Refining the Galleria mellonella Model by Using Stress Marker Genes to Assess Clostridioides difficile Infection and Recuperation during Phage Therapy.
Janet Yakubu NaleMahananda ChutiaJeffrey K J ChengMartha R J ClokiePublished in: Microorganisms (2020)
The Galleria mellonella is an effective model for probing Clostridioides difficile interactions with phages. Despite valuable insights from this model, the larvae are not easily amenable to assessing detailed clinical responses to either bacteria or phages. Here, larval survival, colonisation and toxin levels were compared to expression profiles of 17 G. mellonella stress genes to monitor Clostridiodes difficile infection (CDI), and recuperation during phage therapy. The larvae were infected with a ribotype 014/020 isolate and treated with an optimised phage cocktail. Larvae treated prophylactically with phages and the phage-control larval group were protected, showing the highest survival, and low C. difficile colonisation and toxin rates, compared to co-infection, remedial and bacterial-control larval groups. Expression of growth (9) and reproduction (2) genes were enhanced within prophylaxis and phage-control larval groups compared to the co-infection, remedial and bacterial control groups. In contrast, expression of infection (2), humoral (1) and cellular (3) immunity genes declined in the prophylactic and phage-control groups but increased in the co-infection, remedial and bacterial control larvae. The molecular markers augment the survival, colonisation and toxin data and allow detailed monitoring of CDI and recovery. This data support the use of stress marker genes as tools to analyse clinical symptoms in this model.
Keyphrases
- aedes aegypti
- pseudomonas aeruginosa
- clostridium difficile
- drosophila melanogaster
- genome wide
- escherichia coli
- poor prognosis
- immune response
- magnetic resonance
- bioinformatics analysis
- genome wide identification
- stem cells
- physical activity
- electronic health record
- stress induced
- genome wide analysis
- long non coding rna
- depressive symptoms
- cell therapy
- newly diagnosed
- deep learning
- binding protein
- smoking cessation
- molecular dynamics simulations
- contrast enhanced