Environ. Sci. Technol. 2007, 41, 1799-1800
Response to Comment on “Discrimination of Shifts in a Soil Microbial Community Associated with TNT-Contamination Using a Functional ANOVA of 16S rRNA Hybridized to Oligonucleotide Microarrays” We have reviewed the comments addressed by Pozhitkov and Noble (1) to our manuscript (2). The detailed response to their comments follows. 1. Previous paper (3) showing the effect of grid placement. In this paper, the effect of frame positioning on normalized dissociation curves was analyzed, and several dissociation curves strongly deviated from the general sigmoid shape of the curves (Figure 10, panel B). However, those curves were not observed by Eyers et al. (2), suggesting that the effect of frame positioning was not significant. 2. Table S-1 of Pozhitkov and Noble (1) showing that only 15.6% of the grid frames are exactly placed in the center of the pad. The setting of the grid boxes shown in their Figure S-1 is abnormally small so that the “inner” box is up against the edge of the pad. This increases the sensitivity of grid placement by allowing little variation in grid placement before signal from the pad is integrated as background signal rather than signal from the hybridization. In addition, in Figure 1 of Pozhitkov and Noble (1), the integrated sum of SIs from the gel pad and the surrounding background were used which also increase the sensitivity of grid placement. In the paper of Eyers et al. (2), the inner box was larger, and the software used to capture the melt profile used the mean signal per unit area inside the inner box and the mean signal per unit area in the frame between the inner box and the outer box which mitigates the variation in grid placement. 3. Figure 1 of Pozhitkov and Noble showing 12 dissociation curves. Of note, most of the curves of Figure 1C (1) present a sigmoid shape although in Figure 10, panel B of Pozhitkov et al. (3), they are more strongly deviating from the general sigmoid shape. The reason of these differences between these two figures is not explained by Pozhitkov and Noble. In Figure 1C, two dissociation curves from a total of 12 deviate from the general sigmoid shape. We have shown (Bugli et al., submitted) that the functional ANOVA model identifies “outlier” curves and rejects them from the analysis. In the case of Figure 1C (1), application of the Functional ANOVA model will reject these two dissociation curves from the analysis. Therefore, if misplacement of the grid effectively occurred, the data collected under such conditions will not be taken in the analysis. ‘”Outlier” curves were discussed in our paper, and as we suggested (2), these outlier curves may be the result of several contributing factors, such as movement of the microarray during data collection, sporadic occurrence of fluorescent particles, and the formation of bubbles within the hybridization chamber at high temperature. 4. Large standard deviations of the averaged curves. The standard deviations shown in Figure 1B and Table S-3 of our manuscript (2) correspond to single points in the dissociation curve, and they do not take into account the curves in their entirety. The functional ANOVA model takes into account the curves in their entirety, and the intervals of confidence of the average dissociation curves are rather low (Figure 1D of ref 2). 10.1021/es0629514 CCC: $37.00 Published on Web 01/27/2007
2007 American Chemical Society
5. Data not compliant with MIAME standards. The data in our study are compliant with the voluntary standards suggested by Brazma et al. (4) as image scanning hardware and software, and processing procedures and parameters were provided in our Supporting Information section. Data extraction and processing protocols, normalization, transformation, and data selection procedures and parameters were provided in the Materials and Methods section of the manuscript. Data are available from the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo) under accession numbers GSE3499 and GSE3525, and curve raw data have been provided as a supplementary file (GSE3499_suppl.tar). Normalized data are available when running the Functional ANOVA calculator. 6. Differences in dissociation curves being drowned by noise or due to specific grid placement. In Figure 1D of Eyers et al. (2), the confidence intervals of the curves are actually small, i.e., the “noise” is low and average dissociation curves obtained from hybridizations to PM and MM probes are effectively discriminated. As mentioned above, average signals and a larger “inner” box were used, “outlier” dissociation curves were rejected, triplicates were carried out, and confidence intervals were calculated with the functional ANOVA model. All together, this makes the influence of grid placement low. 7. Curves differing between soil samplesscurves should have been identical. As we discussed in our manuscript (2), George et al. (submitted) have shown that the microbial communities of TNT-contaminated soil samples are strongly impacted by the presence of TNT. In the TNT-contaminated soil samples, a predominance of Pseudomonadaceae was found. In the manuscript of George et al., the same uncontaminated and TNT-contaminated soil sample as used by Eyers et al. (2) were analyzed and a similar strong effect of TNT on soil microbial communities was found. Additional studies of George et al. (manuscript in preparation) showed the absence of Acidobacteria PCR signals in the TNTcontaminated soil samples, although PCR signals were positive in the uncontaminated soil samples. Also, Fuller and Manning (5, 6) showed that Gram-positive bacteria were strongly affected by the presence of TNT. Therefore, microbial communities of TNT-contaminated soils are very different from the ones of uncontaminated soils. In the microarray used in the study of Eyers et al. (2), there were no probes targeting the 16S rRNA of Pseudomonadaceae or Acidobacteria, but probes targeting the Gram-positive bacteria showed differences in SIs and dissociation curves. 8. Attributing curves to particular groups of organisms. We did not attribute the curves to particular groups of organisms, except for probe Eub338 because reference curves were available for this probe (Figure 1A and C of ref 2). This study showed that Functional ANOVA clearly identified differences in dissociation curves between the two soil samples. However, as we stated in our manuscript (2), additional studies are needed to assign particular dissociation curves to particular group of organisms (i.e., obtaining reference curves). 9. Statistical analysis showing no difference between initial SIs and Functional ANOVA results (Tables S-2 and S-3 in ref 1). The analysis of SIs provides information of the abundance of particular target nucleic acids. The analysis of dissociation curves is informative of the sequence of particular target nucleic acids. Since SIs and dissociation curves do not provide the same information, they cannot be directly compared as was done by Pozhitkov and Noble. VOL. 41, NO. 5, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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10. Shifts of soil communities due to artifact of the method. As stated above, parallel molecular and cultivation work in the same soils showed significant shifts in the microbial communities.
Literature Cited (1) Pozhitkov, A. E.; Noble, P. A. Comment on “Discrimination of shifts in a soil microbial community associated with TNTcontamination using a functional ANOVA of 16S rRNA hybridized to oligonucleotide microarrays”. Environ. Sci. Technol. 2007, 41, xxx-xxx. (2) Eyers, L.; Smoot, J. C.; Smoot, L. M.; Bugli, C.; Urakawa, H.; McMurry, Z.; Siripong, S.; El Fantroussi, S.; Lambert, P.; Agathos, S. N.; Stahl, D. A. Discrimination of shifts in a soil microbial community associated with TNT-contamination using a functional ANOVA of 16S rRNA hybridized to oligonucleotide microarrays. Environ. Sci. Technol. 2006, 40, 58675873. (3) Pozhitkov, A.; Chernov, B.; Yershov, G.; Noble, P. A. Evaluation of gel-pad oligonucleotide microarray technology by using artificial neural networks. Appl. Environ. Microbiol. 2005, 71, 8663-8676. (4) Brazma, A.; Hingamp, P.; Quackenbush, J.; Sherlock, G.; Spellman, P.; Stoeckert, C.; Aach, J.; Ansorge, W.; Ball, C. A.; Causton, H. C.; Gaasterland, T.; Glenisson, P.; Hostege, F. C. P.; Kim, I. F.; Markowitz, V.; Matese, J. C.; Parkinson, H.; Robinson, A.; Sarkans, U.; Schulze-Kremer, S.; Stewart, J.; Taylor, R.; Vilo, J.; Vingron, M. Minimum information about a microarray experiment (MIAME)-towards standards for array data. Nat. Genet. 2001, 29, 365-371.
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(5) Fuller, M. E.; Manning, J. F. Aerobic Gram-positive and Gramnegative bacteria exhibit differential sensitivity to and transformation of 2,4,6-trinitrotoluene (TNT). Curr. Microbiol. 1997, 35, 77-83. (6) Fuller, M. E.; Manning, J. F. Evidence for differential effects of 2,4,6-trinitrotoluene and other munitions compounds on specific subpopulations of soil microbial communities. Environ. Toxicol. Chem. 1998, 17, 2185-2195.
L. Eyers Unit of Bioengineering University of Louvain 1348 Louvain-la-Neuve, Belgium
J. C. Smoot C/e- Solutions, Inc. Sacramento, California 95835
D. A. Stahl Civil and Environmental Engineering Department University of Washington Seattle, Washington 98195 ES0629514
