POSTER PRESENTATION Open Access
Integrating neighbor clustering, coexpression
clustering and subsystems analysis to define
dynamic changes in regulatory networks
associated with group A streptococcal
sociomicrobiology and niche adaptation
Ramy K Aziz1,2*, Bruce J Aronow3, William L Taylor4, Sarah L Rowe2,4, Rita Kansal2,5, Mark J Walker6, Malak Kotb2,5,6
From UT-ORNL-KBRIN Bioinformatics Summit 2010
Cadiz, KY, USA. 19-21 March 2010
Background
Bacterial colonies often consist of heterogeneous com-
munities rather than genetically identical cells with har-
monized gene expression profiles[1]. Dramatic changes,
such as the onset of infection, may perturb a colony’s
sociomicrobiology leading a minor subpopulation with a
mutant phenotype to prevail in the host; however, cap-
turing such transitions in real time is difficult. While
differential microarray analysis has become a method of
choice for comparing the transcriptomes of bacterial
subpopulations, current microarray analysis tools are
more optimized to the study of eukaryotic organisms.
Here, we set out to develop a systems biology model
for studying the transcriptional reprogramming underly-
ing the transition of M1T1 group A streptococci[2]
from a virulent to a hypervirulent phenotype [3-5]. In
addition, we aimed at integrating and optimizing micro-
array analysis strategies to better understand bacterial
regulatory networks.
Materials and methods
Using a murine subcutaneous chamber model developed
in our laboratory[6], we sampled the bacteria before and
24 h after infection, and we compared the transcriptomes
of wild type (WT) and animal-passaged (AP) bacteria
in vitro and in vivo (Figure 1). To make biologically rele-
vant discoveries without compromising the statistical
robustness of microarray analysis, we combined multiple
analysis strategies, including coexpression clustering,
neighbor clustering[7], and subsystems analysis[8].
Finally, we visually inspected all computer-generated data
to filter out any artifacts or errors.
Results and conclusion
The integration of these methods revealed extensive
transcriptional alterations of pathoadaptive and meta-
bolic gene sets associated with invasion, immune eva-
sion, and metabolic reprogramming. Comparing
genomic subsystems involved in in vivo adaptation with
those affected by animal-passage demonstrates that the
WT bacteria struggled to adapt to the host environment
* Correspondence: ramy.aziz@salmonella.org
1Department of Microbiology and Immunology, Faculty of Pharmacy, Cairo
University, Cairo, Egypt
Figure 1 Outline of the design of microarray experiments.
Aziz et al. BMC Bioinformatics 2010, 11(Suppl 4):P12
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© 2010 Aziz et al; licensee BioMed Central Ltd.

by altering several virulence and metabolic modules that
made their phenotype more similar to the mutant AP
phenotype; still, WT bacteria failed to survive while the
AP population thrived mainly because of a mutation in
the gene encoding their global environmental sensor,
CovS. We suggest that the transcriptional reprogram-
ming of the WT bacteria was not sufficient for their
adaptation to the host environment and that niche
adaptation was only achieved by the selection/expansion
of covS mutants.
This model allowed us to capture transcriptional snap-
shots of both survivor and extinct subpopulations
in vivo and provided a proof of principle that in vivo
transcriptomics of extinct microbial populations offers
valuable clues to microbial niche adaptation. The inte-
gration of multiple microarray analysis tools and the
customization of these tools to bacterial polycistronic
transcripts demonstrate the importance taking biological
relevance into consideration in high-throughput data
analyses.
Acknowledgments
This work was partly supported by the Research and Development Office,
Medical Research Service, Department of Veterans Affairs (Merit Award to
MK), and by the National Health and Medical Research Council of Australia
(project grant 459103 to MJW and MK).
A detailed study describing the experimental model and the
implementation of these methods in the analysis of group A streptococcal
hypervirulence was recently published[9].
Author details
1Department of Microbiology and Immunology, Faculty of Pharmacy, Cairo
University, Cairo, Egypt. 2The Veterans Affairs Medical Center, Memphis, TN
38104, USA. 3Biomedical Informatics, Children’s Hospital Medical Center,
Cincinnati, OH 45229, USA. 4University of Tennessee, Health Science Center,
Memphis, TN 38163, USA. 5The Veterans Affairs Medical Center, Cincinnati,
OH 45220, USA. 6School of Biological Sciences, University of Wollongong,
Wollongong, Australia.
Published: 23 July 2010
References
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doi:10.1186/1471-2105-11-S4-P12
Cite this article as: Aziz et al.: Integrating neighbor clustering,
coexpression clustering and subsystems analysis to define dynamic
changes in regulatory networks associated with group A streptococcal
sociomicrobiology and niche adaptation. BMC Bioinformatics 2010 11
(Suppl 4):P12.
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