**5. Conclusions**

Mangrove actinomycetia are considered one of the promising sources of novel biologically active compounds. In this study, a total of 521 actinomycetial strains were isolated from underexplored mangrove soils collected in Leizhou Peninsular, China. Our integrative strategies using taxonomical information, bioactivity, and metabolomics tools (PCA, OPLS-DA, molecular networking) for dereplication allowed us to prioritize two

*Streptomyces* strains (H37, M22) with the potential to produce new antibiotics. Two new trioxacarcins were isolated from the scale-up fermentation broth of *Streptomyces* sp. M22. Our study demonstrated that modern metabolomics tools greatly assist classic antibiotic discovery for strain prioritization and improve the efficiency of novel antibiotics discovery. Our data also highlighted that the mangrove in Leizhou Peninsular is an unexploited source with rich microbial diversity and bioactive actinomycetia. In summary, the new strategies presented in this research could set an example to accelerate new antibiotics discovery from mangroves and other highly productive sources, such as rainforests.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/ md19120688/s1, Table S1. Composition of 12 different media used to isolate actinomycetial strains from 13 mangrove soil samples; Table S2. Information on genera distribution of actinomycetial strains isolated from 13 different mangrove soil samples; Table S3. Information on genera distribution of actinomycetial strains recovered from the 12 different cultural media; Table S4. Antibacterial activities of 179 actinomycetial strains by the paper disc diffusion method; Table S5. The trioxacarcin-type antibiotics isolated from the actinomycetial strains; Table S6. The information of mangrove-derived soil samples in different sites of Leizhou Peninsula, China; Figure S1. The UV spectra of three outliers (**1**–**3**) of Y46 and H12. Figure S2. The feature statistic plot of 16.71\_724.4749n (**4**) in all samples; Figure S3. The MS/MS fragment pattern of the outlier 16.71\_724.4749n (**4**) in sample H7; Figure S4. The MS/MS spectra of three false positive compounds in H7 acquired by DDA method; Figure S5. The MS/MS spectra of three revised compounds (**<sup>7</sup>**–**9**) in H7 acquired by DDA method; Figure S6. The positive and negative MS spectra of two revised compounds in the LC-MS of H7; Figure S7. The UV spectra of three compounds (**<sup>7</sup>**–**9)** of H7 eluting with ACN and H2O; Figure S8. The MS/MS spectra of two compounds (**10**–**11**) in H37 acquired by MSE method; Figure S9. The UV spectra of seven trioxacarcin-type compounds (**12**, **14**–**19**) in the UPLC-UV-HRMS chromatograms of M22; Figure S10. The MS spectra of seven trioxacarcin-type compounds (**12**, **14**–**19**) in the UPLC-UV-HRMS chromatograms of M22; Figure S11. The VIP score of selected markers in the OPLS-DA model; Figure S12. Molecular network of the cluster containing the compound 15.40\_566.4171n (**13**) in M22 extract; Figure S13. Molecular network of the cluster containing the compounds 10.64\_900.5435n (**10**) and 11.08\_928.5742n (**11**) in H37 extract. Figures S14–S31. The HRESIMS, UV, IR, and NMR spectra of gutingimycin B (**16**) and trioxacarcin G (**20**); Figures S32–S37. The HRESIMS and NMR spectra of gutingimycin (**12**).

**Author Contributions:** Conceptualization, C.-H.S., H.L. and Q.-P.L.; methodology, Q.-P.L., Y.-M.H. and S.-W.L.; validation, Q.-P.L., Y.-M.H., G.W., Q.Y. and L.-F.L.; formal analysis, Q.-P.L., G.W., S.-W.L. and Q.Y.; investigation, Q.-P.L., Y.-M.H., S.-W.L., G.W., Q.Y., L.-F.L. and H.C.; resources, C.-H.S., H.L., H.-T.Z., Y.Q., T.W., Z.-K.J., J.-J.L. and X.-J.L.; data curation, Q.-P.L., Y.-M.H., G.W., Q.Y., L.-F.L., H.-T.Z. and Y.Q.; writing—original draft preparation, Q.-P.L., Y.-M.H.; writing—review and editing, Q.-P.L., C.-H.S., Y.-M.H. and H.L.; visualization, Q.-P.L., Y.-M.H. and S.-W.L.; supervision, C.-H.S., H.L.; project administration, C.-H.S., H.L.; funding acquisition, C.-H.S., Q.-P.L., H.L., Y.-M.H. and G.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the CAMS Innovation Fund for Medical Sciences (CIFMS 2021-I2M-1-028, CAMS 2017-I2M-B&R-08, and 2017-I2M-1-012), the National Natural Science Foundation of China (81803411 and 82011530051), PUMC Doctoral Innovation Fund Project (2018-1007-16), the Science and Technology Program of Guangdong Province (2019B090905011), the Public Service Platform of South China Sea for R&D Marine Biomedicine Resources (2017C8A), the Guangdong University Youth Innovation Talent Project (2020KQNCX023), the Scientific Research Fund of Guangdong Medical University (GDMUM202002).

**Data Availability Statement:** The sequencing data presented in this study are available in GenBank at NCBI (accession numbers: MW724535-MW724713).

**Acknowledgments:** We greatly thank Chao Zhou from Waters Corporation for his advice in MS acquisition and metabolomics analysis. We greatly thank Rongfeng Li from Department of Chemistry, Johns Hopkins University for his linguistic assistance during the preparation of this manuscript. Our deepest gratitude goes to the anonymous reviewers for their careful work and thoughtful suggestions that have helped improve this paper substantially.

**Conflicts of Interest:** The authors declare no conflict of interest.
