: 
1) Calculation of changes in α-diversity indices for each sample (Δdiv; “baseline” vs. 
“follow-up”) and comparison of diversities correlated to treatment characteristics of the 
patients (e.g. antibiotic versus non-antibiotic, CFTR modified versus non-CFTR 
modified).  
2) We hypothesize that the α-diversity in patient`s microbiome will change in response to 
external stimuli (E.g.: Decrease in response to antibiotic therapy? Increase in response 
to CFTR Modification?). In all patients indices of the three time points “baseline” will be 
summarized and used for the calculation of the α-diversity change. A non-parametric test 
will be applied to test, if there is are significant changes in α-diversity in response the 
initiation of antibiotic oder CFTR modifying treatment. 
3) Fitting of a statistical model for the change in α-diversity in patient`s microbiome. 
4) Correlation of the α-diversity with therapy response in as well as over the entire course 
of the study. 
5) Analysis of the abundance of specific bacteria or fungi over the time course using a zero-
inflated model 
6) Comparison of the composition of the obtained microbial profiles (β-diversity) between 
timepoints and patient`s characteristic groups by PCoA (multiple scaling analysis) 
7) Correlation of microbial parameters (changes in α-diversity or specific organisms) of 
respiratory and intestinal samples 
4.4 Statistical power and confounding 
For Ivacaftor alone, a small study comprising three 
patients described a modified respiratory microbiome 
with a trend towards higher diversity [10]. The study 
comprised only three participants, and sampling was 
Red: Before treatment; Blue: After treatment [10] 

 
 
Bobis/Franke, Kiel 
9 
 
performed at random, unequal intervals. Our study includes 45 patients and has thus an 
increased chance to detect significant changes in the microbial composition. 
It is common sense that microbiome data are highly variable [12]. A number of parameters 
influence the microbial profile, e.g. age, food intake or medication. These known confounders 
will be included in the analysis. A possible technical bias will be minimized by using the same 
chemical reagents throughout the study and by performing NGS in patient-wise batches 
according to standardized protocols. 
5. References 
1. 
Carmody, L.A., et al., The daily dynamics of cystic fibrosis airway microbiota during 
clinical stability and at exacerbation. Microbiome, 2015. 3: p. 12. 
2. 
Hector, A., et al., Microbial colonization and lung function in adolescents with cystic 
fibrosis. J Cyst Fibros, 2016. 
3. 
Kim, S.H., et al., Global Analysis of the Fungal Microbiome in Cystic Fibrosis Patients 
Reveals Loss of Function of the Transcriptional Repressor Nrg1 as a Mechanism of 
Pathogen Adaptation. PLoS Pathog, 2015. 11(11): p. e1005308. 
4. 
Nielsen, S., et al., Disrupted progression of the intestinal microbiota with age in 
children with cystic fibrosis. Sci Rep, 2016. 6: p. 24857. 
5. 
Madan, J.C., Neonatal Gastrointestinal and Respiratory Microbiome in Cystic 
Fibrosis: Potential Interactions and Implications for Systemic Health. Clin Ther, 2016. 
6. 
Huang, Y.J. and J.J. LiPuma, The Microbiome in Cystic Fibrosis. Clin Chest Med, 
2016. 37(1): p. 59-67. 
7. 
Ramsey, B.W., et al., A CFTR potentiator in patients with cystic fibrosis and the 
G551D mutation. N Engl J Med, 2011. 365(18): p. 1663-72. 
8. 
McKone, E.F., et al., Long-term safety and efficacy of ivacaftor in patients with cystic 
fibrosis who have the Gly551Asp-CFTR mutation: a phase 3, open-label extension 
study (PERSIST). The Lancet Respiratory Medicine, 2014. 2(11): p. 902-910. 
9. 
Wainwright, C.E., et al., Lumacaftor-Ivacaftor in Patients with Cystic Fibrosis 
Homozygous for Phe508del CFTR. N Engl J Med, 2015. 373(3): p. 220-31. 
10. 
Bernarde, C., et al., Impact of the CFTR-potentiator ivacaftor on airway microbiota in 
cystic fibrosis patients carrying a G551D mutation. PLoS One, 2015. 10(4): p. 
e0124124. 
11. 
Rehman, A., et al., Geographical patterns of the standing and active human gut 
microbiome in health and IBD. Gut, 2016. 65(2): p. 238-48. 
12. 
Flores, G.E., et al., Temporal variability is a personalized feature of the human 
microbiome. Genome Biology, 2014. 15(12): p. 1-13.