Antiproliferative polyketides from fungus Xylaria cf. Longipes SWUF08-81 in different culture media

doi:10.1007/s13659-023-00427-7

应用天然产物 ›› 2024, Vol. 14 ›› Issue (1): 6-6.DOI: 10.1007/s13659-023-00427-7

Antiproliferative polyketides from fungus Xylaria cf. Longipes SWUF08-81 in different culture media

Kittiwan Sresuksai¹, Sasiphimol Sawadsitang¹, Phongphan Jantaharn¹, Pakin Noppawan², Audomsak Churat¹, Nuttika Suwannasai³, Wiyada Mongkolthanaruk⁴, Thanaset Senawong⁵, Sarawut Tontapha⁶, Pairot Moontragoon^6,7, Vittaya Amornkitbamrung^6,7, Sirirath McCloskey¹

1. Department of Chemistry, Faculty of Science, Center of Excellence for Innovation in Chemistry (PERCH-CIC), Khon Kaen University, Khon Kaen, 40002, Thailand;
2. Department of Chemistry, Faculty of Science, Mahasarakham University, Maha Sarakham, 44150, Thailand;
3. Department of Microbiology, Faculty of Science, Srinakharinwirot University, Bangkok, 10110, Thailand;
4. Department of Microbiology, Faculty of Science, Khon Kaen University, Khon Kaen, 40002, Thailand;
5. Department of Biochemistry, Faculty of Science, Khon Kaen University, Khon Kaen, 40002, Thailand;
6. Department of Physics, Faculty of Science, Khon Kaen University, Khon Kaen, 40002, Thailand;
7. Institute of Nanomaterials Research and Innovation for Energy (IN-RIE), Khon Kaen University, Khon Kaen, 40002, Thailand

收稿日期:2023-10-17 出版日期:2024-02-24 发布日期:2024-02-19
通讯作者: Sirirath McCloskey,E-mail:sirsod@kku.ac.th
基金资助:
This research was supported by the Fundamental Fund of Khon Kaen University. We would like to gratefully acknowledge the Development and Promotion of Science and Technology Talents Project (DPST) and the Center of Excellence for Innovation in Chemistry (PERCH-CIC), Ministry of Higher Education, Science, Research and Innovation, for their financial support.

Antiproliferative polyketides from fungus Xylaria cf. Longipes SWUF08-81 in different culture media

1. Department of Chemistry, Faculty of Science, Center of Excellence for Innovation in Chemistry (PERCH-CIC), Khon Kaen University, Khon Kaen, 40002, Thailand;
2. Department of Chemistry, Faculty of Science, Mahasarakham University, Maha Sarakham, 44150, Thailand;
3. Department of Microbiology, Faculty of Science, Srinakharinwirot University, Bangkok, 10110, Thailand;
4. Department of Microbiology, Faculty of Science, Khon Kaen University, Khon Kaen, 40002, Thailand;
5. Department of Biochemistry, Faculty of Science, Khon Kaen University, Khon Kaen, 40002, Thailand;
6. Department of Physics, Faculty of Science, Khon Kaen University, Khon Kaen, 40002, Thailand;
7. Institute of Nanomaterials Research and Innovation for Energy (IN-RIE), Khon Kaen University, Khon Kaen, 40002, Thailand

Received:2023-10-17 Online:2024-02-24 Published:2024-02-19
Contact: Sirirath McCloskey,E-mail:sirsod@kku.ac.th
Supported by:
This research was supported by the Fundamental Fund of Khon Kaen University. We would like to gratefully acknowledge the Development and Promotion of Science and Technology Talents Project (DPST) and the Center of Excellence for Innovation in Chemistry (PERCH-CIC), Ministry of Higher Education, Science, Research and Innovation, for their financial support.

摘要/Abstract

摘要： Bioactive compounds from the wood-decay fungus Xylaria cf. longipes SWUF08-81, cultivated in three different culture media (GM, YM and PDB), were isolated. Their structures and stereochemistry were deduced from spectroscopic and MS data analysis, together with quantum chemical calculations of ¹³C NMR chemical shifts and electronic circular dichroism (ECD) spectra. Five undescribed polyketides including dibenzofuran (1), mellein (2), dihydroisocoumarin (15), and two pyrans (16, 17), together with twenty-three compounds were determined. Compounds 18 and 20 were significantly toxic against cancer cell lines (HCT116, HT29, MCF-7 and HeLa) based on the MTT assay. Quantification by HPLC showed that 18 was produced three-fold higher in the broth of PDB than YM. These studies showed that the production of different compounds were primarily dependent on nutrition sources and it has given a starting point for the growth optimization conditions for the scaling up of bioactive compounds production.

关键词: Xylaria, Dibenzofuran, Asperentin, Pyran, Antiproliferative activity, HPLC analysis

Abstract: Bioactive compounds from the wood-decay fungus Xylaria cf. longipes SWUF08-81, cultivated in three different culture media (GM, YM and PDB), were isolated. Their structures and stereochemistry were deduced from spectroscopic and MS data analysis, together with quantum chemical calculations of ¹³C NMR chemical shifts and electronic circular dichroism (ECD) spectra. Five undescribed polyketides including dibenzofuran (1), mellein (2), dihydroisocoumarin (15), and two pyrans (16, 17), together with twenty-three compounds were determined. Compounds 18 and 20 were significantly toxic against cancer cell lines (HCT116, HT29, MCF-7 and HeLa) based on the MTT assay. Quantification by HPLC showed that 18 was produced three-fold higher in the broth of PDB than YM. These studies showed that the production of different compounds were primarily dependent on nutrition sources and it has given a starting point for the growth optimization conditions for the scaling up of bioactive compounds production.

Key words: Xylaria, Dibenzofuran, Asperentin, Pyran, Antiproliferative activity, HPLC analysis

Kittiwan Sresuksai, Sasiphimol Sawadsitang, Phongphan Jantaharn, Pakin Noppawan, Audomsak Churat, Nuttika Suwannasai, Wiyada Mongkolthanaruk, Thanaset Senawong, Sarawut Tontapha, Pairot Moontragoon, Vittaya Amornkitbamrung, Sirirath McCloskey. Antiproliferative polyketides from fungus Xylaria cf. Longipes SWUF08-81 in different culture media[J]. 应用天然产物, 2024, 14(1): 6-6.

参考文献

[1] Gurung N, Ray S, Bose S, Rai V. A broader view: microbial enzymes and their relevance in industries, medicine, and beyond. Biomed Res Int. 2013;2013: 329121. https://doi.org/10.1155/2013/329121. [2] Guazzaroni ME, Beloqui A, Vieites JM, Al-ramahi Y, Cortés NL, Ghazi A, et al. Metagenomic mining of enzyme diversity. Handbook of hydrocarbon and lipid microbiology. 2010. p. 2911–27. [3] Liu X, Kokare C. Microbial enzymes of use in industry. Biotechnology of microbial enzymes. 2017. p. 267–98. [4] Singh RS, Singh T, Pandey A. microbial enzymes—an overview. Advances in Enzyme Technology. 2019. p. 1–40. [5] Lammle K, Zipper H, Breuer M, Hauer B, Buta C, Brunner H, et al. Identification of novel enzymes with different hydrolytic activities by metagenome cloning. J Biotechnol. 2007;127(4):575–92. https://doi.org/10.1016/j.jbiotec.2006.07.036. [6] Amann RI, Binder BJ, Olson RJ, Chisholm SW, Devereux R, Stahl DA. Combination of 16S rRNA-targeted oligonucleotide probes with flow cytometry for analyzing mixed microbial populations. Appl Environ Microbiol. 1990;56(6):1919–25. https://doi.org/10.1128/aem.56.6.1919-1925.1990. [7] Glogauer A, Martini VP, Faoro H, Couto GH, Muller-Santos M, Monteiro RA, et al. Identification and characterization of a new true lipase isolated through metagenomic approach. Microb Cell Fact. 2011;10(1):54. https://doi.org/10.1186/1475-2859-10-54. [8] Quince C, Walker AW, Simpson JT, Loman NJ, Segata N. Corrigendum: shotgun metagenomics, from sampling to analysis. Nat Biotechnol. 2017;35(12):1211. https://doi.org/10.1038/nbt1217-1211b. [9] Nayfach S, Roux S, Seshadri R, Udwary D, Varghese N, Schulz F, et al. A genomic catalog of Earth’s microbiomes. Nat Biotechnol. 2021;39(4):499–509. https://doi.org/10.1038/s41587-020-0718-6. [10] Berini F, Casciello C, Marcone GL, Marinelli F. Metagenomics: novel enzymes from non-culturable microbes. FEMS Microbiol Lett. 2017. https://doi.org/10.1093/femsle/fnx211. [11] Itoh N. Metagenomics for improved biocatalysis. Future directions in biocatalysis. 2017. p. 375–84. [12] Colin PY, Kintses B, Gielen F, Miton CM, Fischer G, Mohamed MF, et al. Ultrahigh-throughput discovery of promiscuous enzymes by picodroplet functional metagenomics. Nat Commun. 2015;6(1):10008. https://doi.org/10.1038/ncomms10008. [13] Arnold FH. Combinatorial and computational challenges for biocatalyst design. Nature. 2001;409(6817):253–7. https://doi.org/10.1038/35051731. [14] Robinson SL, Piel J, Sunagawa S. A roadmap for metagenomic enzyme discovery. Nat Prod Rep. 2021;38(11):1994–2023. https://doi.org/10.1039/d1np00006c. [15] Hou Q, Pucci F, Pan F, Xue F, Rooman M, Feng Q. Using metagenomic data to boost protein structure prediction and discovery. Comput Struct Biotechnol J. 2022;20:434–42. https://doi.org/10.1016/j.csbj.2021.12.030. [16] Jeske L, Placzek S, Schomburg I, Chang A, Schomburg D. BRENDA in 2019: a European ELIXIR core data resource. Nucleic Acids Res. 2019;47(D1):D542–9. https://doi.org/10.1093/nar/gky1048. [17] Atkinson HJ, Morris JH, Ferrin TE, Babbitt PC. Using sequence similarity networks for visualization of relationships across diverse protein superfamilies. PLoS ONE. 2009;4(2): e4345. https://doi.org/10.1371/journal.pone.0004345. [18] Lapebie P, Lombard V, Drula E, Terrapon N, Henrissat B. Bacteroidetes use thousands of enzyme combinations to break down glycans. Nat Commun. 2019;10(1):2043. https://doi.org/10.1038/s41467-019-10068-5. [19] Gharechahi J, Vahidi MF, Sharifi G, Ariaeenejad S, Ding XZ, Han JL, et al. Lignocellulose degradation by rumen bacterial communities: new insights from metagenome analyses. Environ Res. 2023;229: 115925. https://doi.org/10.1016/j.envres.2023.115925. [20] Ngara TR, Zhang H. Recent advances in function-based metagenomic screening. Genom Proteom Bioinform. 2018;16(6):405–15. https://doi.org/10.1016/j.gpb.2018.01.002. [21] Patel T, Chaudhari HG, Prajapati V, Patel S, Mehta V, Soni N. A brief account on enzyme mining using metagenomic approach. Front Syst Biol. 2022. https://doi.org/10.3389/fsysb.2022.1046230. [22] Sampaio PS, Fernandes P. Machine learning: a suitable method for biocatalysis. Catalysts. 2023. https://doi.org/10.3390/catal13060961. [23] Scherlach K, Hertweck C. Mining and unearthing hidden biosynthetic potential. Nat Commun. 2021. https://doi.org/10.1038/s41467-021-24133-5. [24] Zaparucha A, de Berardinis V, Vaxelaire-Vergne C. Chapter 1. Genome Mining for Enzyme Discovery. Modern Biocatalysis. Catalysis Series, 2018. p. 1–27. [25] Staley JT, Konopka A. Measurement of in situ activities of nonphotosynthetic microorganisms in aquatic and terrestrial habitats. Annu Rev Microbiol. 1985;39(1):321–46. https://doi.org/10.1146/annurev.mi.39.100185.001541. [26] Uchiyama T, Miyazaki K. Functional metagenomics for enzyme discovery: challenges to efficient screening. Curr Opin Biotechnol. 2009;20(6):616–22. https://doi.org/10.1016/j.copbio.2009.09.010. [27] Daniel R. The soil metagenome–a rich resource for the discovery of novel natural products. Curr Opin Biotechnol. 2004;15(3):199–204. https://doi.org/10.1016/j.copbio.2004.04.005. [28] Yun J, Ryu S. Screening for novel enzymes from metagenome and SIGEX, as a way to improve it. Microb Cell Fact. 2005;4(1):8. https://doi.org/10.1186/1475-2859-4-8. [29] Madhavan A, Sindhu R, Parameswaran B, Sukumaran RK, Pandey A. Metagenome analysis: a powerful tool for enzyme bioprospecting. Appl Biochem Biotechnol. 2017;183(2):636–51. https://doi.org/10.1007/s12010-017-2568-3. [30] Dadheech T, Shah R, Pandit R, Hinsu A, Chauhan PS, Jakhesara S, et al. Cloning, molecular modeling and characterization of acidic cellulase from buffalo rumen and its applicability in saccharification of lignocellulosic biomass. Int J Biol Macromol. 2018;113:73–81. https://doi.org/10.1016/j.ijbiomac.2018.02.100. [31] De Santi C, Altermark B, Pierechod MM, Ambrosino L, de Pascale D, Willassen NP. Characterization of a cold-active and salt tolerant esterase identified by functional screening of Arctic metagenomic libraries. BMC Biochem. 2016;17(1):1. https://doi.org/10.1186/s12858-016-0057-x. [32] Pereira MR, Maester TC, Mercaldi GF, de Macedo Lemos EG, Hyvonen M, Balan A. From a metagenomic source to a high-resolution structure of a novel alkaline esterase. Appl Microbiol Biotechnol. 2017;101(12):4935–49. https://doi.org/10.1007/s00253-017-8226-4. [33] Araujo FJ, Hissa DC, Silva GO, Antunes A, Nogueira VLR, Goncalves LRB, et al. A novel bacterial carboxylesterase identified in a metagenome derived-clone from Brazilian mangrove sediments. Mol Biol Rep. 2020;47(5):3919–28. https://doi.org/10.1007/s11033-020-05484-6. [34] Istvan P, Souza AA, Garay AV, Dos Santos DFK, de Oliveira GM, Santana RH, et al. Structural and functional characterization of a novel lipolytic enzyme from a Brazilian Cerrado soil metagenomic library. Biotechnol Lett. 2018;40(9–10):1395–406. https://doi.org/10.1007/s10529-018-2598-0. [35] Park JM, Kang CH, Won SM, Oh KH, Yoon JH. Characterization of a novel moderately thermophilic solvent-tolerant esterase isolated from a compost metagenome library. Front Microbiol. 2019;10:3069. https://doi.org/10.3389/fmicb.2019.03069. [36] Maruthamuthu M, van Elsas JD. Molecular cloning, , and characterization of four novel thermo-alkaliphilic enzymes retrieved from a metagenomic library. Biotechnol Biofuels. 2017;10(1):142. https://doi.org/10.1186/s13068-017-0808-y. [37] Thomas T, Gilbert J, Meyer F. Metagenomics - a guide from sampling to data analysis. Microb Inform Exp. 2012;2(1):3. https://doi.org/10.1186/2042-5783-2-3. [38] Ovchinnikov S, Park H, Varghese N, Huang PS, Pavlopoulos GA, Kim DE, et al. Protein structure determination using metagenome sequence data. Science. 2017;355(6322):294–8. https://doi.org/10.1126/science.aah4043. [39] Wang Y, Shi Q, Yang P, Zhang C, Mortuza SM, Xue Z, et al. Fueling ab initio folding with marine metagenomics enables structure and function predictions of new protein families. Genome Biol. 2019;20(1):229. https://doi.org/10.1186/s13059-019-1823-z. [40] Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215(3):403–10. https://doi.org/10.1016/S0022-2836(05)80360-2. [41] Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12(1):59–60. https://doi.org/10.1038/nmeth.3176. [42] Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26(19):2460–1. https://doi.org/10.1093/bioinformatics/btq461. [43] Eddy SR. Profile hidden Markov models. Bioinformatics. 1998;14(9):755–63. https://doi.org/10.1093/bioinformatics/14.9.755. [44] Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25(17):3389–402. https://doi.org/10.1093/nar/25.17.3389. [45] Szalkai B, Grolmusz V. MetaHMM: a webserver for identifying novel genes with specified functions in metagenomic samples. Genomics. 2019;111(4):883–5. https://doi.org/10.1016/j.ygeno.2018.05.016. [46] Koutsandreas T, Ladoukakis E, Pilalis E, Zarafeta D, Kolisis FN, Skretas G, et al. ANASTASIA: an automated metagenomic analysis pipeline for novel enzyme discovery exploiting next generation sequencing data. Front Genet. 2019;10:469. https://doi.org/10.3389/fgene.2019.00469. [47] Nobeli I, Favia AD, Thornton JM. Protein promiscuity and its implications for biotechnology. Nat Biotechnol. 2009;27(2):157–67. https://doi.org/10.1038/nbt1519. [48] Elbehery AH, Leak DJ, Siam R. Novel thermostable antibiotic resistance enzymes from the Atlantis II Deep Red Sea brine pool. Microb Biotechnol. 2017;10(1):189–202. https://doi.org/10.1111/1751-7915.12468. [49] Garg R, Srivastava R, Brahma V, Verma L, Karthikeyan S, Sahni G. Biochemical and structural characterization of a novel halotolerant cellulase from soil metagenome. Sci Rep. 2016;6(1):39634. https://doi.org/10.1038/srep39634. [50] Al-Shahib A, Breitling R, Gilbert DR. Predicting protein function by machine learning on amino acid sequences–a critical evaluation. BMC Genomics. 2007;8(1):78. https://doi.org/10.1186/1471-2164-8-78. [51] Bonetta R, Valentino G. Machine learning techniques for protein function prediction. Proteins. 2020;88(3):397–413. https://doi.org/10.1002/prot.25832. [52] Zou Z, Tian S, Gao X, Li Y. mlDEEPre: multi-functional enzyme function prediction with hierarchical multi-label deep learning. Front Genet. 2018;9:714. https://doi.org/10.3389/fgene.2018.00714. [53] Shen HB, Chou KC. EzyPred: a top-down approach for predicting enzyme functional classes and subclasses. Biochem Biophys Res Commun. 2007;364(1):53–9. https://doi.org/10.1016/j.bbrc.2007.09.098. [54] Li YH, Xu JY, Tao L, Li XF, Li S, Zeng X, et al. SVM-Prot 2016: a web-server for machine learning prediction of protein functional families from sequence irrespective of similarity. PLoS ONE. 2016;11(8): e0155290. https://doi.org/10.1371/journal.pone.0155290. [55] Li Y, Wang S, Umarov R, Xie B, Fan M, Li L, et al. DEEPre: sequence-based enzyme EC number prediction by deep learning. Bioinformatics. 2018;34(5):760–9. https://doi.org/10.1093/bioinformatics/btx680. [56] Dalkiran A, Rifaioglu AS, Martin MJ, Cetin-Atalay R, Atalay V, Dogan T. ECPred: a tool for the prediction of the enzymatic functions of protein sequences based on the EC nomenclature. BMC Bioinformatics. 2018;19(1):334. https://doi.org/10.1186/s12859-018-2368-y. [57] Yu T, Cui H, Li JC, Luo Y, Jiang G, Zhao H. Enzyme function prediction using contrastive learning. Science. 2023;379(6639):1358–63. https://doi.org/10.1126/science.adf2465. [58] Shi Z, Deng R, Yuan Q, Mao Z, Wang R, Li H, et al. Enzyme commission number prediction and benchmarking with hierarchical dual-core multitask learning framework. Research. 2023;6:0153. https://doi.org/10.34133/research.0153. [59] Buton N. Datasets and models for EnzBert. Zenodo; 2023. [60] Foroozandeh Shahraki M, Farhadyar K, Kavousi K, Azarabad MH, Boroomand A, Ariaeenejad S, et al. A generalized machine-learning aided method for targeted identification of industrial enzymes from metagenome: a xylanase temperature dependence case study. Biotechnol Bioeng. 2021;118(2):759–69. https://doi.org/10.1002/bit.27608. [61] Foroozandeh Shahraki M, Ariaeenejad S, Fallah Atanaki F, Zolfaghari B, Koshiba T, Kavousi K, et al. MCIC: automated identification of cellulases from metagenomic data and characterization based on temperature and pH dependence. Front Microbiol. 2020;11: 567863. https://doi.org/10.3389/fmicb.2020.567863. [62] Shahraki MF, Atanaki FF, Ariaeenejad S, Ghaffari MR, Norouzi-Beirami MH, Maleki M, et al. A computational learning paradigm to targeted discovery of biocatalysts from metagenomic data: a case study of lipase identification. Biotechnol Bioeng. 2022;119(4):1115–28. https://doi.org/10.1002/bit.28037. [63] Kosloff M, Kolodny R. Sequence-similar, structure-dissimilar protein pairs in the PDB. Proteins. 2008;71(2):891–902. https://doi.org/10.1002/prot.21770. [64] Friedberg I, Margalit H. Persistently conserved positions in structurally similar, sequence dissimilar proteins: roles in preserving protein fold and function. Protein Sci. 2002;11(2):350–60. https://doi.org/10.1110/ps.18602. [65] Littlechild JA. Protein structure and function. Introduction to biological and small molecule drug research and development. 2013. p. 57–79. [66] Petrey D, Honig B. Protein structure prediction: inroads to biology. Mol Cell. 2005;20(6):811–9. https://doi.org/10.1016/j.molcel.2005.12.005. [67] Madej T, Gibrat JF, Bryant SH. Threading a database of protein cores. Proteins. 1995;23(3):356–69. https://doi.org/10.1002/prot.340230309. [68] Bertoline LMF, Lima AN, Krieger JE, Teixeira SK. Before and after AlphaFold2: an overview of protein structure prediction. Front Bioinform. 2023;3:1120370. https://doi.org/10.3389/fbinf.2023.1120370. [69] Yang J, Yan R, Roy A, Xu D, Poisson J, Zhang Y. The I-TASSER Suite: protein structure and function prediction. Nat Methods. 2015;12(1):7–8. https://doi.org/10.1038/nmeth.3213. [70] Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ. The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc. 2015;10(6):845–58. https://doi.org/10.1038/nprot.2015.053. [71] Webb B, Sali A. Comparative protein structure modeling using modeller. Curr Protoc Bioinformatics. 2016;54(1):561–5637. https://doi.org/10.1002/cpbi.3. [72] Biasini M, Bienert S, Waterhouse A, Arnold K, Studer G, Schmidt T, et al. SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res. 2014;42(Web Server issue):W252–8. https://doi.org/10.1093/nar/gku340. [73] Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583–9. https://doi.org/10.1038/s41586-021-03819-2. [74] Baltzer L, Nilsson H, Nilsson J. De novo design of proteins–what are the rules? Chem Rev. 2001;101(10):3153–63. https://doi.org/10.1021/cr0000473. [75] Berendsen HJC, van der Spoel D, van Drunen R. GROMACS: a message-passing parallel molecular dynamics implementation. Comput Phys Commun. 1995;91(1–3):43–56. https://doi.org/10.1016/0010-4655(95)00042-e. [76] Nelson MT, Humphrey W, Gursoy A, Dalke A, Kalé LV, Skeel RD, et al. NAMD: a parallel, object-oriented molecular dynamics program. Int J High Perform Comput Appl. 2016;10(4):251–68. https://doi.org/10.1177/109434209601000401. [77] Seritan S, Bannwarth C, Fales BS, Hohenstein EG, Isborn CM, Kokkila-Schumacher SIL, et al. TeraChem: a graphical processing unit-accelerated electronic structure package for large-scale ab initio molecular dynamics. WIREs Comput Mol Sci. 2020. https://doi.org/10.1002/wcms.1494. [78] Fariselli P, Riccobelli P, Casadio R. Role of evolutionary information in predicting the disulfide-bonding state of cysteine in proteins. Proteins. 1999;36(3):340–6. [79] Ban X, Lahiri P, Dhoble AS, Li D, Gu Z, Li C, et al. Evolutionary stability of salt bridges hints its contribution to stability of proteins. Comput Struct Biotechnol J. 2019;17:895–903. https://doi.org/10.1016/j.csbj.2019.06.022. [80] Ahmed MC, Papaleo E, Lindorff-Larsen K. How well do force fields capture the strength of salt bridges in proteins? PeerJ. 2018;6: e4967. https://doi.org/10.7717/peerj.4967. [81] Dehouck Y, Kwasigroch JM, Gilis D, Rooman M. PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality. BMC Bioinformatics. 2011;12(1):151. https://doi.org/10.1186/1471-2105-12-151. [82] Pucci F, Kwasigroch JM, Rooman M. SCooP: an accurate and fast predictor of protein stability curves as a function of temperature. Bioinformatics. 2017;33(21):3415–22. https://doi.org/10.1093/bioinformatics/btx417. [83] Hubbard RE, Kamran Haider M. Hydrogen bonds in proteins: role and strength. eLS. 2010. [84] McDonald IK, Thornton JM. Satisfying hydrogen bonding potential in proteins. J Mol Biol. 1994;238(5):777–93. https://doi.org/10.1006/jmbi.1994.1334. [85] odinger L. The PyMOL molecular graphics system, version 2.0 Schrödinger, LLC. 2015. [86] Li Y, Roy A, Zhang Y. HAAD: A quick algorithm for accurate prediction of hydrogen atoms in protein structures. PLoS ONE. 2009;4(8): e6701. https://doi.org/10.1371/journal.pone.0006701. [87] Salam NK, Adzhigirey M, Sherman W, Pearlman DA. Structure-based approach to the prediction of disulfide bonds in proteins. Protein Eng Des Sel. 2014;27(10):365–74. https://doi.org/10.1093/protein/gzu017. [88] Katz L, Baltz RH. Natural product discovery: past, present, and future. J Ind Microbiol Biotechnol. 2016;43(2–3):155–76. https://doi.org/10.1007/s10295-015-1723-5. [89] Scott TA, Piel J. The hidden enzymology of bacterial natural product biosynthesis. Nat Rev Chem. 2019;3(7):404–25. https://doi.org/10.1038/s41570-019-0107-1. [90] Medema MH, Kottmann R, Yilmaz P, Cummings M, Biggins JB, Blin K, et al. Minimum information about a biosynthetic gene cluster. Nat Chem Biol. 2015;11(9):625–31. https://doi.org/10.1038/nchembio.1890. [91] Blin K, Shaw S, Augustijn HE, Reitz ZL, Biermann F, Alanjary M, et al. antiSMASH 7.0: new and improved predictions for detection, regulation, chemical structures and visualisation. Nucleic Acids Res. 2023;51(W1):W46–50. https://doi.org/10.1093/nar/gkad344. [92] Skinnider MA, Merwin NJ, Johnston CW, Magarvey NA. PRISM 3: expanded prediction of natural product chemical structures from microbial genomes. Nucleic Acids Res. 2017;45(W1):W49–54. https://doi.org/10.1093/nar/gkx320. [93] Weber T, Rausch C, Lopez P, Hoof I, Gaykova V, Huson DH, et al. CLUSEAN: a computer-based framework for the automated analysis of bacterial secondary metabolite biosynthetic gene clusters. J Biotechnol. 2009;140(1–2):13–7. https://doi.org/10.1016/j.jbiotec.2009.01.007. [94] Li MH, Ung PM, Zajkowski J, Garneau-Tsodikova S, Sherman DH. Automated genome mining for natural products. BMC Bioinformatics. 2009;10:185. https://doi.org/10.1186/1471-2105-10-185. [95] Behsaz B, Bode E, Gurevich A, Shi YN, Grundmann F, Acharya D, et al. Integrating genomics and metabolomics for scalable non-ribosomal peptide discovery. Nat Commun. 2021;12(1):3225. https://doi.org/10.1038/s41467-021-23502-4. [96] Skinnider MA, Johnston CW, Gunabalasingam M, Merwin NJ, Kieliszek AM, MacLellan RJ, et al. Comprehensive prediction of secondary metabolite structure and biological activity from microbial genome sequences. Nat Commun. 2020;11(1):6058. https://doi.org/10.1038/s41467-020-19986-1. [97] Jensen PR. Natural products and the gene cluster revolution. Trends Microbiol. 2016;24(12):968–77. https://doi.org/10.1016/j.tim.2016.07.006. [98] Prihoda D, Maritz JM, Klempir O, Dzamba D, Woelk CH, Hazuda DJ, et al. The application potential of machine learning and genomics for understanding natural product diversity, chemistry, and therapeutic translatability. Nat Prod Rep. 2021;38(6):1100–8. https://doi.org/10.1039/d0np00055h. [99] Walker AS, Clardy J. A machine learning bioinformatics method to predict biological activity from biosynthetic gene clusters. J Chem Inf Model. 2021;61(6):2560–71. https://doi.org/10.1021/acs.jcim.0c01304. [100] Sugimoto Y, Camacho FR, Wang S, Chankhamjon P, Odabas A, Biswas A, et al. A metagenomic strategy for harnessing the chemical repertoire of the human microbiome. Science. 2019. https://doi.org/10.1126/science.aax9176. [101] Tasse L, Bercovici J, Pizzut-Serin S, Robe P, Tap J, Klopp C, et al. Functional metagenomics to mine the human gut microbiome for dietary fiber catabolic enzymes. Genome Res. 2010;20(11):1605–12. https://doi.org/10.1101/gr.108332.110. [102] Buton N, Coste F, Le Cunff Y, Valencia A. Predicting enzymatic function of protein sequences with attention. Bioinformatics. 2023. https://doi.org/10.1093/bioinformatics/btad620. [103] Dodda SR, Hossain M, Kapoor BS, Dasgupta S, Aikat K, et al. Computational approach for identification, characterization, three-dimensional structure modelling and machine learning-based thermostability prediction of xylanases from the genome of Aspergillus fumigatus. Comput Biol Chem. 2021;91:107451. https://doi.org/10.1016/j.compbiolchem.2021.107451. [104] Wang L, Wang Y, Chang S, Gao Z, Ma J, Wu B, et al. Identification and characterization of a thermostable GH11 xylanase from Paenibacillus campinasensis NTU-11 and the distinct roles of its carbohydrate-binding domain and linker sequence. Colloids Surf B Biointerfaces. 2022;209(Pt 1): 112167. https://doi.org/10.1016/j.colsurfb.2021.112167. [105] Ghadikolaei KK, Sangachini ED, Vahdatirad V, Noghabi KA, Zahiri HS. An extreme halophilic xylanase from camel rumen metagenome with elevated catalytic activity in high salt concentrations. AMB Express. 2019;9(1):86. https://doi.org/10.1186/s13568-019-0809-2. [106] Joshi N, Sharma M, Singh SP. Characterization of a novel xylanase from an extreme temperature hot spring metagenome for xylooligosaccharide production. Appl Microbiol Biotechnol. 2020;104(11):4889–901. https://doi.org/10.1007/s00253-020-10562-7. [107] Mon ML, Marrero Díaz de Villegas R, Campos E, Soria MA, Talia PM. Characterization of a novel GH10 alkali-thermostable xylanase from a termite microbiome. Bioresour Bioprocess. 2022. https://doi.org/10.1186/s40643-022-00572-w. [108] Fredriksen L, Stokke R, Jensen MS, Westereng B, Jameson JK, Steen IH, et al. Discovery of a Thermostable GH10 Xylanase with Broad Substrate Specificity from the Arctic Mid-Ocean Ridge Vent System. Appl Environ Microbiol. 2019. https://doi.org/10.1128/AEM.02970-18. [109] Knapik K, Becerra M, Gonzalez-Siso MI. Microbial diversity analysis and screening for novel xylanase enzymes from the sediment of the Lobios Hot Spring in Spain. Sci Rep. 2019;9(1):11195. https://doi.org/10.1038/s41598-019-47637-z. [110] Rajabi M, Nourisanami F, Ghadikolaei KK, Changizian M, Noghabi KA, Zahiri HS. Metagenomic psychrohalophilic xylanase from camel rumen investigated for bioethanol production from wheat bran using Bacillus subtilis AP. Sci Rep. 2022;12(1):8152. https://doi.org/10.1038/s41598-022-11412-4. [111] Hu D, Zhao X. Characterization of a new xylanase found in the rumen metagenome and its effects on the hydrolysis of wheat. J Agric Food Chem. 2022;70(21):6493–502. https://doi.org/10.1021/acs.jafc.2c00827. [112] Wang J, Liang J, Li Y, Tian L, Wei Y. Characterization of efficient xylanases from industrial-scale pulp and paper wastewater treatment microbiota. AMB Express. 2021;11(1):19. https://doi.org/10.1186/s13568-020-01178-1. [113] Ariaeenejad S, Hosseini E, Maleki M, Kavousi K, Moosavi-Movahedi AA, Salekdeh GH. Identification and characterization of a novel thermostable xylanase from camel rumen metagenome. Int J Biol Macromol. 2019;126:1295–302. https://doi.org/10.1016/j.ijbiomac.2018.12.041. [114] Ariaeenejad S, Maleki M, Hosseini E, Kavousi K, Moosavi-Movahedi AA, Salekdeh GH. Mining of camel rumen metagenome to identify novel alkali-thermostable xylanase capable of enhancing the recalcitrant lignocellulosic biomass conversion. Bioresour Technol. 2019;281:343–50. https://doi.org/10.1016/j.biortech.2019.02.059. [115] Ariaeenejad S, Lanjanian H, Motamedi E, Kavousi K, Moosavi-Movahedi AA, Hosseini SG. The stabilizing mechanism of immobilized metagenomic xylanases on bio-based hydrogels to improve utilization performance: computational and functional perspectives. Bioconjug Chem. 2020;31(9):2158–71. https://doi.org/10.1021/acs.bioconjchem.0c00361. [116] Mousavi SH, Sadeghian Motahar SF, Salami M, Kavousi K, Sheykh Abdollahzadeh Mamaghani A, Ariaeenejad S, et al. Invitro bioprocessing of corn as poultry feed additive by the influence of carbohydrate hydrolyzing metagenome derived enzyme cocktail. Sci Rep. 2022;12(1):405. https://doi.org/10.1038/s41598-021-04103-z. [117] Ariaeenejad S, Kavousi K, Zolfaghari B, Roy S, Koshiba T, Hosseini SG. Efficient bioconversion of lignocellulosic waste by a novel computationally screened hyperthermostable enzyme from a specialized microbiota. Ecotoxicol Environ Saf. 2023;252: 114587. https://doi.org/10.1016/j.ecoenv.2023.114587. [118] Pavarina GC, Lemos EGM, Lima NSM, Pizauro JM Jr. Characterization of a new bifunctional endo-1,4-beta-xylanase/esterase found in the rumen metagenome. Sci Rep. 2021;11(1):10440. https://doi.org/10.1038/s41598-021-89916-8. [119] Ariaeenejad S, Kavousi K, Maleki M, Motamedi E, Moosavi-Movahedi AA, Hosseini SG. Application of free and immobilized novel bifunctional biocatalyst in biotransformation of recalcitrant lignocellulosic biomass. Chemosphere. 2021;285: 131412. https://doi.org/10.1016/j.chemosphere.2021.131412. [120] Ariaeenejad S, Motamedi E, Kavousi K, Ghasemitabesh R, Goudarzi R, Salekdeh GH, et al. Enhancing the ethanol production by exploiting a novel metagenomic-derived bifunctional xylanase/beta-glucosidase enzyme with improved beta-glucosidase activity by a nanocellulose carrier. Front Microbiol. 2022;13:1056364. https://doi.org/10.3389/fmicb.2022.1056364. [121] Sanjaya RE, Putri KDA, Kurniati A, Rohman A, Puspaningsih NNT. In silico characterization of the GH5-cellulase family from uncultured microorganisms: physicochemical and structural studies. J Genet Eng Biotechnol. 2021;19(1):143. https://doi.org/10.1186/s43141-021-00236-w. [122] Patel M, Patel HM, Dave S. Determination of bioethanol production potential from lignocellulosic biomass using novel Cel-5m isolated from cow rumen metagenome. Int J Biol Macromol. 2020;153:1099–106. https://doi.org/10.1016/j.ijbiomac.2019.10.240. [123] Stepnov AA, Fredriksen L, Steen IH, Stokke R, Eijsink VGH. Identification and characterization of a hyperthermophilic GH9 cellulase from the Arctic Mid-Ocean Ridge vent field. PLoS ONE. 2019;14(9): e0222216. https://doi.org/10.1371/journal.pone.0222216. [124] Hammami A, Fakhfakh N, Abdelhedi O, Nasri M, Bayoudh A. Proteolytic and amylolytic enzymes from a newly isolated Bacillus mojavensis SA: Characterization and applications as laundry detergent additive and in leather processing. Int J Biol Macromol. 2018;108:56–68. https://doi.org/10.1016/j.ijbiomac.2017.11.148. [125] Nguyen KHV, Dao TK, Nguyen HD, Nguyen KH, Nguyen TQ, Nguyen TT, et al. Some characters of bacterial cellulases in goats’ rumen elucidated by metagenomic DNA analysis and the role of fibronectin 3 module for endoglucanase function. Anim Biosci. 2021;34(5):867–79. https://doi.org/10.5713/ajas.20.0115. [126] Guerrero EB, de Villegas RMD, Soria MA, Santangelo MP, Campos E, Talia PM. Characterization of two GH5 endoglucanases from termite microbiome using synthetic metagenomics. Appl Microbiol Biotechnol. 2020;104(19):8351–66. https://doi.org/10.1007/s00253-020-10831-5. [127] Maleki M, Shahraki MF, Kavousi K, Ariaeenejad S, Hosseini SG. A novel thermostable cellulase cocktail enhances lignocellulosic bioconversion and biorefining in a broad range of pH. Int J Biol Macromol. 2020;154:349–60. https://doi.org/10.1016/j.ijbiomac.2020.03.100. [128] Motamedi E, Sadeghian Motahar SF, Maleki M, Kavousi K, Ariaeenejad S, Moosavi-Movahedi AA, et al. Upgrading the enzymatic hydrolysis of lignocellulosic biomass by immobilization of metagenome-derived novel halotolerant cellulase on the carboxymethyl cellulose-based hydrogel. Cellulose. 2021;28(6):3485–503. https://doi.org/10.1007/s10570-021-03727-8. [129] Ariaeenejad S, Sheykh Abdollahzadeh Mamaghani A, Maleki M, Kavousi K, Foroozandeh Shahraki M, Hosseini Salekdeh G. A novel high performance in-silico screened metagenome-derived alkali-thermostable endo-beta-1,4-glucanase for lignocellulosic biomass hydrolysis in the harsh conditions. BMC Biotechnol. 2020;20(1):56, https://doi.org/ https://doi.org/10.1186/s12896-020-00647-6. [130] Chai S, Zhang X, Jia Z, Xu X, Zhang Y, Wang S, et al. Identification and characterization of a novel bifunctional cellulase/hemicellulase from a soil metagenomic library. Appl Microbiol Biotechnol. 2020;104(17):7563–72. https://doi.org/10.1007/s00253-020-10766-x. [131] Yan Z, Ding L, Zou D, Wang L, Tan Y, Guo S, et al. Identification and characterization of a novel carboxylesterase EstQ7 from a soil metagenomic library. Arch Microbiol. 2021;203(7):4113–25. https://doi.org/10.1007/s00203-021-02398-0. [132] Zhang Y, Ding L, Yan Z, Zhou D, Jiang J, Qiu J, et al. Identification and characterization of a novel carboxylesterase belonging to family VIII with promiscuous acyltransferase activity toward cyanidin-3-O-glucoside from a soil metagenomic library. Appl Biochem Biotechnol. 2023;195(4):2432–50. https://doi.org/10.1007/s12010-021-03614-9. [133] Lu M, Daniel R. A novel carboxylesterase derived from a compost metagenome exhibiting high stability and activity towards high salinity. Genes (Basel). 2021. https://doi.org/10.3390/genes12010122. [134] Ariaeenejad S, Kavousi K, Mamaghani ASA, Motahar SFS, Nedaei H, Salekdeh GH. In-silico discovery of bifunctional enzymes with enhanced lignocellulose hydrolysis from microbiota big data. Int J Biol Macromol. 2021;177:211–20. https://doi.org/10.1016/j.ijbiomac.2021.02.014. [135] Kaushal G, Rai AK, Singh SP. A novel beta-glucosidase from a hot-spring metagenome shows elevated thermal stability and tolerance to glucose and ethanol. Enzyme Microb Technol. 2021;145: 109764. https://doi.org/10.1016/j.enzmictec.2021.109764. [136] Thornbury M, Sicheri J, Slaine P, Getz LJ, Finlayson-Trick E, Cook J, et al. Characterization of novel lignocellulose-degrading enzymes from the porcupine microbiome using synthetic metagenomics. PLoS ONE. 2019;14(1): e0209221. https://doi.org/10.1371/journal.pone.0209221. [137] Salami M, Sadeghian Motahar SF, Ariaeenejad S, Sheykh Abdollahzadeh Mamaghani A, Kavousi K, Moosavi-Movahedi AA, et al. The novel homologue of the human alpha-glucosidase inhibited by the non-germinated and germinated quinoa protein hydrolysates after in vitro gastrointestinal digestion. J Food Biochem. 2022;46(1):e14030. https://doi.org/10.1111/jfbc.14030. [138] Ariaeenejad S, Zolfaghari B, Sadeghian Motahar SF, Kavousi K, Maleki M, Roy S, et al. Highly efficient computationally derived novel metagenome alpha-amylase with robust stability under extreme denaturing conditions. Front Microbiol. 2021;12: 713125. https://doi.org/10.3389/fmicb.2021.713125. [139] Sadeghian Motahar SF, Ariaeenejad S, Salami M, Emam-Djomeh Z, Sheykh Abdollahzadeh Mamaghani A. Improving the quality of gluten-free bread by a novel acidic thermostable alpha-amylase from metagenomics data. Food Chem. 2021;352:129307. https://doi.org/10.1016/j.foodchem.2021.129307. [140] Thakur M, Sharma N, Rai AK, Singh SP. A novel cold-active type I pullulanase from a hot-spring metagenome for effective debranching and production of resistant starch. Bioresour Technol. 2021;320(Pt A): 124288. https://doi.org/10.1016/j.biortech.2020.124288. [141] Sadeghian Motahar SF, Salami M, Ariaeenejad S, Emam‐Djomeh Z, Sheykh Abdollahzadeh Mamaghani A, Kavousi K, et al. Synergistic Effect of metagenome‐derived starch‐degrading enzymes on quality of functional bread with antioxidant activity. Starch Stärke. 2021; doi: https://doi.org/10.1002/star.202100098. [142] Itoh N, Hayashi Y, Honda S, Yamamoto Y, Tanaka D, Toda H. Construction and characterization of a functional chimeric laccase from metagenomes suitable as a biocatalyst. AMB Express. 2021;11(1):90. https://doi.org/10.1186/s13568-021-01248-y. [143] Ariaeenejad S, Kavousi K, Afshar Jahanshahi D, Sheykh Abdollahzadeh Mamaghani A, Ghasemitabesh R, Moosavi-Movahedi AA, et al. Enzymatically triggered delignification through a novel stable laccase: a mixed in-silico /in-vitro exploration of a complex environmental microbiota. Int J Biol Macromol. 2022;211:328–41. https://doi.org/10.1016/j.ijbiomac.2022.05.039. [144] Motamedi E, Kavousi K, Sadeghian Motahar SF, Reza Ghaffari M, Sheykh Abdollahzadeh Mamaghani A, Hosseini Salekdeh G, et al. Efficient removal of various textile dyes from wastewater by novel thermo-halotolerant laccase. Bioresour Technol. 2021;337:125468. https://doi.org/10.1016/j.biortech.2021.125468. [145] Verma SK, Sharma PC. Isolation and biochemical characterization of a novel serine protease identified from solid tannery waste metagenome. Biologia. 2021;76(10):3163–74. https://doi.org/10.1007/s11756-021-00832-8. [146] Verma SK, Kaur S, Tevetia A, Chatterjee S, Sharma PC. Structural characterization and functional annotation of microbial proteases mined from solid tannery waste metagenome. Biologia. 2021;76(6):1829–42. https://doi.org/10.1007/s11756-021-00727-8. [147] Ariaeenejad S, Kavousi K, Mamaghani ASA, Ghasemitabesh R, Hosseini SG. Simultaneous hydrolysis of various protein-rich industrial wastes by a naturally evolved protease from tannery wastewater microbiota. Sci Total Environ. 2022;815: 152796. https://doi.org/10.1016/j.scitotenv.2021.152796. [148] Wu S, Nan F, Jiang J, Qiu J, Zhang Y, Qiao B, et al. Molecular cloning, and characterization of a novel feruloyl esterase from a soil metagenomic library with phthalate-degrading activity. Biotechnol Lett. 2019;41(8–9):995–1006. https://doi.org/10.1007/s10529-019-02693-3. [149] Cavello IA, Hours RA, Cavalitto SF. Enzymatic hydrolysis of gelatin layers of X-Ray films and release of silver particles using keratinolytic serine proteases from Purpureocillium lilacinum LPS # 876. J Microbiol Biotechnol. 2013;23(8):1133–9. https://doi.org/10.4014/jmb.1302.02038. [150] Sarkar J, Dutta A, Pal Chowdhury P, Chakraborty J, Dutta TK. Characterization of a novel family VIII esterase EstM2 from soil metagenome capable of hydrolyzing estrogenic phthalates. Microb Cell Fact. 2020;19(1):77. https://doi.org/10.1186/s12934-020-01336-x. [151] Kaur R, Kumar R, Verma S, Kumar A, Rajesh C, Sharma PK. Structural and functional insights about unique extremophilic bacterial lipolytic enzyme from metagenome source. Int J Biol Macromol. 2020;152:593–604. https://doi.org/10.1016/j.ijbiomac.2020.02.210. [152] Castillo Villamizar GA, Nacke H, Griese L, Tabernero L, Funkner K, Daniel R. Characteristics of the first protein tyrosine phosphatase with phytase activity from a soil metagenome. Genes (Basel). 2019. https://doi.org/10.3390/genes10020101. [153] Karunatillaka I, Jaroszewski L, Godzik A. Novel putative polyethylene terephthalate (PET) plastic degrading enzymes from the environmental metagenome. Proteins. 2022;90(2):504–11. https://doi.org/10.1002/prot.26245. [154] Goncalves TA, Sodre V, da Silva SN, Vilela N, Tomazetto G, Araujo JN, et al. Applying biochemical and structural characterization of hydroxycinnamate catabolic enzymes from soil metagenome for lignin valorization strategies. Appl Microbiol Biotechnol. 2022;106(7):2503–16. https://doi.org/10.1007/s00253-022-11885-3. [155] Sun J, Yao C, Li Y, Wang W, Hao J, Yu Y. A novel salt-tolerant GH42 beta-galactosidase with transglycosylation activity from deep-sea metagenome. World J Microbiol Biotechnol. 2022;38(9):154. https://doi.org/10.1007/s11274-022-03348-8. [156] Creekmore BC, Gray JH, Walton WG, Biernat KA, Little MS, Xu Y, et al. Mouse gut microbiome-encoded beta-glucuronidases identified using metagenome analysis guided by protein structure. mSystems. 2019. https://doi.org/10.1128/mSystems.00452-19. [157] Wierzbicka-Wos A, Henneberger R, Batista-Garcia RA, Martinez-Avila L, Jackson SA, Kennedy J, et al. Biochemical characterization of a novel monospecific endo-beta-1,4-glucanase belonging to GH family 5 from a rhizosphere metagenomic library. Front Microbiol. 2019;10:1342. https://doi.org/10.3389/fmicb.2019.01342. [158] Yasir M, Khan H, Azam SS, Telke A, Kim SW, Chung YR. Cloning and functional characterization of endo-beta-1,4-glucanase gene from metagenomic library of vermicompost. J Microbiol. 2013;51(3):329–35. https://doi.org/10.1007/s12275-013-2697-5. [159] Holck J, Djajadi DT, Brask J, Pilgaard B, Krogh K, Meyer AS, et al. Novel xylanolytic triple domain enzyme targeted at feruloylated arabinoxylan degradation. Enzyme Microb Technol. 2019;129: 109353. https://doi.org/10.1016/j.enzmictec.2019.05.010.

Antiproliferative polyketides from fungus Xylaria cf. Longipes SWUF08-81 in different culture media

Antiproliferative polyketides from fungus Xylaria cf. Longipes SWUF08-81 in different culture media

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 8

编辑推荐

Metrics

本文评价

[1]	Yi-Yun Yuan, Yan Li, Wen-Yu Lu, Ai-Lin Liang, Jing Li, Wen-Xuan Wang. Xylariaides A and B, novel cytochalasans with a unique 5/6/5/3 ring system from a soil fungus Xylaria sp. Y01[J]. 应用天然产物, 2025, 15(3): 23-23.
[2]	Yue-Mei Chen, Nan-Kai Cao, Si-Si Zhu, Meng Ding, Hai-Zhen Liang, Ming-Bo Zhao, Ke-Wu Zeng, Peng-Fei Tu, Yong Jiang. Euchrestifolines A-O, fifteen novel carbazole alkaloids with potent anti-ferroptotic activity from Murraya euchrestifolia[J]. 应用天然产物, 2025, 15(1): 5-5.
[3]	Kai-Liang Ma, Shi-Hui Dong, Hang-Ying Li, Wen-Jun Wei, Yong-Qiang Tu, Kun Gao. Cytochalasins from Xylaria sp. CFL5, an Endophytic Fungus of Cephalotaxus fortunei[J]. 应用天然产物, 2021, 11(1): 87-98.
[4]	Tin Thu Thu Aung, Meng-Yuan Xia, Pyae Phyo Hein, Rong Tang, Dong-Dong Zhang, Jun Yang, Xue-Fei Yang, Dong-Bao Hu, Yue-Hu Wang. Chemical Constituents from the Whole Plant of Cuscuta reflexa[J]. 应用天然产物, 2020, 10(5): 337-344.
[5]	Rong Chen, Jian-Wei Tang, Xing-Ren Li, Miao Liu, Wen-Ping Ding, Yuan-Fei Zhou, Wei-Guang Wang, Xue Du, Han-Dong Sun, Pema-Tenzin Puno. Secondary Metabolites from the Endophytic Fungus Xylaria sp. Hg1009[J]. 应用天然产物, 2018, 8(2): 121-129.
[6]	Li Peng, Yanting Lu, Yuhui Xu, Jing Hu, Fang Wang, Yumei Zhang, Wenyong Xiong. Pyrocincholic acid 3β-O-β-D-quinovopyranosyl-28-O-β-D-glucopyranoside suppresses adipogenesis and regulates lipid metabolism in 3T3-L1 adipocytes[J]. 应用天然产物, 2017, 7(3): 225-234.
[7]	Qingxi Su, Peggy J. Brodie, Yixi Liu, James S. Miller, Naina M. Andrianjafy, Rabodo Antsiferana, Vincent E. Rasamison, David G. I. Kingston. Antiproliferative Triterpenoid Saponins from Leptaulus citroides Baill. from the Madagascar Rain Forest[J]. 应用天然产物, 2016, 6(1): 31-39.
[8]	Fang-Fang Liu, Yan-Ming Wang, Hong-Tao Zhu, Dong Wang, Chong-Ren Yang, Min Xu, Ying-Jun Zhang. Comparative Study on “Long-Dan”, “Qin-Jiao” and Their Adulterants by HPLC Analysis[J]. 应用天然产物, 2014, 4(5): 297-308.