Deinococcus radiodurans R1 (DEIRA) is a bacterium best known for its extreme resistance to the lethal effects of ionizing radiation, but the molecular mechanisms underlying this phenotype remain poorly understood. To define the repertoire of DEIRA genes responding to acute irradiation (15 kGy), transcriptome dynamics were examined in cells representing early, middle, and late phases of recovery by using DNA microarrays covering ≈94% of its predicted genes. At least at one time point during DEIRA recovery, 832 genes (28% of the genome) were induced and 451 genes (15%) were repressed 2-fold or more. The expression patterns of the majority of the induced genes resemble the previously characterized expression profile of recA after irradiation. DEIRA recA , which is central to genomic restoration after irradiation, is substantially up-regulated on DNA damage (early phase) and down-regulated before the onset of exponential growth (late phase). Many other genes were expressed later in recovery, displaying a growth-related pattern of induction. Genes induced in the early phase of recovery included those involved in DNA replication, repair, and recombination, cell wall metabolism, cellular transport, and many encoding uncharacterized proteins. Collectively, the microarray data suggest that DEIRA cells efficiently coordinate their recovery by a complex network, within which both DNA repair and metabolic functions play critical roles. Components of this network include a predicted distinct ATP-dependent DNA ligase and metabolic pathway switching that could prevent additional genomic damage elicited by metabolism-induced free radicals.

To determine the potential of DNA array technology for assessing functional gene diversity and distribution, a prototype microarray was constructed with genes involved in nitrogen cycling: nitrite reductase ( nirS and nirK ) genes, ammonia mono-oxygenase ( amoA ) genes, and methane mono-oxygenase ( pmoA ) genes from pure cultures and those cloned from marine sediments. In experiments using glass slide microarrays, genes possessing less than 80 to 85% sequence identity were differentiated under hybridization conditions of high stringency (65°C). The detection limit for nirS genes was approximately 1 ng of pure genomic DNA and 25 ng of soil community DNA using our optimized protocol. A linear quantitative relationship ( r ² = 0.89 to 0.94) was observed between signal intensity and target DNA concentration over a range of 1 to 100 ng for genomic DNA (or genomic DNA equivalent) from both pure cultures and mixed communities. However, the quantitative capacity of microarrays for measuring the relative abundance of targeted genes in complex environmental samples is less clear due to divergent target sequences. Sequence divergence and probe length affected hybridization signal intensity within a certain range of sequence identity and size, respectively. This prototype functional gene array did reveal differences in the apparent distribution of nir and amo A and pmo A gene families in sediment and soil samples. Our results indicate that glass-based microarray hybridization has potential as a tool for revealing functional gene composition in natural microbial communities; however, more work is needed to improve sensitivity and quantitation and to understand the associated issue of specificity.

Background: Large-scale sequencing of entire genomes has ushered in a new age in biology. One of the next grand challenges is to dissect the cellular networks consisting of many individual functional modules. Defining co-expression networks without ambiguity based on genome-wide microarray data is difficult and current methods are not robust and consistent with different data sets. This is particularly problematic for little understood organisms since not much existing biological knowledge can be exploited for determining the threshold to differentiate true correlation from random noise. Random matrix theory (RMT), which has been widely and successfully used in physics, is a powerful approach to distinguish system-specific, non-random properties embedded in complex systems from random noise. Here, we have hypothesized that the universal predictions of RMT are also applicable to biological systems and the correlation threshold can be determined by characterizing the correlation matrix of microarray profiles using random matrix theory. Results: Application of random matrix theory to microarray data of S. oneidensis, E. coli, yeast, A. thaliana, Drosophila, mouse and human indicates that there is a sharp transition of nearest neighbour spacing distribution (NNSD) of correlation matrix after gradually removing certain elements insider the matrix. Testing on an in silico modular model has demonstrated that this transition can be used to determine the correlation threshold for revealing modular co-expression networks. The co-expression network derived from yeast cell cycling microarray data is supported by gene annotation. The topological properties of the resulting co-expression network agree well with the general properties of biological networks. Computational evaluations have showed that RMT approach is sensitive and robust. Furthermore, evaluation on sampled expression data of an in silico modular gene system has showed that under-sampled expressions do not affect the recovery of gene co-expression network. Moreover, the cellular roles of 215 functionally unknown genes from yeast, E. coli and S. oneidensis are predicted by the gene co-expression networks using guilt-by-association principle, many of which are supported by existing information or our experimental verification, further demonstrating the reliability of this approach for gene function prediction. Conclusion: Our rigorous analysis of gene expression microarray profiles using RMT has showed that the transition of NNSD of correlation matrix of microarray profile provides a profound theoretical criterion to determine the correlation threshold for identifying gene co-expression networks.

Temporal genomic profiling and whole-cell proteomic analyses were performed to characterize the dynamic molecular response of the metal-reducing bacterium Shewanella oneidensis MR-1 to an acute chromate shock. The complex dynamics of cellular processes demand the integration of methodologies that describe biological systems at the levels of regulation, gene and protein expression, and metabolite production. Genomic microarray analysis of the transcriptome dynamics of midexponential phase cells subjected to 1 mm potassium chromate (K₂CrO₄) at exposure time intervals of 5, 30, 60, and 90 min revealed 910 genes that were differentially expressed at one or more time points. Strongly induced genes included those encoding components of a TonB1 iron transport system (tonB1-exbB1-exbD1), hemin ATP-binding cassette transporters (hmuTUV), TonB-dependent receptors as well as sulfate transporters (cysP, cysW-2, and cysA-2), and enzymes involved in assimilative sulfur metabolism (cysC, cysN, cysD, cysH, cysI, and cysJ). Transcript levels for genes with annotated functions in DNA repair (lexA, recX, recA, recN, dinP, and umuD), cellular detoxification (so1756, so3585, and so3586), and two-component signal transduction systems (so2426) were also significantly up-regulated (p < 0.05) in Cr(VI)-exposed cells relative to untreated cells. By contrast, genes with functions linked to energy metabolism, particularly electron transport (e.g. so0902-03-04, mtrA, omcA, and omcB), showed dramatic temporal alterations in expression with the majority exhibiting repression. Differential proteomics based on multidimensional HPLC-MS/MS was used to complement the transcriptome data, resulting in comparable induction and repression patterns for a subset of corresponding proteins. In total, expression of 2,370 proteins were confidently verified with 624 (26%) of these annotated as hypothetical or conserved hypothetical proteins. The initial response of S. oneidensis to chromate shock appears to require a combination of different regulatory networks that involve genes with annotated functions in oxidative stress protection, detoxification, protein stress protection, iron and sulfur acquisition, and SOS-controlled DNA repair mechanisms.

Shewanella oneidensis is an important model organism for bioremediation studies because of its diverse respiratory capabilities. However, the genetic basis and regulatory mechanisms underlying the ability of S. oneidensis to survive and adapt to various environmentally relevant stresses is poorly understood. To define this organism's molecular response to elevated growth temperatures, temporal gene expression profiles were examined in cells subjected to heat stress by using whole-genome DNA microarrays for S. oneidensis . Approximately 15% ( n = 711) of the total predicted S. oneidensis genes ( n = 4,648) represented on the microarray were significantly up- or downregulated ( P < 0.05) over a 25-min period after shift to the heat shock temperature. As expected, the majority of the genes that showed homology to known chaperones and heat shock proteins in other organisms were highly induced. In addition, a number of predicted genes, including those encoding enzymes in glycolysis and the pentose cycle, serine proteases, transcriptional regulators (MerR, LysR, and TetR families), histidine kinases, and hypothetical proteins were induced. Genes encoding membrane proteins were differentially expressed, suggesting that cells possibly alter their membrane composition or structure in response to variations in growth temperature. A substantial number of the genes encoding ribosomal proteins displayed downregulated coexpression patterns in response to heat stress, as did genes encoding prophage and flagellar proteins. Finally, a putative regulatory site with high conservation to the Escherichia coli σ ³² -binding consensus sequence was identified upstream of a number of heat-inducible genes.