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SUPPLEMENTARY TEXT. Systems Analysis with the Gaggle Mrna

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SUPPLEMENTARY TEXT. Systems Analysis with the Gaggle Mrna SUPPLEMENTARY TEXT. Systems analysis with the Gaggle mRNA level changes were analyzed simultaneously with gene/protein functional associations [operons (Moreno-Hagelsieb and Collado-Vides 2002), phylogenetic profile (Pellegrini et al. 1999), chromosomal proximity (Overbeek et al. 1999)], physical interactions [protein-DNA interactions (Facciotti etal, submitted)], putative functions in the SBEAMS database (http://halo.systemsbiology.net) (Bonneau et al. 2004) along with supporting evidence such as matches in protein databank [PDB (Sussman et al. 1998)], protein families [Pfam (Bateman et al. 2000), COG database (Tatusov et al. 2000)] and metabolic pathways [KEGG (Kanehisa 2002)]. Further, data were extracted into sub- matrices using custom filters, normalized and statistically analyzed using the R statistical package (http://www.r-project.org) and TIGR microarray expression viewer [TMeV (Saeed et al. 2003)]. Given the size and heterogeneity of data and software, we used the Gaggle framework (http://gaggle.systemsbiology.net) (Shannon et al, accepted in BMC Bioinformatics) to facilitate analyses and queries. The Gaggle is an open source Java software framework for seamless desktop integration of diverse databases and software applications. Specifically, all genes were visualized in Cytoscape (Shannon et al. 2003) as networks of nodes (genes) and edges (functional associations). The genes were organized into various function categories and node color was mapped to mRNA level changes (red for increased and green for decreased mRNA) (Johnson etal, submitted, Shannon etal, submitted); and node size was mapped to statistical significance (λ) (Baliga et al. 2004; Shannon et al. 2003). With this visualization scheme significant changes (genes appearing as large red or green nodes) in various function categories become immediately evident. This also enabled assignment of putative function to genes of previously unknown function by retrieving matching records in SBEAMS, Pfam, PDB, COG, and KEGG. Function assignments were further confirmed with orthogonal sources of information such as expression correlation and functional associations to genes of known function (Bonneau et al. 2004) (ST2). Physiological reconstruction Deletion of individual subunits of multi-component ABC transporters does not effect any change in metal sensitivity Multi-subunit ATP-binding cassette (ABC) transporters mediate active transport of sugars, ions, peptides, and oligonucleotides (Schneider and Hunke 1998). At least 50 genes of this function category were differentially regulated in one or more metal (ST2). To evaluate whether these transporters play a role in metal resistance, we selected subunits of four ABC transport systems for further analysis on basis of both their putative functions and their differential regulation. While three of these genes, phoX, appA, and 2- ycdH, encode subunits of transport systems for PO4 , peptides, and Mn(II), respectively, the other two, fepC and VNG2562H, are both putative subunits of the same Fe(II) transport system. Each gene deletion strain was then assayed for altered growth characteristics in varying concentrations of selected metals. Absence of defective growth phenotypes of these strains initially suggested that ABC transporters may not contribute towards metal resistance (ST3). However, given that numerous ABC transport systems were differentially regulated in each metal, we cannot rule out that loss of function of individual subunits in one might be complemented by subunits of other related transport systems. Responses of metalloenzymes, a ferritin and putative siderophore biosynthesis genes Metalloenzymes. Several metabolic pathways that require metal co-factors were affected during metal stress; for example genes of cobalamin biosynthesis, a pathway that requires Co(II),were differentially regulated in at least four metals Mn(II), Fe(II), Co(II) and Zn(II). The most notable change was Co(II)-specific down regulation of four of seven genes encoding a segment of the pathway that requires this metal ion. Included among these genes is CobN, a putative Co-chelatase which inserts Co into the corrin ring. A second example was Mn(II)- and Fe(II)-specific up regulation of a putative molybdenum cofactor sulfurase (VNG1735C) implicated in delivering sulphur to diverse metal-sulphur clusters. Ferritin. Structural analysis of the halobacterial ferritin DpsA has demonstrated that it stores iron in its nontoxic Fe3+ form through a process of matrix-controlled biomineralization (Reindel et al. 2002; Zeth et al. 2004). In conjunction with these reports, increase in dpsA transcript during Fe(II) stress points to a regulatory mechanism that ensures increased abundance of DpsA to minimize Fe(II) toxicity as has been reported for other prokaryotes (Munro and Linder 1978; Nair and Finkel 2004; Theil 1987; Wiedenheft et al. 2005). Siderophore biosynthesis. Bacteria synthesize low molecular weight compounds termed siderophores for scavenging Fe (Winkelmann 2002). Four of six genes (gabT, bdb, iucA, iucB, hxyA and iucC) in an operon in Halobacterium NRC-1 putatively encode siderophore biosynthesis: two (IucA and IucC ) match a protein family (PF04183) for synthesis of the siderophore aerobactin (de Lorenzo and Neilands 1986); and the other two (Bdb and GabT) putatively act on L-2,4-diaminobutyrate –a precursor of pyovredine siderophore biosynthesis (Vandenende et al. 2004). Although, none of these genes were differentially regulated in presence of Fe(II), addition of Mn(II) resulted in their up regulation (this is also discussed further later). However, we did not detect any growth defects in the ∆iucA strain, with and without Mn(II), upon adding excess Fe(II) or chelating “free” Fe(II) with the Fe(II)-specific chelator 2, 2’-dipyridyl (DIP) (ST3). This suggests that perhaps Fe(II) uptake studies might be better suited to characterize the role of siderophore biosynthesis in Halobacterium NRC-1 Fe(II) response. Transcription regulators selected for further analysis solely on basis of differential regulation and putative functions We also selected transcription regulators for further analysis merely on the basis of their putative functions and differential regulation Specifically, we selected three putative transcription regulators: CspD1, VNG0703H and VNG5176C. CspD1 was selected because its putative function implicated it in stress response (Schindelin et al. 1994) and it was down regulated in Mn(II) and Fe(II) and up regulated in Cu(II) and Zn(II) (ST2). VNG0703H, on the other hand, was selected on basis of its up regulation in Fe(II) and Zn(II) as well as its proximity in the genome to yvgX, which was implicated in mediating resistance to Cu(II). Finally, VNG5176C was selected because it was up regulated in presence of Zn(II) and it was one of four differentially regulated transcription regulators of the ArsR family, which is characterized by the alpha3N metal binding site implicated in binding and mediating responses to Zn(II) (Turner et al. 1996). Therefore, the VNG5176C mutant was predicted to fair poorly under Zn(II) stress. However, the ∆cspD1, ∆VNG0703H and ∆VNG5176C strains did not have any observable growth defects (ST3) ruling out their roles in directly controlling mechanisms that confer resistance to these metals. SirR may bind up to four different metals with diverse outcomes The putative Mn(II) uptake genes zurA, zurM and ycdH were up regulated by Co(II) and Ni(II) but down regulated by Mn(II) and Fe(II) (Fig 6A). This suggested that SirR, the putative MntR family transcriptional repressor of this uptake system, can functionally bind Mn(II) and Fe(II) to repress these genes. In contrast binding of Co(II) and Ni(II) seems to interfere with normal SirR function. Another possible explanation is that addition of excess Co(II) and Ni(II) changes the balance of the cellular metal ion pool, including relative Mn(II)-availability. Supplementary References Baliga, N.S., S.J. Bjork, R. Bonneau, M. Pan, C. Iloanusi, M.C.H. Kottemann, L. Hood, and J. DiRuggiero. 2004. Systems Level Insights Into the Stress Response to UV Radiation in the Halophilic Archaeon Halobacterium NRC-1. Genome Res. 14: 1025-1035. Bateman, A., E. Birney, R. Durbin, S.R. Eddy, K.L. Howe, and E.L. Sonnhammer. 2000. The Pfam protein families database. Nucleic Acids Res 28: 263-266. Bonneau, R., N.S. Baliga, E.W. Deutsch, P. Shannon, and L. Hood. 2004. Comprehensive de novo structure prediction in a systems-biology context for the archaea Halobacterium sp. NRC-1. Genome Biol 5: R52. de Lorenzo, V. and J.B. Neilands. 1986. Characterization of iucA and iucC genes of the aerobactin system of plasmid ColV-K30 in Escherichia coli. J Bacteriol 167: 350- 355. Kanehisa, M. 2002. The KEGG database. Novartis Found Symp 247: 91-101; discussion 101-103, 119-128, 244-152. Moreno-Hagelsieb, G. and J. Collado-Vides. 2002. A powerful non-homology method for the prediction of operons in prokaryotes. Bioinformatics 18 Suppl 1: S329- 336. Munro, H.N. and M.C. Linder. 1978. Ferritin: structure, biosynthesis, and role in iron metabolism. Physiol Rev 58: 317-396. Nair, S. and S.E. Finkel. 2004. Dps protects cells against multiple stresses during stationary phase. J Bacteriol 186: 4192-4198. Overbeek, R., M. Fonstein, M. D'Souza, G.D. Pusch, and N. Maltsev. 1999. The use of gene clusters to infer functional coupling. Proc Natl Acad Sci U S A 96: 2896- 2901. Pellegrini, M., E.M. Marcotte, M.J. Thompson, D. Eisenberg, and T.O. Yeates. 1999. Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. Proc Natl Acad Sci U S A 96: 4285-4288. Reindel, S., S. Anemuller, A. Sawaryn, and B.F. Matzanke. 2002. The DpsA-homologue of the archaeon Halobacterium salinarum is a ferritin. Biochim Biophys Acta 1598: 140-146. Saeed, A.I., V. Sharov, J. White, J. Li, W. Liang, N. Bhagabati, J. Braisted, M. Klapa, T. Currier, M. Thiagarajan, A. Sturn, M. Snuffin, A. Rezantsev, D. Popov, A. Ryltsov, E. Kostukovich, I. Borisovsky, Z. Liu, A. Vinsavich, V. Trush, and J. Quackenbush. 2003. TM4: a free, open-source system for microarray data management and analysis. Biotechniques 34: 374-378.
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