Computational protocol: Soil features in rookeries of Antarctic penguins reveal sea to land biotransport of chemical pollutants

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Protocol publication

[…] In recent years, techniques based on DNA extraction from soils and its selective amplification by PCR have been widely used to study the microbial communities of edaphic systems and their biodiversity [,]. When multiple samples have to be compared, as in our case, soil diversity have often be assessed by means of denaturing gradient gel electrophoresis (DGGE) and community fingerprinting, which helps to discriminate the composition of the microbial communities in each sample, then complemented by Sanger sequencing of the main bands. In our case the bacterial diversity patterns can be compared to determine the influence of soil features and pollution in the community composition of the studied soils. In order to avoid any possible bias due to the distance and geological features among the sampling sites, the study of the soil microbiota was centred on samples from Deception Island, which present similar soils and thus the differential effect of penguins on soil microbiota can be tested.For DNA extraction and purification, 1 gram of each fresh melted sample was processed using a commercially available kit (E.Z.N.A.™ Soil DNA Kits, Omega Bio-Tek) following the manufacturer’s protocol. After the extraction, a PCR amplification of the 16S rDNA fragments for bacteria was made. The mix per reaction consisted in: 2 μl of 10X Tris HCl buffer, 0.8 μl of MgCl2 50 mM, 0.4 μl of dNTPs 10 mM, 2 μl of each primer, 1 μl of BSA, 0.5 μl of polymerase, 10.3 μl of Milli-Q ultrapure water and 1 μl of the DNA sample. The primers used were 341F-GC (5’-CGC CCG CCG CGC GCG GCG GGC GGG GCG GGG GCA CGG GGG GCC TAC GGG AGG CAG CAG-3’) and 534R (5´-ATT ACC GCG GCT GCT GG-3´). The program setting was 5 min at 94°C, 1 min at 80°C, 35 cycles at 94°C 1 min, 45°C 1 min and 72°C 1 min, and a final elongation step at 72°C for 30 min []. All the amplifications were carried out in an Eppendorf Mastercycler Personal thermocycler. Amplification of the PCR product was checked by agarose gel electrophoresis (2% agarose, 120 V, 40min) by taking 2 μl of the PCR product, 2 μl of the loading buffer and 5 μl of SYBR-Green and placing a marker in the gel to verify the size of the amplified bands.Once amplification was confirmed, a DGGE analysis was performed for each of the different sample sites using a CBS DGGE System (CBS Scientific Company). 18 μl of the PCR product were loaded on a 7% polyacrylamide gel (Acrylamide/Bisacrylamide 37.5:1) containing a denaturant gradient of 40–70% made by urea and formamide. Gels were electrophoresed at 60°C at a constant voltage (250 V) for 5 h and were stained for 40 minutes using SYBR-Green. Bands were recorded to digital images by UV light gel transillumination.Nucleotide sequences of DNA fragments recovered from bands on DGGE gels were determined by excising the bands from a DGGE gel with an adapted 1-ml pipet tip and the DNA was then eluted in 25 μl sterile water at 4°C overnight. The DNA fragment was amplified from the eluted solution by PCR and the mobility on DGGE gels was checked. The primer pair without GC clamp (341F and 534R) was used in the template amplification by PCR. DNA was sent for Sanger sequencing to Macrogen Sequencing Service (Macrogen Inc., Korea). Possible chimeric sequences were screened using Ribosomal Database Project (RDP release 8.1) online Chimera Check program (http://rdp8.cme.msu.edu/html/analyses.html) []. Taxonomic identity of each phylotype was determined using a naive Bayesian rRNA classifier described in the Ribosomal Database Project RDP Classifier 2.0, a with a 50% bootstrap threshold []. [...] All data are directly supplied in the manuscript or as supplementary material. For the statistical analyses, the concentrations of elements below the detection limit were substituted by values one-half of the detection limit. After a preliminary correlation analysis, multivariate analyses were centred on data of significant elements, and those elements uncorrelated (either positively or negatively) with the percentage of organic carbon (%Corg), the main descriptor of penguin activity, were not used for the multivariate analysis. This resulted in the removal of Na, K, Ca, Mg, B, S, Cr, Ni, Be, Bi, Li, Sb, Sr, Ti, Tl and V from further multivariate analyses. A principal component analysis (PCA) was then carried out using log-transformed data of selected physical (pH, EC) and chemical (%C, %Corg, %N, P, and concentrations of Al, As, Cd, Co, Cu, Fe, Mn, Mo, Pb, Se and Zn) features of the soil samples. Since variances were non-homogeneous, non-parametric tests (Median test and Kruskal–Wallis one-way analysis of variance) were applied to identify differences in soil composition between ornithogenic and non-ornithogenic soils, rookeries of Gentoo and Chinstrap penguins, and sites located either in the South Shetland Islands or in the Antarctic Peninsula, respectively. Significance level for null hypothesis rejection was established in 0.05 for all tests. Bivariate correlations were also performed for selected metals concentrations against the % organic C, the latter as an indicator of organic matter content and thus of penguin influence. The software used for the statistical analysis was SPSS Statistics 21 (SPSS, Inc.).Data obtained from rookeries were further compared with those of non-ornithogenic soils (control areas) through two approaches. The first was the comparison of the arithmetic mean, the standard deviation, and the 95% confidence interval about the mean of the metal concentration, with the estimated background range (estimated as the mean plus or minus two standard deviations). The second was the use of the biogenic enrichment factor (BEF), a metric used to rank the elements based on the likelihood that they are enriched by penguin presence and activity. Following Brimble et al. [], this indicator was calculated as the ratio of the average level of each parameter within the rookeries divided by the average level of the same parameter within the control areas.Finally, using the results of the DGGE profiles for Bacteria, a dissimilarity matrix based on the Jaccard coefficient was calculated and a dendrogram was built using the matrix with the presence or absence of bands [,,]. A dendrogram for each site was drawn using the Bio-Rad Quantity One software. […]

Pipeline specifications

Software tools RDP Classifier, SPSS
Databases CORG
Applications Miscellaneous, Phylogenetics, 16S rRNA-seq analysis
Chemicals Arsenic, Cadmium, Carbon, Cobalt, Iron, Selenium, Zinc