Since the soils nutrient composition along with the associated biotic and abiotic factors direct the diversity of the contained microbiome and its potential to produce bioactive compounds, many studies have been focused on sediment types with unique features characteristic of extreme environments. Red Sea lagoon ecosystems are environments with such unique features as they are highly saline. However, not much is known about the potential of their microbiomes to produce bioactive compounds. Here, we explored sediment types such as mangrove mud, microbial mat, and barren soil collected from Rabigh harbor lagoon (RHL) and Al-Kharrar lagoon (AKL) as sources for antibiotic bioprospecting. Our antibiotic bioprospecting process started with a metagenomic study that provides a more precise view of the microbial community inhabiting these sites and serves as a preliminary screen for potential antibiotics. Taking the outcomes of the metagenomic screening into account, the next step we established a library of culturable strains from the analyzed samples. We screened each strain from that library for antibiotic activity against four target strains (Staphylococcus aureus ATCC 25923, Escherichia coli dh5 α, Pseudomonas syringae pv. tomato dc3000 and Salmonella typhimurium dt2) and for the presence of polyketide synthase (PKS) and nonribosomal peptide synthetase (NRPS) genes known to support synthesis of secondary metabolites that act like antimicrobial agents. The metagenomics study showed a shift in dominant phyla consistent with a historical exposure to hydrocarbon contamination and that AKL unexpectedly displayed more contamination than RHL. This may be due to dominant phyla in AKL being consistent with early hydrocarbon exposure (when contamination levels are still high) and the dominant phyla in RHL being consistent with late hydrocarbon exposure (when contamination levels are lower as a result of an extended period of hydrocarbon degradation). Additionally, RHL samples showed a higher percentage of enzymes associated with antibiotic synthesis, PKS and NRPS. When considering sediment type, mangrove mud samples showed a higher percentage of enzymes associated with antibiotic synthesis than microbial mat samples. Taken together, RHL was shown to be the better location with an increased probability of successful antibiotic bioprospecting, while the best sediment type in RHL for this purpose is microbial mat. Moreover, the phylum Actinobacteria tends to be the common target for research when it comes to antibiotic bioprospecting. However this culture-independent metagenomic study suggests the tremendous potential of Proteobacteria, Bacteroidetes, Cyanobacteria and Firmicutes for this purpose. Taking into account that the metagenomic screen suggests other phyla beyond Actinobacteria for antibiotic bioprospecting, the culture-dependent experiments were not designed to target actinobacteria alone. A total of 251 bacterial strains were isolated from the three collected sediments. Phylogenetic characterization of 251 bacterial isolates, based on 16S rRNA gene sequencing, supported their assignment to five different phyla: Proteobacteria, Firmicutes, Actinobacteria, Bacteroidetes, and Planctomycetes. Fifteen putative novel species were identified based on a 16S rRNA gene sequence similarity of ≤ 98 % to other strain sequences in the NCBI database. We demonstrate that 52 of the 251 isolates exhibit the potential to produce an antimicrobial effect. Additionally, at least one type of biosynthetic gene sequence, responsible for the synthesis of secondary metabolites, was recovered from 25 of the 52 isolates. Moreover, 10 of the isolates had a growth inhibition effect towards all target strains. In conclusion, this study demonstrated the significant microbial diversity associated with Red Sea harbor/lagoon systems and their potential to produce antimicrobial compounds and novel secondary metabolites. To the best of our knowledge, this is the first study that has analyzed the microbiomes in Red Sea lagoons for antibiotic bioprospecting.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/622011 |
| Date | 08 December 2016 |
| Creators | Al Amoudi, Soha |
| Contributors | Bajic, Vladimir B., Physical Science and Engineering (PSE) Division, Sarathy, Mani, Gojobori, Takashi, Mijakovic, Ivan |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
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