# Presentation: 128084

## 1Physical Biosciences Division, Lawrence Berkeley National Labs, 2Bioengineering Department, University of California-Berkeley, 3Howard Hughes Medical Institute, 4Oak Ridge National Laboratory  

- Eric Alm1, Yue Wang2, Jizhong Zhou4 and Adam P. Arkin1,2,3
- 1Physical Biosciences Division, Lawrence Berkeley National Labs, 2Bioengineering Department, University of California-Berkeley, 3Howard Hughes Medical Institute, 4Oak Ridge National Laboratory

_**Comparative analysis of **__***E.coli***__** and **__***S. oneidensis ***__**expression data**_

**Abstract**

**Motivation**

_**Searching for ‘generic’ stress response genes**_

**References**

- Bacterial stress response, Storz G and Hengge-Aronis R, ASM press 2000
- 2.	Blattner laboratory website http://www.genome.wisc.edu/functional/microarray.htm

**Results**

- We searched the literature for microarray data describing gene expression over  a wide range of conditions in over 20 bacterial species.  We are compiling these data in a common format in a central repository at the Virtual Institute for Microbial Stress and Survival (VIMSS).  In addition, we are integrating these data with our Comparative Genomics Database which includes hand-curated gene annotations as well our own predictions of the operon and regulon structure of over 125 fully sequenced genomes.

- A wealth of comparative genomic data,  less complicated transcriptional control,  smaller intergenic regions in which to search for regulatory motifs, and the coregulation of genes in operons make prokaryotes an ideal target for building systems-level descriptions of gene expression.  Building such large-scale models will require rigorous testing and training against experimental data such as that obtained from microarray experiments.  Although there is already a large community of researchers generating microarray data in prokaryotic systems, there is currently no central data repository as for model eukaryotic species.  We have obtained a significant fraction of the published data available for over 20 species, and plan to release our completed database freely as a community resource.

- There is little overall correlation in global gene expression between these two species for any given condition.  However,  looking at only those genes that are significantly up or down regulated in both species in response to a given treatment highlights a core of stress-response genes highly specific to a given treatment.  Shown below are the genes most over and under-expressed in response to heat shock treatment in both *E. coli *and *S. oneidensis.*

- We compared data from four different stress treatments (heat shock, low pH, high pH, salt shock) to identify genes consistently up or down regulated in *S. oneidensis.*

- We compared our own predictions of operon and regulon structure in *E. coli* to gene expression data in 14 different conditions obtained from the Blattner laboratory website (Ref. 2).  Shown (right) is the distribution of correlations between the expression profiles of 3 classes of gene pairs: (1) genes predicted to be on the same operon by our operon prediction tool; (2) randomly chosen gene pairs; and (3) genes predicted to be on the same regulon (but not operon) based on the co-occurrence of those genes in operons in multiple unrelated species.  Interestingly,  genes which tend to occur on the same operon in many species are also closely coregulated in *E. coli* even if they occur on different operons.  These results suggest that operon structure in distantly related bacteria can be a good indicator of transcriptional coregulation.

- Correlation

_**Elucidating transcriptional reglatory networks using comparative genomics**_

_**Overview of a global stress response in **__***E. coli***_

- Shown at left (above) is a view of *E. coli *metabolism colored according to changes in gene expression in response to heat shock.  Nodes represent metabolites, and edges are enzymes catalyzing reactions among the metabolites.  Enzymes that are down regulatedin response to heat shock are shown in blue, those upregulated are shown in red and non-changers are shown in black.

_**Comparative view of a global stress response**_

- The global transcriptional response of metabolic enzymes to low pH treatment (below left) is shown for comparison to the heat shock treatment above.  Colors are as described above.

**Acknowledgements**

- We would like to acknowledge our collaborators at Oak Ridge National Laboratory for sharing their expression data on *Shewanella oneidensis*: Haichun Gao, Adam Leaphart, Yongqing Liu, Jizhong Zhou, Matthew Fields, Dorothea Thompson and others.