The experimental treatments used in this study were (1) continuously flooded-As amended (CFA), (2) continuously flooded-control (As unamended) (CFC), and (3) continuously saturated-control (As unamended) (CSC).
Growth protocol
Only soil genimic DNA was used I nthis study and did not involve any live culture growth
Extracted molecule
genomic DNA
Extraction protocol
the rhizosphere and the root-plaque samples were collected from each treatment plot. The microbial community DNA was extracted from the frozen rhizosphere and the root-plaque samples using MO BIO Power Max DNA extraction kits (Qiagen Inc.,).
Label
Cy3
Label protocol
The purified DNA was labelled with Cy-3 using random primers and the Klenow fragment of DNA polymerase I (Wu et al. 2006). Labelled DNA was purified using the QIA quick purification kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s instructions, measured on a NanoDrop ND-1000 spectrophotometer and then dried down in a SpeedVac (ThermoSavant, Milford, MA, USA) at 45 °C for 45 min. Dried DNA was rehydrated with 2.68 lL sample tracking control (NimbleGen) to confirm sample identity. The samples were incubated at 50 °C for 5 min, vortexed for 30 s and then centrifuged to collect all liquid at the bottom of the tube. Hybridization buffer (7.32 lL), containing 40% formamide, 25% SSC, 1% SDS, 2.38% Cy3-labelled alignment oligo (NimbleGen) and 2.8% Cy5-labelled CORS target, was added. The samples were then mixed by vortexing, spun down, incubated at 95 °C for 5 min and maintained at 42 °C until hybridization. An HX12 mixer (NimbleGen) was placed onto the array using NimbleGen’s precision mixer alignment tool, and then, the array was preheated to 42 °C on a hybridization station (MAUI, BioMicro Systems, Salt Lake City, UT, USA) for at least 5 min. Samples (6.8 lL) were then loaded onto the array surface and hybridized approximately 16 h with mixing
Hybridization protocol
not provided
Scan protocol
After hybridization, arrays were scanned at full laser power and 100% photomultiplier tubes gain with a NimbleGen MS 200 Microarray Scanner (Roche NimbleGen). Scanned images were gridded by NimbleScan software using the gridding file containing GeoChip 4 probes and NimbleGen control probes to obtain the signal intensity for each probe. Probe spots with coefficient of variance (CV) >0.8 were removed. In general, a local background that represents the actual background signal for each spot is preferred for signal-to-noise ratio (SNR) calculations and false-positive filtering instead of the global background generated by NimbleScan; so a different method for background calculation was introduced here. To obtain a local background signal for each probe, a customized void gridding file targeting positions without probes was generated. The scanned images were gridded using the void gridding files. The local background signal for each probe was calculated as the mean signal intensity of the four neighbouring void spots. When all of the four neighbouring void spots were not valid, the set of eight void spots surrounding its closest neighbours was used
Description
Soil mciorbial DNA was extarcted, amplified and hybridized, microarray scanned
Data processing
Microarray hybridization array data was processed and normalized by following the data analysis pipeline detailed in Tu et al (37). We further processed the data by replacing all the biological replicates values by a zero value if any of the three replicates had no signal intensity. Further normalization of the data was performed by transforming absolute detection to relative abundance by estimating ratio of each probe to all genes detected by GeoChip for that specific sample. Relative signal intensity ratios were used for all comparative analysis and multivariate statistical analysis. We used specific gene probes for quantifying relative abundance of functional groups. For arsenate reducing bacteria (ARB) we used arsA, arsB and arsC gene probes. For sulfate reducing bacteria (SRB), we used aprA, APS_aprA, APS_aprB, dsrA, and dsrB gene probes. For sulfur oxidizing bacteria (SOB) we used sox gene probes. For iron reducing bacteria (IRB) we used cytochrome gene probes. Principal component analysis was performed using the PAST software (23). The PCA routine in PAST finds the eigenvalues and eigenvectors of the variance-covariance matrix or the correlation matrix. We used the variance-covariance matrix for the gene-probe relative abundance data. The Biplot option was used for projecting the predominant species (gene-probes) constraining the principal components. A one-way non-parametric multivariate analysis of variance (PERMANOVA) was used to test the significant differences between the experimental treatments for relative abundance of functional groups, based on Bray-Curtis similarity index. The canonical correspondence analysis for the relative abundance of arsenate reducing bacteria (ARBs) and sulfate reducing bacteria (SRBs) were compared with their functional associations (other gene probes detected in these organisms). The gene categories and their abundance were estimated for each sample, which was then used as variables (priori) within CCA and were plotted as biplots. Hierarchical clustering analysis and heat maps were created for the relative signal intensities for gene probes in GeoChip using Gplots package within R software. The individual probe data were then grouped by taxonomic phyla for graphing
Niche differentiation of arsenic-transforming microbial groups in rice rhizosphere compartments as impacted by water management and soil-As concentrations
