{"paper_id":"2fe3217d-99cd-44be-9aaa-8c562817d675","body_text":"Impact of Fundão Dam Tailings on Rhizospheric Soil Microbial Communities in Mariana, MG, Brazil | 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 Impact of Fundão Dam Tailings on Rhizospheric Soil Microbial Communities in Mariana, MG, Brazil Janaína Barros Miranda, Edmo Montes Rodrigues, Alessandro Del’Duca, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4824709/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Bioremediation using plants and microorganisms effectively mitigates heavy metal soil contamination and improves soil conditions. Phytoremediation with leguminous plants and rhizobacteria enhances metal bioavailability and promotes plant growth. This study evaluates microbial community structure in soils with varying concentrations of tailings from the Fundão Dam disaster in Mariana, MG, Brazil. Microbial diversity was measured by fluorescent in situ hybridization in treatments with different tailings concentrations and in the presence of Leucaena leucocephala . Higher tailings proportions reduced bacterial densities, with pure ore tailings (T100%) showing the lowest bacteria percentage. However, after 14 months of leucaena cultivation, this treatment had the highest number of prokaryotes. The presence of leucaena plants modified the densities of Bacteroidetes and the Pseudomonas genus. Experimentation time influenced the densities of Actinobacteria, Acidobacteria, and Firmicutes. The interaction between legume presence and sampling time altered the density of Proteobacteria and Gallionella ferruginea . The results show that iron ore tailings impact the microbial community in the plant rhizosphere, offering insights for bioremediation strategies to restore soil quality in mining-affected areas. environmental disaster bioremediation mining waste revegetation leucaena microorganisms proteobacteria Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. INTRODUCTION Bioremediation of soils contaminated with heavy metals is an effective option for reducing the negative effects on ecosystem disturbance. A promising technique for bioremediation is phytoremediation by phytostabilization with the aid of microorganisms, which involves growing plants to remove metals, improving soil conditions for other plant species. Bacteria that colonize the rhizosphere (or plant growth-promoting rhizobacteria) can be useful, promoting plant growth and making contaminants bioavailable for plant uptake (Alshaal et al. 2013 ; Colin et al. 2019 ; Silva et al. 2021 ; Santos et al. 2023 ). Phytoremediation of metals, although technically challenging, offers a sustainable and eco-friendly strategy, especially when the metals are not in a form that is bioavailable to plants, hindering their absorption and accumulation in plant biomass (Etesami 2018 ). The low availability of organic matter in mining tailings also impairs the process (Andrade et al. 2018 ; Zago et al. 2019 ; Couto et al. 2021 ; Bressanin et al. 2022 ). However, microorganisms can associate with plants, promoting their growth through various mechanisms such as peptide production, antibiosis, competition for iron, induction of resistance, phosphate mineralization, nitrogen fixation, production of growth regulators and increased bioavailability of metals (Glick 2010 ; Cristaldi et al. 2017 ). Bacterial diversity can serve as an indicator of soil quality and its assessment allows the identification of taxonomic groups adapted to environments with a high concentration of metals, which is common in mining tailings (Ma et al. 2016 ). The changes caused by soil contamination by mining tailings are studied to develop strategies that facilitate the restoration of soil quality in mining areas and affected regions (Silva et al. 2021 ; Bressanin et al. 2022 ). Restoration is expected to re-establish long-term ecological functions similar to those of the original ecosystem. The integration of soil biological indicators with chemical and physical indicators is an important factor in the evaluation of soil quality and the recovery process (Prado et al. 2019 ). Obtaining information on soil quality and identifying microorganisms that are resistant to the metals in the tailings are also fundamental for selecting bioremediating and biocontrol bacteria. These microorganisms, which promote plant growth and improve soil quality, can be used as inoculants in areas affected by mining tailings (Etesami 2018 ; Andrade et al. 2018 ; Zago et al. 2019 ; Prado et al. 2019 ; Bressanin et al. 2022 ; Santos et al. 2023 ). The recovery of these areas aims to restore the productive capacity of the disturbed land, guaranteeing socio-economic conditions and environmental sustainability. Mining tailings can cause significant environmental, health, economic and social impacts (Carmo et al. 2017 ). Studies indicate that tailings have high levels of heavy metals, such as iron, manganese, aluminum, arsenic, lead and cadmium, as well as presenting cytotoxic and genotoxic risks, affecting the ecosystem for long periods (Escobar 2015 ; Fernandes et al. 2016 ; Segura et al. 2016 ; Guerra et al. 2017 ; Guevara et al. 2018 ). The removal of tailings should not only be aesthetic, but also functional, with a view to reuse in sectors such as construction and industry (Carmo et al. 2017 ). Bioremediation, using plants and microorganisms, can be sustainable alternatives for recovering contaminated soils. These processes not only improve soil fertility, but also facilitate revegetation and restoration of affected areas, promoting long-term sustainability (Etesami 2018 ; Odoh et al. 2019 ; Zago et al. 2019 ; Couto et al. 2021 ; Bressanin et al. 2022 ). Different species of legumes have been used in the recovery of degraded environments, especially in areas with low fertility and the presence of toxic elements. The efficiency of these plants is attributed to their ability to associate with nitrogen-fixing bacteria, which leads to higher concentration of this mineral element in the plant leaves (Chaer et al. 2011 ; Etesami 2018 ). Leucaena leucocephala (Lam.) de Wit is a small, fast-growing mimosoid tree native to southern Mexico and northern Central America. Although it is an exotic species, leucaena was introduced in Brazil due to its high forage potential (Bomfim et al. 2021 ). In addition to the ability to fix N 2 from the atmosphere, the biological characteristics of this species include rapid development and high reproduction, traits that favor competition and establishment in highly degraded environments (Bomfim et al. 2021 ; Sharma et al. 2022 ). The collapse of the Fundão Dam in Mariana, MG, Brazil, in November 2015, resulted in the largest environmental disaster in the history of country, releasing around 60 million m³ of mining tailings. The event caused the Doce River to silt up, covering the ground with mining tailings, damaging agricultural production and changing the physical, chemical and biological characteristics of the soil, affecting native plants and animals (da Força-Tarefa 2016 ; Carmo et al. 2017 ; Andrade et al. 2018 ). The tailings plume reached the mouth of this Doce river, degrading approximately 680 km of water bodies and impacting 39 municipalities in the states of Minas Gerais and Espírito Santo (Aires et al. 2018 ; Queiroz et al. 2018 ; Bernardino et al. 2019 ). In this study, we evaluated the structure of the bacterial community in different concentrations of tailings from the iron mining of the Fundão dam collapse. To this end, different concentrations of tailings were mixed into a fertile substrate and used to plant the legume L. leucocephala . In addition to the treatments with different proportions of tailings in the absence of the plant, the bacterial community of the leucaena rhizospheric soil was also compared at two different times. 2. MATERIAL AND METHODS Samples of tailings from the Fundão dam were collected in November 2016 from material dredged from the lake of the Risoleta Neves Hydroelectric Power Plant (Candonga), located in the municipalities of Rio Doce and Santa Cruz do Escalvado, MG, Brazil (20°12'27.2'' S 42°51'17.3'' W). The material was then transported to the Experimental Station of the Federal University of Juiz de Fora (UFJF). The tailings were stored in covered beds and covered with plastic sheeting to prevent the leaching of chemical elements by the rain. Samples of the tailings were mixed with a substrate prepared with soil/sand/bovine manure in a ratio of 3:2:1 (v/v/v). The mixtures resulted in the following proportions: T0% (100% fertile substrate), T25% (25% mining tailings + 75% fertile substrate), T50% (50% mining tailings + 50% fertile substrate), T75% (75% mining tailings + 25% fertile substrate) and T100% (100% mining tailings). The experiment was conducted in a completely randomized design, with five treatments (T100%, T75%, T50%, T25% and T0%) and five replications, using plants of L. leucocephala (Lam.) de Wit. In each growing container, three individuals approximately 2 cm high were planted in 20-liter pots with the substrates and tailings in the different proportions. The plants were kept in a greenhouse at the Experimental Station. A fraction of all treatments without plants was stored to be used as a control, while another fraction was collected to characterize the substrate with the respective concentrations of tailings (Freitas et al. 2023 ). The samples were collected after 8 months (T1 without and with plant - L. leucocephala ) and 14 months (T2 with plant) of growing the legume in each of the tailing concentrations mixed with the fertile substrate kept in the pots. A sample at a depth of 10 cm was collected with an auger inserted into the soil near the stem of each individual. As a control, 5 samples of substrate without legume cultivation were also collected at the same concentrations as the treatment. All samples were submitted to the fluorescent in situ hybridization (FISH) technique. The samples were initially weighed (0.5 g), fixed in a 2% paraformaldehyde solution and refrigerated until processing. Processing began with the addition of 10 mL of deionized water for sonication in a Vibra Cell VCX 130PB device (Sonics & Materials) at a frequency of 3 Hertz for 1 minute (three times). The samples were then centrifuged at 500 g for 5 minutes and washed with ultrapure water (three times). The three supernatant fractions were pooled, homogenized and a volume of 1 mL was used for filtration on a 0.2 µm polycarbonate membrane. For hybridization, specific probes for 16S rRNA marked with the Cy3 fluorochrome were used to quantify bacterial taxa (Table 1 ). A negative control probe (NON), with no specificity for any bacteria, was used to evaluate the efficiency of the hybridization. All the probes are described in the probeBase online platform (Loy et al. 2007 ). The analyses were carried out under an epifluorescence microscope (Olympus, USA) at 1000x magnification. Counts were made in ten random fields. Statistical comparisons were made between treatments with different proportions of tailings, with and without the presence of plants and between the two sampling times. When the data showed a normal distribution, they were subjected to analysis of variance (ANOVA - Two way) and a posteriori Tukey test. The Kruskal-Wallis test was used for non-normal data. In both cases, values of p < 0.05 were considered significant using the SigmaPlot 12.5 software (Zar 2010 ). Table 1 Probes used for the fish technique with specificity, sequence and concentrations of formamide in the hybridization solution and concentration of NaCl in the washing solution. Probe Specificity Sequence (5' -3') % For NaCl (mM) Reference NON Negative control TAGTGACGCCGTCGA 30 30 (Karner and Fuhrman 1997 ) ACIDO228 Subdivision 1 of Acidobacteria (Acidobacteriales) TAATCCGCCGCGACCCCT 35 35 (Kleinsteuber et al. 2008 ) HCG236 Actinobacteria AACAAGCTGATAGGCCGC 30 30 (Erhart et al. 1997 ) CF319a Flavobacteria, some Bacteroidetes some Sphingobacteria TGGTCCGTGTCTCAGTAC 35 35 (Manz et al. 1996 ) LGC354A Firmicutes TGGAAGATTCCCTATTGC 35 35 (Meier et al. 1999 ) LGC354B CGGAAGATTCCCTACTGC 35 35 LCG354C CCGAAGATTCCCTACTGC 35 35 DELTA495A Many Delta-proteobacteria many Gemmatimonadetes AGTTAGCCGGTGCTTCTT 35 35 (Loy et al. 2002 ) EPSY549 Epsylon-proteobacteria CAGTGATTCCGAGTAACG 30 30 (Lin et al. 2006 ) BET42a Beta-proteobacteria GCCTTCCCACTTCGTTT 30 30 (Manz et al. 1992 ) GAM42a Gamma-proteobacteria GCCTTCCCACATCGTTT 30 30 (Manz et al. 1996 ) Pae997 Pseudomonas spp. TCTGGAAAGTTCTCAGCA 35 35 (Amann et al. 1996 ) GALTS0084 Clones related to Gallionella ferruginea CCACTAACCTGGGAGCAA 40 40 (Hallberg et al. 2006 ) FOR = Formamide 3. RESULTS The densities of bacteria quantified by the FISH technique, varied in the soils containing different concentrations of mining tailings in the absence of L. leucocephala plants (T1) and in the presence of plants at both times, 8 months (T1) and 14 months (T2). In general, there was an increase in the densities of most of the bacteria in the presence of the legume, especially in the treatments with the lowest concentration of tailings. Exceptions were the densities of Acidobacteria and Actinobacteria in the treatments with only tailings without plants in the sampling conducted 8 months after substrate preparation (T1). On the other hand, the density of these bacteria in the treatment without tailings (T0%) were highest (0.31 x 10 8 cells g -1 and 0.36 x 10 8 cells g -1 respectively) 14 months after planting the legume. At the same time, only treatments T25% and T75% showed no statistical differences between Acidobacteria density. Also, in the presence of plants, the treatments without tailings (T0%) and with only 25% tailings in the mixture (T25%) had similar densities of these bacteria after 8 months. However, these densities were different from the treatments with the highest proportions of tailings (T50%, T75% and T100%). In the comparisons between times, the collection after 14 months of L. leucocephala planting showed differences with the collection carried out at 8 months in the treatments with and without plants. (Fig. 1 a). The highest average density of Actinobacteria (0.19 x 10 8 cells g -1 ) in treatments with only tailings and without plants in the first collection differing from the others. The highest densities of these bacteria were found in the treatment without tailings with the presence of the L. leucocephala , after 14 months of planting. These average densities were different from the densities of samples collected at 8 months in treatments with and without plants (Fig. 1 b). The addition of tailings in any proportion did not change the densities of Bacteroidetes without the presence of L. leucocephala . The treatments with different proportions of tailings showed no differences in the average densities of these bacteria found in the first collection without plants. However, with the presence of the legume (T1 with plants), the treatment without tailings (T0%) was different from the others and showed a higher density of this group of bacteria (0.14 x 10 8 cells g -1 ). Density of Bacteroidetes was higher in the treatment with 50% tailings (0.16 x 10 8 cells g -1 ) after 14 months of planting. The lowest average densities of these bacteria were found in the treatments with the highest proportions of tailings (T75% and T100%). The treatments without plants were different from the treatments with L. leucocephala at both collection times (Fig. 1 c). The bacteria of the Firmicutes phylum in the treatments without plants were present in higher densities in the treatment without tailings (T0%) and with 25% tailings (T25%). In the presence of the plants, these treatments and the treatment with 50% tailings (T50%) were also the treatments with the highest density of these bacteria 8 months after planting of L. leucocephala . At 14 months after planting, the treatment without tailings (T0%) showed the greatest increase in bacterial density (0.34 x 10 8 cells g -1 ). On the other hand, the lowest density (0.03 x 10 8 cells g -1 ) was found in the treatment with tailings only (T100%). These Firmicutes densities in the treatments with plants after 14 months were significantly different from the treatments with different proportions of tailings from the first collection, with and without plants (Fig. 1 d). The densities of bacteria from the four classes of Proteobacteria (Beta, Delta, Epsylon and Gamma) were lower in the treatments with only mining tailings (Fig. 2 ). The densities of bacteria from the Beta-proteobacteria class followed the same trend in the treatments with and without plants in both collections. The highest average values were found in the treatments without tailings and decreased as the proportion of tailings in the treatments increased. The lowest densities of these bacteria (0.02 ± 0.01 x10 8 cells g -1 ) were found in the treatment consisting of 100% tailings with L. leucocephala planted 8 months ago. There was a difference between the treatments with and without plants, and between the treatments at the two sampling times (Fig. 2 a). The average density of bacteria from the Delta-proteobacteria class in the first collection without L. leucocephala was higher in treatments T25% and T50%. In the presence of plants, in both samplings, the treatments without tailings (T0%) showed higher densities of this class. The densities of these bacteria did not differ between the two sampling times, nor with the presence or absence of plants of L. leucocephala (Fig. 2 b). The highest densities of bacteria from the Epsylon-proteobacteria class were found in the T0%, T25% and T50% treatments in the treatments without plants. In the first sample, only the treatment with 100% L. leucocephala tailings (T100%) differed from the others. After 14 months of planting, there was a reduction in the average density of these bacteria, especially in the treatments without or with a lower proportion of tailings (T0%, T25% and T50%). At this point, there was no difference in the number of these bacteria between the treatments (Fig. 2 c). The treatments without tailings (T0%) showed the highest average values of Gamma-proteobacteria, the highest being 4.11 x 10 8 cells g -1 in the presence of L. leucocephala after 14 months of cultivation (Fig. 2 d). The average densities of Beta, Epsylon and Gamma-proteobacteria were different when comparing the treatments with the presence and absence of L. leucocephala , and when comparing the two sampling times with the plant. The presence of bacteria of the genus Pseudomonas was not observed in the treatment with 100% mining tailings without the presence of the plant. In the treatments with the other proportions of tailings, no difference was observed in the density of Pseudomonas spp. On the other hand, in the treatments with the presence of the plants, after eight months of cultivation, higher bacterial densities were recorded in the treatments with lower proportions of tailings (T0% and T25%). In addition to these treatments, the T50% treatment also showed a higher density of these bacteria after 14 months of L. leucocephala cultivation (Fig. 3 a). The presence of plants and the time between samples led to differences in the density of Gallionella ferruginea in the treatments with different proportions of tailings (Fig. 3 b). In the first collection, where L. leucocephala were not planted (T1 without plant), there was no difference between the treatments with different concentrations of tailings. However, where the L. leucocephala were grown (T1 with plant), the highest densities were found in the treatments with the highest proportion of tailings (75% and 100%). After 14 months of planting, the average density values of these bacteria in the treatment with tailings only (T100%) decreased, making the lowest value found (0.07 ± 0.02 x 10 8 cells g -1 ). The proportion of the major groups identified by the FISH technique, with the probes used in this work, can be seen in Fig. 4 . Proteobacteria were predominant in all treatments with and without the presence of L. leucocephala in both collections. A higher proportion of Acidobacteria and Actinobacteria was observed in the treatment with 100% tailings without the presence of plants. Less than 50% of the prokaryotes, quantified with DAPI, were identified and quantified by the probes used to characterize the bacteria in the treatments with different concentrations of tailings, in the presence and absence of plants, and at 8 and 14 months of sampling (Fig. 5 ). The exception (69%) was in the treatment with 75% tailings (T75%) stored for 8 months without the legume. The sum of the average densities identified in this treatment was 2.79 x10 8 cells g -1 . The treatments with tailings only (T100%) were the treatments with the lowest proportion of prokaryotes identified. The sums of bacterial densities identified in these treatments ranged from 0.81 to 0.91 x10 8 cells g -1 at the first sampling point with and without plants, respectively. In this treatment with L. leucocephala planted for 14 months, less than 4% of the prokaryotes were identified with the probes used. 4. DISCUSSION Higher taxonomic levels, such as phylum and class, do not always show differences when comparing two different conditions. Metagenomic studies comparing water from the Doce River, impacted after the collapse of the Fundão dam, with water from the Paraguaçu River (not impacted) showed similar bacterial community composition at the phylum level and different at other taxonomic levels (Cordeiro et al. 2019 ). In our study, a similarity in the density of Bacteroidetes was observed in the treatments without plants in the first sampling. In the Epsylon-proteobacteria class, this similarity occurred in the second sampling in the treatments with L. leucocephala . All the other phyla and classes evaluated had differences in at least one of the treatments with different proportions of tailings. A study carried out in the mining spill region of the Iron Quadrangle in the state of Minas Gerais, Brazil, verified the microbial and metabolic diversity in unimpacted and tailings-impacted soils after the revegetation process using sequencing of the V4-V5 region of 16S rRNA. The results revealed that Bacteroidetes were among the predominant microorganisms after revegetation. The predominant phyla they found were also Proteobacteria, followed by Acidobacteria, Verrucomicrobia, Planctomycetes and Bacteroidetes. Bacteria from vegetated soil showed high metabolic diversity and the presence of genes linked to resistance to iron-containing environments (Fernandes et al. 2018 ). The presence of the legume influenced the density of Bacteroidetes and bacteria of the genus Pseudomonas . The lowest densities of these bacteria were found in the treatments without L. leucocephala , which differed from the treatments with the plant in both samplings. Bacteria of the genus Pseudomonas spp. were not found in the tailings without plants. In this same sampling and in the second one, the presence of these bacteria was observed, although in lower densities in this treatment with L. leucocephala . Species of Pseudomonas exhibit remarkable metabolic diversity, allowing them to thrive in various environments and serve as beneficial inoculants and plant growth promoters (Saati-Santamaría et al. 2022 ; Mehmood et al. 2023 ). These bacteria use root exudates, produce compounds that are toxic to pathogenic fungi and bacteria and chelate iron, contributing to plant health and growth (Guzmán-Guzmán and Santoyo 2022 ; Sanow et al. 2023 ). When introduced into metal-contaminated soils, Pseudomonas species have been shown to promote remediation by aiding in the detoxification and immobilization of heavy metals, thus contributing to the restoration of soil health and fertility (Shaheen et al. 2022 ). Their ability to improve plant growth, fight pathogens and aid in metal detoxification underlines the valuable contributions of Pseudomonas to sustainable agriculture and environmental remediation efforts. In addition to Protobacteria and other bacteria, Pseudomonas are ACC deaminase-producing, an enzyme can help alleviate the heavy metals toxicity. This enzyme promotes root growth by hydrolyzing ACC, the immediate precursor of ethylene, a plant hormone related to senescence and oxidative stress (Etesami 2018 ). In addition, mechanisms of Pseudomonas sp. action include nitrogen fixation, nutrient availability (i.e., K and P solubilization with the production of organic acids, siderophores and phosphatases), resistance to pathogens and alterations in soil microbiome (Bressanin et al. 2022 ). Their study with tailings from the Fundão dam found greater growth and survival of Hymenaea courbaril seedlings when the substrate was inoculated with Pseudomonas . Their resistance to metals further underlines their suitability for phytostabilization processes, emphasizing the importance of microbial community dynamics in the successful efforts of these processes. Regardless of the presence or absence of L. leucocephala in the substrates, the density of Acidobacteria, Actinobacteria and bacteria from the Firmicutes phylum differed between the time intervals between samples. Acidobacteria plays significant ecological roles, as evidenced through their active participation in key carbon, nitrogen, and sulfur biogeochemical circuits (Kalam et al. 2020 ). Studies suggest that the abundance of the Actinobacteria and Firmicutes phyla is significantly correlated with metal-contaminated environments (Fernandes et al. 2018 ). Over time, the densities of these bacteria in the rhizosphere increased, especially in the treatments with little or no tailings. Soils with deposits of zinc waste were re-vegetated with eight plant species, and after 5 years sequencing was carried out to verify the structure and diversity of the bacterial community (Luo et al. 2018 ). The authors observed that revegetation promoted an increase in the microbial community in the rhizosphere of the plants, among the phyla that increased were Proteobacteria, Acidobacteria, and Bacteroidetes, and there was also an increase in the abundance of plant growth-promoting bacteria. In an ecosystem belonging to a historical area of ​​the Iron Quadrangle, Minas Gerais, Brazil, which suffered mining activities until its total depletion, observed that Proteobacteria was the most predominant phylum, followed by others with emphasis on Acidobacteria and Bacteroidetes, as also observed in our study (Fernandes et al. 2018 ). According to the authors, this is an important component of natural bioremediation in iron mining areas undergoing a regeneration process. Emenike et al. ( 2023 ) also observed that bioremediation was enhanced in metal-contaminated soil by consortia of proteobacteria. According to the authors, ureolytic (urease-producing) microorganisms such as proteobacteria possess the potential to ameliorate soils contaminated with a vast spectrum of heavy metal(loid)s. Proteobacteria considerably increased the bioreduction rate of As, Cu, Zn, Mn and Cr, making them potential remediation agents for the bioreduction of heavy metal(loid)s in contaminated environments. Acidic environments such as mining tailings can harbor Acidobacteria and Actinobacteria (Johnson and Aguilera 2016 ). These bacteria can live in these acidic, metal-rich environments because they have heavy metal resistance mechanisms. These bacteria can be used as biomarkers after contamination by mining tailings containing iron, as they may possess genes related to the iron cycle (Haferburg and Kothe 2007 ; El Baz et al. 2015 ; Kelly et al. 2023 ). In the iron ore waste used in the present study, the iron concentrations were five times higher in the pure tailings treatments when compared to the treatments without tailings (Freitas et al. 2023 ). The impact of the deposition of tailings from the Fundão dam caused an increase in soil density and silt content, reducing macroporosity, microbial biomass carbon, basal respiration, enzymatic activity and the density of some microbial groups (Silva et al. 2021 ). However, they observed that some microbial functional groups had a higher density in these areas, which shows the potential of revegetation to increase the quality of this environment. The presence of vegetation favors the development of resistant microorganisms by supplying nutrients and energy through the deposition of organic matter, as well as by the release of exudates by the roots (Valentim dos Santos et al. 2016 ). In addition, the authors state that when stressful conditions occur in the soil, such as contamination by heavy metals, contaminant-resistant groups tend to increase their population competitively in relation to less resistant ones. Thus, the population can even reach higher values than those observed in uncontaminated sites. Our results corroborate these findings. In a research into the ecological restoration of mine tailings, significant differences were also observed in the relative abundance of the Alpha- and Delta-proteobacteria classes, and bacteria from the Acidobacteria, Firmicutes and Nitrospira (Li et al. 2016 ). In our work, the number of bacteria from the Firmicutes phylum, and the Delta, Gamma and Beta-proteobacteria classes were lower where there were only mining tailings without the presence of the plant. In the presence of L. leucocephala and over time, the number of bacteria increased in the treatments with the lowest proportions of tailings. In the treatments with 100% tailings, the number of these bacteria remained lower than in the other treatments, possibly due to the influence of the compounds present in the tailings over time. Considering the two factors together, the presence of plants and the time interval, the difference among the treatments was found among the proteobacteria of the epsylon, gamma and beta classes, including the Gallionella ferruginea species. The average density of Epsylon-proteobacteria found was lower in the treatments with only tailings, with and without L. leucocephala in both samplings, indicating that the more tailings the lower the number of these bacteria. However, in the presence of plants, the densities of this class were higher in all treatments at 8 months after planting and lower at 14 months. The presence of the plant seems to have influenced the growth of these bacteria only initially. All the classes of proteobacteria evaluated were the predominant group in all the treatments and over time. This was the case even though the densities of bacteria belonging to the Alpha-proteobacteria class were not evaluated and even though there was no difference in the densities of bacteria belonging to the Delta-proteobacteria class between treatments with or without the presence of L. leucocephala , nor between sampling times. The most abundant class of proteobacteria was the Gamma-proteobacteria class. The presence of Gamma-proteobacteria can improve the environmental stability of tailings. In environments with mining tailings containing iron, copper and gold, when subjected to acidification, they were able to promote the adsorption of metals such as arsenic, cobalt, lead, zinc, nickel and chromium, inhibiting the leaching of these metals(Henne et al. 2019 ). A study that compared the microbial community in a river impacted (Doce River) and another not impacted (Paraguaçu River) by the Fundão dam tailings spill also found a predominance of proteobacteria in both rivers (Cordeiro et al. 2019 ). However, when other taxonomic levels were evaluated, it was observed that in the impacted river there was a predominance of Bacteroidetes, Gamma-proteobacteria and Actinobacteria, and in the non-impacted river there was a predominance of Beta-proteobacteria. The authors concluded that the tailings altered the microbial community of the Doce River. Their genomic analysis of the bacteria found in the impacted samples also showed that there was an increase in genes linked to microbial virulence, respiration, membrane transport (efflux pump for metals), iron and nitrogen metabolism (denitrification and nitrogen fixation), and microbial motility. However, amino acid, fatty acid, carbohydrate and pigment metabolism genes decreased in the bacteria found in the impacted water samples. The density of Gallionella ferruginea bacteria in the treatments without plants showed no statistical difference. After eight months of plant cultivation, the number of bacteria increased in the treatments that did not contain 100% tailings, and after 14 months of cultivation, the T75% and T100% treatments had a decrease in the number of these microorganisms. According to Reis et al. ( 2014 ), absence or reduction in Gallionella density in remediated environments after mining activities can be used as an indicator of reaching the end point of remediation processes. G. ferruginea is an acidophilic bacterium and can grow microaerophilically in an environment with the presence of metals, promoting the oxidation of ferrous iron (Ayangbenro et al. 2018 ). G. ferruginea obtains all of its cellular carbon from CO 2 fixation when grown under aerobic gradient conditions in a mineral salt solution with iron sulfide. Adding a new carbon source, such as glucose, increases carbon uptake and leads to a decrease in CO 2 fixation (Hallbeck and Pedersen 1991 ; Eggerichs et al. 2020 ). Legumes can release exudates through their roots, the exudates contain glucose and fructose, and can promote the acidification of the medium, and the development of microorganisms that adapt to these conditions (Chaer et al. 2011 ). Consequently, this leads to a high average density of microorganisms and a low respiration rate, as observed in the treatments with the presence of leucaena and throughout the study period. Experiments carried out to investigate the effect of temperature (20 and 35°C) and bacterial diversity by sequencing the 16S rRNA gene, in samples of acid drainage from the Carnoulès mine (France), observed that the temperature of 20°C, provides the dominance of iron-oxidizing bacteria, such as Gallionella spp. was associated with almost complete oxidation of iron (98%) (Tardy et al. 2018 ). In experiments using water samples without the influence of human activity from the Sucio River (Costa Rica), which originates from volcanic rocks, 89.39% of the sequences were identified as proteobacteria by sequencing, in particular the Betaproteobacteria class (80.16%). Of these, the largest proportion (43.89%) were bacteria from the genus Gallionella . Bacteria of this species have been considered key in the Sucio river ecosystem, as they participate in iron and sulphur metabolism (Arce-Rodríguez et al. 2017 ). On the other hand, in samples of neutral drainage water from the Elizabeth well mine, Slovinky (Slovakia), the composition of the bacterial population was characterized and the presence of iron-oxidizing Proteobacteria of the Gallionella and Leptothrix genera was observed, the occurrence of which did not change during the years 2008 to 2014 (Kisková et al. 2018 ). Therefore, Gallionella spp. is present in natural environments without anthropogenic action and in mineral extraction environments. The increase in the number of prokaryotes in the treatment with tailings after 1 year and 2 months of planting L. leucocephala does not correspond to the bacteria identified with the probes used. None of the bacteria evaluated showed a higher density in this treatment at that time (T100% with L. leucocephala planted 14 months ago). In this way, not even 10% of the prokaryotes accounted for by DAPI staining were identified. These prokaryotes may be Archaea and/or the probes used do not have the specificity to reach all the bacteria in their taxonomic group. Among the 12% of Archaea found, there was a predominance of the Euryarcheota phylum in the water samples from the areas impacted by the Fundão dam collapse (Cordeiro et al. 2019 ). The potential use of microorganisms in bioremediation depends on their identification and also on their ability to be cultivated and kept alive. Many of the microorganisms found in mine drainage do not grow in known culture media to date. In order to identify them, it is necessary to sequence them, assemble their genomes and compare them with a database, which is constantly being updated (Bressanin et al. 2022 ; Santos et al. 2023 ). Many microorganisms present in extreme environments have yet to be identified and/or classified. The omics era (metagenomics, proteomics, metabolomics, transcriptomics, etc.) has opened up new ways of understanding the nature and functionality of extremophilic microorganisms. They still face limitations related to sampling practices, handling, storage, processing and interpretation of the range of data generated (Gupta et al. 2019 ). Interactions among members of microbial communities are often fundamental to their roles as bioremediators and biostimulators. One microorganism can produce metabolites and be used by another microorganism, and the interaction between them promotes growth. Often, these interactions enable behavior (gene expression) that neither party could perform in isolation (Marx 2009 ; Hillesland 2018 ). Evidence of co-occurrence and metabolic dependence with other bacteria has been found in two bacteria belonging to the Saccharimonadia class from copper mines (Lemos et al. 2019 ). These bacteria found by them have small genomes and do not contain genes associated with the biosynthesis of essential amino acids, nucleotides, fatty acids and cofactors, requiring them to live in symbiosis with other bacteria, such as those of the Hydrotalea genus. Understanding the interactions between microorganisms tends to bring the concept of a microbial community into practice. That is, using a group of bacteria from different species, or even a group of Bacteria, Archaea and Fungi. 5. CONCLUSION The increase in microorganisms in various concentrations of iron ore tailings associated with legumes is a promising discovery (Valentim dos Santos et al. 2016 ; Bressanin et al. 2022 ). Symbiosis can provide valuable insights into the dynamics and recovery of impacted areas, since they are recognized as a crucial factor in the maintenance and functioning of the ecosystem. Future prospects include the possibility of selecting specific microorganisms, or sets of microorganisms, that promote plant growth and improving soil quality. Once selected and cultivated, these microorganisms can be used as inoculants in regions affected by mining tailings, helping to restore the productive capacity of disturbed land and ensuring socio-economic and environmentally sustainable practices. The results of this study allow us to conclude that iron ore tailings influence the microbial community associated with the rhizosphere of plants at different concentrations. The increase in prokaryote density, identified by FISH, observed in the 14-month evaluation, is a strong suggestion of a soil recovery and improvement process in response to its cultivation with L. leucocephala . Declarations ACKNOWLEDGEMENTS We would like to thank all the students and technicians at our institutions for their help at all stages of this work. We would also like to thank Dr. Raúl Marcel González Garcia ( in memorian ) for his collaboration and encouragement. FUNDING This work was supported by Minas Gerais State Research Support Foundation (Fapemig), Project CRA-APQ-01187-16 Technologies for the recovery of the Doce River Basin. COMPETING INTERESTS The authors declare no competing interests. CONSENT TO PARTICIPATE Not applicable CONSENT TO PUBLISH Not applicable ETHICS APPROVAL Not applicable. DATA AVAILABILITY STATEMENT The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request AUTHOR CONTRIBUTIONS Conceptualization were performed by Janaína Barros Miranda, Paulo Henrique Pereira Peixoto, Alessandro Del’Duca and Dionéia Evangelista Cesar. Methodology were performed by Janaína Barros Miranda, Edmo Montes Rodrigues, Cristiano Ferrara de Resende, Raiza dos Santos Azevedo and Dionéia Evangelista Cesar. 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Resende\",\"suffix\":\"\"},{\"id\":349477237,\"identity\":\"97a39199-fd6f-4c96-b8cb-9a17ae159c2e\",\"order_by\":5,\"name\":\"Raiza dos Santos Azevedo\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYFACxgY4U4KhAkgyMzfgUotNyxmQFiQRgkCCsQ3NEGzAvP1w84cff+zs+SWSD974OK82mr8dqOVHxTacWmTOJDYY9rYlM0vOSEu2nLnteO6Mw4wNjD1nbuN2C0NiQwJvAzObwZkzZtK8247lNgC1MDO24dHC/7Dh4J8/9Tz2Z85/k+adcyx3PkEtEomNzTxshyUM2HvYpHkbanI3ENbysJlZtu24gcTxNmPLGccO5G4EajmI1y/86Y8/vvlTbc/fzPzwxoeautx55w8ffPCjArcWdHAYTB4gWj0Q1JGieBSMglEwCkYIAACycVn5wlJOjQAAAABJRU5ErkJggg==\",\"orcid\":\"https://orcid.org/0000-0002-9229-694X\",\"institution\":\"Universidade Federal do Rio Grande\",\"correspondingAuthor\":true,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Raiza\",\"middleName\":\"dos Santos\",\"lastName\":\"Azevedo\",\"suffix\":\"\"},{\"id\":349477238,\"identity\":\"97147fd5-9639-4e5e-82af-eadba095ec54\",\"order_by\":6,\"name\":\"Julliane Dutra Medeiros\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Universidade Federal de Juiz de Fora\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Julliane\",\"middleName\":\"Dutra\",\"lastName\":\"Medeiros\",\"suffix\":\"\"},{\"id\":349477239,\"identity\":\"2611bcf4-192c-41d7-a42c-7f91f9723f6f\",\"order_by\":7,\"name\":\"André Luiz dos Santos Furtado\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Universidade Federal de Juiz de Fora\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"André\",\"middleName\":\"Luiz dos Santos\",\"lastName\":\"Furtado\",\"suffix\":\"\"},{\"id\":349477240,\"identity\":\"ccf12f26-c91d-4af5-ad85-480d2bdc7a27\",\"order_by\":8,\"name\":\"Dionéia Evangelista Cesar\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Universidade Federal de Juiz de Fora\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Dionéia\",\"middleName\":\"Evangelista\",\"lastName\":\"Cesar\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-07-30 00:02:00\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4824709/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4824709/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":66072593,\"identity\":\"cfb60918-3e69-483e-82a1-dec73971f3fa\",\"added_by\":\"auto\",\"created_at\":\"2024-10-07 12:26:13\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":36864,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDensity (10\\u003csup\\u003e8\\u003c/sup\\u003e cells g\\u003csup\\u003e-1\\u003c/sup\\u003e) of bacteria from the Acidobacteria (A), Actinobacteria (B), Bacteroidetes (C) and Firmicutes (D) groups in soils containing different concentrations of mining tailings in the absence and presence of plants (\\u003cem\\u003eL. leucocephala\\u003c/em\\u003e), after 8 months (T1) and after 14 months of planting (T2). Letters above the bars designate averages with significant differences between treatments within the same collection and “*” designate significant difference between sampling times\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Onlinefloatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4824709/v1/58385f7cd77c10bddf927a04.png\"},{\"id\":66072811,\"identity\":\"a5a9fc91-22f7-4855-9a90-298ecd7f2fae\",\"added_by\":\"auto\",\"created_at\":\"2024-10-07 12:34:14\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":144574,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAverage density (10\\u003csup\\u003e8\\u003c/sup\\u003e cells g\\u003csup\\u003e-1\\u003c/sup\\u003e) of proteobacteria in soils containing different concentrations of mining tailings in the absence and presence of plants (\\u003cem\\u003eL. leucocephala\\u003c/em\\u003e), after 8 months of planting (T1) and after 14 months (T2). A - Beta-proteobacteria, B - Delta-proteobacteria, C - Epsylon-proteobacteria, D - Gamma-proteobacteria. Letters above the bars designate means with significant differences between treatments within the same collection and “*” designate significant difference between sampling times\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Onlinefloatimage28.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4824709/v1/d200b114ef81c1463e055c74.png\"},{\"id\":66072596,\"identity\":\"5ce52837-a1f1-4a1e-b8b7-d9e4f163d588\",\"added_by\":\"auto\",\"created_at\":\"2024-10-07 12:26:14\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":19933,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAverage density (10\\u003csup\\u003e8\\u003c/sup\\u003e cells g\\u003csup\\u003e-1\\u003c/sup\\u003e) of \\u003cem\\u003ePseudomonas\\u003c/em\\u003e spp. (A) and \\u003cem\\u003eGallionella ferruginea\\u003c/em\\u003e (B) in soils containing different concentrations of mining tailings in the absence and presence of plants (\\u003cem\\u003eL. leucocephala\\u003c/em\\u003e), after 8 months of planting (T1) and after 14 months (T2). Letters above the bars designate means with significant differences between treatments within the same collection and “*” designate significant difference between sampling times\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Onlinefloatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4824709/v1/f5c3e33bef097c5e2be78984.png\"},{\"id\":66072597,\"identity\":\"5f19c878-5eda-4deb-9ced-f8215bbf15c5\",\"added_by\":\"auto\",\"created_at\":\"2024-10-07 12:26:14\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":43675,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eProportion of large groups of bacteria (%) identified by the FISH technique with the probes used in this work, in soil samples from treatments with different proportions of tailings (0%, 25%, 50%, 75% and 100%), in the absence and presence of \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e, 8 and 14 months after planting\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Onlinefloatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4824709/v1/0131be279b4d3fcea490fa9e.png\"},{\"id\":66072810,\"identity\":\"c8b86e88-2949-4456-a5c8-882621fa0923\",\"added_by\":\"auto\",\"created_at\":\"2024-10-07 12:34:13\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":49363,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eProportion of bacteria identified by the FISH technique in relation to total prokaryotes (%) in soil samples from treatments with different proportions of tailings (0, 25, 50, 75 and 100%), in the absence and presence of \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e, 8 and 14 months after planting\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Onlinefloatimage5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4824709/v1/5f46b3e65985b0b7974620ca.png\"},{\"id\":68075193,\"identity\":\"793be55b-9767-4c1a-850d-3dddf07d4a7f\",\"added_by\":\"auto\",\"created_at\":\"2024-11-02 10:46:44\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":890433,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4824709/v1/86520265-b1b2-4644-968a-6df7417ca402.pdf\"}],\"financialInterests\":\"\",\"formattedTitle\":\"\\u003cp\\u003eImpact of Fundão Dam Tailings on Rhizospheric Soil Microbial Communities in Mariana, MG, Brazil\\u003c/p\\u003e\",\"fulltext\":[{\"header\":\"1. INTRODUCTION\",\"content\":\"\\u003cp\\u003eBioremediation of soils contaminated with heavy metals is an effective option for reducing the negative effects on ecosystem disturbance. A promising technique for bioremediation is phytoremediation by phytostabilization with the aid of microorganisms, which involves growing plants to remove metals, improving soil conditions for other plant species. Bacteria that colonize the rhizosphere (or plant growth-promoting rhizobacteria) can be useful, promoting plant growth and making contaminants bioavailable for plant uptake (Alshaal et al. \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; Colin et al. \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Silva et al. \\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Santos et al. \\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Phytoremediation of metals, although technically challenging, offers a sustainable and eco-friendly strategy, especially when the metals are not in a form that is bioavailable to plants, hindering their absorption and accumulation in plant biomass (Etesami \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). The low availability of organic matter in mining tailings also impairs the process (Andrade et al. \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Zago et al. \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Couto et al. \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Bressanin et al. \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). However, microorganisms can associate with plants, promoting their growth through various mechanisms such as peptide production, antibiosis, competition for iron, induction of resistance, phosphate mineralization, nitrogen fixation, production of growth regulators and increased bioavailability of metals (Glick \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e; Cristaldi et al. \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eBacterial diversity can serve as an indicator of soil quality and its assessment allows the identification of taxonomic groups adapted to environments with a high concentration of metals, which is common in mining tailings (Ma et al. \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). The changes caused by soil contamination by mining tailings are studied to develop strategies that facilitate the restoration of soil quality in mining areas and affected regions (Silva et al. \\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Bressanin et al. \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Restoration is expected to re-establish long-term ecological functions similar to those of the original ecosystem. The integration of soil biological indicators with chemical and physical indicators is an important factor in the evaluation of soil quality and the recovery process (Prado et al. \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eObtaining information on soil quality and identifying microorganisms that are resistant to the metals in the tailings are also fundamental for selecting bioremediating and biocontrol bacteria. These microorganisms, which promote plant growth and improve soil quality, can be used as inoculants in areas affected by mining tailings (Etesami \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Andrade et al. \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Zago et al. \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Prado et al. \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Bressanin et al. \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Santos et al. \\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). The recovery of these areas aims to restore the productive capacity of the disturbed land, guaranteeing socio-economic conditions and environmental sustainability. Mining tailings can cause significant environmental, health, economic and social impacts (Carmo et al. \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Studies indicate that tailings have high levels of heavy metals, such as iron, manganese, aluminum, arsenic, lead and cadmium, as well as presenting cytotoxic and genotoxic risks, affecting the ecosystem for long periods (Escobar \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Fernandes et al. \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Segura et al. \\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Guerra et al. \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Guevara et al. \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe removal of tailings should not only be aesthetic, but also functional, with a view to reuse in sectors such as construction and industry (Carmo et al. \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Bioremediation, using plants and microorganisms, can be sustainable alternatives for recovering contaminated soils. These processes not only improve soil fertility, but also facilitate revegetation and restoration of affected areas, promoting long-term sustainability (Etesami \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Odoh et al. \\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Zago et al. \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Couto et al. \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Bressanin et al. \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eDifferent species of legumes have been used in the recovery of degraded environments, especially in areas with low fertility and the presence of toxic elements. The efficiency of these plants is attributed to their ability to associate with nitrogen-fixing bacteria, which leads to higher concentration of this mineral element in the plant leaves (Chaer et al. \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Etesami \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). \\u003cem\\u003eLeucaena leucocephala\\u003c/em\\u003e (Lam.) de Wit is a small, fast-growing mimosoid tree native to southern Mexico and northern Central America. Although it is an exotic species, leucaena was introduced in Brazil due to its high forage potential (Bomfim et al. \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). In addition to the ability to fix N\\u003csub\\u003e2\\u003c/sub\\u003e from the atmosphere, the biological characteristics of this species include rapid development and high reproduction, traits that favor competition and establishment in highly degraded environments (Bomfim et al. \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Sharma et al. \\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe collapse of the Fund\\u0026atilde;o Dam in Mariana, MG, Brazil, in November 2015, resulted in the largest environmental disaster in the history of country, releasing around 60\\u0026nbsp;million m\\u0026sup3; of mining tailings. The event caused the Doce River to silt up, covering the ground with mining tailings, damaging agricultural production and changing the physical, chemical and biological characteristics of the soil, affecting native plants and animals (da For\\u0026ccedil;a-Tarefa \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Carmo et al. \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Andrade et al. \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). The tailings plume reached the mouth of this Doce river, degrading approximately 680 km of water bodies and impacting 39 municipalities in the states of Minas Gerais and Esp\\u0026iacute;rito Santo (Aires et al. \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Queiroz et al. \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Bernardino et al. \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). In this study, we evaluated the structure of the bacterial community in different concentrations of tailings from the iron mining of the Fund\\u0026atilde;o dam collapse. To this end, different concentrations of tailings were mixed into a fertile substrate and used to plant the legume \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e. In addition to the treatments with different proportions of tailings in the absence of the plant, the bacterial community of the leucaena rhizospheric soil was also compared at two different times.\\u003c/p\\u003e\"},{\"header\":\"2. MATERIAL AND METHODS\",\"content\":\"\\u003cp\\u003eSamples of tailings from the Fund\\u0026atilde;o dam were collected in November 2016 from material dredged from the lake of the Risoleta Neves Hydroelectric Power Plant (Candonga), located in the municipalities of Rio Doce and Santa Cruz do Escalvado, MG, Brazil (20\\u0026deg;12'27.2'' S 42\\u0026deg;51'17.3'' W). The material was then transported to the Experimental Station of the Federal University of Juiz de Fora (UFJF). The tailings were stored in covered beds and covered with plastic sheeting to prevent the leaching of chemical elements by the rain. Samples of the tailings were mixed with a substrate prepared with soil/sand/bovine manure in a ratio of 3:2:1 (v/v/v). The mixtures resulted in the following proportions: T0% (100% fertile substrate), T25% (25% mining tailings\\u0026thinsp;+\\u0026thinsp;75% fertile substrate), T50% (50% mining tailings\\u0026thinsp;+\\u0026thinsp;50% fertile substrate), T75% (75% mining tailings\\u0026thinsp;+\\u0026thinsp;25% fertile substrate) and T100% (100% mining tailings).\\u003c/p\\u003e \\u003cp\\u003eThe experiment was conducted in a completely randomized design, with five treatments (T100%, T75%, T50%, T25% and T0%) and five replications, using plants of \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e (Lam.) de Wit. In each growing container, three individuals approximately 2 cm high were planted in 20-liter pots with the substrates and tailings in the different proportions. The plants were kept in a greenhouse at the Experimental Station. A fraction of all treatments without plants was stored to be used as a control, while another fraction was collected to characterize the substrate with the respective concentrations of tailings (Freitas et al. \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe samples were collected after 8 months (T1 without and with plant - \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e) and 14 months (T2 with plant) of growing the legume in each of the tailing concentrations mixed with the fertile substrate kept in the pots. A sample at a depth of 10 cm was collected with an auger inserted into the soil near the stem of each individual. As a control, 5 samples of substrate without legume cultivation were also collected at the same concentrations as the treatment. All samples were submitted to the fluorescent in situ hybridization (FISH) technique. The samples were initially weighed (0.5 g), fixed in a 2% paraformaldehyde solution and refrigerated until processing. Processing began with the addition of 10 mL of deionized water for sonication in a Vibra Cell VCX 130PB device (Sonics \\u0026amp; Materials) at a frequency of 3 Hertz for 1 minute (three times). The samples were then centrifuged at 500 g for 5 minutes and washed with ultrapure water (three times). The three supernatant fractions were pooled, homogenized and a volume of 1 mL was used for filtration on a 0.2 \\u0026micro;m polycarbonate membrane. For hybridization, specific probes for 16S rRNA marked with the Cy3 fluorochrome were used to quantify bacterial taxa (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). A negative control probe (NON), with no specificity for any bacteria, was used to evaluate the efficiency of the hybridization. All the probes are described in the probeBase online platform (Loy et al. \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e). The analyses were carried out under an epifluorescence microscope (Olympus, USA) at 1000x magnification. Counts were made in ten random fields.\\u003c/p\\u003e \\u003cp\\u003eStatistical comparisons were made between treatments with different proportions of tailings, with and without the presence of plants and between the two sampling times. When the data showed a normal distribution, they were subjected to analysis of variance (ANOVA - Two way) and a posteriori Tukey test. The Kruskal-Wallis test was used for non-normal data. In both cases, values of p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 were considered significant using the SigmaPlot 12.5 software (Zar \\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e).\\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\\u003eProbes used for the fish technique with specificity, sequence and concentrations of formamide in the hybridization solution and concentration of NaCl in the washing solution.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\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=\\\"left\\\" 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=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eProbe\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSpecificity\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSequence (5' -3')\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e% For\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNaCl (mM)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNON\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNegative control\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTAGTGACGCCGTCGA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e(Karner and Fuhrman \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e1997\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eACIDO228\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSubdivision 1 of Acidobacteria (Acidobacteriales)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTAATCCGCCGCGACCCCT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e(Kleinsteuber et al. \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHCG236\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eActinobacteria\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAACAAGCTGATAGGCCGC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e(Erhart et al. \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e1997\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCF319a\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFlavobacteria,\\u003c/p\\u003e \\u003cp\\u003esome Bacteroidetes\\u003c/p\\u003e \\u003cp\\u003esome Sphingobacteria\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTGGTCCGTGTCTCAGTAC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e(Manz et al. \\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e1996\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLGC354A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eFirmicutes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTGGAAGATTCCCTATTGC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e(Meier et al. \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e1999\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLGC354B\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCGGAAGATTCCCTACTGC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e35\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLCG354C\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCCGAAGATTCCCTACTGC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e35\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDELTA495A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMany Delta-proteobacteria\\u003c/p\\u003e \\u003cp\\u003emany Gemmatimonadetes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAGTTAGCCGGTGCTTCTT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e(Loy et al. \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEPSY549\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEpsylon-proteobacteria\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCAGTGATTCCGAGTAACG\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e(Lin et al. \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBET42a\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBeta-proteobacteria\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGCCTTCCCACTTCGTTT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e(Manz et al. \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e1992\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGAM42a\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGamma-proteobacteria\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGCCTTCCCACATCGTTT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e(Manz et al. \\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e1996\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePae997\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003ePseudomonas spp.\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTCTGGAAAGTTCTCAGCA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e(Amann et al. \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e1996\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGALTS0084\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eClones related to \\u003cem\\u003eGallionella ferruginea\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCCACTAACCTGGGAGCAA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e40\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e40\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e(Hallberg et al. \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003e\\u003csup\\u003eFOR = Formamide\\u003c/sup\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e\"},{\"header\":\"3. RESULTS\",\"content\":\"\\u003cp\\u003eThe densities of bacteria quantified by the FISH technique, varied in the soils containing different concentrations of mining tailings in the absence of \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e plants (T1) and in the presence of plants at both times, 8 months (T1) and 14 months (T2). In general, there was an increase in the densities of most of the bacteria in the presence of the legume, especially in the treatments with the lowest concentration of tailings. Exceptions were the densities of Acidobacteria and Actinobacteria in the treatments with only tailings without plants in the sampling conducted 8 months after substrate preparation (T1). On the other hand, the density of these bacteria in the treatment without tailings (T0%) were highest (0.31 x 10\\u003csup\\u003e8\\u003c/sup\\u003e cells g\\u003csup\\u003e-1\\u003c/sup\\u003e and 0.36 x 10\\u003csup\\u003e8\\u003c/sup\\u003e cells g\\u003csup\\u003e-1\\u003c/sup\\u003e respectively) 14 months after planting the legume. At the same time, only treatments T25% and T75% showed no statistical differences between Acidobacteria density. Also, in the presence of plants, the treatments without tailings (T0%) and with only 25% tailings in the mixture (T25%) had similar densities of these bacteria after 8 months. However, these densities were different from the treatments with the highest proportions of tailings (T50%, T75% and T100%). In the comparisons between times, the collection after 14 months of \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e planting showed differences with the collection carried out at 8 months in the treatments with and without plants. (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ea). The highest average density of Actinobacteria (0.19 x 10\\u003csup\\u003e8\\u003c/sup\\u003e cells g\\u003csup\\u003e-1\\u003c/sup\\u003e) in treatments with only tailings and without plants in the first collection differing from the others. The highest densities of these bacteria were found in the treatment without tailings with the presence of the \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e, after 14 months of planting. These average densities were different from the densities of samples collected at 8 months in treatments with and without plants (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eb).\\u003c/p\\u003e \\u003cp\\u003eThe addition of tailings in any proportion did not change the densities of Bacteroidetes without the presence of \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e. The treatments with different proportions of tailings showed no differences in the average densities of these bacteria found in the first collection without plants. However, with the presence of the legume (T1 with plants), the treatment without tailings (T0%) was different from the others and showed a higher density of this group of bacteria (0.14 x 10\\u003csup\\u003e8\\u003c/sup\\u003e cells g\\u003csup\\u003e-1\\u003c/sup\\u003e). Density of Bacteroidetes was higher in the treatment with 50% tailings (0.16 x 10\\u003csup\\u003e8\\u003c/sup\\u003e cells g\\u003csup\\u003e-1\\u003c/sup\\u003e) after 14 months of planting. The lowest average densities of these bacteria were found in the treatments with the highest proportions of tailings (T75% and T100%). The treatments without plants were different from the treatments with \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e at both collection times (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ec).\\u003c/p\\u003e \\u003cp\\u003e The bacteria of the Firmicutes phylum in the treatments without plants were present in higher densities in the treatment without tailings (T0%) and with 25% tailings (T25%). In the presence of the plants, these treatments and the treatment with 50% tailings (T50%) were also the treatments with the highest density of these bacteria 8 months after planting of \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e. At 14 months after planting, the treatment without tailings (T0%) showed the greatest increase in bacterial density (0.34 x 10\\u003csup\\u003e8\\u003c/sup\\u003e cells g\\u003csup\\u003e-1\\u003c/sup\\u003e). On the other hand, the lowest density (0.03 x 10\\u003csup\\u003e8\\u003c/sup\\u003e cells g\\u003csup\\u003e-1\\u003c/sup\\u003e) was found in the treatment with tailings only (T100%). These Firmicutes densities in the treatments with plants after 14 months were significantly different from the treatments with different proportions of tailings from the first collection, with and without plants (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ed).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe densities of bacteria from the four classes of Proteobacteria (Beta, Delta, Epsylon and Gamma) were lower in the treatments with only mining tailings (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). The densities of bacteria from the Beta-proteobacteria class followed the same trend in the treatments with and without plants in both collections. The highest average values were found in the treatments without tailings and decreased as the proportion of tailings in the treatments increased. The lowest densities of these bacteria (0.02\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.01 x10\\u003csup\\u003e8\\u003c/sup\\u003e cells g\\u003csup\\u003e-1\\u003c/sup\\u003e) were found in the treatment consisting of 100% tailings with \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e planted 8 months ago. There was a difference between the treatments with and without plants, and between the treatments at the two sampling times (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ea). The average density of bacteria from the Delta-proteobacteria class in the first collection without \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e was higher in treatments T25% and T50%. In the presence of plants, in both samplings, the treatments without tailings (T0%) showed higher densities of this class. The densities of these bacteria did not differ between the two sampling times, nor with the presence or absence of plants of \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eb). The highest densities of bacteria from the Epsylon-proteobacteria class were found in the T0%, T25% and T50% treatments in the treatments without plants. In the first sample, only the treatment with 100% \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e tailings (T100%) differed from the others. After 14 months of planting, there was a reduction in the average density of these bacteria, especially in the treatments without or with a lower proportion of tailings (T0%, T25% and T50%). At this point, there was no difference in the number of these bacteria between the treatments (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ec). The treatments without tailings (T0%) showed the highest average values of Gamma-proteobacteria, the highest being 4.11 x 10\\u003csup\\u003e8\\u003c/sup\\u003e cells g\\u003csup\\u003e-1\\u003c/sup\\u003e in the presence of \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e after 14 months of cultivation (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ed). The average densities of Beta, Epsylon and Gamma-proteobacteria were different when comparing the treatments with the presence and absence of \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e, and when comparing the two sampling times with the plant.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe presence of bacteria of the genus \\u003cem\\u003ePseudomonas\\u003c/em\\u003e was not observed in the treatment with 100% mining tailings without the presence of the plant. In the treatments with the other proportions of tailings, no difference was observed in the density of \\u003cem\\u003ePseudomonas\\u003c/em\\u003e spp. On the other hand, in the treatments with the presence of the plants, after eight months of cultivation, higher bacterial densities were recorded in the treatments with lower proportions of tailings (T0% and T25%). In addition to these treatments, the T50% treatment also showed a higher density of these bacteria after 14 months of \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e cultivation (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ea). The presence of plants and the time between samples led to differences in the density of \\u003cem\\u003eGallionella ferruginea\\u003c/em\\u003e in the treatments with different proportions of tailings (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eb). In the first collection, where \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e were not planted (T1 without plant), there was no difference between the treatments with different concentrations of tailings. However, where the \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e were grown (T1 with plant), the highest densities were found in the treatments with the highest proportion of tailings (75% and 100%). After 14 months of planting, the average density values of these bacteria in the treatment with tailings only (T100%) decreased, making the lowest value found (0.07\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.02 x 10\\u003csup\\u003e8\\u003c/sup\\u003e cells g\\u003csup\\u003e-1\\u003c/sup\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe proportion of the major groups identified by the FISH technique, with the probes used in this work, can be seen in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e. Proteobacteria were predominant in all treatments with and without the presence of \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e in both collections. A higher proportion of Acidobacteria and Actinobacteria was observed in the treatment with 100% tailings without the presence of plants.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eLess than 50% of the prokaryotes, quantified with DAPI, were identified and quantified by the probes used to characterize the bacteria in the treatments with different concentrations of tailings, in the presence and absence of plants, and at 8 and 14 months of sampling (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). The exception (69%) was in the treatment with 75% tailings (T75%) stored for 8 months without the legume. The sum of the average densities identified in this treatment was 2.79 x10\\u003csup\\u003e8\\u003c/sup\\u003e cells g\\u003csup\\u003e-1\\u003c/sup\\u003e. The treatments with tailings only (T100%) were the treatments with the lowest proportion of prokaryotes identified. The sums of bacterial densities identified in these treatments ranged from 0.81 to 0.91 x10\\u003csup\\u003e8\\u003c/sup\\u003e cells g\\u003csup\\u003e-1\\u003c/sup\\u003e at the first sampling point with and without plants, respectively. In this treatment with \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e planted for 14 months, less than 4% of the prokaryotes were identified with the probes used.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\"},{\"header\":\"4. DISCUSSION\",\"content\":\"\\u003cp\\u003eHigher taxonomic levels, such as phylum and class, do not always show differences when comparing two different conditions. Metagenomic studies comparing water from the Doce River, impacted after the collapse of the Fund\\u0026atilde;o dam, with water from the Paragua\\u0026ccedil;u River (not impacted) showed similar bacterial community composition at the phylum level and different at other taxonomic levels (Cordeiro et al. \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). In our study, a similarity in the density of Bacteroidetes was observed in the treatments without plants in the first sampling. In the Epsylon-proteobacteria class, this similarity occurred in the second sampling in the treatments with \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e. All the other phyla and classes evaluated had differences in at least one of the treatments with different proportions of tailings. A study carried out in the mining spill region of the Iron Quadrangle in the state of Minas Gerais, Brazil, verified the microbial and metabolic diversity in unimpacted and tailings-impacted soils after the revegetation process using sequencing of the V4-V5 region of 16S rRNA. The results revealed that Bacteroidetes were among the predominant microorganisms after revegetation. The predominant phyla they found were also Proteobacteria, followed by Acidobacteria, Verrucomicrobia, Planctomycetes and Bacteroidetes. Bacteria from vegetated soil showed high metabolic diversity and the presence of genes linked to resistance to iron-containing environments (Fernandes et al. \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe presence of the legume influenced the density of Bacteroidetes and bacteria of the genus \\u003cem\\u003ePseudomonas\\u003c/em\\u003e. The lowest densities of these bacteria were found in the treatments without \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e, which differed from the treatments with the plant in both samplings. Bacteria of the genus \\u003cem\\u003ePseudomonas\\u003c/em\\u003e spp. were not found in the tailings without plants. In this same sampling and in the second one, the presence of these bacteria was observed, although in lower densities in this treatment with \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e. Species of \\u003cem\\u003ePseudomonas\\u003c/em\\u003e exhibit remarkable metabolic diversity, allowing them to thrive in various environments and serve as beneficial inoculants and plant growth promoters (Saati-Santamar\\u0026iacute;a et al. \\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Mehmood et al. \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). These bacteria use root exudates, produce compounds that are toxic to pathogenic fungi and bacteria and chelate iron, contributing to plant health and growth (Guzm\\u0026aacute;n-Guzm\\u0026aacute;n and Santoyo \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Sanow et al. \\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). When introduced into metal-contaminated soils, \\u003cem\\u003ePseudomonas\\u003c/em\\u003e species have been shown to promote remediation by aiding in the detoxification and immobilization of heavy metals, thus contributing to the restoration of soil health and fertility (Shaheen et al. \\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Their ability to improve plant growth, fight pathogens and aid in metal detoxification underlines the valuable contributions of \\u003cem\\u003ePseudomonas\\u003c/em\\u003e to sustainable agriculture and environmental remediation efforts. In addition to Protobacteria and other bacteria, \\u003cem\\u003ePseudomonas\\u003c/em\\u003e are ACC deaminase-producing, an enzyme can help alleviate the heavy metals toxicity. This enzyme promotes root growth by hydrolyzing ACC, the immediate precursor of ethylene, a plant hormone related to senescence and oxidative stress (Etesami \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). In addition, mechanisms of \\u003cem\\u003ePseudomonas\\u003c/em\\u003e sp. action include nitrogen fixation, nutrient availability (i.e., K and P solubilization with the production of organic acids, siderophores and phosphatases), resistance to pathogens and alterations in soil microbiome (Bressanin et al. \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Their study with tailings from the Fund\\u0026atilde;o dam found greater growth and survival of \\u003cem\\u003eHymenaea courbaril\\u003c/em\\u003e seedlings when the substrate was inoculated with \\u003cem\\u003ePseudomonas\\u003c/em\\u003e. Their resistance to metals further underlines their suitability for phytostabilization processes, emphasizing the importance of microbial community dynamics in the successful efforts of these processes.\\u003c/p\\u003e \\u003cp\\u003eRegardless of the presence or absence of \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e in the substrates, the density of Acidobacteria, Actinobacteria and bacteria from the Firmicutes phylum differed between the time intervals between samples. Acidobacteria plays significant ecological roles, as evidenced through their active participation in key carbon, nitrogen, and sulfur biogeochemical circuits (Kalam et al. \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Studies suggest that the abundance of the Actinobacteria and Firmicutes phyla is significantly correlated with metal-contaminated environments (Fernandes et al. \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). Over time, the densities of these bacteria in the rhizosphere increased, especially in the treatments with little or no tailings. Soils with deposits of zinc waste were re-vegetated with eight plant species, and after 5 years sequencing was carried out to verify the structure and diversity of the bacterial community (Luo et al. \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). The authors observed that revegetation promoted an increase in the microbial community in the rhizosphere of the plants, among the phyla that increased were Proteobacteria, Acidobacteria, and Bacteroidetes, and there was also an increase in the abundance of plant growth-promoting bacteria. In an ecosystem belonging to a historical area of ​​the Iron Quadrangle, Minas Gerais, Brazil, which suffered mining activities until its total depletion, observed that Proteobacteria was the most predominant phylum, followed by others with emphasis on Acidobacteria and Bacteroidetes, as also observed in our study (Fernandes et al. \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). According to the authors, this is an important component of natural bioremediation in iron mining areas undergoing a regeneration process. Emenike et al. (\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e) also observed that bioremediation was enhanced in metal-contaminated soil by consortia of proteobacteria. According to the authors, ureolytic (urease-producing) microorganisms such as proteobacteria possess the potential to ameliorate soils contaminated with a vast spectrum of heavy metal(loid)s. Proteobacteria considerably increased the bioreduction rate of As, Cu, Zn, Mn and Cr, making them potential remediation agents for the bioreduction of heavy metal(loid)s in contaminated environments.\\u003c/p\\u003e \\u003cp\\u003eAcidic environments such as mining tailings can harbor Acidobacteria and Actinobacteria (Johnson and Aguilera \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). These bacteria can live in these acidic, metal-rich environments because they have heavy metal resistance mechanisms. These bacteria can be used as biomarkers after contamination by mining tailings containing iron, as they may possess genes related to the iron cycle (Haferburg and Kothe \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e; El Baz et al. \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Kelly et al. \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). In the iron ore waste used in the present study, the iron concentrations were five times higher in the pure tailings treatments when compared to the treatments without tailings (Freitas et al. \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). The impact of the deposition of tailings from the Fund\\u0026atilde;o dam caused an increase in soil density and silt content, reducing macroporosity, microbial biomass carbon, basal respiration, enzymatic activity and the density of some microbial groups (Silva et al. \\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). However, they observed that some microbial functional groups had a higher density in these areas, which shows the potential of revegetation to increase the quality of this environment. The presence of vegetation favors the development of resistant microorganisms by supplying nutrients and energy through the deposition of organic matter, as well as by the release of exudates by the roots (Valentim dos Santos et al. \\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). In addition, the authors state that when stressful conditions occur in the soil, such as contamination by heavy metals, contaminant-resistant groups tend to increase their population competitively in relation to less resistant ones. Thus, the population can even reach higher values than those observed in uncontaminated sites. Our results corroborate these findings.\\u003c/p\\u003e \\u003cp\\u003eIn a research into the ecological restoration of mine tailings, significant differences were also observed in the relative abundance of the Alpha- and Delta-proteobacteria classes, and bacteria from the Acidobacteria, Firmicutes and Nitrospira (Li et al. \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). In our work, the number of bacteria from the Firmicutes phylum, and the Delta, Gamma and Beta-proteobacteria classes were lower where there were only mining tailings without the presence of the plant. In the presence of \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e and over time, the number of bacteria increased in the treatments with the lowest proportions of tailings. In the treatments with 100% tailings, the number of these bacteria remained lower than in the other treatments, possibly due to the influence of the compounds present in the tailings over time. Considering the two factors together, the presence of plants and the time interval, the difference among the treatments was found among the proteobacteria of the epsylon, gamma and beta classes, including the \\u003cem\\u003eGallionella ferruginea\\u003c/em\\u003e species. The average density of Epsylon-proteobacteria found was lower in the treatments with only tailings, with and without \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e in both samplings, indicating that the more tailings the lower the number of these bacteria. However, in the presence of plants, the densities of this class were higher in all treatments at 8 months after planting and lower at 14 months. The presence of the plant seems to have influenced the growth of these bacteria only initially. All the classes of proteobacteria evaluated were the predominant group in all the treatments and over time. This was the case even though the densities of bacteria belonging to the Alpha-proteobacteria class were not evaluated and even though there was no difference in the densities of bacteria belonging to the Delta-proteobacteria class between treatments with or without the presence of \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e, nor between sampling times. The most abundant class of proteobacteria was the Gamma-proteobacteria class. The presence of Gamma-proteobacteria can improve the environmental stability of tailings. In environments with mining tailings containing iron, copper and gold, when subjected to acidification, they were able to promote the adsorption of metals such as arsenic, cobalt, lead, zinc, nickel and chromium, inhibiting the leaching of these metals(Henne et al. \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eA study that compared the microbial community in a river impacted (Doce River) and another not impacted (Paragua\\u0026ccedil;u River) by the Fund\\u0026atilde;o dam tailings spill also found a predominance of proteobacteria in both rivers (Cordeiro et al. \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). However, when other taxonomic levels were evaluated, it was observed that in the impacted river there was a predominance of Bacteroidetes, Gamma-proteobacteria and Actinobacteria, and in the non-impacted river there was a predominance of Beta-proteobacteria. The authors concluded that the tailings altered the microbial community of the Doce River. Their genomic analysis of the bacteria found in the impacted samples also showed that there was an increase in genes linked to microbial virulence, respiration, membrane transport (efflux pump for metals), iron and nitrogen metabolism (denitrification and nitrogen fixation), and microbial motility. However, amino acid, fatty acid, carbohydrate and pigment metabolism genes decreased in the bacteria found in the impacted water samples.\\u003c/p\\u003e \\u003cp\\u003eThe density of \\u003cem\\u003eGallionella ferruginea\\u003c/em\\u003e bacteria in the treatments without plants showed no statistical difference. After eight months of plant cultivation, the number of bacteria increased in the treatments that did not contain 100% tailings, and after 14 months of cultivation, the T75% and T100% treatments had a decrease in the number of these microorganisms. According to Reis et al. (\\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e), absence or reduction in \\u003cem\\u003eGallionella\\u003c/em\\u003e density in remediated environments after mining activities can be used as an indicator of reaching the end point of remediation processes. \\u003cem\\u003eG. ferruginea\\u003c/em\\u003e is an acidophilic bacterium and can grow microaerophilically in an environment with the presence of metals, promoting the oxidation of ferrous iron (Ayangbenro et al. \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). \\u003cem\\u003eG. ferruginea\\u003c/em\\u003e obtains all of its cellular carbon from CO\\u003csub\\u003e2\\u003c/sub\\u003e fixation when grown under aerobic gradient conditions in a mineral salt solution with iron sulfide. Adding a new carbon source, such as glucose, increases carbon uptake and leads to a decrease in CO\\u003csub\\u003e2\\u003c/sub\\u003e fixation (Hallbeck and Pedersen \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e1991\\u003c/span\\u003e; Eggerichs et al. \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Legumes can release exudates through their roots, the exudates contain glucose and fructose, and can promote the acidification of the medium, and the development of microorganisms that adapt to these conditions (Chaer et al. \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e). Consequently, this leads to a high average density of microorganisms and a low respiration rate, as observed in the treatments with the presence of leucaena and throughout the study period. Experiments carried out to investigate the effect of temperature (20 and 35\\u0026deg;C) and bacterial diversity by sequencing the 16S rRNA gene, in samples of acid drainage from the Carnoul\\u0026egrave;s mine (France), observed that the temperature of 20\\u0026deg;C, provides the dominance of iron-oxidizing bacteria, such as \\u003cem\\u003eGallionella\\u003c/em\\u003e spp. was associated with almost complete oxidation of iron (98%) (Tardy et al. \\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). In experiments using water samples without the influence of human activity from the Sucio River (Costa Rica), which originates from volcanic rocks, 89.39% of the sequences were identified as proteobacteria by sequencing, in particular the Betaproteobacteria class (80.16%). Of these, the largest proportion (43.89%) were bacteria from the genus \\u003cem\\u003eGallionella\\u003c/em\\u003e. Bacteria of this species have been considered key in the Sucio river ecosystem, as they participate in iron and sulphur metabolism (Arce-Rodr\\u0026iacute;guez et al. \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). On the other hand, in samples of neutral drainage water from the Elizabeth well mine, Slovinky (Slovakia), the composition of the bacterial population was characterized and the presence of iron-oxidizing Proteobacteria of the \\u003cem\\u003eGallionella\\u003c/em\\u003e and \\u003cem\\u003eLeptothrix\\u003c/em\\u003e genera was observed, the occurrence of which did not change during the years 2008 to 2014 (Kiskov\\u0026aacute; et al. \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). Therefore, \\u003cem\\u003eGallionella\\u003c/em\\u003e spp. is present in natural environments without anthropogenic action and in mineral extraction environments.\\u003c/p\\u003e \\u003cp\\u003eThe increase in the number of prokaryotes in the treatment with tailings after 1 year and 2 months of planting \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e does not correspond to the bacteria identified with the probes used. None of the bacteria evaluated showed a higher density in this treatment at that time (T100% with \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e planted 14 months ago). In this way, not even 10% of the prokaryotes accounted for by DAPI staining were identified. These prokaryotes may be Archaea and/or the probes used do not have the specificity to reach all the bacteria in their taxonomic group. Among the 12% of Archaea found, there was a predominance of the Euryarcheota phylum in the water samples from the areas impacted by the Fund\\u0026atilde;o dam collapse (Cordeiro et al. \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe potential use of microorganisms in bioremediation depends on their identification and also on their ability to be cultivated and kept alive. Many of the microorganisms found in mine drainage do not grow in known culture media to date. In order to identify them, it is necessary to sequence them, assemble their genomes and compare them with a database, which is constantly being updated (Bressanin et al. \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Santos et al. \\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Many microorganisms present in extreme environments have yet to be identified and/or classified. The omics era (metagenomics, proteomics, metabolomics, transcriptomics, etc.) has opened up new ways of understanding the nature and functionality of extremophilic microorganisms. They still face limitations related to sampling practices, handling, storage, processing and interpretation of the range of data generated (Gupta et al. \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eInteractions among members of microbial communities are often fundamental to their roles as bioremediators and biostimulators. One microorganism can produce metabolites and be used by another microorganism, and the interaction between them promotes growth. Often, these interactions enable behavior (gene expression) that neither party could perform in isolation (Marx \\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e; Hillesland \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). Evidence of co-occurrence and metabolic dependence with other bacteria has been found in two bacteria belonging to the Saccharimonadia class from copper mines (Lemos et al. \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). These bacteria found by them have small genomes and do not contain genes associated with the biosynthesis of essential amino acids, nucleotides, fatty acids and cofactors, requiring them to live in symbiosis with other bacteria, such as those of the \\u003cem\\u003eHydrotalea\\u003c/em\\u003e genus. Understanding the interactions between microorganisms tends to bring the concept of a microbial community into practice. That is, using a group of bacteria from different species, or even a group of Bacteria, Archaea and Fungi.\\u003c/p\\u003e\"},{\"header\":\"5. CONCLUSION\",\"content\":\"\\u003cp\\u003eThe increase in microorganisms in various concentrations of iron ore tailings associated with legumes is a promising discovery (Valentim dos Santos et al. \\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Bressanin et al. \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Symbiosis can provide valuable insights into the dynamics and recovery of impacted areas, since they are recognized as a crucial factor in the maintenance and functioning of the ecosystem. Future prospects include the possibility of selecting specific microorganisms, or sets of microorganisms, that promote plant growth and improving soil quality. Once selected and cultivated, these microorganisms can be used as inoculants in regions affected by mining tailings, helping to restore the productive capacity of disturbed land and ensuring socio-economic and environmentally sustainable practices. The results of this study allow us to conclude that iron ore tailings influence the microbial community associated with the rhizosphere of plants at different concentrations. The increase in prokaryote density, identified by FISH, observed in the 14-month evaluation, is a strong suggestion of a soil recovery and improvement process in response to its cultivation with \\u003cem\\u003eL. leucocephala\\u003c/em\\u003e.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eACKNOWLEDGEMENTS\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe would like to thank all the students and technicians at our institutions for their help at all stages of this work. We would also like to thank Dr. Ra\\u0026uacute;l Marcel Gonz\\u0026aacute;lez Garcia (\\u003cem\\u003ein memorian\\u003c/em\\u003e) for his collaboration and encouragement.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFUNDING\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was supported by Minas Gerais State Research Support Foundation (Fapemig), Project CRA-APQ-01187-16 Technologies for the recovery of the Doce River Basin.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCOMPETING INTERESTS\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCONSENT TO PARTICIPATE\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCONSENT TO PUBLISH\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eETHICS APPROVAL\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDATA AVAILABILITY STATEMENT\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAUTHOR CONTRIBUTIONS\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eConceptualization were performed by Jana\\u0026iacute;na Barros Miranda, Paulo Henrique Pereira Peixoto, Alessandro Del\\u0026rsquo;Duca and Dion\\u0026eacute;ia Evangelista Cesar. Methodology were performed by Jana\\u0026iacute;na Barros Miranda, Edmo Montes Rodrigues, Cristiano Ferrara de Resende, Raiza dos Santos Azevedo and Dion\\u0026eacute;ia Evangelista Cesar. All authors contributed to the results analysis and first draft writing. The review and editing were performed by Edmo Montes Rodrigues, Alessandro Del\\u0026rsquo;Duca, Julliane Dutra Medeiros, Raiza dos Santos Azevedo and Dion\\u0026eacute;ia Evangelista Cesar. Visualization were performed by Edmo Montes Rodrigues, Alessandro Del\\u0026rsquo;Duca, Julliane Dutra Medeiros, Raiza dos Santos Azevedo, Andr\\u0026eacute; Luiz dos Santos Furtado and Dion\\u0026eacute;ia Evangelista Cesar. All authors read and approved the final manuscript.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eAndrade GF, Paniz FP, Martins AC et al (2018) Agricultural use of Samarco\\u0026rsquo;s spilled mud assessed by rice cultivation: A promising residue use? 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Environ Pollut 249:1029\\u0026ndash;1037. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.envpol.2019.03.060\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.envpol.2019.03.060\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZar JH (2010) Biostatistical Analysis. Upper Saddle River, New Jersey\\u003c/span\\u003e\\u003c/li\\u003e \\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"environmental disaster, bioremediation, mining waste, revegetation, leucaena, microorganisms, proteobacteria\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4824709/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4824709/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eBioremediation using plants and microorganisms effectively mitigates heavy metal soil contamination and improves soil conditions. Phytoremediation with leguminous plants and rhizobacteria enhances metal bioavailability and promotes plant growth. This study evaluates microbial community structure in soils with varying concentrations of tailings from the Fund\\u0026atilde;o Dam disaster in Mariana, MG, Brazil. Microbial diversity was measured by fluorescent in situ hybridization in treatments with different tailings concentrations and in the presence of \\u003cem\\u003eLeucaena leucocephala\\u003c/em\\u003e. Higher tailings proportions reduced bacterial densities, with pure ore tailings (T100%) showing the lowest bacteria percentage. However, after 14 months of leucaena cultivation, this treatment had the highest number of prokaryotes. The presence of leucaena plants modified the densities of Bacteroidetes and the \\u003cem\\u003ePseudomonas\\u003c/em\\u003e genus. Experimentation time influenced the densities of Actinobacteria, Acidobacteria, and Firmicutes. The interaction between legume presence and sampling time altered the density of Proteobacteria and \\u003cem\\u003eGallionella ferruginea\\u003c/em\\u003e. The results show that iron ore tailings impact the microbial community in the plant rhizosphere, offering insights for bioremediation strategies to restore soil quality in mining-affected areas.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Impact of Fundão Dam Tailings on Rhizospheric Soil Microbial Communities in Mariana, MG, Brazil\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-10-07 12:26:09\",\"doi\":\"10.21203/rs.3.rs-4824709/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"39c496b0-cf30-44ff-9f9e-be1438c46c7e\",\"owner\":[],\"postedDate\":\"October 7th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-11-02T10:38:36+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-10-07 12:26:09\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4824709\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4824709\",\"identity\":\"rs-4824709\",\"version\":[\"v1\"]},\"buildId\":\"7rjqhiLT3MXkJMwkYKINL\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}