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In-silico prediction and deep-DNA sequencing validation indicate phase variation in 115 Neisseria meningitidis genes

Background
The Neisseria meningitidis (Nm) chromosome shows a high abundance of simple sequence DNA repeats (SSRs) that undergo stochastic, reversible mutations at high frequency. This mechanism is reflected in an extensive phenotypic diversity that facilitates Nm adaptation to dynamic environmental changes. To date, phase-variable phenotypes mediated by SSRs variation have been experimentally confirmed for 26 Nm genes.

Results
Here we present a population-scale comparative genomic analysis that identified 277 genes and classified them into 52 strong, 60 moderate and 165 weak candidates for phase variation. Deep-coverage DNA sequencing of single colonies grown overnight under non-selective conditions confirmed the presence of high-frequency, stochastic variation in 115 of them, providing circumstantial evidence for their phase variability.

We confirmed previous observations of a predominance of variable SSRs within genes for components located on the cell surface or DNA metabolism. However, in addition we identified an unexpectedly broad spectrum of other metabolic functions, and most of the variable SSRs were predicted to induce phenotypic changes by modulating gene expression at a transcriptional level or by producing different protein isoforms rather than mediating on/off translational switching through frameshifts.

Investigation of the evolutionary history of SSR contingency loci revealed that these loci were inherited from a Nm ancestor, evolved independently within Nm, or were acquired by Nm through lateral DNA exchange.

Conclusions
Overall, our results have identified a broader and qualitatively different phenotypic diversification of SSRs-mediated stochastic variation than previously documented, including its impact on central Nm metabolism.


BMC Genomics, 2016

Autori IIT:

Tipo: Contributo in rivista ISI
Area di disciplina: Computer Science & Engineering

File: RD_bmcgenomics.pdf

Attività: Biologia computazionale