Supplemental Data Materials and Methods
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Supplemental Data
A. Materials and MethodsRetrieval and verification of functional gene sequencesA total of 292 key functional genes or enzymes involved in some important microbially mediated biogeochemical processes, such as C, N, S, P cycling, metal resistance, organic contaminant resistance, and energy process as well as one phylogenetic marker gene gyrB, were chosen as our targets (Table 1). Sequence retrieval was performed by a GeoChip design pipeline (Fig. 1). This pipeline was implemented in Perl scripts integrated with the Common Gateway Interface (CGI) web standard protocol and some open-source modules, such as Bioperl and DBI. A MySQL database was integrated with the pipeline to store and manage all sequence and probe data, such as databases for all initially retrieved candidate sequences, verified sequences, seed sequences, key words, output probes, and the best probes for GeoChip. For each functional gene, the first step was to submit a query to GenBank Protein Database through NCBI Entrez Programming Utilities (eUtils) and fetch all candidate amino acid sequences. Each query submitted usually included various key words such as: the name of the target gene/enzyme, its abbreviation and enzyme commission number (EC), affiliated domains of bacteria, archaea and fungi. The sequences retrieved by the query should be as broad as possible to avoid missing any real sequences but meanwhile quite a few non-target sequences, such as the genes with similar abbreviation or homological sequences, were retrieved as well. Thus, all these candidate sequences have to be verified by a sequence-region-consensus finding program, HMMER 2.3.2 (Eddy, 1998). Generally, more than five full-length sequences with experimentally verified functions were selected as seed sequences for each functional gene, and then ClustalW (Thompson et al., 1994) and hmmbuild (Eddy, 1998) were used to construct a profile hidden Markov model (HMM) based on the alignment of all seed sequences. This model was used to search against all candidate sequences through both HMMER local and global algorithms, and e-values were obtained for the hits. Only those hits with e-values of the global alignment less than 0.1 which was a little bit stricter than the significance criteria of e-value less than 1.0 recommended by HMMER authors (Eddy, 1998) were considered as highly confident targets in this design process. Other hits with local e-values less than 1.0 were listed and manually determined to be targets. Finally, all confirmed protein sequences were searched against GenBank again to obtain their corresponding nucleic acid sequences for probe design. For GeoChip 3.0, all sequences were downloaded from the GenBank database before May 5, 2009 although this developed pipeline can be used for automatic updates at any time in the future. Oligonucleotide probe design, synthesis and microarray fabricationA new version of CommOligo (Li et al., 2005) with group-specific probe design features was used to design both gene- and group-specific oligonucleotide probes based on the following criteria: (i) Gene-specific probes: ≤ 90% sequence identity, ≤ 20-base continuous stretch, and ≥ -35 kcal/mol free energy (Liebich et al., 2006); (ii) Group-specific probes: a group-specific probe has to meet the above requirements for non-target groups, and it also must have ≥96% of sequence identity, ≥ 35-base of continuous stretch, and ≤ -60 kcal/mol of free energy within the group (He et al., 2005). All designed probes were subsequently verified against the GenBank (NR) nucleic acid database for specificity and the criteria for nonspecific hits were >90% sequence identities, > 20bp continuous stretches, or < -35 kcal/mol free. Only the best probe for each sequence or each group of closely related sequences was chosen to be synthesized by Invitrogen (Carlsbad, CA). The concentration of all oligonucleotides was adjusted to 150 pmol/μl. Also, eight degenerate probes for the 16S rRNA gene were synthesized for positive controls and spotted on each sub-grid at least two times, and 672 unique probes designed from hypothetical genes of seven sequenced genomes of hyperthermophiles for negative controls. In addition, a 50-mer common oligonucleotide reference standard (CORS) probe (5’-CCGCACCTCGGACCGCACACAATCGTTTGAGGACGTGTAGCTGTGCTGGC-3') was synthesized and then mixed with each gene or control probe at the concentration of 5% (Liang et al., 2009). The final probe concentration was 100 pmol/μl. All oligonucleotide probes and controls were arrayed onto Corning UltraGAPS (Corning, NY) slides using a Microgrid II Arrayer (Genomic Solutions, Ann Arbor, MI) as described previously (He et al., 2005). Preparations of synthesized oligonucleotid targetsA total of 24 oligonucleotides that are complementary to their corresponding probes on the array were synthesized and labeled at the 5’-end with Cy5 or Cy3 dye by MWG Biotech (High Point, NC) during synthesis (Table S1). 10 pg of each synthesized oligonucleotide was used in hybridization.Preparations of Shewanella genomic DNA targetsShewanella species MR-4 and W3-18-1 were grown at the LB medium, and their genomic DNAs were extracted as previously described (He et al., 2005). 500 ng of gDNA for each organism was used independently for hybridization. BioCON experimental site, plant species and sampling The BioCON (Biodiversity, CO2 and N) experimental site is located at the Cedar Creek Ecosystem Science Reserve, Minnesota, USA (lat. 45° N, Long. 93° W), and its main field experiment has a total of 296 plots (2 x 2 m) evenly distributed in six 20-m diameter rings with three treatments: CO2 (ambient, 368 µmol-1 vs. elevated, 560 µmol-1), N (ambient vs. 4 g N m-2 per year), and plant diversity. Four levels of plant diversity: 1, 4, 9, or 16 species were chosen randomly for each plot from 16 perennial species native or naturalized to the Cedar Creek Ecosystem Science Reserve, including (i) four C3 grasses (Agropyron repens, Bromus inermis, Koeleria cristata, Poa pratensis), (ii) four C4 grasses (Andropogon gerardii, Bouteloua gracilis, Schizachyrium scoparium, Sorghastrum nutans), (iii) four N-fixing legumes (Amorpha canescens, Lespedeza capitata, Lupinus perennis, Petalostemum villosum), and (iv) four non N-fixing herbaceous species (Achillea millefolium, Anemone cylindrica, Asclepias tuberosa, Solidago rigida) (Reich et al., 2001). Similar to our previous study with soil microbial communities under ambient and elevated CO2 at the same site (He et al., 2010), this study analyzed 31 soil samples from ring 2 (ambient CO2 and without N supply) with 11 plots each for 1- and 4-species, 5 for 9-species, and 4 for 16-species in July 2007. Extraction and purification of soil DNAA total of 31 soil samples with different plant diversity (11, 11, 5, and 4 from 1-, 4-, 9-, and 16-species, respectively) were extracted DNA by freeze-grinding mechanical lysis as described previously (Zhou et al., 1996), and purified using a low melting agarose gel followed by phenol extraction. DNA quality and quantificationEach DNA sample from Shewanella species MR-4, W3-18-1, or soil samples was assessed by the ratios of 260 nm/280 nm, and 260/230 nm using a NanoDrop ND-1000 Spectrophotometer (NanoDrop Technologies Inc., Wilmington, DE), and those ratios were more than 1.80 for 260/280, and more than 1.7 for 260/230. Final DNA concentrations were quantified with PicoGreen (Ahn et al., 1996) using a FLUOstar Optima (BMG Labtech, Jena, Germany).Soil DNA amplification In order to produce consistent hybridizations from all 31 soil samples, a whole community genome amplification (WCGA) was used to generate approximately 3.0 µg of DNA with 50 ng purified soil DNA as the template (Wu et al., 2006) using the TempliPhi Kit (GE Healthcare, Piscataway, NJ) following the manufacturer’s instructions. In addition, single-strand binding protein (267 ng μL-1) and spermidine (0.1 mM) were added to the reaction mix to improve the amplification efficiency. The reactions were incubated at 30°C for 3 hours and stopped by heating the mixtures at 65°C for 10 min. Labeling of gDNA and soil DNA targets 500 ng of Shewanella genomic DNA from MR-4 or W3-18-1, or about 3.0 µg of amplified DNA were labeled with the fluorescent dye Cy-5 using random priming method (Wu et al., 2006) as follows. First, the whole amplified products were mixed with 20 μL random primers, denatured at 99.9 °C for 5 min, and then immediately chilled on ice. Following denaturation, the labeling master mix containing 2.5 μL dNTP (5 mM dAGC-TP, 2.5 mM dTTP), 1 μL Cy-5 dUTP (Amersham, Piscataway, NJ), 80 U of the large Klenow fragment (Invitrogen, Carlsbad, CA), and 2.5 μL water were added and then incubated at 37°C for 3 hours, followed by heating at 95 °C for 3 min. Labeled DNA was purified using the QIA quick purification kit (Qiagen, Valencia, CA) according to the manufacturer’s instructions, measured on a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies Inc., Wilmington, DE), and then dried down in a SpeedVac (ThermoSavant, Milford, MA) at 45 °C for 45 min. Hybridization and imaging processing The labeled target was re-suspended in 120 µl hybridization solution containing 50% formamide, 3 x SSC, 10 µg of unlabeled herring sperm DNA (Promega, Madison, WI), and 0.1% SDS, and the mix was denatured at 95°C for 5 min and kept at 50°C until it was deposited directly onto a microarray. Hybridizations were performed with a TECAN Hybridization Station HS4800 Pro (TECAN, US) according to the manufacturer’s recommended method. This equipment allows up to 48 hybridizations at one time, and well reproducible results were obtained. After washing and drying, the microarray was scanned by ScanArray Express Microarray Scanner (Perkin Elmer, Boston, MA) at 633 nm using a laser power of 90% and a photomultiplier tube (PMT) gain of 75%. The ImaGene version 6.0 (Biodiscovery, El Segundo, CA) was then used to determine the intensity of each spot, and identify poor-quality spots. Data pre-processing Raw data from ImaGene were submitted to Microarray Data Manager in our website (http://ieg.ou.edu/microarray/) and analyzed using the data analysis pipeline with the following major steps: (i) The spots flagged as 1 or 3 by Imagene and with a signal to noise ratio (SNR) less than 2.0 (He and Zhou, 2008) were removed as poor-quality spots; (ii) After removing the bad spots, normalized intensity of each spot was calculated by dividing the signal intensity of each spot by the mean intensity of the microarray; (iii) If any of replicates had (signal–mean) more than two times the standard deviation, this replicate was moved as an outlier. This process continued until no such replicates were identified; (iv) At least 0.34 time of the final positive spots (probes), or a minimum of two spots was required for each gene; and (v) If a probe appeared in 25% or fewer samples among the total of samples for each plant species diversity, it was removed for data reliability, resulting in 4012 probes to be further analyzed. Statistical analysis of GeoChip 3.0 data To examine the effects of plant species diversity on the structure, composition, and functional activity, pre-processed GeoChip 3.0 data were further analyzed with different statistical methods. Detrended correspondence analysis (DCA) was used to analyze all (4012) detected genes toexamine the effects of different plant species diversity levels (1-, 4-, 9-, and 16-species) on the overall functional structure and composition of soil microbial communities as described previously (Zhou et al., 2008). DCA is an ordination technique that uses detrending to remove the arch effect, where the data points are organized in a horseshoelike shape, in correspondence analysis (Hill and Gauch, 1980), and DCA was performed by PC-ORD for Windows (McCune and Mefford, 1999) and confirmed by CANOCO 4.5 for Windows (Biometris – Plant Research International, The Netherlands). 114 nifH gene sequences were detected by GeoChip 3.0, and 71 of them detected at least 5 out of 31 samples (11, 11, 5, and 4 from 1, 4, 9, and 16 species, respectively) were used for hierarchical clustering analysis, and the resulting heat-map was visualized with TreeView (Eisen et al., 1998). To determine the number of phylotypes detected by GeoChip 3.0 at different taxonomical levels, we first excluded 534 genes (derived from uncultured organisms) from the totally detected genes (4012) in 31 samples, and then the rest of those genes were mapped to their lineages using the sequence database if possible. Finally, the number of genes detected at different taxonomical levels was counted. B. Supplemental Table Table S1 The list of synthesized oligonucleotides and their target probes
C. Supplemental references Ahn S, Costa J, Emanuel J (1996). PicoGreen quantitation of DNA: effective evaluation of samples pre- or post-PCR. Nucleic Acids Res 24: 2623-2625. Eddy SR (1998). Profile hidden Markov models. Bioinformatics 14: 755-63. Eisen MB, Spellman PT, Brown PO, Botstein D (1998). Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95: 14863-14868. He Z, Wu L, Li X, Fields MW, Zhou J (2005). Empirical establishment of oligonucleotide probe design criteria. Appl Environ Microbiol 71: 3753-60. He Z, Xu M, Deng Y, Kang S, Kellogg L, Wu L et al (2010). Metagenomic analysis reveals a marked divergence in the structure of belowground microbial communities at elevated CO2. Ecol Lett (in press). He Z, Zhou J (2008). Empirical evaluation of a new method for calculating signal-to-noise ratio for microarray data analysis. Appl Environ Microbiol 74: 2957-66. Hill MO, Gauch HG (1980). Deterended correspondence analysis, an improved ordination technique. Vegetatio 42. Li X, He Z, Zhou J (2005). Selection of optimal oligonucleotide probes for microarrays using multiple criteria, global alignment and parameter estimation. Nucleic Acids Res 33: 6114-23. Liang Y, He Z, Wu L, Deng Y, Li G, Zhou J (2009a). Development of a common oligo reference standard (CORS) for microarray data normalization and comparison across different microbial communities. Appl Environ Microbiol (in press). Liebich J, Schadt CW, Chong SC, He Z, Rhee SK, Zhou J (2006). Improvement of oligonucleotide probe design criteria for functional gene microarrays in environmental applications. Appl Environ Microbiol 72: 1688-1691. McCune B, Mefford MJ (1999). PCORD. MjM Software Design, Gleneden Beach, OR. Reich PB, Knops J, Tilman D, Craine J, Ellsworth D, Tjoelker M et al (2001). Plant diversity enhances ecosystem responses to elevated CO2 and nitrogen deposition. Nature 410: 809-812. Thompson JD, Higgins DG, Gibson TJ (1994). CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 22: 4673-80. Wu L, Liu X, Schadt CW, Zhou J (2006a). Microarray-based analysis of subnanogram quantities of microbial community DNAs by using whole-community genome amplification. Appl Environ Microbiol 72: 4931-41. Zhou J, Bruns MA, Tiedje JM (1996). DNA recovery from soils of diverse composition. Appl Environ Microbiol 62: 316-22. Zhou J, Kang S, Schadt CW, Garten CT, Jr. (2008). Spatial scaling of functional gene diversity across various microbial taxa. Proc Natl Acad Sci USA 105: 7768-73. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||