Biosynthetic gene clusters with biotechnological applications in novel Antarctic isolates from 
Actinomycetota have attracted increasing scientific interest in the vast field of microorganisms owing to their importance in the production of biologically and pharmaceutically relevant natural products (Matsumoto and Takahashi ; Albarano et al. ). Natural products derived from microorganisms have a broad spectrum of biological activities, making them promising candidates for the development of new drugs. However, the discovery of new bioactive compounds poses a great challenge to the scientific community due to the need to identify new sources of bioactive compounds with antimicrobial potential (Kumar et al. ; Núñez-Montero et al. ). Within the phylum Actinomycetota , Micrococcaceae plays a key role in the synthesis of bioactive compounds with a wide range of therapeutic properties. Exploration of these microorganisms and their natural products has opened new perspectives to address public health challenges (Borker et al. ; Núñez-Montero et al. ), such as the increasing antimicrobial resistance observed in pathogenic species. Antarctica and its extreme environment have emerged as promising sources for the discovery of extraordinary taxa (Benaud et al. ) and the exploration of novel natural products (Silva et al. ). The extreme environmental conditions present, such as low temperatures, high radiation, and nutrient scarcity, have driven the development of unique adaptations in Antarctic microorganisms, contributing to richer biodiversity and high potential for bioactive compound discovery (Gummerlich et al. ). Actinomycetota , in particular, has been the subject of great attention in Antarctica because of their ability to produce secondary metabolites with pharmacological and biotechnological properties (Oyedoh et al. ) (Reis-Mansur et al. ). In recent years, Antarctic bacteria have been found to produce plant growth promoters, pigments, bioremediation agents, bioactive compounds, antibiofilm agents, nanoparticles, and enzymes for the food industry (Styczynski et al. ; Ramasamy et al. ). On the other hand, advancements in DNA sequencing technology have revolutionized the study of microorganisms and their biosynthetic capabilities (Singh et al. ). Next-generation sequencing and related technologies have significantly accelerated and improved access to genetic information on microorganisms (Scherlach and Hertweck ), aiding the swift and accurate identification of biosynthetic gene clusters responsible for the production of natural products. These bioinformatics tools play a crucial role in the discovery (Tizabi et al. ) and characterization of new bioactive compounds (Albarano et al. ; Benaud et al. ) as well as in understanding the underlying molecular mechanisms of their biosynthesis. While there is no certainty regarding the expression or silencing of the gene clusters identified through genomics, this initial exploration enabled us to detect their presence and structure. Mechanisms for the activation and overexpression of these genes could be proposed based on genome mining to obtain novel bioactive molecules. Therefore, genomic exploration of emerging microorganisms and their biosynthetic gene clusters lays the foundation for advanced metabolomics studies and characterization of novel biomolecules (Javed et al. ). In this context, the objective of our study was to isolate rare Actinomycetota strains from Antarctic soil and sediment samples and identify their metabolic potential based on genome mining and exploration of biosynthetic gene clusters. 

Isolation and sample treatment of actinobacterial strains In this study, soil and sediment samples were collected from Antarctica during the Chilean Antarctic Expedition conducted between 2019 and 2020. For isolation of rare Actinomycetota strains (less frequently isolated species), the samples were treated with high temperature, detergent solutions and plated with antibiotics supplementation to avoid the growth of most frequent species in the soil. To do this, 1 g of each sample was exposed to 100 °C for 60 min, followed by a 10 −1 dilution in each of the following chemical treatments: 1.5% phenol, 0.05% SDS, or 1% chloramine-T, incubating for 30 min at 30 °C. A 0.85% solution was used for the control samples. Dilutions of 10 −2 and 10 −3 of each pre-treated sample were cultured on soil agar (100 g/L Antarctic soil, 18 g/L agar) and oatmeal agar (60 g/L oatmeal, 18 g/L agar) supplemented with nalidixic acid (25 μg/mL). The cultures were incubated for 4 weeks at 15 °C. The resulting colonies were isolated and subsequently plated until purification in ISP-2 agar medium (4 g/L yeast extract, 4 g/L glucose, 10 g/L malt extract, and 20 g/L agar). Pure cultures were preserved by freezing at −80 °C in glycerol (20% v/v) until further use. Culture of actinobacterial strains In this work, nine isolated strains deposited at the Colección Chilena de Cultivos Tipo – CCCT (Universidad de La Frontera, Chile) under accession codes CCCT 24.01–CCCT 24.09 were utilized. These strains were inoculated on ISP-2 agar plates, nutrient agar (Liofilchem, Italy), and R2A agar (Merck Millipore, Germany) and incubated at 15 °C for 2 weeks until suitable microbial growth was observed. DNA extraction from actinobacterial strains From the above cultures, DNA extraction was performed using DNeasy UltraClean Microbial Kit (QIAGEN, Germany) following the manufacturer’s instructions. Subsequently, DNA was quantified using a Qubit dsDNA HS Assay Kit (Invitrogen, USA), considering a minimum range of concentrations equal to or greater than 50 ng/µL. Finally, the integrity of the obtained DNA was visualized on an agarose gel in TAE 1 × 0.9% m/v buffer. Library preparation and whole genome sequencing The samples were sequenced using Illumina and Oxford Nanopore Technologies (ONT). The Illumina library was prepared with 2 × 150 bp paired-end fragments on an Illumina NovaSeq platform. Quality control of the reads was assessed using FastQC v0.11.9 (Andrews ). Subsequently, adapters were cut from the reads and quality filtered using Fastp v0.20.0 tool (Chen et al. ) with the following parameters: --detect_adapter_for_pe -f 12 -F 12. For genome sequencing using long reads, the Rapid Sequencing Kit SQK-RBK004 (ONT) was used for library preparation, and sequencing was performed using an R9.4 flow cell (FLO-MIN106D) on a MinION Mk1C machine (ONT) using MinKNOW v4.3.7 software. Basecalling was performed using Guppy v5.0.12, in the fast mode. Quality control of the reads was assessed using the Nanoplot v1.40.0 tool (De Coster et al. ). Subsequently, the adapters were cut with Porechop v0.2.4, and sequences with a quality equal to or greater than 10 were filtered out using Nanofilt v2.8.0 (De Coster et al. ). Hybrid genome assembly To achieve de novo hybrid assembly, short and long reads that passed quality control and filtering were assembled using Unycicler v.0.4.8 tool (Wick et al. ). Subsequently, the hybrid genomes were polished with Medaka v1.2.3 ( https://github.com/nanoporetech/medaka ) using ONT reads. Then, the genomes were again polished with Polypolish v0.5.0 (Wick and Holt ) and poLCA v4.0.5 (Zimin and Salzberg ) using short reads. Finally, quality and contamination determination of the polished hybrid genomes were performed using Quast v5.0.2 and CheckM v1.1.3 (Gurevich et al. ; Parks et al. ). Genomic and taxonomic annotation Annotation of genomic sequences was achieved using Prokka v1.14 (Seemann ). The taxonomic annotation was performed by uploading the hybridized assembled genomes to the Type Strain Genome Server (TYGS) available under https://tygs.dsmz.de (Meier-Kolthoff and Göker ). Subsequently, the average nucleotide identity (ANI) was calculated using the FastANI v1.32 tool (Jain et al. ), and digital DNA-DNA hybridization (dDDH) was performed using the web tool Genome-to-Genome Distance Calculator (GGDC) (Meier-Kolthoff et al. ). In addition, a genome-based phylogenetic tree was inferred using the autoMLST: Automated Multi-Locus Species Tree pipeline ( https://automlst.ziemertlab.com/ ), where the “denovo mode” was used and the IQ-TREE Ultrafast Bootstrap analysis (1000 replicates) option was selected (Alanjary et al. ). The constructed tree was visualized and annotated using the Interactive Tree Of Life (iTOL) available at https://itol.embl.de/ (Letunic and Bork ). Identification and analysis of biosynthetic gene clusters (BGCs) The biosynthetic gene clusters in the nine strains were identified using antiSMASH v6.1.1 (Blin et al., ) with the following parameters: --taxon bacteria --fullhmmer --cc-mibig --cb-knownclusters --rre --hmmdetection-strictness strict and --genefinding-tool prodigal. Finally, to summarize the number of BGCs identified in the samples, a matrix plot was created using RAWGraphs (Mauri et al. ). Biosynthetic gene clusters network of the family Micrococcaceae Genome assemblies belonging to the family Micrococcaceae were recovered from the Biosample database of the National Center for Biotechnology Information (NCBI), which includes the environmental packages of soil (50), plant-associated (43), water (11), sediments (7), and wastewater/sludge (4). Subsequently, the BGCs were identified using the same methodology described in this manuscript. To determine biosynthetic biodiversity, sequence similarity networks were generated using the “biosynthetic gene similarity clustering and prospecting engine” BiG-SCAPE v1.1.2 (Navarro-Muñoz et al. ) with the PFAM database (v35.0) (Mistry et al. ). A distance matrix was created by calculating the distance between every pair of BGCs within the dataset. This distance matrix incorporates three metrics: (i) Jaccard index, representing the percentage of shared domain types; ii) domain sequence similarity, reflecting the similarity between aligned domain sequences; and (iii) adjacency index, which measures the similarity of domain pair types. In addition, the MiBIG database v3.1 (Minimum Information about a Biosynthetic Gene cluster) (Terlouw et al. ) was used. The .gbk files of the BGCs obtained from antiSMASH were used as the inputs. The following settings were used to run the analysis: --mode auto --mix --no_classify --mibig --cutoffs 0.3 0.4. Finally, the networks obtained were annotated and visualized using Cytoscape v3.6.0 (Shannon et al. ). 

In this study, soil and sediment samples were collected from Antarctica during the Chilean Antarctic Expedition conducted between 2019 and 2020. For isolation of rare Actinomycetota strains (less frequently isolated species), the samples were treated with high temperature, detergent solutions and plated with antibiotics supplementation to avoid the growth of most frequent species in the soil. To do this, 1 g of each sample was exposed to 100 °C for 60 min, followed by a 10 −1 dilution in each of the following chemical treatments: 1.5% phenol, 0.05% SDS, or 1% chloramine-T, incubating for 30 min at 30 °C. A 0.85% solution was used for the control samples. Dilutions of 10 −2 and 10 −3 of each pre-treated sample were cultured on soil agar (100 g/L Antarctic soil, 18 g/L agar) and oatmeal agar (60 g/L oatmeal, 18 g/L agar) supplemented with nalidixic acid (25 μg/mL). The cultures were incubated for 4 weeks at 15 °C. The resulting colonies were isolated and subsequently plated until purification in ISP-2 agar medium (4 g/L yeast extract, 4 g/L glucose, 10 g/L malt extract, and 20 g/L agar). Pure cultures were preserved by freezing at −80 °C in glycerol (20% v/v) until further use. 

In this work, nine isolated strains deposited at the Colección Chilena de Cultivos Tipo – CCCT (Universidad de La Frontera, Chile) under accession codes CCCT 24.01–CCCT 24.09 were utilized. These strains were inoculated on ISP-2 agar plates, nutrient agar (Liofilchem, Italy), and R2A agar (Merck Millipore, Germany) and incubated at 15 °C for 2 weeks until suitable microbial growth was observed. 

From the above cultures, DNA extraction was performed using DNeasy UltraClean Microbial Kit (QIAGEN, Germany) following the manufacturer’s instructions. Subsequently, DNA was quantified using a Qubit dsDNA HS Assay Kit (Invitrogen, USA), considering a minimum range of concentrations equal to or greater than 50 ng/µL. Finally, the integrity of the obtained DNA was visualized on an agarose gel in TAE 1 × 0.9% m/v buffer. 

The samples were sequenced using Illumina and Oxford Nanopore Technologies (ONT). The Illumina library was prepared with 2 × 150 bp paired-end fragments on an Illumina NovaSeq platform. Quality control of the reads was assessed using FastQC v0.11.9 (Andrews ). Subsequently, adapters were cut from the reads and quality filtered using Fastp v0.20.0 tool (Chen et al. ) with the following parameters: --detect_adapter_for_pe -f 12 -F 12. For genome sequencing using long reads, the Rapid Sequencing Kit SQK-RBK004 (ONT) was used for library preparation, and sequencing was performed using an R9.4 flow cell (FLO-MIN106D) on a MinION Mk1C machine (ONT) using MinKNOW v4.3.7 software. Basecalling was performed using Guppy v5.0.12, in the fast mode. Quality control of the reads was assessed using the Nanoplot v1.40.0 tool (De Coster et al. ). Subsequently, the adapters were cut with Porechop v0.2.4, and sequences with a quality equal to or greater than 10 were filtered out using Nanofilt v2.8.0 (De Coster et al. ). 

To achieve de novo hybrid assembly, short and long reads that passed quality control and filtering were assembled using Unycicler v.0.4.8 tool (Wick et al. ). Subsequently, the hybrid genomes were polished with Medaka v1.2.3 ( https://github.com/nanoporetech/medaka ) using ONT reads. Then, the genomes were again polished with Polypolish v0.5.0 (Wick and Holt ) and poLCA v4.0.5 (Zimin and Salzberg ) using short reads. Finally, quality and contamination determination of the polished hybrid genomes were performed using Quast v5.0.2 and CheckM v1.1.3 (Gurevich et al. ; Parks et al. ). 

Annotation of genomic sequences was achieved using Prokka v1.14 (Seemann ). The taxonomic annotation was performed by uploading the hybridized assembled genomes to the Type Strain Genome Server (TYGS) available under https://tygs.dsmz.de (Meier-Kolthoff and Göker ). Subsequently, the average nucleotide identity (ANI) was calculated using the FastANI v1.32 tool (Jain et al. ), and digital DNA-DNA hybridization (dDDH) was performed using the web tool Genome-to-Genome Distance Calculator (GGDC) (Meier-Kolthoff et al. ). In addition, a genome-based phylogenetic tree was inferred using the autoMLST: Automated Multi-Locus Species Tree pipeline ( https://automlst.ziemertlab.com/ ), where the “denovo mode” was used and the IQ-TREE Ultrafast Bootstrap analysis (1000 replicates) option was selected (Alanjary et al. ). The constructed tree was visualized and annotated using the Interactive Tree Of Life (iTOL) available at https://itol.embl.de/ (Letunic and Bork ). 

The biosynthetic gene clusters in the nine strains were identified using antiSMASH v6.1.1 (Blin et al., ) with the following parameters: --taxon bacteria --fullhmmer --cc-mibig --cb-knownclusters --rre --hmmdetection-strictness strict and --genefinding-tool prodigal. Finally, to summarize the number of BGCs identified in the samples, a matrix plot was created using RAWGraphs (Mauri et al. ). 

Micrococcaceae Genome assemblies belonging to the family Micrococcaceae were recovered from the Biosample database of the National Center for Biotechnology Information (NCBI), which includes the environmental packages of soil (50), plant-associated (43), water (11), sediments (7), and wastewater/sludge (4). Subsequently, the BGCs were identified using the same methodology described in this manuscript. To determine biosynthetic biodiversity, sequence similarity networks were generated using the “biosynthetic gene similarity clustering and prospecting engine” BiG-SCAPE v1.1.2 (Navarro-Muñoz et al. ) with the PFAM database (v35.0) (Mistry et al. ). A distance matrix was created by calculating the distance between every pair of BGCs within the dataset. This distance matrix incorporates three metrics: (i) Jaccard index, representing the percentage of shared domain types; ii) domain sequence similarity, reflecting the similarity between aligned domain sequences; and (iii) adjacency index, which measures the similarity of domain pair types. In addition, the MiBIG database v3.1 (Minimum Information about a Biosynthetic Gene cluster) (Terlouw et al. ) was used. The .gbk files of the BGCs obtained from antiSMASH were used as the inputs. The following settings were used to run the analysis: --mode auto --mix --no_classify --mibig --cutoffs 0.3 0.4. Finally, the networks obtained were annotated and visualized using Cytoscape v3.6.0 (Shannon et al. ). 

Antarctic isolates genome sequencing and taxonomic affiliation We successfully isolated nine Actinomycetota strains from different soil and sediment samples from the Antarctic, following different sample treatments (Table ). The hybrid assembly of those nine genomes, isolated from samples collected in the Antarctic territory, exhibited completeness exceeding 98% and contamination of less than 1%. The number of contigs varied between 1 and 5 for all assemblies, demonstrating a high quality of contiguity. Specifically, all assemblies had an L50 value of 1 and a notably high N50 value. The strain Sec 6.3 exhibited the lower contiguity. Furthermore, a large number of reads were obtained for each assembly, contributing to the genomic coverage and continuity (Supplementary Table ). Additionally, all assemblies obtained had a guanine-cytosine content (GC) ranging from 61.5 to 72.9%, with predicted coding sequences numbering between 2348 and 5007, as expected for Actinomycetota phylum (Table ). The genomics-based tree revealed that the strains belonged to the families Micrococcaceae and Dermatophilaceae within the phylum Actinomycetota , as per the information sourced from the Genome Taxonomy Database (available at https://gtdb.ecogenomic.org/ ) (Figure ). Specifically, within the family Micrococcaceae , strains Sec 5.8 and Sec 7.4 were closely grouped, and Sec 5.7 formed a distinct phylogenetic lineage within the genus Arthrobacter . On the other hand, strain Se 16.17 was grouped with Paenarthrobacter nicotinovorans 231Sha2.1M6, while Sec 5.1 formed a distinct phylogenetic lineage within the genus Paenarthrobacter. Strain Sec 6.3 was placed in a separate phylogenetic lineage within the genus Micrococcus . Additionally, strain Sec 5.9 was clustered with the species Janibacter terrae , and strains Sec 6.4 and Soc 4.6 formed a distinct phylogenetic lineage within the Dermatophilaceae family. Comparing the whole genomic sequences using ANI and dDDH values, we found that almost all the strains exhibited the closest ANI values to species obtained through the NCBI database via autoMLST. However, these values were relatively low, except for strain Se 16.17, which shared a high similarity with P. nicotinovorans 231Sha2.1M6, showcasing ANI and dDDH values of 98.8% and 86.6%, respectively, and allowing its affiliation as a P. nicotinovorans strain. Another exception was strain Sec 5.9, showing 98.8% ANI and 90.1% dDDH, classifying it as the species Janibacter terrae (Table ). Based on our results, seven out of nine strains might be representative of novel species of the genera Lapillicoccus sp. (Soc 4.6), Pseudarthrobacter sp. (Sec 5.7, Sec 5.8 and Sec 7.4), Micrococcus sp. (Sec 6.3), and Allobranchiibius sp. (Sec 6.4). Analysis and comparison of biosynthetic gene clusters The nine genomes isolated from the Micrococcaceae and Dermatophilaceae families collectively possess 49 BGCs. Remarkably, about 84% of these BGCs exhibit a similarity of less than 50% with the closest clusters in the MiBIG database. The most prevalent BGC types within the strains were beta-lactones, type III polyketide synthases (T3PKS), and non-alpha-poly amino acids (NAPAA), with all of them containing NAPAA. Furthermore, BGCs corresponding to siderophores, terpenes, thiopeptide, and non-ribosomal peptide synthetase (NRPS), among others, were identified in smaller quantities (Supplementary Figure ). Among the BGCs exhibiting a similarity identity greater than 50% with known clusters in the database, we found siderophores with 75% and 100% similarity to the desferrioxamine E cluster; a type III polyketide synthase with 100% similarity to the alkylresorcinol; a cluster with 75% similarity to the ectoine; and a terpene from strain Sec 6.3, showing 66% similarity to the carotenoid cluster (Supplementary Figure ). The strains belonging to the Micrococcaceae family shared NAPAA, beta-lactone, and T3PKS clusters. Then, we wanted to further investigate whether this feature could be associated with the taxonomic family or could be part of genetic material related to strains inhabiting the extreme environment. Biosynthetic gene cluster network analysis in the Micrococcaceae family To assess the biosynthetic biodiversity of the Micrococcaceae family, we employed the biosynthetic gene similarity clustering and prospecting engine (BiG-SCAPE) to generate sequence similarity networks. The analysis involved 597 BGCs identified from 121 genomic assemblies within the Micrococcaceae family (Fig. and Supplementary Table ). Among these, six assemblies were from strains isolated in Antarctica, while the rest were retrieved from the Biosample database of the NCBI. These genomes were classified under different environmental categories, including soil (50), plant-associated (43), water (11), sediments (7), and wastewater/sludge (4). The similarity cutoff used for the clustering was set at 0.3. The sequence similarity network of the BGCs unveiled six predominant types: beta-lactone, NAPAA, T3PKS, siderophores, terpenes, and ectoine. Moreover, the networks containing BGCs from the genera Micrococcus and Nesterenkonia were distinctly grouped, unlike other genera that shared their BGCs within the same network. Within the identified clusters, two were noteworthy for their annotations from six MiBIG reference BGCs with known functions. Five of these were associated with siderophores, encompassing natural products such as desferrioxamine B (MIBiG BGC0000940 and BGC0000941), desferrioxamine E (MIBiG BGC0001478), and legonoxamine A (MIBiG BGC0002305). Conversely, one was linked to the thiopeptide category but grouped in the same cluster as terpenes, representing a compound called “TP-1161” (MIBiG BGC0000615). This limited representation of networks containing previously characterized BGCs underscores the extensive BGC diversity within the Micrococcaceae family. We attempted to generate networks based on the source of isolation and continent, but no discernible relationships were identified related to the origin of the isolated (Supplementary Figure and ). 

We successfully isolated nine Actinomycetota strains from different soil and sediment samples from the Antarctic, following different sample treatments (Table ). The hybrid assembly of those nine genomes, isolated from samples collected in the Antarctic territory, exhibited completeness exceeding 98% and contamination of less than 1%. The number of contigs varied between 1 and 5 for all assemblies, demonstrating a high quality of contiguity. Specifically, all assemblies had an L50 value of 1 and a notably high N50 value. The strain Sec 6.3 exhibited the lower contiguity. Furthermore, a large number of reads were obtained for each assembly, contributing to the genomic coverage and continuity (Supplementary Table ). Additionally, all assemblies obtained had a guanine-cytosine content (GC) ranging from 61.5 to 72.9%, with predicted coding sequences numbering between 2348 and 5007, as expected for Actinomycetota phylum (Table ). The genomics-based tree revealed that the strains belonged to the families Micrococcaceae and Dermatophilaceae within the phylum Actinomycetota , as per the information sourced from the Genome Taxonomy Database (available at https://gtdb.ecogenomic.org/ ) (Figure ). Specifically, within the family Micrococcaceae , strains Sec 5.8 and Sec 7.4 were closely grouped, and Sec 5.7 formed a distinct phylogenetic lineage within the genus Arthrobacter . On the other hand, strain Se 16.17 was grouped with Paenarthrobacter nicotinovorans 231Sha2.1M6, while Sec 5.1 formed a distinct phylogenetic lineage within the genus Paenarthrobacter. Strain Sec 6.3 was placed in a separate phylogenetic lineage within the genus Micrococcus . Additionally, strain Sec 5.9 was clustered with the species Janibacter terrae , and strains Sec 6.4 and Soc 4.6 formed a distinct phylogenetic lineage within the Dermatophilaceae family. Comparing the whole genomic sequences using ANI and dDDH values, we found that almost all the strains exhibited the closest ANI values to species obtained through the NCBI database via autoMLST. However, these values were relatively low, except for strain Se 16.17, which shared a high similarity with P. nicotinovorans 231Sha2.1M6, showcasing ANI and dDDH values of 98.8% and 86.6%, respectively, and allowing its affiliation as a P. nicotinovorans strain. Another exception was strain Sec 5.9, showing 98.8% ANI and 90.1% dDDH, classifying it as the species Janibacter terrae (Table ). Based on our results, seven out of nine strains might be representative of novel species of the genera Lapillicoccus sp. (Soc 4.6), Pseudarthrobacter sp. (Sec 5.7, Sec 5.8 and Sec 7.4), Micrococcus sp. (Sec 6.3), and Allobranchiibius sp. (Sec 6.4). 

The nine genomes isolated from the Micrococcaceae and Dermatophilaceae families collectively possess 49 BGCs. Remarkably, about 84% of these BGCs exhibit a similarity of less than 50% with the closest clusters in the MiBIG database. The most prevalent BGC types within the strains were beta-lactones, type III polyketide synthases (T3PKS), and non-alpha-poly amino acids (NAPAA), with all of them containing NAPAA. Furthermore, BGCs corresponding to siderophores, terpenes, thiopeptide, and non-ribosomal peptide synthetase (NRPS), among others, were identified in smaller quantities (Supplementary Figure ). Among the BGCs exhibiting a similarity identity greater than 50% with known clusters in the database, we found siderophores with 75% and 100% similarity to the desferrioxamine E cluster; a type III polyketide synthase with 100% similarity to the alkylresorcinol; a cluster with 75% similarity to the ectoine; and a terpene from strain Sec 6.3, showing 66% similarity to the carotenoid cluster (Supplementary Figure ). The strains belonging to the Micrococcaceae family shared NAPAA, beta-lactone, and T3PKS clusters. Then, we wanted to further investigate whether this feature could be associated with the taxonomic family or could be part of genetic material related to strains inhabiting the extreme environment. 

Micrococcaceae family To assess the biosynthetic biodiversity of the Micrococcaceae family, we employed the biosynthetic gene similarity clustering and prospecting engine (BiG-SCAPE) to generate sequence similarity networks. The analysis involved 597 BGCs identified from 121 genomic assemblies within the Micrococcaceae family (Fig. and Supplementary Table ). Among these, six assemblies were from strains isolated in Antarctica, while the rest were retrieved from the Biosample database of the NCBI. These genomes were classified under different environmental categories, including soil (50), plant-associated (43), water (11), sediments (7), and wastewater/sludge (4). The similarity cutoff used for the clustering was set at 0.3. The sequence similarity network of the BGCs unveiled six predominant types: beta-lactone, NAPAA, T3PKS, siderophores, terpenes, and ectoine. Moreover, the networks containing BGCs from the genera Micrococcus and Nesterenkonia were distinctly grouped, unlike other genera that shared their BGCs within the same network. Within the identified clusters, two were noteworthy for their annotations from six MiBIG reference BGCs with known functions. Five of these were associated with siderophores, encompassing natural products such as desferrioxamine B (MIBiG BGC0000940 and BGC0000941), desferrioxamine E (MIBiG BGC0001478), and legonoxamine A (MIBiG BGC0002305). Conversely, one was linked to the thiopeptide category but grouped in the same cluster as terpenes, representing a compound called “TP-1161” (MIBiG BGC0000615). This limited representation of networks containing previously characterized BGCs underscores the extensive BGC diversity within the Micrococcaceae family. We attempted to generate networks based on the source of isolation and continent, but no discernible relationships were identified related to the origin of the isolated (Supplementary Figure and ). 

The Antarctic stands as the coldest, driest, windiest, and most challenging-to-access continent on Earth. Due to its extreme conditions, it has been identified as a potential source for the discovery of novel natural products from bacteria (Núñez-Montero and Barrientos ). We isolated and analyzed nine strains from soil and sediment samples collected across diverse locations within the Antarctic territory. These strains were obtained through selective isolation methods to increase the probability of obtaining rare strains and to facilitate the discovery of natural products. Heating in the pretreatment of the samples, the use of chemicals such as phenol and sodium dodecylsulfate and antimicrobial agents, such as nalidixic acid, was used to reduce the growth of undesirable bacteria and increase the selectivity of rare Actinomycetota strains. This is possible because there are several strains of Actinomycetota resistant to a broad spectrum of antibiotics, together with the addition of chemical compounds toxic to bacteria, which allows for a decrease in the number of bacteria sensitive to these agents, thus enabling the isolation of new strains for the search of new natural products (Subramani and Aalbersberg ). Genomic assembly of the isolated strains was facilitated using a hybrid approach, integrating short reads through Illumina sequencing and long reads using Oxford Nanopore Technology (ONT). Combining these sequencing technologies enhances genome assembly, addressing the inherent limitations of Illumina, which struggles with errors in sequencing repetitive structures and relies on short read sizes, typically less than 500 base pairs. These challenges are mitigated with ONT, generating large reads, often exceeding 10,000 bp. This allows for the assembly of complex genomes, resulting in fewer contigs and enhanced precision and contiguity (De Maio et al. ). This is evidenced by genomic annotation, wherein all assemblies display a high N50 value and a low L50 value, except for strain Sec 6.3, which demonstrates a slightly lower N50 value. The L50 and N50 values represent the number of contigs and contig lengths, respectively, crossing the 50% mark of the assembly. Hence, higher N50 values and lower L50 values signify a more contiguous assembly (Jayakumar and Sakakibara ). Additionally, we achieved a substantial depth of coverage in the genomic assemblies utilizing both technologies, as outlined in the supplementary tables. Consequently, this precise analysis allows for a thorough examination of the biosynthetic gene clusters and various genomic characteristics of the obtained strains. Taxonomic annotation revealed that strains Sec 5.9 and Se 16.17 exhibited high dDDH and ANI values, with their closest genomes being Janibacter terrae NBRC 107854 and Paenarthrobacter nicotinovorans 231Sha2.1M6, respectively. The generally accepted cutoff values for identifying new species within a genus are 95–96% for ANI and AAI and 70% for dDDH (Richter and Rosselló-Móra ; Chun et al. ). Consequently, these strains are identified as belonging to the mentioned species. Strains affiliated with the J. terrae species were previously isolated from environmental water and soil samples, showcasing the capability to degrade environmental pollutants (Lang et al. ). On the other hand, P. nicotinovorans 231Sha2.1M6 was isolated from Arabidopsis thaliana soil samples. As for the remaining seven strains, both their dDDH and ANI values fell below the defined cutoff values, designating them as new species within the Micrococcaceae and Dermatophilaceae families. In the analysis of the identified BGCs within the isolated strains, a variable number, ranging from three to eight BGCs, was observed. Among them, three main types stood out: NAPAA, beta-lactone, and T3PKS. Beta-lactone-associated BGCs have demonstrated applications as anticancer agents, such as Marizomib (salinosporamide A) and antimicrobial and antiobesity agents, like Xenical (Robinson et al. ; Wang et al. , ). T3PKS, responsible for biosynthesizing a plethora of natural products, encompass antibiotics, immunosuppressants, and cancer chemotherapy (Nivina et al. ). NAPAA, such as ε-poly-L-lysine, exhibit bacteriostatic and biodegradable properties, leading to their use as food preservatives and in the pharmaceutical industry (Wang et al. , ). Another noteworthy category of BGCs with potential public health applications includes RiPPs, which feature a domain known as the RiPP recognition element (RRE-containing). This domain is prevalent in various RiPP clusters (Kloosterman et al. ) and has been instrumental in addressing therapeutic challenges, such as combating cystic fibrosis and acting as antimicrobials (Hetrick and Donk ). Furthermore, concerning BGCs with a similarity greater than 50% to known BGCs in the MiBIG database, we identified alkylresorcinol in the Soc 4.6 strain. Alkylresorcinol is a lipophilic molecule with a polyphenol structure known for its antimicrobial, anticancer, antilipidemic, antioxidant, and other properties (Zabolotneva et al. ). Additionally, we found BGCs linked to siderophores, particularly desferrioxamine E in the Sec 5.1, 5.7, 5.9, and Se 16.17 strains, which play a crucial role in microbial growth (Yamanaka et al. ). On a different note, we also identified BGCs associated with more physiological functions, such as terpenes and ectoine. Terpenes are linked to electron transport, light uptake, photoprotection, and signaling (Caulier et al. ), while ectoine is known for its protective properties on enzymes, DNA, cell membranes, and cells against various types of stress, including osmotic, cold, and heat stress (Zhang et al. ). Hence, our results showed that the possibly known molecules are related to primary microbial metabolism and or common functions across. On the other hand, our strains showed a large number of BGCs with low or non-similarity to other clusters, and belonging to types of specialized metabolites previously related to biological activities. Hence, our data highlights the metabolic potential for the production of novel natural products from novel strains isolated from the Antarctic continent. The network analysis of BGCs allows us to visualize a phylogenetic correlation between certain genera of the family Micrococcaceae , especially observed in genera such as Micrococcus and Nesterenkonia . This observation has been observed in studies of other species belonging to the phylum Actinomycetota , such as the genera Amycolatopsis , Salinispora , and Streptomyces , among others, where these correlations were detected using different bioinformatic approaches, such as the identification of gene cluster families, operational biosynthetic units, and BGCs (Doroghazi and Metcalf ; Ziemert et al. ; Adamek et al. ). In addition, it has been detected in the phylum Firmicutes, with the genus Bacillus , where clade- and species-specific BGCs have been found (Steinke et al. ). These BGCs-related families might be associated with core metabolism of the genus. Furthermore, among the three most abundant groups of BGCs within this family, T3PKS have been previously reported for the genus Arthrobacter (Doroghazi and Metcalf ), but not the beta-lactone BGCs, which are grouped in one network and currently do not have a close reference and could be part of the core genome of these strains because of their high level of conservation. In addition, no relationship was found between the source of isolation and the BGCs. This might be because there are many types of environments and different conditions of temperature, humidity, and pressure, among others, which generates a very diverse grouping in the network. On the other hand, there are many BGCs to be studied within this family, since only two groups are similar to any known natural product, being siderophores and thiopeptides. The latter obtained from an Actinomycetota , Nocardiopsis sp., has potent antibiotic properties (Engelhardt et al. ). Therefore, it is important to further investigate BGCs belonging to this family as several beneficial properties have been demonstrated, such as the ability to act as plant growth promoters in various plant species, as seen in members belonging to the genera Arthrobacter and Pseudarthrobacter (Chhetri et al. ; Jiang et al. ; Ham et al. ; Platamone et al. ). In addition, species of the genera Arthrobacter and Micrococcus have been used for the bioremediation of organic compounds and heavy metals (Behera and Das ). For the species belonging to the genus Micrococcus , extracts with pharmaceutical properties, such as antimicrobial, antifungal, and antioxidant properties, have been obtained, which are described in more detail in the review by Tizabi and Hill . In summary, the species isolated and sequenced in this study exhibit BGCs with significant potential across various domains including clinical applications, pharmaceuticals, the food industry, and agriculture. Moreover, we observed a substantial gap in our understanding of the functionality of different BGC groups within the Micrococcaceae family, which holds significant implications for the future. Our research also provides valuable genomics data from untapped microorganisms from rare Actinomycetota strains from extreme environments. Given that this analysis was conducted in silico , we were unable to confirm whether these BGCs are actively expressed or silenced. Future investigations integrating transcriptomics and metabolomics are essential to determine their expression and functionality. This comprehensive approach will pave the way for these BGCs to emerge as promising candidates for future applications and biotechnological advancements. 

Below is the link to the electronic supplementary material. Supplementary file1 (TIF 28338 KB) Supplementary file2 (PDF 615 KB) Supplementary file3 (XLSX 36 KB) 

