in

Lignocellulose-mediated selection of potential halophilic PET-degrading enzymes from mangrove soil


Abstract

Mangroves are ecosystems located at land–sea transition zones, where they are continuously exposed to plant biomass and plastic pollution. Their soils harbor extensive microbial diversity with potential for discovering polymer-degrading enzymes. Here, we perform a microcosm experiment to examine how mangrove soil microbial communities respond to inputs of lignocellulose or polyethylene terephthalate (PET) in the presence and absence of seawater, and to explore the selection of putative PET-active enzymes (PETases) using gene- and genome-resolved metagenomics. Incubation conditions lead to a gradual increase in salinity, resulting in the enrichment of halophilic taxa, including spore-forming bacteria and archaeal species, particularly in seawater-depleted treatments. Lignocellulose input is the primary driver of soil microbial community restructuring, followed by seawater presence. In dry, lignocellulose-amended microcosms (L treatment), microbial diversity is significantly reduced, while lignocellulolytic taxa within the phyla Bacillota and Actinomycetota are enriched. Twelve potential PETases are identified in the L treatment, sharing >70% sequence similarity with known PETases, and three are predicted to be thermostable. Two putative PETases from Microbulbifer species display distinct sequence and structural features, thereby expanding the currently limited PETase sequence landscape. This study demonstrates that perturbing environmental microbiomes with plant-derived polymers represents a promising strategy for capturing novel PETases.

Data availability

Raw sequencing data (16S rRNA gene, ITS2, and metagenomic) and metagenome-assembled genomes are available through the European Nucleotide Archive (ENA) under BioProject ID PRJEB72453. Source data are provided with this paper. Additional data generated in this study are provided in the Supplementary Information/Source Data file. Source data are provided with this paper.

Code availability

Custom scripts for processing, analysis, and visualization of shotgun metagenomic data are available at https://github.com/mariafpv/LignoMangrove-MAGs and at Zenodo: https://doi.org/10.5281/zenodo.18651101131. Code for the structural and physicochemical characterization of putative PETases is available at https://github.com/Robaina/Mangrove-PETases and at Zenodo: https://doi.org/10.5281/zenodo.18656903132.

References

  1. Donato, D. et al. Mangroves among the most carbon-rich forests in the tropics. Nat. Geosci. 4, 293–297 (2011).

    Google Scholar 

  2. Rahman, M. M. et al. Co-benefits of protecting mangroves for biodiversity conservation and carbon storage. Nat. Commun. 12, 3875 (2021).

    Google Scholar 

  3. Andreote, F. D. et al. The microbiome of Brazilian mangrove sediments as revealed by metagenomics. PLoS ONE 7, e38600 (2012).

    Google Scholar 

  4. Jiménez, D. J. et al. Compositional profile of α/β-hydrolase fold proteins in mangrove soil metagenomes: prevalence of epoxide hydrolases and haloalkane dehalogenases in oil-contaminated sites. Microb. Biotechnol. 8, 604–613 (2015).

    Google Scholar 

  5. Allard, S. M. et al. Introducing the Mangrove Microbiome Initiative: identifying microbial research priorities and approaches to better understand, protect, and rehabilitate mangrove ecosystems. mSystems 5, e00658–20 (2020).

    Google Scholar 

  6. Yuan, Z., Zeng, Z. & Liu, F. Community structures of mangrove endophytic and rhizosphere bacteria in Zhangjiangkou National Mangrove Nature Reserve. Sci. Rep. 13, 17127 (2023).

    Google Scholar 

  7. Nag, S. et al. Halotolerant and halophilic bacteria present in the mangrove ecosystem: emerging bioengineering potentials. in (eds Sarma, H & Joshi, S. J.) Biotechnology of emerging microbes. Progress in biochemistry and biotechnology, 143–162. https://doi.org/10.1016/B978-0-443-15397-6.00010-3 (Academic Press, 2024).

  8. Jiménez, D. J. et al. Microbial community characterization in Red Sea-derived samples using a field-deployable DNA extraction system and nanopore sequencing. Environ. Microbiome. 21, 1 (2025).

  9. Vázquez-Rosas-Landa, M. et al. Impact of seasonal flooding and hydrological connectivity loss on microbial community dynamics in mangrove sediments of the southern Gulf of Mexico. PeerJ 13, e19371 (2025).

    Google Scholar 

  10. Hülsen, S., Dee, L. E., Kropf, C. M., Meiler, S. & Bresch, D. N. Mangroves and their services are at risk from tropical cyclones and sea level rise under climate change. Commun. Earth Environ. 6, 262 (2025).

    Google Scholar 

  11. Lacerda, L. D., Ferreira, A. C., Ward, R. & Borges, R. Editorial: mangroves in the Anthropocene: from local change to global challenge. Front. Glob. Change 5, 993409 (2022).

    Google Scholar 

  12. Han, L. et al. Microplastics alter soil structure and microbial community composition. Environ. Int. 185, 108508 (2024).

    Google Scholar 

  13. Jaramillo, F. et al. Effects of hydroclimatic change and rehabilitation activities on salinity and mangroves in the Ciénaga Grande de Santa Marta, Colombia. Wetlands 38, 755–767 (2018).

    Google Scholar 

  14. Wang, X. & Zhu, X. Salinity stress and atmospheric dryness co-limit evapotranspiration in a subtropical monsoonal estuarine mangrove wetland. Environ. Res. Lett. 19, 114067 (2024).

    Google Scholar 

  15. Mendes, D. S., Beasley, C. R., Silva, D. N. N. & Fernandes, M. E. B. Microplastic in mangroves: a worldwide review of contamination in biotic and abiotic matrices. Mar. Pollut. Bull. 195, 115552 (2023).

    Google Scholar 

  16. Deakin, K., Porter, A., Osorio Baquero, A. & Lewis, C. Plastic pollution in mangrove ecosystems: a global meta-analysis. Mar. Pollut. Bull. 218, 118165 (2025).

    Google Scholar 

  17. Ding, J. et al. Depth heterogeneity of lignin-degrading microbiome and organic carbon processing in mangrove sediments. NPJ Biofilms Microbiomes 11, 5 (2025).

    Google Scholar 

  18. Mamidala, H. P. et al. Interspecific variations in leaf litter decomposition and nutrient release from tropical mangroves. J. Environ. Manag. 328, 116902 (2023).

    Google Scholar 

  19. Garcés-Ordóñez, O., Castillo-Olaya, V. A., Granados-Briceño, A. F., Blandón García, L. M. & Espinosa Díaz, L. F. Marine litter and microplastic pollution on mangrove soils of the Ciénaga Grande de Santa Marta, Colombian Caribbean. Mar. Pollut. Bull. 145, 455–462 (2019).

    Google Scholar 

  20. Martin, C. et al. Exponential increase of plastic burial in mangrove sediments as a major plastic sink. Sci. Adv. 6, eaz5593 (2020).

    Google Scholar 

  21. Pawano, O. et al. Exploring untapped bacterial communities and potential polypropylene-degrading enzymes from mangrove sediment through metagenomics analysis. Front. Microbiol. 15, 1347119 (2024).

    Google Scholar 

  22. Paixão, D. A. A. et al. Microbial enrichment and meta-omics analysis identify CAZymes from mangrove sediments with unique properties. Enzym. Microb. Technol. 148, 109820 (2021).

    Google Scholar 

  23. DasSarma, S. & DasSarma, P. Halophiles and their enzymes: negativity put to good use. Curr. Opin. Microbiol 25, 120–126 (2015).

    Google Scholar 

  24. Jiménez, D. J. et al. Engineering the mangrove soil microbiome for selection of polyethylene terephthalate-transforming bacterial consortia. Trends Biotechnol. 43, 162–183 (2025).

    Google Scholar 

  25. Danso, D. et al. New insights into the function and global distribution of polyethylene terephthalate (PET)-degrading bacteria and enzymes in marine and terrestrial metagenomes. Appl. Environ. Microbiol. 84, e02773–17 (2018).

    Google Scholar 

  26. Wei, R., Westh, P., Weber, G., Blank, L. M. & Bornscheuer, U. T. Standardization guidelines and future trends for PET hydrolase research. Nat. Commun. 16, 4684 (2025).

    Google Scholar 

  27. Joo, S. et al. Structural insight into molecular mechanism of poly(ethylene terephthalate) degradation. Nat. Commun. 9, 382 (2018).

    Google Scholar 

  28. Turak, O. et al. A third type of PETase from the marine Halopseudomonas lineage. Protein Sci. 34, e70305 (2024).

  29. Yoshida, S. et al. A bacterium that degrades and assimilates poly(ethylene terephthalate). Science 351, 1196–1199 (2016).

    Google Scholar 

  30. Chen, J. et al. Global marine microbial diversity and its potential in bioprospecting. Nature 633, 371–379 (2024).

    Google Scholar 

  31. Zhang, G. et al. Structural insights and engineering of deep-sea halophilic PET hydrolytic enzymes. Preprint at https://doi.org/10.1101/2025.08.30.673199 (2025)

  32. Wei, R., Weber, G., Blank, L. M. & Bornscheuer, W. T. Process insights for harnessing biotechnology for plastic depolymerization. Nat. Chem. Eng. 2, 110–117 (2025).

    Google Scholar 

  33. Bell, E. L. et al. Directed evolution of an efficient and thermostable PET depolymerase. Nat. Catal. 5, 673–681 (2022).

    Google Scholar 

  34. Cordero, I., Leizeaga, A., Hicks, L. C., Rousk, J. & Bardgett, R. D. High intensity perturbations induce an abrupt shift in soil microbial state. ISME J. 17, 2190–2199 (2023).

    Google Scholar 

  35. Delmont, T. O. et al. Reconstructing rare soil microbial genomes using in situ enrichments and metagenomics. Front. Microbiol. 6, 358 (2015).

    Google Scholar 

  36. Saidu, M. B. et al. Exploring the biodegradation of PET in mangrove soil and its intermediates by enriched bacterial consortia. Environ. Technol. https://doi.org/10.1080/09593330.2025.2521762 (2025)

  37. Zhao, S. et al. Biodegradation of polyethylene terephthalate (PET) by diverse marine bacteria in deep-sea sediments. Environ. Microbiol. 25, 2719–2731 (2023).

    Google Scholar 

  38. Jiménez, D. J., Sanchez, A. & Dini-Andreote, F. Engineering microbiomes to transform plastics. Trends Biotechnol. 42, 265–268 (2024).

    Google Scholar 

  39. Chen, C. C., Dai, L., Ma, L. & Guo, R. T. Enzymatic degradation of plant biomass and synthetic polymers. Nat. Rev. Chem. 4, 114–126 (2020).

    Google Scholar 

  40. Cai, F. M. et al. Guidelines toward ecologically-informed bioprospecting for microbial plastic degradation. Biotechnol. Adv. 82, 108590 (2025).

    Google Scholar 

  41. Jiménez, D. J. et al. Merging plastics, microbes, and enzymes: highlights from an international workshop. Appl. Environ. Microbiol. 88, e0072122 (2022).

    Google Scholar 

  42. Bastida, F. et al. Soil microbial diversity-biomass relationships are driven by soil carbon content across global biomes. ISME J. 15, 2081–2091 (2021).

    Google Scholar 

  43. He, X. et al. Emerging multiscale insights on microbial carbon use efficiency in the land carbon cycle. Nat. Commun. 15, 8010 (2024).

    Google Scholar 

  44. Zhang, J. et al. A global assessment of mangrove soil organic carbon sources and implications for blue carbon credit. Nat. Commun. 15, 8994 (2024).

    Google Scholar 

  45. Jiménez, D. J. et al. Ecological insights into the dynamics of plant biomass-degrading microbial consortia. Trends Microbiol. 25, 788–796 (2017).

    Google Scholar 

  46. Wang, C. & Kuzyakov, Y. Mechanisms and implications of bacterial-fungal competition for soil resources. ISME J. 18, wrae073 (2024).

    Google Scholar 

  47. Ratzke, C., Barrere, J. & Gore, J. Strength of species interactions determines biodiversity and stability in microbial communities. Nat. Ecol. Evol. 4, 376–383 (2020).

    Google Scholar 

  48. Knight, C. G. et al. Soil microbiomes show consistent and predictable responses to extreme events. Nature 636, 690–696 (2024).

    Google Scholar 

  49. Potts, L. D. et al. Chronic environmental perturbation influences microbial community assembly patterns. Environ. Sci. Technol. 56, 2300–2311 (2022).

    Google Scholar 

  50. Rath, K. M., Fierer, N., Murphy, D. V. & Rousk, J. Linking bacterial community composition to soil salinity along environmental gradients. ISME J. 13, 836–846 (2019).

    Google Scholar 

  51. Rath, K. M., Maheshwari, A. & Rousk, J. Linking microbial community structure to trait distributions and functions using salinity as an environmental filter. mBio. 10, e01607–e01619 (2019).

    Google Scholar 

  52. Székely, A. J., Berga, M. & Langenheder, S. Mechanisms determining the fate of dispersed bacterial communities in new environments. ISME J. 7, 61–71 (2013).

    Google Scholar 

  53. Chapman, S. K., Hayes, M. A., Kelly, B. & Langley, J. A. Exploring the oxygen sensitivity of wetland soil carbon mineralization. Biol. Lett. 15, 20180407 (2019).

    Google Scholar 

  54. Luo, M., Huang, J. F., Zhu, W. F. & Tong, C. Impacts of increasing salinity and inundation on rates and pathways of organic carbon mineralization in tidal wetlands: a review. Hydrobiologia 827, 31–49 (2019).

    Google Scholar 

  55. Rodríguez Del Río, Á, Scheu, S. & Rillig, M. C. Soil microbial responses to multiple global change factors as assessed by metagenomics. Nat. Commun. 16, 5058 (2025).

    Google Scholar 

  56. Liu, Y. R. et al. Vulnerability of soil food webs to chemical pollution and climate change. Nat. Ecol. Evol. 9, 1112–1119 (2025).

    Google Scholar 

  57. Mandic-Mulec, I., Stefanic, P. & van Elsas, J. D. Ecology of bacillaceae. Microbiol. Spectr. 3, TBS-0017-2013 (2015).

  58. Yeager, C. M. et al. Polysaccharide degradation capability of Actinomycetales soil isolates from a semiarid grassland of the Colorado Plateau. Appl. Environ. Microbiol. 83, e03020–16 (2017).

    Google Scholar 

  59. Asha, K. & Bhadury, P. Myceligenerans indicum sp. nov., an actinobacterium isolated from mangrove sediment of Sundarbans, India. Arch. Microbiol. 203, 1577–1585 (2021).

    Google Scholar 

  60. Kearns, P. J. et al. Nutrient enrichment induces dormancy and decreases diversity of active bacteria in salt marsh sediments. Nat. Commun. 7, 12881 (2016).

    Google Scholar 

  61. Yang, Y. et al. Differential responses of soil extracellular enzyme activities to salinization: implications for soil carbon cycling in tidal wetlands. Glob. Biogeochem. Cycles 36, e2021GB007285 (2022).

    Google Scholar 

  62. Ekborg, N. A. et al. Saccharophagus degradans gen. nov., sp. nov., a versatile marine degrader of complex polysaccharides. Int. J. Syst. Evol. Microbiol. 55, 1545–1549 (2005).

    Google Scholar 

  63. Klimek, D., Herold, M. & Calusinska, M. Comparative genomic analysis of Planctomycetota potential for polysaccharide degradation identifies biotechnologically relevant microbes. BMC Genom. 25, 523 (2024).

    Google Scholar 

  64. Kündgen, M., Jogler, C. & Kallscheuer, N. Substrate utilization and secondary metabolite biosynthesis in the phylum Planctomycetota. Appl. Microbiol. Biotechnol. 109, 123 (2025).

    Google Scholar 

  65. Hollister, E. B. et al. Shifts in microbial community structure along an ecological gradient of hypersaline soils and sediments. ISME J. 4, 829–838 (2010).

    Google Scholar 

  66. Zhang, C. J. et al. Diversity, metabolism and cultivation of archaea in mangrove ecosystems. Mar. Life Sci. Technol. 3, 252–262 (2020).

    Google Scholar 

  67. Peng, H., Ruiz-Moreno, A. J. & Fu, J. Multi-dimensional metagenomics. Nat. Rev. Bioeng. https://doi.org/10.1038/s44222-025-00346-x (2025).

  68. Kim, N. et al. Genome-resolved metagenomics: a game changer for microbiome medicine. Exp. Mol. Med. 56, 1501–1512 (2024).

    Google Scholar 

  69. Delgado-Baquerizo, M. et al. A global atlas of the dominant bacteria found in soil. Science 359, 320–325 (2018).

    Google Scholar 

  70. Sulaiman, S. et al. Isolation of a novel cutinase homolog with polyethylene terephthalate-degrading activity from leaf-branch compost by using a metagenomic approach. Appl. Environ. Microbiol. 78, 1556–1562 (2012).

    Google Scholar 

  71. Arnal, G. et al. Assessment of four engineered PET degrading enzymes considering large-scale industrial applications. ACS Catal. 13, 13156–13166 (2023).

    Google Scholar 

  72. Perez-Garcia, P. et al. An archaeal lid-containing feruloyl esterase degrades polyethylene terephthalate. Commun. Chem. 6, 193 (2023).

    Google Scholar 

  73. Alam, I. et al. Widespread distribution of bacteria containing PETases with a functional motif across global oceans. ISME J. 19, wraf121 (2025).

    Google Scholar 

  74. Carletti, A. et al. Functional and structural characterization of PETase SM14 from marine-sponge Streptomyces sp. active on polyethylene terephthalate. ACS Sustain. Chem. Eng. 13, 7460–7468 (2025).

    Google Scholar 

  75. Weigert, S. et al. Investigation of the halophilic PET hydrolase PET6 from Vibrio gazogenes. Protein Sci. 31, e4500 (2022).

    Google Scholar 

  76. Meyer-Cifuentes, I. E. et al. B. Synergistic biodegradation of aromatic-aliphatic copolyester plastic by a marine microbial consortium. Nat. Commun. 11, 5790 (2020).

    Google Scholar 

  77. Blázquez-Sánchez, P. et al. Antarctic polyester hydrolases degrade aliphatic and aromatic polyesters at moderate temperatures. Appl. Environ. Microbiol. 88, e0184221 (2022).

    Google Scholar 

  78. Blázquez-Sánchez, P. et al. Engineering the catalytic activity of an Antarctic PET-degrading enzyme by loop exchange. Protein Sci. 32, e4757 (2023).

    Google Scholar 

  79. Ruginescu, R. & Purcarea, C. Plastic-degrading enzymes from marine microorganisms and their potential value in recycling technologies. Mar. Drugs 22, 441 (2024).

    Google Scholar 

  80. Erickson, E. et al. Sourcing thermotolerant polyethylene terephthalate hydrolase scaffolds from natural diversity. Nat. Commun. 13, 7850 (2022).

    Google Scholar 

  81. Qi, X., Ji, M., Yin, C. F., Zhou, N. Y. & Liu, Y. Glacier as a source of novel polyethylene terephthalate hydrolases. Environ. Microbiol. 25, 2822–2833 (2023).

    Google Scholar 

  82. da Costa, C. H. S. et al. Assessment of the PETase conformational changes induced by poly(ethylene terephthalate) binding. Proteins 89, 1340–1352 (2021).

    Google Scholar 

  83. Xu, S., Huo, C. & Chu, X. Unraveling the interplay between stability and flexibility in the design of polyethylene terephthalate (PET) hydrolases. J. Chem. Inf. Model 64, 7576–7589 (2024).

    Google Scholar 

  84. Herlemann, D. P. et al. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 5, 1571–1579 (2011).

    Google Scholar 

  85. Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).

    Google Scholar 

  86. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).

    Google Scholar 

  87. Kursa, M. B. & Rudnicki, W. R. Feature selection with the Boruta package. J. Stat. Softw. 36, 1–13 (2010).

    Google Scholar 

  88. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Google Scholar 

  89. Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012).

    Google Scholar 

  90. Bastian, M., Heymann, S. & Jacomy, M. Gephi: an open source software for exploring and manipulating networks. Proc. Int. AAAI Conf. Web Soc. Media 3, 361–362 (2009).

    Google Scholar 

  91. Andrews, S. FastQC: a quality control tool for high throughput sequence data. 2010. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/.

  92. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Google Scholar 

  93. Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257 (2019).

    Google Scholar 

  94. Lu, J., Breitwieser, F. P., Thielen, P. & Salzberg, S. L. Bracken: estimating species abundance in metagenomics data. PeerJ Comput. Sci. 3, e104 (2017).

    Google Scholar 

  95. Oksanen, J. et al. Vegan: community ecology package. R package version 2.2-0. 2014. http://CRAN.Rproject.org/package=vegan.

  96. Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).

    Google Scholar 

  97. Rideout, J. R. et al. biocore/scikit-bio: scikit-bio 0.5.9: more compositional methods added. Preprint at Zenodo, https://doi.org/10.5281/zenodo.2254379 (2024).

  98. Li, D., Liu, C. M., Luo, R., Sadakane, K. & Lam, T. W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).

    Google Scholar 

  99. Gurevich, A., Saveliev, V., Vyahhi, N. & Tesler, G. QUAST: quality assessment tool for genome assemblies. Bioinformatics 29, 1072–1075 (2013).

    Google Scholar 

  100. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Google Scholar 

  101. Wu, Y. W., Simmons, B. A. & Singer, S. W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605–607 (2016).

    Google Scholar 

  102. Kang, D. D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).

    Google Scholar 

  103. Nissen, J. N. et al. Improved metagenome binning and assembly using deep variational autoencoders. Nat. Biotechnol. 39, 555–560 (2021).

    Google Scholar 

  104. Sieber, C. M. K. et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat. Microbiol. 3, 836–843 (2018).

    Google Scholar 

  105. Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).

    Google Scholar 

  106. Bowers, R. M. et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat. Biotechnol. 35, 725–731 (2017).

    Google Scholar 

  107. Chaumeil, P. A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2019).

    Google Scholar 

  108. Tanizawa, Y., Fujisawa, T. & Nakamura, Y. DFAST: a flexible prokaryotic genome annotation pipeline for faster genome publication. Bioinformatics 34, 1037–1039 (2018).

    Google Scholar 

  109. Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 11, 119 (2010).

    Google Scholar 

  110. Díaz-García, L., Bugg, T. D. H. & Jiménez, D. J. Exploring the lignin catabolism potential of soil-derived lignocellulolytic microbial consortia by a gene-centric metagenomic approach. Microb. Ecol. 80, 885–896 (2020).

    Google Scholar 

  111. Zhang, H. et al. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 46, W95–W101 (2018).

    Google Scholar 

  112. Buchholz, P. C. F. et al. Plastics degradation by hydrolytic enzymes: the plastics-active enzymes database (PAZy). Proteins 90, 1443–1456 (2022).

    Google Scholar 

  113. Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).

    Google Scholar 

  114. Eisenhofer, R., Odriozola, I. & Alberdi, A. Impact of microbial genome completeness on metagenomic functional inference. ISME Commun. 3, 12 (2023).

    Google Scholar 

  115. Espinoza, J. L. & Dupont, C. L. VEBA: a modular end-to-end suite for in silico recovery, clustering, and analysis of prokaryotic, microeukaryotic, and viral genomes from metagenomes. BMC Bioinform. 23, 419 (2022).

    Google Scholar 

  116. Zallot, R., Oberg, N. & Gerlt, J. A. The EFI web resource for genomic enzymology tools: leveraging protein, genome, and metagenome databases to discover novel enzymes and metabolic pathways. Biochemistry 58, 4169–4182 (2019).

    Google Scholar 

  117. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    Google Scholar 

  118. Edgar, R. C. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinform. 5, 113 (2004).

    Google Scholar 

  119. Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2: approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).

    Google Scholar 

  120. Letunic, I. & Bork, P. Interactive Tree of Life (iTOL) v6: recent updates to the phylogenetic tree display and annotation tool. Nucleic Acids Res. 52, W78–W82 (2024).

    Google Scholar 

  121. Teufel, F. et al. SignalP 6.0 predicts all five types of signal peptides using protein language models. Nat. Biotechnol. 40, 1023–1025 (2022).

    Google Scholar 

  122. Hiller, K., Grote, A., Scheer, M., Münch, R. & Jahn, D. PrediSi: prediction of signal peptides and their cleavage positions. Nucleic Acids Res. 32, W375–W379 (2004).

    Google Scholar 

  123. Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).

    Google Scholar 

  124. Corso, G. et al. The discovery of binding modes requires rethinking docking generalization. In Proc. International Conference on Learning Representations (ICLR). https://doi.org/10.5281/zenodo.10656052 (2024).

  125. Rodella, C., Lazaridi, S. & Lemmin, T. TemBERTure: advancing protein thermostability prediction with deep learning and attention mechanisms. Bioinform. Adv. 4, vbae103 (2024).

    Google Scholar 

  126. Zhu, M., Song, Y., Yuan, Q. & Yang, Y. Accurately predicting optimal conditions for microorganism proteins through geometric graph learning and language model. Commun. Biol. 7, 1709 (2024).

    Google Scholar 

  127. Li, B. & Ming, D. GATSol, an enhanced predictor of protein solubility through the synergy of 3D structure graph and large language modeling. BMC Bioinform. 25, 204 (2024).

    Google Scholar 

  128. Boorla, V. S. & Maranas, C. D. CatPred: a comprehensive framework for deep learning in vitro enzyme kinetic parameters. Nat. Commun. 16, 2072 (2025).

    Google Scholar 

  129. Cock, P. J. et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25, 1422–1423 (2009).

    Google Scholar 

  130. Zhang, Y. & Skolnick, J. TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Res. 33, 2302–2309 (2005).

    Google Scholar 

  131. Peña-Valencia, M. F. Lignocellulose-mediated selection of potential halophilic PET-degrading enzymes from mangrove soil. Preprint at Zenodo, https://doi.org/10.5281/zenodo.18651101 (2026).

  132. Robaina, S. Lignocellulose-mediated selection of potential halophilic PET-degrading enzymes from mangrove soil. Preprint at Zenodo, https://doi.org/10.5281/zenodo.18656903 (2026).

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Acknowledgements

We thank the Faculty of Sciences at the Universidad de los Andes (Colombia) for financial and administrative support. DNA samples for sequencing were exported under ANLA permit number 2784. Computational analyses were partially conducted using the ExaCore—IT Core Facility at the Vice Presidency for Research and Creation, Universidad de los Andes. This study was partially funded by the FAPA project (PR.3.2018.5287) awarded to D.J.J. at the Department of Biological Sciences, Universidad de los Andes, and by baseline resources (BAS/1/1096-01-01) provided by A.S.R. at KAUST. A.V. received funding from the Dutch Research Council (grant VI.Veni.212.029). O.T. acknowledges funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project Number 391977956–SFB 1357 Microplastics, subproject C03 at Bayreuth University. We thank Josh L. Espinoza for assistance with metagenomic analyses. O.T. thanks Prof. Birte Höcker for her supervision. We are also grateful to Intikhab Alam at KAUST for M5 motif predictions.

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M.F.P.V.: Metagenomic analyses, statistical analyses, manuscript revision, and figure design. S.R.E.: Development of gene catalogs, screening of PET-active enzymes, protein characterization, and writing. G.F.C.: Amplicon sequencing analysis, statistical analyses, and writing. O.T.: Protein analyses and writing. F.S.: Sampling, microcosm setup, and computational analyses. L.W.M.: Co-occurrence analysis and writing. C.R.L.: Sampling, experimental design, and microcosm setup. J.G.: Development of gene catalogs, screening of PET-active enzymes, protein characterization, and writing. A.V.: Funding acquisition and writing/revision of the final draft. F.D.A.: Advising, computational analyses, and writing/revision of the final draft. A.S.R.: Funding acquisition, coordination, and writing/revision of the final draft. A.R.: Advising, funding acquisition, computational analyses, coordination, and writing/revision of the final draft. D.J.J.: Sampling, experimental design, microcosm setup, conceptualization, figure design, funding acquisition, project coordination, and writing of the first and final drafts of the manuscript.

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Alexandre Soares Rosado, Alejandro Reyes or Diego Javier Jiménez.

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Peña-Valencia, M.F., Robaina-Estévez, S., Custer, G.F. et al. Lignocellulose-mediated selection of potential halophilic PET-degrading enzymes from mangrove soil.
Nat Commun (2026). https://doi.org/10.1038/s41467-026-71548-z

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