4.6 Article

Refining the Galleria mellonella Model by Using Stress Marker Genes to Assess Clostridioides difficile Infection and Recuperation during Phage Therapy

期刊

MICROORGANISMS
卷 8, 期 9, 页码 -

出版社

MDPI
DOI: 10.3390/microorganisms8091306

关键词

Clostridioides difficile; Clostridium difficile; Clostridiodes difficile infection; Galleria mellonella; bacteriophage therapy; qPCR

资金

  1. AmpliPhi Biosciences
  2. University of Leicester [RM63G0556]
  3. BBSRC [RM38G0140]
  4. DBT Overseas Associateship [102/IFD/SAN/2628-2643/2013-14]
  5. DST, New Delhi [EMR/2016/003227]
  6. UKRI BBSRC [1897785]
  7. BBSRC [1897785] Funding Source: UKRI

向作者/读者索取更多资源

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.

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