- Project Title
- In silico reconstruction of Streptococcus pneumoniae cellular networks and their impact on virulence
- Project Type
- Nacional / Public
- Funding Body
- Funding Program
- Call for Funding of Research and Development Projects in all Scientific Domains - 2009
- CEB: 28 908,00
- Total: 126 588,00
- Fundação da Faculdade de Ciências (FFC/FC/UL)
Instituto de Medicina Molecular (IMM/FM/UL)
Universidade do Minho (UM)
- External link
Team Members - CEB
Streptococcus pneumoniae is a major cause of life threatening infections, such as pneumonia and meningitis, and also of less severe
infections such as sinusitis and middle-ear infections. Paradoxically, it is carried asymptomatically in the human nasopharynx. It is
also an important model organism within the Streptococcus genus.
Molecular typing of S. pneumoniae highlighted the heterogeneous behaviour of particular genetic lineages within this species (28).
Virulence, the relative capacity of a pathogen to inflict damage to a susceptible host, is not equally distributed across the
pneumococcal population. Indeed, it is associated with some branches of its highly diverse genetic space. Among the non-redundant
pool of genes present in three pneumococcal sequenced strains (R6, TIGR4 and G54), only 57% of the genes are present in all three
strains. Considering these large differences in gene content, it is possible to identify virulent lineages through the presence or
absence of specific genes. Genomic technologies, as comparative genomic hybridization (CGH) in microarrays, enable the
high-throughput screening for such virulence determining genes (14, 19).
We propose to potentiate this large-scale search through its integration with a systems biology approach, using the metabolic and
transcriptional networks of S. pneumoniae to search for network motifs associated with virulence. If the function of a gene impacts
on virulence, we expect neighbour genes in the metabolic or transcriptional networks to have a similar influence, even if indirectly,
by affecting the activity of the first gene. The search for network motifs is more powerful than the identification of single gene
associations. First, the cumulative association of the motif can be significant although none of the constituting genes presents a
sufficiently strong association by itself. Second, the motif localization within the network provides more insights into the mechanism
underlying its impact in pathogenesis. Networks can also be used to map the interactions of virulence determinants, which may
suggest explanations for conflicting findings between laboratory studies and the distribution of virulence genes in natural
A current approach to integrate genomic data with other information sources is gene set enrichment analysis (GSEA) (18, 23). In
the latter, one first identifies a group of genes that are individually associated with virulence and then searches for motifs with more
nodes included in that set than expected by chance. In this project we propose a different method that evaluates directly network
motif significance. This can improve the method performance in the detection of associations between data sources.
The project will start with the reconstruction of the metabolic and transcriptional networks of S. pneumoniae from database and
literature sources. Both will be major achievements, useful for the study of virulence determinants, and for the vast community
studying fundamental biology and pathogenesis of S. pneumoniae. Such an effort will constitute an important model for other
Next, we will develop new methods to integrate network information with CGH and epidemiological data for a collection of
pneumococcal strains. These will allow us to identify nodes or motifs significantly associated with virulence. Lastly, topological
analysis of the virulence motifs and their insertion spots within the networks will answer questions about the typical structural
properties of significant motifs. Study of network motifs has been a pivotal tool to understand the connection between complex
network topology and cellular function (13). It is our strong believe that it can also be a fruitful tool in the clarification of virulence
molecular mechanisms, where, to our knowledge, it has never been applied.
The project capitalizes on efforts and data from previous projects (“Population based identification of pneumococcal virulence and
colonization factors” from NIAID/NIH and “Population and genomic consequences of vaccination against Streptococcus pneumoniae”
(PTDC/SAU-ESA/64888/2006)) and builds upon team member experience in metabolic network reconstruction (24, 25), gene
expression analysis (7, 18, 27), CGH array analysis (19), ontotlogy and semantic similarity analysis (16, 17), pneumococcal
molecular epidemiology (1, 2, 28), and non-linear correlation analysis in the integration of heterogeneous data (21, 22).
Discovery of new virulence determinants and hypothesis for virulence factor interactions may shed a new light on our understanding
of pneumococcal pathogenesis and lead to new therapeutic or vaccination strategies. This project also innovates through the
introduction of a concept - the virulence associated network motifs - and the methodology to search for them. This methodology can
be applied to other pathogens, broadening the scientific impact of the proposed workplan beyond the pneumococcal field.