Chemical and biological dispersants differently affect the bacterial communities of uncontaminated and oil-contaminated marine water

Camila Rattes de Almeida Couto; Deborah Catharine de Assis Leite; Diogo Jurelevicius; Jan Dirk van Elsas; Lucy Seldin

doi:10.1007/s42770-019-00153-8

. 2019 Oct 14;51(2):691–700. doi: 10.1007/s42770-019-00153-8

Chemical and biological dispersants differently affect the bacterial communities of uncontaminated and oil-contaminated marine water

Camila Rattes de Almeida Couto ¹, Deborah Catharine de Assis Leite ¹, Diogo Jurelevicius ¹, Jan Dirk van Elsas ², Lucy Seldin ^1,^3,^✉

PMCID: PMC7203333 PMID: 31612432

Abstract

The use of dispersants in marine environments is a common practice worldwide for oil spill remediation. While the effects of chemical dispersants have been extensively studied, those of biosurfactants, mainly surfactin that is considered one of the most effective surfactants produced by bacteria, have been less considered. We constructed microcosms containing marine water collected from Grumari beach (W_GB, Brazil) and from Schiermonnikoog beach (W_SI, The Netherlands) with the addition of oil (WO), Ultrasperse II plus oil (WOS), surfactin plus oil (WOB), and both dispersants (WS or WB) individually. In these treatments, the composition of bacterial communities and their predictive biodegradation potential were determined over time. High-throughput sequencing of the rrs gene encoding bacterial 16S rRNA revealed that Bacteroidetes (Flavobacteria class) and Proteobacteria (mainly Gammaproteobacteria and Alphaproteobacteria classes) were the most abundant phyla found among the W_GB and W_SI microbiomes, and the relative abundance of the bacterial types in the different microcosms varied based on the treatment applied. Non-metrical multidimensional scaling (NMDS) revealed a clear clustering based on the addition of oil and on the dispersant type added to the GB or SI microcosms, i.e., WB and WOB were separated from WS and WOS in both marine ecosystems studied. The potential presence of diverse enzymes involved in oil degradation was indicated by predictive bacterial metagenome reconstruction. The abundance of predicted genes for degradation of petroleum hydrocarbons increased more in surfactin-treated microcosms than those treated with Ultrasperse II, mainly in the marine water samples from Grumari beach.

Electronic supplementary material

The online version of this article (10.1007/s42770-019-00153-8) contains supplementary material, which is available to authorized users.

Keywords: Dispersants, Biosurfactant, Bacterial community, Oil, Marine water

Introduction

Oil spills occur recurrently in marine environments worldwide. Dispersants, i.e., compounds that are able to split oil slicks into micron-sized droplets, are applied as a strategy to enhance the bioavailability of petroleum hydrocarbons to indigenous microorganisms and, consequently, to improve environmental remediation [1, 2]. However, different studies show contradictory results regarding the efficiency of chemical dispersants that have been applied, such as Ultrasperse II (a mixture of alcohol, alcohol sulfate, and the fatty ester ethoxylate diluted in a solvent containing non-aromatic hydrocarbons) and Corexit 9500 (a mixture of hydrocarbons, glycols, and dioctylsulfosuccinate—DOSS) [3, 4].

Hamdan and Fulmer [5] showed that high concentrations of Corexit 9500 reduced the viability of different oil-degrading bacteria isolated from the beach sand of Elmer’s Island (LA, USA) contaminated with oil from the Deep Water Horizon spill. By contrast, Kleindienst et al. [3] demonstrated that the addition of only Corexit 9500 altered the microbial community composition, with the increase of the relative abundance of Colwellia in a study using microcosms containing seawater sampled from an active natural hydrocarbon seep in the northern Gulf of Mexico. Techtmann et al. [6] also observed that the addition of Corexit 9500 resulted in a decrease in the relative abundance of Marinobacter in microcosms constructed using samples collected near the site of the Deepwater Horizon oil spill and incubated at 25 °C. However, other OTUs were stimulated by the addition of the dispersant, many of which were identified as known hydrocarbon-degrading bacteria.

The effect of Ultrasperse II has been less studied than that of Corexit 9500 in marine oil-contaminated environments, even though it has been used in a few countries including Brazil [7]. A recent study of our group showed that the addition of Ultrasperse II to oil-contaminated marine water did not increase the abundance of the total bacterial community and of the alkane-degrading bacteria [7]. However, these results did not elucidate the effect of this substance on the composition and diversity of the oil-contaminated and uncontaminated marine water bacterial communities.

Although widely used, chemical surfactants are generally derived from petroleum and/or have hydrocarbons in their composition, thus being toxic to different life forms such as plants, animals, and microorganisms, including hydrocarbon-degrading bacteria [8–10]. On the other hand, biosurfactants produced by several microorganisms are considered to be environmentally friendly. They represent an alternative to chemical dispersants, as they are active under conditions of extreme temperature, pH, and salinity and are less toxic than synthetic surfactants to some invertebrate species [8, 11, 12]. However, they are still understudied for marine bioremediation applications. Among the well-known biosurfactants, surfactin—a polypeptide produced by Bacillus subtilis and related species—is considered to be one of the most effective [13]. With respect to its effects, Couto et al. [7] demonstrated that the abundance of genes potentially involved in alkane degradation was higher in surfactin-treated than in Ultrasperse II-treated oil-contaminated marine waters. However, the effects of either surfactin or Ultrasperse II on the structural and functional composition of bacterial communities of oil-contaminated and uncontaminated marine water are still not well known.

The aim of this study was to compare the effects of two dispersants—Ultrasperse II and surfactin—on the relative abundance and diversity of the bacterial communities in oil-contaminated and uncontaminated marine water samples collected from Grumari beach (Rio de Janeiro, Brazil) and from Schiermonnikoog beach, Island of Schiermonnikoog (Groningen, The Netherlands)—two countries with contrasting climatic conditions (tropical and temperate weather). We used high-throughput sequencing of the rrs gene encoding 16S rRNA to achieve our goals, thereby contributing to the understanding of the possible effects on marine bacterial communities.

Materials and methods

Dispersant sources

The biosurfactant produced by Bacillus velezensis H₂O-1, a strain originally isolated from connate water from an oil reservoir in Brazil and previously described by Korenblum et al. [14] and Guimarães et al. [15], was produced and purified as previously described by Guimarães et al. [15]. In brief, it was produced in a mineral salt medium with the following composition (% w/v): glucose 1.0, NaCl 1.0, Na₂HPO₄ 0.5, KH₂PO₄ 0.2, MgSO₄ 0.02, and (NH₄)₂SO₄ 0.2. The medium was inoculated with 20 mg cells L⁻¹ of a pre-inoculum of strain H₂O-1 grown in the same culture medium for 14 h at 30 °C and 170 rpm. The biosurfactant was characterized as surfactin by Korenblum et al. [16]. Ultrasperse II was obtained from Oxiteno, São Paulo, Brazil.

Sample sites and construction of the microcosms

Marine water samples (5 L) were collected from Grumari beach (GB) in Rio de Janeiro, Brazil (23° 2′ 59″ S 43° 31′ 35″ W), and from Schiermonnikoog Island (SI), located in the Netherlands (53° 29′ 14″ N 6° 14′ 3″ E) in December 2014 and April 2015, respectively. The oil samples were supplied by Petrobras in Brazil and by Royal Dutch Shell in the Netherlands. Both were considered medium oils. Microcosms were constructed in triplicate using 25 mL of water, and they were submitted to different treatments as previously described in detail in Couto et al. [7]. They were as follows: (a) control—microcosms containing only water—W; (b) microcosms with water and the addition of biosurfactant—WB (surfactin, 40 μg mL⁻¹); (c) microcosms with water and the addition of chemical surfactant—WS (Ultrasperse II, 1 μL mL⁻¹); (d) microcosms with water contaminated with crude oil—WO (1% v/v); (e) microcosms with water contaminated with crude oil and the addition of biosurfactant—WOB; and (f) microcosms with water contaminated with crude oil and the addition of chemical surfactant—WOS. The microcosms were incubated at 20 °C, under shaking conditions (75 rpm) and in the dark, during 30 days.

DNA extraction

After 30 days of incubation, the content of each microcosm (samples in triplicate) was filtered through a Millipore membrane (0.45 μm), and the community DNA was then extracted using FastDNA® Spin Kit for Soil (BIO 101 Systems, OH, USA) and then stored at 4 °C prior to PCR amplification.

High-throughput sequencing and data analyses

DNA obtained from the different microcosms was PCR-amplified using primers 515F/806R [17], which target the V4 region of the 16S rRNA encoding gene. Amplification, pooling, and purification were performed by Macrogen (South Korea). All samples were sequenced using a MiSeq Platform and the MiSeq Reagent kit v3 (Illumina, USA). All subsequent analyses were carried out with the software package QIIME (Quantitative Insights into Microbial Ecology toolkit; [18]). The sequences were trimmed using parameters such as quality score (> 30) and sequence length (> 100), maximum homopolymer length of 6, and 0 mismatched bases in the primers and barcodes. Barcodes and adapters were also removed. The forward and reverse sequences were merged using the fastq-join tool [19], demultiplexed and quality-filtered as described by Bokulich et al. [20]. The sequence analysis tool USEARCH was used to cluster sequences in operational taxonomic units (OTUs) at 97% sequence identity and to remove chimeras from the sequences. Then, representative sequences from each OTU were aligned with the Greengenes Core Set [21] using PyNAST [22]. Taxonomy was assigned to sequences using the BLAST tool [23]. Before analysis, singletons, chloroplast plastid, mitochondrion, and archaeal sequences were manually removed from the dataset. All sequences were deposited in the sequence read archive (SRA) of the National Center for Biotechnology Information (NCBI) under the accession number SRP151584.

Statistical analyses

The OTU-generated matrices were exported into PAST software [24] for non-metrical multidimensional scaling (NMDS—Bray-Curtis distance) analyses. ANOVA was used to check whether the sampling sites (GB and SI) and the applied treatments significantly influenced the bacterial communities, and Tukey’s test was used to determine the significance of differences (p < 0.05).

Bacterial community functional predictions

Predicted genes potentially related to metabolic pathways involved in petroleum hydrocarbon degradation were analyzed in all microcosms containing only oil and in those with oil added with either surfactin or Ultrasperse II using the software Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) [25]. Metagenomic functional predictions were compared using Tukey’s pairwise comparisons (p < 0.05).

Results

Diversity and composition of the bacterial communities in the different microcosms

The diversity and composition of the bacterial communities from the different microcosms (36 in total) were evaluated using high-throughput sequencing of the 16S rRNA gene after 30 days of incubation. A total of 1,885,728 sequences were obtained. After filtering out of low-quality sequences, OTU tables were rarified to analyze 12,000 sequences for each sample (based on the lowest number of sequences obtained per sample).

OTU numbers and the diversity measures obtained from the GB and SI samples are shown in Tables 1 and 2, respectively. Rarefaction curve expressing the estimated OTUs obtained from Grumari Beach and Schiermonnikoog Island samples and a graphic representation expressing the values of Chao and Shannon alpha diversity are shown in Figs. S1 and S2, respectively. Significant differences within the diversity values were determined using Tukey’s pairwise comparison tests (p < 0.05). Among the GB samples, the bacterial richness values (Chao1) were highest in WOS_GB (347.24 ± 40.56), whereas the lowest richness values were found in the W_GB (255.39 ± 32.63) and WO_GB (249.01 ± 26.02) samples (Table 1). However, W_GB and WO_GB were statistically similar to WB_GB, WS_GB, and WOB_GB. Conversely, bacterial diversity values (Shannon-Weaver index) were higher in the W_GB (6.10 ± 0.39) and WO_GB (5.96 ± 0.61) systems, as compared to WS_GB (4.30 ± 0.35), WOB_GB (4.29 ± 0.15), and WOS_GB (4.29 ± 0.39). In the SI systems, bacterial richness values (Chao1) were lower in WO_SI (207.40 ± 26.08) than in all other systems (Table 2). Moreover, whereas bacterial diversity (Shannon-Weaver index) was high in W_SI (5.60 ± 0.40), samples WS_SI (4.35 ± 0.23) and WOS_SI (4.58 ± 0.37) showed the lowest diversity indices (Table 2). Nonetheless, WS_SI and WOS_SI were statistically similar to the other samples.

Table 1.

Estimated OTUs, richness, and diversity indices based on the OTU-generated matrices of samples from Grumari Beach

Samples	Observed OTUs	Chao (richness)	Shannon (diversity)	Phylogenetic distance
W_GB	222.53 ± 37.17^a*	255.39 ± 32.63^a	6.10 ± 0.39^a	24.87 ± 1.2^a
WB_GB	248.3 ± 18.45^a	311.85 ± 28.63^ab	5.03 ± 0.60^ab	23.74 ± 1.04^a
WS_GB	243.73 ± 8.97^a	331.01 ± 4.32^ab	4.30 ± 0.35^b	23.5 ± 1.51^a
WO_GB	207.8 ± 28.19^a	249.01 ± 26.02^a	5.96 ± 0.61^a	24.8 ± 1.25^a
WOB_GB	210.1 ± 25.17^a	270.35 ± 37.10^ab	4.29 ± 0.15^b	22.13 ± 2.15^a
WOS_GB	259.63 ± 26.62^a	347.24 ± 40.56^b	4.29 ± 0.39^b	23.82 ± 2.14^a

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GB, Grumari Beach; W, water; WB, water and surfactin; WS, water and Ultrasperse II; WO, water and oil; WOB, water, oil, and surfactin; WOS, water, oil and Ultrasperse II

*Different letters indicate statistically significant differences based on Tukey’s test (p < 0.05)

Table 2.

Estimated OTUs, richness, and diversity indices based on the OTU-generated matrices of samples from Schiermonnikoog Island

Samples	Observed OTUs	Chao (richness)	Shannon (diversity)	Phylogenetic distance
W_SI	247.65 ± 8.41^a*	293.44 ± 24.72^a	5.60 ± 0.40^a	24.36 ± 2.37^a
WB_SI	224.4 ± 18.61^a	292.69 ± 17.02^a	5.07 ± 0.11^ab	22.84 ± 1.50^ac
WS_SI	227.96 ± 8.39^a	295.80 ± 12.30^a	4.35 ± 0.23^b	21.91 ± 1.25^ac
WO_SI	165.7 ± 16.60^b	207.40 ± 26.08^b	4.93 ± 0.71^ab	19.46 ± 1.35^bc
WOB_SI	218.73 ± 23.60^a	271.63 ± 25.71^a	5.23 ± 0.48^ab	20.63 ± 1.45^bc
WOS_SI	225.13 ± 21.47^a	275.78 ± 26.60^a	4.58 ± 0.37^b	20.48 ± 1.83^bc

Open in a new tab

SI, Schiermonikoog Island; W, water; WB, water and surfactin; WS, water and Ultrasperse II; WO, water and oil; WOB, water, oil, and surfactin; WOS, water, oil, and Ultrasperse II

*Different letters indicate statistically significant differences based on Tukey’s test (p < 0.05)

Multivariate analyses of 16S rRNA gene–based OTUs

NMDS ordination based on the OTU-generated matrices was used to compare the GB or SI samples, in separate and together. The analysis revealed clear clusterings based on the parameters “addition of oil” and “dispersant type” added to the GB or SI microcosms (Fig. 1A, B). WB and WOB were separated from W, WO, WS, and WOS in both marine waters studied, indicating the effect of the biosurfactant used. In general, oil-contaminated samples were separated from uncontaminated samples in GB (Fig. 1A). WB and WOB showed to be closer to each other (Fig. 1A). The separation of WS and WB in SI is clearly shown in Fig. 1B.

Fig. 1 — Non-metric multidimensional scaling (NMDS) ordination diagram based on the OTU-generated matrices obtained after sequencing using primers for the 16S rRNA-coding gene. A—samples from Grumari beach (GB); B—samples from Schiermonikoog Island (SI), and C and D—all samples analyzed together. ○ water; ● water and oil; Δ water and surfactin; ▲ water, oil, and surfactin; □ water and ultrasperse II; ■ water, oil, and ultrasperse II. Black and red colors represent GB and SI, respectively

Analysis of all samples (GB and SI) using NMDS revealed a separation of the samples with the addition of oil and/or dispersants—WS, WB, WOS, and WOB—from those only contaminated with oil—W and WO (when coordinates 1 and 2 were analyzed; Fig. 1C), suggesting an influence of the dispersant addition. The influence of the type of dispersant used was observed when the samples contaminated with Ultrasperse II (WS_GB, WOS_GB, WS_SI, and WOS_SI) were separated from those samples treated with surfactin (WB_GB, WOB_GB, WB_SI, and WOB_SI) when coordinates 2 and 3 were analyzed (Fig. 1D). Furthermore, a clear distinction between the GB and SI samples was observed (Fig. 1D).

Relative abundance of bacterial types in the different microcosms

In a separate analysis of each replicate with respect to the OTU types, we found similar bacterial groups at similar percentages of the total among the replicates of the same treatment. Hence, in further analyses, we considered the averages of the relative abundances of the different taxa found in the different microcosms incubated for 30 days. Only the taxonomic groups with more than 5% relative abundance were considered for further analyses, and the remainder (less than 5% of the relative abundance) being denoted as “others” (Fig. 2).

Fig. 2 — Relative abundances of the most abundant classes found in the marine water samples from Grumari Beach (GB) and Schiermonnikoog Island (SI) as determined by high-throughput sequencing. W, microcosms containing only water; WB, microcosms with water and surfactin; WS, microcosms with water and Ultrasperse II; WO, microcosms with water contaminated with crude oil (1% v/v); WOB, microcosms with oil-contaminated water plus surfactin; WOS, microcosms with oil-contaminated water plus Ultrasperse II

Identical phyla were found among the W_GB and W_SI microbial communities, albeit at different relative abundances. Thus, Bacteroidetes (Flavobacteria class) and Proteobacteria (mainly Gammaproteobacteria and Alphaproteobacteria classes) were the most abundant phyla found. The presence of oil and dispersants in the GB and SI samples incited different responses in the respective bacterial communities (Fig. 2).

In the GB samples, slight increases of Alphaproteobacteria and Gammaproteobacteria and decreases of Bacteroidetes were observed with the addition of surfactin (W_GB versus WB_GB). On the other hand, an increase of Bacteroidetes and a decrease of Gammaproteobacteria were observed with the addition of Ultrasperse II (WS_GB). The presence of oil (WO_GB) resulted in an increase of Alphaproteobacteria and a decrease of Gammaproteobacteria. The most prominent alterations were observed with the combined addition of oil and dispersants. When surfactin was added to the oil-contaminated microcosms (WOB_GB), the relative abundance of Planctomycetes increased up to more than 30% when compared with W_GB. In addition, a decrease of Gammaproteobacteria and an increase of Alphaproteobacteria were also detected. The main effect was observed with the combination of oil and Ultrasperse II (WOS_GB). While a considerable increase of the relative abundance of OTUs related to Bacteroidetes was observed, increases of those related to Planctomycetes were not found in WOS_GB (as observed in WOB_GB).

The results obtained in the GB and SI systems were different (Fig. 2). With the addition of surfactin but not of Ultrasperse II in SI, an increase of the relative abundance of OTUs related to Bacteroidetes was observed. Conversely, an increase of Gammaproteobacteria was observed in WS_SI compared with W_SI. With oil addition (WO_SI), we observed the appearance of OTUs related to Actinobacteria which were not found in the surfactin- or Ultrasperse II-amended water systems. Furthermore, an increase of Alphaproteobacteria was also observed in WO_SI. OTUs related to the Gammaproteobacteria dominated in the systems treated with oil and Ultrasperse II (WOS_SI).

Using the genus level, different bacterial communities were observed in GB versus SI (Fig. 3A and B, respectively). Briefly, OTUs related to Alcanivorax, Sinobacteriaceae and Parvibaculum were enriched by the presence of surfactin in the GB samples. Members of the Flavobacteriaceae were enriched with the addition of Ultrasperse II with or without the presence of oil (WS_GB or WOS_GB). While OTUs related to Planctomyces increased with the addition of surfactin and oil (in WOB_GB), they were not detected with Ultrasperse II as the dispersant used (WOS_GB) (Fig. 3A).

The relative abundance of OTUs related to Rhodobacteriaceae, Microbacteriaceae, and Loktanella increased in oil-containing SI samples (WO_SI). Marinobacter, Kordia, and Winogradskyella were enriched with the addition of surfactin (WB_SI) but decreased with the presence of oil (WOB_SI). OTUs related to Pseudoalteromonas were mainly enriched in oil- and Ultrasperse II-containing samples (representing 24% in WOS_SI) (Fig. 3B).

Predicting functions involved in oil degradation

Using PICRUSt, fifteen predicted genes potentially related to metabolic pathways involved in petroleum hydrocarbon degradation were analyzed in all microcosms containing only oil and in those with oil added with either surfactin or Ultrasperse II (Fig. 4). The abundances of genes encoding enzymes potentially involved in aminobenzoate, benzoate, chloroalkane, chloroalkene, chlorocyclohexane, chlorobenzene, fluorobenzene, naphthalene, styrene, toluene and xylene degradation, and butanoate metabolism were significantly increased in surfactin-treated GB samples (Fig. 4A). Conversely, 9 and 14 of the 15 predicted genes potentially related to hydrocarbon degradation significantly decreased in Ultrasperse II-treated GB and SI microcosms, respectively, when compared with oil-contaminated microcosms (Fig. 4A, B).

Fig. 4 — Abundance of predicted genes coding for metabolic pathways related to hydrocarbon degradation in samples (using PICRUSt) from (A) Grumari beach—GB and (B) Schiermonikoog Island—SI containing oil (WO), oil with surfactin (WOB), and oil with Ultrasperse II (WOS). Different letters indicate statistically significant differences (p < 0.05)

Discussion

The search for an efficient and environmentally friendly technology to remediate spills of crude oil and derived products is still a challenge. The rate of oil biodegradation is related to the presence of populations of microorganisms that will transform oil components into degradable compounds; this process is governed by pollutant bioavailability [26, 27]. Therefore, the use of dispersants is a common practice in terrestrial and marine environments to improve the availability of oil to microbial degraders.

In a previous study, Couto et al. [7] used quantitative PCR (alkB genes) and genetic fingerprint analyses (PCR-DGGE) to demonstrate that the addition of either surfactin or Ultrasperse II influenced the structure and abundance of total and oil-degrading bacterial communities of the oil-contaminated and uncontaminated marine waters collected from two regions with contrasting climatic conditions also used in this study. The enhancement of the oil-degrading bacteria with the addition of surfactin was greater than that observed with Ultrasperse II [7]. Here, the compositions of the bacterial communities of these two marine waters are compared using sequence analyses to better understand the effects of oil, surfactin, and Ultrasperse II on their dynamics and diversity.

Our finding that the diversity indices (Shannon) decreased with the addition of either Ultrasperse II or surfactin (with or without oil) in both GB and SI was in line with that of Liu et al. [28], who showed that the application of oil and dispersant typically led to the lowest alpha diversity of microbial communities from on-ship microcosms set up immediately after water collection in Eastern Mediterranean Deep Sea. Possibly, both agents incited selective (toxicity or growth related) pressures resulting in such lowered diversities.

Evaluating bacterial community structures in the oil collected from the sea surface and sediment in the northern Gulf of Mexico after the Deepwater Horizon oil spill, Liu and Liu [29] showed that Proteobacteria was the most dominant phylum in both the oil spill and the control site. Lee and Eom [30] also demonstrated that the phylum Proteobacteria accounted for 68.44% of the bacterial community in the seawater collected from Mallipo, South Korea. Similarly, we observed more than 50% and about 40% Proteobacteria (Gammaproteobacteria and Alphaproteobacteria)-related OTUs in uncontaminated GB and in SI samples, respectively. Indeed, both marine waters contained largely the same phyla but at different proportions (Fig. 2). However, with the introduction of the different dispersants, the bacterial communities responded differently. With the addition of Ultrasperse II in GB, an increase of the OTU numbers related to Bacteroidetes was observed. This effect was accentuated with the introduction of oil when Bacteroidetes corresponded to more than 60% of the OTUs. On the other hand, the major effect of Ultrasperse II in SI was the increase of Gammaproteobacteria (constituting almost 70% of the OTUs). Considering the use of the biosurfactant, OTUs related to Planctomycetes were clearly stimulated with the addition of surfactin in GB, reaching over 30% abundance with the addition of oil. Oppositely, Yu et al. [31] suggested strong negative impacts of the oil contamination and/or biostimulation treatments on Planctomycetes and other phyla in the saline soil of Yellow River Delta, China. In that environment, Alcanivorax-related Gammaproteobacteria rapidly dominated and contributed to the biodegradation of easily degradable portion of the heavy crude oil [31].

Various drivers of observed differences included OTUs affiliated with bacterial groups that are considered (or at least suspected) to be hydrocarbon degraders [32]. In treatments containing surfactin in GB, we observed the increase of OTUs related to Alcanivorax, Planctomyces, and Kordiimonadaceae. These two latter groups were stimulated with the addition of oil. Alcanivorax has been previously related to petroleum hydrocarbon degradation [31–33]. Flavobacteriaceae- and Kordiimonadaceae-related OTUs were present in Ultrasperse II–treated GB microcosms. While the former is stimulated with oil addition, the other was not found among the most abundant OTUs in WOS_GB. In treatments containing surfactin in SI, significant increases in the abundance of OTUs related to Winogradskyella and Marinobacter were observed, also suggesting an ability of these microorganisms to take advantage of the presence of biosurfactant and outcompete other microorganisms. Members of Winogradskyella have been already involved with the degradation of naphthalene and fluorene [34] and the genus Marinobacter has also been reported as a suspected taxa known to contain oil-degraders [5, 6, 32, 35]; however, in our study, OTUs related to this genus decreased in abundance with oil addition. On the other hand, other hydrocarbon degraders, such as Hyphomonas and others, were stimulated by the presence of oil. A similar situation was observed with the addition of Ultrasperse II in SI microcosms. OTUs related to Pseudoalteromonas reached to 25% of the total in WOS_SI. This latter genus was found in oil-polluted water column of the North Sea as a potential hydrocarbon degrader [36].

Finally, PICRUSt was used in an attempt to assign a function to phylogeny. It is very useful to build hypotheses, based on data concerning the enrichment of bacterial groups related to the hydrocarbon degradation in oil and/or dispersant-marine water samples. For example, after the DWH oil spill, many genes related to aromatic and aliphatic biodegradation were found to be enriched in the bacterial communities from both surface and deep water and strong biodegradation of those compounds was detected [34, 37, 38]. In our study, PICRUSt revealed a potential enrichment of several genes that were potentially involved in oil degradation, mainly after the surfactin addition in GB (Fig. 4). In contrast, a decrease of the abundance of several predicted biodegradative genes was observed after the use of Ultrasperse II in either oil-contaminated GB or SI. Couto et al. [7] have previously shown that significantly higher alkane-monooxygenase AlkB (alkB) copy numbers were observed in surfactin and oil-containing microcosms from GB and SI, suggesting that the addition of surfactin stimulated oil-degrading bacteria more than the chemical surfactant used here. Therefore, data obtained in the present study not only corroborate surfactin as a promising alternative to the recovery of oil-contaminated environments but also demonstrate its effects on the dynamics and diversity of the bacterial communities in two marine waters.

Conclusions

The effects of chemical surfactant (Ultrasperse II) and biosurfactant (surfactin) on the bacterial communities differed in both oil-contaminated and uncontaminated marine water from both regions (GB and SI). The dispersants used influenced more the original bacterial communities than the addition of oil. Different bacteria related to hydrocarbon degradation were stimulated by the addition of both surfactants. The use of surfactin resulted in an increase of gene abundance related to hydrocarbon degradation mainly in the tropical region (GB), while the use of Ultrasperse II led to the decrease of abundance of these genes in both regions studied (GB and SI). Therefore, surfactin is a promising alternative to the petroleum industry, with the potential to contribute to the restoration of oil-contaminated environments.

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Funding information

This study was supported by grants from the National Research Council of Brazil (CNPq), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Footnotes

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7.Couto CRA, Jurelevicius DA, Alvarez VM, van Elsas JD, Seldin L. Response of the bacterial community in oil-contaminated marine water to the addition of chemical and biological dispersants. J Environ Manag. 2016;184:473–479. doi: 10.1016/j.jenvman.2016.10.039. [DOI] [PubMed] [Google Scholar]
8.Edwards KR, Lepo JE, Lewis MA. Toxicity comparison of biosurfactants and synthetic surfactants used in oil spill remediation to two estuarine species. Mar Pollut Bull. 2003;46:1309–1316. doi: 10.1016/S0025-326X(03)00238-8. [DOI] [PubMed] [Google Scholar]
9.George-Ares A, Clark JR. Aquatic toxicity of two Corexit dispersants. Chemosphere. 2000;40:897–906. doi: 10.1016/S0045-6535(99)00498-1. [DOI] [PubMed] [Google Scholar]
10.Overholt WA, Marks KP, Romero IC, Hollander DJ, Snell TW, Kostka JE. Hydrocarbon-degrading bacteria exhibit a species-specific response to dispersed oil while moderating ecotoxicity. Appl Environ Microbiol. 2016;82:518–527. doi: 10.1128/AEM.02379-15. [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Kapadia SG, Yagnik BN. Current trend and potential for microbial biosurfactants. Asian J Exp Biol Sci. 2013;4:1–8. [Google Scholar]
12.Silva RCFS, Almeida DG, Rufino RD, Luna JM, Santos VA, Sarubbo LA. Applications of biosurfactants in the petroleum industry and the remediation of oil spills. Int J Mol Sci. 2014;15:12523–12542. doi: 10.3390/ijms150712523. [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Karlapudi AP, Venkateswarulu TC, Tammineedi J, Kanumuri L, Ravuru BK, Dirisala VR, Kodali VP. Role of biosurfactants in bioremediation of oil pollution - a review. Petroleum. 2018;4:241–249. doi: 10.1016/j.petlm.2018.03.007. [DOI] [Google Scholar]
14.Korenblum E, der Weid I, Santos AL, Rosado AS, Sebastián GV, Coutinho CM, Magalhães FC, Paiva MM, Seldin L. Production of antimicrobial substances by Bacillus subtilis LFE-1, B. firmus H2O-1 and B. licheniformis T6-5 isolated from an oil reservoir in Brazil. J Appl Microbiol. 2005;98:667–675. doi: 10.1111/j.1365-2672.2004.02518.x. [DOI] [PubMed] [Google Scholar]
15.Guimarães CR, Pasqualino IP, da Mota FF, Godoy MG, Seldin L, Castilho LVA, Freire DMG. Surfactin from Bacillus velezensis H2O-1: production and physicochemical characterization for postsalt applications. J Surfactant Deterg. 2019;22:451–462. doi: 10.1002/jsde.12250. [DOI] [Google Scholar]
16.Korenblum E, de Araujo LV, Guimarães CR, de Souza LM, Sassaki G, Abreu F, Nitschke M, Lins U, Freire DMG, Eliana Barreto-Bergter E, Seldin L. Purification and characterization of a surfactin-like molecule produced by Bacillus sp. H2O-1 and its antagonistic effect against sulfate reducing bacteria. BMC Microbiol. 2012;12:252. doi: 10.1186/1471-2180-12-252. [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, Fierer N, Knight R. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci U S A. 2011;108:4516–4522. doi: 10.1073/pnas.1000080107. [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Peña AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–336. doi: 10.1038/nmeth.f.303. [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Aronesty E (2011) ea-utils: Command-line tools for processing biological sequencing data. http://code.google.com/p/ea-utils
20.Bokulich NA, Subramanian S, Faith JJ, Gevers D, Gordon JI, Knight R, Mills DA, Caporaso JG. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat Methods. 2013;10:57–59. doi: 10.1038/nmeth.2276. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72:5069–5072. doi: 10.1128/AEM.03006-05. [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Caporaso JG, Bittinger K, Bushman FD, DeSantis TZ, Andersen GL, Knight R. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics. 2010;26:266–267. doi: 10.1093/bioinformatics/btp636. [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PS I-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25:3389–3402. doi: 10.1093/nar/25.17.3389. [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Hammer Ø, Harper DAT, Ryan PD (2001) PAST: paleontological statistics software package for education and data analysis. Palaeontol Electron 4(1):9pp. http://palaeo-electronica.org/2001_1/past/issue1_01.htm
25.Langille MG, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, Clemente JC, Burkepile DE, Vega Thurber RL, Knight R, Beiko RG, Huttenhower C. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol. 2013;31(9):814–821. doi: 10.1038/nbt.2676. [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Onwurah INE, Ogugua VN, Onyike NB, Ochonogor AE, Otitoju OF Crude oil spills in the environment, effects and some innovative clean-up biotechnologies. Int J Environ Res. 2007;1:307–320. [Google Scholar]
27.Yakimov MM, Timmis KN, Golyshin PN. Obligate oil-degrading marine bacteria. Curr Opin Biotechnol. 2007;18:257–266. doi: 10.1016/j.copbio.2007.04.006. [DOI] [PubMed] [Google Scholar]
28.Liu J, Techtmann SM, Woo HL, Ning D, Fortney JL, Hazen TC. Rapid response of Eastern Mediterranean Deep Sea microbial communities to oil. Sci Rep. 2017;7:5762. doi: 10.1038/s41598-017-05958-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Liu Z, Liu J. Evaluating bacterial community structures in oil collected from the sea surface and sediment in the northern Gulf of Mexico after the Deepwater Horizon oil spill. MicrobiologyOpen. 2013;2(3):492–504. doi: 10.1002/mbo3.89. [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Lee S-Y, Eom Y-B. Analysis of microbial composition associated with freshwater and seawater. Biomed Sci Lett. 2016;22:150–159. doi: 10.15616/BSL.2016.22.4.150. [DOI] [Google Scholar]
31.Yu S, Li S, Tang Y, Wu X. Succession of bacterial community along with the removal of heavy crude oil pollutants by multiple biostimulation treatments in the Yellow River Delta, China. J Environ Sci. 2011;23(9):1533–1543. doi: 10.1016/S1001-0742(10)60585-2. [DOI] [PubMed] [Google Scholar]
32.Varjani SJ. Microbial degradation of petroleum hydrocarbons. Bioresour Technol. 2017;223:277–286. doi: 10.1016/j.biortech.2016.10.037. [DOI] [PubMed] [Google Scholar]
33.Brooijmans RJW, Pastink MI, Siezen RJ. Hydrocarbon-degrading bacteria: the oil-spill clean-up crew. Microb Biotechnol. 2009;2:587–594. doi: 10.1111/j.1751-7915.2009.00151.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Kappell AD, Wei Y, Newton RJ, Van Nostrand JD, Zhou J, McLellan SL, et al. The polycyclic aromatic hydrocarbon degradation potential of Gulf of Mexico native coastal microbial communities after the Deepwater Horizon oil spill. Front Microbiol. 2014;5:205. doi: 10.3389/fmicb.2014.00205. [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Fathepure BZ. Recent studies in microbial degradation of petroleum hydrocarbons in hypersaline environments. Front Microbiol. 2014;5:173. doi: 10.3389/fmicb.2014.00173. [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Chronopoulou P-M, Sanni GO, Silas-Olu DI, van der Meer JR, Timmis KN, Brussaard CPD, McGenity TJ. Generalist hydrocarbon-degrading bacterial communities in the oil-polluted water column of the North Sea. Microb Biotechnol. 2015;8(3):434–447. doi: 10.1111/1751-7915.12176. [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Lu Z, Deng Y, Van Nostrand JD, He Z, Voordeckers J, Zhou A, Lee Y-J, Mason OU, Dubinsky EA, Chavarria KL, Tom LM, Fortney JL, Lamendella R, Jansson JK, D'haeseleer P, Hazen TC, Zhou J. Microbial gene functions enriched in the Deepwater Horizon deep-sea oil plume. ISME J. 2012;6:451–460. doi: 10.1038/ismej.2011.91. [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Mason OU, Scott NM, Gonzalez A, Robbins-Pianka A, Balum J, Kimbrel J, Bouskill NJ, Prestat E, Borglin S, Joyner DC, Fortney JL, Jurelevicius D, Stringfellow WT, Alvarez-Cohen L, Hazen TC, Knight R, Gilbert JA, Jansson JK. Metagenomics reveals sediment microbial community response to Deepwater Horizon oil spill. ISME J. 2014;8(7):1464–1475. doi: 10.1038/ismej.2013.254. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

ESM 1^{(204KB, doc)}

(DOC 204 kb)

[CR1] 1.Prince RC, Mcfarlin KM, Butler JD, Febb EJ, Wang FCY, Nedwed TJ. The primary biodegradation of dispersed crude oil in the sea. Chemosphere. 2013;90:521–526. doi: 10.1016/j.chemosphere.2012.08.020. [DOI] [PubMed] [Google Scholar]

[CR2] 2.Rahsepar S, Smit MP, Murk AJ, Rijnaarts HH, Langenhoff AA. Chemical dispersants: oil biodegradation friend or foe? Mar Pollut Bull. 2016;108:113–119. doi: 10.1016/j.marpolbul.2016.04.044. [DOI] [PubMed] [Google Scholar]

[CR3] 3.Kleindienst S, Seidel M, Ziervogel K, Grim S, Loftis K, Harrison S, Malkin SY, Perkins MJ, Field J, Sogin ML, Dittmar T, Passow U, Medeiros PM, Joye SB. Chemical dispersants can suppress the activity of natural oil-degrading microorganisms. Proc Natl Acad Sci U S A. 2015;112(48):14900–14905. doi: 10.1073/pnas.1507380112. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR4] 4.Kujawinski EB, Kido Soule MC, Valentine DL, Boysen AK, Longnecker K, Redmond MC. Fate of dispersants associated with the Deepwater Horizon oil spill. Environ Sci Technol. 2011;45:1298–1306. doi: 10.1021/es103838p. [DOI] [PubMed] [Google Scholar]

[CR5] 5.Hamdan LJ, Fulmer PA. Effects of COREXIT® EC9500A on bacteria from a beach oiled by the Deepwater Horizon spill. Aquat Microb Ecol. 2011;63:101–109. doi: 10.3354/ame01482. [DOI] [Google Scholar]

[CR6] 6.Techtmann SM, Zhuang M, Campo P, Holder E, Elk M, Hazen TC, Conmy R, Domingo JWS. Corexit® 9500 enhances oil biodegradation and changes active bacterial community structure of oil enriched microcosms. Appl Environ Microbiol. 2017;83:e03462–e03416. doi: 10.1128/AEM.03462-16. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR7] 7.Couto CRA, Jurelevicius DA, Alvarez VM, van Elsas JD, Seldin L. Response of the bacterial community in oil-contaminated marine water to the addition of chemical and biological dispersants. J Environ Manag. 2016;184:473–479. doi: 10.1016/j.jenvman.2016.10.039. [DOI] [PubMed] [Google Scholar]

[CR8] 8.Edwards KR, Lepo JE, Lewis MA. Toxicity comparison of biosurfactants and synthetic surfactants used in oil spill remediation to two estuarine species. Mar Pollut Bull. 2003;46:1309–1316. doi: 10.1016/S0025-326X(03)00238-8. [DOI] [PubMed] [Google Scholar]

[CR9] 9.George-Ares A, Clark JR. Aquatic toxicity of two Corexit dispersants. Chemosphere. 2000;40:897–906. doi: 10.1016/S0045-6535(99)00498-1. [DOI] [PubMed] [Google Scholar]

[CR10] 10.Overholt WA, Marks KP, Romero IC, Hollander DJ, Snell TW, Kostka JE. Hydrocarbon-degrading bacteria exhibit a species-specific response to dispersed oil while moderating ecotoxicity. Appl Environ Microbiol. 2016;82:518–527. doi: 10.1128/AEM.02379-15. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR11] 11.Kapadia SG, Yagnik BN. Current trend and potential for microbial biosurfactants. Asian J Exp Biol Sci. 2013;4:1–8. [Google Scholar]

[CR12] 12.Silva RCFS, Almeida DG, Rufino RD, Luna JM, Santos VA, Sarubbo LA. Applications of biosurfactants in the petroleum industry and the remediation of oil spills. Int J Mol Sci. 2014;15:12523–12542. doi: 10.3390/ijms150712523. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR13] 13.Karlapudi AP, Venkateswarulu TC, Tammineedi J, Kanumuri L, Ravuru BK, Dirisala VR, Kodali VP. Role of biosurfactants in bioremediation of oil pollution - a review. Petroleum. 2018;4:241–249. doi: 10.1016/j.petlm.2018.03.007. [DOI] [Google Scholar]

[CR14] 14.Korenblum E, der Weid I, Santos AL, Rosado AS, Sebastián GV, Coutinho CM, Magalhães FC, Paiva MM, Seldin L. Production of antimicrobial substances by Bacillus subtilis LFE-1, B. firmus H2O-1 and B. licheniformis T6-5 isolated from an oil reservoir in Brazil. J Appl Microbiol. 2005;98:667–675. doi: 10.1111/j.1365-2672.2004.02518.x. [DOI] [PubMed] [Google Scholar]

[CR15] 15.Guimarães CR, Pasqualino IP, da Mota FF, Godoy MG, Seldin L, Castilho LVA, Freire DMG. Surfactin from Bacillus velezensis H2O-1: production and physicochemical characterization for postsalt applications. J Surfactant Deterg. 2019;22:451–462. doi: 10.1002/jsde.12250. [DOI] [Google Scholar]

[CR16] 16.Korenblum E, de Araujo LV, Guimarães CR, de Souza LM, Sassaki G, Abreu F, Nitschke M, Lins U, Freire DMG, Eliana Barreto-Bergter E, Seldin L. Purification and characterization of a surfactin-like molecule produced by Bacillus sp. H2O-1 and its antagonistic effect against sulfate reducing bacteria. BMC Microbiol. 2012;12:252. doi: 10.1186/1471-2180-12-252. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR17] 17.Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, Fierer N, Knight R. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci U S A. 2011;108:4516–4522. doi: 10.1073/pnas.1000080107. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR18] 18.Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Peña AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–336. doi: 10.1038/nmeth.f.303. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR19] 19.Aronesty E (2011) ea-utils: Command-line tools for processing biological sequencing data. http://code.google.com/p/ea-utils

[CR20] 20.Bokulich NA, Subramanian S, Faith JJ, Gevers D, Gordon JI, Knight R, Mills DA, Caporaso JG. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat Methods. 2013;10:57–59. doi: 10.1038/nmeth.2276. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR21] 21.DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72:5069–5072. doi: 10.1128/AEM.03006-05. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR22] 22.Caporaso JG, Bittinger K, Bushman FD, DeSantis TZ, Andersen GL, Knight R. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics. 2010;26:266–267. doi: 10.1093/bioinformatics/btp636. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR23] 23.Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PS I-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25:3389–3402. doi: 10.1093/nar/25.17.3389. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR24] 24.Hammer Ø, Harper DAT, Ryan PD (2001) PAST: paleontological statistics software package for education and data analysis. Palaeontol Electron 4(1):9pp. http://palaeo-electronica.org/2001_1/past/issue1_01.htm

[CR25] 25.Langille MG, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, Clemente JC, Burkepile DE, Vega Thurber RL, Knight R, Beiko RG, Huttenhower C. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol. 2013;31(9):814–821. doi: 10.1038/nbt.2676. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR26] 26.Onwurah INE, Ogugua VN, Onyike NB, Ochonogor AE, Otitoju OF Crude oil spills in the environment, effects and some innovative clean-up biotechnologies. Int J Environ Res. 2007;1:307–320. [Google Scholar]

[CR27] 27.Yakimov MM, Timmis KN, Golyshin PN. Obligate oil-degrading marine bacteria. Curr Opin Biotechnol. 2007;18:257–266. doi: 10.1016/j.copbio.2007.04.006. [DOI] [PubMed] [Google Scholar]

[CR28] 28.Liu J, Techtmann SM, Woo HL, Ning D, Fortney JL, Hazen TC. Rapid response of Eastern Mediterranean Deep Sea microbial communities to oil. Sci Rep. 2017;7:5762. doi: 10.1038/s41598-017-05958-x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR29] 29.Liu Z, Liu J. Evaluating bacterial community structures in oil collected from the sea surface and sediment in the northern Gulf of Mexico after the Deepwater Horizon oil spill. MicrobiologyOpen. 2013;2(3):492–504. doi: 10.1002/mbo3.89. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR30] 30.Lee S-Y, Eom Y-B. Analysis of microbial composition associated with freshwater and seawater. Biomed Sci Lett. 2016;22:150–159. doi: 10.15616/BSL.2016.22.4.150. [DOI] [Google Scholar]

[CR31] 31.Yu S, Li S, Tang Y, Wu X. Succession of bacterial community along with the removal of heavy crude oil pollutants by multiple biostimulation treatments in the Yellow River Delta, China. J Environ Sci. 2011;23(9):1533–1543. doi: 10.1016/S1001-0742(10)60585-2. [DOI] [PubMed] [Google Scholar]

[CR32] 32.Varjani SJ. Microbial degradation of petroleum hydrocarbons. Bioresour Technol. 2017;223:277–286. doi: 10.1016/j.biortech.2016.10.037. [DOI] [PubMed] [Google Scholar]

[CR33] 33.Brooijmans RJW, Pastink MI, Siezen RJ. Hydrocarbon-degrading bacteria: the oil-spill clean-up crew. Microb Biotechnol. 2009;2:587–594. doi: 10.1111/j.1751-7915.2009.00151.x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR34] 34.Kappell AD, Wei Y, Newton RJ, Van Nostrand JD, Zhou J, McLellan SL, et al. The polycyclic aromatic hydrocarbon degradation potential of Gulf of Mexico native coastal microbial communities after the Deepwater Horizon oil spill. Front Microbiol. 2014;5:205. doi: 10.3389/fmicb.2014.00205. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR35] 35.Fathepure BZ. Recent studies in microbial degradation of petroleum hydrocarbons in hypersaline environments. Front Microbiol. 2014;5:173. doi: 10.3389/fmicb.2014.00173. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR36] 36.Chronopoulou P-M, Sanni GO, Silas-Olu DI, van der Meer JR, Timmis KN, Brussaard CPD, McGenity TJ. Generalist hydrocarbon-degrading bacterial communities in the oil-polluted water column of the North Sea. Microb Biotechnol. 2015;8(3):434–447. doi: 10.1111/1751-7915.12176. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR37] 37.Lu Z, Deng Y, Van Nostrand JD, He Z, Voordeckers J, Zhou A, Lee Y-J, Mason OU, Dubinsky EA, Chavarria KL, Tom LM, Fortney JL, Lamendella R, Jansson JK, D'haeseleer P, Hazen TC, Zhou J. Microbial gene functions enriched in the Deepwater Horizon deep-sea oil plume. ISME J. 2012;6:451–460. doi: 10.1038/ismej.2011.91. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR38] 38.Mason OU, Scott NM, Gonzalez A, Robbins-Pianka A, Balum J, Kimbrel J, Bouskill NJ, Prestat E, Borglin S, Joyner DC, Fortney JL, Jurelevicius D, Stringfellow WT, Alvarez-Cohen L, Hazen TC, Knight R, Gilbert JA, Jansson JK. Metagenomics reveals sediment microbial community response to Deepwater Horizon oil spill. ISME J. 2014;8(7):1464–1475. doi: 10.1038/ismej.2013.254. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Chemical and biological dispersants differently affect the bacterial communities of uncontaminated and oil-contaminated marine water

Camila Rattes de Almeida Couto

Deborah Catharine de Assis Leite

Diogo Jurelevicius

Jan Dirk van Elsas

Lucy Seldin

Abstract

Electronic supplementary material

Introduction

Materials and methods

Dispersant sources

Sample sites and construction of the microcosms

DNA extraction

High-throughput sequencing and data analyses

Statistical analyses

Bacterial community functional predictions

Results

Diversity and composition of the bacterial communities in the different microcosms

Table 1.

Table 2.

Multivariate analyses of 16S rRNA gene–based OTUs

Fig. 1.

Relative abundance of bacterial types in the different microcosms

Fig. 2.

Fig. 3.

Predicting functions involved in oil degradation

Fig. 4.

Discussion

Conclusions

Electronic supplementary material

Funding information

Compliance with ethical standards

Conflict of interest

Footnotes

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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