Joshua Talavera Veluz ^1* Paul Christian Tantengco Gloria ^1,2 Laurence Anthony Navarro Mallari ^1,2 Ann Elizabeth Rollon Enova ³ Maria Auxilia Tan Siringan ¹

• ¹ Natural Sciences Research Institute, University of the Philippines Diliman, Quezon City, Philippines
• ² Institute of Biology, College of Science, University of the Philippines Diliman, Quezon, National Capital Region, Philippines
• ³ Marine Science Institute, University of the Philippines Diliman, Quezon City, Philippines

Submarine groundwater discharges (SGDs) are conduits linking the marine and terrestrial environments allowing groundwater to flow from land into the ocean through continental margins (George et al., 2021). SGD-associated sites are recognized as biogeochemical hotspots, driven by diverse microorganisms that are influenced by fluctuating environmental conditionsSGD-associated sites are known as biogeochemical hotspots where diverse microorganisms are major drivers and are influenced by fluctuating environmental conditions (Ruiz-González et al., 2021). Exploring these marine microorganisms is vital for understanding their ecological significance, contribution to environmental health (Adyasari et al., 2020), and potential biotechnological applications (Fenical, 2020;Shi et al., 2024).In Mabini, Batangas, Philippines, SGD-influenced sites contained sedimentary rocks covered by microbial mats. Microbial mats are multi-layered and self-sustaining microbial communities typically thriving in aqueous environments (Rabus et al., 2015;Stal & Noffke, 2011). Mat-associated microbiota may play key roles in cycling essential elements such as carbon, hydrogen, nitrogen, oxygen, phosphorus, and sulfur and consequently, in ecosystem functioning in the marine environment (Glockner et al., 2012;Parkes et al., 2014). These marine microorganisms are potential sources of bioactive metabolites (Jensen et al., 2005;Magarvey et al., 2004), offering promising biotechnological, environmental, and pharmaceutical applications. Notable examples include Salinosporamide A and Didemnin B, which exhibit anticancer activity, Anthracimycin and Marinopyrrole A, identified as antibiotics, and Cyclomarin A, known for its anti-inflammatory properties (Fenical, 2020). A review by Shi et al. (2024) further underscores the diversity of bioactive compounds from marine microbes, highlighting their antibacterial, antiviral, antimalarial, anticancer, anti-inflammatory, and antibiofilm activities.This data report presents a preliminary description of the diversity and functional profiles of marine microorganisms inhabiting the microbial mats of an SGD-influenced site in Mabini, Batangas, Philippines, as revealed through shotgun metagenomics.This study employs shotgun metagenomics to explore the diversity and functional profiles of the marine microorganisms present in the microbial mats of an SGD-influenced site in Mabini, Batangas, Philippines. Using the metagenome data, metagenomeassembled genomes (MAGs) were extracted. Assembling genomes from metagenomic data, though putative, is a culture-independent approach that provides comprehensive and valuable insights into microbial diversity and functional potential (Wilkins et al., 2019). This approach enables the recovery of previously inaccessible genetic information from uncultivable microbes in SGD environments in the Philippines.The paper presents the functional and structural metrics, identities, genes linked to nutrient metabolism, and BGCs of these MAGs. To the best of our knowledge, this is the first documentation of MAGs from a mat-associated microbial community in an SGD-influenced site in the Philippines. It addresses the dearth of data and knowledge on the microbial dimensions of SGD areas in the country and provides a baseline for future research using experimental or targeted approaches. Furthermore, investigating microbial mats in specific marine ecosystems can also offer valuable insights into climate change conditions, such as ocean acidification and warming (Mazière et al., 2023).To the best of our knowledge, this marks the first documentation of MAGs from a mat-associated microbial community in an SGD-influenced site in the Philippines, thereby contributing novel insights into the microbial ecology of these unique environments. 2 transported to the Microbiological Research Services Laboratory, Natural Sciences Research Institute, at the University of the Philippines -Diliman, Quezon City. Upon arrival, the samples were stored in an ultra-low freezer (NuAire, USA) set at <-80°C until required for processing. The samples collected from the Acacia site were pooled for DNA extraction and processed as a single composite sample.The total DNA was extracted from the microbial mats that were subsampled into four separate microcentrifuge tubes, each containing approximately 250 mg of the material. The extraction was carried out with the DNeasy PowerSoil® Pro Kit (QIAGEN, Netherlands), following the manufacturer's protocol with minimal modifications. Lysis was performed individually on all four subsamples, and the resulting lysates were pooled to achieve a final DNA concentration of >100 ng/µL. All subsequent procedures were carried out according to the manufacturer's protocol. The use of four subsamples was based on previous extractions from microbial mats where this approach consistently yielded sufficient DNA concentrations. DNA concentration was assessed using Denovix's dsDNA Broad Range kit (DeNovix, USA) and fluorometer following the manufacturer's protocol. The extracts were then sent to Macrogen, Inc. (South Korea) for shotgun metagenomic sequencing using the NovaSeq™ 6000 system with a throughput of 25 Gb at 150 bp paired-end setting.Total DNA was extracted from microbial mats, weighing about 250 mg, using DNeasy PowerSoil® Pro Kit (QIAGEN, Netherlands), following the manufacturer's protocol with minimal modifications. DNA concentration was assessed using Denovix's dsDNA Broad Range kit (DeNovix, USA) and fluorometer following the manufacturer's protocol.Extracts, with a concentration of >100 ng/µL were sent to Macrogen, Inc. (South Korea) for shotgun metagenomics sequencing using the NovaSeq™ 6000 system with a throughput of 25Gb at 150 bp pair-end setting.Upon receipt of raw reads, the forward and reverse sequences underwent merging, trimming, assembly, and analysis, employing various bioinformatics tools, using KBase v2.7.11 (Arkin et al., 2018). The forward and reverse reads were interleaved during the importing stage and the interleaved reads were subsequently subjected to Trimmomatic v0.36 (Bolger et al., 2014) with a sliding window size of 4 to minimum quality of 30.The Q30-trimmed reads were assembled using metaSPAdes v3.15.3 (Nurk et al., 2017;Prjibelski et al., 2020), MEGAHIT v.1.2.9 (Li et al., 2015), and IDBA-UD v.1.1.3 (Peng et al., 2012) with a minimum contig length of 2,000 bp. Quality Assessment Tool (QUAST) v.5.2.0 (Mikheenko et al., 2018) was used to evaluate the assemblies, and the detailed results can be found in Supplementary File 1. Among the three assemblies, metaSPAdes produced the longest assembly length at 250,173,012 bp, surpassing MEGAHIT by 60,524,281 bp and IDBA-UD by 132,767,228 bp, leading to its selection for assembling MAGs.The metaSPAdes assembled reads were subjected to CONCOCT v1.1 (Alneberg et al., 2014), MaxBin2 v2.2.4 (Wu et al., 2016), and MetaBAT2 Contig Binning v1.7 (Kang et al., 2019) to cluster the assembled metagenomic sequences to putative genomes known as bins. All bins from the three different binning tools were optimized using the DAS tool v1.1.2, with DIAMOND as the gene identification tool (Sieber et al., 2018). The tool employed a score threshold of 0.5, a duplicate penalty of 0.6, and a megabin penalty of 0.5. The optimized bins were then filtered using CheckM v1.0.18 (Parks et al., 2015) to completeness and contamination scores of at least 90 and 5, respectively.The filtered bins were extracted using MetagenomeUtils v1.1.1 (Arkin et al., 2018). Following extraction, these bins underwent taxonomic identification using the Genome Taxonomy Database (GTDB) toolkit v2.3.2 (Chivian et al., 2022). To further elucidate the relationships between the bins and other available GenBank genomes, a phylogenetic tree was reconstructed using SpeciesTreeBuilder v0.1.4 (Arkin et al., 2018). This tool leverages 49 clusters of orthologous groups to estimate relatedness and determine the nearest existing genomes to the bin set. The outgroup Thermoplasma acidophilum DSM 1728 (GCF 000195915.1), an archaean, was also included in the tree for reference.All genomes were annotated using the Rapid Annotation using Subsystem Technology (RAST) through SEED Viewer v2.0 (Overbeek et al., 2013). The focus of this study was on genes related to nitrogen, phosphorus, potassium, iron acquisition and metabolism, and sulfur metabolism, as these pathways are crucial for growth, survival, and biogeochemical cycles.Finally, biosynthetic gene clusters (BGCs) were identified using antiSMASH v7.0 (Blin et al., 2023) and PRISM 4 (Skinnider et al., 2020). This data report provides an initial overview of predicted biosynthetic gene clusters (BGCs) associated with putative microbes in a microbial mat influenced by submarine groundwater discharge (SGD) in the Philippines. The default settings of antiSMASH and PRISM 4 were used for BGC mining across all 17 MAGs. AntiSMASH was recognized to have a prediction accuracy of 97.7% (Medema et al., 2011) and PRISM 4 demonstrates a known prediction accuracy of 96% (Skinnider et al., 2020). All predicted BGCs were cataloged based on their types, regardless of their similarity scores with existing BGCs in antiSMASH and PRISM databases. This analysis was applied specifically to the bins to highlight the potential of these putative marine microorganisms as sources of various natural products. As this is a preliminary analysis, no experimental data confirming the expression of these BGCs is provided; the findings are intended solely as a baseline reference. A. Data The assembly characteristics of the seventeen optimized and filtered bins extracted from the Q30trimmed metaSPAdes assembly are presented in Figure 1A and Supplementary File 2. Extracted MAGs have CheckM completeness and contamination percentages of >90% and <5%, respectively following the standards on the minimum information about a metagenome-assembled genome (MIMAG) of Bowers et al. (2017). The analysis revealed that bins 017, 036, and 049 stand out with CheckM completeness scores exceeding 98%, with bin 049 achieving a completeness score of 100%. This indicates that these assembled genomes encompass a highly substantial portion of the marker genes required to define their positions within reference genomes (Parks et al., 2015). Moreover, bins 012, 035, and 039 exhibit 0% CheckM contamination, signifying minimal to no redundancy of marker genes, which are typically present as single copies within a genome (Parks et al., 2015). Additionally, Benchmarking Universal Single-Copy Orthologs (BUSCO) v5.4.6 (Simão et al., 2015) The identities of the 17 MAGs are presented in Figure 1B. Through GTDB, all MAGs were determined to be under the Domain Bacteria and classified into three phyla, namely Pseudomonadota (12 bins), Bacteroidota (4 bins), and Planctomycetota (1 bin). The presence of three bacterial phyla-Pseudomonadota (syn. Proteobacteria), Bacteroidota (syn. Bacteroidetes), and Planctomycetota (syn.Planctomycetes)-was confirmed in the metagenome Q30-trimmed reads using both Kaiju v.1.3.4 (Menzel et al., 2016) and GOTTCHA2 v.0.0.7 (Freitas et al., 2015) analyses (Supplementary 6).According to Kaiju, Pseudomonadota, Bacteroidota (part of the FCB group), and Planctomycetota (part of the PVC group) comprised approximately 68%, 16%, and 8% of the detected bacterial population, respectively. In contrast, GOTTCHA2 detected only Pseudomonadota and Bacteroidota, which were estimated to constitute 74% and 10% of the bacterial community in the metagenome reads, (Jain et al., 2018). Such low ANI values, along with the absence of ANI placements for the other bins in the GTDB reference database (Supplementary File 3), suggest that these genomes may not have close representatives in GTDB. These observations may also highlight the potential novelty of these genomes, offering valuable insights into both characterized and yet-to-bediscovered microbes. It expands our understanding of microbial diversity in SGD ecosystems. A phylogenetic tree was reconstructed incorporating existing GenBank genome sequences.Additionally, genes involved in the metabolism of nitrogen, phosphorus, potassium, and sulfur and genes related to iron acquisition and metabolism were identified. The gene counts for all genomes are presented in Figure 2, while the specific roles in each nutrient metabolism are detailed in Supplementary File 4.Among the 17 bins, 16 exhibited genes linked to nitrogen metabolism, with ammonia assimilation genes being the most prevalent among them (Supplementary File 4 Figure 1). Most GenBank genome sequences analyzed also contained genes related to nitrogen metabolism, with Litoreibacter albidus DSM 26922 exhibiting the highest number among them (Figure 2). L. albidus DSM 26922 formed a clade near bin 012 (Brevirhabdus sp.) with a bootstrap value of 1.0 and bin 039 (Planktomarina sp.)with a bootstrap value of 0.99 (Figure 2).Among the 17 bins, 16 exhibited genes linked to nitrogen metabolism, particularly those involved in nitrogen fixation, denitrification, nitrate and nitrite ammonification, nitrogen fixation, nitrosative stress, and ammonia assimilation (Supplementary File 4 Figure 1). Genes linked to ammonia assimilation were determined to be present in 14 out of 17 bins, underscoring their prevalence among the nitrogen metabolism-related genes. Notably, bin 010, identified as Marinibacterium sp., exhibited the highest number of nitrogen metabolism-related genes, totaling 38. Additionally, most GenBank genome sequences analyzed also contained genes related to nitrogen metabolism, with Litoreibacter albidus DSM 26922 exhibiting the highest number among them. These genes perform similar roles previously discussed, with additional genes for amidase clustered with urea and nitrile hydratase functions.Notably, L. albidus DSM 26922 formed a clade near bin 012 (Brevirhabdus sp.) with a bootstrap value of 1.0 and bin 039 (Planktomarina sp.) with a bootstrap value of 0.99 (Figure 2). Lastly, the outgroup T.acidophilum DSM 1728 showed no genes related to nitrogen metabolism.All bins contained genes associated with phosphorus metabolism, with the polyphosphate-related genes being the most prevalent (Supplementary File 4 Figure 2). Similarly, all GenBank genome sequences analyzed contained genes related to phosphorus metabolism (Supplementary File 4 Figure 2). Among these, Aestuariivita boseongensis BS-B2 had the most abundant phosphorus metabolism genes and formed a clade with bin 010 (Marinibacterium sp.) with a bootstrap value of 1.00 (Figure 2).All bins contained genes associated with phosphorus metabolism, such as those involved in high-affinity phosphate transporter and phosphate regulon, phosphate metabolism, and polyphosphate synthesis (Supplementary File 4 Figure 2). Polyphosphate-related genes were observed in all bins. Moreover, bin 005 (Ruegeria sp.) showcased the highest number of genes for phosphorus metabolism, with a total of 27 genes. Similarly, all GenBank genome sequences analyzed contained genes related to phosphorus metabolism, performing similar roles discussed above. Among these, Aestuariivita boseongensis BS-B2 had the most abundant phosphorus metabolism genes and formed a clade with bin 010 with a bootstrap value of 1.00 (Figure 2).Genes related to potassium metabolism, specifically potassium homeostasis-related genes, were identified in all bins (Supplementary File 4 Figure 3). A similar trend was observed in all analyzed GenBank genomes (Supplementary File 4 Figure 3). Notably, Psychroserpens mesophilus JCM 13413 had the highest potassium metabolism genes (Figure 2). Phylogenetically, this genome was distant from other bins, with the closest bin being bin 059, identified within the family Flavobacteriaceae (Figure 2).Genes related to potassium metabolism, specifically potassium homeostasis-related genes, were identified in all bins (Supplementary File 4 Figure 3). Bins 005 (Ruegeria sp.) and 036 (UWMA-0217) displayed the most abundant genes associated with potassium metabolism, totaling nine. Similarly, all GenBank genome sequences exhibited genes associated with potassium homeostasis. Among these, Psychroserpens mesophilus JCM 13413 had the highest potassium metabolism genes. This genome Formatted: Font: 11 pt, Not Bold, Font color: Black was phylogenetically distant from other bins, with the closest bin being bin 059, identified within the family Flavobacteriaceae (Figure 2).All bins demonstrated the presence of genes involved in sulfur metabolism, with thioredoxin-disulfide reductase genes being the most prevalent (Supplementary File 4 Figure 4). A similar pattern was seen in the GenBank genome sequences (Supplementary File 4 Figure 4). Hyunsoonleella jejuensis DSM 21035 had the highest number of sulfur metabolism genes and bin 059, under Flavobacteriaceae family, was determined to be the closest bin (Figure 2).All bins demonstrated the presence of genes involved in sulfur metabolism (Supplementary File 4 Figure 4). These sulfur metabolism genes involved genes linked to galactosylceramide and sulfatide metabolism, sulfate assimilation-related cluster, sulfite reduction-associated complex DsrMKJOP and co-clustering genes, and thioredoxin-disulfide reductase. Genes related to thioredoxin-disulfide reductase were identified in 15 bins, indicating its prevalence among the sulfur metabolism genes. Bin 005 stood out by displaying the most abundant sulfur metabolism-related genes, totaling 11. A similar pattern was observed in GenBank genome sequences, with genomes exhibiting similar genes.Hyunsoonleella jejuensis DSM 21035 exhibited the highest number of sulfur metabolism genes and bin 059 was determined to be the closest bin.Only six bins were determined to possess genes associated with iron acquisition and metabolism (Figure 2). Of these, five bins exhibited iron acquisition genes similar to Streptococcus, showcasing its prevalence (Supplementary File 4 Figure 5). Similarly, only a few GenBank genome sequences exhibited genes associated with these functions (Supplementary File 4 Figure 5). Among these, L.albidus DSM 26922 and A. boseongensis BS-B2 exhibited the highest number of iron acquisition and metabolism-related genes, with bins 010 (Marinibacterium sp.), 012 (Brevirhabdus sp.), and 039 (Planktomarina sp.) identified to be the closest bin (Figure 2).Only 6 bins, namely bins 005, 010, 012, 017 (JANTGD01), 039, and 045 (Sedimenticolaceae), were determined to possess genes associated with iron acquisition and metabolism (Supplementary File 4 Figure 5). These genes included those associated with iron acquisition genes, hemin transport system, and encapsulating proteins for DyP-type peroxidase and ferritin-like protein oligomers. Five of these bins exhibited iron acquisition genes similar to Streptococcus, showcasing its prevalence among the iron acquisition and metabolism genes. Remarkably, bin 005, once again, displayed the highest number of such genes, totaling six. Similarly, a few GenBank genome sequences exhibited genes related to iron acquisition and metabolism, namely A. boseongensis BS-B2, Gaetbulibacter saemankumensis DSM17032, L. albidus DSM 26922, Nereida ignava CECT 5292, and Planktomarina temperata RCA23.A boseongensis BS-B2 formed a clade with bin 010 (bootstrap value 1.00), and P. temperata RCA23 formed a clade with bin 039 (bootstrap value 1.00). Additionally, the clade of L. albidus DSM 26922 and N. ignava CECT was determined to be near bins 012 (bootstrap value 1.00) and 039 (bootstrap value 0.99). All of these bins exhibited iron acquisition and metabolism-related genes similar to those in the GenBank genome sequences they clustered with or near. Bins 005 and 010 had the most BGCs, particularly for acyl homoserine and polyketides, with bin 005 also containing ectoine. No BGCs were found in bins 008, 029 (Lutibacter sp.), and 035 (UBA1924). All bins contained genes related to phosphorus, potassium, and sulfur metabolism, with most also possessing genes for nitrogen metabolism. However, only six bins had genes for iron metabolism and acquisition. These genes suggest a significant role for these microorganisms in cycling phosphorus, potassium, sulfur, nitrogen, and iron at the SGD-influenced site. Notably prevalent were genes involved in ammonia assimilation (nitrogen), polyphosphate (phosphorus), potassium homeostasis (potassium), thioredoxin-disulfide reductase (sulfur), and Streptococcus iron acquisition (iron acquisition and metabolism).Ammonia assimilation is a pivotal component of the nitrogen cycle wherein ammonia is incorporated into organic compounds, that can be utilized by living organisms for survival and growth (Wright & Lehtovirta-Morley, 2023). Polyphosphate is a biopolymer implicated in cellular functions such as antibiotic resistance, biofilm formation, cell cycle control, energy storage, motility, stress response, and virulence (Akbari et al., 2021;Pokhrel et al., 2019). Potassium homeostasis is vital in adjusting membrane potential and electrical signaling, activating enzymes, maintaining pH levels, regulating osmotic pressure, and synthesizing proteins in bacteria (Stautz et al., 2021). The genes encoding potassium channels and transporters are essential, as they facilitate these critical functions. The bacterial enzyme thioredoxin-disulfide reductase is known to be involved in colonization, stress response, namely oxygen and disulfide stress, and virulence (Felix et al., 2021). Lastly, according to the study of Ge & Sun (2014), iron acquisition genes observed in Streptococcus are crucial for iron uptake, which are essential for activating oxygen, amino acid & nucleoside production, and electron transport that may impact the bacteria's survival and virulence. Similarly, putative marine microbes harboring comparable genes may also perform similar functions. The prevalence of the aforementioned genes may be influenced by the nutrients present in the water emitted by the SGD vents in Acacia, to which the microbial mats are continuously exposed.The nutrient levels in the SGD vent water were analyzed, revealing the following average concentrations: 0.13 µM nitrite, 1.77 µM ammonium, 7.76 µM nitrate, 110.45 µM total dissolved nitrogen (TDN), 9.66 µM dissolved inorganic nitrogen (DIN), and 100.78 µM dissolved organic nitrogen (DON).For phosphorus compounds, the average concentrations were 0.04 µM phosphate, 0.29 µM total dissolved phosphorus (TDP), and 0.25 µM dissolved organic phosphorus (DOP). Additionally, an iron concentration of 0.03 ppm was detected. The nutrient data are also detailed in Supplementary File 7.The varying levels of different nitrogen forms in the SGD vent water may indicate an environment where nitrogen cycling and various nitrogen-utilization pathways are crucial for the survival of the putative microbes in the microbial mats. This observation aligns with the detection of diverse nitrogenmetabolism genes across the MAGs. Additionally, the relatively low average concentrations of both phosphorus and iron may explain the prevalence of polyphosphate-related and iron acquisition-related genes among the MAGs. These genes enable microbes to store phosphate as polyphosphate, which can later serve as an energy source-an advantageous trait in environments with limited phosphate availability (Achbergerová and Nahalka, 2011). The ability to concentrate and store nutrients within the matrix is critical, as these microbes are anchored to surfaces, like rocks, and cannot relocate to nutrientrich areas. Similarly, the presence of iron acquisition genes, similar to those found in Streptococcus Formatted: Font: 11 pt, Not Bold, Font color: Black species, suggests adaptation to environments with restricted free iron. These genes are known to be utilized by Streptococcus in low-iron conditions (Ge et al., 2009).Based on the obtained nutrient and genomic data, the prevalence of specific genes, such as those involved in polyphosphate and iron acquisition, appears to correlate with the phosphorus-and ironlimited conditions of the SGD vent water. These genes, along with those associated with potassium homeostasis and thioredoxin-disulfide, supported by their known roles in growth and survival, are likely essential for the putative marine microbes to thrive in an SGD-influenced area. Additionally, the presence of various nitrogen metabolism genes across all MAGS suggests that these putative microbes may play a role in nitrogen cycling, supporting other marine life (Hunter-Cevera et al., 2005) and contributing to biogeochemical processes in this environment.The genes involved in the aforementioned functions likely play a role in the growth and survival of the putative marine microorganisms in this study. These predicted genes also reveal that these microbes are potential contributors to various biogeochemical cycles. As such, these microbes may significantly influence the survival of other marine life forms that rely on the nutrients cycled by these marine microorganisms [18].The GenBank genomes with the highest number of nutrient metabolism genes identified in the phylogenetic tree (Figure 2 (Gavriilidou et al., 2020;Pujalte et al., 2014;Riedel et al., 2013).In Figure 2, bins 010 (Marinibacterium sp.) and 039 (Planktomarina sp.) formed monophyletic clades with A. boseongensis BS-B2 (Park et al., 2014) and P. temperata RCA23 (Giebel et al., 2013), respectively, each with a bootstrap value of 1.00. Notably, bin 10 and A. boseongensis BS-B2, both under Rhodobacteraceae, share similar genome sizes (~4.0 Mbp and ~3.9 Mbp, respectively) and G+C content of 62.2% and 63.6%, respectively (Park et al., 2014;Figure 1B; Supplementary File 2). Similarly, bin 39 and P. temperata RCA23 share the same genus, with genome sizes of ~2.2 Mbp and ~3.3 Mbp, and G+C content of 52.0% and 53.5%, respectively (Giebel et al., 2013;Figure 1B; Supplementary File 2).The high bootstrap values for these bins indicate a significant genomic similarity with the referenced GenBank genomes. Given these families are involved in ocean nutrient cycling, it is likely that bins 010 and 039 also contribute to nutrient cycling processes within the SGD-influenced environment of Acacia, Mabini, Batangas.In the case of the GenBank genomes, it is noteworthy that those with the highest abundance of genes involved in various nutrient metabolism processes formed distinct clades or clustered very closely with some of the generated MAGs. Specifically, bins 10 and 39 formed a monophyletic clade with A. Carrión et al., 2023;Voget et al., 2015;Giebel et al., 2019).The different capabilities of the microbes discussed above and their roles in nutrient cycling suggest that the generated MAGs, which form a clade with or are closely related to these GenBank genomes, may also exhibit similar functional roles. Notably, these microorganisms thrive in marine environments, supporting the fact that the generated MAGs indeed inhabit a marine ecosystem.In addition to nutrient metabolism genes, BGCs were also identified in the majority of the MAGs. BGCs are groups of two or more neighboring genes that are encoded together to produce specific secondary metabolites (Medema et al., 2015). For BGCs, RiPP-like BGCs were determined to be the most prevalent class detected in the bins. This class, previously recognized as bacteriocin-encoding genes (Blin et al., 2021), has been extensively studied in relation to their anticancer, antibiotics, and biopreservative potential (Negash & Tsehai, 2020;Thapar & Salooja, 2023). In PRISM, BGCs associated with polyketides stood out to be the most abundant BGC among the assembled genomes.Polyketides are known to have promising applications in the field of medicine, such as antibiotics, immunosuppressants, and anticancer agents (Sanchez & Demain, 2011;Zhang & Liu, 2016), and biotechnology, such as hydrocarbon biofuels (Gayen, 2022). In an ecological context, the presence or prevalence of these types of BGCs likely represents a survival strategy by producing secondary metabolites that inhibit the growth of rival microorganisms, as highlighted by Chen et al., (2020). This prevalence may also be associated with adaptation to nutrient-limited environments, where competition for resources drives the need for such defensive mechanisms.The identified BGCs in the MAGs not only provide a competitive advantage to the putative marine microbes but also are known to exhibit several health-related benefits, such as antimicrobial activity and cytotoxic properties (Kwon & Hovde, 2024). Although these microbes are often unculturable, BGCs could be harnessed for metabolite production using a molecular approach. For instance, BGCs can be synthesized and expressed in culturable hosts (Lin et al. 2020), potentially generating not only the intended metabolites but also novel variants. Nguyen et al. (2022) demonstrated this by co-expressing RiPP BGCs in Escherichia coli, yielding diverse metabolite forms. Such strategies can be applied to the BGCs identified in MAGs from SGD microbial mats in Acacia, Mabini, Batangas, opening avenues for future discoveries in natural products and synthetic biology, with promising implications for advancements in medicine and biotechnology. Formatted: Space Before: 0 pt