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Article

Bacterial Community Structure and Heavy Metal Adaptation in Soils from a Gold–Copper Mining Area in Bulgaria

by
Michaella Petkova
1,
Gergana Dimitrova
1,
Evan Gatev
1,
Mariana Hristova
2,
Nikolai Dinev
2 and
Galina Radeva
1,*
1
Roumen Tsanev Institute of Molecular Biology, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl. 21, 1113 Sofia, Bulgaria
2
N. Poushkarov Institute of Soil Science, Agrotechnologies and Plant Protection, Agricultural Academy, 7 Shosse Bankya St., 1331 Sofia, Bulgaria
*
Author to whom correspondence should be addressed.
Soil Syst. 2026, 10(8), 91; https://doi.org/10.3390/soilsystems10080091
Submission received: 30 June 2026 / Revised: 3 August 2026 / Accepted: 6 August 2026 / Published: 11 August 2026
(This article belongs to the Special Issue Challenges and Future Trends of Soil Ecotoxicology)
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Abstract

Heavy metal/loid (HM) pollution of soils, primarily as a consequence of mining and ore-processing activities, poses significant risks to ecosystems and human health. Soil microbial communities play essential roles in maintaining key ecosystem functions, including nutrient cycling, carbon sequestration, and soil stability. The purpose of this study was to characterize the taxonomic composition and diversity of bacterial communities and evaluate their functional adaptation to heavy metal stress in soils affected by long-term gold–copper mining activities in Bulgaria. Ten soil samples representing a Cu pollution gradient (53–860 mg kg−1) were categorized into five pollution classes. High-throughput sequencing of 16S rRNA gene amplicons revealed the dominance of the phyla Pseudomonadota (mean relative abundance 32%), Acidobacteriota (22%), and Actinomycetota (16%). At the class level, Alphaproteobacteria (18%), Terriglobia (16%), and Gammaproteobacteria (14%) were the most abundant taxa, indicating their adaptation to long-term heavy metal contamination. The genus Z2-YC6860 exhibited significant tolerance to Cu, whereas Bradyrhizobium_503372 was negatively associated with As and Zn concentrations. Functional predictions suggested enrichment of key pathways related to heavy metal resistance, including efflux systems and detoxification. The study design spans a broad Cu pollution gradient across river-associated and industrially impacted sites, providing an ecologically relevant framework for evaluating microbial responses to long-term metal stress.

Graphical Abstract

1. Introduction

Environmental heavy metal pollution is a globally widespread problem, and its effects on the soil microbiome have been extensively investigated. Heavy metals (HMs) are released into the environment through anthropogenic activities such as mining and metallurgical operations [1]. Their accumulation in soils exerts toxic effects on bacterial communities, ultimately impairing soil quality and ecosystem functioning. Soil bacteria play a crucial role in nutrient cycling, soil fertility, and carbon sequestration, all of which are essential for maintaining soil ecological functions [2].
Previous studies have identified several general trends in the response of soil bacterial communities to heavy metals and metalloids, including decreased microbial diversity [3,4,5], reduced bacterial abundance [6,7], increased proportions of metal-resistant taxa [3,8,9], and alterations in microbial biomass and metabolic activity [10]. Metagenomic approaches combined with Illumina high-throughput sequencing have revealed that soil microbial communities in mining-impacted areas are typically dominated by the phyla Pseudomonadota, Actinomycetota, Bacteroidota, Acidobacteriota, and Chloroflexota [11,12,13,14], with marked shifts in their relative abundances across different mining sites. For example, Epelde et al. [15] reported a decline in Actinomycetota and Acidobacteriota in Pb–Zn mining soils, accompanied by an increased prevalence of Chloroflexota, particularly members of the class Ktedonobacteria. Fajardo et al. [16] identified Firmicutes as the most resistant phylum under high concentrations of heavy metals, which selectively displaced other bacterial phyla such as Pseudomonadota, Actinomycetota, and Verrucomicrobiota. Zhao et al. [8] also observed the dominance of Pseudomonadota and Firmicutes across various mining regions and reported a positive correlation between these phyla and heavy metal concentrations (Cu, Zn, Pb).
At the class and genus levels, several studies have highlighted the abundance of Alphaproteobacteria, with the genus Bradyrhizobium showing increased relative abundance in highly contaminated mining sites [17,18]. Other frequently reported classes include Gammaproteobacteria [19,20], Bacilli [21], and additional metal-tolerant taxa [21,22].
Based on metagenomic data, the ecological functions of bacterial communities have frequently been inferred using PICRUSt2 [23]. Functional metagenomic approaches provide insights into genes involved in essential soil microbial processes and enable the identification of microbial taxa harboring genes associated with resistance to heavy metals and metalloids. Such microorganisms constitute a valuable resource for the development of bioremediation strategies [17]. In mining-impacted soils, predicted functional profiles frequently indicate enhanced expression of metal resistance genes (such as znuD, zntA, pbrB, and pbrT, ars operon, copA), revealing adaptive pathways for HM resistance [17,18,19,24].
The adaptive capacity of soil microbiota depends not only on heavy metal concentrations and bioavailability, but also on local soil attributes [11]. Several studies have demonstrated that soil physicochemical parameters, particularly soil pH, organic carbon content, and total heavy metal concentrations, strongly influence the distribution and structure of bacterial communities across different soil types [3,8,25,26].
Despite extensive research, our understanding of the diversity and composition of bacterial communities across different soil habitats within mining regions, as well as the relative contributions of environmental factors and varying levels of heavy metal pollution, remains limited. The main objectives of this study were to: (i) characterize the taxonomic composition and diversity of bacterial communities in mining-impacted heavy metal polluted soils; and (ii) evaluate their functional adaptation to heavy metal stress. For this purpose, high-throughput sequencing of the 16S rRNA gene (V3–V4 region) was performed to characterize bacterial community composition and PICRUSt2 was used to predict their functional profiles.
We hypothesized that heavy metal pollution and local soil properties jointly shape bacterial community structure by selecting for bacterial taxa well adapted to heavy metal stress. The study was conducted in the Zlatitsa–Pirdop Valley region in Western Bulgaria, which has been affected by copper–gold mining for decades. The region contains one of the largest gold-copper deposits on the Balkan Peninsula. Long-term monitoring data indicate persistent soil pollution of diffuse or pedogenic origin, primarily with Cu, Zn, Pb, Cd, and As [27,28]. By integrating river-associated and industrially impacted agricultural soils spanning a broad Cu pollution gradient (53–860 mg kg−1), this study provides new insights into microbial adaptation and resilience and supports the development of microbial indicators for soil health assessment and bioremediation strategies.

2. Materials and Methods

2.1. Study Area and Soil Sampling

Soil samples were collected in May 2024 from different land-use types in the Zlatitsa–Pirdop Valley region, Western Bulgaria, along a gradient of Cu pollution and co-contaminants Zn, Pb, Cd, and As (Figure 1). The soil samples S1 (42°40′26.3″ N, 24°03′11.5″ E), S2 (42°39′16.9″ N, 24°03′42.5″ E), S3 (42°41′25.8″ N, 24°04′09.4″ E), S7 (42°41′23.6″ N, 24°03′49.5″ E) and S10 (42°42′05.0″ N, 24°05′50.5″ E) were collected from highly industrialized area, characterized by numerous mines and processing plants for copper and other non-ferrous metals. The soil samples, noted S4 (soil) and S5 (sediment) (42°39′14.0″ N, 24°09′02.8″ E), S6 (42°39′26.6″ N, 24°08′14.3″ E), S8 (42°39′47.0″ N, 24°08′10.0″ E) and S9 (42°36′40.7″ N, 24°00′46.8″ E) were collected in the vicinity of the Topolnitsa River and its tributaries (Medetska, Zlatishka and Bunovska Rivers), which are impacted by mining and metallurgical activities and serve as the main irrigation network for the surrounding agricultural lands. Soil samples were collected from agricultural fields with industrial and oilseed crops (Lavandula vera L., Gossypium sp., Brassica napus) and from pastures. The soils were classified as Fluvisols [29]; sampling sites encompassed both alluvial settings along the Topolnitsa River system and deluvial-alluvial settings within the adjacent valley and foothill areas. The Zlatitsa–Pirdop Valley is a Sub-Balkan valley situated between the Balkan Mountains to the north and Sredna Gora to the south, with the Topolnitsa River and its tributaries forming the main drainage network [27,28]. The study area therefore represents a mixed industrial-agricultural landscape in which mining and metallurgical activities occur in proximity to agricultural fields, pastures, settlements, and waters used for irrigation [27,28]. Sampling locations were selected to represent contrasting positions along the Cu-dominated contamination gradient, including highly industrialized sites and river-associated agricultural settings.
At each site, five subsamples (approximately 200 g each) were randomly collected within a 100 m × 100 m quadrat from the upper 0–20 cm layer to account for small-scale spatial heterogeneity. The five subsamples were combined and homogenized to obtain one representative composite sample per site. After collection, the sample material was sieved, and each composite sample was divided into two portions: one stored at −80 °C for molecular analysis and the other at 4 °C for physicochemical analysis.

2.2. Soil Physicochemical Properties

Soil pH (H2O) (1:2.5 soil/water ratio) was determined according to BDS EN ISO 10390:2022 [30], and organic carbon (TOC) was measured according to [31]. Soil moisture (SM) was calculated after oven drying (105 °C).

2.3. Heavy Metal Content and the Level of Pollution

The concentrations of HMs were determined by inductively coupled plasma optical emission spectrometry (ICP-OES) using an Agilent 5800 instrument (Agilent Technologies, Santa Clara, CA, USA) after microwave-assisted aqua regia digestion (ETHOS EASY, Milestone, Sorisole (BG), Italy) of soil samples, in accordance with ISO 11047:1998 [32]. The Single Pollution Index (PI) and the integrated Nemerow pollution index (NPI) were calculated to assess the level of HM pollution [33]. NPI values were interpreted as follows: <0.7, clean; 0.7–1.0, warning limit; 1.0–2.0, slight pollution; 2.0–3.0, moderate pollution; and >3.0, high pollution [33]. The maximum permissible concentrations (MPC) of heavy metals specified by Bulgarian Regulation 3/2008 were used as a reference for calculating the PI and NPI (Ministry of Environment and Water of the Republic of Bulgaria, National Legislation (Soils), available at: https://www.moew.government.bg/bg/pochvi/zakonodatelstvo/nacionalno-zakonodatelstvo/ (accessed on 1 August 2008)). For S5, which is a sediment sample, the corresponding soil thresholds were used solely as comparative screening values and do not represent legally applicable sediment-quality criteria.

2.4. DNA Extraction and Illumina Sequencing

Total DNA was extracted from 0.5 g of soil using the E.Z.N.A. DNA Soil Kit (Omega Bio-tek, Norcross, GA, USA) following the manufacturer’s protocol. DNA concentration and purity were measured using a Qubit 4 Fluorometer (Thermo Scientific, Waltham, MA, USA) and a NanoDrop 1000 spectrophotometer (Thermo Scientific, Waltham, MA, USA), respectively. DNA quality was assessed by 1% agarose gel electrophoresis. Targeted sequencing of 16S amplicons (V3–V4 region) was performed with barcoded primers 341F (5′-CCTACGGGNGGCWGCAG-3′) and 806R (5′-GACTACHVGGGTATCTAATCC-3′) [34]. Amplicon libraries were prepared and sequenced on an Illumina MiSeq platform (Macrogen, Seoul, Republic of Korea) with 2 × 300 bp reads.

2.5. Bioinformatics and Data Processing

After trimming the barcodes, the raw sequencing data were processed with the standard QIIME2 pipeline [35]. DADA2 was used to cut primer sequences, denoise and select representative sequences [36]. Predicted functional changes in the bacterial community were assessed with the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) software package, v.2.6.3. [23]. The resulting KO IDs were manually annotated using the KEGG database to estimate the abundances of HM resistance genes.
Taxonomic classification of bacterial sequences was performed with a Naive Bayes classifier pre-trained on the Greengenes 13_8 99% OTU reference database (2024.09.taxonomy) [37,38]. ASVs were clustered at 99% sequence identity. Chimera checking was conducted on aligned 16S rRNA sequences using the UCHIME algorithm [39]. Alpha diversity indices, including observed features (ASVs), ACE, Chao1, and Shannon, were calculated using the QIIME 2 platform.

2.6. Data Analyses

Statistical analyses and graphical outputs were performed in R version 4.4.1, using the pheheatmap, ggplot2, tidyr and dplyr packages [40]. Relationships between bacterial taxonomic abundance at the genus level, soil physicochemical properties, and HM concentrations were analyzed using Spearman’s rank correlation at a significance level of p < 0.05. Benjamini–Hochberg false discovery rate (FDR) correction was also performed to account for multiple comparisons.

3. Results

3.1. Heavy Metal Content, Level of Pollution, and Soil Physicochemical Properties

The concentrations of Cu in S1, S4–S10 soil samples exceeded the corresponding maximum permissible concentrations (MPCs) under Bulgarian legislation (Table 1). Pollution indices (PIs) (Table S1) and NPI were calculated as the ratios of measured heavy metal content and MPCs to assess the pollution levels of the soils [33]. Cu had the highest mean single-element pollution index (mean PI(Cu) = 3.0) and was the major contributor to the overall pollution assessment, which categorized the soils as moderately contaminated. The overall heavy metal pollution, based on the calculated NPI, follows the order: S1 and S2 (clean, uncontaminated) < S3 (warning) < S4, S5 and S6 (slight) < S7 and S8 (moderate) < S9 and S10 (high) (Table 1). Pb exceeded the applicable soil MPC only in S10. Elevated As concentrations were recorded in S2, S3, S7, S8, and S10; the corresponding PI values are reported in Table S1. Soils were nutrient-rich, with TOC ranging from 12.5 g·kg−1 to 42.6 g·kg−1, except for S9 at 5.9 g·kg−1. pH varied from moderately acidic 5.3 to slightly alkaline 7.3. SM (%) was higher in S4, S8, and S9.

3.2. Alpha Diversity of Bacterial Communities

A total of 136,036 high-quality bacterial reads were obtained from 16S rRNA sequencing of the 10 topsoil samples, and detected sequences of each sample varied from 12,834 to 15,563, with a length of 301 bp (Table 2). The valid sequences were clustered into 5133 ASVs based on 99% similarity, which were taxonomically classified into 25 phyla, 47 classes, and 320 genera (105 genera were >1.5%).
The alpha diversity indices (ACE and Chao1) showed considerable variation among the studied soils, while showing a similar pattern across samples with ACE values ranging from 325.01 to 770.39 and Chao1 values from 331.33 to 756.06. Four samples exhibited relatively low estimated richness (331.33 to 517.40), whereas others showed substantially higher richness (597.45–756.06), suggesting marked variation in bacterial taxon richness across the sampled soils. This difference indicates that some soils may harbor approximately twice the number of bacterial taxa compared with others. Shannon index ranged from 7.97 to 8.89, which suggests that the soils had high bacterial richness and diversity.

3.3. Taxonomic Composition and Structure of Soil Bacterial Communities

Bacterial community analyses on soil samples revealed a total of 25 bacterial phyla, and only 18 phyla displayed a relative abundance <1%. Pseudomonadota, Acidobacteriota and Actinomycetota represented 70% of the bacterial sequences (Figure 2a). Pseudomonadota showed higher relative abundance in clean, warning, slight (S5), moderate (S8) and high (S9) (31–45%) levels of HM pollution. In contrast, its abundance decreased markedly in soils S4, S7, and S10, where it accounted for only 17–20% of the community. An opposite trend was observed for Acidobacteriota, whose relative abundance increased substantially in these samples, reaching a maximum of 43.54% in S4. Actinomycetota were relatively evenly distributed among the clean, warning, and highly polluted (S10) soils, with relative abundances ranging from 21.87% to 26.40%. However, a pronounced increase was observed in the moderately polluted soil S7, where this phylum represented 39.49% of the bacterial community.
Among the subdominant phyla, Bacteroidota exhibited elevated relative abundances in the slightly polluted (S5), moderately polluted (S8), and highly polluted (S9) soils, ranging from 12.75% to 20.87%. Verrucomicrobiota were relatively evenly distributed across most soils; however, their abundance decreased in the moderately polluted (S7 and S8) and highly polluted (S10) soils, declining from 3.47% to 2.27%, respectively. Notably, increased abundances of Bacillota_I and Chloroflexota were observed in specific samples. Bacillota_I reached 16.25% in the slightly polluted soil S4, whereas Chloroflexota attained its highest relative abundance in sample S6 (6.23%).
At the class level, 31 bacterial classes (relative abundance < 1%) were detected across soils along the Cu pollution gradient (Figure 2b). The bacterial communities were predominantly composed of Alphaproteobacteria, Gammaproteobacteria, and Terriglobia. Alphaproteobacteria displayed a relatively stable distribution across the soils, contributing 13.69–25.0% of the total community. In contrast, the relative abundance of Gammaproteobacteria varied considerably among samples, ranging from 2.0% in S4 to 25.0% in S5. Terriglobia showed an inverse distribution, with the highest abundances in the slightly polluted soils S4 (39.71%) and S6 (27.58%), followed by a pronounced decline in S5 (2.27%). Actinomycetes were particularly enriched in S7 (approximately 37%) and remained among the dominant classes in S1, S2, S3, and S10, whereas Bacteroidia were more abundant in S5, S8, and S9. Blastocatellia showed a relatively uniform distribution across samples but increased in highly polluted soils (S9 and S10), reaching approximately 10%. Members of Verrucomicrobiae accounted for 3.47–7.55% of the bacterial community in most soils but were less abundant in S7 and S8. Bacilli_A was markedly enriched in S4 (16%) and was absent from S8 and S9.
At the genus level, the relative abundance of the 13 most dominant bacterial genera across the soil samples is presented in Figure 3. Distinct distribution patterns were observed along the Cu pollution gradient. Sphingomicrobium_483192 (class Alphaproteobacteria) was among the dominant genera in the uncontaminated, warning and slightly polluted soils (S1–S4), reaching its highest relative abundance in warning S3 soil (12.01%). Its abundance markedly decreased in the highly polluted soils (S9–S10), remaining below 5%.
In contrast, Z2-YC6860 (Alphaproteobacteria) was more abundant in moderately and highly polluted soils, particularly in S8–S10, where its relative abundance ranged from approximately 5.0% to 7.4%, with the highest value recorded in S10. Lower abundances were observed in the slightly polluted soils.
Palsa-1315 (class Nitrospiria) exhibited a relatively stable distribution across most soil samples, with abundances around 5%. However, its abundance increased in the highly polluted soils, reaching a maximum of 6.11% in S9. PSRF01 (class Blastocatellia) displayed considerable variation among the soil samples. Relatively low abundances were detected in the moderately polluted soils S7 and S8 (3.28% and 4.09%, respectively), whereas substantially higher abundances were observed in the highly polluted soils S9 (12.05%) and S10 (8.09%). Notably, a high abundance was also recorded in the uncontaminated S2 (9.40%), indicating that factors other than heavy metal pollution may also influence its distribution. The results revealed a shift in the dominant bacterial genera along the pollution gradient, with Sphingomicrobium_483192 prevailing in the less contaminated soils, whereas Z2-YC6860, Palsa-1315, and PSRF01 tended to increase in abundance under higher levels of heavy metal pollution.

3.4. Relationships of Heavy Metal Concentrations, Soil Properties and Bacterial Genera

The relationships between HM concentrations, soil physicochemical properties, and bacterial genera were assessed using Spearman’s rank correlation analysis, with statistical significance determined at p < 0.05 (Figure 4). It showed that several bacterial genera exhibited distinct responses to HM pollution and local soil properties. Z2-YC6860 and Reyranella (class Alphaproteobacteria), PSRF01 (class Blastocatellia), and Palsa-1315 (class Nitrospiria) generally showed positive correlations with most HMs. Among them, Z2-YC6860 exhibited a significant positive correlation with Cu concentration (Spearman’s ρ = 0.7504, p < 0.05), suggesting a potential adaptation to Cu-enriched environments. In contrast, Bradyrhizobium_503372 (class Alphaproteobacteria) was negatively correlated with all investigated HMs. Significant negative correlations were observed with As (Spearman’s ρ = −0.6828, p < 0.05) and Zn (Spearman’s ρ = −0.6760, p < 0.05). Similarly, Mycobacterium (Actinomycetes) showed a significant negative correlation with Cd concentration (Spearman’s ρ = −0.6964, p < 0.05).
Regarding soil physicochemical properties, Reyranella, PSRF01, and Chryseolinea_899021 (Bacteroidia) were significantly negatively correlated with TOC, with Spearman’s correlation coefficients of −0.6487, −0.6788, and −0.6555, respectively (p < 0.05). Negative correlations with (SM) and pH were also observed for Streptomyces_400150 and Mycobacterium, while Angelobacter (Terriglobia) was negatively correlated with pH. However, among these relationships, only the correlation between Streptomyces_400150 and SM was statistically significant (Spearman’s ρ = −0.6713, p < 0.05). Although several significant correlations were observed between bacterial genera, HMs and soil physicochemical properties, none remained significant after FDR correction, likely due to the limited number of soils and high number of comparisons.
The correlation analysis indicated that several bacterial genera, particularly Z2-YC6860, Reyranella, and PSRF01, were positively associated with elevated HM concentrations, whereas Bradyrhizobium_503372 and Mycobacterium appear to be negatively affected by increasing metal levels. These patterns suggest differential ecological adaptation of bacterial taxa to HM pollution and local environmental conditions.

3.5. Predicted Bacterial Metabolic Pathways Related to HM Resistance

Using the KEGG database, the relative abundance of the most prevalent heavy metal (HM) resistance-related functional pathways (>2% abundance in at least one soil) was identified (Table S2), mainly associated with Cu, As, Zn, and Cd resistance. Figure 5 presents a heatmap of the relative abundance of the predicted KEGG orthologs involved in HM resistance.
Based on their relative abundance, the identified KEGG orthologs could be classified into four groups. The most abundant genes were arsR (K03892) with relative abundances ranging from 14.35% to 18.00%, and copA (K17686), ranging from 11.23% to 14.68% across slightly and highly polluted soils. The second group included K03709 (troR), K16264 (czcD), K15726 (czcA), and K15727 (czcB), with relative abundances ranging from 5.00% to 10.69%. The third group comprised K01534 (zntA), K01533 (copB), K02077 (ABC.ZM.S), K07787 (cusA), K07238 (TC.ZIP), K03322 (mntH), and K15725 (czcC), with abundances of up to 6.46% (Table S2). The fourth and least abundant group included K07245 (pcoD), which reached approximately 4% abundance in samples S3 and S7, as well as K09816 (znuB), K09815 (znuA), K02074 (ABC.ZM.A), K02075 (ABC.ZM.P), K03893 (arsB), and K07665 (cusR), all of which exhibited relative abundances below 4%.

4. Discussion

4.1. Heavy Metal Pollution and Soil Properties

The Zlatitsa–Pirdop Valley has been subjected to decades of copper mining and metallurgical activities, resulting in persistent Cu-dominated soil pollution consistent with previous studies from the region and other mining-affected areas [28,41]. The soils covered five NPI pollution categories (Table 1). The mobility and toxicity of HM and their effects on microbial communities are strongly modulated by soil properties such as pH, organic matter, texture, and land use [1,28], underscoring the importance of these interactions for ecological protection and sustainable land management [3,21,42].

4.2. Taxonomic Composition and Structure of Bacterial Communities

Alpha bacterial diversity observed across the different pollution levels was relatively stable (Table 2). Some authors reported that microbial community structure is more affected by HM toxicity than microbial diversity [7,10,43].
Pseudomonadota, Acidobacteriota, and Actinomycetota dominated the bacterial communities across the Cu pollution gradient, consistent with previous studies in both unpolluted and HM polluted soils [3,6,10,19,44,45,46,47,48,49,50,51,52,53]. Their persistence reflects broad ecological adaptability to metal stress [20]. Although Pseudomonadota were slightly more abundant in some highly polluted soils, no consistent relationship with Cu concentration was observed (Figure 2a). Many authors have reported Pseudomonadota as dominant in HM polluted soils, which might be related to their relatively high tolerance to some heavy metals and ability to live in extreme environments [8,10,20,52,53].
Bacteroidota increased, whereas Verrucomicrobiota declined, with increasing Cu pollution (Figure 2a). The enrichment of Bacteroidota agrees with previous studies showing that its metabolic versatility promotes persistence in HM polluted soils and contributes to microbial adaptation and altered nutrient cycling [52]. The decline of Verrucomicrobiota in moderately and highly polluted soils suggests sensitivity to heavy metal stress. This phylum is typically associated with stable soil environments and its reduced abundance under metal contamination is consistent with previous reports identifying HM as key drivers of bacterial community restructuring [21].
Alphaproteobacteria, Gammaproteobacteria, and Terriglobia dominated the bacterial communities (Figure 2b), in agreement with previous studies of HM polluted soils [19,20,52,54,55]. At the genus level (Figure 3), Sphingomicrobium_483192 declined with increasing Cu pollution, whereas Z2-YC6860 and Palsa-1315 increased, suggesting contrasting sensitivities and adaptation to heavy metal stress [56,57]. In contrast, PSRF01 occurred in both polluted and unpolluted soils, indicating that factors other than heavy metal contamination also influence its distribution [24,58,59,60].

4.3. Associations Among Heavy Metal Concentrations, Soil Properties and Bacterial Genera

Spearman’s rank correlation analysis revealed taxon-specific relationships between bacterial genera, HM concentrations, and soil physicochemical properties (Figure 4), indicating differences in ecological adaptation to long-term pollution. Z2-YC6860 was positively associated with Cu, whereas Bradyrhizobium_503372 and Mycobacterium showed negative associations with HMs, suggesting contrasting responses to metal stress. These patterns are consistent with previous studies reporting enrichment of metal-tolerant taxa and decline of sensitive microorganisms in contaminated soils [3,19,52,61,62]. Several genera, including Reyranella, PSRF01, and Chryseolinea_899021, were negatively correlated with TOC, while Streptomyces_400150, Mycobacterium, and Angelobacter were negatively associated with SM and/or pH. These results support the important role of soil physicochemical properties, particularly pH and organic carbon, in shaping bacterial communities by influencing metal bioavailability and resource availability [52]. Although none of the correlations remained significant after FDR correction, the observed trends agree with previous studies mentioned above and support the concept that long-term HM pollution, together with local soil properties, acts as a key driver of bacterial community assembly.

4.4. Predicted Bacterial Resistance to Heavy Metals

It should be noted that the functional profiles of bacterial communities were obtained from 16S rRNA gene amplicon sequencing and taxonomic composition. Consequently, the identified KEGG orthologs represent predicted functional potential based on the known ecological characteristics of the detected taxa. They should therefore be interpreted as indicative of possible microbial responses to long-term metal stress. The predicted functional profiles suggest that bacterial communities inhabiting Cu-polluted soils possess an increased potential for metal tolerance and resistance. The relatively high predicted abundance of KEGG orthologs associated with As and Cu resistance (K03892, K17686), together with orthologs related to Cu transport and detoxification (K01533, K07245, K07665), may reflect adaptive responses to prolonged exposure to elevated HM concentrations (Figure 5, Table S2). Similarly, the predicted enrichment of orthologs associated with Co–Zn–Cd efflux systems (K15725, K15726, K15727 and K16264) is consistent with previous metagenomic studies reporting enhanced metal resistance potential in HM polluted soils [12,17,19,63]. The relatively stable predicted abundance of arsenic-related orthologs (K03892 and K03893) across all soils may indicate that As resistance is a widespread adaptive feature of soil bacterial communities rather than a response exclusively to elevated As concentrations [64,65]. In contrast, the greater representation of predicted functions associated with Cu and Zn resistance suggests that these metals may show stronger selective pressure on bacterial communities in the study soils. The enrichment of predicted Co–Zn–Cd efflux-related functions is similarly consistent with previous observations from polluted soils, where these transport systems have been proposed to contribute to microbial tolerance under chronic metal exposure [19,66].
Long-term exposure to Cu, Zn and Cd has been shown to favour metal-tolerant microorganisms and may also contribute to the co-selection of metal and antibiotic resistance determinants, thereby increasing the potential abundance of antibiotic resistance genes (ARGs) in polluted soils [67,68,69,70]. In addition, the predicted occurrence of orthologs involved in metal homeostasis and oxidative stress responses (K03709 and K03322) may indicate that maintaining intracellular metal balance and mitigating oxidative damage represent important components of microbial adaptation to metal stress [71,72].
Although these predicted functions cannot be assigned with certainty to individual taxa, they were associated with the dominant bacterial taxa Pseudomonadota, Acidobacteriota and Actinomycetota identified in this study. These phyla are widely recognized for being representatives capable of diverse metal tolerance mechanisms, including metal efflux, intracellular sequestration and other detoxification processes [17,19,24,69,73]. Overall, the predicted functional profiles suggested that long-term metal contamination may promote the selection of bacterial communities with an enhanced functional potential for metal regulation, transport and detoxification. Nevertheless, these predicted functions require confirmation using metagenomic and functional genomic approaches.

5. Conclusions

Long-term Cu pollution altered the bacterial community structure, whereas bacterial diversity and abundance remained relatively stable across a broad Cu pollution gradient. Several bacterial genera showed strong associations with HM concentrations and soil physicochemical properties, highlighting the combined influence of metal pollution and local environmental conditions on the soil bacterial communities. KEGG functional analysis revealed both widespread common HM defense pathways and more complex mechanisms for HM-resistant bacteria. These results need to be further validated in upcoming studies with integration of metagenomic and metatranscriptomic approaches. These findings improve our understanding of microbial adaptation to chronic metal contamination and provide a scientific basis for the development of microbial indicators for soil health assessment in agricultural soils affected by long-term gold–copper mining.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/soilsystems10080091/s1, Table S1: Single pollution level (PI) of the soils, Table S2: Definition and relative abundance of the KEGG pathways providing heavy metal resistance of bacterial communities in soils.

Author Contributions

M.P., writing—original draft preparation, methodology, investigation, formal analysis; G.D., formal analysis, validation, data curation; E.G., formal analysis, validation, data curation; M.H., methodology, investigation, formal analysis, data curation; N.D., project administration, funding acquisition; G.R., writing—review and editing, conceptualization, methodology, supervision. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Bulgarian Science Fund, grant number KP-06-N76/9/2023.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets generated and analyzed during the current study are available in the NCBI Bioproject database under accession number PRJNA1480567.

Conflicts of Interest

The authors declare no conflicts of interest.

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Soilsystems 10 00091 g001
Figure 1. Map of the sampling sites in the region of Zlatitsa–Pirdop Valley, Western Bulgaria.
Figure 1. Map of the sampling sites in the region of Zlatitsa–Pirdop Valley, Western Bulgaria.
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Figure 2. Relative abundance of bacterial phyla (a) and bacterial classes (b) in soils. ‘Others’ represents the summed relative abundance of phyla/classes < 1%.
Figure 2. Relative abundance of bacterial phyla (a) and bacterial classes (b) in soils. ‘Others’ represents the summed relative abundance of phyla/classes < 1%.
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Figure 3. Heatmap showing the relative abundance of the dominant 13 bacterial genera (<1.5%) across all soils. Color intensity represents the relative abundance (%).
Figure 3. Heatmap showing the relative abundance of the dominant 13 bacterial genera (<1.5%) across all soils. Color intensity represents the relative abundance (%).
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Figure 4. Correlation analysis between dominant bacterial genera, heavy metals and soil physicochemical properties. Bubble plots represent the Spearman correlation coefficients between bacterial taxa and environmental variables. Red circles indicate positive correlations, while blue circles indicate negative correlations; circle size corresponds to the correlation strength. Asterisks (*) indicate statistically significant correlations at p < 0.05.
Figure 4. Correlation analysis between dominant bacterial genera, heavy metals and soil physicochemical properties. Bubble plots represent the Spearman correlation coefficients between bacterial taxa and environmental variables. Red circles indicate positive correlations, while blue circles indicate negative correlations; circle size corresponds to the correlation strength. Asterisks (*) indicate statistically significant correlations at p < 0.05.
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Figure 5. Heatmap showing the relative abundance of predicted KEGG Orthologs (KOs) associated with heavy metal resistance in soils.
Figure 5. Heatmap showing the relative abundance of predicted KEGG Orthologs (KOs) associated with heavy metal resistance in soils.
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Table 1. HMs content, level of pollution, and soil physicochemical properties.
Table 1. HMs content, level of pollution, and soil physicochemical properties.
Soil VariablesS1S2S3S4S5S6S7S8S9S10
Cu mg·kg−186.553132109233170270536710860
Zn mg·kg−142.086.587.880.410457.080.0178145180
Pb mg·kg−131.635.040.836.027.5319.067.597.235.9176
Cd mg·kg−10.490.610.680.850.990.710.632.961.670.82
As mg·kg−110.735.836.23.697.963.3638.942.423.245.6
pH (H2O)6.005.306.005.707.055.805.507.007.305.90
TOC g·kg−117.414.017.842.618.1016.824.412.55.916.9
SM %8.605.004.3029.97.5312.47.3031.629.874.70
NPI0.490.680.781.041.241.572.542.913.507.91
TOC, Total Organic Carbon; SM, Soil Moisture; NPI, Nemerow Pollution Index. Heavy metal concentrations that exceeded Bulgarian maximum permissible concentrations (MPC) of HMs (mg·kg−1) according to Regulation 3/2008 are given in bold.
Table 2. Diversity estimation statistics of bacterial communities.
Table 2. Diversity estimation statistics of bacterial communities.
SoilsTotal ReadsASVACEChao1Shannon
S112,834.00322.00325.01331.337.97
S215,563.00387.00387.99388.208.26
S313,370.00411.00412.24418.508.31
S410,709.00516.00522.52517.408.32
S514,170.00728.00735.19728.998.89
S613,487.00596.00604.68597.458.55
S715,029.00384.00385.17387.338.20
S814,412.00753.00770.39756.068.85
S913,497.00669.00684.43671.458.59
S1012,965.00367.00367.31367.148.09
Abbreviations: ASV—Amplicon Sequence Variant; ACE—Abundance-based Coverage Estimator; Chao1—Chao1 richness estimator; Shannon—Shannon diversity index.
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Petkova, M.; Dimitrova, G.; Gatev, E.; Hristova, M.; Dinev, N.; Radeva, G. Bacterial Community Structure and Heavy Metal Adaptation in Soils from a Gold–Copper Mining Area in Bulgaria. Soil Syst. 2026, 10, 91. https://doi.org/10.3390/soilsystems10080091

AMA Style

Petkova M, Dimitrova G, Gatev E, Hristova M, Dinev N, Radeva G. Bacterial Community Structure and Heavy Metal Adaptation in Soils from a Gold–Copper Mining Area in Bulgaria. Soil Systems. 2026; 10(8):91. https://doi.org/10.3390/soilsystems10080091

Chicago/Turabian Style

Petkova, Michaella, Gergana Dimitrova, Evan Gatev, Mariana Hristova, Nikolai Dinev, and Galina Radeva. 2026. "Bacterial Community Structure and Heavy Metal Adaptation in Soils from a Gold–Copper Mining Area in Bulgaria" Soil Systems 10, no. 8: 91. https://doi.org/10.3390/soilsystems10080091

APA Style

Petkova, M., Dimitrova, G., Gatev, E., Hristova, M., Dinev, N., & Radeva, G. (2026). Bacterial Community Structure and Heavy Metal Adaptation in Soils from a Gold–Copper Mining Area in Bulgaria. Soil Systems, 10(8), 91. https://doi.org/10.3390/soilsystems10080091

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