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Transcriptomic buffering of cryptic genetic variation contributes to meningococcal virulence

View Article: PubMed Central - PubMed

ABSTRACT

Background: Commensal bacteria like Neisseria meningitidis sometimes cause serious disease. However, genomic comparison of hyperinvasive and apathogenic lineages did not reveal unambiguous hints towards indispensable virulence factors. Here, in a systems biological approach we compared gene expression of the invasive strain MC58 and the carriage strain α522 under different ex vivo conditions mimicking commensal and virulence compartments to assess the strain-specific impact of gene regulation on meningococcal virulence.

Results: Despite indistinguishable ex vivo phenotypes, both strains differed in the expression of over 500 genes under infection mimicking conditions. These differences comprised in particular metabolic and information processing genes as well as genes known to be involved in host-damage such as the nitrite reductase and numerous LOS biosynthesis genes. A model based analysis of the transcriptomic differences in human blood suggested ensuing metabolic flux differences in energy, glutamine and cysteine metabolic pathways along with differences in the activation of the stringent response in both strains. In support of the computational findings, experimental analyses revealed differences in cysteine and glutamine auxotrophy in both strains as well as a strain and condition dependent essentiality of the (p)ppGpp synthetase gene relA and of a short non-coding AT-rich repeat element in its promoter region.

Conclusions: Our data suggest that meningococcal virulence is linked to transcriptional buffering of cryptic genetic variation in metabolic genes including global stress responses. They further highlight the role of regulatory elements for bacterial virulence and the limitations of model strain approaches when studying such genetically diverse species as N. meningitidis.

Electronic supplementary material: The online version of this article (doi:10.1186/s12864-017-3616-7) contains supplementary material, which is available to authorized users.

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Related in: MedlinePlus

GC content variation in potential promoter regions based on the MC58 genome sequence. a Scatter plot of the GC content variation averaged over a 5-bp sliding window within 100 bp upstream regions for genes highly expressed in MC58 (red and yellow lines) or α522 (light and dark blue lines) in human blood. The black line gives the GC content of the respective upstream regions for genes not differently expressed. Regulatory regions are indicated at the top of the panel based on the average length of 5’-untranslated regions in N. gonorrhoeae [75]. The insert gives the number of genes in each gene set. b Box-and-whiskers plot depicting differences in the mean GC content of the putative discriminator (left) and Hfq-binding regions (right) between genes highly expressed in MC58 (red) or α522 (blue) in human blood as depicted in panel (a). The line within each box gives the median and the upper and lower margins the upper and the lower quartile, respectively. The whiskers denote the highest and the lowest values, respectively, and the open circles outliers. *: p < 0.05, **: p < 0.01 (Wilcoxon test)
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Fig6: GC content variation in potential promoter regions based on the MC58 genome sequence. a Scatter plot of the GC content variation averaged over a 5-bp sliding window within 100 bp upstream regions for genes highly expressed in MC58 (red and yellow lines) or α522 (light and dark blue lines) in human blood. The black line gives the GC content of the respective upstream regions for genes not differently expressed. Regulatory regions are indicated at the top of the panel based on the average length of 5’-untranslated regions in N. gonorrhoeae [75]. The insert gives the number of genes in each gene set. b Box-and-whiskers plot depicting differences in the mean GC content of the putative discriminator (left) and Hfq-binding regions (right) between genes highly expressed in MC58 (red) or α522 (blue) in human blood as depicted in panel (a). The line within each box gives the median and the upper and lower margins the upper and the lower quartile, respectively. The whiskers denote the highest and the lowest values, respectively, and the open circles outliers. *: p < 0.05, **: p < 0.01 (Wilcoxon test)

Mentions: Although sequence analyses of the 200 bp upstream regions of the 524 genes that were differently expressed between both strains in cross-strain comparisons failed to identify any consistent sequence differences or overrepresented bona fide TF binding sites for any of the cross-strain comparisons, the analysis of GC content variation yet revealed a 6 bp region immediately upstream of the predicted ribosome binding site (RBS) having a significantly lower GC content in genes that were expressed at higher levels in MC58 than in α522 in blood (GCMC58 = 37% vs. GCα522 = 42%, Wilcoxon test, p < 0.01) (Fig. 6). At the mRNA level, such AU-rich elements next to the RBS are often targets for the Hfq-mediated binding of small non-coding RNAs which thus post-transcriptionally regulate the degradation and/or translation efficiency of the corresponding mRNA [73]. In strain MC58, the RNA chaperone Hfq was already shown to be involved in the regulation of amino acid and energy metabolism, the oxidative stress response and required for survival in human blood [74]. The comparison of the differently expressed genes showed that of the 18 genes that are part of the Hfq regulon and that were included in this study, 9 were differently expressed in both strains, all higher in MC58. Therefore, these data suggest that Hfq contributes to gene regulation differences between both strains in blood.Fig. 6


Transcriptomic buffering of cryptic genetic variation contributes to meningococcal virulence
GC content variation in potential promoter regions based on the MC58 genome sequence. a Scatter plot of the GC content variation averaged over a 5-bp sliding window within 100 bp upstream regions for genes highly expressed in MC58 (red and yellow lines) or α522 (light and dark blue lines) in human blood. The black line gives the GC content of the respective upstream regions for genes not differently expressed. Regulatory regions are indicated at the top of the panel based on the average length of 5’-untranslated regions in N. gonorrhoeae [75]. The insert gives the number of genes in each gene set. b Box-and-whiskers plot depicting differences in the mean GC content of the putative discriminator (left) and Hfq-binding regions (right) between genes highly expressed in MC58 (red) or α522 (blue) in human blood as depicted in panel (a). The line within each box gives the median and the upper and lower margins the upper and the lower quartile, respectively. The whiskers denote the highest and the lowest values, respectively, and the open circles outliers. *: p < 0.05, **: p < 0.01 (Wilcoxon test)
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Fig6: GC content variation in potential promoter regions based on the MC58 genome sequence. a Scatter plot of the GC content variation averaged over a 5-bp sliding window within 100 bp upstream regions for genes highly expressed in MC58 (red and yellow lines) or α522 (light and dark blue lines) in human blood. The black line gives the GC content of the respective upstream regions for genes not differently expressed. Regulatory regions are indicated at the top of the panel based on the average length of 5’-untranslated regions in N. gonorrhoeae [75]. The insert gives the number of genes in each gene set. b Box-and-whiskers plot depicting differences in the mean GC content of the putative discriminator (left) and Hfq-binding regions (right) between genes highly expressed in MC58 (red) or α522 (blue) in human blood as depicted in panel (a). The line within each box gives the median and the upper and lower margins the upper and the lower quartile, respectively. The whiskers denote the highest and the lowest values, respectively, and the open circles outliers. *: p < 0.05, **: p < 0.01 (Wilcoxon test)
Mentions: Although sequence analyses of the 200 bp upstream regions of the 524 genes that were differently expressed between both strains in cross-strain comparisons failed to identify any consistent sequence differences or overrepresented bona fide TF binding sites for any of the cross-strain comparisons, the analysis of GC content variation yet revealed a 6 bp region immediately upstream of the predicted ribosome binding site (RBS) having a significantly lower GC content in genes that were expressed at higher levels in MC58 than in α522 in blood (GCMC58 = 37% vs. GCα522 = 42%, Wilcoxon test, p < 0.01) (Fig. 6). At the mRNA level, such AU-rich elements next to the RBS are often targets for the Hfq-mediated binding of small non-coding RNAs which thus post-transcriptionally regulate the degradation and/or translation efficiency of the corresponding mRNA [73]. In strain MC58, the RNA chaperone Hfq was already shown to be involved in the regulation of amino acid and energy metabolism, the oxidative stress response and required for survival in human blood [74]. The comparison of the differently expressed genes showed that of the 18 genes that are part of the Hfq regulon and that were included in this study, 9 were differently expressed in both strains, all higher in MC58. Therefore, these data suggest that Hfq contributes to gene regulation differences between both strains in blood.Fig. 6

View Article: PubMed Central - PubMed

ABSTRACT

Background: Commensal bacteria like Neisseria meningitidis sometimes cause serious disease. However, genomic comparison of hyperinvasive and apathogenic lineages did not reveal unambiguous hints towards indispensable virulence factors. Here, in a systems biological approach we compared gene expression of the invasive strain MC58 and the carriage strain &alpha;522 under different ex vivo conditions mimicking commensal and virulence compartments to assess the strain-specific impact of gene regulation on meningococcal virulence.

Results: Despite indistinguishable ex vivo phenotypes, both strains differed in the expression of over 500 genes under infection mimicking conditions. These differences comprised in particular metabolic and information processing genes as well as genes known to be involved in host-damage such as the nitrite reductase and numerous LOS biosynthesis genes. A model based analysis of the transcriptomic differences in human blood suggested ensuing metabolic flux differences in energy, glutamine and cysteine metabolic pathways along with differences in the activation of the stringent response in both strains. In support of the computational findings, experimental analyses revealed differences in cysteine and glutamine auxotrophy in both strains as well as a strain and condition dependent essentiality of the (p)ppGpp synthetase gene relA and of a short non-coding AT-rich repeat element in its promoter region.

Conclusions: Our data suggest that meningococcal virulence is linked to transcriptional buffering of cryptic genetic variation in metabolic genes including global stress responses. They further highlight the role of regulatory elements for bacterial virulence and the limitations of model strain approaches when studying such genetically diverse species as N. meningitidis.

Electronic supplementary material: The online version of this article (doi:10.1186/s12864-017-3616-7) contains supplementary material, which is available to authorized users.

No MeSH data available.


Related in: MedlinePlus