Comparative genome analysis of a large Dutch Legionella pneumophila strain collection identifies five markers highly correlated with clinical strains

Genomic heterogeneity differentiates clinical and environmental subgroups of Legionella pneumophila sequence type 1

Legionella spp. are the cause of a severe bacterial pneumonia known as Legionnaires' disease (LD). In some cases, current genetic subtyping methods cannot resolve LD outbreaks caused by common, potentially endemic L. pneumophila (Lp) sequence types (ST), which complicates laboratory investigations and environmental source attribution. In the United States (US), ST1 is the most prevalent clinical and environmental Lp sequence type. In order to characterize the ST1 population, we sequenced 289 outbreak and non-outbreak associated clinical and environmental ST1 and ST1-variant Lp strains from the US and, together with international isolate sequences, explored their genetic and geographic diversity. The ST1 population was highly conserved at the nucleotide level; 98% of core nucleotide positions were invariant and environmental isolates unassociated with human disease (n = 99) contained ~65% more nucleotide diversity compared to clinical-sporadic (n = 139) or outbreak-associated (n = 28) ST1 subgroups. The accessory pangenome of environmental isolates was also ~30-60% larger than other subgroups and was enriched for transposition and conjugative transfer-associated elements. Up to ~10% of US ST1 genetic variation could be explained by geographic origin, but considerable genetic conservation existed among strains isolated from geographically distant states and from different decades. These findings provide new insight into the ST1 population structure and establish a foundation for interpreting genetic relationships among ST1 strains; these data may also inform future analyses for improved outbreak investigations.

Multiplex polymerase chain reaction of genetic markers for detection of potentially pathogenic environmental Legionella pneumophila isolates

Background & objectives:

Genomic constitution of the bacterium Legionella pneumophila plays an important role in providing them a pathogenic potential. Here, we report the standardization and application of multiplex polymerase chain reaction (PCR) for the detection of molecular markers of pathogenic potential in L. pneumophila in hospital environment.

Methods:

Culture of the standard strains of L. pneumophila was performed in buffered charcoal-yeast extract agar with L-cysteine at pH 6.9. Primers were designed for multiplex PCR, and standardization for the detection of five markers annotated to L. pneumophila plasmid pLPP (11A2), lipopolysaccharide synthesis (19H4), CMP-N-acetylneuraminic acid synthetase (10B12), conjugative coupling factor (24B1) and hypothetical protein (8D6) was done. A total of 195 water samples and 200 swabs were collected from the hospital environment. The bacterium was isolated from the hospital environment by culture and confirmed by 16S rRNA gene PCR and restriction enzyme analysis. A total of 45 L. pneumophila isolates were studied using the standardized multiplex PCR.

Results:

The PCR was sensitive to detect 0.1 ng/μl DNA and specific for the two standard strains used in the study. Of the 45 hospital isolates tested, 11 isolates had four markers, 12 isolates had three markers, 10 isolates had two markers, nine isolates had one marker and three isolates had none of the markers. None of the isolates had all the five markers.

Interpretation & conclusions:

The findings of this study showed the presence of gene markers of pathogenic potential of the bacterium L. pneumophila. However, the genomic constitution of the environmental isolates should be correlated with clinical isolates to prove their pathogenic potential. Rapid diagnostic methods such as multiplex PCR reported here, for elucidating gene markers, could help in future epidemiological studies of bacterium L. pneumophila.

Multiplex real-time PCR assay for Legionella species

Legionella pneumophila serogroup 1 (sg1) accounts for the majority of infections in humans, but other Legionella species are also associated with human disease. In this study, a new SYBR Green I-based multiplex real-time PCR assay in a single reaction was developed to allow the rapid detection and differentiation of Legionella species by targeting specific gene sequences. Candidate target genes were selected, and primer sets were designed by referring to comparative genomic hybridization data of Legionella species. The Legionella species-specific groES primer set successfully detected all 30 Legionella strains tested. The xcpX and rfbA primers specifically detected L. pneumophila sg1-15 and L. pneumophila sg1, respectively. In addition, this assay was validated by testing clinical samples and isolates. In conclusion, this novel multiplex real-time PCR assay might be a useful diagnostic tool for the rapid detection and differentiation of Legionella species in both clinical and epidemiological studies.

Prediction of the origin of French Legionella pneumophila strains using a mixed-genome microarray

Background

Legionella is a water and soil bacterium that can infect humans, causing a pneumonia known as Legionnaires’ disease. The pneumonia is almost exclusively caused by the species L. pneumophila, of which serogroup 1 is responsible for 90% of patients. Within serogroup 1, large differences in prevalence in clinical isolates have been described. A recent study, using a Dutch Legionella strain collection, identified five virulence associated markers. In our study, we verify whether these five Dutch markers can predict the patient or environmental origin of a French Legionella strain collection. In addition, we identify new potential virulence markers and verify whether these can predict better. A total of 219 French patient isolates and environmental strains were compared using a mixed-genome micro-array. The micro-array data were analysed to identify predictive markers, using a Random Forest algorithm combined with a logistic regression model. The sequences of the identified markers were compared with eleven known Legionella genomes, using BlastN and BlastX; the functionality for each of the predictive markers was checked in the literature.

Results

The five Dutch markers insufficiently predicted the patient or environmental origin of the French Legionella strains. Subsequent analyses identified four predictive markers for the French collection that were used for the logistic regression model. This model showed a negative predictive value of 91%. Three of the French markers differed from the Dutch markers, one showed considerable overlap and was found in one of the Legionella genomes (Lorraine strain). This marker encodes for a structural toxin protein RtxA, described for L. pneumophila as a factor involved in virulence and entry in both human cells and amoebae.

Conclusions

The combination of a mixed-genome micro-array and statistical analysis using a Random Forest algorithm has identified virulence markers in a consistent way. The Lorraine strain and related Dutch and French Legionella strains contain a marker that encodes a RtxA protein which probably is involved in the increased prevalence in clinical isolates. The current set of predictive markers is insufficient to justify its use as a reliable test in the public health field in France. Our results suggest that genetic differences in Legionella strains exist between geographically distinct entities. It may be necessary to develop region-specific mixed-genome microarrays that are constantly adapted and updated.

Genome analysis of Legionella pneumophila strains using a mixed-genome microarray

BACKGROUND

Legionella, the causative agent for Legionnaires' disease, is ubiquitous in both natural and man-made aquatic environments. The distribution of Legionella genotypes within clinical strains is significantly different from that found in environmental strains. Developing novel genotypic methods that offer the ability to distinguish clinical from environmental strains could help to focus on more relevant (virulent) Legionella species in control efforts. Mixed-genome microarray data can be used to perform a comparative-genome analysis of strain collections, and advanced statistical approaches, such as the Random Forest algorithm are available to process these data.

METHODS

Microarray analysis was performed on a collection of 222 Legionella pneumophila strains, which included patient-derived strains from notified cases in The Netherlands in the period 2002-2006 and the environmental strains that were collected during the source investigation for those patients within the Dutch National Legionella Outbreak Detection Programme. The Random Forest algorithm combined with a logistic regression model was used to select predictive markers and to construct a predictive model that could discriminate between strains from different origin: clinical or environmental.

RESULTS

Four genetic markers were selected that correctly predicted 96% of the clinical strains and 66% of the environmental strains collected within the Dutch National Legionella Outbreak Detection Programme.

CONCLUSIONS

The Random Forest algorithm is well suited for the development of prediction models that use mixed-genome microarray data to discriminate between Legionella strains from different origin. The identification of these predictive genetic markers could offer the possibility to identify virulence factors within the Legionella genome, which in the future may be implemented in the daily practice of controlling Legionella in the public health environment.

Comparative molecular analyses of Borrelia burgdorferi sensu stricto strains B31 and N40D10/E9 and determination of their pathogenicity

Background

Lyme disease in the United States is caused primarily by B. burgdorferi sensu stricto while other species are also prevalent in Europe. Genetic techniques have identified several chromosomal and plasmid-borne regulatory and virulence factors involved in Lyme pathogenesis. B31 and N40 are two widely studied strains of B. burgdorferi, which belong to two different 16 S-23 S rRNA spacer types (RST) and outer surface protein C (OspC) allelic groups. However, the presence of several known virulence factors in N40 has not been investigated. This is the first comprehensive study that compared these two strains both in vitro and using the mouse model of infection.

Results

Phylogenetic analyses predict B31 to be more infectious. However, our studies here indicate that N40D10/E9 is more infectious than the B31 strain at lower doses of inoculation in the susceptible C3H mice. Based-upon a careful analyses of known adhesins of these strains, it is predicted that the absence of a known fibronectin-glycosaminoglycan binding adhesin, bbk32, in the N40 strain could at least partially be responsible for reduction in its binding to Vero cells in vitro. Nevertheless, this difference does not affect the infectivity of N40D10/E9 strain. The genes encoding known regulatory and virulence factors critical for pathogenesis were detected in both strains. Differences in the protein profiles of these B. burgdorferi strains in vitro suggest that the novel, differentially expressed molecules may affect infectivity of B. burgdorferi. Further exacerbation of these molecular differences in vivo could affect the pathogenesis of spirochete strains.

Conclusion

Based upon the studies here, it can be predicted that N40D10/E9 disseminated infection at lower doses may be enhanced by its lower binding to epithelial cells at the site of inoculation due to the absence of BBK32. We suggest that complete molecular analyses of virulence factors followed by their evaluation using the mouse infection model should form the basis of determining infectivity and pathogenicity of different strains rather than simple phylogenetic group analyses. This study further emphasizes a need to investigate multiple invasive strains of B. burgdorferi to fully appreciate the pathogenic mechanisms that contribute to Lyme disease manifestations.

Transient dynamics of reduced-order models of genetic regulatory networks

In systems biology, a number of detailed genetic regulatory networks models have been proposed that are capable of modeling the fine-scale dynamics of gene expression. However, limitations on the type and sampling frequency of experimental data often prevent the parameter estimation of the detailed models. Furthermore, the high computational complexity involved in the simulation of a detailed model restricts its use. In such a scenario, reduced-order models capturing the coarse-scale behavior of the network are frequently applied. In this paper, we analyze the dynamics of a reduced-order Markov Chain model approximating a detailed Stochastic Master Equation model. Utilizing a reduction mapping that maintains the aggregated steady-state probability distribution of stochastic master equation models, we provide bounds on the deviation of the Markov Chain transient distribution from the transient aggregated distributions of the stochastic master equation model.

A systems biology perspective on plant-microbe interactions: biochemical and structural targets of pathogen effectors

Plants have biochemical defences against stresses from predators, parasites and pathogens. In this review we discuss the interaction of plant defences with microbial pathogens such as bacteria, fungi and oomycetes, and viruses. We examine principles of complex dynamic networks that allow identification of network components that are differentially and predictably sensitive to perturbation, thus making them likely effector targets. We relate these principles to recent developments in our understanding of known effector targets in plant-pathogen systems, and propose a systems-level framework for the interpretation and modelling of host-microbe interactions mediated by effectors. We describe this framework briefly, and conclude by discussing useful experimental approaches for populating this framework.