Investigating pathogen-host interactions and adaptation with network biology approaches

Olbei, Marton (2021) Investigating pathogen-host interactions and adaptation with network biology approaches. Doctoral thesis, University of East Anglia.

[thumbnail of 2021OlbeiMPhDthesis.pdf]
Download (37MB) | Preview


Serovars of the genus Salmonella are widespread enteric pathogens, causing acute inflammatory gut infections. However, a subgroup of Salmonella adapted to a systemic lifestyle instead of a mucosal one. A systems-level understanding of how molecular level changes accompanying this adaptive process potentially modify the behaviour of these invasive strains is crucial for future intervention processes, and possible treatments.

In this thesis, I generated and analysed multi-layered interaction networks for 20 strains in the genus Salmonella. I collated protein-protein, transcriptional regulatory, and metabolic interaction data from low and high-throughput experiments and performed predictive measures to add further connections to the systems. The resulting networks culminated in the update to SalmoNet, the first integrated network database for Salmonella serovars. Through comparative network approaches, users can highlight elements under selection in these invasive serovars, increasing our understanding of the host adaptation process leading to their systemic lifestyle.

During the last year of my PhD, I redeployed for 6 months to work on COVID-19 related research. This effort led to a systematic literature curation highlighting different cytokine responses in patients caused by SARS-CoV-2 compared to other similar viruses. I also led the effort to establish a new network resource, CytokineLink, aimed at highlighting avenues of cell-to-cell communication mediated by cytokines, to better understand inflammatory and infectious diseases.

Overall, the work presented in this thesis has increased our understanding of the Salmonella host adaptation process, by highlighting specific elements under selection, while also exhibiting how network information can be created, and used for understanding such evolutionary processes.

Item Type: Thesis (Doctoral)
Faculty \ School: Faculty of Science > School of Biological Sciences
Depositing User: Chris White
Date Deposited: 16 Mar 2022 14:29
Last Modified: 16 Mar 2022 14:29


Downloads per month over past year

Actions (login required)

View Item View Item