Abstract
Despite the arsenal of technologies employed to control foodborne nontyphoidal Salmonella (NTS), infections have not declined in decades. Poultry is the primary source of NTS outbreaks, as well as the fastest growing meat sector worldwide. With recent FDA rules for phasing-out antibiotics in animal production, pressure is mounting to develop new pathogen reduction strategies. We report on a technology to reduce Salmonella enteritidis in poultry. We engineered probiotic E. coli Nissle 1917, to express and secrete the antimicrobial peptide, Microcin J25. Using in vitro experiments and an animal model of 300 turkeys, we establish the efficacy of this technology. Salmonella more rapidly clear the ceca of birds administered the modified probiotic than other treatment groups. Approximately 97% lower Salmonella carriage is measured in a treated group, 14 days post-Salmonella challenge. Probiotic bacteria are generally regarded as safe to consume, are bile-resistant and can plausibly be modified to produce a panoply of antimicrobial peptides now known. The reported systems may provide a foundation for platforms to launch antimicrobials against gastrointestinal tract pathogens, including ones that are multi-drug resistant.
Original language | English (US) |
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Article number | 40695 |
Journal | Scientific reports |
Volume | 7 |
DOIs | |
State | Published - Jan 17 2017 |
Bibliographical note
Funding Information:This work was supported by a grant from the National Institutes of Health (GM111358) and a grant from the National Science Foundation (CBET1412283). We thank Professors T. Johnson (Veterinary and Biomedical Sciences) and S. Noll (Animal Science) at the University of Minnesota for useful discussions and T. Johnson for donating the SE isolate (MH91989). We thank Professor J. Links (Chemical and Biological Engineering, Princeton University) for providing the PJP3 plasmid vector. We thank the University of Minnesota Genomics Center for their microbiome sequencing service and the Minnesota Supercomputing Institute for tools used in bioinformatics analysis. This work utilized the high-performance computational resources of the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1053575. We thank the University of Minnesota Research Animal Resources for their aid in housing and caring for the animals. Support from the University of Minnesota Digital Technology Center, the Biotechnology Institute and the MN-DRIVE program are gratefully acknowledged
Publisher Copyright:
© The Author(s) 2017.