... für Studierende der Informatik
Es sind mehrere Masterprojekte für Studierende der Informatik verfügbar (Stand 10. Juli 2023: ein Projekt).
- Ein Einstieg in das Projekt ist ab sofort bis zum Februar 2024 möglich (in Absprache mit den Projektverantwortlichen).
- Die Bewerbung erfolgt über das DigiStrucMed-Programm.
- Bei Interesse an den Projekten sowie bei inhaltlichen Fragen wenden Sie sich bitte direkt an die jeweilige Projektleitung.
P06 - Development of a bioinformatical tool to predict clinical responses to CFTR modulator therapy by integrating microbial metagenomics, inflammatory cell transcriptomics and secretomics
In chronic lung diseases, shifts in microbial communities in the airways lead to a dysbiotic microbial composition, which interacts bi-directionally with the host’s immune system to promote chronic inflammation, leading to progressive loss of lung function. Dysbiosis in cystic fibrosis (CF) lung disease is the result of a single treatable trait, dysfunction of the chloride channel “CFTR”. Dysbiosis in CF lung disease has been deeply phenotyped, yet the exact contribution of CFTR dysfunction leading to microbial dysbiosis and inflammation and their contribution to progressive lung disease remain incompletely characterized.
We recently showed that novel therapeutic drugs, so-called “CFTR modulators” permit improvement of CFTR function up to 40-50% of wildtype (1). Airway microbial metagenomics from patients treated with these novel drugs show significant changes in their airway dysbiosis (2) associated with significant reductions in different parameters of systemic inflammation (unpublished data) and drastic improvements of pulmonary disease (3). However, individual patients show large variations in clinical responses concerning lung function and body mass index (BMI), as central clinical read-out parameters of CF disease and sweat chloride, as a surrogate parameter for functional CFTR improvement.
We hypothesize that microbial dysbiosis and systemic inflammation are central determinants of an individual’s clinical response towards CFTR modulator therapy. For the projects outlined here, we aim to combine the highly granular data on microbial metagenomics (expertise Wiehlmann (2, 4-23), inflammatory transcriptome (expertise Wiehlmann (11, 12, 14, 15, 21), inflammatory secretome (expertise Dittrich (24-27) and clinical data (expertise Dittrich (3, 28, 29) from two cohorts of CF patient by complex bioinformatical approaches (expertise Wiehlmann (2, 10-23). Ultimately, this multi-dimensional data base will permit us to develop a bioinformatics tool to predict CFTR modulator response with respect to the three hallmarks of CF disease, lung function, BMI and sweat chloride.
We have collected, analyzed and deposited airway microbial metagenomics data from twenty-four paired samples, obtained from probands at baseline, three and twelve months post-initiation of CFTR modulator therapy (2) and their associated clinical (3) and functional CFTR data (1), which will permit development of the prediction tool by the bioinformatics student. We are currently analyzing transcriptomes and secretomes of restimulated peripheral blood leukocytes from the same cohort to be added to the data set by the bioinformatics student. We have collected identical biomaterials and clinical data from a second group of patients, 6-11 years old, of which the medical student will sequence the airway metagenome and analyze the clinical data. The bioinformatics student will consecutively analyze, add and exploit the 6-11 year old data set to validate the prediction tool.
Our findings on the predictive value of cellular and soluble mediators as downstream effectors of microbial dysbiosis have the potential to provide unique starting points to progressively individualize diagnostic and therapeutic approaches for chronic diseases characterized by dysbiosis.
Pediatric Pneumology, Allergy and Neonatology
Hannover Medical School