metagenomic analysis reveals the presence of...

Keywords: metagenomics, metal response genes, shotgun sequencing

Metagenomic Analysis Reveals the Presence of Heavy Metal Response Genes from Cyanobacteria

Thriving in Balatoc Mines, Benguet Province, Philippines

1Institute of Biology (IB), College of Science (CS) University of the Philippines (UP) Diliman, Quezon City 1101 Philippines

2Natural Sciences Research Institute (NSRI), CS, UP Diliman, Quezon City 1101 Philippines

Libertine Rose S. Sanchez1 and Ernelea P Cao1,2

Tailing ponds of mining sites heavily contaminated with metals is a serious problem in many parts of the world. Metagenomic sequencing and bioinformatics analysis of water samples from the Balatoc mine tailings – an abandoned mining site in Itogon, Benguet, Philippines – revealed microbial communities, particularly cyanobacteria consortia that implied their ability to survive in metal-stressed environments. Thus, their presence can be further investigated for applications in bioremediation. Surface water samples were collected from three sampling points in the Balatoc mine tailings. Physicochemical properties of the samples were also determined. Genomic DNA was extracted from all water samples and subjected to shotgun sequencing using Illumina NextSeq2500 2 x 150 paired ends. Thirty-eight (38) Gbases raw reads obtained from three data sets showed similar microbial assemblages using St. Petersburg genome assembler (SPAdes v3.10.1). Taxonomic assignments to contigs using CLAssfier based on reduced K-mers (CLARK) revealed the relative abundance of 97% Bacteria and 3% Archaea. All sampling sites were found to have relatively the same physicochemical properties. The abandoned Balatoc tailing site exhibited high temperature (31.50 ̊ C), alkaline pH (8.42), and elevated levels of copper (Cu2+) (1.53 mg/L) and zinc (Zn2+) (0.077 mg/L). A CLARK v1.2.5 custom database of cyanobacteria was also used to determine the classification, taxonomic assignment, as well as the estimation of percentage relative abundance of the cyanobacteria. Taxonomic assignments of all metadata revealed a dominant cyanobacterium, classified as Leptolyngbya sp., which comprises about 3% of the assembled contigs. Prokka v1.12 was used for annotation and protein-coding sequences (CDS) were evaluated for gene ontology (GO) using the evolutionary genealogy of genes-Non-supervised Orthologous Groups (eggNOG) Mapper v4.5. The genes conferring stress-response to metal ions Cu2+, Zn2+, lead (Pb2+), and cadmium (Cd2+) are reported to be involved in efflux/transport functions and heavy metal resistance that can be major attributes of Leptolyngbya sp. for survival to extreme metal conditions.

Philippine Journal of Science148 (S1): 71-82, Special Issue on GenomicsISSN 0031 - 7683Date Received: 18 Mar 2019

*Corresponding Author: [email protected]

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INTRODUCTIONCyanobacteria are a group of diverse Gram-negative, oxygenic, photoautotrophic prokaryotes. They have high adaptability; hence, they are widely distributed in extreme environments like radioactive wastewater and polluted watersheds (Liu et al. 2015, Smucker et al. 2018); mine tailings (Orlekowsky et al. 2013. Dhal and Sar 2014, Goswami et al. 2015, Hazarika et al. 2015, Seiderer et al. 2017, McCutcheon et al. 2017, Sibanda et al. 2019); geothermal habitats (Debnath et al. 2009, Ward et al. 2012, Dadheech et al. 2013, Roy et al. 2014, 2017, Castenholz 2015); ice-capped lakes (Singh and Elster 2007, Cirés et al. 2017); and Arctic and Antarctic rocks (Whitton and Potts 2000, Strunecky et al. 2012). They are also reportedly found in industrial effluents (Vijayakumar 2005, 2012; Vijayakumar et al. 2005, 2007) such as those found in an oil refinery, fertilizer factory, brewery (Kumar et al. 1974), tannery (Dhamotharan et al. 2008), and even in the dye industry (Vijayakumar et al. 2005). Cyanobacteria have a high multiplication rate and metal absorption capacity (Vijayakumar 2012). They are commonly regarded as blue-green algae, which have a superficial resemblance to green algae due to chlorophyll a (chlora) pigments and phycocyanin, with some species proven to be agents for bioremediation (Ananya and Ahmad 2014). Studies conducted by Kuenen et al. 1986 on cyanobacteria revealed that these photosynthetic prokaryotes provide a favorable condition for the removal of heavy metals from the environment since their interior pH is almost two units higher than the surrounding liquid. Cyanobacteria forming mats, such as Phormidium and Oscillatoria, were reported to absorb hexavalent chromium (Cr6+) from tannery effluents (Balaji et al. 2016) and copper from mine wastewater (Chaturvedi et al. 2013). An immobilized cyanobacterium Anabaena doliolum exhibited an increased uptake of Fe3+ and Cu2+ than that of the free-living cells (Rai and Mallic 1992). Cyanobacteria are able to protect cells in the presence of excessive metals by sequestering them (Huertas et al. 2014). They have been found to possess several mechanisms for metal homeostasis, as well as proteins that can be used to assess their metal bioremediation potential.

In the Philippines, several studies have been conducted on the existence of heavy-metal-resistant cyanobacteria in industrial effluents and mine tailings. Hapalosiphon welwitschii isolated near the mining site in Mogpog, Marinduque, by De Guzman and Cao (2010) exhibited an efflux mechanism of the intracellular bound Cd2+ under controlled conditions. Jao (2004) isolated several species of cyanobacteria in a major mining site in Benguet province where gold, copper, silver and nickel are the primary metals being mined. Sanchez and co-authors (2017) conducted an initial study on copper-tolerant

cyanobacterial consortia found at Balatoc, Antamok, Acupan, and Philex mine tailings in the said province.

Balatoc mines, owned by Benguet Corporation, started in 1903. It is the first and the oldest mining company in the Philippines (Benguet Corporation 2018). While it has been officially declared as abandoned since 1992, some small-scale mining activities continue to exist (Cawis 2018) where recovery of minerals is done by placer mining by the local communities in the area. Copper, gold, silver, chromium, nickel, lead, zinc, and magnesium are the metals being mined. The area is characterized by an average rainfall of 2,585 mm per year wherein February is the driest month with 4 mm rainfall, while the greatest amount of precipitation at 586 mm occurs in August. The average annual temperature is 23.4 °C. The warmest month of the year is May at an average temperature of 25 °C, while the lowest temperature occurs in January at 21.6 °C (Climate-data.org 2019). There is sparse vegetation and uncovered slopes in Balatoc mine tailings, and those surfaces are exposed to the erosive forces of wind and water (Seiderer et al. 2017). This would eventually lead to decreased infiltration of water resulting in run-off, sedimentation, erosion, and air pollution (Hattingh and van Deventer 2004). This could pose a serious threat to the health of the people living near the vicinity of the tailings. Furthermore, high alkalinity of the water and the presence of acids in mine tailings have a synergistic effect in causing biotoxicity since resulting low pH increases the bioavailability of metals (Stevenson and Cole 1999, Seiderer et al. 2017). This also explains why mine tailings are devoid of vegetation (Moynahan et al. 2002, Mendez and Maier 2008, Seiderer et al. 2017).

The interactions between cyanobacteria and other microbial symbionts came to existence billions of years ago (Douzery et al. 2004, Rodriguez-Espeleta 2005). These associations resulted in difficult isolation and purification of axenic cultures of cyanobacteria. Most of the time, unicyanobacterial cultures were obtained with some associations to other microbes, giving rise to microbial consortia (Alvarenga et al. 2017). There are also instances when a cyanobacterium is impossible to purify at all because it lives epiphytically or endosymbiotically (Bergman et al. 2007) with other cyanobacteria or microbes. The molecular methods provided by next-generation sequencing give updated techniques and insights to examine the microbial community structures in environmental samples, particularly for microorganisms that are difficult to isolate and culture (Mortazavi et al. 2015, Semedo-Aguiar et al. 2018). The advent of metagenomic approaches came to solve these problems for achieving axenic growth and that individual genomes can already

Special Issue on Genomics Sanchez and Cao: Metagenomic Analysis of Cyanobacteria from Balatoc mines

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be obtained from consortia. Metagenomics deals with data from mixed samples made up of distinct microbial populations (McHardy and Rigoutsos 2007). This has been a useful approach to reveal the structure, composition, metabolism, and genetics of both natural and artificial microbial consortia (Song and Thomas 2014). Metagenomic approaches can also be used to manage time-consuming efforts of attaining axenic cultures of cyanobacteria from mine tailing sites. The emergence of shotgun metagenomic sequencing help researchers from various fields to evaluate diversity, detect the relative abundance of organisms, and comprehensively examine the genes in a given sample (Illumina 2015).

This study aimed to identify the dominant cyanobacterium present in the consortia isolated from the Balatoc mining sites using the shotgun sequencing approach. It also intended to analyze and validate metagenomic data using different bioinformatics tools. This study also highlighted the baseline information obtained on the metal response genes present in the consortium and the identification of cyanobacteria in mine tailings that could probably start re-vegetation by producing biological crusts.

MATERIALS AND METHODS

Sample CollectionTwo liters (2 L) of water samples were collected from three sampling points in Balatoc Mines in Itogon, Benguet province, Philippines with the GPS coordinates of 16°21'55'' N, 121°40'22'' during the month of August 2017 (Figure 1). The water samples were green in color with large amounts of ore residues after copper extraction and contain high levels of toxic metals (Courtney 2013, Rzymski et al. 2017).

Samples were collected from the three water replicates of the Balatoc mine tailings that were 500 m apart. Water samples were obtained from a depth of ≦ 90 cm since photosynthetic cyanobacteria usually thrive near the surface of water tailings. The samples were placed in an iced container and stored between 0 and 4°C while being transported to the Plant Genetics and Cyanobacterial Biotechnology Laboratory (PGBCL), IB, CS, UP Diliman within 24 hours prior to DNA extraction. The measurement of physicochemical parameters was done in triplicate on site using a pH meter (Exstik®Extech PH100) for hydrogen ion concentration (pH), and a multi-parameter probe (Exstik®Extech EC400) for temperature, total dissolved solids (TDS), salinity, and conductivity. The results were reported as the mean of the data for the abovementioned parameters.

Processing of Collected SamplesTwo liters (2 L) of tailing samples were filtered using a 47 mm diameter, 0.2 µm thick Cyclopore® track-etched membrane (Whatman, USA) for genomic DNA extraction. One liter (1 L) of the pooled filtered tailing samples was sent to the Research and Analytical Services Laboratory of NSRI, UP Diliman for initial determination of total elemental concentrations of copper (Cu2+), magnesium (Mg2+), lead (Pb2+), zinc (Zn2+), chromium (Cr3+), calcium (Ca2+), and potassium (K+).

Genomic DNA Extraction, Library Construction, and Metagenomic SequencingMicrobial eDNAs were extracted from the filter-etched membrane of each of the three water samples using DNeasyPowerWater® Kit (Qiagen®, USA). Nucleic acids were purified using silica particles according to the manufacturer’s instructions, quantified by fluorometry using Qubit 3.0 fluorometer (Invitrogen, Thermo Fisher Scientific™).

Bioinformatics Processing and Assembly of Metagenome SequencesAll fluorometric quantification of gDNA passed the standards for quality control. Other parameters tested include the sequences/reads, length of reads, % guanine-cytosine (GC) content, and per base N content. Nucleic

Figure 1. Map showing the A) entire Balatoc tailings pond; B) three sampling points (B1, B2, B3); and C) mining tenement in Itogon, Benguet, Philippines.


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acids were then subjected to library construction and Illumina HiSeq 2500 paired-end (2 x 150) shotgun sequencing at Apical Scientific (formerly Axon Scientific and 1st BASE) in Malaysia.

Illumina bcl2fastq 2.17 (“bcl2fastq and bcl2fastq2 Conversion Software,” 2019) was used for demultiplexing each constructed library. The paired-end raw reads were trimmed of 5’ -and 3’-adaptors using Trimmomatic v0.36 (Bolger et al. 2014) with a minimum quality score of Phred 33 to maintain the reliability of the data. FastQC (Andrews 2010) was used to assess the quality of the trimmed reads. Clean reads were then assembled using SPAdes v3.10.1 (Nurk et al. 2017) at k-mer sizes of 21, 33, and 55. QUAST v4.3 (Gurevich et al. 2013) was used to assess the quality of assembled reads. Scaffolds with a length of ≧ 500 bp were extracted and broken into contigs. CLARK v1.2.5 (Ounit et al. 2015, Ounit and Lonardi 2016) was used to determine classification and taxonomic assignment using scaffolds, as well as the estimation of relative abundance of all operational taxonomic units. It utilizes specific databases based on k-mer spectra from RefSeq genomes (Escobar-Zepeda et al. 2018) A custom database containing all cyanobacterial sequences downloaded from the National Center for Biotechnology Information nucleotide database was created, which served as a target in CLARK v1.2.5 (Ounit et al. 2015, Ounit and Lonardi 2016) used for specific classification of cyanobacterial contigs.

Gene Prediction and Functional AnnotationFor each of the assemblies, contigs classified as cyanobacteria were then annotated using Prokka v1.12 a rapid prokaryotic genome annotation tool. It coordinates a set of existing software tools to achieve a rich, reliable, and rapid annotation of genomic bacterial sequences. It relies on prediction tools in identifying the coordinates of genomic features within contigs (Seemann 2014). This includes Prodigal (Hyatt et al. 2010) for predicting the CDS, RNAmmer (Lagesen et al. 2007) for rRNA, Aragorn (Laslett and Canback 2004) for tRNA, SignalP (Petersen et al. 2011) for signal leader peptides, and Infernal (Kolbe and Eddy 2011) for non-coding RNA.

CDS of metal resistance genes from the annotations were subjected to GO analysis using the eggNOG mapper v4.5 (Huerta-Cepas et al. 2016, 2017; Buchfink et al. 2015). This tool is designed to annotate large collections of sequences based on fast orthology mappings, particularly targeting translated gene-coding regions from genomes, metagenomes, and transcriptomics data. Orthology predictions for functional annotation permit a higher precision than traditional homology searches (Huerta-Cepas et al. 2016, 2017).

RESULTS AND DISCUSSION

Physicochemical Properties of Water SamplesThe initial water characteristics were determined for each sampling point in Balatoc mines (Table 1). Results showed that the pH value is consistent among the three replicates being alkaline at pH 8.42. The temperature condition is 31.5 °C since it is situated at a lower altitude. The area exhibited a higher salinity and conductivity values of 464 mg/L and 957.33 µs/cm, respectively, since it is an abandoned mining site.

Table 1. Physicochemical properties of water samples.

Samplingsite pH

T TDS* Salinity Conductivity

(°C) (mg/L) (mg/L) (µs/cm)

Balatoc 1 8.50 31.00 623.00 453.00 949.00

Balatoc 2 8.42 31.50 668.00 467.00 959.00

Balatoc 3 8.34 32.00 674.00 472.00 964.00

Mean 8.42 ± 0.08

31.50 ± 0.50

655 ± 27.88

464 ± 9.50 957 ± 7.64

*TDS – total dissolved solids

Metal analysis (Table 2) revealed that Cu2+ and Zn2+ concentrations were 1.53 mg/L and 0.077 mg/L, respectively. This is relatively high, which might be due to an inefficient mining system in Balatoc sites as there are still small-scale mining operations in the area. The physicochemical analysis revealed that the sampled tailings materials promote harsh conditions unsuitable for any vegetative growth.

Shotgun Metagenomic Assembly of ReadsFrom three paired-end data sets, a total of 38 Gbases yielded was obtained from metagenomic sequencing. Unique molecular identifiers or barcodes were further removed by demultiplexing. Appendix Table I shows the index information used in demultiplexing, the number of raw reads, FastQC output of data quality measurements such as the overall %GC of all bases in all sequences, and the clean reads after trimming the adaptors prior to assembly.

All three data sets were successfully assembled and assessed using St. Petersburg genome assembler (SPAdes) v3.10.1 (Nurk et al. 2017) and QUAST v4.3 (Gurevich et al. 2013), respectively. Metagenomic sequences are available under the BioProject ID PRJNA504923. This Whole Genome Shotgun project has been deposited at DDBJ/ENA/GenBank under the accession VFQM00000000, VFQN00000000, and VFQO00000000. The version described in this paper is version VFQM01000000, VFQN01000000, VFQO01000000.


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Metrics information based on contigs ≧ 500 bp and %GC content ranging from 50 to 60% (Table 3) indicated that the assemblies were sufficient for the study. Balatoc 2 has the most number of contigs at 204,338 and was able to retrieve the contig with the longest length at 2,625,434 bp. The lengths of the largest contigs in all sites for both shotgun metagenomics and cyanobacterial assembly have the same values, implying that the longest contig in the metagenomic assembly is found in the cyanobacterial phylum.

Taxonomic Composition of Microbial Communities in Proportion to Classified ContigsFor each of the generated assemblies, taxonomic assignments to contigs were performed using CLARK v1.2.5 (Ounit et al. 2015, Ounit and Lonardi 2016). For the three data sets (Figure 2), the Bacteria domain has a relatively higher proportion of classified contigs at 97% compared to Archaea, which consisted of only 3%. There

were 39 taxa at the phylum level where all sampling sites share five dominant phyla belonging to Bacteria – with the proportion of classified contigs at 49% Proteobacteria, 17% Actinobacteria, 11% Firmicutes, 8% Bacteroidetes, and 3% Cyanobacteria; while the Euryarchaeota has 3% proportion of classified contigs dominating the Archaea. The usage of metagenomics shotgun sequencing in this study yielded the highest prevalence of Proteobacteria, followed by Actinobacteria and Cyanobacteria – which is consistent with the results of several metagenomic studies conducted in copper, manganese, and gold mine tailings (Van Rossum et al. 2016, Tessler et al. 2017, Ghosh and Das 2018, Sibanda et al. 2019).

Identification of Cyanobacterial Contigs/Scaffolds Present in the AssemblyFor the three data sets, CLARK v1.2.5 (Ounit et al. 2015, Ounit and Lonardi 2016) classified the cyanobacterial community in Balatoc mines. Table 4 shows a summary of the assembly statistics specific to the cyanobacterial contigs. The proportion of the cyanobacterial contigs ranged from 2.7 to 4.3% of the metagenomic assembly.

A total of 93, 90, and 86 cyanobacterial species comprised the Balatoc 1 (Figure 3), Balatoc 2 (Figure 4), and Balatoc 3 (Figure 5) sampling points, respectively. Taxonomic assignments showed a large number of sequences attributed to the 22 strains of unicellular Synechococcus sp., followed by four strains of filamentous Leptolyngbya sp. Synechococcus sp. comprised 83 to 85% of the sequences that could be assigned to the species level and ranged from 22 to 24% of the reads assigned to the

Table 2. Initial metal analysis of water samples using atomic absorption spectrophotometry.

Sampling site

Ca Mg K Cr Cu Pb Zn

(mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L)

Balatoc 132 1.17 19.4<MDL*

1.53<MDL*

0.077MDL = 0.04 MDL = 0.05

*MDL – method detection limit**LOQ – limit of quantification

Table 3. Metrics for shotgun metagenomic assembly.

Sampling site N50* L50** No. of

contigsLargest contig

length (bp)Total assembly

length (bp) %GC

Balatoc 1 2,891 15,253 188,014 501,653 317,033,810 56.57

Balatoc 2 10,195 8,644 204,338 2,625,434 544,798,236 60.31

Balatoc 3 2,668 14,334 170,686 708,782 283,254,888 54.49

Note: Values computed above are based on contigs ≧ 500 bp in length.*N50 – the length of the smallest contig in the set of largest contigs that have a combined length that represents at least 50% of the assembly**L50 – the minimal number of contigs in the set of largest contigs that cover 50% of the assembly

Figure 2. Summary of the distribution of taxa at the phylum level across all sampling sites (Others ≤ 0.90%).


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Figure 3. Krona chart showing the proportion of classified contigs at the species level comprising the cyanobacterial community in Balatoc 1 sampling point. A total of 93 strains of cyanobacteria belonging to Taxon ID 1117 were recovered with an average score of 14.7097 sequencing coverage.


Table 4. Metrics for cyanobacterial contigs assembly.

Sampling site N50* L50** Number of contigs Largest contig length

(bp)Total assembly length

(bp) %GC Proportion from total contigs

Balatoc 1 29,783 742 14,380 501,653 99,380,287 62.04 2.74145Balatoc 2 48,206 942 17,838 2,625,434 213,765,044 62.57 4.34690Balatoc 3 31,826 606 13,241 708,782 92,812,812 62.38 2.98724

Note: Values computed above are based on contigs ≧500 bp in length.*N50 – the length of the smallest contig in the set of largest contigs that have a combined length that represents at least 50% of the assembly**L50 – the minimal number of contigs in the set of largest contigs that cover 50% of the assembly


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phylum level, while Leptolyngbya sp. comprised 11% to 13% of the sequences that could be assigned to the species level and ranged from 4% to 5% of the reads assigned to phylum cyanobacteria.

Gene Annotation of Cyanobacterial ContigsContigs classified to be of cyanobacterial origin were collected from the assemblies. These contigs were then annotated using the annotation pipeline implemented by Prokka v1.12 (Seemann 2014). The pipeline was able to identify CDS, tRNA, tmRNA, and rRNA. Table 5 shows the number of annotated DNA features. A relatively higher CDS in Balatoc 2 were determined as a result of the most number of contigs retrieved during SPAdes assembly.

Search for Metal Response GenesThe functional annotation was performed using emapper-0.12.7 based on eggNOG v4.5 orthology data (Huerta-Cepas et al. 2016, 2017; Buchfink et al. 2015). Sequence searches were performed using DIAMOND (Buchfink et al. 2015). CDS from the annotations were subjected to GO analysis. The sequences of all features that are found to be functionally associated with a metal response based on homology were gathered. Appendix Table II shows the GO accession number and functions of each heavy metal response genes. Figure 6 shows the total number of metal response genes annotated from the cyanobacterial contigs. The top five heavy metals with the most number of cellular and stress response genes include copper, zinc, silver, aluminum, and cadmium. Likewise, a total of 55 CDS for smt genes had been annotated, which


Table 5. Number of Prokka v1.12 (Seemann 2014) annotated genes of cyanobacteria.

Sample Bases CDS tmRNA tRNA rRNA Total

Balatoc 1 107,523,043 97,621 34 2,157 203 100,015

Balatoc 2 218,496,389 202,987 35 2,381 196 205,599

Balatoc 3 98,429,374 90,762 28 1,838 165 92,793


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are responsible for the molecular response of metals to ion binding. These metallothioneins (MTs) refer to cysteine-rich low molecular weight polypeptides that bind metal ions in metal-thiolate clusters. They function in zinc homeostasis, detoxification, and oxidative stress protection (Turner and Robinson 1995). Class II MTs had been isolated from the marine cyanobacterium Synechococcus RRIMP NI (Olafson et al. 1979, 1980) and freshwater strains Synechococcus UTEX-625 and Synechococcus TX-20 (Olafson 1984). Hence, the MT gene sequenced from them is called smtA (Robinson et al. 1990) and a divergently transcribed gene smtB (Huckle et al. 1993). A 100-bp operator-promoter region is found between smtA and smtB protein coding regions that contain divergent promoters (Morby et al. 1993). SmtA transcripts increase in response to elevated concentrations of Cr3+, Hg2+, Pb2+, Cd2+, Co2+, Cu2+, Ni2+, and Zn2+ (Huckle et al. 1993). It could be inferred that the presence of smtA transcripts from all sampling points in Balatoc mines might be due to elevated concentrations of Cu2+, Zn2+, and Cd2+. A total of 11 smtA encodes Class II MT, while 43 smtB codes for the metal-dependent repressor of smtA.

On the other hand, the Mn transporter gene mntC in Synechocystis PCC6803 functions to operate in giving protection against stresses triggered by Cd, as well as high concentrations of Cu2+, Fe2+, Mn2+, and Zn2+ (Chen et al. 2014). There are six manganese response genes in

Balatoc mines that might have been triggered by elevated levels of 1.53 mg/L Cu2+ and 0.077 mg/L Zn2+, which is greater than the standard method detection limit (MDL = 0.02) (Table 2).

Figure 7 shows the common metal response genes found in all sampling points of Balatoc mines. For copper, there are six efflux, transport, and chaperone proteins while the others are metabolic proteins that function for transcription, resistance, tolerance, and homeostasis. The presence of arsenic resistance transcriptional protein, as well as cobalt-zinc-cadmium resistance protein, suggests that the metabolic functions of these metals are related to the copper response of the cyanobacterium Leptolyngbya sp. to survive in copper-contaminated mine tailings. Gloeothece sp. observed in the soils of Benguet province was previously reported to produce essential attributes for removing copper and lead and that the simultaneous presence of these two heavy metals caused a mutual inhibition in the adsorption of each metal (Pereira et al. 2011).

Figure 6. Count of annotated heavy metal response genes in each sampling point using eggNOG v4.5 mapper tool (Huertas et al. 2016, 2017; Buchfink et al. 2015).

Figure 7. Annotated cyanobacterial heavy metal response genes of copper, zinc, and cadmium found in all sampling points of the Balatoc mine tailings.

There are three zinc transport proteins and a single ATM1-type heavy metal exporter annotated for lead. There are also three transport and chaperone proteins as well as four metabolic proteins annotated for cadmium. The presence of mercuric resistance operon might be related to the response of Leptolyngbya sp. to elevated levels of Cd.

The extraction process employed by the small-scale mining operations near the abandoned major Balatoc


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tailings might not be 100% efficient, leaving some of the metals within the water contained in the tailing ponds. Cyanobacteria can survive in these mine tailings since they have an adaptation to high metal concentrations (Sanchez et al. 2017), such as in the case of Microcoleus vaginatus and Phormidium sp. that were isolated from the gold mine tailings in South Africa (Orlekowsky et al. 2013).

CONCLUSIONThis study investigated the microbial composition in Balatoc mine tailings using a shotgun metagenomic approach. Sequence assembly identified the dominant cyanobacterium Leptolyngbya sp. inhabiting extreme metal-rich conditions prevalent in the mining area. It also revealed the presence of metal response genes that can be major attributes of Leptolyngbya sp. for its resistance and tolerance to extreme metal conditions. Enhanced growth of Leptolyngbya sp. might also lead to the probable formation of viable biological crusts initiating a revegetation process. This study also provided insights into the catalytic potential of a novel native biofilm of cyanobacteria as agents for bioremediation of heavy metal polluted mine tailings in Benguet province.

ACKNOWLEDGMENTSGrateful acknowledgment is given to the following: Philippine Council for Industry, Energy, and Emerging Technology Research and Development of the Department of Science and Technology (DOST) and the NSRI of UP Diliman for the financial assistance of this study; to the staff of the Core Facility for Bioinformatics (CFB) of the Philippine Genome Center for assistance on data processing and bioinformatics consultation, namely Francis Tablizo, Joshua Dizon, and El King Morado; to Angelo Joshua A. Victoria, M.Sc. for the bioinformatics assistance; to Amor M. Damatac II, Alysa G. Estopace, and Daisy Santos of the PGBCL, IB for laboratory assistance; and to the DOST – Accelerated Science and Technology Human Resource Development Program for the graduate scholarship granted to LRS Sanchez. The authors would also like to thank the European Molecular Biology Laboratory in Heidelberg, Germany for the metagenomics and bioinformatics training attended by LRS Sanchez.

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