Katja Koeppen, PhD 

Research Scientist
Department of Microbiology and Immunology
The Geisel School of Medicine at Dartmouth
Remsen, Room 604
Hanover, NH 03755

E-mail: Katja.Koeppen@Dartmouth.edu

Areas of expertise: cystic fibrosis, host-pathogen interactions, bioinformatics

Katja Koeppen is a biochemist with extensive experience in molecular genetics, ion channels, cystic fibrosis research and host-pathogen interactions. She received her PhD from the University of Tuebingen, Germany.
Her current research interest focuses on host pathogen interactions in the lung. Chronic airway infections are a leading cause of death in many lung diseases including cystic fibrosis, chronic obstructive pulmonary disease (COPD) and ventilator-associated pneumonia. The emergence of antibiotic resistance necessitates novel approaches for controlling these infections. If we can understand better how pathogenic bacteria communicate with host cells in order to establish and maintain chronic infections, we can find ways to disrupt this communication and thereby reduce mortality of bacterial infections.
Katja Koeppen employs a wide array of molecular, biochemical and bioinformatic techniques to understand complex scientific problems in microbiology and immunology. Her expertise encompasses primary cell culture and transfection, purification of bacterial outer membrane vesicles, qPCR, ELISA, biotinylation, fluorescence microscopy and electrophysiology (patch-clamp and Ussing recordings).
She routinely uses advanced bioinformatics tools such as the R statistical programming language, Ingenuity Pathway Analysis and CLC Genomics Workbench to study gene and microRNA interactions and interpret RNA-Seq, microarray, nCounter and proteomics data. Katja Koeppen is developing collaborations throughout the Dartmouth Lung Biology Center to leverage her bioinformatics skills and assist others with the interpretation of quantitative data.


ScanGEO Web Application
ScanGEO is a tool for rapid meta-analysis of differential gene expression across NCBI Gene Expression Omnibus datasets.