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Metagenomic analysis for taxonomic and functional potential of Polyaromatic hydrocarbons (PAHs) and Polychlorinated biphenyl (PCB) degrading bacterial communities in steel industrial soil

  • Monika Sandhu,

    Roles Conceptualization, Investigation, Methodology, Software, Writing – original draft, Writing – review & editing

    Affiliation Department of Biological Sciences, Birla Institute of Technology and Science Pilani, Pilani, Rajasthan, India

  • Atish T. Paul,

    Roles Conceptualization, Data curation, Formal analysis, Visualization, Writing – review & editing

    Affiliation Department of Pharmacy, Birla Institute of Technology and Science Pilani, Pilani, Rajasthan, India

  • Prabhat N. Jha

    Roles Conceptualization, Supervision, Writing – review & editing

    prabhatjha@pilani.bits-pilani.ac.in

    Affiliation Department of Biological Sciences, Birla Institute of Technology and Science Pilani, Pilani, Rajasthan, India

Abstract

Iron and steel industries are the major contributors to persistent organic pollutants (POPs). The microbial community present at such sites has the potential to remediate these contaminants. The present study highlights the metabolic potential of the resident bacterial community of PAHs and PCB contaminated soil nearby Bhilai steel plant, Chhattisgarh (India). The GC-MS/MS analysis of soil samples MGB-2 (sludge) and MGB-3 (dry soil) resulted in identification of different classes of POPs including PAHs {benzo[a]anthracene (nd; 17.69%), fluorene (15.89%, nd), pyrene (nd; 18.7%), benzo(b)fluoranthene (3.03%, nd), benzo(k)fluoranthene (11.29%; nd), perylene (5.23%; nd)} and PCBs (PCB-15, PCB-95, and PCB-136). Whole-genome metagenomic analysis by Oxford Nanopore GridION Technology revealed predominance of domain bacteria (97.4%; 97.5%) followed by eukaryote (1.4%; 1.5%), archaea (1.2%; 0.9%) and virus (0.02%; 0.04%) in MGB-2 and MGB-3 respectively. Proteobacteria (44.3%; 50.0%) to be the prominent phylum followed by Actinobacteria (22.1%; 19.5%) in MBG-2 and MBG-3, respectively. However, Eukaryota microbial communities showed a predominance of phylum Ascomycota (20.5%; 23.6%), Streptophyta (18.5%, 17.0%) and unclassified (derived from Eukaryota) (12.1%; 12.2%) in MGB-2 and MGB-3. The sample MGB-3 was richer in macronutrients (C, N, P), supporting high microbial diversity than MGB-2. The presence of reads for biphenyl degradation, dioxin degradation, PAH degradation pathways can be further correlated with the presence of PCB and PAH as detected in the MGB-2 and MGB-3 samples. Further, taxonomic vis-à-vis functional analysis identified Burkholderia, Bradyrhizobium, Mycobacterium, and Rhodopseudomonas as the keystone degrader of PAH and PCB. Overall, our results revealed the importance of metagenomic and physicochemical analysis of the contaminated site, which improves the understanding of metabolic potential and adaptation of bacteria growing under POP contaminated environments.

Introduction

Persistent organic pollutants (POPs) are anthropogenic chemicals that are enlisted as priority environmental pollutants due to their toxicity and persistence in the environment for a prolonged period [1]. PAHs/PCBs are strongly lipophilic and hence they easily enter the food chains. These characteristics are important since they are responsible for the detrimental effect on the environment and induce health threats to plants, animals, and humans [2]. Increased industrialization has led to the extensive production of such POPs, which are also emitted during steel production [3]. The rise in these pollutants has led to adverse health and the environment effects, resulting in extensive studies on the remediation of contaminated soil. Various physical and chemical technologies, including chemical oxidation, electrokinetic remediation, solvent extraction, photocatalytic degradation, and thermal treatment, are widely applied in remediation [4]. However, most of these treatment methods are unsustainable, disruptive and carry these PCBs/PAHs to the environment. Therefore, the utilization of existing contaminated soil-based bacterial communities can be an alternative strategy for effective and viable degradation of POPs [5] as it has comparatively fewer technical hindrances than other remediation technologies.

A series of studies have been performed by the culture-dependent approach to isolate the most efficient biodegrader from such polluted sites [6]. The contaminated soil environment consists of the genetic, species, and metabolic diversity of microbial biodegraders. Only a minor fraction of POP-degrading bacteria can be obtained using a culture-dependent method o. Furthermore, it has been reported that enrichment of these cultures under lab conditions is less efficient in biodegradation than indigenous bacteria present in the contaminated soil [7]. To date, information related to taxonomic and functional interaction amongst the microbial communities during the biodegradation process within the contaminated environment is skewed. The recent development of powerful culture-independent metagenomic approaches and the advancement of next-generation sequencing (NGS) technology provides a comprehensive insight into the total microbial community inhabiting contaminated sites and their metabolic capabilities. Several metagenomics studies conducted on PAH/PCB contaminated soil samples [8] have highlighted microbial interaction playing a pivotal part in bioremediation of these POPs. However, most of these studies are based on 16S rRNA gene sequence, which does not highlight the metabolic potential of resident microorganisms. Therefore, the present study aimed to investigate taxonomic diversity and their metabolic potential to degrade POPs employing Oxford Nanopore Technology (ONT). ONT is very sensitive and can detect very low abundant microbial members that are otherwise missed in the metagenome.

The PCBs congeners and PAHs have been reported to be present in the waste sites of the industrialized area of this steel plant, India [9]. The major pollution source of steel industries includes sinter, coke, and the blast furnace [10]. Therefore, the present study aimed at investigating and providing an insight into the diversity of the bacterial community, metabolic potential of the dominant bacterial community in the contaminated soil collected from nearby regions of Bhilai steel plant (one of Asia’s biggest steel plants) in Chhattisgarh, India, and to correlate their functional characteristics to the biodegradation pathways. From our result, we concluded that our results provided potential bacterial candidates for the exploitation bioremediation of PAH and PCB.

Materials and methods

Study site and sampling

The soil samples were collected from 2 different sites i.e., sludge site (Metagenomics Bhilai; MGB-2) and dry soil waste site (Metagenomics Bhilai; MGB-3) from the polluted area near Bhilai steel plant, Chhattisgarh (21.1915° N, 81.4041° E), in India. Soil samples were collected in sterile containers from a depth of about 0 to 10 cm of two sampling sites. The soil samples were randomly collected from three sites of each site and pooled for further analysis. It was then transported on ice pack and stored at 4°C (to be used immediately) in the lab for analysis. Physicochemical parameters such as pH, electrical conductivity, organic C, N, P, Mg, K, Na, Cl, Ca, S, Zn, Fe, Cu, and Mn of MGB-2 and MGB-3 were estimated using the standard protocol at the National Horticultural Research and Development Foundation, Nasik, India.

Extraction and determination of PAH and PCB in sediments

PCB and PAH were extracted following He et al. [11] with minor modification. Briefly, 5 g of collected sample (dry weight) was added into 50 ml Milli Q (MQ) water and was homogenized by vortexing for 15 min. After allowing it to stand for 30 min, 10 ml of acetone and hexane (1:1; v/v) were added to the falcon and vortexed for 3 min. 2g NaCl was added and shaken vigorously for a few min. It was then centrifuged at 4000 x g for 5 min. The supernatant was subjected for further solid-phase extraction (SPE) of PCB and PAH using bond elute cartridge as per manufacturer’s instruction (Agilent technologies, USA). Further, the sample elution was performed with methanol and hexane 1:1 (v/v) in 5 ml MQ by centrifuging for 2 min at 1000 x g. The final elute was then collected through a Polytetrafluoroethylene (PTFE) filter (0.22 μ) in a separate vial and adjusted to 1 ml with nitrogen [12].

GCMS-TQ8040 (Shimadzu, Japan) fitted with Scan/SIM was used to qualitatively analyze PAHs and PCBs that are potentially present in the MGB-2 and MGB-3 samples. GC-MS/MS fitted with Flame ionization detector (FID), and an RTX-5 column (30 m × 0.32 mm × 0.25 μm) was used for analysis. GC conditions were set at 40°C with a 2 min hold and 10°C/min increment to 80°C, then 6°C/min to 225°C with 10 min hold. The presence of PCB was detected through SIM mode of GC-MS/MS.

Metagenome sequencing and analysis

DNA extraction and processing for metagenome.

Two soil samples were collected in triplicate and pooled together for each sample. DNA extraction was done using Powersoil® DNA Isolation Kit (Qiagen, USA) following the manufacturer’s instructions. The metagenomic DNA was checked for integrity by agarose gel (1%) using a BioRad Gel documentation system and was quantified by Qubit 3.0 Fluorometer (Invitrogen, USA).

Preparation of library and whole metagenome sequencing.

Metagenomic DNA extracted from the collected soil (MGB-2 and MGB-3) were end-repaired using NEBnext ultra II kit (New England Biolabs, USA), cleaned up with 1x AmPure beads (Beckmann Coulter, USA). Native barcode ligation was performed with NEB blunt/TA ligase using NBD103 and cleaned with 1x AmPure beads. Qubit quantified barcode ligated DNA samples were pooled at an equimolar concentration to attain a 1 μg pooled sample. Adapter ligation (BAM), cleaning of library mix and elution of sequencing library was done as per Kumar et al., 2021 [13] and was further used for whole-genome sequencing. The whole-genome library was prepared by using a Native Barcoding kit (EXP-NBD103). Barcode sequences are detailed in the (S1 Table). The sequencing was performed using SpotON flow cell (R9.4) on MinKNOW 2.1 v18.05.5 with a 48 h sequencing protocol [14] on GridION X5 (Oxford Nanopore Technology (ONT, UK).

Data processing and analysis

The Nanopore raw reads (‘fast5’ format) were base-called (‘fastq5’ format) and demultiplexed using Albacore v2.3.1 and were uploaded to MG-RAST server (version 4.0.3) for taxonomic and functional analysis. Functional annotation by SEED subsystems helps in predicting the abundance of genes assigned to metabolic pathways in soil. The sequenced reads were interpreted using a multisource non-redundant ribosomal RNA database for taxonomic diversity. They were determined using the contigLCA algorithm against the M5NR database for samples analyzed via whole-genome sequencing (WGS) (MG-RAST metagenome MGB-2 and MGB-3 identification numbers = mgm4822000.3, mgm4822001.3). The interpretation was based on E-value cut-off = 1 x e-5 and sequence identity of 60% [15, 16]. Raw reads of whole genome metagenome shotgun sequence of two sample MGB-2 and MGB-3 were deposited to the NCBI Sequence Read Archive under BioProject PRJNA765179 with the accession numbers SRR16004303 and SRR16004304, respectively.

Statistical analysis

Various alpha diversity indices were calculated to study species richness and evenness of the MGB-2 and MGB-3 using PAST4.03 software. The principal component analysis (PCA) plot was constructed using Bray-Curtis matrices with R studio v3.1.2. Comparison of samples MGB-2 and MGB-3 was done using Statistical Analysis of Metagenomic Profiles software [17] with a two-sided G-test (w/Yates’+ Fischer’s). Comparative metagenome analysis was done mainly with RefSeq and SEED subsystem to obtain genus/functional abundance, respectively. Cytoscape software v3.7.1 was used to generate networking plots for the study of the interaction of microbial communities of MGB-2 and MGB-3 involved in xenobiotic biodegradation pathways.

Results and discussion

Physico-chemical analysis of the MGB-2 and MGB-3

Microbial community structure and function are determined by various environmental factors, including nutritional status and other parameters, such as pH, salinity, presence of metals, and various physicochemical parameters. Therefore, the physicochemical properties of the MGB-2 and MGB-3 were determined and are summarized in Table 1. The sample MGB-3 was richer in terms of macronutrients such as carbon (C), nitrogen (N), and phosphorus (P) which greatly influenced the composition of the microbial community. The organic carbon content, representing the energy flow in the carbon cycle, was 0.85% (slightly high) in MGB-2 and 1.39% (high) in MGB-3 compared to reference values. Our results indicated high carbon content in the given samples because of aromatic organic hydrocarbons present in the contaminated soil. Industrial soil and effluent are considered sources of organic contaminants including POPs like PAHs [18] and PCBs. Because of the hydrophobic nature of these POPs, they tend to bind with the soil and hence add to the organic carbon content of the soil. The sample MGB-3 had very high nitrogen and phosphorus content (N, 734 kg ha-1; P, 56.9 kg ha-1 respectively) whereas MGB-2 had moderate nitrogen (430 kg ha-1) and low phosphorus (17.66 kg ha-1) content. MGB-3 exhibited high P and low C/P ratios, indicating the possibility of higher microbial diversity than the MGB-2. Several studies have confirmed that the soil with high microbial diversity has high P and low C/P ratios, while the environment with less P and high C/P ratios shows a low microbial diversity [19]. A higher level of these micronutrients in MGB-2 can be due to the high water content in the sample. Further, these results indicate that both MGB-2 and MGB-3 can support diverse microbial communities.

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Table 1. Physio chemical parameters of the contaminated soil sample MGB-2 and MGB-3 from polluted near Bhilai steel plant.

https://doi.org/10.1371/journal.pone.0266808.t001

Overall, micronutrients, including K, Mg, Na, and Mn, were higher in MGB-2 than MGB-3. The level of K (1232 kg ha-1) and Mg (768 kg ha-1) was found to be higher than the reference value in MGB-2. The availability of inorganic nutrients serves for structural as well as catalytic functions. Therefore, the bacterial communities’ total taxonomic and functional profile is predominantly driven by the availability of C and N and the presence of inorganic nutrients, i.e., Ca, K and Mg, to some extent [20]. The result of the metal analysis indicated that the MGB-3 had a comparatively higher metal content (Zn, Cu, and Fe) than the MGB-2 (Table 1). The result also showed a high percentage of Mn (44.12 mg kg-1) in MGB-2 and Fe (14.98 mg kg-1) in MGB-3, providing a suitable environment for microbes to undergo anaerobic biodegradation. The high metal content in both samples could be due to the additives used in a steel factory. The presence of the metals in a soil sample can limit many microbial species, whereas they can support the survival and growth of metal tolerant species. The presence of metals is in accordance with several reports highlighting heavy metals and organic contaminants such as PAH/ PCB from iron and steel industrial soil sites [21]. In addition, Mn (IV) and Fe (III) are known to act as terminal electron acceptors that efficiently remove aromatic compounds from the soil. Fe is the most widely found cofactor involved in deoxygenation reactions in biodegradation studies. It has been reported that Fe containing dioxygenases is incorporated into the active site either as iron centre, Rieske [2Fe-2S] cluster or as heme prosthetic group during PAH and PCB biodegradation [22].

Determination of PAH and PCB residues in MGB-2 and MGB-3

Cities with long industrial history contribute to the addition of organic pollutants in soil [23, 24]. Hence it was deemed fit to estimate the level of POPs mainly PAHs and PCBs, in given soil samples by GC-MS/MS using Scan/SIM mode. GC-MS/MS triple quadrupole allows detection at very low (femtogram) limits in the matrix through the use of even greater selectivity with selected reaction monitoring (SIM) mode. Based on the data obtained from GC-MS/MS analysis, various PAHs and PCB in industrial soil samples were identified. The structure and the relative percentage abundance of PAH identified in MGB-2 and MGB-3 are given in Fig 1A. F (15.89%) and BkF (11.29%) were found to be dominant PAH species in MGB-2, while Pyr (18.7%) and BaA (17.69%) in MGB-3. High-molecular-weight (HMW; 4–6 rings) PAHs namely BcPhe (0.51%), BkF (11.29%), F (15.89%), IcdP (6.63%), Per (5.23%), and Tpl (4.70%), predominated in the MGB-2 sediments, which were not detected in MGB-3 (Fig 1B). Similar to our results, PAHs such as Indeno (1, 2, 3-cd) pyrene, benzo (k) fluoranthene, dibenzo (a, h) anthracene, chrysene, fluoranthene, acenaphthene, and fluorene have also been reported to be abundantly present in different operational units of steel industry [25].

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Fig 1. Structure and relative abundance of PAH and PCB identified in MGB-2 and MGB-3.

(A) PAH found in MGB-2 & MGB-3 were 9,10-Anthracenedione (AnD), 11H-Benzo[a]fluoren-11-one (BaFo), 11H-Benzo[b]fluorene (BbFl), Benzo[def]fluorene (BbFl), Benzo[b]fluoranthene (BbF), Benzo[b]naphtho[2,1-d]thiophene (BbNT), Benzo[b]triphenylene (BbTPl), Benzo[c]phenanthrene (BcPhe), Benzo[ghi]fluoranthene (BghiF), Benzo[k]fluoranthene (BkF), Fluorene (F), Indeno[1,2,3-cd] pyrene (IcdP), Perylene (Per), Phenanthrene (Phe), Pyrene (Pyr) and Triphenylene (Tpl) and are classified based on the numbers of aromatic rings 2, 3, 4, 5 and 6 membered rings. Bph, PCB-15, PCB-95 and PCB-136 represents biphenyl, 1,1’-biphenyl, 4,4’-dichloro, biphenyl, 1,1’-biphenyl 2,2’,3,’,5,6 pentachloro and 1,1’-biphenyl 2,2’,3,3’,6,6’ hexachloro respectively. (B) PCoA plot depicting relative abundance using Bray-Curtis matrix of PAH in MGB-2 and MGB-3.

https://doi.org/10.1371/journal.pone.0266808.g001

In addition to PAHs, MGB-2 and MGB-3 were also found to be contaminated with PCBs such as biphenyl, 1,1’-biphenyl, 4,4’-dichloro (PCB-15) in MGB-2 and biphenyl, 1,1’-biphenyl 2,2’,3,5’,6 pentachloro (PCB-95) and 1,1’-biphenyl 2,2’,3,3’,6,6’ hexachloro (PCB-136) in MGB-3 (S1 and S2 Figs) [26]. It is well studied that the carcinogenic risk increases with the molecular weight or the aromatic ring of PAHs [27] and with the chlorine atoms in the case of PCBs. However, the data of source of PCBs has been scarce in industrial regions. The present results corroborate with previous studies conducted on the Bhilai steel plant (Raipur, Chhattisgarh) soil, reporting the presence of PCBs ranging from di-chlorinated to hexachlorinated biphenyl in the sludge [28]. From the data of organic contaminants, it appears that the high carbon content in MGB-2 and MGB-3 mentioned in the previous section could be co-related with the abundance of PAH and PCB in the soil sample.

Metagenomic analysis

Whole genome sequencing and assembly summary.

The metagenomic approach provides a complete picture of biodegradation vis-a-vis microbes present within the environment and the functional genes involved in the bioremediation of contaminants. Whole-genome metagenomics studies are used not only to study the taxonomic diversity but also to elucidate the metabolic pathways required for understanding pollutant degradation [29]. In the present study, the ONT platform for community analysis was used to enable unbiased assembly of complete genome sequencing [30]. Nanopore GridION X5 generated real-time, long-read, high-fidelity DNA sequence data. MG-RAST statistical analysis of dataset provided 275,844 sequences (totalling 539,360,072 bp; average length 1,955 bp) for MGB-2 and 193,221 (totalling 532,031,111 bp; average length 2,753 bp) for MGB-3. The downstream analyses of the total number of reads are detailed in S2 Table. The datasets were used for various taxonomic, ecological, and functional analyses as described in the previous section.

Analysis of sequence data for the extent of microbial diversity.

The horizontal rarefaction curve indicated the significant sampling depth, representing sufficient sample coverage for diversity analysis (S3 Fig). MGB2 and MGB-3 comprised 1839 and 1884 species, respectively, indicating higher species richness in MGB-3 that is also evident from the data of Chao-1 and Shannon’s diversity. The various diversity indices depicted in Table 2 suggest equivalent overall diversity that considers both richness and abundance. Beta diversity among MGB-2 and MGB-3 based on Bray-Curtis dissimilarity (Fig 2A) defined the overall distribution pattern of bacterial communities in MGB-2 and MGB-3 samples, with Principal coordinates showing 98.7%, bacterial communities’ similarity in the two sites (MGB-2 and MGB-3).

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Fig 2.

(A).Taxonomic analysis beta diversity of MGB-2 and MGB-3 on Bary -Curtis dissimilarity.(B) Krona plot demonstrated the relative abundance of bacterial taxa across phylum to genus level hierarchy of a. MGB-2; b. MGB-3.

https://doi.org/10.1371/journal.pone.0266808.g002

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Table 2. Alpha diversity for bacterial, archaea and eukaryota communities of collected soil from polluted site near Bhilai steel plant.

https://doi.org/10.1371/journal.pone.0266808.t002

Taxonomic diversity and total microbial community.

The collected samples were explored for taxonomic diversity and functional potential. ONT and MG-RAST database search provided detailed microbial taxonomy of MGB-2 and MGB-3. The differences were noticeable at the domain level, where MG-RAST derived reads accounted majorly for bacteria (97.4%; 97.5%) followed by eukaryote (1.4%; 1.5%), archaea (1.2%; 0.9%) and virus (0.02%; 0.04%) in MGB-2 and MGB-3 respectively. The abundance of bacteria at the level of phylum, order, class, family, and genus in two samples is shown in Krona plot (Fig 2B and 2C). Among bacteria, phylum Proteobacteria (45.0%; 50.0%) was the most predominant followed by Actinobacteria (22.1%; 19.5%) and Firmicutes (6.3%; 5.4%) in both MGB-2 and MGB-3 samples. Both samples included all six classes of Proteobacteria namely Alphaproteobacteria (18.6%; 22.4%), Betaproteobacteria (8.8%; 10.7%), Deltaproteobacteria (8.2%; 7.1%), Epsilonproteobacteria (0.3%; 0.3%), Gammaproteobacteria (7.2%; 8.9%) and Zetaproteobacteria (0.04%; 0.03%) with difference in their relative abundance, which indicates the presence of diverse members playing a vital role in photosynthesis, nitrogen fixation, sulfur, and phosphorus metabolism [31]. At the class level, Actinobacteria (26.2%) was observed to be dominant in MGB-2 while Alphaproteobacteria (22.1%) in MGB-3. STAMP analysis with G-test (w/Yates’+ Fischer’s) two-way comparison showed the degree of closeness and difference across MGB-2 and MGB-3 at the class level. The analysis showed close similarity in both metagenomes; however, they differ in relative abundance. Among the order, Actinomycetales (85.7%; 88.4%) were distinctly observed in MGB-2 and MGB-3, followed by Solirubrobacterales (8.6%; 6.7%).

At the genus level, the composition of the bacterial community in the two samples was similar, but they differed in terms of their relative abundance. Streptomyces (3.7%) and Candidatus Solibacter (2.7%) were the most predominant genera in MGB-2, while in MGB-3, it was Mycobacterium (3.1%) and Streptomyces (3.0%). RefSeq annotations at highest taxonomic classification, i.e., at genus level possess significant abundance level with p < 1 e-5 for Streptomyces, Candidatus Koribacter, Gemmata, Conexibacter, Anaeromyxobacter and Nocardioides in MGB-2, while Hypomicrobium, Pseudomonas, Mycobacterium, Roseomonas, and Methylobacterium with p < 1 e-5 in MGB-3. The correlation coefficient (r2) indicated a linear relationship between MGB-2 and MGB-3, as depicted on the scatter plot (S4 Fig). The dominance of Mycobacterium in MGB-3 can be correlated with the presence of pyrene as the given genus is known to degrade pyrene [32]. The presence of the dominant genera in samples suggests the potential for degrading organic pollutants by the members of these genera. For instance, Anaeromyxobacter is known to be capable of anaerobic respiration and possesses genes involved in reductive dechlorination processes at contaminated sites [33]. Similarly, members of Mycobacterium, Bradyrhizobium, Burkholderia [34, 35], and Pseudomonas [36, 37] are well-known aromatic pollutant degraders in contaminated soils [38].

Within the metagenome of MGB-2 and MGB-3, Archaeal communities showed a dominance of Euryarchaeota (76.7%; 81.3%) at phylum level followed by Crenarchaeota (13.7%; 13.2%) Thaumarchaeota (8.7%; 4.8%) and Korarchaeota (0.9%; 0.7%). At genus level relative abundance for methane-producing Methanosarcina (10.5%; 12.1%) Methanoculleus (3.7%; 3.2%) ammonia-oxidizing Nitrosopumilus (5.9%; 3.3%), Sulfur reducing bacteria like Thermococcus (4.3%; 4.7%) unclassified (derived from Euryarchaeota) (4.3%; 4.2%), Pyrococcus (3.9%; 3.3%), Sulfolobus (3.6%; 3.3%) dominated in both MGB-2 and MGB-3. However, a two-way statistical comparison of Archaea at genus level revealed Nitrosopumilus (p = 1.24 e-4) and Cenarchaeum (p = 5.18 e-3) in MGB-2 while Methanobrevibacterium (p = 0.012) in MGB-3 to predominate indicating more methanogenesis occurring in MGB-3 [39]. The contaminated soil also possesses many members of Methanosarcina, Halobacterium, Euryarchaeota, and Crenarchaeota of uncultured genera. The diversity of Archaea is found to be higher within hydrocarbon-degrading communities in the contaminated environment than the non-contaminated counterpart [40]. Relative abundance of Archaeal communities were more in MGB-2, which was associated with the higher soil moisture and POPs content.

Eukaryota microbial communities showed a predominance of phylum Ascomycota (20.5%; 23.6%), Streptophyta (18.5%, 17.0%) and unclassified (derived from Eukaryota) (12.1%; 12.2%) in MGB-2 and MGB-3. Several studies conducted on contaminated soils have revealed the presence of Ascomycota as the most dominant eukaryotic phylum, as the indigenous ascomycete can transform or remove the pollutants [41].

Functional diversity and metabolic potential of MGB-2 and MGB-3.

The SEED subsystem analysis in MG-RAST assigned reads based on various functions that identified 60% of the total function constituting metabolisms of amino acid, carbohydrate, energy, lipid, cofactors, vitamins, biosynthesis of glycan, polyketides, terpenoids, xenobiotic biodegradation, and biosynthesis of secondary metabolites. The remaining 40% functions include cellular processes, organismal systems, genetic & environmental processing, and human diseases.

General metabolism of microbial community.

Predicted genes from the MGB-2 and MGB-3 metagenome were used as input in the KEGG mapper for functional annotation. Annotated genes involved in methane (Table 3A), N, and S metabolism pathways were identified (Table 3B). Comparative analysis of both the metagenome based on their metabolism at SEED level 1 revealed significant dominance of methane metabolism (p = 5.61 e-4), citrate cycle (p = 1.62 e-3), carbon fixation (p = 2.61 e-3), pyruvate metabolism (5.80 e-3), amino benzoate degradation (p = 0.031), selenocompound metabolism (p = 0.038) and benzoate degradation (p = 0.045) in both samples. However, lipoic acid metabolism (p = 7.58 e-4), drug metabolism (p = 7.81 e-3), folate biosynthesis (p = 0.17), fatty acid metabolism (p = 0.042) and lysine degradation (p = 0.042) were found to be comparatively high in abundance in MGB-3 (Fig 3A). The results indicated the active metabolism is represented by bacteria belonging to phylum Actinobacteria in MGB-2 while Proteobacteria in MGB-3 (Fig 3B).

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Fig 3. Comparative analysis of metagenome MGB-2 and MGB-3 using STAMP.

(A) SEED level based on metabolism (B) genus level with RefSeq for metabolism.

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Table 3. Annotated gene and enzyme identified in metagenome MGB-2 and MGB-3.

https://doi.org/10.1371/journal.pone.0266808.t003

The comparative analysis of gene sequences revealed the abundance of P transporter in MGB-2, which can correlate with the low P content than MGB-3. Various genes encoding proteins involved in phosphate-recycling mechanisms, such as phnA, phnE, phnW and phnX, (phosphonate transporters), pstA, pstB, pstC, and pstS (high-affinity phosphate transporters), and phoR, phoA, phoP and phoD (two-component systems) were detected across both the metagenome (S5 Fig). The availability number and abundance of reads for phosphate mechanism in low phosphate-phosphorous environments indicated these mechanisms that help them cope within such environments. Moreover, bacteria can survive due to resistant genes toward toxic metals such as copper, lead, and nickel as a part of their defense mechanism, which is recruited further for cleaning the contaminated environments. The high abundance of Mn content in MGB-2 can be correlated with the significantly high reads for Mn transporter in MGB-2 than MGB-3. Also, a comparison between MGB-2 and MGB-3 of functional gene annotation using SEED subsystem for membrane transport revealed a significant level (p > 0.05) of abundance for Na+/H+ antiporters and Mn transporter MntH, Mn ABC transporter SitD, TadA, Zn ABC transporter ZnuA (S5 Fig). MGB-3 showed higher Na+/H+ transporters that/ which indicate the exchange of the ions across the membrane to maintain homeostasis.

Abundance of Xenobiotics degradation and metabolism gene related to biodegradation.

Overall, 708 (MGB-2) and 760 (MGB-3) annotated genes corresponding to 17 pathways linked with xenobiotic biodegradation and metabolism were identified though at varying levels of abundance (S6 Fig) in two samples. The annotated pathways for degradation and metabolism of xenobiotic compounds, including POPs enlisted by the US Environmental Protection Agency, accounted for 2% of the 60% metabolic functions (Fig 4A). The presence of reads for biphenyl degradation, dioxin degradation, PAH degradation pathways can be further correlated with the presence of PCBs and PAHs as detected in the MGB-2 and MGB-3 samples (Fig 4B and 4C). Further, chlorocyclohexane and chlorobenzene [PATH: Ko00361], and benzoate [PATH: Ko00362] degrading pathway were most prominent in MGB-2 (33.76%; 37.39%) and MGB-3 (39.55%; 33.96%). In addition to these degradative pathways, dioxin [PATH: Ko00621] and PAH [PATH: Ko00624] degradation pathways were also observed in MGB-2 (3.27%; 2.54%) and MGB-3 (4.29%; 2.80%), respectively. Similar to our observations, the presence of chlorobenzene, PAH, PCB, and benzoate are known to be prominent contaminants of dye and steel industries [9, 10]. The annotated genes encoding enzymes, namely chqB, pcpB, pheA, clcD, hadL, bedC1/todC1,bphC,catA, and catB, highlighted the degradation of chlorocyclohexane and chlorobenzene compounds within the two communities (Table 4A). The presence of the above-mentioned enzymes suggests that the degradation of chlorobenzene is catalyzed via the ortho -cleavage pathway in two communities [42], and the meta-cleavage pathway [43].

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Fig 4. SEED subsystem analysis in MG-RAST assigned reads in MGB-2 and MGB-3 based on various (A) functions (B) Xenobiotic biodegradation pathways.

https://doi.org/10.1371/journal.pone.0266808.g004

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Table 4. Annotated enzyme and the assigned genera in metagenome MGB-2 and MGB-3.

https://doi.org/10.1371/journal.pone.0266808.t004

The degradation pathway mainly of PAHs and PCBs is highlighted in the present study as the collected soil samples MGB-2 and MGB-3 were found to be highly contaminated with these organic pollutants. The annotated genes encoding enzymes required in PAH degradation pathway in MGB-2 and MGB-3 are listed in Table 4B. The presence of genes encoding hydroxychromene-2-carboxylate isomerase (nahD), naphthalene dioxygenase (nahAc), salicylate hydroxylase (nahG), and other genes including nidA, nidB, nidD, phdF, phdG, phdI, and phdJ indicated the presence of a complete pathway of PAH degradation. The presence of reads for nidA gene could be correlated with the degradation of pyrene [5], which was found to be abundantly present in MGB-3 as detected by GC-MS/MS. There are several reports indicating nidA gene being responsible for the synthesis of the large subunit of PAH dioxygenase involved in the degradation of PAHs such as phenanthrene, pyrene, benzo[a]pyrene, etc. [47]. The pathway analysis demonstrated that the enzymes involved in the PAH biodegradation pathway were affiliated to the members of genera Pseudoalteromonas, Aromatoleum, Dechloromonas, Agrobacterium, Mesorhizobium in MGB-2, while Mycobacterium, Parvibaculum, Ruegeria, Burkholderia, Aromatoleum, Bradyrhizobium in MGB-3 communities. The contribution of enzymes for degradation by different genera suggests synergistic degradation of these PAH.

The annotated reads for various genes, including bphA, bphC, bphD, bphE, and bphF, corresponding to the biphenyl degradation pathway, were identified in both samples in Table 4B. The presence of bphA in both metagenomes MGB-2 and MGB-3 indicated biphenyl degradation assigned to genus Mycobacterium in both samples and Polaromonas in MGB-3. Biphenyl 2, 3-diol, 1, 2-dioxygenase (bphC) is another important enzyme that was noted in both the metagenome. The bphC genes were assigned to Mycobacterium in MGB-2, whereas Alkalilimnicola, Mycobacterium, and Polynucleobacter in MGB-3. Metabolic pathways of polychlorinated biphenyls degradation by Pseudomonas, Polaromonas, Mycobacterium, and Polynucleobacter have been studied in detail [12, 22, 35, 37, 44, 45].

Further, the annotated gene/enzyme and the bacterial genera involved in the benzoate degradation pathway were identified in MGB-2 and MGB-3 as listed in Table 4C. The presence of benzoate ligase in MGB-2 indicated anaerobic degradation of benzoate. Benzoate is the most common intermediate in the metabolism of aromatic compounds, and in the anaerobic condition, it is converted into benzoyl-CoA by benzoate ligase [46]. The presence of genes encoding anaerobic degradation via benzoyl CoA (badA, badD, badE, badF, badH, badI, hbaA, hbaB, hcrC) and aerobic via protocatechuate (ligAB, ligC, ligI, ligJ, pobA, pcaB, pcaC, pcaDF, pcaGH and pcaIJ) catechol (catE/ dmpB) indicates complete pathway for degradation of benzoate. Protocatechuate can be mineralized via ortho- cleavage by protocatechuate 3, 4-dioxygenase (PcaGH), and meta cleavage by protocatechuate 4, 5-dioxygenase (LigAB). Anaerobic degradation via benzoyl-CoA has also been documented in a variety of facultative anaerobes, including the denitrifying Thauera [47], Magnetospirillum strains [48], and the photoheterotroph Rhodopseudomonas [49]. These enzymes are contributed possibly by members of genera Aromatoleum, Arthrobacter, Burkholderia, Bradyrhizobium, Cupriavidus, Magnetospirullum, Methylobacterium, Rhodomicrobium, Rhodopseudomonas, and Polaromonas in both the metagenome communities suggesting a synergistic degradation of benzoate. Analysis of functional gene annotation using SEED subsystem by STAMP for the presence of peripheral degradation pathway in MGB-2 and MGB-3 revealed the abundance of, muconate cycloisomerase ring hydroxylating dioxygenase, naphthalene dioxygenase, acetaldehyde dehydrogenase, 2-hydroxy-6-oxo-6-phenyl hexa-2,4-dienoate hydrolase, and benzoate transport protein in MGB-2. On the other hand, genes encoding biphenyl 2,3 diol dioxygenase, benzaldehyde dehydrogenase, nap/bph dioxygenase, ortho-halobenzoate 1,2- dioxygenase, phenol dioxygenase, and 1,2-dihydroxycyclohexa-3,5-diene-1-carboxylate dehydrogenate were abundant in MGB-3 (S7 Fig). Similarly, a two-way comparison between MGB-2 and MGB-3 of functional gene annotation using SEED subsystem for aromatic degradation revealed significant level of abundance for benzoate ligase (p = 2.63 e-3) and 4-hydroxyphenylacetate 3-monooxygenase (p = 8.93 e-3) in MGB-2. The presence of 2-hydroxycyclohexanecarboxyl dehydrogenase with p = 2.39 e-3 indicated anaerobic degradation of benzoate in MGB-3 as well. However, aerobic degradation of biphenyl was also found in MGB-3 which is evident by the abundance of biphenyl- 2, 3-diol 1, 2-dioxygenase with p = 0.035 in MGB-3 (S8 Fig). Furthermore, a comparison between MGB-2 and MGB-3 at functional gene annotation using RefSeq for aromatic degradation revealed a significant level of abundance of Acidothermus (p = 2.68 e-3), Nakamurella (p = 6.39 e-3) in MGB-2 while Mycobacterium (p = 3.30 e-3), Leptothrix (p = 7.89 e-3) and Polynuclearbacter (p = 0.034) in MGB-3 (S9 Fig).

Our findings suggest that biphenyl (PCB) and pyrene/phenanthrene (PAH) biodegradation pathways could be linked together via a common intermediate protocatechuate pathway and undergoes complete degradation through the common protocatechuate branch of the β-ketoadipate pathway. Therefore, the reconstruction of complete biphenyl/PCB and PAH degradative pathways, based on the annotated genes, were done and shown in Fig 5. Cytoscape-based networking revealed microbial interaction in the xenobiotic biodegradation pathway. The key biodegraders in xenobiotic biodegradation pathways (aromatic halogenated, dioxin, PAH, PCB/biphenyl, catechol, protocatechuate, benzoyl-CoA) were found to be Arthrobacter chlorophenolicus, Meiothermus ruber, Cupriavidus metallidurans, Burkholderia xenovorans, Rubrobacter xylanophilus, Bradyrhizobium sp. BTAi1, Bradyrhizobium japonicum, Sphingobium japonicum, Pseudomonas aeruginosa, Polaromonas sp. JS666 and Rhizobium leguminosarum in MGB-2 (Fig 6A). In MGB-3 it was Bradyrhizobium japonicum, Rhizobium leguminosarum, Mycobacterium sp.KMS, Polaromonas sp. JS666, Rhodopseudomonas palustris, Xanthobacter autotrophicus, Mycobacterium smegmatis, Burkholderia cenocepacia, and Burkholderia xenovorans (Fig 6B). The greater abundance of these genes and genera in sample MGB-2 compared to sample MGB-3 suggests a higher degrading capacity in sample MGB-2. However, the key biodegraders were Bradyrhizobium, Burkholderia, Mycobacterium, and Rhodopseudomonas in both the metagenome.

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Fig 5. Reconstruction of complete Biphenyl/P.C.B. and PAH degradation pathways based on annotated genes identified.

Blue, biphenyl degradation; red, benzoate degradation via catechol; pink, benzoate degradation via protocatechuate; orange, benzoate degradation via benzoyl CoA degradation; Black, PAH (Pyrene and Phenanthrene) degradation enter central pathway via protocatechuate intermediate.

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Fig 6.

Cytoscape-based networking depicted interrelationship of key biodegraders in PAH, biphenyl, dioxin, halogenated, catechol and protocatechuate pathways Key biodegraders presented in the middle and the interrelated pathways are given in red lines (A) MGB-2 (B) MGB-3.

https://doi.org/10.1371/journal.pone.0266808.g006

Conclusions

The present metagenomic study highlighted microbial function annotation, extensive degradation capabilities in terms of xenobiotic degradation pathways and correlated with the presence of PAH and PCB in the contaminated soil of steel plants. In addition, physicochemical profiling of the soil samples provided valuable information regarding the presence of organic (C/N/P), inorganic nutrient (Ca, K, Mg, Na, Mn), and metal (Fe, Mn, Cu, Zn) present that can be an essential parameter for designing biodegradation strategies. Higher proportions of Proteobacteria and Actinobacteria indicated the two samples possess good biodegradation potential. Moreover, the coordination among different biodegraders and the presence of functional genes involved in biodegradation pathways and energy metabolism has provided an in-depth understanding of their survival under stress conditions of persistent organic pollutants. Moreover, these potential biodegraders such as Bradyrhizobium, Burkholderia, Mycobacterium, Rhodopseudomonas, and Pseudomonas identified in the present study can be further selected and further could be exploited for enhancing bioremediation. Therefore, it can be concluded that investigating microbial community and exploring their potential for biodegradation is a critical factor in maximizing the efficacy of the bioremediation process.

Supporting information

S2 Table. The total number of unassembled and assembled reads used for downstream analyses.

https://doi.org/10.1371/journal.pone.0266808.s002

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S3 Fig. Rarefaction curve represented species richness indicating function of number of reads (y-axis) per MGB-2 and MGB-3 (x- axis).

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S4 Fig. The scatter plot indicated a linear relationship between MGB-2 and MGB-3.

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S5 Fig.

Comparison between MGB-2 and MGB-3 of functional gene annotation using STAMP using SEED subsystem a.) Tol and Ton b.) two-component system c.) membrane transport d.) Phosphate transporter.

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S6 Fig. Annotated pathways for xenobiotic degradation and metabolism accounted for metagenome MGB-2 and MGB-3.

https://doi.org/10.1371/journal.pone.0266808.s008

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S7 Fig. Comparative analysis of metagenome MGB-2 and MGB-3 based on Peripheral aromatic degradation metabolism at SEED subsystem level by using STAMP.

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S8 Fig. Comparative analysis of metagenome MGB-2 and MGB-3 based on biphenyl degradation metabolism SEED level by using STAMP.

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S9 Fig. Comparative analysis of metagenome MGB-2 and MGB-3 based on aromatic degradation metabolism at ReFSeq genus level by using STAMP.

https://doi.org/10.1371/journal.pone.0266808.s011

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Acknowledgments

MS acknowledges financial assistance provided by Department of Science and Technology, New Delhi, India under Women Scientist Scheme B [SR/WOS-B/570/2016], and BITS Pilani. Our special thanks to Central Instrumentation Facility, Birla Institute of Technology and Sciences (BITS), Pilani campus for providing GC-MS/MS facility.

References

  1. 1. Jones KC, de P Voogt P. Persistent organic pollutants (POPs): state of the science. Environ Pollut. 1998; 100:209–221.
  2. 2. Engraff M, Solere C, Smith KEC, et al. Aquatic toxicity of PAHs and PAH mixtures at saturation to benthic amphi pods: linking toxic effects to chemical activity. Aquat Toxicol. 2011;102:142–149. pmid:21356176
  3. 3. Zhang W, Wei C, An G. Distribution, partition and removal of polycyclic aromatic hydrocarbons (PAHs) during coking wastewater treatment processes. Environ Sci Process Impacts. 2015;17(5):975–984. pmid:25865172
  4. 4. Van Gerven T, Geysen D, Vandecasteele C. Estimation of the contribution of a municipal waste incinerator to the overall emission and human intake of PCBs in Wilrijk, Flanders. Chemosphere. 2004;54:1303–1308. pmid:14659423
  5. 5. Lu C, Hong Y, Liu J, et al. A PAH-degrading bacterial community enriched with contaminated agricultural soil and its utility for microbial bioremediation. J Env Pol. 2019; 251:773–782. pmid:31121542
  6. 6. Isaac P, Sanchez LA, Bourguignon N, et al. Indigenous PAH-degrading bacteria from oil-polluted sediments in Caleta Cordova, Patagonia Argentina. Int Biodeterior Biodegradation. 2013;82:207–214. https://doi.org/10.1016/j.ibiod.2013.03.009
  7. 7. Isaac P, Martinez FL, Bourguignon N, et al. Improved PAHs removal performance by a defined bacterial consortium of indigenous Pseudomonas and Actinobacteria from Patagonia, Argentina. Int Biodeterior Biodegrad. 2015;101:23–31. https://doi.org/10.1016/j.ibiod.2015.03.014
  8. 8. Zhang S, Hu Z, Wang H. Metagenomic analysis exhibited the co-metabolism of polycyclic aromatic hydrocarbons by bacterial community from estuarine sediment. Environ Int. 2019;129:308–319. pmid:31150973
  9. 9. Yadav A, Rajhans KP, Ramteke S, et al. Contamination of Industrial Waste Water in Central India. J Environ Prot. 2016:7, 72–8.
  10. 10. Jiun-Hornga T, Kuo-Hsiungb L, Chih-Yub C, et al. Chemical constituents in particulate emissions from an integrated iron and steel facility. J Haz Mat. 2007;147:111–119. http://doi.org/10.1016/j.jhazmat.2006.12.054
  11. 11. He Z, Wang L, Peng Y, et al. Determination of selected polychlorinated biphenyls in soil and earthworm (Eisenia fetida) using a QuEChERS‐based method and gas chromatography with tandem MS J Sep Sci. 2015; 38(21):3766–3773. pmid:26332829
  12. 12. Weiland-Brauer N, Fischer MA, Schramm KW, et al. Polychlorinated Biphenyl (PCB)-Degrading Potential of Microbes Present in a Cryoconite of Jamtalferner Glacier. Front Microbiol. 2017; 8:1105. pmid:28663747
  13. 13. Kumar P, Meghvansi MK, Kamboj DV. Phenotypic Characterization and Whole-Genome Analysis of a Novel Bacteriophage HCF1 Infecting Citrobacter amalonaticus and C. freundii. Front Microbiol. 2021;12:1–15. pmid:33569047
  14. 14. Laver T, Harrison J, O’Neill PA, et al. Assessing the performance of the Oxford Nanopore Technologies MinION. Biomol Detect Quantif. 2015;3:1–8. pmid:26753127
  15. 15. Randle-Boggis RJ, Helgason T, Sapp M, Ashton PD. Evaluating techniques for metagenome annotation using simulated sequence data. FEMS Microbiol Ecol. 2016;92:95. pmid:27162180
  16. 16. Brumfield KD, Huq A, Colwell RR, et al. Microbial resolution of whole genome shotgun and 16S amplicon metagenomic sequencing using publicly available NEON data. PLoS One. 2020;15 (2):e0228899. pmid:32053657
  17. 17. Parks DH, Beiko RG. Identifying biologically relevant differences between metagenomic communities. Bioinformatics. 2010;26(6):715–21. pmid:20130030
  18. 18. Cai Y, Mo H, Wu T, et al. Bioremediation of polycyclic aromatic hydrocarbons (PAHs)-contaminated sewage sludge by different composting processes. J Hazard Mater. 2007;142(1–2):535–542. pmid:17029776
  19. 19. Delgado-Baquerizo M, Eldridge DJ, Ochoa V, et al. Soil microbial communities drive the resistance of ecosystem multi functionality to global change in drylands across the globe. Ecol Lett. 2017;20:1295–1305. pmid:28921861
  20. 20. Nicolitch O, Feucherolles M, Churin JL, et al. A microcosm approach highlights the response of soil mineral weathering bacterial communities to an increase of K and Mg availability. Sci Rep. 2019;9:14403. pmid:31591410
  21. 21. Bano S, Pervez S, Chow JC, Matawle JL, et al. Coarse particle (PM10–2.5) source profiles for emissions from domestic cooking and industrial process in Central India. J Sci Tot Env. 2018;627:1137–1145. pmid:29426131
  22. 22. Langenhoff AAM, Zehnder AJB, Schraa G. Behaviour of toluene, benzene and naphthalene under anaerobic conditions in sediment columns. Biodegradation.1996;7:267–274. https://doi.org/10.1007/BF00058186
  23. 23. Jiang L, Luo C, Zhang D, et al. Biphenyl-Metabolizing Microbial Community and a Functional Operon Revealed in E-Waste-Contaminated Soil. Environ Sci Technol. 2018; 52(15):8558–8567. pmid:29733586
  24. 24. Ontiveros-Cuadras JF, Ruiz-Fernandez AC, Sanchez-Cabeza JA, et al. Recent history of persistent organic pollutants (PAHs, PCBs, PBDEs) in sediments from a large tropical lake. J Hazardous Mat. 2019;368, 264–273. pmid:30684764
  25. 25. Khaparde VV, Bhanarkar AD, Majumdar D, Rao CVC. Characterization of polycyclic aromatic hydrocarbons in fugitive PM10 emissions from an integrated iron and steel plant. J Sci Tot Env. 2016; 562:155–163. pmid:27099996
  26. 26. Aydin YM, Kara M, Dumanoglu Y, et al. Source apportionment of Polycyclic Aromatic Hydrocarbons (PAHs) and Polychlorinated Biphenyls (PCBs) in ambient air of an industrial region in Turkey. J Atmos Env. 2014;97:271–285.
  27. 27. Yang HH, Lai SO, Hsieh LT, et al. Profiles of PAH emission from steel and iron industries. Chemosphere. 2002;48: 1061–1074. pmid:12227511
  28. 28. Patel KS, Ramteke S, Sahu BL, et al. Polychlorinated Biphenyls Contamination of Sludge in India. AJAC. 2015;6:867–877. https://dx.doi.org/10.4236/ajac.2015.611082
  29. 29. Bouhajja E, Agathos SN, George IF. Metagenomics: Probing pollutant fate in natural and engineered ecosystems. Biotechnol Adv. 2016;34(8):1413–1426. pmid:27825829
  30. 30. Cummings PJ, Olszewicz J, Obom KM. Nanopore DNA Sequencing for Metagenomic Soil Analysis. J Vis Exp.2017;130:55979. pmid:29286470
  31. 31. Zhou Z, Tran PQ, Kieft K., et al. Genome diversification of globally distributed novel marine proteobacteria is linked to environmental adaptation. ISME. 2020; 14:2060–2077. pmid:32393808
  32. 32. Cheung PY, Kinkle BK. Mycobacterium diversity and pyrene mineralization in petroleum-contaminated soils. Appl Environ Microbiol. 2001;67(5):2222–2229. pmid:11319104
  33. 33. Stanford RA, Cole JR, Tiedje JM. Characterization and Description of Anaeromyxobacter dehalogenans gen. nov., sp. nov., an Aryl-Halorespiring Facultative Anaerobic Myxobacterium. Appl Environ Microbiol. 2002;68 (2):893–900. pmid:11823233
  34. 34. Goris J, De Vos P, Caballero-Mellado J, et al. Classification of the biphenyl- and polychlorinated biphenyl-degrading strain LB400T and relatives as Burkholderia xenovorans sp. nov. Int J Syst Evol Microbiol. 2004;54 (5): 21. pmid:15388727
  35. 35. Jorge LM, Rodrigues CAK, Michael RA, et al. Degradation of Aroclor 1242 Dechlorination Products in Sediments by Burkholderia xenovorans LB400 (ohb) and Rhodococcus sp. Strain RHA1(fcb). Appl Environ Microbiol. 2006;72 (4):2476–2482. pmid:16597946
  36. 36. Wittich RM, Strompl C, Moore ERB, et al. Interactions of Sphingomonas and Pseudomonas strains in the degradation of chlorinated dibenzofurans. J Ind Microbiol Biotechnol. 1999;23:353–358. pmid:11423955
  37. 37. Hatamian-Zarmi A, Shojaosadati SA, Vasheghani-Farahani E, et al. Extensive biodegradation of highly chlorinated biphenyls and Aroclor 1242 by Pseudomonas aeruginosa TMU56 isolated from contaminated soils. Int Biodeter Biodegr. 2009;63:788–794.
  38. 38. Seo JS, Keum YS, Li QX Bacterial degradation of aromatic compounds. Int J Environ Res Public Health. 2009;6 (1):278–309. pmid:19440284
  39. 39. Samson R, Shah M, Yadav R, Sarode P, et al. Metagenomic insights to understand transient influence of Yamuna River on taxonomic and functional aspects of bacterial and archaeal communities of River Ganges. J Sci Tot Env. 2019;674:288–299. pmid:31005831
  40. 40. Zhang De-C, Mortelmaier C, Margesin R. Characterization of the bacterial archaeal diversity in hydrocarbon-contaminated soil. Sci Tot Environ. 2012;421–422:184–196. https://doi.org/10.1016/j.scitotenv.2012.01.043
  41. 41. Aranda E. Promising approaches towards biotransformation of polycyclic aromatic hydrocarbons with Ascomycota fungi. Curr Opin Biotechnol.2016;38: 1–8. pmid:26722717
  42. 42. Mars AE, Kingma J, Kaschabek SR, Reineke W, Janssen DB. Conversion of 3-chlorocatechol by various catechol 2,3-dioxygenases and sequence analysis of the chlorocatechol dioxygenase region of Pseudomonas putida GJ31. J Bact. 1999;181:1309–1319. pmid:9973359
  43. 43. Mars AE, Kasberg T, Kaschabek SR, van Agteren MH, Janssen DB, Reineke W. Microbial degradation of chloroaromatics: use of the meta-cleavage pathway for mineralization of chlorobenzene. J Bact. 1997; 179:4530–4537. pmid:9226262
  44. 44. Komancova M, Jurcova I, Kochankov L, Burkhard J. Metabolic pathways of polychlorinated biphenyls degradation by Pseudomonas sp. 2. Chemosphere. 2003;50 (4): 537–543. pmid:12685753
  45. 45. Pagnout C, Frache G, Poupin P, et al. Isolation and characterization of a gene cluster involved in PAH degradation in Mycobacterium sp. strain SNP11:expression in Mycobacterium smegmatis mc(2) 155. Res Microbiol. 2007;158:175–86. pmid:17258432
  46. 46. Valderrama JA, Durante-Rodriguez G, Blazquez B, Garcia JL, Carmona M, Diaz E (2012) Bacterial Degradation of Benzoate cross-regulation between aerobic and anaerobic pathways. J Biol Chem 287 (13):10494–10508 pmid:22303008
  47. 47. Heider J, Boll M, Breese K, et al. Differential induction of enzymes involved in anaerobic metabolism of aromatic compounds in the denitrifying bacterium Thauera aromatica. Arch Microbiol. 1998;170:120–131. pmid:9683649
  48. 48. Meyer-Cifuentes I, Martinez-Lavanchy PM, Marin-Cevada V, et al. Isolation and characterization of Magnetospirillum sp. strain 15–1 as a representative anaerobic toluene-degrader from a constructed wetland model. PLoS One. 2017;12(4). pmid:28369150
  49. 49. Egland PG, Gibson J, Harwood CS. Benzoate-coenzyme A ligase, encoded by badA, is one of three ligases able to catalyze benzoyl-coenzyme A formation during anaerobic growth of Rhodopseudomonas palustris on benzoate. J Bacteriol. 1995;177 (22):6545–6551 pmid:7592432