Microorganisms are useful environmental indicators, able to deliver essential insights to processes regarding mine land rehabilitation. To compare microbial communities from a chronosequence of mine land rehabilitation to pre-disturbance levels from references sites covered by native vegetation, we sampled non-rehabilitated, rehabilitating and reference study sites from the Urucum Massif, Southwestern Brazil. From each study site, three composed soil samples were collected for chemical, physical, and metagenomics analysis. We used a paired-end library sequencing technology (NextSeq 500 Illumina); the reads were assembled using MEGAHIT. Coding DNA sequences (CDS) were identified using Kaiju in combination with non-redundant NCBI BLAST reference sequences containing archaea, bacteria, and viruses. Additionally, a functional classification was performed by EMG v2.3.2. Here, we provide the raw data and assembly (reads and contigs), followed by initial functional and taxonomic analysis, as a base-line for further studies of this kind. Further investigation is needed to fully understand the mechanisms of environmental rehabilitation in tropical regions, inspiring further researchers to explore this collection for hypothesis testing.
Machine-accessible metadata file describing the reported data (ISA-tab format)
Background & Summary
In many countries, the environmental rehabilitation of mine lands as close as possible to its pre-disturbance levels is a legal requirement to reduce net losses of biodiversity and ecosystem functions1,2. It is necessary to monitor rehabilitating sites to meet targets of environmental licensing agencies2. To date, there is no consensus on the best indices available from science to evaluate the monitoring process3. Therefore, multidisciplinary approaches aiming at providing such parameters have been proposed recently4,5.
Besides vegetation or fauna surveys6, the examination of microbial communities can detect environmental alterations in short time scales7, thus able to deliver insights about the fulfillment of rehabilitation targets8,9. Metagenomic approaches provide insights into environmental variations10,
In Brazil, one of the world’s leading raw iron export nations16, iron ore deposits occur in open-cast mines in different regions. Ferriferous savanna ecosystems named ‘cangas’17,18 cover the deposits in the Iron Quadrangle (Minas Gerais), the Carajás mountains (Pará), the Caetité region (Bahia), and the Urucum Massif (Mato Grosso do Sul). Due to particular environmental conditions such as high concentrations of metal ions, especially iron, high radiation, elevated temperatures, and ample rainfall seasonal amplitudes, these diverse and endemic ecosystems19,
Impacted by iron ore extraction27 reshaping entire landscapes28 by the removal of ore and mining wastes, the environmental rehabilitation of the impacted ecosystems is desired aiming at the preservation of biotic resources and ecosystem services for future generations. Insights to composition, diversity and functional characterization of microbial soil communities along environmental rehabilitation gradients are useful variables for measuring the success of rehabilitation, able to provide valuable feedback to improve the rehabilitation practice.
The goal of this study was to identify changes in microbial community composition, diversity and functional processes resulting from mine land rehabilitation and compare to pre-disturbance levels from references sites covered by native canga vegetation. We sampled three study sites before rehabilitation efforts, seven study sites spanning different rehabilitation stages and three reference canga sites associated with two iron ore mines from Corumbá (Urucum Massif). Environmental rehabilitation comprises topographic reformulation after removal of the iron ore, liming, fertilization and the application of biomass before native canga species are seeded or planted.
At each study site, we installed three plots of 10 × 10 m; in each plot, a composed soil sample was collected (depth 0–2 cm) for metagenomics analysis. An additional sample (depth 0–10 cm) was collected for physical and chemical analysis. In this study, we applied a paired-end sequencing technology (NextSeq 500 Illumina) after DNA extraction, purification and amplification to construct metagenomic libraries. The Illumina reads were assembled using MEGAHIT. Subsequently, nucleotide sequences coding for proteins (CDS), were extracted from assemblies. Functional and taxonomic classification of coding DNA sequences (CDS) was performed using EMG and Kaiju.
Here, we provide the complete metagenomic data set, without detailed analysis of results or discussion to highlight its outstanding comprehensive view into soil microbial communities from the rehabilitation of a canga ecosystem occurring in Southwestern Brazil. We furthermore present the annotated metagenome assembly, containing taxonomic and functional classification as well as chemical soil properties (i.e., pH, cation exchange capacity, organic matter contents, micro- and macro nutrient as well as aluminum availability) and soil texture. The present collection is the first high-throughput sequencing-based survey from non-rehabilitated and reference sites as well as sites under rehabilitation after iron ore mining from a tropical region, thus representing base-line data for further studies of this kind. With its publication, researchers can explore this collection for hypothesis testing related to environmental rehabilitation in tropical regions, especially after mining activities. The consistency in experimental design, sequencing methodology and sample sources ensures the value of this collection for on-going studies about environmental rehabilitation after anthropogenic impacts, in particular, those about mine land rehabilitation.
Data were collected in October 2016 in 13 study sites from open-cast iron ore mines situated in the Urucum Massif, Mato Grosso do Sul, Brazil (Fig. 1). The altitude of the massif varies between 600 and 1,065 m a.s.l. With a mean annual temperature of 25 °C and mean annual precipitation of 1,070 mm29, the climate of the region corresponds a tropical warm, savanna climate (Aw in the Koppen classification), characterized by dry winters and rainy summers. The natural vegetation is a mosaic of seasonal deciduous and semi-deciduous forests on slopes and near watercourses. Furthermore, different savanna formations, ranging from arborized physiognomies to treeless grasslands stock on the upper parts of the massif 30.
Iron ore mining in the region is restricted to the outcrops of ferruginous jaspilites and fixed hematites from the Santa Cruz Formation31 and begins with the suppression of vegetation and removal of topsoil layers. Environmental rehabilitation after mining includes topological reformulation, topsoil application, liming and fertilization of mine soils. Organic matter originating from suppressed areas is added. The rehabilitation targets are native open savanna formations, i.e., pre-mining formations on ironstone outcrops. Thus, plants rescued from suppressed areas and seedlings of native species produced in a tree nursery are planted to trigger environmental rehabilitation of mine lands. Additionally, seed mixtures of native species collected in the vegetation remnants were applied. On-demand, further activities, such as re-plantation of seedlings, further applications of seeds, and combating alien invasive species, were executed.
Study sites comprise three bare soil areas immediately before rehabilitation activities are carried out, seven sites from different rehabilitating stages (two-, three- and six-year-old stands) as well as three reference sites covered by native vegetation, i.e., open savanna formations (Table 1). At each study site, three plots (10 × 10 m) were installed in homogeneous vegetation without signs of external disturbances.
Two mixed soil samples were collected from each plot. For each sample, the substrate from five homogeneously distributed sampling points within each plot was mixed. The first sample collected at a depth of 0–10 cm was air dried and submitted to analysis of chemical properties and texture. The pH in water (pH(H2O)) and in potassium chloride (pH(KCl)), organic matter (OM), available phosphorus (P), potassium (K), sulfur (S), calcium (Ca), magnesium (Mg), aluminum (Al), boron (B), zinc (Zn), iron (Fe), manganese (Mn) and copper (Cu) concentrations as well as effective cation exchange capacity (ECEC) of the samples were determined following standardized protocols32. Soil texture was detected by particle-size distribution analysis using the pipette method.
A mixed superficial soil sample (depth 0–2 cm) was collected for metagenomics analysis from each plot. Immediately after collection, soil samples were cooled in a fridge to avoid DNA degeneration. At the lab, the samples were stored in a freezer of −80 °C until analysis.
DNA extraction and shotgun sequencing
From 250 mg soil from each sample, total DNA was extracted using the PowerSoil DNA Isolation Kit (Mobio Laboratories, USA) following the manufacturer’s instructions. DNA samples were quantified using Qubit 3.0 fluorometer (Thermo Fisher Scientific Inc.).
Shotgun metagenomic paired-end libraries were then constructed from 50 ng of pure DNA. For that, samples were subjected to a random enzymatic fragmentation in which the DNA was simultaneously fragmented and bound to adapters using the QXT SureSelect kit (Agilent Technologies). The fragmented DNA was purified using AmPure XP beads (Beckman Coulter) and subjected to an amplification reaction using primers complementary to the Illumina flowcell adapters. Amplified libraries were again purified using AmPure XP beads (Beckman Coulter), quantified using the Qubit 3.0 Fluorometer (Thermo Fisher Scientific Inc.) and checked for fragments size in the 2100 Bioanalyzer (Agilent Technologies®) using a High Sensitivity DNA kit (Agilent Technologies).
After that, the libraries were adjusted to a concentration of 4 nM, pooled, denatured and diluted to a running concentration of 1.8 pM. The sequencing run was performed in the NextSeq 500 Illumina platform using a NextSeq 500 v2 kit high-output with 150 cycles.
Genome assembly, taxonomic and functional classification
Using a locally installed EMG v2.3.2 pipeline34, coding DNA sequences (CDS) were extracted from contigs output as .fnn files. Furthermore, the pipeline produces the functional classification output as .ipr files. Subsequently, the taxonomic classification was performed on CDS using Kaiju v.1.4.4 (running mode: greedy, with up to 5 substitutions; minimum match: 12; minimum match score: 70)35. As reference database, we used the non-redundant NBCI BLAST protein sequences (access on December, 8th, 2016, containing 81 M protein sequences from Bacteria, Archaea, and Viruses). We estimated average coverage as the fraction of the observed microbial community covered by the NBCI BLAST protein sequence by package Nonpareil v3.3.336, using forward reads with quality scores greater than Q20, as recommended by the tool.
For data validation, taxonomic and functional counting matrices were generated. Differences in entire microorganism richness, i.e., the taxonomic matrix containing all CDS identified until genus level, between non-rehabilitated, rehabilitating and reference study sites were outlined using one-way ANOVA followed by post-hoc Tukey HSD tests after checking for normality and homogeneity of variance. Diversity was estimated as Shannon’s diversity index H’, using package vegan v2.5-237 in R Environment.
We used package pvclust v2.038 in R Environment v3.4.139 to compute the clusters from the taxonomic counting matrix, considering genus-level predictions from Kaiju. Cluster consistency was tested using the approximately unbiased (au) and the bootstrap probability (bp) statistics40. Both statistics return p-values ranging from 0 to 1, where 0 represents a weak consistency and 1 represents a strong consistency for all formed clusters. As au is a better approximation to unbiased p-value than bp, we considered only with au values larger than 0.95, which represents a strong similarity between the grouped samples.
Finally, an integral analysis of taxonomy was performed by MGCOMP41 to observe the relationship among sample profiles and sites. In order to reduce the influence of rare organisms in this analysis, we considered only the top 30 most abundant genera for each sample, which corresponds to the smallest number of genera covering 50% of the analyzed sequences. Based on these top 30 genera, we performed a two-level clustering of all identified genera for this analysis. In the first level, the samples that showed similar genus abundances were grouped and in the second level, a second grouping was carried out in each cluster considering only the samples belonging to the respective group. After the grouping, the genera present in all first level groupings (denominated core taxa), the genera present exclusively in each of the first level groupings (denominated exclusive taxa) and the other genera (denominated neutral taxa) were identified.
The raw nucleotide sequences of 1,192,347,558 reads and 2,608,990 contigs extracted from 34 soil samples were deposited as fastq and fasta files at NBCI (Data Citation 1: NCBI Sequence Read Archive SRP153411 and Table 2 (available online only)). As required, fastq files contain four lines for each read, that is an identifier of the read, the nucleotide sequence, the placeholder ‘ + ’ for optional annotations (not used here) and the Phred quality score of each nucleotide. fasta files are composed of two elements for each contig, an identifier and the sequence of the contig.
Further data were deposited in Open Science Framework (Data Citation 2: Open Science Framework http://doi.org/10.17605/OSF.IO/EZNMH). Here, the “supplementary” folder contains quality reports for forward and reverse reads from each sample as well as chemical and physical soil properties. Soil properties are furnished as comma delimited .csv file, named SoilSamples.csv. Read quality reports contains 12 section entitled 1) Basic Statistics, 2) Per base sequence quality, 3) Per tile sequence quality, 4) Per sequence quality scores, 5) Per base sequence content, 6) Per sequence GC content, 7) Per base N content, 8) Sequence Length Distribution, 9) Sequence Duplication Levels, 10) Overrepresented sequences, 11) Adapter Content and 12) Kmer Content. The file README.txt, available in the same folder, contains a brief explanation for each section.
Additionally, the “cluster_analysis” folder contains three subordinated folders. The “inputs” folder contains files regarding CDS detected within assembled contigs whereas the “output” folder contains the taxonomic and the functional classification that were used to generate counting matrices by the corresponding scripts, deposited in the “script” folder.
The “inputs” folder contains three zipped files. First, kaiju_input.tar.gz contains a file for each sample with all identified CDS. The file lists CDS identifiers and their sequences. Second, kaiju_output.tar.gz contains the taxonomic classification for each CDS, stored as individual, tab-delimited files for each sample. An upper case letter indicates the success of taxonomic classification (U is unclassified, C is classified) and is followed by the CDS identifier, the NCBI taxonomy ID for the identified taxon and a string showing taxonomic identification containing domain, phylum, class, order, family, genus and species, separated by semicolons, for each CDS. The identifier is composed of CDS ID, containing the contig ID as well as the initial and final nucleotide positions of the CDS within the contig, all of them joined by underlines to a single string. The interpro_output.tar.gz contains the functional classification. Individual comma-delimited files (.csv) contains the enzyme list detected within each sample. Each file is composed of three columns containing an identifier, the name of the protein as well as the number of occurrences within the analyzed sample.
The “output” folder contains three comma separated files within a zipped folder (output.tar.gz). The files correspond to the expected taxonomic (taxa.csv) and the functional matrices (functions.csv). Additionally, taxa_30.csv shows the taxonomic matrix for the 30 top genera only.
Furthermore, five R scripts used to produce the taxonomic matrix (taxonomic_analysis.R), plot samples clustered by taxonomic composition (taxonomic_cluster_plot.R), plot taxonomic composition of each sample (taxonomic_stacked_plot.R), produce the functional matrix (functional_analysis.R) and to plot samples clustered by functions (functional_cluster_plot.R) are available in the “scripts” folder.
Altogether, 2,166,372 CDS were detected. A total of 2.064 genera were present in 1,290,491 CDS, among them 127 archaea, 1,853 bacteria, and 84 virus genera. Richness varies from 739 to 1,894 within samples (Table 3). 273,799 CDS (12.64% of all CDS) remain completely unclassified, and for an additional 875,881 CDS (40.43% of all CDS), only partial matches are available. Functional classification of identified contigs distinguished 10,913 proteins.
All micro-organism diversity within samples (measured on genus level) varied from 4.5 to 5.5 (Fig. 3) and was significantly higher in non-rehabilitated than in rehabilitating study sites (ANOVA, F = 4.137, p = 0.0255, Fig. 3). Significant differences in community composition were detected. First, the cluster analysis separated the samples into two clusters. The larger cluster groups samples from rehabilitating and reference sites, whereas samples from non-rehabilitated sites were grouped outside (Fig. 4).
Additionally, the complete analysis of taxonomy separated the dataset into four groups by taxonomic profile, three of them divided into subgroups (Fig. 5). As shown in Table 4, samples from all three treatments (non-rehabilitated, rehabilitating and reference sites) were clustered in groups A and B, while a single reference sample forms group D and group C is composed exclusively of non-rehabilitating samples. All analysis carried out here show that taxonomic composition of microorganism communities from rehabilitating and reference sites is highly similar, indicating that rehabilitating activities after iron ore mining in the Urucum massif can rehabilitate soil microorganisms successfully.
Contigs and the taxonomic and functional classifications have been generated using an automated process without manual assessment, i.e., represent a draft assembly only. As such, all downstream research should independently assess the accuracy of reads, contigs, and taxonomic and functional assignments for organisms of interest. Nevertheless, this study presents a baseline for further studies of this kind.
The dataset contains a significant amount of taxa and functions previously identified, but a high portion of unclassified or incompletely classified CDS indicates the presence of a sizable portion of unseen biodiversity within soils along the sampled rehabilitation chronosequence. The identification of this unseen biodiversity may require additional alignments, eventually using different genome assemblers as well as combinations with further reference databases. Furthermore, there is a need for manual assessment of the quality of functional and taxonomic classification in some cases. This analysis of outstanding seen and unseen biodiversity within this dataset is expected to produce helpful insights to microbial community ecology along rehabilitation chronosequences after iron ore mining.
How to cite this article: Gastauer, M. et al. A metagenomic survey of soil microbial communities along a rehabilitation chronosequence after iron ore mining. Sci. Data. 6:190008 https://doi.org/10.1038/sdata.2019.8 (2019).
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Gastauer, M. et al. Open Science Framework http://doi.org/10.17605/OSF.IO/EZNMH (2018)
We are grateful for Felipe Tadashi Asoa Coelho and Rosilene Silva from DIFC/Vale for their support during the field campaign. The authors would like to thank Vale for the financial support. SJR and GO are grateful for CNPq supported productivity scholar grants.