BMC Genomics 2008, 9:627 http://www.biomedcentral.com/1471-2164/9/627 taining Tween 80, ergosterol, and silicon antifoam (SSG- TEA) [51] with shaking (300 rpm) for 3 – 4 days prior to inoculating a NewBrunswick BioFlo III fermentor (3.5 l working volume) [52]. Batch fermentor cultures were inoculated with a volume of pre-culture to allow for at least six generations of aerobic growth before reaching a cell density of ≈ 1 Klett unit. A control sample was then harvested and 1 μM antimycin A (final concentration; Sigma, St. Louis, MO) was added to the medium. Ten samples were collected (0.04, 0.08, 0.13, 0.19, 0.25, 0.38, 0.5, 1, 2, and 3 generations) over the next three genera- tions of aerobic growth. The sparge gas (1.2 vol. of gas/ vol. of medium per min) was then switched from air to 2.5% CO2 in O2-free N2 and the same generation-specific samples were taken over three generations of anaerobic growth. Dissolved O2 was controlled and monitored as described previously [52]. Two batch fermentor experi- ments, in which every other time point was sampled, were conducted to complete the entire time series, which was repeated in triplicate. Cells were harvested using a vacuum filtration apparatus onto filters as described previously [53]. The filtered cells were washed with either sterile de- oxygenated or oxygenated water (as appropriate), flash- frozen in liquid N2 within one minute of initiating the sampling, and stored at -80°C for later RNA isolation. RNA extraction, cDNA synthesis, and microarray hybridization Total RNA was extracted from the filtered cells using hot phenol as described previously [53]. Thirty μg of total RNA was used for first-strand cDNA synthesis, and micro- array target preparation was performed as described else- where [53]. A reference design was used for hybridizing the custom 70-mer oligonucleotide microarrays [5]. The reference consisted of a pool of equal masses of RNA from a 24-h anaerobic culture and a 4-h aerobic culture har- vested after prior exposure to 24 h of anaerobiosis. Micro- array hybridizations, washing, and scanning were conducted as described previously [5]. Statistical analyses and data clustering GenePix Pro software (v4.1, Axon, Union City, CA) was used for spot identification and fluorescence intensity quantification. Data normalization and statistical analy- ses were performed as described previously [5] using SAS (v9.1, SAS Institute, Cary, NC). After normalization, sepa- rate ANOVAs (SAS MIXED procedure with repeated meas- ures) were used to identify genes whose transcript levels responded significantly to three different treatments: (A) anaerobiosis, using data published previously from our laboratory [6]; (B) antimycin A treatment during aerobio- sis; and (C) anaerobiosis in the presence of antimycin A. The same generation-specific sampling regime was used for each treatment (0 [control], 0.04, 0.08, 0.13, 0.19, 0.25, 0.38, 0.5, 1, 2, and 3 generations). A step-down Bon- ferroni post hoc P-value adjustment was used to minimize the false discovery rate. After identifying genes that responded significantly to each treatment, the temporal profiles were separately clus- tered using Kohonen's self-organizing map (SOM) algo- rithm http://www.cis.hut.fi/research/som_pak/[54]) with 1D-string topology and standard correlation as the dis- tance metric. To determine an appropriate K value (cluster number) for gene network recovery, two quality assess- ment metrics were calculated from the results obtained using a range of K values (from 2 to 30). Consensus Share (CS) is the percentage of genes that were clustered exactly the same over 10 replicate runs of the SOM algorithm (an indicator of clustering robustness), and the Feature Con- figuration Statistic (FCS) is the probability that the observed configuration of a transcription-factor motif (TFM) among gene clusters arose by chance alone from its multinomial distribution dictated by cluster sizes [5]. For FCS, we assessed the configuration of 2,892 transcription- factor consensus binding sequences or motifs (TFMs) compiled from both experimental and comparative phyl- ogenomic analyses [see Additional file 1]. To narrow the range of potentially interesting K values, we first elimi- nated those with a CS value < 0.9. We then calculated FCS q values [55] on a K-by-K basis and eliminated any TFMs whose value was > 0.01 for all remaining K values. We then calculated an average FCS P value for all remaining TFMs as a function of K. The clustering result (K value) for each treatment that resulted in the lowest average FCS P value was chosen for gene network analysis. In addition to examining the configuration of consensus binding sequences among gene clusters (FCS P value), and calcu- lating their hypergeometric enrichment P values within each cluster, we also used MDscan [56] to identify over- represented sequences in each of the gene clusters that may not be included in our TFM list. For training, we used a set of ≈ 30 expression profiles, but no more than 50% of any cluster, that were closest to the mean expression pro- file in each cluster. FunSpec [57] was used to calculate hypergeometric P values for enriched MIPS functional cat- egories in each gene cluster or for subgroups of genes identified with MDscan. All microarray data from this study have been deposited in NCBI Gene Expression Omnibus (accession numbers GSE3705 and GSE3706). The anaerobic dataset to which the antimycin A treatments are compared is a subset of accession number GSE2246. Authors' contributions L-CL helped grow the strains, carried out the microarray studies, conducted the statistical and clustering analyses, Page 11 of 14 (page number not for citation purposes)