Comparative genome characterization of Echinicola marina sp. nov., isolated from deep-sea sediment provide insight into carotenoid biosynthetic gene cluster evolution

Abstract

Echinicola, carotenoid-pigmented bacteria, are isolated from various hypersaline environments. Carotenoid accumulation in response to salt stress can stabilize the cell membrane in order to survive. A pink-colored strain SCS 3–6 was isolated from the deep-sea sediment of the South China Sea. Growth was found to occur at 10–45 °C. The strain could tolerate 10% (w/v) NaCl concentration and grow at pH 5–9. The complete genome of SCS 3–6 comprises 5053 putative genes with a total 5,693,670 bp and an average G + C content of 40.11 mol%. The 16S rRNA gene sequence analysis indicated that strain SCS 3–6 was affiliated with the genus Echinicola, with the closely strains were Echinicola arenosa CAU 1574T (98.29%)and Echinicola shivajiensis AK12T (97.98%). For Echinicola species with available genome sequences, pairwise comparisons for average nucleotide identity (ANI) and in silico DNA-DNA hybridization (DDH) revealed ANIb values from 70.77 to 74.71%, ANIm values from 82.72 to 88.88%, and DDH values from 18.00 to 23.40%. To identify their genomic features, we compared their genomes with those of other Echinicola species. Phylogenetic analysis showed that strain SCS 3–6 formed a monophyletic clade. Genomic analysis revealed that strain SCS 3–6 possessed a complete synthetic pathway of carotenoid and speculated that the production was astaxanthin. Based on phenotypic and genotypic analyses in this study, strain SCS 3–6 is considered to represent a novel species of the genus Echinicola for which the name Echinicola marina sp. nov. is proposed. The type strain is SCS 3-6T (= GDMCC 1.2220T = JCM 34403T).

Marine Microbial-Derived Resource Exploration: Uncovering the Hidden Potential of Marine Carotenoids

“…For the discovery of novel smBGCs, an approach using machine learning-based methods such as ClusterFinder [67] and DeepBGC [68] is required. However, the drawback of this machine learning-based method is the high false-positive rate [69]. Nevertheless, the combination of these two approaches is expected to provide an accurate and representative list of smBGCs.…”