SummaryMajor pathogenic clonal complexes (cc) of Neisseria meningitidis differ substantially in their point prevalence among healthy carriers. We show that frequently carried pathogenic cc (e.g. sequence type ST-41/44 cc and ST-32 cc) depend on extracellular DNA (eDNA) to initiate in vitro biofilm formation, whereas biofilm formation of cc with low point prevalence (ST-8 cc and ST-11 cc) was eDNA-independent. For initial biofilm formation, a ST-32 cc type strain, but not a ST-11 type strain, utilized eDNA. The release of eDNA was mediated by lytic transglycosylase and cytoplasmic N-acetylmuramyl-L-alanine amidase genes. In late biofilms, outer membrane phospholipase A-dependent autolysis, which was observed in most cc, but not in ST-8 and ST-11 strains, was required for shear force resistance of microcolonies. Taken together, N. meningitidis evolved two different biofilm formation strategies, an eDNA-dependent one yielding shear force resistant microcolonies, and an eDNA-independent one. Based on the experimental findings and previous epidemiological observations, we hypothesize that most meningococcal cc display a settler phenotype, which is eDNA-dependent and results in a stable interaction with the host. On the contrary, spreaders (ST-11 and ST-8 cc) are unable to use eDNA for biofilm formation and might compensate for poor colonization properties by high transmission rates.

Summary:A novel point process model continuous in space-time is proposed for quantifying the transmission dynamics of the two most common meningococcal antigenic sequence types observed in Germany 2002Germany -2008. Modelling is based on the conditional intensity function (CIF) which is described by a superposition of additive and multiplicative components. As an epidemiological interesting finding, spread behaviour was shown to depend on type in addition to age: basic reproduction numbers were 0.25 (95% CI 0.19-0.34) and 0.11 (95% CI 0.07-0.17) for types B:P1.7-2,4:F1-5 and C:P1.5,2:F3-3, respectively. Altogether, the proposed methodology represents a comprehensive and universal regression framework for the modelling, simulation and inference of self-exciting spatio-temporal point processes based on the CIF. Usability of the modelling in biometric practice is promoted by an implementation in the R package surveillance.

A longstanding question in infection biology addresses the genetic basis for invasive behavior in commensal pathogens. A prime example for such a pathogen is Neisseria meningitidis. On the one hand it is a harmless commensal bacterium exquisitely adapted to humans, and on the other hand it sometimes behaves like a ferocious pathogen causing potentially lethal disease such as sepsis and acute bacterial meningitis. Despite the lack of a classical repertoire of virulence genes in N. meningitidis separating commensal from invasive strains, molecular epidemiology suggests that carriage and invasive strains belong to genetically distinct populations. In recent years, it has become increasingly clear that metabolic adaptation enables meningococci to exploit host resources, supporting the concept of nutritional virulence as a crucial determinant of invasive capability. Here, we discuss the contribution of core metabolic pathways in the context of colonization and invasion with special emphasis on results from genome-wide surveys. The metabolism of lactate, the oxidative stress response, and, in particular, glutathione metabolism as well as the denitrification pathway provide examples of how meningococcal metabolism is intimately linked to pathogenesis. We further discuss evidence from genome-wide approaches regarding potential metabolic differences between strains from hyperinvasive and carriage lineages and present new data assessing in vitro growth differences of strains from these two populations. We hypothesize that strains from carriage and hyperinvasive lineages differ in the expression of regulatory genes involved particularly in stress responses and amino acid metabolism under infection conditions.

Meningococcal disease clustering was found by DNA sequence–based finetyping and cluster detection software.

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