Toxicogenomic analysis incorporating operon-transcriptional coupling and toxicant concentration-expression response: analysis of MX-treated Salmonella

Abstract

Background: Deficiencies in microarray technology cause unwanted variation in the hybridization signal, obscuring the true measurements of intracellular transcript levels. Here we describe a general method that can improve microarray analysis of toxicant-exposed cells that uses the intrinsic power of transcriptional coupling and toxicant concentration-expression response data. To illustrate this approach, we characterized changes in global gene expression induced in Salmonella typhimurium TA100 by 3-chloro-4-(dichloromethyl)-5-hydroxy-2(5H)-furanone (MX), the primary mutagen in chlorinated drinking water. We used the co-expression of genes within an operon and the monotonic increases or decreases in gene expression relative to increasing toxicant concentration to augment our identification of differentially expressed genes beyond Bayesian-t analysis.

Results: Operon analysis increased the number of altered genes by 95% from the list identified by a Bayesian t-test of control to the highest concentration of MX. Monotonic analysis added 46% more genes. A functional analysis of the resulting 448 differentially expressed genes yielded functional changes beyond what would be expected from only the mutagenic properties of MX. In addition to gene-expression changes in DNA-damage response, MX induced changes in expression of genes involved in membrane transport and porphyrin metabolism, among other biological processes. The disruption of porphyrin metabolism might be attributable to the structural similarity of MX, which is a chlorinated furanone, to ligands indigenous to the porphyrin metabolism pathway. Interestingly, our results indicate that the lexA regulon in Salmonella, which partially mediates the response to DNA damage, may contain only 60% of the genes present in this regulon in E. coli. In addition, nanH was found to be highly induced by MX and contains a putative lexA regulatory motif in its regulatory region, suggesting that it may be regulated by lexA.

Conclusion: Operon and monotonic analyses improved the determination of differentially expressed genes beyond that of Bayesian-t analysis, showing that MX alters cellular metabolism involving pathways other than DNA damage. Because co-expression of similarly functioning genes also occurs in eukaryotes, this method has general applicability for improving analysis of toxicogenomic data.

Figure 1

Average mutagenicity of MX in Salmonella TA100 from four experiments. The average % survival of log-phase cells was 95, 97, and 73% at 1.15-, 2.3-, and 4.6-μM MX, respectively.

Figure 2

Principal component analysis of differentially expressed genes in MX-treated Salmonella TA100. PCA#1 accounts for 31% of the variation in the data and PCA#2 accounts for 16%.

Figure 3

Monotonically increasing gene expression in MX-treated Salmonella TA100. The x-axis represents the concentration of MX at 4 concentrations, 0-, 1.15-, 2.3-, and 4.6-μM MX, respectively. The log-scaled y-axis represents expression values that are the mean of 12 background-corrected intensities (4 biological replicates and 3 technical replicates for each biological replicate) normalized to the DNA reference. Double asterisks represent those genes that are known members of the LexA regulon.

Figure 4

Monotonically decreasing gene expression in MX-treated Salmonella TA100. The x-axis represents the concentration of MX at 4 concentrations, 0-, 1.15-, 2.3-, and 4.6-μM MX, respectively. The log-scaled y-axis represents expression values that are the mean of 12 background-corrected intensities (4 biological replicates and 3 technical replicates for each biological replicate) normalized to the DNA reference.

Figure 5

Structures of furan, MX, and pyrrole.