Computational protocol: Global Distribution of Polaromonas Phylotypes   Evidence for a Highly Successful Dispersal Capacity

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[…] To describe the global biogeographic distribution of Polaromonas phylotypes we used previously published and unpublished sequences (see for references) from GenBank and new sequences that we obtained from periglacial sediments from our previously described sites in the Himalayas , Colorado Rocky Mountains , Andes and Alaska . Sediment samples (0 to 4 cm deep) were collected sterilely during the summer at all sites in a grid pattern in order to obtain spatial representation as described in King et al. , . The location of each sample site was logged using a Garmin 60CSx gps unit. Samples were frozen in the field and shipped to Colorado where they were kept at −80°C until DNA was extracted. shows the global sites used in these analyses.To extract DNA, 0.4 grams of sediment from each sample was processed with the Mo Bio PowerSoil™ DNA isolation Kit (Carlsbad, CA, USA) and 3 µl of each extraction was PCR amplified in 25 µl reaction volumes using primers 8F (5′-agagtttgatcctggctcag-3′) and 1391R (5′-gacgggcggtgwgtrca-3′) . The reaction conditions consisted of 1 µM of each primer, 250 µM each dA, dT, dG, dC, 0.25 µL bovine serum albumen, 1 unit of OmniKlen™ Taq polymerase, and 3 µl DNA extract as template. For negative controls, sterile Millipore water was used as template. Denaturing temperature was 94°C (1 minute), the annealing temperature was 53°C (30 seconds) and the extension temperature was 72°C (2 minutes 30 seconds). PCR products were purified using the Quiaquick gel extraction protocol (Qiagen, Valencia, CA, USA), with HyperLadder II™ as a reference. Plasmids were cloned into OneShot™ E. coli using the Invitrogen Topo TA™ cloning kit (Invitrogen, Carlsbad, CA, USA). Colonies were grown on selective media for 18 hours, pelleted and sent overnight on dry ice to Functional Biosciences (Madison, WI, USA) and sequenced bi-directionally using sequencing primers T7 and M13R.Sequencher 4.6 (Gene Codes Co., Ann Arbor, MI, USA) was used to interpret the chromatograms, edit out unreliable data, and assemble contigs. Sequences were imported into ARB v. 9.4 , and aligned using the SILVA reference database . Sequences unique to this study were deposited in GenBank under accession numbers JF719322-JF719338 and JF729309. Other glacial Polaromonas sequences, as well as known Polaromonas guide sequences were downloaded from GenBank and imported into ARB. All Polaromonas sequences were aligned in ARB, and then filtered by base frequency to exclude any position in the alignment that had below 30% identity across all sequences. Sequences were exported from ARB to a FASTA file, and Mesquite was used to convert between file formats.Phylogenetic trees were constructed using two robust methods in order to clearly define a well-supported Polaromonas clade. RAxML was used to make a maximum likelihood (ML) trees with 500 bootstraps, and MrBayes – was run for 5 million generations at a temperature value of 0.02. The Bayes and ML trees were then compared for structural similarity and mutual support of the node separating the out-group genera (Variovorax and Rhodoferax) from Polaromonas phylotypes. The Bayes tree had a posterior probability of 0.99 for this node, and in the ML tree, 100% of bootstraps contained that node, confirming that the Polaromonas clade discussed below is indeed monophyletic.Genomic and metagenomic comparisons were performed using the RAST and MG-RAST annotation and comparison platforms, which utilize the manually curated SEED database , . The genomes of Polaromonas naphthalenivorans CJ2 (CP000529) and Polaromonas sp. JS666 (CP000316) , as well as the glacier metagenome (SRX000607) were obtained from the NCBI database. Comparisons of annotated metabolic subsystems between the genomic data were sorted by identity and function.Geographic distances between sample sites were computed in R using the Fields package and used to construct a geographic distance matrix using in-house software. An uncorrected genetic distance matrix was exported from ARB using the same filter that was used to export sequences for the trees. To test for a correlation between these matrices, Mantel tests were performed in R using 1000 randomized permutations per test. A Mantel correlogram was constructed to the specifications set forth by Legendre and Legendre , . The application of Sturge's rule resulted in the data being partitioned into 12 distances classes each containing 115 pairwise comparisons so that each distance class had the same statistical power. Mantel tests were carried out on each of the 12 distance classes, and a Bonferroni correction was applied to the original alpha value of 0.05 resulting in a corrected alpha value of 0.004. Using this approach, only 2 of the 12 distances classes showed significant P-values (P<0.004), however the first distance class was not testable (showed a null result for P and rM values) because it contained no geographic variation. Guide sequences and the outgroup sequences were not included in biogeographic analyses. Several groupings of our Polaromonas sequences were tested in this manner, to see whether variables in addition to geographic distance, such as sequence length, distance from glaciers, elevation, or whether the sequences were obtained from culture-dependent or culture-independent studies were correlated with spatial structuring. However at the present time we do not have enough environmental data to disentangle the effects of geographic distance from environmental variation across the 2 distance classes that showed significant spatial structuring. […]

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