Nano-enabled Antibacterial Combination Therapy (NeACT) Targeting Intracellular Salmonella enterica ser. Typhimurium to Treat Intestinal Infections in Swine
Bibliographic record
Abstract
Salmonellosis is a zoonotic infection caused by gram-negative Salmonella spp. and is amongst the top four bacterium causing foodborne infections globally. Transmission generally occurs through animal-based food such as pork. There exist many Salmonella enterica. serovars and S. Typhimurium (ST) has shown a global prevalence. Furthermore, ST infections pose a significant threat to the swine industry due to reduced production, lack of treatment, economic losses, etc. The prevalence of the infection and the rise of antimicrobial resistance (AMR) in livestock warrants intervention strategies that can reduce and treat infection. This thesis research focused on two main aspects: (1) determination of the phenotypic and genotypic profile of a swine Salmonella enterica ser. Typhimurium isolate obtained from an animal farm in Quebec for extreme drug resistance, and (2) developing a Nano-enabled Antibacterial Combination Therapy to eliminate intracellular pathogenesis of multidrug-resistance (MDR) Salmonella enterica ser. Typhimurium.The first phase of this study consisted of the phenotypic and genotypic study of the swine Salmonella enterica ser. Typhimurium isolate from a Quebec animal farm. The isolate was characterized for its AMR genes using whole genome sequencing (WGS) where resistance to more than 8 antibiotic classes was identified. Generally, the WGS data validated the phenotypic results. The WGS data predicted genes aph(6)-Id and aph(3'')-Ib to confer resistance to streptomycin alone, however, resistance was phenotypically observed with streptomycin as well. This is a result of the two belonging to the same antibiotic group: aminoglycosides and having similar modes of action. Furthermore, we investigated their intracellular infection in an in vitro swine epithelial intestinal model, IPEC-J2 cells. Remarkably, the antibiotics reported to be effective against ST, when tested were unsuccessful in remediating intracellular infection in IPEC-J2 cells. Consequently, alternate and novel treatments are required to target this multi-drug resistance (MDR) intracellular pathogen. Combination therapy was sought as a solution to repurpose antibiotics and polyphenols because of their accessibility and their use in the swine industry. Accordingly, the first part of thesis addresses assessing the effectiveness of antibiotic-adjuvant combinations for eliminating MDR ST. Studies conducted using checkerboard assay and identifying antibacterial synergism using their fractional inhibitory concentration index (FICI) revealed, amoxicillin (AMOX) and tazobactam (TAZO) to be the best combination among the tested molecules and was therefore selected for further analysis. The above combination was further encapsulated in a cyclodextrin (CD) and liposome (LP) as the selected nano-carriers for their drug delivery abilities. The final drug, LP-CAT was of 232.34±4.76 nm in size and exhibited surface charge of -41.87±5.11 mV. The encapsulation efficiency was determined spectrophotometrically, AMOX and TAZO was found to be 46.5±1.11% and 49±1.79% respectively. LP-CAT showed improved antibacterial efficiency and the ability to reduce intracellular pathogenicity against ST in IPEC-J2 cells. The results of this study highlight the potential of LP-CAT (NeACT) as an alternative to target intracellular pathogens in animal agriculture
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".