Disputation Anna Górska
am Donnerstag, 31. Januar 2019, um 13:00 Uhr in Raum A104, Sand 1
Bioinformatics approaches to study antibiotics resistance emergence across levels of biological organization
Berichterstatter 1: Prof. Dr. Daniel Huson
Berichterstatter 2: Prof. Dr. Kay Nieselt
The emergence of antibiotic resistance constitutes a severe threat to the whole of the modern medical practice. The novel antibiotic discovery has so far been too slow to combat the emergence. Therefore, antibiotic stewardship gains importance. However, the details of the mechanisms of the resistance emergence and its relationship with the antibiotic therapies or patient’s characteristics remain poorly described. Consequently, it is still impracticable to include resistance emergence into the stewardship. This dissertation focuses on the antibiotic resistance emergence at multiple levels of biological organization, aiming to describe the emergence mechanisms and to provide tools for antibiotic stewardship.
Firstly, we examined changes in the genomes of MRSA strains isolated from several patients throughout antibiotic therapies. We observed the MRSA strains differed in the number and types of the virulence factors responsible for interacting with the human body. The changes were attributed to the mobile genetic elements.
Secondly, we investigated shifts in the microbiome of the human gut caused by antibiotic therapy. We showed that prompted by an antibiotic, the resistance genes transfer from the bacterial genomes onto plasmids, prophages, and finally, to the free bacteriophages. Moreover, the antibiotic resistance genes remained on the mobile genetic elements and bacterial chromosomes long after the therapy had stopped.
Thirdly, we analyzed the impact of the antibiotic therapies on the hospitalized patients. We applied machine learning methods to investigate the relationship between the patient's medical history, and the colonization with multi-drug resistant bacteria. We showed that antibiotic therapies differ regarding the probability of the colonization with multi-drug resistant bacteria. The final classifiers were made available on the AskSaturn website where the doctors can compare their antibiotic therapies concerning the colonization probability.
These findings together with the developed bioinformatics methods provide crucial components needed for the personalization of the therapeutic antibiotic usage aiming at curbing emergence of antibiotic resistance.