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Research and Scientists
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Model Systems: Halobacterium
Nitin Baliga
in collaboration with Richard Bonneau
of New York University (NYU) and Jocelyne DiRuggierro
from the University of Maryland Biotechnology Institute,
is characterizing regulatory networks in Halobacterium using complementary systems approaches
(physically mapping interactions and statistically learning functional associations from systems biology data),
to understand physiological implications of these regulatory influences at the systems level.
Significance:
The insights gleaned from our studies of Halobacterium are applicable to other systems. Specifically, we have
extended the general understanding of how a global regu latory circuit can be assembled via an expansion of GTFs.
This has also shed considerable insight on how archaeal organisms can modulate transcription of a large number
of genes, as do bacteria, using multiple sigma factors and eukaryotes with multiple RNA polymerases and GTFs.
We have also confirmed the general utility of the computational tools we have developed using the Halobacterium model
system by discovering co-regulated gene clusters in Helicobacter pylori, yeast (Saccharomyces cerevisiae), and E coli.
As expected, several of these clusters have recapitulated known biology, but, more importantly, they have provided several
new experimentally testable hypotheses.
Research and Results:
We have developed an approach of clustering genes and conditions (biclustering) and have subsequently inferred a regulatory
network using a machine-learning algorithm. Using this approach we have narrowed 2,400 genes in Halobacterium to ~250
clusters each of which represents a group of genes that are co-regulated under a defined set of environmental conditions.
The transcriptional regulation of genes in these biclusters are modeled as a function of corresponding changes in
transcript levels of 72 transcription regulators and concentrations of 10 environmental factors. We have delineated
a physical map of protein-DNA interactions to unravel a global circuit specified by 7 general transcription factors
(GTFs). Remarkably, these factors exercise transcriptional control over more than half of all genes under a range of
environmental conditions. The circuits deciphered using these two complementary approaches have a great deal of
overlap and are being used in tandem to bootstrap towards a complete regulatory circuit for Halobacterium. The
computational approaches developed are generally applicable to systems of higher complexity and will be implemented
to disseminate the methods to the Center.
To date we have identified reference peptides for ~1,600 proteins, and are applying this information for designing
strategies for absolute rather than relative protein quantitation. We have also developed a novel algorithm to
compare changes at transcriptional and translational levels and are now looking at using mRNA levels as a
proxy for protein levels.
Plans:
We plan to expand the gene regulation network to include information on non-coding RNA, and proteins.
We'd also like to expand and enhance our understanding of gene regulatory networks in Halobacterium
and use observed data to improve and expand our regulatory inference model. This work will include
absolute quantitation of the Halobacterium proteome, refinement of the sub-networks that control relocation
of cells in response to environmental perturbations, and the investigation of protein-DNA interactions
over the time dimension. Data collected from these experiments will be used to improve the regulatory
network inference algorithms which will benefit other research projects.
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Institute for Systems Biology
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Center for Systems Biology at the Institute for Systems Biology
