Appendix A. Additional methods used for DNA fingerprinting, substrate preparations, Quantitative PCR, and data analysis.
Oligonucleotide fingerprinting of rRNA genes (OFRG)
Clones obtained from fungal rRNA sequences were subjected to a series of hybridization experiments, each containing a single 10-nucleotide DNA probe. Briefly, rRNA gene clones were printed onto nylon membranes (11 × 7 cm) using a QPix robot (Genetix, Hampshire, UK). The arrays were fixed by UV crosslinking (70 mJ), denatured with 0.5 N NaOH/1.5 M NaCl, neutralized with 50 mM Na phosphate pH 7.2, covered with boiling 0.1% SDS, and allowed to cool for 10 min. Arrays were then hybridized overnight at 11°C with 33P-labeled DNA oligonucleotide probes. Arrays were washed twice in 1X SSC for 30 min at 11°C, dried, and phosphorimaged.
The probe set used in this study was: F1, AGTTTTTGGG; F2, CAAGCCGATG; F6, CGCTGGCTTC; F8, TGGCCGGAAG; F9, GAAACTCACC; F10, CGTGCGGTTC; F11, GTGGAGCCTG; F12, GGGACTATCG; F13, GGGATCGGGC; F21, TCACCTTGGC; F23, CAGGTCTGTG; F29, CACCACCAGG; F30, CTGGTCGCCG; F31, TTTGCGGGCC; F32, ACCTGCTAAA; F33, GGCACCTTAC; F35, AGGGACAGTC; F36, CGGTCCGCAT; F37, CTTTGGCTGG; F40, ACTGCGAAAG; F41, TCTAGGACCG; F42, ATAGCCCGGC; F43, AAGTTTTTGG; F44, GGTCCGGGTA; F45, CTGACAGAGC; F46, GTCTGGGTAA; F49, CCAGCGAGTT; S5, GCTTCTTAGA; S7, GGTCTGGGTA; S9, TCCAGACACA; S11, TTATTGAAGA; S13, AGGTCTGGGT; L2, GGGCATTAGT; L3, TAACCTTGGC; L4, GTCGGGGGCA; L5, TTTGGGTTCT; L7, GAGTGGAGCC; L8, AGCGAGTTTA; L12, CAATTGTCAG; L13, GACTATCGGC; L14, GAGAGGTCTG; L25, AGTATGGTCG; L26, GCCGGCTTCT; L28, GTGCGGTTCT; L30, GGTTAATTCC.
The results from the hybridization experiments were used to create a fingerprint for each clone using the classification scheme described in Jeske et al. (2007). A UPGMA dendogram of the fingerprints was constructed using GCPAT (Figueroa et al. 2004). This dendrogram combined with the nucleotide sequences of representative clones from the most abundant fingerprints (i.e., those represented by at least 15 clones each) were used to refine the groupings into phylotypes (hereafter “taxa”). The number of clones represented within each taxon is an index of the relative abundance of that taxon in the litter subsample.
Preparation of lignocellulose and tannin-protein
To make lignocellulose, we ground live Pinus canariensis needles collected on the UCI campus in liquid N until they achieved the consistency of flour. Twenty five g of the material was washed in 250 mL deionized water at 80 ºC for 4 h, then refluxed in 250 mL 1:1 cyclohexane-ethanol at 80–85 ºC for 4 h. The material was transferred to a fresh solution of cyclohexane-ethanol, and the reflux step was repeated. We then refluxed the material in 250 mL ethanol at 85–90 ºC for two h, and repeated this step until the ethanol remained colorless. The material was added to 250 mL of deionized water, washed for 2 h at 80 ºC, and then filtered to remove the water. The remaining material (purified lignocellulose) was dried for 24 h at 60 ºC.
To prepare the tannin-protein solution, we mixed 2 g bovine serum albumin in 1 L 0.17 M sodium chloride to the tannic acid dissolved in deionized water. The solutions were combined in a 1:2 (tannin:protein) ratio by volume. The solution was allowed to stand for 15 minutes, and then was centrifuged at 5000 g for 15 minutes. The supernatant was discarded, and the pellet (tannin-protein complexes) was dried at 60 ˚C for three days.
Quantitative reactions were conducted in triplicate and contained 400 nM of each primer, 0.5 mL SYBRGreen (BioRad) per mL reaction mixture, and 0.1 mL DNA template per mL reaction mixture. A MYiQ single color real‑time PCR detection system (BioRad) was used with the following cycling conditions: initial denaturation at 95°C for 15 min, followed by 37 cycles of 15 s at 94°C, 30 s at 56°C, and 30 s at 72°C. We specified the following heating conditions to generate melting curves: 55°C starting temperature increasing by 0.10°C increments for 80 cycles, followed by a 10 min final elongation step at 72°C and holding temperature at 4°C. MYiQ software (BioRad) was used to conduct analyses.
Cohen's D effect size calculation
Cohen's d effect size was calculated useing the following equation:
Where is the mean of the substrate addition treatment (arginine, glutamate, lignocellulose, or tannin-protein complexes);mean of the control; n, replicate size; and s, standard deviation. Larger Cohen’s d statistics indicate more significant responses to substrates. We considered positive values as an indicator of substrate use. To calculate effect size of litter type, we substituted spruce and aspen litter values in place of those for substrate addition and controls, respectively.
We calculated the net relatedness index (NRI) for each sample using the 'comstruct' command, and separately examining the effects of substrate addition, litter type, and substrate addition within each litter type. The NRI metric calculates the mean phylogenetic distance for each sample and compares this value to the mean phylogenetic distance for a null community that is randomly generated. Positive NRI values indicate that traits within communities are phylogenetically clustered, whereas negative NRI values reflect trait overdispersion. Null communities for our analyses were generated using the independent swap algorithm (Gotelli and Entsminger 2003) with 999 runs. We also employed the 'rao' command to estimate total phylogenetic diversity within and across treatments in response to substrate addition and litter types.
Calculation of genetic and phenotypic distance
To generate the genetic distance matrix, we used the The Kimura 2 Parameter (K2P) model of DNA evolution, which assumes different rates of transitions and transversions and equal nucleotide frequency. The distance matrix was generated by PAUP*(Swofford 2002). Euclidean distance was calculated as:
Between taxa i and h where j through p are substrates (or litter type) and α is effect size.
Figueroa, A., J. Borneman, and T. Jiang. 2004. Clustering binary fingerprint vectors with missing values for DNA array data analysis. Journal of Computational Biology 11:887–901.
Gotelli, N. J., and G. L. Entsminger. 2003. Swap algorithms in null model analysis. Ecology 84:532–535.
Jeske, D. R., Z. Liu, E. Bent, and J. Borneman. 2007. Classification rules that include neutral zones and their application to microbial community profiling. Communications in Statistics-Theory and Methods 36:1965–1980.
Swofford, D. L. 2002. PAUP*. Phylogentic Analysis Using Parsimony (*and Other Methods). in. Sinauer Associates, Sunderland, Massachusetts, USA.