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Genetic, molecular and physiological basis of variation in Drosophila gut immunocompetence.

Bou Sleiman MS, Osman D, Massouras A, Hoffmann AA, Lemaitre B, Deplancke B - Nat Commun (2015)

Bottom Line: Gut immunocompetence involves immune, stress and regenerative processes.Using genome-wide association analysis, we identify several novel immune modulators.This genetic and molecular variation is physiologically manifested in lower ROS activity, lower susceptibility to ROS-inducing agent, faster pathogen clearance and higher stem cell activity in resistant versus susceptible lines.

View Article: PubMed Central - PubMed

Affiliation: 1] Global Health Institute, School of Life Sciences, Station 19, EPFL, 1015 Lausanne, Switzerland [2] Institute of Bioengineering, School of Life Sciences, Station 19, EPFL, 1015 Lausanne, Switzerland.

ABSTRACT
Gut immunocompetence involves immune, stress and regenerative processes. To investigate the determinants underlying inter-individual variation in gut immunocompetence, we perform enteric infection of 140 Drosophila lines with the entomopathogenic bacterium Pseudomonas entomophila and observe extensive variation in survival. Using genome-wide association analysis, we identify several novel immune modulators. Transcriptional profiling further shows that the intestinal molecular state differs between resistant and susceptible lines, already before infection, with one transcriptional module involving genes linked to reactive oxygen species (ROS) metabolism contributing to this difference. This genetic and molecular variation is physiologically manifested in lower ROS activity, lower susceptibility to ROS-inducing agent, faster pathogen clearance and higher stem cell activity in resistant versus susceptible lines. This study provides novel insights into the determinants underlying population-level variability in gut immunocompetence, revealing how relatively minor, but systematic genetic and transcriptional variation can mediate overt physiological differences that determine enteric infection susceptibility.

No MeSH data available.


Related in: MedlinePlus

GWAS reveals genetic loci underlying susceptibility to infection.(a) Manhattan plot of the P-values (y axis) for the association between genomic variants in DGRP lines and Pseudomonas entomophila susceptibility. A non-parametric Kruskal–Wallis test was performed using proportion death at day 3 as phenotype. The x axis represents the genomic location. Multiple variants in a single gene are bounded by a box. (b) Susceptibility of DGRP lines grouped by the Nrk allele (GWAS P=3.6e−6) that changes the coding sequence at position 306 of the protein (at chr2R:9048897). Note that Drosophila simulans, Drosophila sechelia, Drosophila yakuba, and Drosophila erecta all have the variant G-allele. (c) Knockdown of the top GWAS hit, Nrk, using a ubiquitous driver (da-gal4) highly reduces the activity of the immune activation reporter Dpt-lacZ in the gut as revealed with X-Gal staining (P. entomophila A 50 was used to avoid the anticipated inhibition of translation effect of P. entomophila at A 100 (ref. 31)). UC=unchallenged flies. (d) RT–qPCR experiments on gut total RNA from females show that four susceptible DGRP lines harbouring the G-allele at the Gyc76C locus (chr3L:19769316) express Gyc76C at higher levels after P. entomophila infection, in comparison to resistant lines carrying the A-allele. Dpt transcript induction is higher in susceptible DGRP lines carrying the G-allele in Gyc76C (ANOVA P for allele effect in the challenged condition for Gyc76C and Dpt is 0.00205 and 0.0344, respectively). (e) Gyc76C knockdown in enterocytes using the thermosensitive MyoIA-gal4 driver shows that Gyc76C regulates the induction of Dpt transcript in the gut 4h and 16 h post-P. entomophila infection (ANOVA P=0.00741 for line effect; error bars represent standard deviation around the mean of three replicates). (f) Survival analysis of females that are orally infected with P. entomophila shows a lower survival rate of MyoIAts>Gyc76C-IR flies compared to wild type (Log-Rank test P=0.0351 for comparison between Gyc76C knockdown and wild type in challenged condition). d–f data is based on at least three independent biological replicates.
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f3: GWAS reveals genetic loci underlying susceptibility to infection.(a) Manhattan plot of the P-values (y axis) for the association between genomic variants in DGRP lines and Pseudomonas entomophila susceptibility. A non-parametric Kruskal–Wallis test was performed using proportion death at day 3 as phenotype. The x axis represents the genomic location. Multiple variants in a single gene are bounded by a box. (b) Susceptibility of DGRP lines grouped by the Nrk allele (GWAS P=3.6e−6) that changes the coding sequence at position 306 of the protein (at chr2R:9048897). Note that Drosophila simulans, Drosophila sechelia, Drosophila yakuba, and Drosophila erecta all have the variant G-allele. (c) Knockdown of the top GWAS hit, Nrk, using a ubiquitous driver (da-gal4) highly reduces the activity of the immune activation reporter Dpt-lacZ in the gut as revealed with X-Gal staining (P. entomophila A 50 was used to avoid the anticipated inhibition of translation effect of P. entomophila at A 100 (ref. 31)). UC=unchallenged flies. (d) RT–qPCR experiments on gut total RNA from females show that four susceptible DGRP lines harbouring the G-allele at the Gyc76C locus (chr3L:19769316) express Gyc76C at higher levels after P. entomophila infection, in comparison to resistant lines carrying the A-allele. Dpt transcript induction is higher in susceptible DGRP lines carrying the G-allele in Gyc76C (ANOVA P for allele effect in the challenged condition for Gyc76C and Dpt is 0.00205 and 0.0344, respectively). (e) Gyc76C knockdown in enterocytes using the thermosensitive MyoIA-gal4 driver shows that Gyc76C regulates the induction of Dpt transcript in the gut 4h and 16 h post-P. entomophila infection (ANOVA P=0.00741 for line effect; error bars represent standard deviation around the mean of three replicates). (f) Survival analysis of females that are orally infected with P. entomophila shows a lower survival rate of MyoIAts>Gyc76C-IR flies compared to wild type (Log-Rank test P=0.0351 for comparison between Gyc76C knockdown and wild type in challenged condition). d–f data is based on at least three independent biological replicates.

Mentions: To uncover genetic determinants underlying variation in immunocompetence, we performed a genome-wide association study (GWAS) on survival using both a non-parametric (Fig. 3a) and parametric test (Supplementary Fig. 4a). Unlike a previous study dealing with survival to viral infection in DGRP lines in which one quantitative trait locus (QTL) with large effect was identified24, we obtained 27 QTLs at an arbitrary P-value of 10−5, even though there was no clear point of departure from expectations in the Q–Q plot (Supplementary Fig. 4b). The results were largely consistent between both GWAS analysis procedures and a maximum of 19% of the phenotypic variance could be explained by a single QTL (Supplementary Table 3). The small sample size and the truncated distribution from which QTLs are chosen to estimate effect sizes can result in an overestimation of the proportion of variance explained, a phenomenon known as the ‘Beavis effect'33. This could be further exacerbated by linkage between SNPs (Supplementary Fig. 4a). To account for redundancy between linked SNPs, we also performed an iterative multiple-SNP regression34. Interestingly, as few as four SNPs can explain ∼50% of the phenotypic variance (Supplementary Table 4). Moreover, we performed a permutation analysis to evaluate the Beavis effect. In short, we sampled groups of lines of different sizes, ranging from 70 to 140, and performed multi-SNP regression. For each sample size, we performed 100 permutations with random resampling (Supplementary Fig. 5). We found that the proportion of variance explained, R2, decreases as the sample size increases, as expected, yet starts levelling-off at larger sample sizes, suggesting that the correct proportion of variance accounted by the SNPs is being approached at the larger sample sizes.


Genetic, molecular and physiological basis of variation in Drosophila gut immunocompetence.

Bou Sleiman MS, Osman D, Massouras A, Hoffmann AA, Lemaitre B, Deplancke B - Nat Commun (2015)

GWAS reveals genetic loci underlying susceptibility to infection.(a) Manhattan plot of the P-values (y axis) for the association between genomic variants in DGRP lines and Pseudomonas entomophila susceptibility. A non-parametric Kruskal–Wallis test was performed using proportion death at day 3 as phenotype. The x axis represents the genomic location. Multiple variants in a single gene are bounded by a box. (b) Susceptibility of DGRP lines grouped by the Nrk allele (GWAS P=3.6e−6) that changes the coding sequence at position 306 of the protein (at chr2R:9048897). Note that Drosophila simulans, Drosophila sechelia, Drosophila yakuba, and Drosophila erecta all have the variant G-allele. (c) Knockdown of the top GWAS hit, Nrk, using a ubiquitous driver (da-gal4) highly reduces the activity of the immune activation reporter Dpt-lacZ in the gut as revealed with X-Gal staining (P. entomophila A 50 was used to avoid the anticipated inhibition of translation effect of P. entomophila at A 100 (ref. 31)). UC=unchallenged flies. (d) RT–qPCR experiments on gut total RNA from females show that four susceptible DGRP lines harbouring the G-allele at the Gyc76C locus (chr3L:19769316) express Gyc76C at higher levels after P. entomophila infection, in comparison to resistant lines carrying the A-allele. Dpt transcript induction is higher in susceptible DGRP lines carrying the G-allele in Gyc76C (ANOVA P for allele effect in the challenged condition for Gyc76C and Dpt is 0.00205 and 0.0344, respectively). (e) Gyc76C knockdown in enterocytes using the thermosensitive MyoIA-gal4 driver shows that Gyc76C regulates the induction of Dpt transcript in the gut 4h and 16 h post-P. entomophila infection (ANOVA P=0.00741 for line effect; error bars represent standard deviation around the mean of three replicates). (f) Survival analysis of females that are orally infected with P. entomophila shows a lower survival rate of MyoIAts>Gyc76C-IR flies compared to wild type (Log-Rank test P=0.0351 for comparison between Gyc76C knockdown and wild type in challenged condition). d–f data is based on at least three independent biological replicates.
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Related In: Results  -  Collection

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f3: GWAS reveals genetic loci underlying susceptibility to infection.(a) Manhattan plot of the P-values (y axis) for the association between genomic variants in DGRP lines and Pseudomonas entomophila susceptibility. A non-parametric Kruskal–Wallis test was performed using proportion death at day 3 as phenotype. The x axis represents the genomic location. Multiple variants in a single gene are bounded by a box. (b) Susceptibility of DGRP lines grouped by the Nrk allele (GWAS P=3.6e−6) that changes the coding sequence at position 306 of the protein (at chr2R:9048897). Note that Drosophila simulans, Drosophila sechelia, Drosophila yakuba, and Drosophila erecta all have the variant G-allele. (c) Knockdown of the top GWAS hit, Nrk, using a ubiquitous driver (da-gal4) highly reduces the activity of the immune activation reporter Dpt-lacZ in the gut as revealed with X-Gal staining (P. entomophila A 50 was used to avoid the anticipated inhibition of translation effect of P. entomophila at A 100 (ref. 31)). UC=unchallenged flies. (d) RT–qPCR experiments on gut total RNA from females show that four susceptible DGRP lines harbouring the G-allele at the Gyc76C locus (chr3L:19769316) express Gyc76C at higher levels after P. entomophila infection, in comparison to resistant lines carrying the A-allele. Dpt transcript induction is higher in susceptible DGRP lines carrying the G-allele in Gyc76C (ANOVA P for allele effect in the challenged condition for Gyc76C and Dpt is 0.00205 and 0.0344, respectively). (e) Gyc76C knockdown in enterocytes using the thermosensitive MyoIA-gal4 driver shows that Gyc76C regulates the induction of Dpt transcript in the gut 4h and 16 h post-P. entomophila infection (ANOVA P=0.00741 for line effect; error bars represent standard deviation around the mean of three replicates). (f) Survival analysis of females that are orally infected with P. entomophila shows a lower survival rate of MyoIAts>Gyc76C-IR flies compared to wild type (Log-Rank test P=0.0351 for comparison between Gyc76C knockdown and wild type in challenged condition). d–f data is based on at least three independent biological replicates.
Mentions: To uncover genetic determinants underlying variation in immunocompetence, we performed a genome-wide association study (GWAS) on survival using both a non-parametric (Fig. 3a) and parametric test (Supplementary Fig. 4a). Unlike a previous study dealing with survival to viral infection in DGRP lines in which one quantitative trait locus (QTL) with large effect was identified24, we obtained 27 QTLs at an arbitrary P-value of 10−5, even though there was no clear point of departure from expectations in the Q–Q plot (Supplementary Fig. 4b). The results were largely consistent between both GWAS analysis procedures and a maximum of 19% of the phenotypic variance could be explained by a single QTL (Supplementary Table 3). The small sample size and the truncated distribution from which QTLs are chosen to estimate effect sizes can result in an overestimation of the proportion of variance explained, a phenomenon known as the ‘Beavis effect'33. This could be further exacerbated by linkage between SNPs (Supplementary Fig. 4a). To account for redundancy between linked SNPs, we also performed an iterative multiple-SNP regression34. Interestingly, as few as four SNPs can explain ∼50% of the phenotypic variance (Supplementary Table 4). Moreover, we performed a permutation analysis to evaluate the Beavis effect. In short, we sampled groups of lines of different sizes, ranging from 70 to 140, and performed multi-SNP regression. For each sample size, we performed 100 permutations with random resampling (Supplementary Fig. 5). We found that the proportion of variance explained, R2, decreases as the sample size increases, as expected, yet starts levelling-off at larger sample sizes, suggesting that the correct proportion of variance accounted by the SNPs is being approached at the larger sample sizes.

Bottom Line: Gut immunocompetence involves immune, stress and regenerative processes.Using genome-wide association analysis, we identify several novel immune modulators.This genetic and molecular variation is physiologically manifested in lower ROS activity, lower susceptibility to ROS-inducing agent, faster pathogen clearance and higher stem cell activity in resistant versus susceptible lines.

View Article: PubMed Central - PubMed

Affiliation: 1] Global Health Institute, School of Life Sciences, Station 19, EPFL, 1015 Lausanne, Switzerland [2] Institute of Bioengineering, School of Life Sciences, Station 19, EPFL, 1015 Lausanne, Switzerland.

ABSTRACT
Gut immunocompetence involves immune, stress and regenerative processes. To investigate the determinants underlying inter-individual variation in gut immunocompetence, we perform enteric infection of 140 Drosophila lines with the entomopathogenic bacterium Pseudomonas entomophila and observe extensive variation in survival. Using genome-wide association analysis, we identify several novel immune modulators. Transcriptional profiling further shows that the intestinal molecular state differs between resistant and susceptible lines, already before infection, with one transcriptional module involving genes linked to reactive oxygen species (ROS) metabolism contributing to this difference. This genetic and molecular variation is physiologically manifested in lower ROS activity, lower susceptibility to ROS-inducing agent, faster pathogen clearance and higher stem cell activity in resistant versus susceptible lines. This study provides novel insights into the determinants underlying population-level variability in gut immunocompetence, revealing how relatively minor, but systematic genetic and transcriptional variation can mediate overt physiological differences that determine enteric infection susceptibility.

No MeSH data available.


Related in: MedlinePlus