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Following septic insults, healthy insects, just like vertebrates, mount a complex immune response to contain and destroy pathogens. The failure to efficiently clear bacterial infections in immuno-compromised fly mutants leads to higher mortality rates which provide a powerful indicator for genes with important roles in innate immunity. The following protocol is designed to reproducibly inject a known amount of non-pathogenic E. coli into otherwise sterile flies and to measure the survival of flies after infection. The protocol can be easily adapted to different types of bacteria.
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[Abstract] Following septic insults, healthy insects, just like vertebrates, mount a complex immune response to contain and destroy pathogens. The failure to efficiently clear bacterial infections in immuno-compromised fly mutants leads to higher mortality rates which provide a powerful indicator for genes with important roles in innate immunity. The following protocol is designed to reproducibly inject a known amount of non-pathogenic E. coli into otherwise sterile flies and to measure the survival of flies after infection. The protocol can be easily adapted to different types of bacteria.
Keywords: Drosophila, Innate immunity, Bacterial infections, Tolerance, Survival
[Background] Classic infection experiments involve infecting Drosophila orally (Chakrabarti et al., 2016) or with a needle dipped in a concentrated bacterial solution (Romeo and Lemaitre, 2008). Unlike these protocols, our experimental procedure allows us to determine the site of infection and precisely control the dose of bacteria injected into each fly. This provides homogeneity and reproducibility, and allows us to adapt bacterial load for different experiments (Akbar et al., 2011 and 2016).
Materials and Reagents
Equipment
Software
Procedure
Data analysis
Recorded data are entered into Prism (GraphPad) software which is used to display survival curves (Figure 4) and analyze statistical significance of differences in survival between genotypes. Log-Rank (Mantel-Cox) test was used to assess statistical significance of differences between survival curves (Figure 4). Alternatively, multiple Log-rank analysis software packages are available online at: http://astatsa.com/ and http://bioinf.wehi.edu.au/software/russell/logrank. Both sides offer detailed information on how to enter data. Figure 4. Example of statistical analysis of fly survival post bacterial infection. The diagram shows the fraction of surviving wild-type flies (OreR) or Vps33B mutants after injection with heat-killed E. coli, similar to experiments previously published (Akbar et al., 2016). Statistical analysis was performed in Prism using the Log-rank (or Mantel-Cox) test to determine a P value smaller than 0.0001.
Notes
Recipes
Acknowledgments
The work herein was supported by NIH Grants EY010199, EY021922. This protocol has been adapted and modified from our previously published work (Akbar et al., 2016; Akbar et al., 2011). The authors declare no conflicts of interest.
References
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