ABSTRACT
Microbial mats are biofilm formations that reflect early Earth ecosystems. To investigate their microbial diversity, an indirect DNA extraction method was applied to benthic ephemeral microbial mats from Fraternidad Saltern Lagoon during rainy and dry seasons. This approach yields high molecular DNA, suitable for metabolic and diversity analysis.
KEYWORDS: microbial mats, hypersaline, metagenomics, metagenomic libraries
ANNOUNCEMENT
Microbial mats are composed of diverse communities of microorganisms that may resemble Earth’s earliest life forms (1–4). It is theorized that these ecosystems played a role in Earth’s atmosphere oxygenation, leading to oxygen-dependent life (5, 6). Molecular techniques advancements have revealed niche differentiations across microbial mat layers and their microbial communities, leading to the discovery of microbes and metabolic functions (7, 8). To assess taxonomic and metabolic diversity, we employed an indirect DNA extraction method of the benthic ephemeral hypersaline microbial mats of the Fraternidad Saltern Lagoon (FSL) enhancing DNA molecular weight recovery, facilitating functional metagenomic, and metabolic analysis (9, 10).
Benthic ephemeral mat samples were collected from the FSL, Cabo Rojo, Puerto Rico (17.98˚N, 67.21˚W) during the dry and rainy seasons (11), transported at room temperature and stored at 4°C. High-molecular-weight DNA was extracted using a modified indirect extraction protocol (12–14). Following homogenization and washing, microbial cells were embedded in low-melting-point agarose into plugs, cooled on ice, and stored at 4°C prior to cell lysis by incubating in buffer (0.01 M Tris, 0.05 M NaCl, 0.2 M EDTA [pH 8.0], 1% sarkosyl, 1% sodium deoxycholate, and 1 mg/mL lysozyme) for 3 h at 37°C. Protein digestion was performed with proteinase K (1 mg/mL) in ESP buffer (1% sarkosyl, 0.5 M EDTA [pH 8.0]) at 55°C for 24 h, followed by treatment with PMSF (phenylmethylsulfonyl fluoride; 1 mM, 2 h, room temperature) to inhibit enzymatic activity. DNA fragments exceeding 20 kb molecular weight were recovered by electrophoresis and electroelution (13, 14). Metagenomic DNA was assessed with a NanoDrop (Thermo Scientific NanoDrop Products) for quality (15), purified DNA was ligated into the fosmid vector pCC1FOS and packaged into lambda phages using Epicentre MaxPlax system and transduced into EPI300-T1R cells (16). DNA was purified with the QIAGEN Plasmid Miniprep Kit, fragmented, adapter-ligated, diluted to 4.0 nM, and sequenced on the Illumina MiSeq platform (2 × 300 bp paired-end reads, 30 cycles) with MiSeq Reagent Kit v3 (https://www.mrdnalab.com/). Total clone count was 1,400 for the rainy season and 30,000 for the dry season. 2,807,462 sequences were generated: 1,803,636 from the dry season and 1,003,826 from the rainy season. Raw reads were filtered using BBMap (bbmap.sh) with default settings and ambig=all to remove reads mapping to reference contaminants (EPI300-T1R host genome and pCC1FOS plasmid) (17) (Table 1). Data were analyzed using the National Microbiome Data Collaborative (NMDC) standardized metagenomic workflow (18), which included quality control (rqcfilter2 on BBTools v38.94), assembly (MetaSPAdes v4.0.0), gene prediction (Prodigal), taxonomic classification (Centrifuge v1.0.4), and functionally classified based on the DRAM metabolism hierarchy model (19–27). The bioinformatic workflow was processed in October 2025.
TABLE 1.
Summary of annotation, quality metrics, and coding DNA sequences (CDS) for metagenomic data sets of Fraternidad Lagoon metagenomic libraries during the dry and rainy seasons
| Season | Category | Metric | Value | Method |
|---|---|---|---|---|
| Dry | Annotation | Number of CDS | 1,443 | Prodigal v2.6.3 |
| Dry | Annotation | Total CDS length | 4,807,285 bp | Prodigal v2.6.3 |
| Dry | Annotation | Median CDS length | 657 bp | Prodigal v2.6.3 |
| Dry | Annotation | Average CDS length | 799.183 bp | Prodigal v2.6.3 |
| Dry | Annotation | Shortest CDS | 75 bp | Prodigal v2.6.3 |
| Dry | Annotation | Longest CDS | 9,075 bp | Prodigal v2.6.3 |
| Dry | Annotation | CDS length standard deviation | 609.897 bp | Prodigal v2.6.3 |
| Dry | Annotation | Predicted features | 6,014 | Prodigal v2.6.3 |
| Dry | Quality | Coding density | 89.42% | |
| Dry | Quality | Genes per Mb | 1,167.23 | |
| Dry | Quality | Sequences per Mb | 253.02 | |
| Dry | MAGs | 0 | ||
| Dry | Contig N50 | bp | 83 | |
| Rainy | Annotation | Number of CDS | 13,827 | Prodigal v2.6.3 |
| Rainy | Annotation | Total CDS length | 19,893,291 bp | Prodigal v2.6.3 |
| Rainy | Annotation | Median CDS length | 528 bp | Prodigal v2.6.3 |
| Rainy | Annotation | Average CDS length | 666.018 bp | Prodigal v2.6.3 |
| Rainy | Annotation | Shortest CDS | 75 bp | Prodigal v2.6.3 |
| Rainy | Annotation | Longest CDS | 13,632 bp | Prodigal v2.6.3 |
| Rainy | Annotation | CDS length standard deviation | 530.592 bp | Prodigal v2.6.3 |
| Rainy | Annotation | Predicted features | 29,869 | Prodigal v2.6.3 |
| Rainy | Quality | Coding density | 89.71% | |
| Rainy | Quality | Genes per Mb | 1,407.51 | |
| Rainy | Quality | Sequences per Mb | 591.7 | |
| Rainy | MAGs | 9 | ||
| Rainy | Contig N50 | bp | 1,635 |
Figure 1 analysis showed that both dry- and rainy-season mats were dominated by the genera Streptomyces (13.3% and 10.7%), Pseudomonas (8.7% and 10.3%), and Burkholderia (4.8% and 7.0%), respectively. Functional profiling indicated dry-season samples were enriched in carbon metabolism, amino acid biosynthesis, and membrane transport pathways. In contrast, rainy-season samples were characterized by cofactors biosynthesis, carbon metabolism, and amino acid biosynthesis. Although composition was similar, relative abundance between seasons' taxonomy and functional profiles might have been affected by weather changes.
Fig 1.
Taxonomic composition and functional profiles of benthic microbial mats from Fraternidad Saltern Lagoon across the dry and rainy seasons. (A) Relative abundance of the top 25 microbial genera of each benthic ephemeral microbial mat samples collected during the dry and rainy seasons. (B and C) Top 20 KEGG pathways identified from metagenomic data obtained during the dry (B) and rainy (C) seasons. Functional annotation was based on KEGG orthology (KO) assignments, and pathway abundance is expressed as percentages of the total counts.
ACKNOWLEDGMENTS
This work was supported by the Microbial Observatory (grant MCB-0455620) from the National Science Foundation, and the United States Department of Agriculture (USDA - CSREES 2007-02386-18042). In addition, Luis R. Morales-Valle helped to upload the sequences on the NCBI database.
Contributor Information
Carlos Rios-Velazquez, Email: carlos.rios5@upr.edu.
Frank J. Stewart, Montana State University, Bozeman, Montana, USA
DATA AVAILABILITY
Under BioProject PRJNA1357061, sequence raw archives (SRA) are available under accessions SRS26982951 (Fraternidad Dry) and SRS26982952 (Fraternidad Rain). Corresponding assembled metagenomes are available under BioSample accessions SAMN53094954 (Dry Season) and SAMN53094955 (Rainy Season). Additionally, assembly, annotation, raw reads, and MAGs information (quality and sequences) are public in the Zenodo repository under the following link: https://doi.org/10.5281/zenodo.18644825.
REFERENCES
- 1. Walter MR, Buick R, Dunlop JSR. 1980. Stromatolites 3,400–3,500 Myr old from the North Pole area, Western Australia. Nature 284:443–445. doi: 10.1038/284443a0 [DOI] [Google Scholar]
- 2. Allen MA, Goh F, Burns BP, Neilan BA. 2009. Bacterial, archaeal and eukaryotic diversity of smooth and pustular microbial mat communities in the hypersaline lagoon of Shark Bay. Geobiology 7:82–96. doi: 10.1111/j.1472-4669.2008.00187.x [DOI] [PubMed] [Google Scholar]
- 3. Ruvindy R, White RA 3rd, Neilan BA, Burns BP. 2016. Unravelling core microbial metabolisms in the hypersaline microbial mats of Shark Bay using high-throughput metagenomics. ISME J 10:183–196. doi: 10.1038/ismej.2015.87 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Spring S, Brinkmann N, Murrja M, Spröer C, Reitner J, Klenk HP. 2015. High diversity of culturable prokaryotes in a lithifying hypersaline microbial mat. Geomicrobiol J 32:332–346. doi: 10.1080/01490451.2014.913095 [DOI] [Google Scholar]
- 5. Hoehler TM, Bebout BM, Des Marais DJ. 2001. The role of microbial mats in the production of reduced gases on the early Earth. Nature 412:324–327. doi: 10.1038/35085554 [DOI] [PubMed] [Google Scholar]
- 6. Des Marais DJ. 2003. Biogeochemistry of hypersaline microbial mats illustrates the dynamics of modern microbial ecosystems and the early evolution of the biosphere. Biol Bull 204:160–167. doi: 10.2307/1543552 [DOI] [PubMed] [Google Scholar]
- 7. Berg IA, Kockelkorn D, Buckel W, Fuchs G. 2007. A 3-hydroxypropionate/4-hydroxybutyrate autotrophic carbon dioxide assimilation pathway in archaea. Science 318:1782–1786. doi: 10.1126/science.1149976 [DOI] [PubMed] [Google Scholar]
- 8. Rinke C, Schwientek P, Sczyrba A, Ivanova NN, Anderson IJ, Cheng J-F, Darling A, Malfatti S, Swan BK, Gies EA, Dodsworth JA, Hedlund BP, Tsiamis G, Sievert SM, Liu W-T, Eisen JA, Hallam SJ, Kyrpides NC, Stepanauskas R, Rubin EM, Hugenholtz P, Woyke T. 2013. Insights into the phylogeny and coding potential of microbial dark matter. Nature 499:431–437. doi: 10.1038/nature12352 [DOI] [PubMed] [Google Scholar]
- 9. Berry AE, Chiocchini C, Selby T, Sosio M, Wellington EMH. 2003. Isolation of high molecular weight DNA from soil for cloning into BAC vectors. FEMS Microbiol Lett 223:15–20. doi: 10.1016/S0378-1097(03)00248-9 [DOI] [PubMed] [Google Scholar]
- 10. Lam KN, Cheng J, Engel K, Neufeld JD, Charles TC. 2015. Recursos actuales y futuros para la metagenómica funcional. Front Microbiol 6. doi: 10.3389/fmicb.2015.01196 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Torres-Zapata I. 2012. Generation of large insert metagenomic libraries from subtropical hypersaline microbial mats and their screening for antibiotic resistance. University of Puerto Rico Institutional Repository. Available from: https://hdl.handle.net/20.500.11801/655 [Google Scholar]
- 12. Casillas-Martinez L, Gonzalez ML, Fuentes-Figueroa Z, Castro CM, Nieves-Mendez D, Hernandez C, Ramirez W, Sytsma RE, Perez-Jimenez J, Visscher PT. 2005. Community structure, geochemical characteristics and mineralogy of a hypersaline microbial mat, cabo rojo, PR. Geomicrobiol J 22:269–281. doi: 10.1080/01490450500182672 [DOI] [Google Scholar]
- 13. Liles MR, Williamson LL, Rodbumrer J, Torsvik V, Goodman RM, Handelsman J. 2008. Recovery, purification, and cloning of high-molecular-weight DNA from soil microorganisms. Appl Environ Microbiol 74:3302–3305. doi: 10.1128/AEM.02630-07 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Torres-Zapata I, Gonzalez-Montalvo A, Castro-Ruiz C, Rios-Velazquez C. 2010. Generation of large insert metagenomic libraries using indirect DNA extraction methods from benthic and ephemeral tropical hypersaline microbial mats, p 1569–1575. In Méndez-Vilas A (ed), Current research, technology and education topics in applied microbiology and microbial biotechnology. Formatex Research Center. [Google Scholar]
- 15. Desjardins P, Conklin D. 2010. NanoDrop microvolume quantitation of nucleic acids. J Vis Exp PMCID:2565. doi: 10.3791/2565 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Rodriguez-Reyes MG, Rios-Velazquez C. 2021. Stool microbiome dataset of the critically endangered Puerto Rican parrot (Amazona vittata). Data Brief 37:107175. doi: 10.1016/j.dib.2021.107175 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Bushnell Brian. 2014. BBMap: a fast, accurate. Splice-Aware Aligner. [Google Scholar]
- 18. Eloe-Fadrosh EA, Ahmed F, Anubhav MB, Baumes J, Borkum M, Bramer L, Canon S, Christianson DS, Corilo YE, Davenport KW. 2022. The national microbiome data collaborative data portal: an integrated multi-omics microbiome data resource. Edited by Wood-Charlson Elisha M., Xu Yan, Chain Patrick S. G., McCue Lee Ann, Mans Douglas, Mungall Christopher J., Mouncey Nigel J., and Fagnan Kjiersten. Nucleic Acids Res 50:D828–D836. doi: 10.1093/nar/gkab990 [DOI] [Google Scholar]
- 19. Bushnell B. 2014. BBMap: a fast, accurate, splice-aware aligner, Lawrence Berkeley National Laboratory. https://escholarship.org/uc/item/1h3515gn. [Google Scholar]
- 20. Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. 2017. metaSPAdes: a new versatile metagenomic assembler. Genome Res 27:824–834. doi: 10.1101/gr.213959.116 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Chan PP. 2019. tRNAscan-SE: searching for tRNA genes in. Edited by Kollmar M.. Gene Prediction 1962:1–14. doi: 10.1007/978-1-4939-9173-0_1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Griffiths-Jones S, Moxon S, Marshall M, Khanna A, Eddy SR, Bateman A. 2005. Rfam: annotating non-coding RNAs in complete genomes. Nucleic Acids Res 33:D121–4. doi: 10.1093/nar/gki081 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Bland C, Ramsey TL, Sabree F, Lowe M, Brown K, Kyrpides NC, Hugenholtz P. 2007. CRISPR recognition tool (CRT): a tool for automatic detection of clustered regularly interspaced palindromic repeats. BMC Bioinformatics 8:209. doi: 10.1186/1471-2105-8-209 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Hyatt D, Chen G-L, Locascio PF, Land ML, Larimer FW, Hauser LJ. 2010. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11:119. doi: 10.1186/1471-2105-11-119 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Besemer J, Lomsadze A, Borodovsky M. 2001. GeneMarkS: A self-training method for prediction of gene starts in microbial genomes. Nucleic Acids Res 29:2607–2618. doi: 10.1093/nar/29.12.2607 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Kim D, Song L, Breitwieser FP, Salzberg SL. 2016. Centrifuge: rapid and sensitive classification of metagenomic sequences. Genome Res 26:1721–1729. doi: 10.1101/gr.210641.116 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Shaffer M, Borton MA, McGivern BB, Zayed AA, La Rosa SL, Solden LM, Liu P, Narrowe AB, Rodríguez-Ramos J, Bolduc B, Gazitúa MC, Daly RA, Smith GJ, Vik DR, Pope PB, Sullivan MB, Roux S, Wrighton KC. 2020. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res 48:8883–8900. doi: 10.1093/nar/gkaa621 [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
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Data Availability Statement
Under BioProject PRJNA1357061, sequence raw archives (SRA) are available under accessions SRS26982951 (Fraternidad Dry) and SRS26982952 (Fraternidad Rain). Corresponding assembled metagenomes are available under BioSample accessions SAMN53094954 (Dry Season) and SAMN53094955 (Rainy Season). Additionally, assembly, annotation, raw reads, and MAGs information (quality and sequences) are public in the Zenodo repository under the following link: https://doi.org/10.5281/zenodo.18644825.