Justin S. Hogg

Comparative Genomic Analyses of Seventeen Streptococcus pneumoniae Strains: Insights into the Pneumococcal Supragenome

The distributed-genome hypothesis (DGH) states that pathogenic bacteria possess a supragenome that is much larger than the genome of any single bacterium and that these pathogens utilize genetic recombination and a large, noncore set of genes as a means of diversity generation. We sequenced the genomes of eight nasopharyngeal strains of Streptococcus pneumoniae isolated from pediatric patients with upper respiratory symptoms and performed quantitative genomic analyses among these and nine publicly available pneumococcal strains. Coding sequences from all strains were grouped into 3,170 orthologous gene clusters, of which 1,454 (46%) were conserved among all 17 strains. The majority of the gene clusters, 1,716 (54%), were not found in all strains. Genic differences per strain pair ranged from 35 to 629 orthologous clusters, with each strains genome containing between 21 and 32% noncore genes. The distribution of the orthologous clusters per genome for the 17 strains was entered into the finite-supragenome model, which predicted that (i) the S. pneumoniae supragenome contains more than 5,000 orthologous clusters and (ii) 99% of the orthologous clusters ( approximately 3,000) that are represented in the S. pneumoniae population at frequencies of >or=0.1 can be identified if 33 representative genomes are sequenced. These extensive genic diversity data support the DGH and provide a basis for understanding the great differences in clinical phenotype associated with various pneumococcal strains. When these findings are taken together with previous studies that demonstrated the presence of a supragenome for Streptococcus agalactiae and Haemophilus influenzae, it appears that the possession of a distributed genome is a common host interaction strategy.

Characterization and modeling of the Haemophilus influenzae core and supragenomes based on the complete genomic sequences of Rd and 12 clinical nontypeable strains

BackgroundThe distributed genome hypothesis (DGH) posits that chronic bacterial pathogens utilize polyclonal infection and reassortment of genic characters to ensure persistence in the face of adaptive host defenses. Studies based on random sequencing of multiple strain libraries suggested that free-living bacterial species possess a supragenome that is much larger than the genome of any single bacterium.ResultsWe derived high depth genomic coverage of nine nontypeable Haemophilus influenzae (NTHi) clinical isolates, bringing to 13 the number of sequenced NTHi genomes. Clustering identified 2,786 genes, of which 1,461 were common to all strains, with each of the remaining 1,328 found in a subset of strains; the number of clusters ranged from 1,686 to 1,878 per strain. Genic differences of between 96 and 585 were identified per strain pair. Comparisons of each of the NTHi strains with the Rd strain revealed between 107 and 158 insertions and 100 and 213 deletions per genome. The mean insertion and deletion sizes were 1,356 and 1,020 base-pairs, respectively, with mean maximum insertions and deletions of 26,977 and 37,299 base-pairs. This relatively large number of small rearrangements among strains is in keeping with what is known about the transformation mechanisms in this naturally competent pathogen.ConclusionA finite supragenome model was developed to explain the distribution of genes among strains. The model predicts that the NTHi supragenome contains between 4,425 and 6,052 genes with most uncertainty regarding the number of rare genes, those that have a frequency of <0.1 among strains; collectively, these results support the DGH.

Insights into the genome of large sulfur bacteria revealed by analysis of single filaments.

Marine sediments are frequently covered by mats of the filamentous Beggiatoa and other large nitrate-storing bacteria that oxidize hydrogen sulfide using either oxygen or nitrate, which they store in intracellular vacuoles. Despite their conspicuous metabolic properties and their biogeochemical importance, little is known about their genetic repertoire because of the lack of pure cultures. Here, we present a unique approach to access the genome of single filaments of Beggiatoa by combining whole genome amplification, pyrosequencing, and optical genome mapping. Sequence assemblies were incomplete and yielded average contig sizes of approximately 1 kb. Pathways for sulfur oxidation, nitrate and oxygen respiration, and CO2 fixation confirm the chemolithoautotrophic physiology of Beggiatoa. In addition, Beggiatoa potentially utilize inorganic sulfur compounds and dimethyl sulfoxide as electron acceptors. We propose a mechanism of vacuolar nitrate accumulation that is linked to proton translocation by vacuolar-type ATPases. Comparative genomics indicates substantial horizontal gene transfer of storage, metabolic, and gliding capabilities between Beggiatoa and cyanobacteria. These capabilities enable Beggiatoa to overcome non-overlapping availabilities of electron donors and acceptors while gliding between oxic and sulfidic zones. The first look into the genome of these filamentous sulfur-oxidizing bacteria substantially deepens the understanding of their evolution and their contribution to sulfur and nitrogen cycling in marine sediments.

Generation of Genic Diversity among Streptococcus pneumoniae Strains via Horizontal Gene Transfer during a Chronic Polyclonal Pediatric Infection

Although there is tremendous interest in understanding the evolutionary roles of horizontal gene transfer (HGT) processes that occur during chronic polyclonal infections, to date there have been few studies that directly address this topic. We have characterized multiple HGT events that most likely occurred during polyclonal infection among nasopharyngeal strains of Streptococcus pneumoniae recovered from a child suffering from chronic upper respiratory and middle-ear infections. Whole genome sequencing and comparative genomics were performed on six isolates collected during symptomatic episodes over a period of seven months. From these comparisons we determined that five of the isolates were genetically highly similar and likely represented a dominant lineage. We analyzed all genic and allelic differences among all six isolates and found that all differences tended to occur within contiguous genomic blocks, suggestive of strain evolution by homologous recombination. From these analyses we identified three strains (two of which were recovered on two different occasions) that appear to have been derived sequentially, one from the next, each by multiple recombination events. We also identified a fourth strain that contains many of the genomic segments that differentiate the three highly related strains from one another, and have hypothesized that this fourth strain may have served as a donor multiple times in the evolution of the dominant strain line. The variations among the parent, daughter, and grand-daughter recombinant strains collectively cover greater than seven percent of the genome and are grouped into 23 chromosomal clusters. While capturing in vivo HGT, these data support the distributed genome hypothesis and suggest that a single competence event in pneumococci can result in the replacement of DNA at multiple non-adjacent loci.

Comparative supragenomic analyses among the pathogens Staphylococcus aureus, Streptococcus pneumoniae, and Haemophilus influenzae Using a modification of the finite supragenome model

BackgroundStaphylococcus aureus is associated with a spectrum of symbiotic relationships with its human host from carriage to sepsis and is frequently associated with nosocomial and community-acquired infections, thus the differential gene content among strains is of interest.ResultsWe sequenced three clinical strains and combined these data with 13 publically available human isolates and one bovine strain for comparative genomic analyses. All genomes were annotated using RAST, and then their gene similarities and differences were delineated. Gene clustering yielded 3,155 orthologous gene clusters, of which 2,266 were core, 755 were distributed, and 134 were unique. Individual genomes contained between 2,524 and 2,648 genes. Gene-content comparisons among all possible S. aureus strain pairs (n = 136) revealed a mean difference of 296 genes and a maximum difference of 476 genes. We developed a revised version of our finite supragenome model to estimate the size of the S. aureus supragenome (3,221 genes, with 2,245 core genes), and compared it with those of Haemophilus influenzae and Streptococcus pneumoniae. There was excellent agreement between RASTs annotations and our CDS clustering procedure providing for high fidelity metabolomic subsystem analyses to extend our comparative genomic characterization of these strains.ConclusionsUsing a multi-species comparative supragenomic analysis enabled by an improved version of our finite supragenome model we provide data and an interpretation explaining the relatively larger core genome of S. aureus compared to other opportunistic nasopharyngeal pathogens. In addition, we provide independent validation for the efficiency and effectiveness of our orthologous gene clustering algorithm.

Compartmental rule-based modeling of biochemical systems

Rule-based modeling is an approach to modeling biochemical kinetics in which proteins and other biological components are modeled as structured objects and their interactions are governed by rules that specify the conditions under which reactions occur. BioNetGen is an open-source platform that provides a simple yet expressive language for rule-based modeling (BNGL). In this paper we describe compartmental BNGL (cBNGL), which extends BNGL to enable explicit modeling of the compartmental organization of the cell and its effects on system dynamics. We show that by making localization a queryable attribute of both molecules and species and introducing appropriate volumetric scaling of reaction rates, the effects of compartmentalization can be naturally modeled using rules. These properties enable the construction of new rule semantics that include both universal rules, those defining interactions that can take place in any compartment in the system, and transport rules, which enable movement of molecular complexes between compartments.

Ensemble models of neutrophil trafficking in severe sepsis.

A hallmark of severe sepsis is systemic inflammation which activates leukocytes and can result in their misdirection. This leads to both impaired migration to the locus of infection and increased infiltration into healthy tissues. In order to better understand the pathophysiologic mechanisms involved, we developed a coarse-grained phenomenological model of the acute inflammatory response in CLP (cecal ligation and puncture)-induced sepsis in rats. This model incorporates distinct neutrophil kinetic responses to the inflammatory stimulus and the dynamic interactions between components of a compartmentalized inflammatory response. Ensembles of model parameter sets consistent with experimental observations were statistically generated using a Markov-Chain Monte Carlo sampling. Prediction uncertainty in the model states was quantified over the resulting ensemble parameter sets. Forward simulation of the parameter ensembles successfully captured experimental features and predicted that systemically activated circulating neutrophils display impaired migration to the tissue and neutrophil sequestration in the lung, consequently contributing to tissue damage and mortality. Principal component and multiple regression analyses of the parameter ensembles estimated from survivor and non-survivor cohorts provide insight into pathologic mechanisms dictating outcome in sepsis. Furthermore, the model was extended to incorporate hypothetical mechanisms by which immune modulation using extracorporeal blood purification results in improved outcome in septic rats. Simulations identified a sub-population (about of the treated population) that benefited from blood purification. Survivors displayed enhanced neutrophil migration to tissue and reduced sequestration of lung neutrophils, contributing to improved outcome. The model ensemble presented herein provides a platform for generating and testing hypotheses in silico, as well as motivating further experimental studies to advance understanding of the complex biological response to severe infection, a problem of growing magnitude in humans.

Exact hybrid particle/population simulation of rule-based models of biochemical systems.

Detailed modeling and simulation of biochemical systems is complicated by the problem of combinatorial complexity, an explosion in the number of species and reactions due to myriad protein-protein interactions and post-translational modifications. Rule-based modeling overcomes this problem by representing molecules as structured objects and encoding their interactions as pattern-based rules. This greatly simplifies the process of model specification, avoiding the tedious and error prone task of manually enumerating all species and reactions that can potentially exist in a system. From a simulation perspective, rule-based models can be expanded algorithmically into fully-enumerated reaction networks and simulated using a variety of network-based simulation methods, such as ordinary differential equations or Gillespies algorithm, provided that the network is not exceedingly large. Alternatively, rule-based models can be simulated directly using particle-based kinetic Monte Carlo methods. This “network-free” approach produces exact stochastic trajectories with a computational cost that is independent of network size. However, memory and run time costs increase with the number of particles, limiting the size of system that can be feasibly simulated. Here, we present a hybrid particle/population simulation method that combines the best attributes of both the network-based and network-free approaches. The method takes as input a rule-based model and a user-specified subset of species to treat as population variables rather than as particles. The model is then transformed by a process of “partial network expansion” into a dynamically equivalent form that can be simulated using a population-adapted network-free simulator. The transformation method has been implemented within the open-source rule-based modeling platform BioNetGen, and resulting hybrid models can be simulated using the particle-based simulator NFsim. Performance tests show that significant memory savings can be achieved using the new approach and a monetary cost analysis provides a practical measure of its utility.

Virulence phenotypes of low-passage clinical isolates of Nontypeable Haemophilus influenzae assessed using the chinchilla laniger model of otitis media

BackgroundThe nontypeable Haemophilus influenzae (NTHi) are associated with a spectrum of respiratory mucosal infections including: acute otitis media (AOM); chronic otitis media with effusion (COME); otorrhea; locally invasive diseases such as mastoiditis; as well as a range of systemic disease states, suggesting a wide range of virulence phenotypes. Genomic studies have demonstrated that each clinical strain contains a unique genic distribution from a population-based supragenome, the distributed genome hypothesis. These diverse clinical and genotypic findings suggest that each NTHi strain possesses a unique set of virulence factors that contributes to the course of the disease.ResultsThe local and systemic virulence patterns of ten genomically characterized low-passage clinical NTHi strains (PittAA – PittJJ) obtained from children with COME or otorrhea were stratified using the chinchilla model of otitis media (OM). Each isolate was used to bilaterally inoculate six animals and thereafter clinical assessments were carried out daily for 8 days by blinded observers. There was no statistical difference in the time it took for any of the 10 NTHi strains to induce otologic (local) disease with respect to any or all of the other strains, however the differences in time to maximal local disease and the severity of local disease were both significant between the strains. Parameters of systemic disease indicated that the strains were not all equivalent: time to development of the systemic disease, maximal systemic scores and mortality were all statistically different among the strains. PittGG induced 100% mortality while PittBB, PittCC, and PittEE produced no mortality. Overall Pitt GG, PittII, and Pitt FF produced the most rapid and most severe local and systemic disease. A post hoc determination of the clinical origins of the 10 NTHi strains revealed that these three strains were of otorrheic origin, whereas the other 7 were from patients with COME.ConclusionCollectively these data suggest that the chinchilla OM model is useful for discriminating between otorrheic and COME NTHi strains as to their disease-producing potential in humans, and combined with whole genome analyses, point the way towards identifying classes of virulence genes.

BioNetGen 2.2: advances in rule-based modeling

: BioNetGen is an open-source software package for rule-based modeling of complex biochemical systems. Version 2.2 of the software introduces numerous new features for both model specification and simulation. Here, we report on these additions, discussing how they facilitate the construction, simulation and analysis of larger and more complex models than previously possible. AVAILABILITY AND IMPLEMENTATION Stable BioNetGen releases (Linux, Mac OS/X and Windows), with documentation, are available at http://bionetgen.org Source code is available at http://github.com/RuleWorld/bionetgen CONTACT: bionetgen.help@gmail.comSupplementary information: Supplementary data are available at Bioinformatics online.