Publication details

The Pioneer platform: A novel approach for selection of selective anti-cancer cytotoxic activity in bacteria through co-culturing with engineered human cells

Authors

GARLAND Gavin D PATIL Kiran R TURNER Suzanne Dawn WILLIS Anne E

Year of publication 2023
Type Article in Periodical
Magazine / Source Plos one
MU Faculty or unit

Central European Institute of Technology

Citation
Web https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0286741
Doi http://dx.doi.org/10.1371/journal.pone.0286741
Keywords REDOX PROTEIN AZURIN; COLICIN-M; CHLORAMPHENICOL ACETYLTRANSFERASE; MAMMALIAN-CELLS
Description Most of the small-molecule drugs approved for the treatment of cancer over the past 40 years are based on natural compounds. Bacteria provide an extensive reservoir for the development of further anti-cancer therapeutics to meet the challenges posed by the diversity of these malignant diseases. While identifying cytotoxic compounds is often easy, achieving selective targeting of cancer cells is challenging. Here we describe a novel experimental approach (the Pioneer platform) for the identification and development of 'pioneering' bacterial variants that either show or are conduced to exhibit selective contact-independent anti-cancer cytotoxic activities. We engineered human cancer cells to secrete Colicin M that repress the growth of the bacterium Escherichia coli, while immortalised non-transformed cells were engineered to express Chloramphenicol Acetyltransferase capable of relieving the bacteriostatic effect of Chloramphenicol. Through co-culturing of E. coli with these two engineered human cell lines, we show bacterial outgrowth of DH5 alpha E. coli is constrained by the combination of negative and positive selection pressures. This result supports the potential for this approach to screen or adaptively evolve 'pioneering' bacterial variants that can selectively eliminate the cancer cell population. Overall, the Pioneer platform demonstrates potential utility for drug discovery through multi-partner experimental evolution.

You are running an old browser version. We recommend updating your browser to its latest version.

More info