Early ecological succession and functional persistence of chemolithoautotrophic bacterial communities of mine tailings

Early ecological succession and functional persistence of chemolithoautotrophic bacterial communities of mine tailings | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Early ecological succession and functional persistence of chemolithoautotrophic bacterial communities of mine tailings Lizbeth Vazquez-Hernandez, Ricardo Monterrubio-López, Juan M. Vigueras-Cortés, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8991199/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Mine tailings, resulting from industrial processes, constitute extreme environments due to toxic heavy metals, metalloids, and low concentrations of inorganic nitrogen, phosphorus, and organic carbon. Despite these selective pressures, pioneer microorganisms colonize these substrates, utilize limited resources, alter environmental conditions, and initiate ecological succession. This study compared the bacterial community diversity in mine tailings of different ages (1.5 and 5 years) from Durango, Mexico. Metagenomic DNA was extracted from the tailings, and the V4-V5 region of the bacterial 16S rRNA gene was amplified and sequenced using a metabarcoding approach. No metagenomic DNA was recovered from recently deposited samples; however, 127 bacterial genera were identified across both mine tailings, with 47 genera shared. The 1.5-year-old tailings exhibited greater genus richness than the 5-year-old samples. Proteobacteria dominated both communities, followed by actinomycetes. The chemolithoautotrophic genus Thiobacillus , capable of oxidizing sulfide, sulfur, and thiosulfate, was most abundant. Chemolithoheterotrophic thiosulfate-oxidizing Limnobacter and Sulfurifustis were prevalent in young and old tailings, respectively. Chemoorganoheterotrophic bacteria, including Nocardioides and Kribella (Actinobacteria), as well as Hydrogenophaga and Pseudomonas (Betaproteobacteria), were also detected. The relative abundance of chemolithoautotrophic bacteria corresponded with the environmental conditions of the mine tailings, which lack organic carbon and contain abundant reduced inorganic sulfur compounds as energy sources. Early ecological succession at the bacterial genus level was evident, primarily involving Sulfurifustis , Nocardioides , and Kribella . bacterial communities ecological succession heavy metals mine tailings chemolithoheterotrophic bacteria and chemolithoautotrophic bacteria Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Background Mine tailings, the byproducts of metal extraction, are among the primary anthropogenic contaminants of terrestrial soils [ 1 , 2 ]. These fine sands contain residual and toxic heavy metals (HM), metalloids such as arsenic, and cyanide, and are stored in human-made dams, from which they may be dispersed by wind or leached into the ground [ 3 , 4 ]. Typically, mine tailings lack organic compounds and assimilable combined inorganic or organic nitrogen sources. These characteristics contribute to the formation of a relatively novel extreme environment in terrestrial soils. Furthermore, mine tailings contain substantial amounts of reduced inorganic sulfur compounds, including pyrite (FeS2), chalcopyrite (CuFeS2), sphalerite (ZnS), galena (PbS) [ 5 , 6 , 7 ], and thiocyanate (SCN-), a product of biological cyanide detoxification [ 8 ]. Depending on the concentrations of HM and metalloids, young mine tailings are seldom colonized by plants [ 9 ]. Nevertheless, certain microbes are capable of colonizing these environments, accumulating organic compounds, promoting ecological succession, and ultimately facilitating plant colonization [ 10 , 11 , 12 ]. Within mine tailing environments, metabolic capacities such as HM tolerance and transformation, cyanotrophy, chemolithoautotrophy, carbon-oligotrophy, nitrogen fixation, and nitrogen oligotrophy are essential for pioneer microorganisms [ 12 , 13 , 14 ]. These microbial colonizers initiate natural bioremediation, introduce organic carbon and nitrogen sources, promote elemental cycling, and modify the environment to support soil formation and subsequent colonization by metallophytic plants. Plants adapted to these conditions may be utilized in both natural and artificial phytoremediation strategies [ 15 , 16 ]. Bacterial guilds isolated, cultured, and characterized from these residues include nitrogen fixers [ 17 ], iron- and sulfur-oxidizers [ 18 ], ammonia- and carbon-oligotrophs [ 14 ], HM-tolerant bacteria and HM-oxidizers [ 19 , 20 ], as well as cyanotrophic bacteria [ 21 ]. The bacterial composition of select mine tailings has been characterized using metabarcoding and metagenomic approaches [ 22 – 25 ]. These microbial profiles are influenced by the chemical composition of the tailings, ore extraction procedures, anthropogenic activities, environmental factors, and the age of the tailings. Variations in microbial populations, nutritional content, and physicochemical properties contribute to the dynamics of these ecosystems. Mining is among the oldest industries in human history, with the first metals for tool fabrication obtained during the Neolithic period [ 26 ]. This industry has generated substantial volumes of toxic compounds globally through environmentally disruptive procedures [ 27 ]. Remediation of mine tailings is challenging due to insufficient environmental regulations and the vast quantities of tailings deposited in opencast mines. Autochthonous bacteria offer more cost-effective and environmentally sustainable alternatives to conventional physicochemical treatments, and can be employed in natural attenuation and artificial bioremediation strategies to promote ecological succession [ 28 ]. This study examined the early ecological succession of bacteria in a tailing dam at the former “La Parrilla” mine in Durango, Mexico, characterized by low concentrations of organic matter and assimilable combined nitrogen, high levels of reduced inorganic sulfur compounds, and elevated HM and arsenic concentrations. The bacterial composition was determined using 16S rRNA metagenomic analysis of DNA from 1.5- and 5-year-old tailing samples. Early ecological succession of bacterial genera was observed, although the relative abundance of putative functional traits persisted. 2. Methods 2.1. Sampling Site The “La Parrilla” mine, located in Durango, Mexico (23°44’16’’N, 104°06’26’’W) (Fig. 1 C), began operations in 2004 and is primarily used for silver and lead extraction via cyanidation. The mine is situated in a semi-arid environment, with average annual temperatures between 12°C and 25°C. The tailing dams at this site are devoid of vegetation. In February 2019, tailings were collected from a mine dam at a depth of 0.3 m, representing three deposition ages: 0 years (recently deposited, Fig. 1 D), 1.5 years (YMT: young mine tailing, Fig. 1 E), and 5 years (OMT: old mine tailing, Fig. 1 F). Samples were transported in sterile, sealed bags to a laboratory in Mexico City for further analysis. 2.2. Physicochemical characteristics, and carbon, cyanide, and nitrogen contents. The pH was measured in a 1:2 mine tailings to water suspension using a pH meter (Corning, NY, USA). Electrical conductivity was assessed with the HI993310 kit (Hanna Instruments, RI, USA). Cyanide and nitrogen concentrations were determined using the Cyaniver kit (Corning, NY, USA) and the Total Nitrogen Reagent Set, HR, TNT (Hach, CO, USA), respectively. For total carbon determination, 5 g of each dried sample (oven-dried at 40°C) was heated in a muffle furnace for 6 hours at 600°C, and the carbon content was calculated by weight difference [ 29 ]. 2.3. HM and arsenic quantification Total concentrations of silver (Ag), mercury (Hg), iron (Fe), copper (Cu), lead (Pb), zinc (Zn), chromium (Cr), cobalt (Co), nickel (Ni), cadmium (Cd), vanadium (V), and arsenic (As) were determined by atomic absorption spectroscopy following the “Norma Oficial Mexicana” NOM-147-SEMARNAT/SSA1-2004 [ 30 ]. 2.4. Metagenomic DNA extraction Metagenomic DNA extraction from mine tailings was standardized according to deposition time, utilizing a combination of mechanical and enzymatic methods [ 31 , 32 ]. For each sample, 5 g was pretreated with 5 mL of pretreatment solution (Tris-HCl 100 mM, NaH2PO4 100 mM, EDTA 500 mM, pH 8) and mixed by orbital shaking at 100 rpm for 3 hours. After centrifugation at 2,000 x g for 2 minutes, the pretreatment solution was discarded. For young and old mine tailings, pellets were resuspended in 15 mL of lysis solution (Tris-HCl 100 mM, EDTA 20 mM, NaCl 150 mM, SDS 2%) with 3.5 g of glass beads. The suspension was vortexed for 1 minute, incubated on ice for 1 minute, and this cycle was repeated three times. Enzymatic lysis was performed by adding 2.5 mg of lysozyme (20,000 U/mg; Affymetrix, CA, USA) and incubating at 37°C for 30 minutes. Subsequently, 0.5 mg of Proteinase K (20 U/mg; Invitrogen, MA, USA) was added, followed by incubation at 60°C for 30 minutes and a cold shock at -20°C. The suspension was centrifuged at 1,340 x g for 10 minutes at room temperature (approximately 23°C), and the supernatant was collected. This was treated with 1/5 volume of EDTA 0.5 M (pH 8.0) and 1/10 volume of potassium acetate 5 M (pH 5.5), incubated at room temperature for 20 minutes, and centrifuged at 18,500 x g for 10 minutes. The supernatant was collected, mixed with an equal volume of isopropanol, and incubated at -20°C for 2 hours. After centrifugation at 18,500 x g for 10 minutes, the pellet was dried and resuspended in 500 µL of TE buffer and 1 mL of chloroform:isoamyl alcohol (24:1), then microcentrifuged for 10 minutes at 13,000 rpm. The aqueous phase was recovered, mixed with an equal volume of absolute isopropanol, and incubated overnight at -20°C to precipitate DNA. The solution was centrifuged for 10 minutes at 18,500 x g , and the supernatant was removed. Remaining alcohol was evaporated using a centrifuged concentrator (Eppendorf, Hamburg, Germany) at room temperature. The pellet was resuspended in 30 µL of sterile distilled water. DNA quality was assessed by electrophoresis on 1% agarose gels in 1× TAE buffer and staining with 0.5 µg mL − 1 ethidium bromide (ETB). DNA concentrations were determined spectrophotometrically, with an A260/A280 ratio of 1.8 considered acceptable. 2.5. Metabarcoding approach by amplification of bacterial 16S rRNA V4-V5 gene region Metagenomic DNA from the samples was amplified and sequenced by Novogene Bioinformatics Technology Co., Ltd., Tianjin, China. The V4-V5 variable regions of the bacterial 16S rRNA gene were amplified using primers 515F (GTGCCAGCMGCCGCGGTAA) and 907R (CCGTCAATTCCTTTGAGTTT). PCR reactions were performed with Phusion High-Fidelity PCR Master Mix (New England Biolabs, Ipswich, MA) and GC Buffer under the following conditions: initial denaturation at 94°C for 3 minutes (1 cycle), followed by 35 cycles of 94°C for 45 seconds, 50°C for 60 seconds, and 72°C for 90 seconds, with a final extension at 72°C for 10 minutes. PCR products were purified using the Qiagen gel extraction kit (Qiagen, Hilden, Germany) and sequenced on the Illumina MiSeq platform with a v3 chemistry kit (2x300 bp). 2.6. Bacterial community composition analysis Bacterial 16S rRNA sequences were analyzed using Quantitative Insights into Microbial Ecology (QIIME2) software version 2022.2 [ 33 ]. Read quality and length were assessed with FastQC [ 34 ]. DADA2 was employed for quality filtering, dereplication of amplicon sequence variants (ASV), trimming, and chimera removal [ 35 ]. Taxonomic annotation was assigned to representative sequences in the ASV table using the classify-sklearn function with a Naïve Bayes supervised learning algorithm and the Silva database v138 [ 36 ]. Taxonomy was collapsed at all taxonomic levels (1–7) using the QIIME taxa collapse argument. 2.7. Bacterial community putative functionality Bacterial taxonomic information was assigned to putative functional groups using Functional Annotation of Prokaryotic Taxa (FAPROTAX v1.2) [ 37 ], based on the abundance table at the genus level (following taxonomy collapse at level 6). Relative abundances of functional groups were calculated using the local-sum scaling procedure for each sample. 2.8. Diversity and statistical analyses Diversity and statistical analyses, as well as data visualization, were conducted in the R environment v. 4.0.2 [ 38 ]. Alpha diversity of bacterial community structure and putative functionality was assessed at different q orders: q = 0 (species richness), q = 1 (frequent species), and q = 2 (dominant species), using Hill numbers [ 39 ] with the hillR package v. 0.5.1 [ 40 ]. Analyses utilized the frequency table (ASV) at the genus taxonomic level and the frequency table of putative functional traits. Frequency tables reporting ASV at various taxonomic levels and putative functional traits (at the genus level) were subjected to compositional analyses, as sequencing data possess an arbitrary total imposed by the instrument [ 41 ]. Sequencing data underwent centered log-ratio transformation using the Aldex.clr function in the Aldex2 package version 1.21.1 [ 42 ]. Principal Component Analysis (PCA) was performed using the prcomp function of the stats package in R to assess variations in bacterial composition between YMT and OMT samples. Results were visualized with the ggplot2 v.3.3.6 [ 43 ] package. Bubble plots were generated to visualize the relative abundance of the ten most prevalent bacterial genera and the eight most abundant putative functional traits across treatments. Shifts in bacterial community composition between YMT and OMT were estimated using log₂ fold change (log2FC) values based on relative abundance for each genus as follows: $${\text{l}\text{o}\text{g}}_{2}\text{F}\text{C}={\text{l}\text{o}\text{g}}_{2}\left(\frac{\text{OMT}}{\text{YMT}}\right)$$ Positive log2FC values indicate enrichment in OMT, while negative values indicate enrichment in YMT. All taxa with a relative abundance greater than 0.01% were included in the analysis. Similarly, to assess functional shifts in putative profiles inferred from the 16S rRNA gene between conditions, log2FC values were calculated from relative functional abundances. When predicted functions were absent in one condition (i.e., zero values), a pseudocount of 0.001 was added to both OMT and YMT values prior to log2FC calculation to prevent division by zero and infinite values: $${{log}_{2}\text{F}\text{C}=\text{l}\text{o}\text{g}}_{2}\left(\frac{\text{OMT}+0.001}{\text{YMT}+0.001}\right)$$ This approach enables quantitative comparison of enrichment patterns while minimizing distortion of relative abundances. Differential abundance was evaluated using an ANOVA-like differential expression approach implemented in the ALDEx2 package [ 47 ]. Compositional data were CLR-transformed with aldex.clr, and group differences were assessed using Welch’s t-test and Wilcoxon rank-sum test via aldex.test. Effect sizes were calculated with Aldex.effect, and significance was determined based on Benjamini–Hochberg adjusted expected p-values from Welch’s t-test. Effect sizes between 0.8 and 3 were considered large, and values of 3 or greater were considered very large. Volcano plots were used to display effect sizes versus expected p-values. The core bacterial community at the genus level was identified by shared taxa across groups and visualized with a Venn diagram ( https://bioinformatics.psb.ugent.be/webtools/Venn/ ). 3. Results 3.1. Physicochemical characteristics, carbon, cyanide, nitrogen, and HM contents in mine tailings The pH and electrical conductivity values were consistent across all mine tailings, with pH values remaining near neutral. In newly deposited mine tailings, organic matter and total nitrogen concentrations were initially very low; however, organic matter increased over time. Total nitrogen values in YMT were higher than in OMT, measuring 79.47 and 49.07 mg kg-1, respectively. In contrast, cyanide content declined rapidly over time. The most recent mine tailing exhibited a cyanide content of 869 mg kg-1, whereas cyanide was not detected in the young and old samples (Table 1 ). With the exception of mercury, whose concentration decreased with the age of the mine tailings, heavy metal and metalloid concentrations remained similar in both young and old samples, persisting within the same order of magnitude over time (Table 1 ). The “Norma Oficial Mexicana” NOM-147-SEMARNAT/SSA1-2004 establishes criteria for determining remediation concentrations for soils contaminated with arsenic, barium, beryllium, cadmium, hexavalent chromium, mercury, nickel, silver, lead, selenium, thallium, and/or vanadium. According to this NOM, the As and Pb concentrations were considered high according to the total reference measurements for industrial soils. Similarly, the concentrations of the other heavy metals were considered moderately high in agricultural, residential, and commercial-use soils. Table 1 Characteristics of the mine tailings of RMT, YMT and OMT years old from the mine “La Parrilla”, Mexico. Sample Deposition age (years) pH EC (mS/cm) OM TN CN − Heavy metals and arsenic As Ag Hg Fe Cu Pb Zn Cr Co Ni Cd V RMT 0 7.59 0.94 0 204 869 568 18 0.4 53,400 174 8,840 11,450 30 16 30 84 126 YMT 1.5 7.28 1.73 400 79 0 324 20 0.2 45,200 127 6,160 5,480 27 13 28 35 168 OMT 5 7.02 2.02 1100 49 0 563 20 0.07 47,500 192 7,590 11,170 27 16 28 78 183 EC, Electrical conductivity; OM, Organic matter; TN, Total nitrogen; CN − , Cyanide; RMT, recent mine tailings; YMT, young mine tailings; OMT, old mine tailings. The unspecified units correspond to mg Kg − 1 . 3.2. Sequencing results No metagenomic DNA or bacterial 16S rRNA amplification products were obtained from the recently deposited mining tailings (Fig. 1 D). In contrast, older samples (YMT and OMT) produced PCR products. Following assembly and quality filtering, the metabarcoding approach generated 966,455 sequences, representing 12 to 15 phyla, 36 classes, 81 orders, 110 families, and 128 bacterial genera (Table 2 ). Table 2 Sequencing data and diversity indexes according to Hill numbers at the genus level of mine tailings of 1.5 (YMT) and 5 (OMT) years old from the mine “La Parrilla”, Mexico. Sample Sequences Phylum Class Order Family Genus Putative functional traits* Hill diversity indexes at genus level q0** q1** q2** YMT1 174879 12 20 50 64 58 31 86 2.54 1.77 YMT2 171437 12 21 56 69 65 28 103 1.56 1.15 YMT3 163442 13 20 45 57 51 24 76 2.96 1.9 OMT1 115366 15 21 49 66 54 29 84 2.76 2.21 OMT2 172438 13 19 50 64 58 31 92 2.88 2.2 OMT3 168893 12 19 51 67 59 31 93 2.83 2.83 Total 966455 17 36 81 110 127 38 * Putative functional traits were predicted through FAPROTAX. ** q0, Species richness; q1, Frequent species; q2, Dominant species 3.3 Sample dispersion and bacterial alpha diversity Principal component analysis (PCA) of clr-transformed data grouped the samples according to tailings age. YMT samples exhibited greater dispersion compared to OMT samples. Additionally, based on Hill numbers, OMT samples demonstrated higher richness (q0) and diversity (q1 and q2) at the bacterial genus level than YMT samples (Fig. 2 ). 3.4. Bacterial communities of mine tailings: diversity and composition Metabarcoding analysis identified Proteobacteria as the most prevalent phylum in both young and old mine tailings (96.5–97.5% in YMT samples and 91.9–92.9% in OMT samples), followed by Actinobacteria (1.7–3.2% in YMT and 6.2–7.2% in OMT). At the order level, Burkholderiales exhibited the highest relative abundance in both treatments (95.6–97.2% in YMT and 52.2–55.8% in OMT), while Acidoferrobacterales was more abundant in OMT (35.4–39.3%) than in YMT (0.31–0.94%). At the genus level, 15 bacterial genera each accounted for at least 0.1% relative abundance, collectively representing 98.9% of the total bacterial community (Fig. 3 A). Within Burkholderiales, Thiobacillus was the most prevalent genus in both mine tailings (69.7–93.2% in YMT and 53.3–56.7% in OMT), followed by Limnobacter (0.3–20.7% in YMT). The genus Sulfurifustis , belonging to Acidiferrobacterales, was the second most abundant in OMT (36.5–40.9%) (Fig. 3 B). Although the relative abundances of Pseudomonas and Hydrogenophaga ( Proteobacteria ), as well as Nocardioides and Kribbella (Actinobacteria), were low, these genera were widely distributed among samples. Log 2 fold change analysis indicated significant directional shifts in dominant bacterial genera over deposition time (Fig. 1 C). Several taxa, including Sulfurifustis (log2FC = + 5.95), Promicromonospora (log2FC = + 5.00), Kribbella (log2FC = + 4.82), and Qipengyuania (log2FC = + 4.48), exhibited strong enrichment after 5 years (OMT). In contrast, Limnobacter (log2FC = − 5.66), Enterobacter (log2FC = − 4.91), Pseudomonas (log2FC = − 4.47), and Hydrogenophaga (log2FC = − 4.35) showed marked declines, reflecting substantial reductions in their relative contributions to community structure at the later deposition stage. Thiobacillus remained the dominant genus at both time points, although it exhibited a moderate decrease (log2FC = − 0.48), indicating persistence with partial restructuring. In contrast, Sphingopyxis displayed minimal variation (log2FC = − 0.15), suggesting relative temporal stability. YMT and OMT samples shared a core bacterial richness comprising 47 genera, with 51 and 29 exclusive genera identified in YMT and OMT, respectively (Figure S1 ). Hydrogenophaga exerted a very large effect on the assembly of the YMT bacteriome, while Pseudomonas , Enterobacter , Acidovorax , Dietzia , Cavicella , Acinetobacter , Knoellia , and Silanimonas had a large effect. In OMT, Kribella and Sulfurifustis demonstrated a very large effect on bacteriome assembly, whereas Mamoricola , Pedobacter , Parviterribacter , Nocardioides , Halothiobacillus , and Bosea exhibited a large effect (Fig. 3 D and F, Table S2). 3.5. Putative functional profile The putative functional profile predicted by FAPROTAX indicated capacities for chemoheterotrophy, fermentation, and nitrogen metabolism. The most prevalent predicted functions in both YMT and OMT were related to sulfur chemolithotrophy, specifically dark oxidation of sulfur compounds (43.28–46.11% in YMT and 45.72–46.17% in OMT) and dark sulfide oxidation (43.28–46.09% in YMT and 45.68–16.15% in OMT) (Fig. 4 ). In OMT samples, functional traits with very large effects included aromatic compound degradation, dark sulfur oxidation, and dark thiosulfate oxidation. In YMT, dark hydrogen oxidation, methylotrophy, and methanol oxidation were the predictive functional traits with very large effects. Analysis of functions inferred from bacterial diversity profiles revealed substantial differences in metabolic structure between YMT and OMT. The bubble plot of relative abundances for the most abundant potential functional traits indicated a chemolithoautotrophic predominance in both YMT and OMT, including processes such as dark sulfur oxidation, dark sulfur thiosulfate oxidation, and dark hydrogen oxidation (Fig. 4 A). YMT exhibited differential functions associated with both chemolithoautotrophic and heterotrophic metabolism, whereas OMT was characterized by a predominance of chemolithoautotrophic processes (Fig. 4 B). Log2FC values confirmed a functional transition, with heterotrophic profiles enriched in YMT and functions related to dark sulfur oxidation and thiosulfate enriched in OMT (Fig. 3 C). Differential analysis using ALDEx2 further supported these distinctions, highlighting a subset of functions significantly overrepresented at each stage (Fig. 3 D and E). Additionally, features associated with mammal gut, human gut, and human-associated animal parasites or symbionts were identified (Table S3). 4. Discussion The mining industry exerts a substantial negative impact on the environment. Elevated concentrations of heavy metals (HM) and metalloids in tailing dams present significant risks to human health, regional ecosystems, and living organisms [ 44 , 45 ]. Beyond metal toxicity, mine tailings are generally defined by low organic matter, limited nutrient availability, and unfavorable physicochemical properties, which collectively create highly restrictive conditions for biological colonization. Nevertheless, specific microbial communities are capable of colonizing mine tailings and functioning as pioneer organisms. This study investigated the composition of bacterial communities in mine tailings of varying ages (0, 1.5, and 5 years) using a 16S rRNA metabarcoding approach, and examined their potential relationships with environmental conditions. No metagenomic DNA could be obtained from recently extracted and deposited mine tailings, although it was present at undetectable levels and insufficient for subsequent protocols. However, the DNA extraction for the other samples was optimized, and their quality enabled amplification and massive sequencing. The bacterial communities of the samples were grouped in the PCA according to the age of the mine tailings. Bacterial diversity, estimated by Hill indexes (richness, frequency, and dominance) at the genus level, was higher in older samples (5 years old, OMT) than in younger samples (1.5 years old, YMT). Similarly, the Simpson diversity and Chao richness indexes of bacterial communities of Pb-Zn tailing soils in the Qinling Mountains were higher in the older tailings (31 years old) than in the younger tailings (13 years old) [ 46 ]. Without remediation strategies, the residence time of mine tailings within a dam serves as a primary ecological factor influencing microbial community assembly. As deposition time increases, ongoing physicochemical changes affect microbial colonization dynamics. Previous research indicates that microbial diversity and richness generally increase over time, ultimately leading to a more stable community structure as ecological succession advances [ 47 ]. This temporal progression is frequently linked to gradual increases in organic matter and the development of plant cover, both of which enhance nutrient availability and support greater bacterial diversity [ 48 ]. In the “La Parrilla” mine tailings, however, bacterial diversity did not correlate with concentrations of organic carbon, total nitrogen, or levels of heavy metals and arsenic (data not shown). In the mine tailings analyzed, most heavy metals and arsenic remained at concentrations similar to their original levels, indicating minimal mobilization, leaching, or sequestration of HM. Such processes can alter the original HM content of mine tailings [ 49 ]. The five-year observation period may be insufficient to detect significant changes in HM composition. Mercury was the only element to exhibit a decrease in concentration over time, likely due to its volatility [ 50 ]. These persistent metal-stress conditions are expected to continue exerting selective pressure on microbial communities, underscoring the significance of residence time in shaping both environmental constraints and bacterial community composition. The toxic effects of HM and metalloids on cells include protein dysfunction, production of reactive oxygen species, antioxidant depletion, impaired membrane function, disruption of nutrient uptake, and genotoxicity [ 51 ]. Consequently, early colonizers typically exhibit metabolic traits such as heavy-metal tolerance and transformation, chemolithoautotrophy, carbon and nitrogen fixation, and oligotrophic growth, enabling survival and function under nutrient-limited and metal-stressed conditions [ 18 , 52 ]. In this study, HM- and metalloid-resistant bacteria were not identified in the functional analysis of genera. However, the genus Pseudomonas , known for HM resistance, was detected in all metagenomic DNA samples from mine tailings and is frequently isolated or detected in similar environments [ 53 – 56 ]. Previous studies have also reported autochthonous HM-resistant Pseudomonas strains in mine tailings from the same mining region in Zacatecas and Durango, Mexico [ 17 , 21 ]. Recent mine tailings exhibited high concentrations of toxic cyanide, which appeared to be rapidly removed in samples aged 1.5 and 5 years. Cyanide in aqueous solution at neutral pH is insoluble and may volatilize into the atmosphere [ 57 ]. Previously, the cyanotrophic Pseudomonas mendocina P6115 strain was isolated from an alkaline pool at the “La Parrilla” mine in Durango and demonstrated adaptive phenotypic traits in mine tailing environments, including siderophore production, moderate HM resistance, arsenite and arsenate tolerance, and the ability to oxidize arsenite [ 21 ]. A further significant selective pressure influencing microbial communities in mine tailings is the scarcity of organic matter. In the absence of organic carbon and energy sources, but with abundant inorganic reduced sulfur compounds, chemolithoautotrophic bacteria are expected to dominate [ 58 ]. Consistent with this, the principal bacterial genera and inferred functional traits identified in this study were associated with chemolithoautotrophic and chemolithoheterotrophic metabolism. Chemolithoautotrophic sulfide, sulfur, and thiosulfate-oxidizing Thiobacillus was the dominant genus, followed by chemolithoheterotrophic thiosulfate-oxidizing Limnobacter and Sulfurifustis in young and old mine tailings, respectively. The marked shifts in dominant bacterial genera between 1.5 and 5 years of mine tailings deposition indicate a directional ecological succession driven by persistent selection pressures related to environmental conditions and resource availability. In this study, the relative abundance of Sulfurifustis increased substantially over time (log2FC = + 5.95), whereas genera characteristic of early colonizers, such as Limnobacter and Pseudomonas , declined markedly. These findings suggest that microbial community assembly in tailings is governed by processes favoring taxa with metabolic capabilities adapted to the changing geochemical environment. This pattern of successional shifts in microbial assemblages under evolving habitat conditions is consistent with observations from other mine tailings ecosystems, where community composition and function change predictably during remediation or natural attenuation [ 59 ]. Furthermore, these three bacterial genera have been isolated or detected in mine tailings from diverse global locations [ 22 , 60 – 62 ]. Nitrogen content in mine tailings is significantly lower than in adjacent surface soils, thereby limiting biodiversity in these environments. Nitrogen concentrations in woodland soils in Durango, Mexico, range from 0.28 to 0.46% [ 63 ] and from 0.11 to 0.174% [ 64 ], whereas in the same semi-arid region, mine tailings contain nitrogen at two orders of magnitude lower (0.0049% in YMT and 0.0079% in OMT). During the early stages of biological succession in tailings and soils, nitrogen concentration is a critical factor [ 48 , 65 ]. Therefore, the capacity of pioneer bacteria in mine wastes to fix nitrogen is advantageous [ 17 , 66 ]. Autotrophic, nitrogen-fixing bacteria have been detected in mine tailings, where they facilitate nutrient acquisition for other microbes and plants, likely driving ecological succession. A significant correlation has been observed between the frequencies of genes involved in sulfur oxidation (soxB) and nitrogen fixation (nifH) [ 12 ]. However, in “La Parrilla” mine tailings, sulfur- and thiosulfate-oxidizing bacterial taxa were detected, but nitrogen-fixing bacteria were not, and the correlation between sulfur oxidation and nitrogen fixation was not confirmed. In contrast, several nitrogen-fixing and oligotrophic ammonium bacteria were isolated or detected in bulk soil and the rhizosphere of pioneer plants growing on nearby mine tailings [ 14 , 17 ]. The genus Hydrogenophaga exerted a significant influence on the bacterial community in the YMT condition. Hydrogenophaga has been reported to heterotrophically oxidize arsenite [ 67 ], a trait advantageous for bioremediation since arsenite is more toxic to plants and mammals than arsenate [ 68 – 70 ]. Pseudomonas , Enterobacter , and Acidovorax also had substantial effects in the YMT bacterial community, with all three genera involved in nitrogen metabolism. Pseudomonas mendocina S16 and Enterobacter cloacae strains CF-S27 and DS’5 are capable of simultaneous nitrification and denitrification under heterotrophic conditions [ 71 , 72 ], while Acidovorax spp. can reduce nitrate [ 73 ]. Hydrogenophaga , Pseudomonas , and Enterobacter possess heterotrophic metabolisms [ 74 – 76 ] and were more abundant in YMT than in OMT conditions. These bacteria may originate from the surrounding soil or be introduced through anthropogenic activities related to mining, and their abundance declined over time due to insufficient metabolic traits for survival in the tailings. The reduced levels of organic matter and total nitrogen, combined with high concentrations of HM and metalloids, underscore the oligotrophic and extreme character of the ecosystem. Consequently, chemoautotrophic bacteria capable of nitrogen fixation and of tolerance to HM and metalloids were anticipated. The most abundant sulfur-oxidizing bacteria identified in “La Parrilla” mine tailings were Thiobacillus , Sulfurifustis , and Limnobacter , genera commonly found in other mine tailings [ 66 , 76 – 81 ]. Sulfur- and iron-oxidizing Thiobacillus can perform nitrate reduction coupled with Fe oxidation under anaerobic conditions [ 82 , 83 ]. Moreover, it expresses genes involved in autotrophic carbon fixation, a convenient feature in environments poor in organic carbon, such as mine tailings [ 84 ]. Sulfurifustis , the second most abundant bacterium in OMT samples, is a sulfur oxidizer harboring genes involved in sulfur oxidation and carbon fixation pathways that grows optimally at pH 6.8–8.2 [ 80 ]. In YMT samples, Limnobacter was the second most abundant genus, a heterotrophic bacterium capable of oxidizing thiosulfate and a prevalent inhabitant of mineral soils under nitrifying conditions [ 58 , 78 , 79 ]. Besides, Nocardioides , the fourth most abundant genus in both “La Parrilla” mine tailings, harbors genes for dissimilatory nitrate reduction to ammonia, particularly nirB and HM resistance [ 11 , 85 ]. The genera Kribella and Sulfurifustis had pronounced effects on the bacterial community in OMT samples. While Kribella has not previously been reported in mine tailings, members of the order Propionibacteriales have been identified in cadmium-contaminated soils [ 86 ]. The reasons for Kribella's substantial impact on the OMT bacterial community remain unclear, and further metagenomic or phenotypic studies are required to elucidate its ecological niche in tailings. Sulfurifustis exhibited a marked increase in 5-year tailings (log2FC = + 5.95) and is part of a group of sulfur-oxidizing chemolithotrophic bacteria described in aquatic and microaerophilic environments [ 87 ]. The presence of redundant genes for sulfur oxidation and carbon fixation in the Sulfurifustis variabilis genome supports its capacity to utilize sulfur compounds as a primary energy source under specific geochemical conditions [ 80 ]. Metagenomic studies of tailings have also shown that sulfur-oxidizing operational taxonomic units (OTUs), including Sulfurifustis , are associated with environmental parameters such as pH and metal concentration, indicating a functional role in microbiome structuring during succession in contaminated sites [ 76 ]. The observed increase in sulfur-oxidizing and carbon-fixing bacteria over time, along with the significant decline in heterotrophic taxa, suggests a progressive metabolic restructuring of the community toward energy acquisition from inorganic substrates under oligotrophic conditions and elevated concentrations of arsenic, lead, and other heavy metals. Several putative functional traits with effects in YMT samples were characteristic of heterotrophic and chemolithoautotrophic metabolism. These functions suggest that the early-stage community was more metabolically versatile than the older-stage community. In OMT, the increased relative abundance of chemolithoautotrophic bacteria was consistent with a mature community exploiting sulfur compounds as a primary inorganic energy source in these mine tailings. In the La Parrilla mine tailings in Mexico, an environmental filtration process reconfigures the microbial community's structure and function. In the early stages of YMT, metabolically versatile chemolithoautotrophic and heterotrophic communities are favored, exerting a strong influence on community structure. This is consistent with succession and the overall community composition in disturbed soils [ 88 , 89 ]. As time progresses and strong selection pressures prevail, the system transitions to functionally more specialized OMT communities, enriched in chemolithoautotrophic functions such as sulfur and thiosulfate oxidation and carbon fixation—characteristics particularly associated with extreme environments and acid mine drainage [ 90 , 91 ]. This reinforces the concept of “environmental filtration” as the dominant structuring force under abiotic conditions with strong selection pressures [ 92 , 93 ]. Taken together, the scheme integrates putative taxonomic and functional evidence to propose that the succession of relationships involves not only species replacement but also a directed transition from heterotrophic versatility and chemolithoautotrophy of taxa to chemolithoautotrophic adaptation and specialization, with implications for biogeochemical stability and microbial restoration strategies in mining ecosystems. This conceptual model illustrates microbial community dynamics from young mine tailings (1.5 years) to older tailings (5 years). Cyanide present in recent mine tailings is rapidly removed through volatilization and biological activity. Early-stage YMT communities are primarily characterized by chemolithoautotrophic functions, such as thiosulfate oxidation and methylotrophy, and are dominated by genera including Thiobacillus and Limnobacter , with limited heterotrophic activity. Elevated heavy metal concentrations, low organic carbon, and minimal combined and assimilable nitrogen favor taxa tolerant to HM and nutritional stress. In the OMT community, increased richness and bacterial diversity result in functional specialization toward sulfur-based chemolithoautotrophy and carbon fixation, with enrichment of Sulfurifustis , Kribbella , and Nocardioides . Despite taxonomic turnover, the Proteobacteria phylum remains dominant across stages, underscoring its ecological versatility during succession. The primary objective of mine tailings remediation is to create a soil-like substrate capable of supporting plant growth while minimizing hazardous compound concentrations. However, low nitrogen and carbon levels, elevated HM and metalloid concentrations, and the inability of sulphidic base-metal tailings to sustain plants present significant challenges to phytostabilization [ 48 ]. Pioneer bacterial activity may facilitate vegetation establishment in mine tailings, thereby accelerating microbial community succession. Key metabolic activities of microbial communities that support pioneer plant survival and promote ecological succession include: (1) increasing organic carbon and assimilable and combined nitrogen; (2) inhibiting or immobilizing toxic HM and metalloids; and (3) colonization by bacteria, genes, and plasmids acquired through horizontal transfer, which are involved in chemolithoautotrophic metabolism and HM resistance to address environmental selective pressures [ 95 ]. This study examined the bacterial community at the taxonomic level; however, based on putative functions and published literature regarding the most abundant genera, the community's metabolism is implicated in survival strategies under the stressful conditions present in mine tailings. It is anticipated that the community's metabolism will adapt as an integrated system to changing substrate conditions, potentially fostering the development of a heterotrophic bacterial community. Further metagenomic studies involving samples with greater age differences are necessary to elucidate the role of pioneer microorganisms in ecological succession within mine tailings. 5. Conclusion The pioneer bacterial community in mine tailings is shaped by strong selective pressures that favor chemolithotrophy, heavy-metal tolerance, and nitrogen-related metabolism. Proteobacteria dominated both young (1.5 years) and older (5 years) tailings, demonstrating ecological versatility under metal stress and nutrient limitation. At the genus level, a clear successional shift was observed: in younger tailings, Thiobacillus and Limnobacter were predominant, along with heterotrophic and nitrogen-cycling genera. Over time, the relative abundance of heterotrophic bacteria decreased, while chemolithoautotrophic taxa, particularly Sulfurifustis , increased significantly. These patterns indicate a progressive metabolic restructuring from an early community influenced by both heterotrophs and chemolithotrophs to a more specialized assemblage dominated by chemolithotrophic sulfur-oxidizing and metal-tolerant bacteria. This transition is consistent with adaptation to persistent carbon limitation and elevated metal concentrations in mine tailings. Abbreviations YMT Young mine tailings OMT Old mine tailings HM Heavy metals Declarations Consent for publication Not applicable. Availability of data and materials The datasets generated and analyzed from bacteria metagenomic approach were deposited in the GenBank repository (https://www.ncbi.nlm.nih.gov/bioproject/953647). BioProject PRJNA953647 and biosamples accession numbers SAMN34035880 - SAMN34035885. Competing interests The authors declare that they have no conflicts of interest. Funding The Secretaría de Investigación y Posgrado-IPN funded this research (SIP 20220795, 20231480, 20240945, and 2383-20151163) Authors' contributions LV-H, CH-R, LV-T and JMV-C conducted the sampling. RM-L conducted the spectroscopy analysis. LV-H main experimental procedures. LV-H and AM-C conducted the analyses. AM-C and LV-H wrote the first draft of the manuscript. CH-R supervised and directed the research. CH-R and LV-T obtained funding. All authors have read and agreed to the published version of the manuscript. Acknowledgements LV-H thanks the Consejo Nacional de Humanidades Ciencia y Tecnología (CONAHCYT) and BEIFI-IPN for scholarships. LV-T and CH-R are fellows of EDI-IPN, COFAA-IPN, and SNI-CONAHCYT. References Wilkinson BH, McElroy BJ. The impact of humans on continental erosion and sedimentation. Geol Soc Am Bull. 2007;119(1–2):140–56. 10.1130/B25899.1 . Cacciuttolo C, Cano D. 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Supplementary Files Supplemetarymaterial.pptx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 17 Apr, 2026 Reviews received at journal 15 Apr, 2026 Reviews received at journal 13 Apr, 2026 Reviews received at journal 09 Apr, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers agreed at journal 22 Mar, 2026 Reviewers agreed at journal 10 Mar, 2026 Reviewers invited by journal 08 Mar, 2026 Editor assigned by journal 03 Mar, 2026 Submission checks completed at journal 03 Mar, 2026 First submitted to journal 27 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8991199","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":604251461,"identity":"a918cc45-af3f-4cb0-bcd6-127530816b2c","order_by":0,"name":"Lizbeth Vazquez-Hernandez","email":"","orcid":"","institution":"Instituto Politécnico Nacional","correspondingAuthor":false,"prefix":"","firstName":"Lizbeth","middleName":"","lastName":"Vazquez-Hernandez","suffix":""},{"id":604251462,"identity":"0939f7c0-81e7-4ae6-b12a-0098c9a9e121","order_by":1,"name":"Ricardo Monterrubio-López","email":"","orcid":"","institution":"Instituto Politécnico Nacional","correspondingAuthor":false,"prefix":"","firstName":"Ricardo","middleName":"","lastName":"Monterrubio-López","suffix":""},{"id":604251464,"identity":"c9161d26-f7b5-46bd-a043-0a88753d73f3","order_by":2,"name":"Juan M. Vigueras-Cortés","email":"","orcid":"","institution":"Instituto Politécnico Nacional","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"M.","lastName":"Vigueras-Cortés","suffix":""},{"id":604251465,"identity":"5152d46a-150a-4833-9a27-b7b5bfb164cd","order_by":3,"name":"Lourdes Villa-Tanaca","email":"","orcid":"","institution":"Instituto Politécnico Nacional","correspondingAuthor":false,"prefix":"","firstName":"Lourdes","middleName":"","lastName":"Villa-Tanaca","suffix":""},{"id":604251467,"identity":"5738dc83-f824-4890-9f55-de7b37987db9","order_by":4,"name":"Alejandra Miranda-Carrazco","email":"","orcid":"","institution":"Universidad Autónoma Metropolitana","correspondingAuthor":false,"prefix":"","firstName":"Alejandra","middleName":"","lastName":"Miranda-Carrazco","suffix":""},{"id":604251468,"identity":"87f3365c-e78a-4d12-b9e1-4716b0e81d82","order_by":5,"name":"César Hernández-Rodríguez","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYDACCQYDBoYfNnIGYJ6BBZFaGHvSjA0YmEFaJIi1he1w4gawFgYitPDPbt748AdPWvp29v6jG34USDDwt3cn4LfkzrFiAwkLm9ydPYfZbvYAHSZx5uwGvFoMJHLMJAx40nI33Ehmu8ED1GIgkUtQi/mPBLbD6QZALTf/EKnFjOEA2+EEkJbbRNkicSOtWLKxJ81ww5nDZrdlDCR4CPqFf0byxo8/ftjIGxxvfHbzzR8bOf72XvxaMAAPacpHwSgYBaNgFGAFAGbyReW8zWh0AAAAAElFTkSuQmCC","orcid":"","institution":"Instituto Politécnico Nacional","correspondingAuthor":true,"prefix":"","firstName":"César","middleName":"","lastName":"Hernández-Rodríguez","suffix":""}],"badges":[],"createdAt":"2026-02-27 19:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8991199/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8991199/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104513589,"identity":"ba0b8375-ea23-4e5f-83ec-a5b5d075d0a8","added_by":"auto","created_at":"2026-03-12 16:44:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":854911,"visible":true,"origin":"","legend":"\u003cp\u003eSampling site of “La Parrilla” mine tailings. (A) Location of Durango state in Mexico. (B) Town of San Jose de La Parrilla in Durango. (C) Mining company “La Parrilla” and the three sampling points. (D) RMT sampling point. (E) YMT sampling point. (F) OMT sampling point. (G) Coordinates by deposition time. Figures A–C were obtained as satellite images from Google Earth (accessed February 10, 2026). RMT: recent mine tailings; YMT: young mine tailings; OMT: old mine tailings.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8991199/v1/129db218040c10365799478a.png"},{"id":104513584,"identity":"3e00941b-00ba-4415-9e5c-439b8e54847b","added_by":"auto","created_at":"2026-03-12 16:44:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":49651,"visible":true,"origin":"","legend":"\u003cp\u003ePCA showing the distribution of the bacterial communities of “La Parrilla” mine tailings of 1.5 (YMT) and 5 (YMT) years old, Durango, Mexico.\u003c/p\u003e\n\u003cp\u003eA, Boxplot of Hill numbers: B, Diversity index at genus taxonomic level.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8991199/v1/68caa846213f34b870e78b88.png"},{"id":104513585,"identity":"ff632b13-e3e5-4c74-8253-5f2915252cd0","added_by":"auto","created_at":"2026-03-12 16:44:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":125466,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal shifts in bacterial community structure and differential taxonomic abundance from the mine tailings “La Parrilla”, Durango, Mexico. (A) stacked bar plot showing the relative abundance of the 15 dominant bacterial genera in mine tailings at 1.5 years (YMT) and 5 years (OMT) after deposition. (B) Bubble plot with the relative abundance estimated by the metabarcoding approach of the 10 most abundant bacterial genera of mine tailing samples of 1.5 (YMT1, YMT2, and YMT3) and 5 (OMT1, OMT2, and OMT3) years after deposition. (C) Horizontal bar plot of log2 fold change (log2 Fold Change: OMT/YMT) for dominant genera, illustrating the magnitude and direction of temporal shifts. Positive values indicate enrichment in OMT, whereas negative values indicate higher relative abundance in YMT. (D) Volcano plot based on ALDEx2 analysis, highlighting differentially abundant genera between YMT and OMT. Each point represents a genus; the x-axis shows the effect size, and the y-axis the adjusted p-value (q-value). Genera significantly enriched in either time point are indicated. (E) Genera with the largest effect in the bacterial community assembling of YMT and OMT samples. Effect sizes ≥ 0.8 and \u0026lt; 3 were considered large, and values ≥ 3 were very large.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8991199/v1/9d1438df1283fb1c59185a25.png"},{"id":106092830,"identity":"c8c46297-113f-41e6-98d2-eca2931532e6","added_by":"auto","created_at":"2026-04-03 11:26:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":120409,"visible":true,"origin":"","legend":"\u003cp\u003eunctional changes associated with bacterial succession from the mine tailings “La Parrilla”, Durango, Mexico. (A) Bubble plot with the relative abundance of the 10 most abundant putative functional traits obtained through FAPROTAX according to the genus taxonomic level in samples in mine tailings at 1.5 years (YMT) and 5 years (OMT) after deposition. (B) Stacked bar plot showing the relative abundance of putative functional traits with a significant difference between YMT and OMT. (C) Horizontal bar plot of log2 fold change (OMT/YMT) for putative functional traits, illustrating the magnitude and direction of temporal metabolic shifts. Positive values indicate enrichment in OMT, whereas negative values indicate higher relative abundance in YMT. (D) Volcano plot based on ALDEx2 analysis, highlighting the effect size of the putative functional traits between YMT and OMT. Each point represents a putative functional trait; the x-axis shows the effect size, and the y-axis the adjusted p-value (q-value). Putative functional traits significantly enriched in either time point are indicated. € Putative functional traits with the largest effect in the bacterial function assembling of YMT and OMT. Effect sizes ≥ 0.8 and \u0026lt; 3 were considered large, and values ≥ 3 were very large. S: sample.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8991199/v1/1ddc83b5a3713f7cf041fe0e.png"},{"id":104513586,"identity":"3d34bebe-1d57-414f-8d74-ca8066d36226","added_by":"auto","created_at":"2026-03-12 16:44:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":718491,"visible":true,"origin":"","legend":"\u003cp\u003eCommunity assembly and early ecological succession in mine tailings over time.\u003cbr\u003e\nThis conceptual model illustrates microbial community dynamics from young mine tailings (1.5 years) to older tailings (5 years). Cyanide present in recent mine tailings is rapidly removed through volatilization and biological activity. Early-stage YMT communities are primarily characterized by chemolithoautotrophic functions, such as thiosulfate oxidation and methylotrophy, and are dominated by genera including \u003cem\u003eThiobacillus\u003c/em\u003e and \u003cem\u003eLimnobacter,\u003c/em\u003e with limited heterotrophic activity. Elevated heavy metal concentrations, low organic carbon, and minimal combined and assimilable nitrogen favor taxa tolerant to HM and nutritional stress. In the OMT community, increased richness and bacterial diversity result in functional specialization toward sulfur-based chemolithoautotrophy and carbon fixation, with enrichment of \u003cem\u003eSulfurifustis\u003c/em\u003e, \u003cem\u003eKribbella\u003c/em\u003e, and \u003cem\u003eNocardioides\u003c/em\u003e. Despite taxonomic turnover, the Proteobacteria phylum remains dominant across stages, underscoring its ecological versatility during succession.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8991199/v1/3f0e8e2f2669c5aee2af89bb.png"},{"id":106095484,"identity":"f41990f6-9ec1-431a-8812-dd0726012b2c","added_by":"auto","created_at":"2026-04-03 11:48:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2983148,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8991199/v1/778c1e7e-0128-410e-b519-aa3615a41d7b.pdf"},{"id":104513588,"identity":"28b34849-44f9-4882-bee3-0c762deb5787","added_by":"auto","created_at":"2026-03-12 16:44:36","extension":"pptx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":438067,"visible":true,"origin":"","legend":"","description":"","filename":"Supplemetarymaterial.pptx","url":"https://assets-eu.researchsquare.com/files/rs-8991199/v1/31febfe570e7e827ffc4d06f.pptx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Early ecological succession and functional persistence of chemolithoautotrophic bacterial communities of mine tailings","fulltext":[{"header":"1. Background","content":"\u003cp\u003eMine tailings, the byproducts of metal extraction, are among the primary anthropogenic contaminants of terrestrial soils [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These fine sands contain residual and toxic heavy metals (HM), metalloids such as arsenic, and cyanide, and are stored in human-made dams, from which they may be dispersed by wind or leached into the ground [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Typically, mine tailings lack organic compounds and assimilable combined inorganic or organic nitrogen sources. These characteristics contribute to the formation of a relatively novel extreme environment in terrestrial soils. Furthermore, mine tailings contain substantial amounts of reduced inorganic sulfur compounds, including pyrite (FeS2), chalcopyrite (CuFeS2), sphalerite (ZnS), galena (PbS) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and thiocyanate (SCN-), a product of biological cyanide detoxification [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Depending on the concentrations of HM and metalloids, young mine tailings are seldom colonized by plants [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Nevertheless, certain microbes are capable of colonizing these environments, accumulating organic compounds, promoting ecological succession, and ultimately facilitating plant colonization [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWithin mine tailing environments, metabolic capacities such as HM tolerance and transformation, cyanotrophy, chemolithoautotrophy, carbon-oligotrophy, nitrogen fixation, and nitrogen oligotrophy are essential for pioneer microorganisms [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These microbial colonizers initiate natural bioremediation, introduce organic carbon and nitrogen sources, promote elemental cycling, and modify the environment to support soil formation and subsequent colonization by metallophytic plants. Plants adapted to these conditions may be utilized in both natural and artificial phytoremediation strategies [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBacterial guilds isolated, cultured, and characterized from these residues include nitrogen fixers [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], iron- and sulfur-oxidizers [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], ammonia- and carbon-oligotrophs [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], HM-tolerant bacteria and HM-oxidizers [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], as well as cyanotrophic bacteria [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe bacterial composition of select mine tailings has been characterized using metabarcoding and metagenomic approaches [\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These microbial profiles are influenced by the chemical composition of the tailings, ore extraction procedures, anthropogenic activities, environmental factors, and the age of the tailings. Variations in microbial populations, nutritional content, and physicochemical properties contribute to the dynamics of these ecosystems.\u003c/p\u003e \u003cp\u003eMining is among the oldest industries in human history, with the first metals for tool fabrication obtained during the Neolithic period [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This industry has generated substantial volumes of toxic compounds globally through environmentally disruptive procedures [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Remediation of mine tailings is challenging due to insufficient environmental regulations and the vast quantities of tailings deposited in opencast mines. Autochthonous bacteria offer more cost-effective and environmentally sustainable alternatives to conventional physicochemical treatments, and can be employed in natural attenuation and artificial bioremediation strategies to promote ecological succession [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study examined the early ecological succession of bacteria in a tailing dam at the former \u0026ldquo;La Parrilla\u0026rdquo; mine in Durango, Mexico, characterized by low concentrations of organic matter and assimilable combined nitrogen, high levels of reduced inorganic sulfur compounds, and elevated HM and arsenic concentrations. The bacterial composition was determined using 16S rRNA metagenomic analysis of DNA from 1.5- and 5-year-old tailing samples. Early ecological succession of bacterial genera was observed, although the relative abundance of putative functional traits persisted.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Sampling Site\u003c/h2\u003e \u003cp\u003eThe \u0026ldquo;La Parrilla\u0026rdquo; mine, located in Durango, Mexico (23\u0026deg;44\u0026rsquo;16\u0026rsquo;\u0026rsquo;N, 104\u0026deg;06\u0026rsquo;26\u0026rsquo;\u0026rsquo;W) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), began operations in 2004 and is primarily used for silver and lead extraction via cyanidation. The mine is situated in a semi-arid environment, with average annual temperatures between 12\u0026deg;C and 25\u0026deg;C. The tailing dams at this site are devoid of vegetation.\u003c/p\u003e \u003cp\u003eIn February 2019, tailings were collected from a mine dam at a depth of 0.3 m, representing three deposition ages: 0 years (recently deposited, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), 1.5 years (YMT: young mine tailing, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE), and 5 years (OMT: old mine tailing, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). Samples were transported in sterile, sealed bags to a laboratory in Mexico City for further analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Physicochemical characteristics, and carbon, cyanide, and nitrogen contents.\u003c/h2\u003e \u003cp\u003eThe pH was measured in a 1:2 mine tailings to water suspension using a pH meter (Corning, NY, USA). Electrical conductivity was assessed with the HI993310 kit (Hanna Instruments, RI, USA). Cyanide and nitrogen concentrations were determined using the Cyaniver kit (Corning, NY, USA) and the Total Nitrogen Reagent Set, HR, TNT (Hach, CO, USA), respectively. For total carbon determination, 5 g of each dried sample (oven-dried at 40\u0026deg;C) was heated in a muffle furnace for 6 hours at 600\u0026deg;C, and the carbon content was calculated by weight difference [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. HM and arsenic quantification\u003c/h2\u003e \u003cp\u003eTotal concentrations of silver (Ag), mercury (Hg), iron (Fe), copper (Cu), lead (Pb), zinc (Zn), chromium (Cr), cobalt (Co), nickel (Ni), cadmium (Cd), vanadium (V), and arsenic (As) were determined by atomic absorption spectroscopy following the \u0026ldquo;Norma Oficial Mexicana\u0026rdquo; NOM-147-SEMARNAT/SSA1-2004 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Metagenomic DNA extraction\u003c/h2\u003e \u003cp\u003eMetagenomic DNA extraction from mine tailings was standardized according to deposition time, utilizing a combination of mechanical and enzymatic methods [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. For each sample, 5 g was pretreated with 5 mL of pretreatment solution (Tris-HCl 100 mM, NaH2PO4 100 mM, EDTA 500 mM, pH 8) and mixed by orbital shaking at 100 rpm for 3 hours. After centrifugation at 2,000 x g for 2 minutes, the pretreatment solution was discarded. For young and old mine tailings, pellets were resuspended in 15 mL of lysis solution (Tris-HCl 100 mM, EDTA 20 mM, NaCl 150 mM, SDS 2%) with 3.5 g of glass beads. The suspension was vortexed for 1 minute, incubated on ice for 1 minute, and this cycle was repeated three times. Enzymatic lysis was performed by adding 2.5 mg of lysozyme (20,000 U/mg; Affymetrix, CA, USA) and incubating at 37\u0026deg;C for 30 minutes. Subsequently, 0.5 mg of Proteinase K (20 U/mg; Invitrogen, MA, USA) was added, followed by incubation at 60\u0026deg;C for 30 minutes and a cold shock at -20\u0026deg;C. The suspension was centrifuged at 1,340 x \u003cem\u003eg\u003c/em\u003e for 10 minutes at room temperature (approximately 23\u0026deg;C), and the supernatant was collected. This was treated with 1/5 volume of EDTA 0.5 M (pH 8.0) and 1/10 volume of potassium acetate 5 M (pH 5.5), incubated at room temperature for 20 minutes, and centrifuged at 18,500 x \u003cem\u003eg\u003c/em\u003e for 10 minutes. The supernatant was collected, mixed with an equal volume of isopropanol, and incubated at -20\u0026deg;C for 2 hours. After centrifugation at 18,500 x \u003cem\u003eg\u003c/em\u003e for 10 minutes, the pellet was dried and resuspended in 500 \u0026micro;L of TE buffer and 1 mL of chloroform:isoamyl alcohol (24:1), then microcentrifuged for 10 minutes at 13,000 rpm. The aqueous phase was recovered, mixed with an equal volume of absolute isopropanol, and incubated overnight at -20\u0026deg;C to precipitate DNA. The solution was centrifuged for 10 minutes at 18,500 x \u003cem\u003eg\u003c/em\u003e, and the supernatant was removed. Remaining alcohol was evaporated using a centrifuged concentrator (Eppendorf, Hamburg, Germany) at room temperature. The pellet was resuspended in 30 \u0026micro;L of sterile distilled water. DNA quality was assessed by electrophoresis on 1% agarose gels in 1\u0026times; TAE buffer and staining with 0.5 \u0026micro;g mL\u0026thinsp;\u0026minus;\u0026thinsp;1 ethidium bromide (ETB). DNA concentrations were determined spectrophotometrically, with an A260/A280 ratio of 1.8 considered acceptable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Metabarcoding approach by amplification of bacterial 16S rRNA V4-V5 gene region\u003c/h2\u003e \u003cp\u003eMetagenomic DNA from the samples was amplified and sequenced by Novogene Bioinformatics Technology Co., Ltd., Tianjin, China. The V4-V5 variable regions of the bacterial 16S rRNA gene were amplified using primers 515F (GTGCCAGCMGCCGCGGTAA) and 907R (CCGTCAATTCCTTTGAGTTT). PCR reactions were performed with Phusion High-Fidelity PCR Master Mix (New England Biolabs, Ipswich, MA) and GC Buffer under the following conditions: initial denaturation at 94\u0026deg;C for 3 minutes (1 cycle), followed by 35 cycles of 94\u0026deg;C for 45 seconds, 50\u0026deg;C for 60 seconds, and 72\u0026deg;C for 90 seconds, with a final extension at 72\u0026deg;C for 10 minutes. PCR products were purified using the Qiagen gel extraction kit (Qiagen, Hilden, Germany) and sequenced on the Illumina MiSeq platform with a v3 chemistry kit (2x300 bp).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Bacterial community composition analysis\u003c/h2\u003e \u003cp\u003eBacterial 16S rRNA sequences were analyzed using Quantitative Insights into Microbial Ecology (QIIME2) software version 2022.2 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Read quality and length were assessed with FastQC [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. DADA2 was employed for quality filtering, dereplication of amplicon sequence variants (ASV), trimming, and chimera removal [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Taxonomic annotation was assigned to representative sequences in the ASV table using the \u003cem\u003eclassify-sklearn\u003c/em\u003e function with a Na\u0026iuml;ve Bayes supervised learning algorithm and the Silva database v138 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Taxonomy was collapsed at all taxonomic levels (1\u0026ndash;7) using the QIIME taxa collapse argument.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Bacterial community putative functionality\u003c/h2\u003e \u003cp\u003eBacterial taxonomic information was assigned to putative functional groups using Functional Annotation of Prokaryotic Taxa (FAPROTAX v1.2) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], based on the abundance table at the genus level (following taxonomy collapse at level 6). Relative abundances of functional groups were calculated using the local-sum scaling procedure for each sample.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Diversity and statistical analyses\u003c/h2\u003e \u003cp\u003eDiversity and statistical analyses, as well as data visualization, were conducted in the R environment v. 4.0.2 [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Alpha diversity of bacterial community structure and putative functionality was assessed at different q orders: q\u0026thinsp;=\u0026thinsp;0 (species richness), q\u0026thinsp;=\u0026thinsp;1 (frequent species), and q\u0026thinsp;=\u0026thinsp;2 (dominant species), using Hill numbers [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] with the hillR package v. 0.5.1 [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Analyses utilized the frequency table (ASV) at the genus taxonomic level and the frequency table of putative functional traits.\u003c/p\u003e \u003cp\u003eFrequency tables reporting ASV at various taxonomic levels and putative functional traits (at the genus level) were subjected to compositional analyses, as sequencing data possess an arbitrary total imposed by the instrument [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Sequencing data underwent centered log-ratio transformation using the Aldex.clr function in the Aldex2 package version 1.21.1 [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Principal Component Analysis (PCA) was performed using the prcomp function of the stats package in R to assess variations in bacterial composition between YMT and OMT samples. Results were visualized with the ggplot2 v.3.3.6 [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] package.\u003c/p\u003e \u003cp\u003eBubble plots were generated to visualize the relative abundance of the ten most prevalent bacterial genera and the eight most abundant putative functional traits across treatments. Shifts in bacterial community composition between YMT and OMT were estimated using log₂ fold change (log2FC) values based on relative abundance for each genus as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${\\text{l}\\text{o}\\text{g}}_{2}\\text{F}\\text{C}={\\text{l}\\text{o}\\text{g}}_{2}\\left(\\frac{\\text{OMT}}{\\text{YMT}}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ePositive log2FC values indicate enrichment in OMT, while negative values indicate enrichment in YMT. All taxa with a relative abundance greater than 0.01% were included in the analysis.\u003c/p\u003e \u003cp\u003eSimilarly, to assess functional shifts in putative profiles inferred from the 16S rRNA gene between conditions, log2FC values were calculated from relative functional abundances. When predicted functions were absent in one condition (i.e., zero values), a pseudocount of 0.001 was added to both OMT and YMT values prior to log2FC calculation to prevent division by zero and infinite values:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$${{log}_{2}\\text{F}\\text{C}=\\text{l}\\text{o}\\text{g}}_{2}\\left(\\frac{\\text{OMT}+0.001}{\\text{YMT}+0.001}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThis approach enables quantitative comparison of enrichment patterns while minimizing distortion of relative abundances.\u003c/p\u003e \u003cp\u003eDifferential abundance was evaluated using an ANOVA-like differential expression approach implemented in the ALDEx2 package [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Compositional data were CLR-transformed with aldex.clr, and group differences were assessed using Welch\u0026rsquo;s t-test and Wilcoxon rank-sum test via aldex.test. Effect sizes were calculated with Aldex.effect, and significance was determined based on Benjamini\u0026ndash;Hochberg adjusted expected p-values from Welch\u0026rsquo;s t-test. Effect sizes between 0.8 and 3 were considered large, and values of 3 or greater were considered very large. Volcano plots were used to display effect sizes versus expected p-values. The core bacterial community at the genus level was identified by shared taxa across groups and visualized with a Venn diagram (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinformatics.psb.ugent.be/webtools/Venn/\u003c/span\u003e\u003cspan address=\"https://bioinformatics.psb.ugent.be/webtools/Venn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Physicochemical characteristics, carbon, cyanide, nitrogen, and HM contents in mine tailings\u003c/h2\u003e \u003cp\u003eThe pH and electrical conductivity values were consistent across all mine tailings, with pH values remaining near neutral. In newly deposited mine tailings, organic matter and total nitrogen concentrations were initially very low; however, organic matter increased over time. Total nitrogen values in YMT were higher than in OMT, measuring 79.47 and 49.07 mg kg-1, respectively. In contrast, cyanide content declined rapidly over time. The most recent mine tailing exhibited a cyanide content of 869 mg kg-1, whereas cyanide was not detected in the young and old samples (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWith the exception of mercury, whose concentration decreased with the age of the mine tailings, heavy metal and metalloid concentrations remained similar in both young and old samples, persisting within the same order of magnitude over time (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe \u0026ldquo;Norma Oficial Mexicana\u0026rdquo; NOM-147-SEMARNAT/SSA1-2004 establishes criteria for determining remediation concentrations for soils contaminated with arsenic, barium, beryllium, cadmium, hexavalent chromium, mercury, nickel, silver, lead, selenium, thallium, and/or vanadium. According to this NOM, the As and Pb concentrations were considered high according to the total reference measurements for industrial soils. Similarly, the concentrations of the other heavy metals were considered moderately high in agricultural, residential, and commercial-use soils.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the mine tailings of RMT, YMT and OMT years old from the mine \u0026ldquo;La Parrilla\u0026rdquo;, Mexico.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"20\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDeposition age (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003cp\u003e(mS/cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCN\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"13\" nameend=\"c20\" namest=\"c8\"\u003e \u003cp\u003eHeavy metals and arsenic\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eCo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003eCd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c19\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e53,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e8,840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e11,450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e45,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e6,160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e5,480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e47,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e7,590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e11,170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c20\" namest=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eEC, Electrical conductivity; OM, Organic matter; TN, Total nitrogen; CN\u003csup\u003e\u0026minus;\u003c/sup\u003e, Cyanide; RMT, recent mine tailings; YMT, young mine tailings; OMT, old mine tailings. The unspecified units correspond to mg Kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Sequencing results\u003c/h2\u003e \u003cp\u003eNo metagenomic DNA or bacterial 16S rRNA amplification products were obtained from the recently deposited mining tailings (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). In contrast, older samples (YMT and OMT) produced PCR products. Following assembly and quality filtering, the metabarcoding approach generated 966,455 sequences, representing 12 to 15 phyla, 36 classes, 81 orders, 110 families, and 128 bacterial genera (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSequencing data and diversity indexes according to Hill numbers at the genus level of mine tailings of 1.5 (YMT) and 5 (OMT) years old from the mine \u0026ldquo;La Parrilla\u0026rdquo;, Mexico.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSequences\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePhylum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOrder\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFamily\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePutative functional traits*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eHill diversity indexes at genus level\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eq0**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eq1**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eq2**\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYMT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e174879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYMT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e171437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYMT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e163442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOMT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOMT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e172438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOMT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e168893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e966455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e*\u003c/sup\u003ePutative functional traits were predicted through FAPROTAX.\u003c/p\u003e \u003cp\u003e \u003csup\u003e**\u003c/sup\u003e q0, Species richness; q1, Frequent species; q2, Dominant species\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Sample dispersion and bacterial alpha diversity\u003c/h2\u003e \u003cp\u003ePrincipal component analysis (PCA) of clr-transformed data grouped the samples according to tailings age. YMT samples exhibited greater dispersion compared to OMT samples. Additionally, based on Hill numbers, OMT samples demonstrated higher richness (q0) and diversity (q1 and q2) at the bacterial genus level than YMT samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Bacterial communities of mine tailings: diversity and composition\u003c/h2\u003e \u003cp\u003eMetabarcoding analysis identified Proteobacteria as the most prevalent phylum in both young and old mine tailings (96.5\u0026ndash;97.5% in YMT samples and 91.9\u0026ndash;92.9% in OMT samples), followed by Actinobacteria (1.7\u0026ndash;3.2% in YMT and 6.2\u0026ndash;7.2% in OMT). At the order level, Burkholderiales exhibited the highest relative abundance in both treatments (95.6\u0026ndash;97.2% in YMT and 52.2\u0026ndash;55.8% in OMT), while Acidoferrobacterales was more abundant in OMT (35.4\u0026ndash;39.3%) than in YMT (0.31\u0026ndash;0.94%). At the genus level, 15 bacterial genera each accounted for at least 0.1% relative abundance, collectively representing 98.9% of the total bacterial community (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Within Burkholderiales, \u003cem\u003eThiobacillus\u003c/em\u003e was the most prevalent genus in both mine tailings (69.7\u0026ndash;93.2% in YMT and 53.3\u0026ndash;56.7% in OMT), followed by \u003cem\u003eLimnobacter\u003c/em\u003e (0.3\u0026ndash;20.7% in YMT). The genus \u003cem\u003eSulfurifustis\u003c/em\u003e, belonging to Acidiferrobacterales, was the second most abundant in OMT (36.5\u0026ndash;40.9%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Although the relative abundances of \u003cem\u003ePseudomonas\u003c/em\u003e and \u003cem\u003eHydrogenophaga (\u003c/em\u003eProteobacteria\u003cem\u003e), as well as Nocardioides\u003c/em\u003e and \u003cem\u003eKribbella\u003c/em\u003e (Actinobacteria), were low, these genera were widely distributed among samples.\u003c/p\u003e \u003cp\u003eLog\u003csub\u003e2\u003c/sub\u003e fold change analysis indicated significant directional shifts in dominant bacterial genera over deposition time (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Several taxa, including \u003cem\u003eSulfurifustis\u003c/em\u003e (log2FC\u0026thinsp;=\u0026thinsp;+\u0026thinsp;5.95), \u003cem\u003ePromicromonospora\u003c/em\u003e (log2FC\u0026thinsp;=\u0026thinsp;+\u0026thinsp;5.00), \u003cem\u003eKribbella\u003c/em\u003e (log2FC\u0026thinsp;=\u0026thinsp;+\u0026thinsp;4.82), and \u003cem\u003eQipengyuania\u003c/em\u003e (log2FC\u0026thinsp;=\u0026thinsp;+\u0026thinsp;4.48), exhibited strong enrichment after 5 years (OMT). In contrast, \u003cem\u003eLimnobacter\u003c/em\u003e (log2FC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;5.66), \u003cem\u003eEnterobacter\u003c/em\u003e (log2FC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;4.91), \u003cem\u003ePseudomonas\u003c/em\u003e (log2FC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;4.47), and \u003cem\u003eHydrogenophaga\u003c/em\u003e (log2FC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;4.35) showed marked declines, reflecting substantial reductions in their relative contributions to community structure at the later deposition stage.\u003c/p\u003e \u003cp\u003e \u003cem\u003eThiobacillus\u003c/em\u003e remained the dominant genus at both time points, although it exhibited a moderate decrease (log2FC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.48), indicating persistence with partial restructuring. In contrast, \u003cem\u003eSphingopyxis\u003c/em\u003e displayed minimal variation (log2FC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.15), suggesting relative temporal stability.\u003c/p\u003e \u003cp\u003eYMT and OMT samples shared a core bacterial richness comprising 47 genera, with 51 and 29 exclusive genera identified in YMT and OMT, respectively (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). \u003cem\u003eHydrogenophaga\u003c/em\u003e exerted a very large effect on the assembly of the YMT bacteriome, while \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eEnterobacter\u003c/em\u003e, \u003cem\u003eAcidovorax\u003c/em\u003e, \u003cem\u003eDietzia\u003c/em\u003e, \u003cem\u003eCavicella\u003c/em\u003e, \u003cem\u003eAcinetobacter\u003c/em\u003e, \u003cem\u003eKnoellia\u003c/em\u003e, and \u003cem\u003eSilanimonas\u003c/em\u003e had a large effect. In OMT, \u003cem\u003eKribella\u003c/em\u003e and \u003cem\u003eSulfurifustis\u003c/em\u003e demonstrated a very large effect on bacteriome assembly, whereas \u003cem\u003eMamoricola\u003c/em\u003e, \u003cem\u003ePedobacter\u003c/em\u003e, \u003cem\u003eParviterribacter\u003c/em\u003e, \u003cem\u003eNocardioides\u003c/em\u003e, \u003cem\u003eHalothiobacillus\u003c/em\u003e, and \u003cem\u003eBosea\u003c/em\u003e exhibited a large effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD and F, Table S2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Putative functional profile\u003c/h2\u003e \u003cp\u003eThe putative functional profile predicted by FAPROTAX indicated capacities for chemoheterotrophy, fermentation, and nitrogen metabolism. The most prevalent predicted functions in both YMT and OMT were related to sulfur chemolithotrophy, specifically dark oxidation of sulfur compounds (43.28\u0026ndash;46.11% in YMT and 45.72\u0026ndash;46.17% in OMT) and dark sulfide oxidation (43.28\u0026ndash;46.09% in YMT and 45.68\u0026ndash;16.15% in OMT) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In OMT samples, functional traits with very large effects included aromatic compound degradation, dark sulfur oxidation, and dark thiosulfate oxidation. In YMT, dark hydrogen oxidation, methylotrophy, and methanol oxidation were the predictive functional traits with very large effects.\u003c/p\u003e \u003cp\u003eAnalysis of functions inferred from bacterial diversity profiles revealed substantial differences in metabolic structure between YMT and OMT. The bubble plot of relative abundances for the most abundant potential functional traits indicated a chemolithoautotrophic predominance in both YMT and OMT, including processes such as dark sulfur oxidation, dark sulfur thiosulfate oxidation, and dark hydrogen oxidation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). YMT exhibited differential functions associated with both chemolithoautotrophic and heterotrophic metabolism, whereas OMT was characterized by a predominance of chemolithoautotrophic processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Log2FC values confirmed a functional transition, with heterotrophic profiles enriched in YMT and functions related to dark sulfur oxidation and thiosulfate enriched in OMT (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Differential analysis using ALDEx2 further supported these distinctions, highlighting a subset of functions significantly overrepresented at each stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD and E). Additionally, features associated with mammal gut, human gut, and human-associated animal parasites or symbionts were identified (Table S3).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe mining industry exerts a substantial negative impact on the environment. Elevated concentrations of heavy metals (HM) and metalloids in tailing dams present significant risks to human health, regional ecosystems, and living organisms [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Beyond metal toxicity, mine tailings are generally defined by low organic matter, limited nutrient availability, and unfavorable physicochemical properties, which collectively create highly restrictive conditions for biological colonization. Nevertheless, specific microbial communities are capable of colonizing mine tailings and functioning as pioneer organisms.\u003c/p\u003e \u003cp\u003eThis study investigated the composition of bacterial communities in mine tailings of varying ages (0, 1.5, and 5 years) using a 16S rRNA metabarcoding approach, and examined their potential relationships with environmental conditions.\u003c/p\u003e \u003cp\u003eNo metagenomic DNA could be obtained from recently extracted and deposited mine tailings, although it was present at undetectable levels and insufficient for subsequent protocols. However, the DNA extraction for the other samples was optimized, and their quality enabled amplification and massive sequencing. The bacterial communities of the samples were grouped in the PCA according to the age of the mine tailings. Bacterial diversity, estimated by Hill indexes (richness, frequency, and dominance) at the genus level, was higher in older samples (5 years old, OMT) than in younger samples (1.5 years old, YMT). Similarly, the Simpson diversity and Chao richness indexes of bacterial communities of Pb-Zn tailing soils in the Qinling Mountains were higher in the older tailings (31 years old) than in the younger tailings (13 years old) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWithout remediation strategies, the residence time of mine tailings within a dam serves as a primary ecological factor influencing microbial community assembly. As deposition time increases, ongoing physicochemical changes affect microbial colonization dynamics. Previous research indicates that microbial diversity and richness generally increase over time, ultimately leading to a more stable community structure as ecological succession advances [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. This temporal progression is frequently linked to gradual increases in organic matter and the development of plant cover, both of which enhance nutrient availability and support greater bacterial diversity [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In the \u0026ldquo;La Parrilla\u0026rdquo; mine tailings, however, bacterial diversity did not correlate with concentrations of organic carbon, total nitrogen, or levels of heavy metals and arsenic (data not shown).\u003c/p\u003e \u003cp\u003eIn the mine tailings analyzed, most heavy metals and arsenic remained at concentrations similar to their original levels, indicating minimal mobilization, leaching, or sequestration of HM. Such processes can alter the original HM content of mine tailings [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The five-year observation period may be insufficient to detect significant changes in HM composition. Mercury was the only element to exhibit a decrease in concentration over time, likely due to its volatility [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. These persistent metal-stress conditions are expected to continue exerting selective pressure on microbial communities, underscoring the significance of residence time in shaping both environmental constraints and bacterial community composition.\u003c/p\u003e \u003cp\u003eThe toxic effects of HM and metalloids on cells include protein dysfunction, production of reactive oxygen species, antioxidant depletion, impaired membrane function, disruption of nutrient uptake, and genotoxicity [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Consequently, early colonizers typically exhibit metabolic traits such as heavy-metal tolerance and transformation, chemolithoautotrophy, carbon and nitrogen fixation, and oligotrophic growth, enabling survival and function under nutrient-limited and metal-stressed conditions [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. In this study, HM- and metalloid-resistant bacteria were not identified in the functional analysis of genera. However, the genus \u003cem\u003ePseudomonas\u003c/em\u003e, known for HM resistance, was detected in all metagenomic DNA samples from mine tailings and is frequently isolated or detected in similar environments [\u003cspan additionalcitationids=\"CR54 CR55\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Previous studies have also reported autochthonous HM-resistant \u003cem\u003ePseudomonas\u003c/em\u003e strains in mine tailings from the same mining region in Zacatecas and Durango, Mexico [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent mine tailings exhibited high concentrations of toxic cyanide, which appeared to be rapidly removed in samples aged 1.5 and 5 years. Cyanide in aqueous solution at neutral pH is insoluble and may volatilize into the atmosphere [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Previously, the cyanotrophic \u003cem\u003ePseudomonas mendocina\u003c/em\u003e P6115 strain was isolated from an alkaline pool at the \u0026ldquo;La Parrilla\u0026rdquo; mine in Durango and demonstrated adaptive phenotypic traits in mine tailing environments, including siderophore production, moderate HM resistance, arsenite and arsenate tolerance, and the ability to oxidize arsenite [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA further significant selective pressure influencing microbial communities in mine tailings is the scarcity of organic matter. In the absence of organic carbon and energy sources, but with abundant inorganic reduced sulfur compounds, chemolithoautotrophic bacteria are expected to dominate [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Consistent with this, the principal bacterial genera and inferred functional traits identified in this study were associated with chemolithoautotrophic and chemolithoheterotrophic metabolism. Chemolithoautotrophic sulfide, sulfur, and thiosulfate-oxidizing \u003cem\u003eThiobacillus\u003c/em\u003e was the dominant genus, followed by chemolithoheterotrophic thiosulfate-oxidizing \u003cem\u003eLimnobacter\u003c/em\u003e and \u003cem\u003eSulfurifustis\u003c/em\u003e in young and old mine tailings, respectively.\u003c/p\u003e \u003cp\u003eThe marked shifts in dominant bacterial genera between 1.5 and 5 years of mine tailings deposition indicate a directional ecological succession driven by persistent selection pressures related to environmental conditions and resource availability. In this study, the relative abundance of \u003cem\u003eSulfurifustis\u003c/em\u003e increased substantially over time (log2FC\u0026thinsp;=\u0026thinsp;+\u0026thinsp;5.95), whereas genera characteristic of early colonizers, such as \u003cem\u003eLimnobacter\u003c/em\u003e and \u003cem\u003ePseudomonas\u003c/em\u003e, declined markedly. These findings suggest that microbial community assembly in tailings is governed by processes favoring taxa with metabolic capabilities adapted to the changing geochemical environment. This pattern of successional shifts in microbial assemblages under evolving habitat conditions is consistent with observations from other mine tailings ecosystems, where community composition and function change predictably during remediation or natural attenuation [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Furthermore, these three bacterial genera have been isolated or detected in mine tailings from diverse global locations [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan additionalcitationids=\"CR61\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNitrogen content in mine tailings is significantly lower than in adjacent surface soils, thereby limiting biodiversity in these environments. Nitrogen concentrations in woodland soils in Durango, Mexico, range from 0.28 to 0.46% [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] and from 0.11 to 0.174% [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], whereas in the same semi-arid region, mine tailings contain nitrogen at two orders of magnitude lower (0.0049% in YMT and 0.0079% in OMT). During the early stages of biological succession in tailings and soils, nitrogen concentration is a critical factor [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Therefore, the capacity of pioneer bacteria in mine wastes to fix nitrogen is advantageous [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Autotrophic, nitrogen-fixing bacteria have been detected in mine tailings, where they facilitate nutrient acquisition for other microbes and plants, likely driving ecological succession. A significant correlation has been observed between the frequencies of genes involved in sulfur oxidation (soxB) and nitrogen fixation (nifH) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, in \u0026ldquo;La Parrilla\u0026rdquo; mine tailings, sulfur- and thiosulfate-oxidizing bacterial taxa were detected, but nitrogen-fixing bacteria were not, and the correlation between sulfur oxidation and nitrogen fixation was not confirmed. In contrast, several nitrogen-fixing and oligotrophic ammonium bacteria were isolated or detected in bulk soil and the rhizosphere of pioneer plants growing on nearby mine tailings [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe genus \u003cem\u003eHydrogenophaga\u003c/em\u003e exerted a significant influence on the bacterial community in the YMT condition. Hydrogenophaga has been reported to heterotrophically oxidize arsenite [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], a trait advantageous for bioremediation since arsenite is more toxic to plants and mammals than arsenate [\u003cspan additionalcitationids=\"CR69\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eEnterobacter\u003c/em\u003e, and \u003cem\u003eAcidovorax also had substantial\u003c/em\u003e effects in the YMT bacterial community, with all three genera involved in nitrogen metabolism. \u003cem\u003ePseudomonas mendocina\u003c/em\u003e S16 and \u003cem\u003eEnterobacter cloacae\u003c/em\u003e strains CF-S27 and DS\u0026rsquo;5 \u003cem\u003eare capable of\u003c/em\u003e simultaneous nitrification and denitrification under heterotrophic conditions [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e], while \u003cem\u003eAcidovorax\u003c/em\u003e spp. can reduce nitrate [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. \u003cem\u003eHydrogenophaga\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, and \u003cem\u003eEnterobacter\u003c/em\u003e possess heterotrophic metabolisms [\u003cspan additionalcitationids=\"CR75\" citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e] and were more abundant in YMT than in OMT conditions. These bacteria may originate from the surrounding soil or be introduced through anthropogenic activities related to mining, and their abundance declined over time due to insufficient metabolic traits for survival in the tailings.\u003c/p\u003e \u003cp\u003eThe reduced levels of organic matter and total nitrogen, combined with high concentrations of HM and metalloids, underscore the oligotrophic and extreme character of the ecosystem. Consequently, chemoautotrophic bacteria capable of nitrogen fixation and of tolerance to HM and metalloids were anticipated. The most abundant sulfur-oxidizing bacteria identified in \u0026ldquo;La Parrilla\u0026rdquo; mine tailings were \u003cem\u003eThiobacillus\u003c/em\u003e, \u003cem\u003eSulfurifustis\u003c/em\u003e, and \u003cem\u003eLimnobacter\u003c/em\u003e, genera commonly found in other mine tailings [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan additionalcitationids=\"CR77 CR78 CR79 CR80\" citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. Sulfur- and iron-oxidizing \u003cem\u003eThiobacillus\u003c/em\u003e can perform nitrate reduction coupled with Fe oxidation under anaerobic conditions [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. Moreover, it expresses genes involved in autotrophic carbon fixation, a convenient feature in environments poor in organic carbon, such as mine tailings [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. \u003cem\u003eSulfurifustis\u003c/em\u003e, the second most abundant bacterium in OMT samples, is a sulfur oxidizer harboring genes involved in sulfur oxidation and carbon fixation pathways that grows optimally at pH 6.8\u0026ndash;8.2 [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. In YMT samples, \u003cem\u003eLimnobacter\u003c/em\u003e was the second most abundant genus, a heterotrophic bacterium capable of oxidizing thiosulfate and a prevalent inhabitant of mineral soils under nitrifying conditions [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Besides, \u003cem\u003eNocardioides\u003c/em\u003e, the fourth most abundant genus in both \u0026ldquo;La Parrilla\u0026rdquo; mine tailings, harbors genes for dissimilatory nitrate reduction to ammonia, particularly \u003cem\u003enirB\u003c/em\u003e and HM resistance [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe genera \u003cem\u003eKribella\u003c/em\u003e and \u003cem\u003eSulfurifustis had pronounced\u003c/em\u003e effects on the bacterial community in OMT samples. While \u003cem\u003eKribella\u003c/em\u003e has not previously been reported in mine tailings, members of the order Propionibacteriales have been identified in cadmium-contaminated soils [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. The reasons for Kribella's substantial impact on the OMT bacterial community remain unclear, and further metagenomic or phenotypic studies are required to elucidate its ecological niche in tailings. \u003cem\u003eSulfurifustis\u003c/em\u003e exhibited a marked increase in 5-year tailings (log2FC\u0026thinsp;=\u0026thinsp;+\u0026thinsp;5.95) and is part of a group of sulfur-oxidizing chemolithotrophic bacteria described in aquatic and microaerophilic environments [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. The presence of redundant genes for sulfur oxidation and carbon fixation in the \u003cem\u003eSulfurifustis variabilis\u003c/em\u003e genome supports its capacity to utilize sulfur compounds as a primary energy source under specific geochemical conditions [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. Metagenomic studies of tailings have also shown that sulfur-oxidizing operational taxonomic units (OTUs), including \u003cem\u003eSulfurifustis\u003c/em\u003e, are associated with environmental parameters such as pH and metal concentration, indicating a functional role in microbiome structuring during succession in contaminated sites [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. The observed increase in sulfur-oxidizing and carbon-fixing bacteria over time, along with the significant decline in heterotrophic taxa, suggests a progressive metabolic restructuring of the community toward energy acquisition from inorganic substrates under oligotrophic conditions and elevated concentrations of arsenic, lead, and other heavy metals.\u003c/p\u003e \u003cp\u003eSeveral putative functional traits with effects in YMT samples were characteristic of heterotrophic and chemolithoautotrophic metabolism. These functions suggest that the early-stage community was more metabolically versatile than the older-stage community. In OMT, the increased relative abundance of chemolithoautotrophic bacteria was consistent with a mature community exploiting sulfur compounds as a primary inorganic energy source in these mine tailings. In the La Parrilla mine tailings in Mexico, an environmental filtration process reconfigures the microbial community's structure and function. In the early stages of YMT, metabolically versatile chemolithoautotrophic and heterotrophic communities are favored, exerting a strong influence on community structure. This is consistent with succession and the overall community composition in disturbed soils [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. As time progresses and strong selection pressures prevail, the system transitions to functionally more specialized OMT communities, enriched in chemolithoautotrophic functions such as sulfur and thiosulfate oxidation and carbon fixation\u0026mdash;characteristics particularly associated with extreme environments and acid mine drainage [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. This reinforces the concept of \u0026ldquo;environmental filtration\u0026rdquo; as the dominant structuring force under abiotic conditions with strong selection pressures [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. Taken together, the scheme integrates putative taxonomic and functional evidence to propose that the succession of relationships involves not only species replacement but also a directed transition from heterotrophic versatility and chemolithoautotrophy of taxa to chemolithoautotrophic adaptation and specialization, with implications for biogeochemical stability and microbial restoration strategies in mining ecosystems.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis conceptual model illustrates microbial community dynamics from young mine tailings (1.5 years) to older tailings (5 years). Cyanide present in recent mine tailings is rapidly removed through volatilization and biological activity. Early-stage YMT communities are primarily characterized by chemolithoautotrophic functions, such as thiosulfate oxidation and methylotrophy, and are dominated by genera including \u003cem\u003eThiobacillus\u003c/em\u003e and \u003cem\u003eLimnobacter\u003c/em\u003e, with limited heterotrophic activity. Elevated heavy metal concentrations, low organic carbon, and minimal combined and assimilable nitrogen favor taxa tolerant to HM and nutritional stress. In the OMT community, increased richness and bacterial diversity result in functional specialization toward sulfur-based chemolithoautotrophy and carbon fixation, with enrichment of \u003cem\u003eSulfurifustis\u003c/em\u003e, \u003cem\u003eKribbella\u003c/em\u003e, and \u003cem\u003eNocardioides\u003c/em\u003e. Despite taxonomic turnover, the Proteobacteria phylum remains dominant across stages, underscoring its ecological versatility during succession.\u003c/p\u003e \u003cp\u003eThe primary objective of mine tailings remediation is to create a soil-like substrate capable of supporting plant growth while minimizing hazardous compound concentrations. However, low nitrogen and carbon levels, elevated HM and metalloid concentrations, and the inability of sulphidic base-metal tailings to sustain plants present significant challenges to phytostabilization [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Pioneer bacterial activity may facilitate vegetation establishment in mine tailings, thereby accelerating microbial community succession. Key metabolic activities of microbial communities that support pioneer plant survival and promote ecological succession include: (1) increasing organic carbon and assimilable and combined nitrogen; (2) inhibiting or immobilizing toxic HM and metalloids; and (3) colonization by bacteria, genes, and plasmids acquired through horizontal transfer, which are involved in chemolithoautotrophic metabolism and HM resistance to address environmental selective pressures [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study examined the bacterial community at the taxonomic level; however, based on putative functions and published literature regarding the most abundant genera, the community's metabolism is implicated in survival strategies under the stressful conditions present in mine tailings. It is anticipated that the community's metabolism will adapt as an integrated system to changing substrate conditions, potentially fostering the development of a heterotrophic bacterial community. Further metagenomic studies involving samples with greater age differences are necessary to elucidate the role of pioneer microorganisms in ecological succession within mine tailings.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe pioneer bacterial community in mine tailings is shaped by strong selective pressures that favor chemolithotrophy, heavy-metal tolerance, and nitrogen-related metabolism. Proteobacteria dominated both young (1.5 years) and older (5 years) tailings, demonstrating ecological versatility under metal stress and nutrient limitation. At the genus level, a clear successional shift was observed: in younger tailings, \u003cem\u003eThiobacillus\u003c/em\u003e and \u003cem\u003eLimnobacter\u003c/em\u003e were predominant, along with heterotrophic and nitrogen-cycling genera. Over time, the relative abundance of heterotrophic bacteria decreased, while chemolithoautotrophic taxa, particularly \u003cem\u003eSulfurifustis\u003c/em\u003e, increased significantly.\u003c/p\u003e \u003cp\u003eThese patterns indicate a progressive metabolic restructuring from an early community influenced by both heterotrophs and chemolithotrophs to a more specialized assemblage dominated by chemolithotrophic sulfur-oxidizing and metal-tolerant bacteria. This transition is consistent with adaptation to persistent carbon limitation and elevated metal concentrations in mine tailings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eYMT Young mine tailings\u003c/p\u003e\n\u003cp\u003eOMT Old mine tailings\u003c/p\u003e\n\u003cp\u003eHM Heavy metals\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed from bacteria metagenomic approach were deposited in the GenBank repository (https://www.ncbi.nlm.nih.gov/bioproject/953647). BioProject PRJNA953647 and biosamples accession numbers SAMN34035880 - SAMN34035885.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Secretar\u0026iacute;a de Investigaci\u0026oacute;n y Posgrado-IPN funded this research (SIP 20220795, 20231480, 20240945, and 2383-20151163)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLV-H, CH-R, LV-T and JMV-C conducted the sampling. RM-L conducted the spectroscopy analysis. \u0026nbsp;LV-H\u0026nbsp;main experimental procedures. LV-H and AM-C conducted the analyses. AM-C and LV-H wrote the first draft of the manuscript. CH-R supervised and directed the research. CH-R and LV-T obtained funding. All authors have read and agreed to the published version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLV-H thanks the Consejo Nacional de Humanidades Ciencia y Tecnolog\u0026iacute;a (CONAHCYT) and BEIFI-IPN for scholarships. LV-T and CH-R are fellows of EDI-IPN, COFAA-IPN, and SNI-CONAHCYT.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWilkinson BH, McElroy BJ. The impact of humans on continental erosion and sedimentation. Geol Soc Am Bull. 2007;119(1\u0026ndash;2):140\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1130/B25899.1\u003c/span\u003e\u003cspan address=\"10.1130/B25899.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCacciuttolo C, Cano D. 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Appl Environ Microbiol. 2004;70(3):1698\u0026ndash;707. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1128/AEM.70.3.1698-1707.2004\u003c/span\u003e\u003cspan address=\"10.1128/AEM.70.3.1698-1707.2004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"bacterial communities, ecological succession, heavy metals, mine tailings, chemolithoheterotrophic bacteria and chemolithoautotrophic bacteria","lastPublishedDoi":"10.21203/rs.3.rs-8991199/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8991199/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMine tailings, resulting from industrial processes, constitute extreme environments due to toxic heavy metals, metalloids, and low concentrations of inorganic nitrogen, phosphorus, and organic carbon. Despite these selective pressures, pioneer microorganisms colonize these substrates, utilize limited resources, alter environmental conditions, and initiate ecological succession. This study compared the bacterial community diversity in mine tailings of different ages (1.5 and 5 years) from Durango, Mexico. Metagenomic DNA was extracted from the tailings, and the V4-V5 region of the bacterial 16S rRNA gene was amplified and sequenced using a metabarcoding approach. No metagenomic DNA was recovered from recently deposited samples; however, 127 bacterial genera were identified across both mine tailings, with 47 genera shared. The 1.5-year-old tailings exhibited greater genus richness than the 5-year-old samples. Proteobacteria dominated both communities, followed by actinomycetes. The chemolithoautotrophic genus \u003cem\u003eThiobacillus\u003c/em\u003e, capable of oxidizing sulfide, sulfur, and thiosulfate, was most abundant. Chemolithoheterotrophic thiosulfate-oxidizing \u003cem\u003eLimnobacter\u003c/em\u003e and \u003cem\u003eSulfurifustis\u003c/em\u003e were prevalent in young and old tailings, respectively. Chemoorganoheterotrophic bacteria, including \u003cem\u003eNocardioides\u003c/em\u003e and \u003cem\u003eKribella\u003c/em\u003e (Actinobacteria), as well as \u003cem\u003eHydrogenophaga\u003c/em\u003e and \u003cem\u003ePseudomonas\u003c/em\u003e (Betaproteobacteria), were also detected. The relative abundance of chemolithoautotrophic bacteria corresponded with the environmental conditions of the mine tailings, which lack organic carbon and contain abundant reduced inorganic sulfur compounds as energy sources. Early ecological succession at the bacterial genus level was evident, primarily involving \u003cem\u003eSulfurifustis\u003c/em\u003e, \u003cem\u003eNocardioides\u003c/em\u003e, and \u003cem\u003eKribella\u003c/em\u003e.\u003c/p\u003e","manuscriptTitle":"Early ecological succession and functional persistence of chemolithoautotrophic bacterial communities of mine tailings","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-12 16:44:31","doi":"10.21203/rs.3.rs-8991199/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-17T05:19:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-15T09:24:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-13T07:05:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-09T16:45:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"136917029259675642147811193478383486258","date":"2026-03-23T14:17:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"119603227797976341311803843061453103441","date":"2026-03-23T09:17:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252886373655777027732062531783947854590","date":"2026-03-22T14:53:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29378008164842682511831125392997571330","date":"2026-03-10T07:51:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-09T03:39:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-03T10:50:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-03T10:46:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2026-02-27T19:24:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5f725446-5719-4440-b6ec-0f4c550d11ca","owner":[],"postedDate":"March 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T07:55:03+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-12 16:44:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8991199","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8991199","identity":"rs-8991199","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}