Environmental impact on the genome shaping of putative new Streptomyces species

Abstract

Background

The bacterial evolution and the emergence of new species are likely influenced by multiple forces, including long-term environmental pressure such as living in extreme conditions. In this study, the genomes of two potentially new Streptomyces species isolated from a former mine heap in Tarnowskie Góry in Poland, were analyzed.

Results

A bioinformatic approach revealed notable phylogenetic and metabolic differences between the studied Streptomyces strains, despite originating from the same environment. While both strains are characterized by genetic features common to actinomycetes, additional unique biosynthetic gene clusters were also predicted in their genomes. The comparative genomic analysis with other Streptomyces spp. revealed a high conservation in heavy metal adaptive mechanisms, indicating a preadaptation to extreme conditions. The difference observed in the cad and mer operons could be attributed to the specific adaptations to heavy metal contamination. The high metal tolerance of examined strains was also confirmed by an agar dilution assay in the presence of several heavy metals. The confirmed siderophore production represents an additional mechanism allowing streptomycetes to survive in extreme conditions. On the other hand, both of studied genomes show significant differences in energy acquisition processes and the production of putative novel secondary metabolites. The isolates showed these differences not only among themselves but also compared to other Streptomyces species, indicating their uniqueness.

Conclusions

Our results demonstrate that extreme environmental conditions can lead to the development of various adaptation mechanisms in the Streptomyces spp. Furthermore, the results indicate that diverse Streptomyces species have developed conserved adaptation mechanisms against the heavy metals under extreme conditions, indicating the emergence of preadaptations that allow bacteria to respond rapidly to polluted environments and evolve their genomes accordingly up to the evolution of new species.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-025-03779-x.

Background

The investigation of microorganisms living in extreme environments and their adaptation mechanisms helps to understand the evolutionary and ecological processes of microorganisms on Earth and to use their ecological and economic potential in a wide range of industries, as they represent a more efficient, sustainable and cost-effective option compared to conventional methods [1, 2].

Streptomyces spp. are ubiquitous Gram-positive bacteria belonging to the phylum Actinomycetota. There are more than 720 validly described species according to the List of Prokaryotic Names with Standing in Nomenclature [3]. Streptomycetes mainly inhabit soil, although they can also be found in water or plants [4]. They are generally characterized by the largest genomes among bacteria, typically ranging in size from 6 to 11 Mbp [5]. The genome consists of core genes that are essential for replication, transcription, translation, primary metabolism, and adaptive genes encoding secondary metabolites [6].

Streptomycetes can produce a large number of secondary metabolites with antimicrobial, antiviral, anticancer and antifungal properties. The high variability of metabolites allows them to survive in diverse environmental habitats and to constitute an evolutionarily very successful bacterial group within microbial communities [7]. Moreover, their rich secondary metabolic activity has a great application potential in a wide range of industries, such as bioremediation, biodegradation, biorefinery or biofuel production [2, 8]. Based on these findings, modern research has focused on environments where the microbiota is still poorly explored (e.g., deserts, deep seas or volcanics), which may be a source of microorganisms with unique properties [9]. The investigation of streptomycetes could lead to the discovery of new species producing novel active compounds [10]. However, the genomes of streptomycetes contain many cryptic biosynthetic gene clusters (BGCs) that can be expressed only in response to specific cues [11, 12]. With genome mining, it is possible to find potential gene clusters that cannot be identified by traditional laboratory methods [13–15].

As mentioned above, streptomycetes are frequently isolated from soil environments contaminated with heavy metals [16]. While heavy metals occur naturally in trace amounts and pose minimal threats to bacterial life, they can negatively affect the biological functions of microorganisms in higher concentrations, culminating in bacterial death [17]. Streptomycetes can tolerate heavy metals through a wide range of specialized adaptation mechanisms, such as efflux pumps, superoxide dismutases, siderophore production, biomineralization, extracellular binding with chelators or oxidation/reduction of toxic metal ions into less harmful forms [6, 18].

Several genetic determinants associated with heavy metal tolerance, such as the mer operon (merABPT) [19], ars operon or copACDZ genes [18, 20, 21], were identified in Streptomyces spp. The ter operon involved in metalloid tellurite tolerance was detected in several studies [22, 23].

The main aim of this study was to investigate the genetic relatedness between two streptomycete strains isolated from the same extreme environment and to identify genetic determinants involved in adaptation processes to heavy metal contaminated environments. In addition, we tried to assess the impact of environmental conditions on the evolution of this bacterial group.

Methods

Origin of the isolates

The P9 and P17 isolates were obtained from the lead-silver-zinc material of the mine heap in Tarnowskie Góry in Poland (50°24′56’’ N, 18°51′16’’ E), which was subsequently processed according to Nosalova et al. [24]. The soil analysis of the material revealed the presence of high concentrations of lead (10,388 mg/kg), zinc (65,658 mg/kg), iron (258,933 mg/kg), manganese (6,360 mg/kg), cadmium (510 mg/kg) and other heavy metals. Bacteria identified in the material showed a high tolerance to heavy metals such as zinc (500 mg/L), cadmium (1,000 mg/L) and lead (1,500 mg/L) [24].

Sampling and bacteria cultivation

The isolates were initially cultivated on Lindenbein selective medium for Streptomyces [25] at laboratory temperature (20–23 °C) for seven days and Streptomyces-like colonies were selected for further analysis. The morphology of the bacterial colonies was observed using the Leica EZ4 D stereo microscope (Leica Microsystems, Germany), and subsequently the isolates were stained by Gram’s method and observed using the Olympus BX-41 optical microscope equipped with a digital camera (Olympus Corporation, Japan) and the QuickPHOTO Micro v2.2 software to capture images (Promica, Czech Republic).

Isolates were first identified based on the 16S rRNA gene sequence and then subjected to whole-genome sequencing.

DNA extraction

The total genomic DNA of the P9 and P17 strains was isolated from the overnight bacterial culture cultivated in liquid Tryptic Soy Broth medium (TSB, Sigma-Aldrich, USA) at 25 °C. DNA extraction was performed using the modified method according to Pospiech and Neumann [26]. Bacterial cells (1.5 mL of the overnight bacterial culture) were centrifuged in microcentrifuge tubes for 5 min at 6,000 g. The pellet was resuspended in 600 µL of a lysis SET solution (75 mM NaCl, 25 mM EDTA, 20 mM Tris-HCl, pH 8), 200 µL of lysozyme solution (2.115 × 10⁶ U/mL) (Serva, Germany) and 10 µL of RNase A Solution (20 mg/mL) (Sigma-Aldrich, USA) were added and the mixture was incubated for 30 min at 37 °C. Subsequently, 120 µL of 10% SDS and 5 µL of proteinase K (20 mg/mL) (Sigma-Aldrich, USA) were added and incubation was continued for 1 h at 55 °C. After that, 240 µL of 5 M NaCl and 960 µL of chloroform were added. The mixture was incubated for 30 min at 25 °C with gentle shaking, followed by centrifugation for 10 min at 10,000 g. The upper layer was collected into a clean microcentrifuge tube and the chloroform purification process was repeated two more times. After centrifugation, an equal volume of isopropanol was added to the collected upper phase and the mixture was incubated for 15 min at 25 °C. Then the mixture was centrifuged for 10 min at 10,000 g. The pellet was washed with 500 µL of 70% ethanol, centrifuged and the residual ethanol was carefully removed with a pipette. The pellet was dried at 37 °C in an open tube. The obtained DNA was dissolved in 50 µL of sterile ultrapure water. The quality of extracted DNA was analyzed by electrophoresis in a 1% agarose gel stained with ethidium bromide to a final concentration of 0.5 µg/L and visualized under UV light using the ChemiDoc™ XRS + System (BIO-RAD, USA). The DNA concentration was measured by NanoDrop 2000c Spectrophotometer (Thermo Scientific, USA).

Identification based on the 16S rRNA gene sequence analysis

The isolated DNA was used as a template for PCR amplification of the 16S rRNA gene using Taq Core Kit (Jena Bioscience, Germany). PCR was performed in a 25 µL volume, with the reaction mixture consisting of 1x Crystal Buffer, 200 µM of dNTP Mix, 1 µM of fD1 (5´-AGAGTTTGATCCTGGCTCAG-3´) and rP2 primer (5´-ACGGCTACCTTGTTACGACTT-3´) [27], 1.25 U of Taq DNA polymerase and 1 µL of DNA. PCR amplification was performed using Mastercycler^® Pro S thermocycler (Eppendorf, Hamburg, Germany) under the following conditions: initial denaturation at 95 °C for 5 min, followed by 35 cycles of denaturation at 95 °C for 1 min, annealing at 56 °C for 1 min and elongation at 72 °C for 1.5 min. The final elongation step was conducted at 72 °C for 10 min.

The PCR products were separated by electrophoresis in a 1% agarose gel and visualized under UV light using the ChemiDoc™ XRS + System (BIO-RAD, USA). The PCR products were purified using the EXO/SAP kit (Jena Bioscience, Germany) according to the manufacturer’s protocol and sequenced in both directions using the Sanger method by Eurofins Genomics Sequencing GmbH (Köln, Germany). The obtained sequences were processed using the tools implemented in the MEGAX v10.2.4 software [28] and compared against the GenBank database using the BlastN search tool [29]. The sequences obtained were deposited in the GenBank database under accession numbers MT482723.1 (P9) and MT482729.1 (P17).

Whole-genome sequence analysis

The whole-genome sequencing of the P9 and P17 isolates was performed by Eurofins Genomics Europe Sequencing GmbH (Konstanz, Germany) using Illumina NovaSeq technology by paired-end strategy (2 × 150) in the NovaSeq 6000 sequence mode by S4 PE150 XP kit. The raw reads were processed by tools implemented in the Unipro UGENE v47.0 bioinformatics software [30]. Sequences with a low-quality score (average quality threshold of 20) were removed from the analysis using the Trimmomatic v0.39 tool. The sequences were assembled de novo using SPAdes v3.12.0. Finally, contigs shorter than 200 bp were excluded from further analysis.

The draft genomes of Streptomyces sp. P9 and P17 were deposited in the GenBank database under the accession numbers JAUPWM000000000 and JAVRBF000000000, respectively. Both genomes were annotated by NCBI Prokaryotic Genome Annotation Pipeline (PGAP), Rapid Annotation using Subsystem Technology (RAST) [31–33] and Bakta Web 1.9.1 [34]. The results were also checked by the BlastX search tool [29].

The genomic relatedness of the P9 and P17 strains to closely related species was determined using the in silico digital DNA: DNA hybridization (dDDH) analysis performed by Type (Strain) Genome Server (TYGS) with a species cut-off value of 70% [35]. In addition, the Average Nucleotide Identity (ANI) analysis was performed by JSpeciesWS Online Service with a minimal cut-off value of 95% indicating the same species [36].

The OrthoVenn3 software was used to perform the comparative genome analysis and predict highly conserved proteins among the selected Streptomyces spp. strains isolated from heavy metal contaminated soils [37].

The heavy metal tolerance gene determinants were predicted by the RAST annotation server, the Bakta Web and the BacMet database [38]. The results were checked by the BlastN and BlastX searching tools.

Additional genomes used in the comparative genome analysis were obtained from the GenBank database and were also annotated by the RAST server.

The correlation analyses between isolates found in heavy metal contaminated and non-contaminated sites were visualized by heatmaps created using the ClustVis software [39].

The antimicrobial resistance genes were predicted using the CARD database [40].

The antiSMASH 7.0 tool was applied to analyze the potential biosynthetic gene clusters associated with the possible production of secondary metabolites. The identified clusters were compared against known gene clusters available in the MIBiG database [41, 42]. Siderophores in bacteria play a significant role in the accumulation of iron from the environment [43, 44]. Therefore, we focused on a more detailed analysis of the BGCs associated with their production. The sequences encoding proteins involved in the siderophore production based on the antiSMASH analysis were checked by the BlastX searching tool of NCBI, grouped into the siderophore types according to their functional groups and compared with other Streptomyces spp. strains from heavy metal contaminated and non-contaminated environments using the ClustVis software [39].

Siderophore production

Based on the identified BGCs, the siderophore production was also investigated in the isolates P9 and P17. Siderophore production was examined using the O-CAS agar method according to Pérez-Miranda et al. and Louden et al. [45, 46]. Isolates were cultivated on the Duxbury medium in a Petri dish [47] for 7 days at the laboratory temperature. Grown isolates were overlaid with CAS medium. The change in the color of the medium was observed the next day.

Heavy metal tolerance assay

The heavy metal tolerance of the P9 and P17 strains was examined by the agar dilution method and the minimal inhibitory concentration (MIC) of the metal was determined [48]. The isolates were inoculated onto the Mueller-Hinton agar (Merck KGaA, Germany) containing a metal in the form of a salt solution of final concentration, such as CoCl₂ × 6H₂O (ranging from 100 to 500 mg/L), NiCl₂ × 6H₂O (ranging from 100 to 500 mg/L), CuCl₂ × 2H₂O (ranging from 100 to 750 mg/L), ZnCl₂ (ranging from 200 to 1,200 mg/L), CdSO₄ × 8H₂O (ranging from 200 to 1,200 mg/L) and Pb(C₂H₃O₂)₂ × 3H₂O (ranging from 200 to 1,500 mg/L) (Centralchem, Slovakia). Bacterial cultures were incubated for seven days at laboratory temperature (20–23 °C). The MIC was determined as the lowest concentration of metal in the medium with no visible growth of the bacteria.

Results

Morphological characterization

Slight morphological differences were observed between colonies of the P9 and P17 strains. The colonies of the P9 isolate appear beige in color and circular in shape with scalloped margins (Fig. 1a). Colonies of the P17 isolate were wrinkled in shape with undulated margins and white in color (Fig. 1b). The Gram staining allowed the visualization of Gram-positive filamentous structures characteristic for Streptomyces species (Fig. 1c, d).

Fig. 1.

Morphology of Streptomyces spp. strains obtained from the mine heap in Tarnowskie Góry (Poland). Colony shape of the isolate P9 (a) and P17 (b); Cell morphology of the isolate P9 (c) and P17 (d), microscopic observation at 1000x total magnification

The 16S rRNA gene sequence–based identification and the whole genome analysis

The 16S rRNA gene sequence analysis confirmed that both isolates P9 and P17 are members of the Streptomyces genus. The P9 strain showed 100% similarity to 11 different Streptomyces species, and the P17 strain showed the highest similarity (98.91%) to the Streptomyces flavofungini strain NBRC 13,371 (NR_041125.1) when compared against the 16S rRNA database of bacteria and archaea.

Using whole-genome sequencing, we obtained the draft genome of Streptomyces sp. P9 with a size of 8,077,218 bp and a GC content of 70.8%. The genome consists of 7,634 predicted protein coding sequences, 263 pseudogenes and 67 tRNAs. The draft genome size of Streptomyces sp. P17 is 8,790,338 bp with a GC content of 69.6%. It consists of 8,623 predicted protein coding sequences, 459 pseudogenes and 72 tRNAs (Table 1).

Table 1.

Genome features of Streptomyces sp. P9 and P17

Feature	Stretomyces sp. P9	Streptomyces sp. P17
Genome length (bp)	8,077,218	8,790,338
Raw reads	5,081,370	10,150,900
Genome coverage	170x	338x
GC content (%)	70.8	69.6
N50 (bp)	161,298	97,668
L50	12	31
Genes (total)	7,975	9,166
CDS (total)	7,897	9,082
Genes (coding)	7,634	8,623
CDS (with protein)	7,634	8,623
tRNA	67	72

The P9 isolate formed a clade with Streptomyces lateritius JCM 4389 (NZ_BMTO00000000.1) and the P17 isolate formed a clade with Streptomyces cupreus PSKA01 (NZ_JACMSF000000000.1) according to the Genome BLAST Distance Phylogeny (GBDP) analysis performed using TYGS (Fig. 2). However, in silico dDDH values between the P9 and P17 isolates and their closest relatives, did not reach the threshold values of 70% for classification into the same species (Table S1, Table S2).

Fig. 2.

The phylogenetic placement of the P9 and P17 isolates within the Streptomyces genus based on the GBDP analysis. The GBDP phylogram was generated using TYGS. The branch lengths are scaled in terms of GBDP distance formula d5. The numbers under branches are GBDP pseudo-bootstrap support values based on 100 replications. Mycobacterium tuberculosis H37Rv was used as the outgroup. Leaf labels are annotated by affiliation to species, subspecies, G + C content, delta values, overall genome sequence length and number of proteins [35]

The ANI value between the P9 strain and its closest relative S. lateritius JCM 4389 was 88.36%; and the ANI value between the P17 strain and its closest relative S. cupreus PSKA01 reached 92.56%. In addition, the ANI value between the P9 and P17 isolates was only 85.76% (Fig. S1).

The obtained results demonstrate the phylogenetic differences between isolates P9 and P17, although they were obtained from the same environment. In addition, the genome analysis using the RAST server showed some differences in the subsystem feature between the P9 and P17 strains, mainly in the “Metabolism of aromatic compounds”, “Carbohydrates” or “Fatty acids, lipids, and isoprenoids” categories (Table 2).

Table 2.

The genome feature subsystems of the P9 and P17 strains determined using the RAST server [31–33]

Subsystem Categories/Isolates	P9 No. of genes	P17 No. of genes
Sulfur metabolism	9	11
Cofactors, vitamins, prosthetic groups, pigments	212	213
Dormancy and sporulation	7	1
Metabolism of aromatic compounds	25	64
Potassium metabolism	13	13
Membrane transport	65	51
Nucleosides and nucleotides	99	117
Phosphorus metabolism	31	30
Secondary metabolism	0	6
Nitrogen metabolism	18	25
Stress response	53	72
Respiration	123	137
DNA metabolism	103	123
RNA metabolism	49	62
Miscellaneous	35	37
Phages, prophages, transposable elements, plasmids	14	24
Regulation and cell signaling	16	24
Virulence, disease, and defense	66	71
Iron acquisition and metabolism	47	27
Cell wall and capsule	45	55
Amino acids and derivatives	410	434
Protein metabolism	195	226
Carbohydrates	291	435
Fatty acids, lipids, and isoprenoids	148	214

A linear megaplasmid (NODE_22, size of 116,112 bp in length) was identified in the P17 genome based on the annotation of its sequence. The genes encoding the plasmid replication/partition related protein ParB, plasmid mobilization relaxosome protein MobC and relaxase/mobilization nuclease domain-containing protein were identified and checked by the BlastX tool. However, no genes related to heavy metal tolerance were identified in the plasmid sequence.

The pangenome analysis

According to the OrthoVenn3 bioinformatic tool, a total of 7,634 protein-coding sequences identified in the P9 genome were grouped into 5,375 orthologous clusters and 8,623 proteins in the P17 genome were grouped into 6,148 clusters. In addition, we performed the genome comparative analysis using four different Streptomyces strains isolated from heavy metal contaminated areas (Table 3).

Table 3.

Streptomyces genomes isolated from various heavy metal contaminated sites used in the comparative genome analysis

Isolate	GenBank accession no.	Country	Environment	Reference
Streptomyces sp. P9	JAUPWM000000000.1	Tarnowskie Góry, Poland	former lead-silver-zinc mining site	this study
Streptomyces sp. P17	JAVRBF000000000.1	Tarnowskie Góry, Poland	former lead-silver-zinc mining site	this study
S. cadmiisoli ZFG47	CP030073.1	XiangTan China	cadmium-contaminated soil	Li et al. [49]
S. cyaneochromogenes MK-45	CP034539.1	Hunan, China	manganese-contaminated soil	Tang et al. [50]
S. zinciresistens K42	AGBF00000000.1	Shaanxi Province, China	copper-zinc mine tailings	Lin et al. [51]
S. mirabilis P16B-1	CP074102.1	Eastern Thuringia, Germany	former uranium mining site	Brangsch et al. [8]

The analysis revealed that 3,286 orthologous clusters were shared between all six genomes, representing a core genome among examined species (Fig. 3).

Fig. 3.

Venn diagram generated through the OrthoVenn3 server showing shared and unique ortholog gene clusters among six Streptomyces spp. strains isolated from different heavy metal contaminated environments

Shared genes were identified in the biological process category, the molecular function category and in the cellular component category. Primary metabolic process (GO:0044238), heterocycle metabolic process (GO:0046483) or cellular metabolic process (GO:0044237) formed a small part of the predicted Gene Ontology (GO) terms in the “biological process” category. The highest number of proteins was predicted in the metabolic process (GO:0008152) category. In the “molecular functions” category, some of the predicted GO subcategories were the hydrolase activity (GO:0016787), oxidoreductase activity (GO:0016491) or transferase activity (GO:0016740). In the “cellular component” category, the highest number of proteins was predicted in the cell part (GO:0044464) subcategory (Fig. S2).

The phylogenetic tree constructed based on highly conserved single-copy gene sequences confirmed the separate positions within the analyzed species (Fig. S3).

Identification of heavy metal tolerance genes

The genome analysis revealed several genetic determinants associated with the heavy metal tolerance in both studied isolates (Table 4). A complete ars operon associated with arsenic tolerance was predicted in both genomes.

Table 4.

Metal resistance genes identified in the genomes of Streptomyces sp. P9 and Streptomyces sp. P17

Mechanisms	Gene	Function	Corresponding protein accession numbers
			Streptomyces sp. P9	Streptomyces sp. P17
Efflux transporters	arsA	arsenic efflux ATP-binding protein	MDT9693060	MDT9694422
	arsB	arsenical pump membrane protein	MDT9687420	MDT9701266
	cadA	cadmium-translocating ATPase	MDT9692968	MDT9696547
	copA	copper-translocating ATPase	MDT9692973	MDT9698404
	corA	magnesium/cobalt transport protein	MDT9692096	MDT9697520
	corC	magnesium/cobalt efflux protein	MDT9690691	MDT9700755
	czcA	cobalt/zinc/cadmium efflux transporter	-	MDT9702863
	czcD	cobalt/zinc/cadmium efflux transporter	MDT9687400	MDT9697327
	rcnA	nickel/cobalt transporter	-	MDT9696211
	mntH	manganese transport protein	-	MDT9702396
	emrE	multidrug efflux SMR transporter	MDT9687724	MDT9696300
		ABC-type multidrug transport system	MDT9687278	MDT9696831
		RND multidrug efflux protein	MDT9688013	MDT9701006
		MATE family efflux transporter	MDT9692020	MDT9694415
Metal resistance	arsC	arsenate reductase	MDT9687427	MDT9701270
	arsH	arsenic resistance protein	MDT9694184	-
	arsO	flavoprotein monooxygenase, associated with arsenic resistance	MDT9687423	RMT89_36725
	cadD	cadmium resistance protein	-	RMT89_07855
	copC	copper resistance protein	MDT9691962	MDT9698369
	copD	copper resistance protein	MDT9689729	MDT9698376
	copZ	copper chaperone	MDT9692972	MDT9698402
	merA	mercuric reductase	MDT9687224	MDT9696631
	merB	alkylmercury lyase	MDT9687418	MDT9696633
	mmco	multicopper oxidase	MDT9692999	MDT9696601
	terA	tellurium resistance protein	MDT9692219	MDT9698469
	terB	tellurium resistance protein	MDT9691431	MDT9701583
	terC	tellurium resistance protein	MDT9687769	MDT9694611
	terD	tellurium resistance protein	MDT9692217	MDT9699692
	terE	tellurium resistance protein	MDT9692220	MDT9701745
	terZ	tellurium resistance protein	MDT9687771	MDT9694609
Regulators	arsR	arsenical resistance operon repressor	MDT9687425	MDT9696893
	cadC	cadmium resistance transcriptional regulatory protein	-	MDT9696672
	merR	mercuric resistance operon regulatory protein	MDT9687419	MDT9696537

- the absence of the gene

We found genes linked with the cadmium tolerance in both genomes, however, the cadA gene was found only in the P9 genome. The cop operon associated with the copper tolerance was identified in both genomes. Additionally, both strains possess multicopper oxidase, which is crucial for copper detoxification in various bacterial species. Both genomes contain the cobalt/zinc/cadmium efflux system gene czcD. In the genome of P17, the czcA gene was also predicted. Genes of the mer operon associated with mercury tolerance were identified in both strains. Furthermore, strain P17 harbors the gene for cobalt/nickel efflux (rcnA). terABCDEZ genes, responsible for tellurium tolerance, were found in both genomes. Additionally, multiple efflux transporters were identified in both strains. The most prevalent efflux transporter proteins belong to the ABC transporters. Additionally, many RND transporters and multidrug and toxic compound extrusion (MATE) family efflux transporters were identified. We observed the presence of sodN gene encoding nickel superoxide dismutase (NiSOD) and the nearby located sodX gene encoding nickel-type superoxide dismutase maturation protease in both genomes. We also observed genes encoding iron/zinc-containing superoxide dismutase.

The correlation analysis of heavy metal tolerance in Streptomyces spp. strains isolated from heavy metal contaminated and non-contaminated environments revealed the absence of genes associated with mercury (merA, merB) and cadmium tolerance (cadA, cadC, cadD) in the majority of strains from non-contaminated environments. The occurrence of other metal resistance genes was very similar in both groups of isolates (Fig. 4).

Fig. 4.

The occurrence of the heavy metal resistance genes in Streptomyces spp. strains isolated from heavy metal contaminated (strain designation in blue) and non-contaminated (strain designation in black) areas. The correlation strength between the variables on each axis is represented by the color intensity. A red color represents a positive correlation, while a blue color indicates a negative correlation. Focusing on the strain-specific resistance profiles, the genes presented in all strains (arsA, arsC, arsR, copA, corA, corC, merR, terA, terB, terC, terD, terZ) were excluded from the correlation analysis by the ClustVis software

Identification of antimicrobial resistance genes

The presence of genes associated with the rifampin (helR, rox) and vancomycin (vanW, vanR) antibiotic resistance was predicted in both Streptomyces genomes (Table 5).

Table 5.

Antimicrobial resistance genes identified in the genomes of Streptomyces sp. P9 and Streptomyces sp. P17

Antibiotic resistance	Gene	Protein	Streptomyces sp. P9	Streptomyces sp. P17
Rifampin resistance	helR	RNA polymerase recycling motor ATPase HelR	MDT9690185	MDT9698480
	rox	Rifampin monooxygenase	MDT9693313	-
Vancomycin resistance	vanW	VanW family protein	MDT9692821	MDT9696596
	vanR	Response regulator transcription factor	-	MDT9695143

- the absence of the gene

Secondary metabolites prediction

In Streptomyces sp. P9, 27 gene clusters involved in secondary metabolite production were identified, including non-ribosomal peptide synthetase (NRPS), NRPS-like, polyketide synthase (PKS) type I, II and III, PKS-like, ribosomally synthesized and post-translationally modified peptide (RiPP)-like, terpene, ectoine, thiopeptide, b-lactam, NRP-metallophore, melanin, siderophore and arylpolyene. All identified clusters were compared with the reference gene clusters from the MIBiG database. For example, in NODE_1, four BGCs were identified with a relatively low percentage of similarity to known clusters. Specifically, ulleungmycin showed only 5% similarity, gausemycin A and B 2%, thiazostatin/watasemycin 26% and combamide 55% similarity. On NODE_7, desferrioxamine B and desferrioxamine E (siderophores) were identified with 100% similarity to the reference gene clusters encoding desferrioxamine B obtained from S. griseus subsp. griseus NBRC 13350 (MIBiG: BGC0000941). Similarly, ectoine identified on NODE_12 has 100% similarity to the gene clusters encoding ectoine identified in S. anulatus (MIBiG: BGC0000853). In contrast, zorbamycin predicted on NODE_41 showed only 4% similarity to the gene cluster identified in S. flavoviridis (MIBiG: BGC0001058). Sixteen putative gene clusters showed less than 60% similarity to the known clusters in the MIBiG database. NODE_11 and 19 were predicted to contain RiPP-like BGCs, which, however, did not match any reference gene clusters from the MIBiG database. Similarly, regions with predicted clusters encoding possible arylpolyene, PKS-like and terpene type BGCs were found on NODE_30, 35 and 40.

There were identified 38 BGCs in Streptomyces sp. P17, including PKS type I, II and III, NRPS, NRPS-like, RiPP-like, terpene, NI-siderophore, ectoine, NRP-metallophore, furan, melanin, lanthipeptide-class-i, arylpolyene, butyrolactone and NAPAA. Ectoine, albaflavenone, informatipeptin, scabichelin or desferrioxamine B were identified, showing 100% similarity to the known gene clusters in the MIBiG database. On NODE_5, the gene cluster encoding griseusin production was identified with only 5% similarity. Nineteen putative gene clusters showed less than 60% similarity to known clusters. Gene clusters encoding various types of BGCs (including NRPS, T1PKS, butyrolactone and RiPP-like) were detected in nine cases, revealing no similarity to known BGCs, suggesting potential new compounds. All BGCs are displayed in Table S3 and Table S4.

The antiSMASH software predicted the presence of three putative hydroxamate-type siderophores in the P9 genome and three hydroxamate-type and one mixed-type siderophores in the P17 genome (Table S3, Table S4). The spectrum of siderophore genes was identified among streptomycete strains analyzed; however, the correlation analysis did not confirm any significant relationship between the abundance of siderophore genes and origin of Streptomyces strains (heavy metal contaminated and non-contaminated environments) (Fig. 5, Fig. S4).

Fig. 5.

The occurrence of the putative siderophores in Streptomyces spp. strains isolated from heavy metal contaminated (strain designation in blue) and non-contaminated (strain designation in black) areas grouped into four types based on their functional groups. The correlation strength between the variables on each axis is represented by the color intensity. A red color represents a positive correlation, while a blue color indicates a negative correlation

Siderophore production

Both isolates (P9 and P17) produced siderophores as indicated by the change in color of the cultivation medium using the O-CAS method [43, 46] (Fig. 6).

Fig. 6.

The siderophore production by the P9 and P17 strains determined by the color change of the cultivation medium from blue to orange around the colonies using the O-CAS method [43, 46]

Heavy metal tolerance assay

The P9 and P17 isolates showed the highest tolerance to lead (1,500 mg/L), cadmium (1,200 mg/L and 1,000 mg/L) and zinc (1,200 mg/L and 1,000 mg/L), lower tolerance to copper (500 mg/L) and nickel (500 mg/L) and the lowest tolerance to cobalt (200 mg/L) (Fig. 7).

Fig. 7.

Minimal inhibitory concentration of selected metals affecting the growth of P9 and P17 isolates

Discussion

Microorganisms living in extreme environments possess several adaptation mechanisms involving structural, physiological and metabolic specializations [52]. The investigation of these mechanisms via genomic analysis is a key to understanding how these organisms can thrive under challenging conditions.

In our study, all phylogenetic analyses (dDDH, GBDP, ANI) confirmed that the P9 and P17 isolates represent two new, phylogenetically different species within the genus Streptomyces. Nevertheless, further analyzes are needed to confirm these isolates as new bacterial species.

We confirmed the presence of several genes associated with a rich metabolism and the production of secondary metabolites. According to Schniete et al. [53], the metabolic robustness is a significant adaptation mechanism used by microorganisms to cope with harsh environmental conditions by achieving gene family expansions through gene duplication or horizontal gene transfer. In this regard, the carbohydrate metabolism is one of the most interesting mechanisms of energy acquisition in bacteria. Different Streptomyces species are able to utilize different carbon sources through highly regulated enzymes that can affect the production of secondary metabolites [54–56]. This finding could explain the difference in the number of genes involved in the carbohydrate metabolism in our isolates. Moreover, the rich carbohydrate metabolism may also be a response to stress factors, including exposure to heavy metals [57].

The P9 and P17 isolates, as well as four other Streptomyces spp. strains isolated from heavy metal contaminated areas shared 3,286 orthologous genes with each other, suggesting that these genes are well conserved and provide essential functions for bacterial survival.

Due to the homologous functions of these gene clusters, they probably evolved from a common ancestor and represent the core genome [58]. Most gene clusters in the core genome are involved in primary metabolic processes of bacteria. On the other hand, unique genes are mainly associated with the secondary metabolism. A similar distribution of core and accessory genes was observed in the study of Khushboo et al. [59] who compared ten selected species of Streptomyces.

Our findings showed that the heavy metal tolerance of the studied Streptomyces strains is provided by several specific (e.g. cadmium and copper ATPases) and non-specific “multidrug” efflux pumps such as MATE or multidrug efflux SMR transporters. Generally, most bacterial tolerance to drugs and toxic compounds is provided by efflux pumps. They participate in the uptake of nutrients from the environment, in the metabolite excretion into the environment as well as in the efflux of drugs and toxic substances from cells [60]. Zhou et al. [61] identified more than 600 predicted transport proteins in Streptomyces coelicolor, as the model organism of the Actinomycetota phylum, with a high proportion of ABC and MFS transporters, ensuring the transport of drugs and toxins through cell membranes.

In addition, several specific genes involved in heavy metal tolerance mechanisms were identified, including metalloid tellurium (ter operon), e.g., the complete ars operon involved in the arsenic tolerance, the mer operon providing the tolerance to mercury or the cop operon involved in the copper tolerance. Some of the identified genetic determinants were very similar among all strains (from the heavy metal contaminated and non-contaminated areas) included in the comparative genome analysis. However, e.g., genes of the ars operon are widespread in microorganisms and they are known to occur even in environments that are not contaminated by the presence of arsenic [62]. On the other hand, genes encoding CadA and CadC proteins associated with the cadmium tolerance appear to be present mostly in the strains isolated from the heavy metal contaminated environments, as well as the merA and merB genes involved in the mercury tolerance mechanisms. Streptomyces spp. species can cope with stress factors through various resistance mechanisms such as oxidation/reduction, efflux pumps, extracellular binding by chelators, siderophores or superoxide dismutases. These mechanisms evolve over time in response to environmental pressure in order to survive, regardless of their phylogeny [63]. These findings point out that the genes carried by the species isolated from the non-contaminated areas are probably acquired through the horizontal gene transfer among Streptomyces species and may thus represent a form of pre-adaptation to environments with heavy metals, resulting in the evolution of response to harsh conditions.

The presence of genetic determinants in the bacterial genomes predicted a high metal tolerance of isolates P9 and P17, which was also confirmed by MIC determination using the agar dilution method. The high tolerance to several heavy metals was also observed in other Streptomyces species inhabiting mining areas [64, 65]. However, the studies mentioned used different culture media, which may affect the toxicity of metals [10].

In addition, the presence of sodN gene encoding NiSOD and sodX gene encoding nickel-type superoxide dismutase maturation protease was observed in both isolates. NiSOD is a unique type of superoxide dismutase found only in a few species of actinomycetes and cyanobacteria [66]. We also observed iron/zinc-containing superoxide dismutase. The presence of superoxide dismutases can play a crucial role in bacterial tolerance to heavy metals by removing reactive oxygen forms that arise when exposed to heavy metals [67].

We also revealed the presence of several antibiotic resistance genes belonging to the rifamycins (e.g., rifampin, rifaximin, rifabutin) and glycopeptides (e.g., vancomycin, teicoplanin) antibiotic drug classes in the P9 and P17 genomes. Rifamycins are antibiotics that inhibit bacterial transcription of RNA polymerase. Surette et al. [68] reported that 49 of 500 soil isolates were highly resistant to rifamycins. The rifamycin resistance is generally associated with the mutations in a β-subunit of RNA polymerase affecting the binding site of rifamycin [69]. In a recent study, the ability of HelD/HelR protein to dissociate the non-functional rifamycin-bound RNA polymerase complex was characterized, representing a new mechanism of resistance to this antibiotic [70]. However, the streptomycetes are believed to possess the mechanisms that ensure the natural rifamycin resistance [69].

The glycopeptide antibiotics inhibit bacterial cell wall synthesis in Gram-positive bacteria; thus, they are used for the treatment of infections caused by multidrug-resistant Gram-positive pathogens. In S. coelicolor, a vancomycin resistance gene cluster consisting of genes vanSRJKHAX was identified. The functionality of these genes is dependent on the gene vanR. However, we were unable to identify the remaining genes of this cluster in our strains, suggesting an incomplete vancomycin resistance mechanism [71].

Nevertheless, the antibiotic resistance and the production of antimicrobial compounds by Streptomyces spp. is an extensive topic that is beyond the specific focus of this study.

Some traits, such as metal tolerance and antibiotic resistance, are often encoded by plasmids [8], but no genes associated with adaptation mechanisms were found on the predicted linear plasmid NODE_22 in the P17 strain.

Bacteria belonging to the Streptomyces genus represent one of the most diverse groups of secondary metabolite producers. Notably, their genomes contain a large amount of BGCs, much more than other Actinobacteria [72]. In recent decades, there has been an ongoing effort to find new compounds to combat bacterial resistance, with streptomycetes emerging as the primary reservoir of antimicrobial agents. Lacey and Rutledge [73] assumed that there are 150,000 more antimicrobial compounds than are currently known. However, many genes remain cryptic, resulting in their inability to produce secondary metabolites under laboratory conditions. However, genome mining and bioinformatics approaches allow us to identify potential clusters in Streptomyces genomes, that cannot be found by traditional laboratory methods [13–15]. This approach revealed a remarkable diversity of BGCs not only among different species of streptomycetes but also among various strains within the same species and play a key role in the exploration and discovery of potential new products [15]. The annotation of the P9 and P17 genomes by the antiSMASH database revealed the presence of 18 different major BGC classes in both strains, including gene clusters that have not yet been identified. Desferrioxamine B, a natural siderophore that accumulates ferric ions and other metal ions in cells, is used for the treatment of iron overdose in humans [15, 74], ectoine, a compound preventing bacteria from osmotic stress and used in the cosmetic industry as a skin protector against various environmental stress factors [75, 76] or melanin with a competitive advantage in certain environments [77] are present in the P9 and P17 genomes. Both strains can produce multiple metabolites with antibacterial, antifungal and antitumor activity. Some of them are not fully described because of their low similarity with known compounds. As previously mentioned, several compounds with the antimicrobial activity were predicted, such as ulleungmycin, gausemycin, aborycin or istamycin and several other antibiotics exhibiting antibacterial activity against several Gram-positive or Gram-negative bacteria [78–81]. Actinobacteria are also known for producing substances with antitumor activity used in medicine, including bleomycin, doxorubicin, daunomycin, mithramycin, etc. Their production was also confirmed in Streptomyces spp [82]. Our findings demonstrated the presence of clusters that could be sources of important antitumor products, such as zorbamycin, stambomycin or showdomycin. All these metabolites show low similarity to previously described compounds, so they could be quite interesting in terms of their use in medicine.

We also confirmed the high variability of these metabolites in the P9 and P17 strains. In addition to common Actinomyces compounds, another 15 BGCs were identified in the P9 genome, which did not share homology with the 22 unique BGCs found in the P17 genome.

Additionally, we detected five BGCs in the P9 genome that did not match any reference gene clusters in the MIBiG database and nine BGCs in the P17 genome with unknown functions, suggesting the production of novel compounds.

The production of siderophores plays an important role in iron homeostasis in bacteria. Iron in soil has a low bioavailability for bacteria, and siderophores help bind Fe³⁺ ions and transport them into the cell [43, 44]. Siderophores can also interact with other heavy metals, such as zinc, cadmium or nickel, to provide the protection against toxic metals [43, 83]. As mentioned above, several BGCs encoding siderophores were predicted in the P9 and P17 genomes, and their presence was compared with other Streptomyces strains used in this study. Since the siderophore production is widespread among Streptomyces spp. due to the iron requirement for proper cell wall functioning [84, 85], it is not surprising that they were identified in strains from both heavy metal contaminated and non-contaminated environments. However, more detailed analyses are needed for identification and characterization to determine which siderophores are involved in heavy metal tolerance mechanisms. The putative siderophores in strains P9 and P17 were experimentally confirmed by the formation of orange haloes around colonies grown on CAS agar plates, indicating the production of hydroxamate-type siderophores [43].

Our data indicate that the extreme environments, mostly the poor or unexplored areas, could be considered promising sources of new Streptomyces species with bioactive compound production potential. Moreover, comparative genomic analyses indicate that while common adaptation mechanisms are used for survival in the environment, the isolates are equipped with a unique BGC set to compete with bacteria in the given environment.

Conclusion

Based on obtained results, we can assume that an extreme environment could have a significant impact on the emergence of high variability in the properties of microorganisms and the evolution of new species.

In this study, two different Streptomyces strains isolated from the same environment polluted with heavy metals were investigated. The obtained results indicate that strains represent two different species of the genus Streptomyces, that differ in phylogeny, metabolism and have the potential to synthesize different types of BGCs. On the other hand, adaptation to high concentrations of heavy metals in the environment is ensured in both isolates by very similar mechanisms (similar heavy metal tolerance genes and efflux pumps). Additionally, the comparative analysis with other Streptomyces spp. isolated from heavy metal contaminated and non-contaminated areas revealed that some of the adaptive mechanisms (heavy metal tolerance genes and siderophore production) may be considered as preadaptations to these conditions.

Electronic supplementary material

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Author contributions

ZL performed the heavy metal tolerance testing, genome analysis and wrote the main manuscript text; MC, MP and SA performed the laboratory processing of samples, including the isolation and identification of bacterial isolates; PP was responsible for leading the research team and designing the research project; JK performed genome data analysis, wrote the manuscript text and was responsible for the final editing of the manuscript, its submission for publication and correspondence with the editors of the journal. All authors read and approved the final manuscript.

Funding

The work was financially supported by the Grant Agency of the Ministry of Education, Science, Research and Sport, Slovak Republic and the Slovak Academy of Sciences VEGA, Grant No. 1/0779/21.

Data availability

The data that support the findings of this study are openly available in the GenBank database. Accession numbers of genomes are listed in Table S5.

Declarations

Ethical approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.