Computational protocol: Molecular Analysis of Bacterial Communities and Detection of Potential Pathogens in a Recirculating Aquaculture System for Scophthalmus maximus and Solea senegalensis

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Protocol publication

[…] Analysis of the DGGE banding profile was performed with the software package BioNumerics v6.6 (Applied Maths, Belgium). Band standardization was carried out automatically by the program, but was always confirmed visually with changes made when necessary. Subsequently, the program constructed a matrix that incorporated the position and intensity of each band. Briefly, the matrix containing both band position and intensity were processed in a spreadsheet and transformed into relative abundance. The relative abundance matrix was uploaded to R, log10 (x+1) transformed and a distance matrix constructed with the Bray Curtis similarity coefficient using the vegdist() function in the vegan package [] in R (version 2.11.1; http://www.r-project.org/). Variation in composition was visualised using principal coordinates analysis (PCO) with the cmdscale() function in R. Differences in the bacterial composition of RAS and water supply were tested using the adonis() function in vegan.Pyrosequencing libraries were first analysed using QIIME (Quantitative Insights Into Microbial Ecology) (http://qiime.sourceforge.net/). First, data was filtered using the split_libraries.py script, which removed forward primers, barcodes and reverse primers. Sequences shorter than 200 base pairs were also removed. Operational taxonomic units (OTUs) were selected (97% similarity) using the pick_otus.py script with the 'usearch_ref' method and the most recent Greengenes release (Greengenes 12_10; http://qiime.wordpress.com/2012/10/16/greengenes-12_10-is-released/). Chimera were identified and removed using de novo and reference based chimera checking based on a reference fasta file from the Greengenes 12_10 release. Representative sequences were selected using the pick_rep_set.py script with the 'most_abundant' method and taxonomic identity was determined using the assign_taxonomy.py script with the Ribosomal Database Project (RDP) method []. We used the make_out_table.py script in QIIME to produce an OTU by sample table containing the abundance and taxonomic assignment of all OTUs. Unique OTUs were identified by assigning them to arbitrary numbers. This table was uploaded to R and non-bacteria, chloroplasts and mitochondria were removed from the analysis. Rarefaction curves were made for each sampling compartment using a self-written function in R []. Variation in OTU composition was visualised using principal coordinates analysis following the same method used for DGGE band data. Variation in the relative abundance of the most abundant bacterial taxa (two phyla, eight classes and ten orders) was assessed using bar plot graphs. The relative abundance was calculated considering the total of reads for each taxonomic level. In addition to this, OTUs taxonomically classified into genera known to be fish pathogens [-] () were selected and their phylogeny investigated. BLAST search (http://www.ncbi.nlm.nih.gov/) was used to obtain the closest relatives of selected OTUs (pathogens and abundant taxa, i.e., where the number of sequences > 400). These sequences were also aligned using ClustalW and a phylogenetic tree was constructed using the neighbour-joining method in Mega 5.1 (http://www.megasoftware.net/). The evolutionary distances were computed using the Maximum Composite Likelihood method with a gamma distribution (four categories) and 500 bootstraps. The DNA sequences generated in this study were submitted to the NCBI SRA: Accession number SRP026529. […]

Pipeline specifications

Software tools BioNumerics, vegan, QIIME, USEARCH, Clustal W, MEGA
Databases SRA
Application Phylogenetics
Organisms Scophthalmus maximus, Solea senegalensis, Photobacterium damselae, Serratia marcescens
Diseases Pulmonary Fibrosis, Mastocytosis, Systemic