Mining Small Molecules from Strains Isolated from Philippine Teredinidae.

Endosymbiotic relationship has played a significant role in the evolution of marine species, allowing for the development of biochemical machinery for the synthesis of diverse metabolites. In this work, we explore the chemical space of exogenous compounds from shipworm endosymbionts using LC-MS-based metabolomics. Priority strains (1022X.S.1B.7A, 991H.S.0A.06B, 1675L.S.0A.01) that displayed antimicrobial activity, isolated from shipworms collected from several sites in the Philippines were cultured, and fractionated extracts were subjected for profiling using ultrahigh-performance liquid chromatography with high-resolution mass spectrometry quadrupole time-of-flight mass analyzer (UHPLC-HRMS QTOF). T7901 was used as a reference microorganism for dereplication analysis. Tandem MS data were analyzed through the Global Natural Products Social (GNPS) molecular networking, which resulted to 93 clusters with more than two nodes, leading to four putatively annotated clusters: lipids, lysophosphatidylethanolamines, cyclic dipeptides, and rhamnolipids. Additional clusters were also annotated through molecular networking with cross-reference to previous publications. Tartrolon D cluster with analogues, turnercyclamycins A and B; teredinibactin A, dechloroteredinibactin, and two other possible teredinibactin analogues; and oxylipin (E)-11-oxooctadec-12-enoic acid were putatively identified as described. Molecular networking also revealed two additional metabolite clusters, annotated as lyso-ornithine lipids and polyethers. Manual fragmentation analysis corroborated the putative identification generated from GNPS. However, some of the clusters remained unclassified due to the limited structural information on marine natural products in the public database. The result of this study, nonetheless, showed the diversity in the chemical space occupied by shipworm endosymbionts. This study also affirms the use of bioinformatics, molecular networking, and fragmentation mechanisms analysis as tools for the dereplication of high-throughput data to aid the prioritization of strains for further analysis.