Molecular Pathogens 2024, Vol.15, No.2, 72-82 http://microbescipublisher.com/index.php/mp 76 Figure 1 Phylogenetic tree of 5’COI sequences. Values at nodes indicate bootstrap support obtained by ML/BI analysis. Bootstrap supports >95 in both analyses are indicated by a thicker line. Reference sequences from public databases are printed in italics and using the identities given in the original publications. ITS1 sequences are available for specimens shown in bold. The colours and letters behind the strain names indicate the geographic origin and host species, respectively. Origins: black = South Africa; orange = Chile; pink = New Zealand; light blue = Arctic; grey = Canadian Pacific coast; dark blue = Brittany; red = Helgoland; green = UK; brown = Kiel, western Baltic; yellow = Korea. Hosts: a = Ecklonia maxima; b = Macrocystis pyrifera; c = Saccharina sessilis; d = Lessonia berteroana; e = Laminaria hyperborea; f = Saccharina latissima; g = Costaria costata; h = Saccharina nigripes; i= Laminaria digitata; j =Saccharina japonica; * = grown from incubated substratum. (Adopted from Bringloe et al., 2021) Figure 2 (A) Scanning electron micrograph of Nereida sp. MMG025. (B) Maximum likelihood phylogeny constructed using the codon tree method through PATRIC with 100 single-copy genes and proteins identified using cross-genus families (PGfams) (10, 21-27). The phylogeny root is indicated by an arrow for clarity. The GenBank accession numbers of the sequences used in this analysis are as follows: CVPC00000000 (Nereida ignava CECT 5292), CP003744 (Octadecabacter arcticus 238), CP006967 (Phaeobacter gallaeciensis DSM 26640), and QBKU00000000 (Sulfitobacter mediterraneus DSM 12244) (Adapted from Alker et al., 2022) 4.3 Bioinformatics tools and software Bioinformatics tools and software are essential for analyzing and interpreting genomic data. Various tools have been developed to address specific challenges in genomic analysis. For instance, Taxoblast is a pipeline used for the post-assembly detection of contaminating sequences in kelp genomes, helping to identify bacterial contaminants and hybrid scaffolds (Dittami and Corre, 2017). Another example is the Genomic Approximation Method for Bacterial Identification and Tracking (GAMBIT), which uses k-mer based strategies for the identification of bacteria from whole genome sequence reads, ensuring high confidence in pathogen identification (Lumpe et al., 2022). Additionally, bioinformatics systems have been developed to eliminate contamination and low-complexity sequences from draft genomes, improving the accuracy of metagenomic analyses (Lu and Salzberg, 2018). The integration of advanced DNA extraction and sequencing techniques, comprehensive genomic annotation, and sophisticated bioinformatics tools is pivotal for the isolation, identification, and genomic analysis of kelp pathogens. These methodologies enable researchers to uncover the complex interactions between kelp and their associated microbial communities, providing insights into the ecological and functional roles of these organisms. 5 Pathogen Genomics and Virulence Factors 5.1 Genomic features of pathogens The genomic features of kelp pathogens can be elucidated through comparative genomics, which reveals significant insights into their genetic makeup and potential virulence factors. For instance, the complete genomes of Edwardsiella piscicida and Edwardsiella anguillarum were sequenced using the Oxford-Nanopore MinION platform, revealing distinct genomic features such as the number of coding genes, rRNA, and tRNA, which were 8322, 25, and 98 for E. anguillarum, and 5458, 25, and 98 for E. piscicida, respectively. These differences
RkJQdWJsaXNoZXIy MjQ4ODYzNA==