Comparative metagenomics reveals different hydrocarbon degradative abilities from enriched oil-drilling waste

Accepted Manuscript Comparative metagenomics reveals different hydrocarbon degradative abilities from enriched oil-drilling waste Amanda P. Napp, José Evandro S. Pereira, Jorge S. Oliveira, Rita C.B. Silva-Portela, Lucymara F. Agnez-Silva, Maria C.R. Peralba, Fátima M. Bento, Luciane M.P. Passaglia, Claudia E. Thompson, Marilene H. Vainstein PII:

S0045-6535(18)31144-5

DOI:

10.1016/j.chemosphere.2018.06.068

Reference:

CHEM 21600

To appear in:

ECSN

Received Date: 18 October 2017 Revised Date:

24 May 2018

Accepted Date: 10 June 2018

Please cite this article as: Napp, A.P., Pereira, José.Evandro.S., Oliveira, J.S., Silva-Portela, R.C.B., Agnez-Silva, L.F., Peralba, M.C.R., Bento, Fá.M., Passaglia, L.M.P., Thompson, C.E., Vainstein, M.H., Comparative metagenomics reveals different hydrocarbon degradative abilities from enriched oil-drilling waste, Chemosphere (2018), doi: 10.1016/j.chemosphere.2018.06.068. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT Comparative Metagenomics Reveals Different Hydrocarbon Degradative Abilities

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from Enriched Oil-Drilling Waste

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Amanda P. Napp1, José Evandro S. Pereira1, Jorge S. Oliveira2,3, Rita C. B. Silva-

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Portela3, Lucymara F. Agnez-Silva3, Maria C. R. Peralba4, Fátima M. Bento5, Luciane

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M. P. Passaglia6, Claudia E. Thompson1,7, Marilene H. Vainstein1,*.

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RS, 91501-070, Brazil.

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Centro de Biotecnologia, Universidade Federal do Rio Grande do Sul, Porto Alegre,

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INESC-ID/IST-Instituto de Engenharia de Sistemas e Computadores/Instituto Superior

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Técnico, Universidade de Lisboa, Lisboa, 1000-029, Portugal.

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Federal do Rio Grande do Norte, Natal, RN, 59072-970, Brazil.

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Porto Alegre, RS, 91500-970, Brazil.

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Rio Grande do Sul, Porto Alegre, RS, 90050-170, Brazil.

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RS, 91500-970, Brazil.

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Porto Alegre, Porto Alegre, RS, 90050-170, Brazil.

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Departamento de Química Inorgânica, Universidade Federal do Rio Grande do Sul,

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Departamento de Microbiologia, Imunologia e Parasitologia, Universidade Federal do

Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre,

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Departamento de Biologia Celular e Genética, Centro de Biociências, Universidade

Departamento de Farmacociências, Universidade Federal de Ciências da Saúde de

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Amanda P. Napp ([email protected])

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José E. S. Pereira ([email protected])

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Jorge S. Oliveira ([email protected])

Rita C. B. Silva-Portela ([email protected])

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Lucymara F. Agnez-Silva ([email protected])

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Maria C. R. Peralba ([email protected])

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Fátima M. Bento ([email protected])

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Luciane M. P. Passaglia ([email protected])

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Claudia E. Thompson ([email protected])

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Marilene H. Vainstein ([email protected])

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* Corresponding author. Tel.: +55 (51) 33086060; fax: +55 (51) 33087309 (M. H.

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Vainstein). E-mail address: [email protected]

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● The study reports comparative metagenomics of distinct oil drilling wasteenriched samples.

● It comparatively evaluates the phylogenetic and functional diversity of two

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HIGHLIGHTS

different oil drilling waste-enriched consortia. ● The sequences and metabolic pathways involved in oil degradation have been

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reported.

● The degradation treatments were satisfactory for a broad range of petroleum hydrocarbons.

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ABSTRACT The oil drilling process generates large volumes of waste with inadequate treatments. Here, oil drilling waste (ODW) microbial communities demonstrate

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different hydrocarbon degradative abilities when exposed to distinct nutrient

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enrichments as revealed by comparative metagenomics. The ODW was enriched in

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Luria Broth (LBE) and Potato Dextrose (PDE) media to examine the structure and

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functional variations of microbial consortia. Two metagenomes were sequenced on Ion

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Torrent platform and analyzed using MG-RAST. The STAMP software was used to

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analyze statistically significant differences amongst different attributes of metagenomes.

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The microbial diversity presented in the different enrichments was distinct and

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heterogeneous. The metabolic pathways and enzymes were mainly related to the aerobic

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hydrocarbons degradation. Moreover, our results showed efficient biodegradation after

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15 days of treatment for aliphatic hydrocarbons (C8-C33) and polycyclic aromatic

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hydrocarbons (PAHs), with a total of about 50.5% and 46.4% for LBE and 44.6% and

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37.9% for PDE, respectively. The results obtained suggest the idea that the enzymatic

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apparatus have the potential to degrade petroleum compounds.

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Keywords: Oil drilling waste; Comparative metagenomics; Xenobiotics pathways;

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Hydrocarbon degradation; Microbial diversity.

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1. Introduction With the aid of technology, the petrochemical industry plays a significant role in the global socioeconomic environment. They work hand in hand, contributing towards

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the economic and social development of countries worldwide (Cerqueira et al., 2011;

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Jafarinejad, 2017). The continuous increase in demand for petroleum-based products

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calls for the creation of more extraction sites every year, both on land (onshore) and at

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sea (offshore).

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The process of drilling oil and gas wells generates large volumes of petrochemical

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mud waste all over the world (Jafarinejad, 2017). This petrochemical mud is composed

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of harmful compounds, and its improper disposal creates a serious environmental threat.

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Accidents involving oil spills have drastically risen due to the increased amount of oil

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exploration and production nowadays, creating a major challenge concerning the

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treatment of contaminated areas (Das and Chandran, 2011).

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Generally, drilling waste generated by the petrochemical industry is heterogeneous, soil-like waste, and contains a significant percentage of hydrocarbon-heavy metals and

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salts (Hu et al., 2013). Petroleum hydrocarbons in crude oil are composed of a complex

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mixture of aliphatics, alicyclics and PAHs. These hydrocarbons can vary in

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compositional and physical properties according to the reservoir’s origin (Peixoto et al.,

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2011).

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Petroleum reservoirs establish geological environments of deep subsurface

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sediment, which become microbial habitats capable of hosting a variety of different

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organisms (Sierra-García et al., 2014). Understanding these microbial communities and

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their metabolic activities regarding petrochemical waste is crucial for the future

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development of biotechnological applications in favor of achieving the degradation of

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the relentless accumulation of oil hydrocarbons in nature.

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Bioremediation is a natural process where autochthonous and/or indigenous microorganisms use carbon from harmful organic pollutants as a source of energy,

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breaking down toxic substances into less toxic or non-toxic substances. There is

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significant interest in the process of decontamination by bioremediation, as it is a

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relatively simple process, while being cost-efficient and environmentally friendly.

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Characterizing petroleum-degrading strains and their metabolic pathways has been

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proven extremely useful in the enhancement of remediation approaches. A wide variety

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of microorganisms have been identified with the capability to use the carbon contained

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in the xenobiotic chemicals of petrochemical waste as their sole source of energy

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(Cerqueira et al., 2012; Das and Chandran, 2011; Das and Kazy, 2014). Some of these

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microorganisms not only produce specific enzymes for hydrocarbon degradation, as

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produce adjuvant molecules, called biosurfactants (BS), that together can contribute to

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the partial and complete systemic breakdown of the original pollutant (Belgacem et al.,

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2015; Oliveira et al., 2015; Owsianiak et al., 2009; Tremblay et al., 2017).

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Biosurfactants are produced by a wide variety of organisms in a constitutive or induced

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manner, from various substrates, including sugars, oils, and residues (Chrzanowski et

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al., 2012; Moro et al., 2018; Pacwa-Płociniczak et al., 2011).

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Metagenomic approaches have emerged as a strategy to analyze microbial

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communities in the environment, enabling access to the genetic potential of

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microorganisms without the need for cultivation in the laboratory (Mirete et al., 2016).

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Through metagenomics, the presence of hydrocarbon-degrading enzymes and

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microorganisms useful for xenobiotic degradation was evaluated. Additionally, the

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comparatively analyzed, and the specific sequences and microorganisms involved in

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hydrocarbon-degradation were identified. The data provided in this study demonstrates

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the biotechnological potential of this environment.

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2. Methods

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2.1 Sample collection

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Oil drilling waste (ODW) samples were obtained from one petroleum production well at the onshore Potiguar Basin (Rio Grande do Norte, Brazil). The Potiguar Basin is

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located at the Brazilian equatorial margin, covering an area of about 94,043 km2. The

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basin produces an average of 3,398.13 m3 of petroleum per year and constitutes one of

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the largest Brazilian petroleum producers (ANP, 2016).

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2.2 Microbial Enrichments

ODW (8 g) were homogenized and used as an inoculum for the microbial enrichment cultures. These were grown in a 250 mL Erlenmeyer flask, containing 50

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mL Luria-Bertani (LB) (Thermo Fisher Scientific, USA) or Potato Dextrose (PD)

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medium (potato extract 0.4%; dextrose 2%). Each medium was supplemented with 1%

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crude oil, in order to increase selective pressure.

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The total incubation period for the assays was 21 days, at a temperature of 30 °C.

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Within that incubation period, the inoculum was transferred to a freshly prepared media

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every 7 days. After the total incubation period, the LB and PD enrichments (LBE and

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PDE) were submitted for total DNA extraction. Medium aliquots were separated and

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stored in 30% glycerol at -80 °C.

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2.3 DNA Extraction and Sequencing Total DNA was extracted from 1.8 mL aliquots of samples using the UltraClean®

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Microbial DNA Isolation Kit (MoBio Laboratories, Inc., Carlsbad, CA), following the

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manufacturer’s instructions.

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The sequencing libraries were constructed using 100 ng of genomic DNA and the Ion Xpress Plus Fragment library kit (Thermo Fisher Scientific, USA), following the

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manufacturer’s protocols. The genomic DNA was sheared by enzymatic lysis using the

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Ion Shear Plus Reagents, linked to adapter P1 and to Ion Xpress barcode adapter, and

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concomitantly nick repaired to complete the connection between the adaptor and DNA

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insert.

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The size-select of the library was performed with the E-Gel SizeSelect 2% agarose (Invitrogen, USA). An amplification reaction was set up in a final volume of 130 µL

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with the following cycling profile: 95 °C for 5 min; followed by 8 cycles of 95 °C for

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15 sec, 58 °C for 15 sec, and 70 °C for 60 sec. After each step, a purification was

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performed using magnetic beads on Agencourt® AMPure® XP Kit (Beckman Coulterc,

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USA). Template preparation was conducted using the Ion PGM Template OT2 400 Kit

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(Thermo Fisher Scientific, USA). Library was diluted and 26 pM was added to an

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aqueous master mix containing Ion Sphere Particles (ISPs) using the manufacturer’s

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specified proportions. Emulsion was created using an Ion OneTouchTM 2 Instrument.

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After emulsion PCR, DNA positive ISPs were recovered and enriched according to

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standard protocols. A sequencing primer was annealed to DNA positive ISPs and the

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sequencing polymerase bound, prior to loading of ISPs into Ion 318 sequencing chip.

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Sequencing of the sample was conducted according to the Ion PGM™ Sequencing 400

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Kit Protocol (Thermo Fisher Scientific, USA). The run was programmed to include 850

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nucleotide flows to deliver 400 base read lengths, on average. The library was

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sequenced on Ion Torrent Personal Genome Machine.

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2.4 Taxonomic Distribution Profile

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Metagenomic reads were assigned with the MetaGenome Rapid Annotation using Subsystem Technology (MG-RAST) server (Meyer et al., 2008). In MG-RAST, the

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species richness was computed as the antilog of the Shannon diversity. The abundance

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of the microbial communities was identified using the lowest common ancestor (LCA)

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method with the following parameters: maximum e-value cutoff of 1 e -5, a minimum

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identity of 60% and 15 bp as the minimum alignment length. Statistical analysis for

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distinct taxonomic levels was performed using the Statistical analyses of Metagenomic

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Profiles (STAMP) (Parks and Beiko, 2010) software. Results with q < 0.05 (corrected

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p-value) were considered significant; unclassified reads were removed from the

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analysis. The biological relevance of the taxonomic statistics was determined by

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applying a difference of at least 1% between the proportions.

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The RefSeq classification was normalized using count-relative abundances and

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total Open Reading Frames (ORFs) obtained per metagenome. The statistical

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significance of relative proportion in taxonomic distribution of LBE and PDE

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metagenomes was evaluated using the two-sided Fisher’s exact test and the standard

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asymptomatic approach for ratio proportions. Since p-value was not uniformly

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distributed, Benjamin-Hochberg FDR was applied as a correction method. Results with

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p < 0.05 were considered statistically significant.

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2.5 Functional Annotation

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Functional profiles were initially identified using the SEED subsystems annotation source of the MG-RAST server, with maximum e-value cutoff of 1 e -5, a minimum

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identity of 60% and 15 bp as the minimum alignment length. The metabolic pathways

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of xenobiotics degradation and the annotation of gene sequences involved in the

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degradation of hydrocarbons and surfactants production were generated using the

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Biosurfactants and Biodegradation Database (BioSurfDB) (Oliveira et al., 2015).

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BioSurfDB has a connection between each protein and one or more metabolic

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pathways, allowing the creation of a histogram of the most abundant metabolic

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pathways in each sample. The relative functional abundance was determined by

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considering the total reads for the metabolic pathways and biosurfactants analyzed. The

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significance of differences in the relative proportions of functional levels, metabolic

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pathways, and proteins were statistically analyzed in the STAMP software, using the

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same parameters described for the taxonomic analysis (Parks and Beiko, 2010).

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2.6 Metagenomic Consortia Potential for Hydrocarbons Degradation The microbial consortia (0.2%) (LBE and PDE) were inoculated in 250 mL

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Erlenmeyer flasks containing oil (1%) and 50 mL of LB or PD medium each. The cell

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growth was monitored for 12 days at 30 °C. The petroleum biodegradation ability of the

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consortia was evaluated with Triphenyl Tetrazolium Chloride (TTC) redox indicator,

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according to Braddock and Catterall, (1999). Each consortium (0.8 OD600nm) was

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inoculated in 5 mL of minimum mineral 1 (MM1) containing the TTC redox indicator

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and petroleum (1%) as a carbon source. The tubes were incubated at 30 °C for up to 168

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h. As negative control, sterile water was used to replace the inoculum. Positive reactions

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were obtained by observing the color change of the medium. The capacity of producing

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biosurfactants was evaluated through the emulsification index, drop collapse and oil

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spreading assays (Belgacem et al., 2015; Cooper and Goldenberg, 1987; Youssef et al.,

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2004).

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2.7 Hydrocarbons Degradation Assay

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Metagenomic consortia (LBE and PDE) were evaluated for their biodegradation ability using a mix of aliphatic (C8-C33) and PAHs (anthracene, phenanthrene and

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pyrene) petroleum hydrocarbons. LBE and PDE were pre-cultured in 250 mL

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Erlenmeyer flasks containing 50 mL of LB or PD medium. This was followed by

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incubation on a rotatory shaker (180 rpm) at 30 °C for 7 days. The cells were

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centrifuged (8,000 rpm, 10 min, 4 °C) and the pellet was washed twice with sterile

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distilled water. The pools (1.5 OD600nm) were suspended in natural marine water. The

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cultures were inoculated in 40 mL of sterile natural marine water containing a solution

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of hydrocarbons (1%) and incubated on a rotatory shaker (180 rpm) at 30 °C for 15

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days. Biodegradation negative controls were performed using natural marine water plus

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hydrocarbon with no addition of microbial inoculum.

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2.8 Quantitative analysis of hydrocarbons

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The glassware was washed with detergent, rinsed in distilled water, ultra-pure water

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and acetone. Finally, they were kept at 300 ºC for 3 h. After 15 days of incubation with

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the microbial consortia, petroleum hydrocarbon fractions (F1 and F2) were subjected to

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a liquid-liquid extraction process. The extract was concentrated in a rotary evaporator

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(40 ºC, 800 mbar for 10 min) and submitted to preparative liquid chromatography in

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order to fractionate (clean) the aliphatic (F1) and aromatic (F2) fractions (Cerqueira et

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performed in three replicates and monitored by Gas Chromatography with Flame

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Ionization Detector (GC-FID), adapted from Ferreira et al., (2012). The identification of

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the constituents of fractions F1 and F2 was based on the respective retention times of

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analytical standards. A mixture of C8-C36 saturated alkanes (Supelco, 1,000 µg mL-1),

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Pristane (Sigma-Aldrich, 1,000 µg mL-1) and Phytane (Sigma-Aldrich, 1,000 µg mL-1)

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were used as standards for F1, while a mixture of 16 PAHs standards were used for F2

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(Supelco, 2,000 µg mL-1) (Dörr de Quadros et al., 2016). The aliphatic hydrocarbons

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and PAHs were analyzed by GC-FID on a PerkinElmer Clarus 600 chromatograph

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equipped with a Built-in Auto-Sampler injector. The samples were injected (1 µL) in

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split mode (10:1) and nitrogen carrier gas at constant flow of 1.3 mL min-1. The injector

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and detector temperatures were 280 and 310 ºC, respectively. For the F1 fraction, the

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chromatographic separation was performed on a capillary column (5% phenyl, 95%

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dimethylpolysiloxane, Petrocol® DH 50.2, 50 m, 0.20 mm i.d., x 0.50 µm film

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thickness). The initial oven temperature was 70 ºC during 3 min. The heating ramp was

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in the rate of 30 ºC min-1 to 310 ºC, kept at this temperature for 34 min. For the analysis

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of fraction F2, the capillary column was the same used for F1, but with the initial oven

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temperature at 190 ºC for 2 min. The heating rate of the first ramp was 8 ºC min-1 to 200

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ºC for 2 min. The second heating ramp was 10 ºC min-1 to 260 ºC for 1 min. For the last

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ramp, the rate was 15 ºC min-1 to 310 °C and remained for 19.42 min. The

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quantification of the biodegradation percentages of the F1 and F2 fractions after the

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incubation period with the microbial consortia was performed using the external

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standardization method (Aly Salem et al., 2014; NBR 15764, 2009).

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3. Results and discussion

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3.1 The Features of Oil-Drilling Waste ODW is generated in large quantities as a result of oil exploration activities, and is

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one of the most complex types of residues among those created by the petrochemical

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industry. The chemical and physical properties of petrochemical waste can vary

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significantly according to the location of the well, the regional archaeology, the drilling

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techniques, and the method of disposal (Al-Ansary and Al-Tabbaa, 2007; Hu et al.,

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2013; Jafarinejad, 2017). The physical, chemical, and organic characteristics of the

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ODW samples are shown in Table 1.

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The ODW used in the experiment is slightly alkaline with a pH of 7.5. The most

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prevalent metals, arranged in descending order, are Ca (23.83 g dm-3), Na (3090 mg dm-

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classified as “sandy loam”, exhibiting low contents of moisture (1.49%) and clay (20 g

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kg-1), with a relatively larger proportion of sand (690 g kg-1).

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), K (793 mg dm-3), and Mn (12.01 mg dm-3). The sample of ODW collected is

The collected sample contained a high quantity of organic matter (120 g dm-3),

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most likely due to residue hydrocarbons (108.9 g kg-1), shown in Table 1. The detailed

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composition of the aliphatic and PAHs fractions is available in Table S1. A chemical

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analysis of the sample revealed that the length of the petroleum hydrocarbons ranged

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from C8 to C35, with concentrations ranging between 350.7 to 7,637.6 mg kg-1; this

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characterizes the residue as having a high oil content (Al-Ansary and Al-Tabbaa, 2007).

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Aromatic compounds represented only 6.24% of sample hydrocarbons (6.8 g kg-1).

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Several reports describe microorganisms capable of developing in a multitude of

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environments, including environments with residues of petrochemical waste, depending

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on their biotic and abiotic characteristics (Mirete et al., 2016). Properties such as

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texture, moisture, pH, and nutrient availability, can determine the taxonomic and

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functional composition of the microorganisms present in this waste (Dörr de Quadros et

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al., 2016; Sierra-García et al., 2014).

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3.2 General Characteristics of the Metagenomes

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LBE and PDE metagenomes were generated from ODW. These were analyzed

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using the MG-RAST server. The whole DNA genome sequencing resulted in 1,007,566

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and 1,553,843 reads from LBE and PDE, with a total of 249,315,001 bp and

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204,050,838 bp, and an average length of 247 and 131 bp, respectively (Table 2). The

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reads were deposited in the MG-RAST server with accession numbers 4643476.3

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(LBE) and 4643480.3 (PDE). Rarefaction curves were asymptotic for two

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metagenomes, indicating that the sampling was sufficient to cover the overall richness

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of sequences. Reads identified as artificial duplicates were automatically removed. After

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this, 753,882 proteins were predicted for the LBE sample, with approximately 17% of

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total reads annotated with unknown functions; while 83% were predicted as functionally

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assigned proteins. For the PDE metagenome, 1,081,245 proteins were predicted, where

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21% of sequences corresponded to unknown proteins and 79% of total reads showed

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functionally (Table 2).

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3.3 Taxonomic Profiles Annotation of Metagenomes Microorganisms grow and survive in the natural environment, and the use of an

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enriched environment can stimulate the population growth of any desired organism

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(Palková, 2004). For microbial propagation, certain biochemical and biophysical

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conditions must be provided. An important distinction between the growth media (LB

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and PD) used in this study is the nutrient composition, which they make available to the

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microbes. Even though the same samples of ODW were used, the different growth

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media gave rise to completely different structures. Table 2 shows a total of 6.35% and 7.41% of the sequences associated with rRNA

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genes for the LBE and PDE samples, respectively. The species richness estimated

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through the α-diversity index showed that the LBE metagenome possessed a diversity of

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160, whereas the PDE metagenome possessed a diversity of 49. This much higher

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diversity in the LBE sample strongly indicates that different environmental enrichments

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have a significant influence on the microbial composition grown in them.

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At the domain level, the majority of reads were assigned to Bacteria for both

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metagenomes, and no statistical difference was found between them. LBE had a total

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abundance of 99.8%, while PDE that of 98.7% (Table 3). Concerning the Eukaryota

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domain, the number of reads was statistically different between each sample, with the

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PDE sample showing an abundance of 1.18% when compared to LBE, which had one

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of 0.8%. The Archaeal community was the less abundant fraction within the microbial

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consortia, with a representation of 0.03% (LBE) and 0.003% (PDE) reads. Previous

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studies corroborate the results obtained at the domain level. LB broth is the most widely

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used medium for bacterial growth (Sezonov et al., 2007). In contrast, PD broth is widely

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recommended for the isolation and enumeration of yeasts and molds (Atlas, 2010).

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Therefore, it is not surprising that PDE medium showed a higher number of Eukaryota

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reads (1.18%).

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Sequencing data revealed that microbial communities were predominantly

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composed by the Phylum Proteobacteria (79.6% for LBE and 92.6% for PDE). In the

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LBE metagenome, Firmicutes represented the second most abundant Phylum (12.1%),

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abundant Phylum was Actinobacteria (2.7%), followed by Ascomycota (1.0%). Forty-

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two additional Phyla representing less that 1% of the relative abundance of the

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microbial communities were also present in both metagenomes. Betaproteobacteria,

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Gammaproteobacteria, Alphaproteobacteria and Actinobacteria (class) were

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predominant in the PD enrichments.

The results obtained were similar to those in existing reports, where Proteobacteria

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are the dominant Phylum found in petrochemical waste samples (Dörr de Quadros et al.,

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2016; Joshi et al., 2014; Yadav et al., 2015). Members of this phylum have an important

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role in hydrocarbon degradation processes with diverse metabolic functions (Joshi et al.,

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2014).

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Table 4 shows the 10 most abundant bacterial genera identified in the metagenomics analyzes. This taxonomic distribution was distinct for LBE and PDE

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metagenomes and statistically significant differences were observed for all genera. In

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the LB-enriched sample, the main genera found belonged to the prokaryotic group, with

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abundances of Pseudomonas (10.5%), Bacillus (6.82%), Bordetella (2.51%),

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Achromobacter (1.55%), Paenibacillus (0.84%), Burkholderia (0.81%), Xanthomonas

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(0.77%), Pseudoxanthomonas (0.5%), Stenotrophomonas (0.35%), and Lysinibacillus

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(0.34%), which represented approximately 25% of the total abundance of the reads. In

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the PD-enriched sample, the most frequent genera were Stenotrophomonas (26.9%),

350

Pantoea (6.3%), Brevundimonas (3.65%), Xanthomonas (1.47%), Roseomonas (0.95%),

351

Erwinia (0.63%), Pseudomonas (0.42%), Escherichia (0.28%), Clavibacter (0.18%),

352

and Enterobacter (0.16%), these represented around 41% of the total abundance of the

353

reads.

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ACCEPTED MANUSCRIPT The most commonly found bacteria in ODW enrichments have been previously

355

described as inhabitants of petroleum-associated environments (Chaudhary et al., 2015;

356

Das and Chandran, 2011; Obi et al., 2016). The majority of these genera have common

357

characteristics that can explain their predominance in ODW enrichments, such as the

358

ability to grow in aerobic conditions and to degrade aliphatic and aromatic compounds

359

(Bezza and Chirwa, 2017; Cerqueira et al., 2012; Chaudhary et al., 2015; Choi et al.,

360

2013; Das and Chandran, 2011; Das and Kazy, 2014; Deng et al., 2014; Emtiazi et al.,

361

2005; Mansur et al., 2014; Viñas et al., 2005; Yousaf et al., 2011). Among the assessed

362

organisms, bacteria of the most abundant genera such as Pseudomonas, Bacillus,

363

Stenotrophomonas and Pantoea are widely reported as an alternative for sustainable

364

treatment of environmental passives (Arslan et al., 2014; Cerqueira et al., 2012; Das and

365

Chandran, 2011; Das et al., 2008; Das and Kazy, 2014; Obi et al., 2016; Vasileva-

366

Tonkova and Gesheva, 2007). Furthermore, some reports have classified these genera as

367

bio-emulsifiers with the potential to increase the bioavailability of hydrocarbons for

368

improved biodegradation.

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Concerning the Eukaryota domain, a higher number of reads was identified in the

370

PDE metagenome (Table S2), with the number of assigned sequences around 15 times

371

higher than that in the LBE sample. The most abundant eukaryotic genera in PDE were

372

Aspergillus, Pyrenophora, Phaeosphaeria, Aureobasidium, Leptosphaeria and

373

Penicillium, while for LBE, all genera represented less than 0.002% of abundance. The

374

biodegradation of hydrocarbons in the environment has been reported by several species

375

of fungi, with an efficiency of up to 90% (Cerniglia and Sutherland, 2010). It has been

376

reported by Singh (2006), that fungi such as Aspergillus, Penicillium, and

377

Aureobasidium can be potential degraders of crude oil.

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ACCEPTED MANUSCRIPT 378

The heterogeneous microbial diversity reported in the samples show strong potential with regards to the remediation of environments contaminated by petroleum

380

hydrocarbons. Different microorganisms provide distinct levels of hydrocarbon

381

degradation. Associations of microorganisms increase their ability to utilize a large

382

number of hydrocarbons as a sole carbon source for survival. These microorganisms can

383

completely mineralize petroleum compounds through the metabolic actions of one or

384

more strains (Jacques et al., 2005; Kadali et al., 2012; Oliveira et al., 2017).

385

Furthermore, even if many microorganisms do not solely survive on oil/ petroleum

386

hydrocarbons, they still contribute to the overall microorganism community with their

387

unique metabolic pathways. Literature has extensively described that

388

hydrocarbonoclastic bacteria are not the only ones capable of degrading hydrocarbons

389

and/or producing biosurfactants (Chronopoulou et al., 2014; Schneiker et al., 2006).

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In this way, the application of mixed cultures in the ODW-contaminated

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environments has shown to be more advantageous than pure cultures, due to the effect

392

of synergistic interactions between members of association. Complete degradation of

393

hydrocarbons involves the use of microbial consortia including prokaryotic and

394

eukaryotic forms (Head et al., 2006; Guerra et al., 2018; Kostka et al., 2011; Owsianiak

395

et al., 2009; Rizzo et al., 2006; Tremblay et al., 2017).

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3.4 Comparative Functional Profiles To reveal the microbial functional profile of the enriched samples, the reads were

399

annotated according to the SEED subsystems database. The major metabolic and

400

functional protein profiles were similar for the two metagenomic samples, in terms of

401

the percentages of genes identified (Fig. 1).

17

ACCEPTED MANUSCRIPT 402

At level 1, the clustering-based subsystem was the most predominant subsystem found in both metagenomes, which corresponds to groups of hypothetical protein

404

families (Fig. 1). In total, 4,371 ORFs were identified for the LBE metagenome; 13.8%

405

of these ORFs corresponded to hypothetical proteins with unknown functions, and

406

86.2% were assigned to specific proteins. Concerning the PDE metagenome, 4,163

407

ORFs were identified; 13.7% of these ORFs were hypothetical, and 86.3% were

408

allocated to specific proteins (Fig. 1). These wide percentages of proteins with unknown

409

functions indicate the enormous biotechnological potential of these ODW enrichments,

410

which could lead to the elucidation and understanding of additional hydrocarbon-

411

degradation pathways, as well as new enzymes.

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The distribution pattern of the assigned proteins highlights the significant dominance of carbohydrates and amino acid subsystems, along with 24.3% and 23.8%

414

of the total abundance for LBE and PDE metagenomes, respectively (Fig. 1).

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The predominance of proteins belonging to these subsystems may indicate the ability of the PDE and LBE metagenomes to obtain energy from different conditions

417

and substrates. These features are crucial for microbial survival and adaptation in

418

polluted environments.

The functional analysis also allows for the classification of subsystems into

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differing subsystems related to the essential metabolic processes in both metagenomes.

421

Thus, more than 25% of the total metagenomic genes were assigned to the metabolism

422

of proteins, cofactors, RNA, fatty acids, DNA, aromatic compounds, and iron. In

423

contrast, some subsystems were underrepresented in the metagenomics data, including

424

sequences involved in photosynthesis, secondary metabolism, dormancy, and potassium

425

metabolism (Fig. 1).

18

ACCEPTED MANUSCRIPT 426

Biodegradation of petroleum hydrocarbons occurs gradually through the sequential metabolism of its compounds. The functional diversity found in ODW samples

428

contributes towards the enzymatic flexibility of microorganisms in the transformation of

429

contaminants into non-harmful final products, which can then be incorporated into

430

natural biogeochemical cycles (Cerniglia and Sutherland, 2010; Fuchs et al., 2011;

431

Peixoto et al., 2011; Rojo, 2009). To summarize, our results indicate a versatile

432

potential for metagenomes in aiding the process of hydrocarbon degradation.

433

435

3.5 Hydrocarbon Degradation Functions

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The BioSurfDB database was also used to evaluate the specific functional composition of the ODW metagenomes. The metabolic and biosynthesis pathways

437

identified were similar to those found using the SEED database (Fig. 2). Several

438

metabolic pathways are related to the aerobic degradation of both aliphatic and aromatic

439

hydrocarbons compounds by microorganisms. Aerobic catabolism is generally faster

440

because of the metabolic advantage of the availability of O2 as the main electron

441

acceptor.

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An in-depth examination of the xenobiotic metabolic pathways revealed the predominance of subgroups as benzoate, fatty acid, xylene, aromatic compounds,

444

chloroalkane, and chloroalkene degradation pathways and methane metabolism with a

445

significant number of putative genes potentially involved in the metabolism of

446

hydrocarbons (Fig. 2A). In total, more than 54% of the sequences annotated belonged to

447

these main categories. Besides that, approximately 15% of the identified genes were

448

related to naphthalene, aminobenzoate, chlorocyclohexane, chlorobenzene, styrene and

449

toluene degradation pathways. Additional pathways, such as fluorobenzoate,

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19

ACCEPTED MANUSCRIPT nitrotoluene and propanoate, representing less than 1% of the total abundance were also

451

present in both metagenomes. The majority of sequences found in our metagenomes for

452

linear and aromatic hydrocarbons mineralization related to O2-dependent degradation

453

are shown in Table 5.

454

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Aromatic compounds represent the second most abundant organic compounds in nature, following carbohydrates, and compromises one-quarter of the earth's biomass.

456

Consequently, benzoate is recognized as a model compound for the catabolism of

457

aromatic rings (Fuchs et al., 2011). The classical aerobic degradation of aromatic

458

hydrocarbons usually generates intermediate compounds by hydroxylation, such as

459

catechol (1,2 dihydroxybenzene) or protocatechuate (3,4 dihydroxybenzoate) (Fuchs

460

et al., 2011). The intermediates may then be dearomatized by the action of enzymes

461

known as oxygenases. Monooxygenases and dioxygenases enzymes perform the

462

addition of molecular oxygen atoms to the aromatic ring with subsequent cleavage of

463

these compounds. The cleavage of ring intermediates is performed in two forms by

464

ortho- or meta-cleavage dioxygenases. After cleavage of the aromatic compounds, they

465

are transformed into metabolic pathway intermediates of the tricarboxylic acid (Krebs

466

Cycle) cycle, such as succinate, pyruvate and acetyl-CoA, which can be completely

467

degraded into CO2 (Fuchs et al., 2011; Peixoto et al., 2011).

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We identified a high number of oxygenases enzymes (190 and 135, for LBE and

469

PDE, respectively), as catechol 1,2-dioxygenase, catechol 2,3-dioxygenase,

470

protocatechuate 3,4-dioxygenases and protocatechuate 4,5-dioxygenase genes in both

471

metagenomic samples (Table 5). Catechol intermediates are produced during bacterial

472

degradation, whereas protocatechuate is generated in most fungi and some bacterial

473

peripheral pathways (Fuchs et al., 2011). Our results corroborate these findings, as a

20

ACCEPTED MANUSCRIPT superior read abundance was found for catechol enzymes in the LBE metagenome,

475

whereas protocatechuate enzymes were more prevalent in the PDE metagenome.

476

Moreover, Figure S1 shows the distinct abundance for each metagenome of specific

477

genes of the benzoate degradation pathway. By analyzing this specific pathway, we can

478

also observe that some enzymes were predicted specifically for either the LBE or the

479

PDE metagenome.

We observed a broad potential for the degradation of aliphatic compounds, most

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likely due to the predominant composition of linear hydrocarbons in ODW, and the

482

addition of crude oil in the initial enrichment (Fig 2A). The main constituents of crude

483

oil are alkanes, which are extremely reduced molecules with a high energy and carbon

484

content; they act as good carbon and energy sources for microorganisms (Wang and

485

Shao, 2013). Catabolism of saturated aliphatic hydrocarbons is generally described by

486

the oxidation of a terminal methyl group to produce an alcohol, which is then further

487

oxidized to the equivalent aldehyde, and finally converted into a fatty acid. Fatty acids

488

are further processed to acetyl-CoA, which is metabolized in the citric acid cycle and

489

finally oxidized to CO2 and water (Fuchs et al., 2011; Peixoto et al., 2011).

490

Several enzymes related to the degradation of alkanes like aldehyde

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dehydrogenase, alcohol dehydrogenase, alkane hydroxylase, methane monooxygenase,

492

and enzymes that belong to a family of soluble cytochrome P-450 were present in the

493

metagenomes (Table 5). In addition, we identified a significant number of proteins

494

involved with synthesis of biosurfactants (18.14 and 17.92% for LBE and PDE,

495

respectively). The predominant pathways of biosurfactants biosynthesis (approximately

496

63%) belonged to putisolvins, streptomycin, polyketide sugar unit, iturin A, and

497

alnumycin for both metagenomes (Fig. 2B).

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ACCEPTED MANUSCRIPT 498

Several bacteria that able to degrade hydrocarbons also produce biosurfactants of diverse chemical structure that allow for the emulsification of low solubility

500

compounds (Banat et al., 2010; Franzetti et al., 2010; Oliveira et al.; 2017).

501

Biosurfactants are reported to act in several ways to provide bacteria with an advantage,

502

for instance by having antibiotic properties, providing motility, promoting biofilm

503

development, or acting as adjuvants in the degradation of heavy oils (Belgacem et al.,

504

2015; Chrzanowski et al., 2012; Franzetti et al., 2010; Ławniczak et al., 2013; Moro et

505

al., 2018). Although the precise mechanism of degradation enhancement by surfactants

506

is still unclear, and recent articles state opposite ideas, it is also accepted that production

507

of biosurfactants and biodegradation of hydrocarbons are correlated events as we are

508

showing (Chrzanowski et al., 2012; Dell’Anno et al., 2018; Kumari et al., 2018;

509

Owsianiak et al., 2009; Oliveira et al., 2015; Roy et al., 2018; Souza et al., 2014;

510

Suganthi et al., 2018; Tremblay et al., 2017).

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As seen in figure 2B, both media allowed production of biosurfactants, only with slight differences in the gene diversity. The efficiency of biosurfactant production is

513

dependent on the carbon source, nitrogen, and trace elements (Banat et al., 2010; Janek

514

et al., 2013; Moro et al., 2018), and so it is possible that the medium composition has

515

favored the increased production of some biosurfactants.

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The pathway for putisolvin was significantly more abundant in our

517

metagenomes and this biosurfactant have often been reported to be produced by

518

Pseudomonas spp. (Janek et al., 2013), the first most abundant genus found in the LBE

519

(see Table 4). Moreover, amongst the most efficient biosurfactant-producing

520

microorganisms are those from the genus Bacillus, the second most abundant in the

521

LBE consortia. This genus are frequently reported as being the largest producers of

22

ACCEPTED MANUSCRIPT lipopeptide biosurfactants, mainly due to its ability to produce cyclic lipopeptides

523

bearing a long acyclic side chain (Chakraborty and Das, 2014.). The three main families

524

of lipopeptides produced by Bacillus spp. are surfactins, iturins, and fengycins (Geissler

525

et al., 2017).

526

528

3.6 Hydrocarbon Assimilation by Metagenomics Samples.

In order to assess the hydrocarbon assimilation potential, the microbial growth

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behaviors of LBE and PDE in cultures containing 1% crude oil were evaluated (Fig.

530

S2). The assay was monitored for 12 days and both metagenomic consortia

531

demonstrated the ability to grow in the conditions used. A period of 7 days for pre-

532

cultivation was chosen for the subsequent experiments, as this is the time during which

533

the cells are experiencing exponential growth. Interestingly, PDE demonstrated a

534

cellular growth 4 times higher than that of LBE after 7 days of culture (Fig. S2).

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The ability of the two consortia to metabolize the hydrocarbons was initially assessed using the TTC indicator redox method. Positive results are shown in Table S3.

537

This methodology is based on the microorganism’s expression of its own metabolic

538

activity through the use and degradation of the contaminant as a source of carbon, and,

539

hence, energy. This indicator then becomes an artificial acceptor of electrons, replacing

540

oxygen, necessary for aerobic metabolism in the compounds (Braddock and Catterall,

541

1999; Richard and Vogel, 1999). It is also an important indicator of intracellular

542

enzymes, such as dehydrogenases, which have the ability to catalyze redox reactions of

543

organic compounds (Olga et al., 2008).

544 545

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Several biosurfactants were identified in the samples, which reflect the detection of potential biosurfactant producers (Table S4). Both enrichments showed satisfactory

23

ACCEPTED MANUSCRIPT results for the emulsification rate (>60%) and oil spreading. However, only the LBE

547

consortia were positive for the other tests: drop collapse and hemolytic activity. These

548

results corroborate the functional data, where we observed a high predominance of

549

biosurfactant genes. We can therefore suggest that there is a significant production of

550

surfactants by the metagenomes (Fig. 2B). The most abundant genes in each

551

metagenome for biosurfactant synthesis are described in Table S5. In general, the LBE

552

metagenome presented an increased versatility for the production of biosurfactants.

553

According to the preliminary results suggesting the degradative capacity of the

554

microbial enrichments, we evaluated the effectiveness of bioremediation concerning the

555

enriched metagenomic consortia after 15 days of bioaugmentation treatment in

556

contaminated seawater with aliphatic and aromatic hydrocarbon solution (1%) (Fig. 3).

557

The microbial consortia from the two enrichments were applied and evaluated as a

558

decontamination strategy for the hydrocarbon-polluted marine environment. Figures 3A

559

and 3B show the percentage of biodegradation of a wide range of aliphatic

560

hydrocarbons (C8-C33), for LBE and PDE, respectively. The most efficient n-alkane

561

degradation occurred at the fraction between C8-C12 chains for both enrichments

562

(above 95%).

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The PDE enrichment showed superior degradation for the C13, C14, and C15

564

chains, as well as phytane (65 to 89%). Interestingly, the compound with C33 also

565

appeared with a high rate of degradation in this consortium (53%), while the other

566

hydrocarbon chains (C19-C32) remained lower (Fig. 3B). On the other hand, the

567

mineralization of n-alkanes with C13-C19 reached the highest degradation percentages

568

(51 to 90%) by LBE enrichment (Fig. 3A). This sample also demonstrated a greater

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24

ACCEPTED MANUSCRIPT 569

metabolic capacity for the long hydrocarbon chains (C22-C29) when compared to the

570

PDE inoculum.

571

In addition, due to the promising data found for aromatic compound metabolism, three PAHs were selected to evaluate the degradation rate (Fig. 3C and D). Our results

573

showed degradation efficiencies in total of about 46.4 and 37.9% after 15 days of

574

treatment, for LBE and PDE, respectively. The bioaugmentation with LBE specifically

575

demonstrated greater effectiveness for anthracene (55.7%), phenanthrene (60.2%) and

576

pyrene (23.3%) (Fig. 3C) when compared with PDE (43.3%, 48.9% and 21.5%,

577

respectively) (Fig. 3D).

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According to Mohamed et al. (2006), the biodegradability of petroleum components follows a preferred order of degradation: n-alkanes> branched alkane chains> branched

580

alkenes> monoaromatic> cycloalkanes> polyaromatic> asphaltene chains. In this way,

581

the degradation of long chains of carbon and aromatic rings is difficult in the short-term.

582

Our results for the bioremediation treatments is in accordance with our functional data

583

and are considered satisfactory, especially when compared with the short time used for

584

the degradation of petroleum hydrocarbons (Cerqueira et al., 2011; Dörr de Quadros et

585

al., 2016).

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4. Conclusion

This study presents a practical method for understanding microbial communities in

589

ODW. The comparative metagenomics approach used in this study demonstrates the

590

effect of different enrichment conditions on the composition of microbial communities

591

and enzymatic apparatus. The LBE enrichment exhibited a superior degradation

592

capacity, most likely due to a higher abundance related to degradation genes of

25

ACCEPTED MANUSCRIPT 593

xenobiotics. The microbial community structure and degradative pathways generated in

594

this study could be used to further enhance existing bioremediation strategies.

595

597

Acknowledgements

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This work was supported by grants from the Brazilian agencies Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Coordenação de

599

Aperfeiçoamento de Pessoal do Ensino Superior (CAPES).

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Fig. 1. Functional assignment of the metagenomes using the SEED subsystem database.

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*Differences statistically significant between LBE and PDE samples (p>0.05), as

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obtained by STAMP.

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Fig. 2. Specific functional profile of the enriched ODW metagenomes for (A)

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xenobiotic degradation pathways and (B) biosurfactants biosynthesis. The reads were

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annotated according to SEED subsystems database. *Differences statistically significant

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between LBE and PDE samples (p>0.05) as obtained by STAMP.

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Fig. 3. Qualitative assessment of enriched oil-drilling samples for the removal of

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representative hydrocarbon compounds. Aliphatic hydrocarbon degradation by (A) LBE

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and (B) PDE metagenomes. Polycyclic aromatic hydrocarbon degradation by (C) LBE

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and (D) PDE metagenomes.

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Fig. S1. Partial KEEG pathway identified for benzoate metabolism from the LBE and

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PDE metagenomes. The red boxes indicate the enzymes identified in the sequence data

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of the LBE metagenome, while the blue boxes indicate the enzymes identified in the

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sequence data of the PDE metagenomes. The empty boxes indicate undetected enzymes.

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Fig. S2. Cell growth of the enriched oil-drilling samples during 12 days of incubation at

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30 °C with crude oil 1% (m/v) in LB and PD medium for LBE (filled circles) and PDE

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(filled squares), respectively. Error bars represent standard deviation (n = 3).

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Table 1. Organic and physical–chemical characteristics of oil-drilling waste (ODW) sample. (1:25) (%)

Organic matter Ca Mg Al H + Al

120.25 23.83 0.43 nd nd

(g dm-3)

P K Na Fe Zn Cu Mn Ni Pb Cd Cr B

1 793 3,090 6.48 1.33 1.55 12.01 0.80 1.55 0.17 0.20 8.83

(mg dm-3)

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Granulometry (g kg-1) Sand 690 Clay 20 Silt 290 Texture classification Sandy loam TPH 108.9

(g kg-1)

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TPH: Total Petroleum Hydrocarbons. nd: not detected.

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Base pairs (bp) Number of reads Length average of reads (bp) Ribosomal RNA genes

249,315,001 1,007,566 247 63,946

204,050,838 1,553,843 131 115,069

Annotated Proteins Unknown Proteins

623,193 130,689

855,273 225,972

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Table 3. Comparative taxonomic profile of LBE and PDE metagenomes. abundance (%) Domain LBE PDE * Archaea 0.03 0.003 Bacteria 99.8 98.7 * Eukaryota 0.08 1.18 * Other sequences 0.001 0.005 * Unclassified sequences 0.022 0.001 * Viruses 0.012 0.094

Differences statistically significant between LBE and PDE samples (p>0.05) by STAMP.

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Table 4. Evaluation of the10 most abundant genera of LBE and PDE metagenomes. abundance (%) LBE* PDE* Pseudomonas 10.5 Stenotrophomonas 26.9 Bacillus 6.82 Pantoea 6.63 Bordetella 2.51 Brevundimonas 3.65 Achromobacter 1.55 Xanthomonas 1.47 Paenibacillus 0.84 Roseomonas 0.95 Burkholderia 0.81 Erwinia 0.63 Xanthomonas 0.77 Pseudomonas 0.42 Pseudoxanthomonas 0.50 Escherichia 0.28 Stenotrophomonas 0.35 Clavibacter 0.18 Lysinibacillus 0.34 Enterobacter 0.16 Differences statistically significant between LBE and PDE samples (p>0.05) by STAMP.

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PDE (reads) 373 315 36 28 22 44 116 82 4 10 88 58 21 182 3 59 121 101 9 0 66 6 62 4 156 224 2190

SC

LBE (reads) 1273 1088 207 44 114 114 331 411 45 33 290 199 98 167 57 141 353 409 81 4 37 6 153 15 2684 459 8813

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Enzymes Aldehyde dehydrogenase Alcohol dehydrogenase Alkane monooxygenase Benzene 1,2-dioxygenase Benzoate 1,2-dioxygenase Biphenyl 2,3-diol-1,2-dioxygenase Carbon-monoxide dehydrogenase Catalase Catechol 1,2-dioxygenase Catechol 2,3-dioxygenase Ferredoxin Methanol dehydrogenase Naphthalene dioxygenase Nitrilase P450 monooxygenase Peroxiredoxin Phenol hydroxylase Phenylacetaldehyde dehydrogenase Pentachlorophenol 4-monooxygenase Protocatechuate 2,3-dioxygenase Protocatechuate 3,4-dioxygenase Protocatechuate 4,5-dioxygenase Salicyladehyde dehydrogenase Xylene monooxygenase Hypothetical protein Unknown protein Total

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Supplementary Material

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Table S1. Composition of aliphatic and polycyclic aromatic hydrocarbons in sample of oildrilling waste (ODW). Mean Mean Aromatic Aliphatics C number mg kg-1 PAHs rings mg kg-1 Octane C8 388.1 Naphthalene 2 nd Acenaphthene Nonane C9 385.7 3 439.9 Acenaphthylene Decane C10 350.7 3 109.2 Undecane C11 470.3 Fluorene 3 204.9 Dodecane C12 471.8 Phenathrene 3 152.5 Anthracene Tridecane C13 1364.6 3 1493.6 Tetradecane C14 4917.8 Fluoranthene 4 899.3 Pentadecane C15 4840.8 Pyrene 4 128.4 Benz[a]anthracene Hexadecane C16 4672.2 4 864.1 Chrysene Heptadecane C17 5328.7 4 81,5 Benzo[b]fluoranthene Octadecane C18 6128.2 5 2137.9 Benzo[k]fluoranthene Nonadecane C19 6096.3 5 237.2 Benzo[a]pyrene Pristane C19 5421.6 5 nd Dibenzo[a,h]anthracene Eicosane C20 7637.6 5 83.0 Benzo[g,h,i]perylene Phytane C20 6607.7 6 nd Indeno[1,2,3-cd]pyrene Heneicosane C21 6676.7 6 nd Docosane C22 5965.6 Tricosane C23 5775.3 Tetracosane C24 5300.6 Pentacosane C25 6407.0 Hexacosane C26 3690.6 Heptacosane C27 3225.6 Octacosane C28 2627.6 Nonacosane C29 1995.7 Hentriacontane C30 1544.7 Dotriacontane C31 985.2 Tritriacontane C32 1042.9 Tetratriacontane C33 584.6 Triacontane C34 726.5 Pentatriacontane C35 433.1 nd: not detected.

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Table S2. Evaluation of the 10 most abundant Eukayota of LBE and PDE metagenomes. abundance (%) LBE* PDE* Sordaria 0.0020 Aspergillus 0.0199 Penicillium 0.0012 Pyrenophora 0.0197 Aspergillus 0.0002 Phaeosphaeria 0.0157 Ajellomyces 0.0002 Aureobasidium 0.0149 Filobasidiella 0.0002 Leptosphaeria 0.0111 Metarhizium 0.0002 Penicillium 0.0109 Magnaporthe 0.0002 Botryotinia 0.0096 Podospora 0.0002 Sordaria 0.0091 Blastocystis 0.0002 Nectria 0.0074 − Glomerella 0.0062 Differences statistically significant between LBE and PDE samples (p>0,05) by STAMP.

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CN: negative control. (+) Indicates the start of reduction of the redox indicator in MM1.

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++++ ++

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Table S3. Evaluation of petroleum hydrocarbons degradation potential by microbial consortia using Triphenyl Tetrazolium Chloride redox indicator. Microbial Consortia Time 48h 72h 96h 120h 144h CN LBE + ++++ ++++ +++ ++++ PDE + +++ +++ +++ +++

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Table S4. Evaluation of biosurfactants production by microbial metagenomes. Metagenomic Consortia LBE PDE + Hemolytic activity − + Drop collapse − Oil spreading (mm) 25.6 ± 3.0 7.0 ± 3.6 Emulsification index (%) 68 ± 1.5 72 ± 2.0 (+) Indicates postive reaction.

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Table S5. Enzymes discovered in microbial enrichemnts related to biosurfactants production. Enzymes LBE (reads) PDE (reads) Alnumycin 1632 947 Amphisin synthetase 9 12 Arthrofactin synthetase 317 131 Bacillomycin synthetase 509 117 Iturin synthetase 1320 286 Lichenysin synthetase 696 205 Malto-oligosyltrehalose synthase 331 669 Phosphatidylethanolamine methyltransferase 156 50 Plipastatin synthetase 530 174 Polyketide synthase 6 3 Putisolvin synthetase 3803 1142 Rhamnosyltransferase 42 30 Serrawettin synthetase 40 22 Surfactin synthetase 587 187 Trehalose-6-phosphate synthase 174 142 Total 10152 4117 912

AC C

EP

TE D

913

50

ACCEPTED MANUSCRIPT Fig. S1

M AN U

SC

RI PT

914

915

EP AC C

917

TE D

916

51

ACCEPTED MANUSCRIPT Fig. S2

SC

RI PT

918

AC C

EP

TE D

M AN U

919

52