Publisher’s version / Version de l'éditeur:
L’accès à ce site Web et l’utilisation de son contenu sont assujettis aux conditions présentées dans le site
Environmental Microbiology, 21, 7, pp. 2307-2319, 2019-07-01
READ THESE TERMS AND CONDITIONS CAREFULLY BEFORE USING THIS WEBSITE.
https://nrc-publications.canada.ca/eng/copyright
NRC Publications Archive Record / Notice des Archives des publications du CNRC :
https://nrc-publications.canada.ca/eng/view/object/?id=6875a2bf-3c74-47fb-abf3-71f5e5ce37d3 https://publications-cnrc.canada.ca/fra/voir/objet/?id=6875a2bf-3c74-47fb-abf3-71f5e5ce37d3
NRC Publications Archive
Archives des publications du CNRC
This publication could be one of several versions: author’s original, accepted manuscript or the publisher’s version. / La version de cette publication peut être l’une des suivantes : la version prépublication de l’auteur, la version acceptée du manuscrit ou la version de l’éditeur.
For the publisher’s version, please access the DOI link below./ Pour consulter la version de l’éditeur, utilisez le lien DOI ci-dessous.
https://doi.org/10.1111/1462-2920.14609
Access and use of this website and the material on it are subject to the Terms and Conditions set forth at
Metagenomic and metatranscriptomic responses of natural oil
degrading bacteria in the presence of dispersants
Tremblay, Julien; Fortin, Nathalie; Elias, Miria; Wasserscheid, Jessica; King,
Thomas L.; Lee, Kenneth; Greer, Charles W.
Metagenomic and metatranscriptomic responses of natural oil degrading
bacteria in the presence of dispersants.
Running title: Oil degradation both coasts NGS
Scope: genomics, functional genomics, environmental genomics/metagenomics, bioinformatic analyses and comparative genomics
Julien Tremblay1*, Nathalie Fortin1, Miria Elias1, Jessica Wasserscheid1, Thomas L. King2,
Kenneth Lee3 and Charles W. Greer1*
1: Energy, Mining and Environment, National Research Council Canada, 6100 Royalmount
Avenue, Montreal, QC, Canada, H4P2R2.
2: Centre for Offshore Oil, Gas and Energy Research (COOGER), Fisheries and Oceans
Canada, Dartmouth,NS, Canada B2Y4A2
3: Fisheries and Oceans Canada, Bedford Institute of Oceanography, PO Box 1006, Dartmouth,
NS, Canada, B2Y4A2
*: Corresponding authors – julien.tremblay@nrc-cnrc.gc.ca; charles.greer@nrc-cnrc.gc.ca
Abstract
Oil biodegradation has been extensively studied in the wake of the Deepwater Horizon spill, but the application of dispersant to oil spills in marine environments remains controversial. Here we report metagenomic (MG) and metatranscriptomic (MT) data mining from microcosm experiments investigating the oil degrading potential of Canadian west and east coasts in order to estimate the gene abundance and activity of oil degrading bacteria in the presence of dispersant. We found that the addition of dispersant to crude oil mainly favors the abundance of Thalassolituus in the summer and Oleispira in the winter, two key natural oil degrading bacteria. We found a high abundance of genes related not only to n-alkane and aromatics degradation, but also associated with transporters, two-component systems, bacterial motility, secretion systems and bacterial chemotaxis.
Introduction
Crude oil enters the marine environment through natural geophysical processes at an estimated rate of 700 million liters per year (Committee on Oil in the Sea: Inputs, Fates, and Effects et al., 2003; Kvenvolden and Cooper, 2003). Continuous exposure of native microbes to low concentrations of hydrocarbons enables the maintenance in the environment of
energy sources (Head et al., 2006; Widdel et al., 2010). In the context of the recent Deepwater Horizon (DWH) MC252 oil spill in the Gulf of Mexico which released an estimated 750 million liters of oil into the gulf, there is scientific consensus that hydrocarbonoclastic bacteria played a major role in the removal of hydrocarbons from the ecosystem (Biello, 2010; American Academy of Microbiology, 2011).
For many decades, chemical dispersants have been used in catastrophic oil spills to help augment oil biodegradation rates and minimize the amount of oil accessing shorelines where clean up becomes considerably more problematic (Harris and Chris, 1995; Law and Carole, 2004; Henry and Charlie, 2005; Steen et al., 2008; Bejarano et al., 2013). Application of dispersants lowers the interfacial tension between oil and water leading to oil emulsification into tiny droplets which in turn increase bioavailability of crude oil to natural hydrocarbon degraders. Because of ecological tradeoffs (Smith, 1968), dispersant deployment is controversial and its effectiveness is debated (Committee on Understanding Oil Spill Dispersants: Efficacy and Effects et al., 2005; Hazen et al., 2010; Kleindienst, Seidel, et al., 2015; Brakstad et al., 2017).
The Douglas Channel, a fjord on the northwest coast of Canada is currently being considered to host liquid natural gas plant installations and an oil pipeline with an endpoint at the industrial town of Kitimat (Johannessen et al., 2015). There is also a proposal to transport diluted bitumen (a heavy tar mixed with light oils) in a pipeline from northern Alberta to Kitimat and then ship this diluted bitumen down the Douglas Channel to access the Pacific Ocean. In order to evaluate the environmental effects of the aforementioned proposals we applied 16S
rRNA gene amplicon sequencing (Schreiber et al., submitted) and shotgun metagenomic and metatranscriptomic analyses (this study) to evaluate the natural microbial community response following hypothetical crude oil spills or various blends. The biodegradation of physically and chemically dispersed crude oil was monitored by conducting microcosm studies using summer and winter seawater freshly procured from three different locations on each coast. By making use of high throughput sequencing technology, recent efforts from our group have focused on understanding the dynamics of oil degrading microbial communities on Canadian coasts with an emphasis on the evaluation of the effect of adding chemical dispersants on oil biodegradation rates. We have shown that the addition of dispersant to oil enhances n-alkane degradation rates in microcosms from the east coast (Tremblay et al., 2017), but had little effect in west coast microcosms (Schrieber et al., - submitted). This latter study reports oil degrading data observed in the west coast microcosms for which we present the corresponding shotgun metagenomic and metatranscriptomic sequencing data in this study. With the objective of getting a broad overview of the microbial communities and metabolic functions involved in oil degradation supplemented with dispersants from both west and east Canadian coasts, we integrated the data of the current study to the sequencing data previously published by our group from the east coast (Tremblay et al., 2017) which also consists of shotgun metagenomic and
metatranscriptomic sequencing data.
Our bioinformatics analyses resulted in 2,426 metagenome bins (SI - Table S4). From a total of 4,465,294 assembled contigs, 631,164 were part of these bins which represented an integration rate of 14.13%. We computed alpha diversity (Observed bins index) metrics and as expected, found that T0 controls host more diverse communities than early and late microcosms (SI - Figure S1). In many cases, oil/disp early microcosms are less diverse than their oil only homologs. In terms of beta diversity (Bray-Curtis), oil/disp samples segregate farther than oil only samples in early microcosms (SI - Figure S2). Late microcosms tended to cluster together to form a post-oil degradation state.
In order to determine a broader picture of the microbial community structures in both west and east coast microcosms, we narrowed down our metagenome bin data by selecting taxonomic groups that are known to degrade oil (Figure 1). One of the most noticeable elements from this figure is that winter microcosms are distinct from summer microcosms for all sampling locations. On the west coast (figure 1, left panels), this is evident by the heavy dominance of Oleispira and to a lesser extent, Colwellia and Polaribacter. Oleispira is known to prosper in cold sea water (Yakimov et al., 2003, 2007) and has been observed in oil plumes (Hazen et al., 2010; Garneau et al., 2016), but it is to our knowledge, the first time that these bacteria are explicitly observed in such overwhelming abundance in seawater containing oil with dispersant. This genus was also found to be quite abundant in early oil only west coast microcosms during the winter and virtually absent in their corresponding east coast microcosms. Our data suggests that these Oleispira bins are actually more abundant in the west coast T0 microcosms (SI - Figure S3) and that east coast waters were less conducive for this bacterium to grow.
2016). Polaribacter seemed to be favored in west coast oil only compared to oil/disp
microcosms (Figure 1). It is possible that the presence of dispersant provided an advantage for Oleispira, which enabled it to outcompete other taxa such as Polaribacter and Colwellia. On the east coast, winter microcosms were dominated by Pseudoalteromonas (Hibernia and Terra Nova) while Cycloclasticus prevailed in Thebaud microcosms that contained gas condensate instead of crude oil (mostly PAHs), possibly explaining the abundance of this bacterium, a well-known PAH degrader (Harayama et al., 2004; Head et al., 2006; Coulon et al., 2007; McKew et al., 2007; Yakimov et al., 2007; Cui et al., 2008; Niepceron et al., 2010).
In the majority of early summer microcosms (east and west coasts) addition of
dispersant to oil favored the proliferation of Thalassolituus, consolidating the importance of this taxonomic group as being central to oil degradation in the presence of dispersant, as previously reported (Tremblay et al., 2017). Microorganisms belonging to the order Oceanospirillales have been observed in abundance in oil contaminated environments (Hazen et al., 2010; Valentine et al., 2010; Kessler et al., 2011; Redmond and Valentine, 2012; Gutierrez et al., 2013) and the hydrocarbon degrading capabilities of Thalassolituus is well established (Yakimov, 2004), but has only recently been suggested to play an important role in oil degradation in presence of dispersant (Tremblay et al., 2017).
Temporal yearly variations were generally observed at all stations (Figure 1). In FOC1A early microcosms, communities were different between 2014 (dominance of Thalassolituus in microcosms with dispersant) and 2015 (prevalence of Alteromonas, Glaciecola and
of Thalassolituus when dispersant was added to oil, compared to the summer of 2015 where samples were enriched with Alteromonas in the presence of dispersant.
Late summer microcosms from both coasts (28-42 days) were generally similar in their taxonomic composition with a dominance of Alcanivorax. In contrast, some notable differences were observed between early west and east coast summer microcosms. The latter ones are populated with Glaciecola, Pseudoalteromonas and Colwellia which was not always the case on the west coast with the recurrent absence of these three taxa. More generally, Glaciecola were found in high abundance in late summer west microcosms, but in very low abundance in late east microcosms. Differences were also observed regarding the abundance of Cycloclasticus. This taxon was dominant on the east coast at the Thebaud station in the winter. It was also found in significant abundance on the west coast in FOC1A winter microcosms. Late winter west coast microcosms were also different from their east coast equivalents. Alcanivorax made up a large proportion of the microbial populations in late winter east coast microcosms, but was practically absent in the west coast equivalents. Many types of oil degradation succession have been described in marine environments (Röling et al., 2002; Head et al., 2006; Yakimov et al., 2007; Berthe-Corti* and Nachtkamp, 2010; Greer, 2010; McGenity et al., 2012; Xia et al., 2018), low-temperature marine environments (Garneau et al., 2016; Ferguson et al., 2017; Tremblay et al., 2017; Ribicic et al., 2018) and the DWH spill (Atlas and Hazen, 2011; Joye et al., 2014; Kostka et al., 2014; King et al., 2015) and they usually end up with the proliferation and enrichment of a few distinct taxonomic groups (Alcanivorax, Colwellia, Glaciecola, etc.). However, all these studies, including this one, present some level of variation between successional patterns over time which reinforce the hypothesis that early and late microbial
community assemblages are influenced by both deterministic and stochastic processes (Dini-Andreote et al., 2015). However, when putting these taxonomic profiles in context with their corresponding oil degradation rates (Tremblay et al., 2017) Schreiber et al., submitted), it appears that regardless of which oil degrading microbes are present, oil is being degraded.
Addition of dispersant to oil triggers a response in DNA and transcript abundance belonging to specific taxa.
Usage of dispersant in oil spills remains controversial (Kleindienst, Paul, et al., 2015). Some recent studies suggest that dispersants enhance oil degradation rates (Tremblay et al., 2017), while others found a negative impact (Kleindienst, Seidel, et al., 2015) or no impact (Brakstad et al., 2017). We previously reported that adding dispersant to oil slightly favored n-alkane degradation in east coast microcosms (Tremblay et al., 2017) and did not have a significant impact in west coast microcosms (Schrieber et al, - submitted). Even if addition of dispersant to oil does not necessarily have an impact on oil degradation, we show here that adding dispersant to oil microcosms does result in modulating microbial community structure (Fig. 1; SI - Fig. S1-S2). We expanded this further by extracting all significantly differentially abundant genes between oil/disp vs oil early microcosms and found many genes whose
abundance and expression was driven upward in the presence of dispersant (Fig. 2; Table S5). Consistent with the population profiles shown in Figure 1, these genes mostly belong to
Thalassolituus and Oleispira in early microcosms and Alcanivorax and Marinobacter in late microcosms (Fig. 2). Thalassolituus was previously shown to proliferate in the presence of dispersant (Tremblay et al., 2017). Oleispira is known to reside in cold seawater (Yakimov et al.,
2003, 2007) and we also observed this microorganism in our winter microcosms. However, this genus has not previously been reported to thrive in the presence of dispersant, which may be due to the fact that most recent studies on seawater oil degradation were done using early next-generation sequencing technologies (i.e. short read-based shotgun metagenomics and 16S rRNA gene amplicons) which are concomitant with low-resolution classification. Consequently, Thalassolituus and Oleispira (genus level) might have been present in these studies, but were possibly classified as an Oceanospirillales (order) which have been reported ubiquitous in oil degrading communities. Marinobacter appeared to be favored by the absence of dispersant at Hibernia, which is consistent with previous findings (Kleindienst, Seidel, et al., 2015). In late microcosms, there were notable differences observed between metatranscriptomic and metagenomic profiles, with some taxa being virtually absent in metagenome data, but highly abundant in metatranscriptome data and vice-versa. Differences in gene abundance between metagenomic and metatranscriptomic data types were previously reported (Hawley et al., 2017; Fortunato et al., 2018) and are challenging to integrate into data interpretation: they might be caused by a real biological response involving low replication, but high transcription. These differences could also be in part explained by biases introduced by DNA vs RNA extraction and rRNA depletion methodologies, a topic that has not been comprehensively addressed to date.
Addition of dispersant to oil results in the enrichment and expression of specific metabolic functions.
To get an overview of the metabolic functions being triggered by the addition of
microbial community characterization in oil degrading contexts have been exhaustively studied from a taxonomic perspective, only a few shotgun metagenomic/metatranscriptomic studies focusing on comprehensive functional prospecting have been reported (Rivers et al., 2013). In that regard, in order to obtain a broad overview of all metabolic functions at play in our study, we expanded our analyses to all differentially abundant genes of our MG and MT datasets whether or not they were part of a metagenome bin. Briefly, each predicted gene from our metagenomic data was blasted (Blastp) against the KEGG database to assign a function. The abundance of all genes belonging to the same metabolic identifier and found to be differentially abundant in MG and/or MT data were then summarized to obtain an aggregated abundance value for each KEGG pathway. This was performed for each station/season/year group for all genes found to be significantly more abundant in oil/disp vs oil only microcosms. The global abundance of a selection of relevant pathways in both metagenome and metatranscriptome data are shown In Figure 3. We directed our focus to genes belonging to certain metabolic functions involved in the detection, assimilation, and degrading mechanisms involved in oil degradation allowing marine bacteria to quickly respond to energy-rich conditions (Yooseph et al., 2010). We hypothesized that bacterial chemotaxis (ko02030) should logically be the initial step in oil degrading metabolism where MCP receptor proteins showing affinity towards dispersant or bioavailable emulsified oil, transmit an intracellular stimulus signal in the presence of oil and dispersant. Through phospho-relay of two component systems (ko02020 and ko02022), this should influence bacterial motility (ko02035) apparatus transcription to guide bacterial cells towards emulsified oil. Two component systems could also act in phospho-relay transfer and increase transcription of mRNA coding for transporters (ko02000), ABC transporters (ko02010)
and secretion systems (ko02044). Extracellular surface-active compounds synthesized by hydrocarbon degraders can enhance the bioavailability of hydrocarbons (Ron and Rosenberg, 2001) and we suspected that transporters and secretion systems could be involved in this process. Fatty acid degradation (ko00071), which includes alkB, is a crucial metabolic function for oil degradation (van Beilen et al., 2006; Rojo, 2009).
In addition to the selected metabolic pathways presented in figure 3, there were also many other diverse metabolic functions found to be more abundant in early oil with dispersant vs oil only microcosms, as illustrated in SI - Figure S5, which contains all the pathways having values greater than or equal to 5,000 CPMs in their MG cumulative abundance. Notably,
functions related to amino acid metabolism, lipid metabolism, carbohydrate metabolism, energy metabolism and xenobiotic degradation and metabolism were highly enriched in early
microcosms with dispersant. This could hint that the presence of dispersant in oil triggers certain metabolic functions aiming at generating energy using dispersant molecules as a substrate. It could also point to specific energy related functions sensitive to degradation of dispersed oil by the bacterial cells and imply an energy requirement for transportation. Enrichment of functions linked to genetic information processing, such as translation, transcription, replication and repair could be related to a high growth rate induced by the presence of dispersant. Other functions like membrane transport, transport and catabolism, [folding, sorting and degradation], cell motility, cellular community, signal transduction and enzyme families could be linked to the whole process of bacterial chemotaxis towards emulsified oil and the subsequent transport of these molecules inside bacterial cells.
From all the differentially abundant genes between oil with dispersant and oil only microcosms, alkB and other genes of known importance in oil degradation (Rivers et al., 2013; Wang and Shao, 2014) were extracted using sequence similarity search (Blastp) against the KEGG gene database, and their abundance was summed in order to get an aggregated
abundance (metagenomic) and expression (metatranscriptomic) value for each KEGG ortholog (Figure 4). Concordantly with the taxonomic profiles of Figures 1 and 2, these selected genes were mainly associated with Thalassolituus, Oleispira, Alcanivorax, Colwellia and/or
Marinobacter depending on the station, season and year. Some experimental conditions
showed more heterogeneity in the taxonomic structure of these selected genes which harbored multiple dominant taxonomic groups: HEC1A-Su2015, was mainly dominated by Alcanivorax and Alteromonas groups. FOC1A-Su2015 was associated with Pseudoalteromonas,
Alteromonas, Glaciecola, Oleispira and Alcanivorax. However, regardless of taxon abundance, the gene abundance (metagenome) profiles for a given experimental condition were strikingly similar from one experimental condition to another. Abundance of K02454/GspE, K02455/GspF, K02660/PilJ, K16089/OmpS and K06076/OmpT1-3 was consistently high on the west coast and to a more varying degree on the east coast (Figure 4). A similar trend was observed for
metatranscriptomic data, but with more dispersion in gene abundance across experimental points (i.e. station/season/year). Also of interest is the wide variation in the abundance of certain gene functions between metagenomic and metatranscriptomic data. In particular, the n-alkane initial oxidation genes (K00496/AlkB and K00529/Rubredoxin) were highly expressed
(metatranscriptome) but showed gene abundance (metagenome) magnitude orders lower in winter microcosms (except Hibernia). A similar trend was observed for K02660/PilJ and
K00114/PQQ alcohol dehydrogenase, which also showed high gene expression but low gene abundance during the winter. For the degradation of aromatics, a comparable pattern was observed: high gene expression of K00154 (benzoate 1,2-dioxygenase alpha subunit), but low abundance in the metagenomic counterpart. This is especially noticeable at the Thebaud station during the winter where the value of K00154 is soaring in gene expression, but not in gene abundance. This station contained gas condensate enriched in aromatic molecules, which could explain the high abundance of that gene function. We have examined our data to see if the ratio of metatranscriptomic vs metagenomic abundance was higher in the winter for each gene of every station/season, but could not find any significant trends, which suggests that the high ratio of gene expression vs gene abundance observed in Figure 4 may be limited to the selected oil degradation genes investigated in this study.
Temperature is known to modulate specific gene expression patterns in E. coli (Oliveira et al., 2016) and our results suggest that low temperature could also be a trigger for the
following genes: K00496/AlkB, K00529/Rubredoxin, K02660/PilJ, K00114/PQQ, and K00154. The results also suggest that Oleispira shifts to distinct expression patterns, were associated with increasing transcription of these specific genes in the presence of oil and dispersant at low temperatures.
Gene abundance (metagenomic) patterns in Figs. 3 and 4a suggested a consistent functional response towards dispersant (but not taxonomic response) regardless of the location and temporal variables. Microbial succession (Brakstad and Bonaunet, 2006; Brakstad et al., 2015) and functions (Ribicic et al., 2018) associated with oil degradation in cold temperature seawater and from the DWH spill (Valentine et al., 2010; Kostka et al., 2011; Dubinsky et al.,
2013) are well documented. Microbial processes were also investigated for the DWH spill (Hazen et al., 2010; Baelum et al., 2012; Lu et al., 2012; Mason et al., 2012). These studies reported the recurrent importance of key microbial groups, usually at the broader family or order levels, with some variation between investigated locations. However, to our knowledge, this is the first time that a taxa-independent persistent functional profile specific to oil/disp degradation is being reported. This is in agreement with the proposal that indigenous marine bacterial communities constitute a diverse and versatile cosmopolitan group of which certain members hold the potential to bloom if presented with the correct energy rich conditions (Nealson and Craig Venter, 2007; Yooseph et al., 2010) - in our case, massive affluence of hydrocarbons. In accordance with other studies, our results suggest that the usual key players in marine oil degradation are highly susceptible to proliferation following exposure to hydrocarbons or emulsified hydrocarbons. Our data also suggest that when looking solely at taxonomic profiles (Fig. 1), oil degrading communities in the presence of dispersant are usually constituted of a diverse assemblage of microbes with a few dominating members. However, when further processing the data and looking only at gene functions that are known to be involved in oil degradation (Fig. 4), we find indications that oil degradation per-se can be done by a diverse community, but that it can also be potentially performed by a single taxon. For example, at HEC1A-Su2014/2015, oil degrading genes were found to be associated with as many as eight different taxa: Thalassolituus, Pseudoalteromonas, Halioglobus, Glaciecola, Alteromonas, Alcanivorax and Acinetobacter. In contrast, the FOC1A-Su2014 microcosms were essentially dominated by one taxon: Thalassolituus. In the winter (FOC1A-Wi2016 and KSK1A-2016), even though Pseudoalteromonas and Colwellia were present (Fig. 1), Oleispira alone seems to be
actively involved in oil degradation (Fig. 4b). Our study further suggests the existence of a common core set of metabolic pathways specific to emulsified oil metabolism. For instance, for all of the selected KEGG pathway function categories showcased in our study (Fig. 3), the number of significantly expressed (metatranscriptomic) genes is lower than the number of significantly abundant (metagenomic) genes (SI – Table S5), which could mean that for critical oil metabolism gene functions in the presence of dispersant, a large pool of potential functional genes is actually available for transcription, but only a subset of them are effectively transcribed. It could also be an indication that metatranscriptome data, by its intrinsic nature, is more
variable and that fewer genes could meet the statistical cutoffs to be considered differentially abundant. It could also simply mean that there are more copies of these genes being
transcribed than replicated in the winter, suggesting that winter oil degrading bacteria (mainly Oleispira) adopt an energy saving strategy where they favor transcription of selected oil degradation genes over replication of the entire chromosome.
Conclusions
A multi-year taxonomic and functional profile of the oil degrading bacterial communities from both the east and west coasts of Canada was performed in this study. The structures of early microbial communities exposed to hydrocarbon varied between season and location, and in the longer term, converged to be mainly dominated by Alcanivorax and Marinobacter. We also present in-depth analyses of the gene functions and metabolic pathways that are
potentially active in the degradation of oil by bacterial communities in the presence and absence of dispersant in seawater. We provide evidence that on both Canadian coasts, genes that are
known to be involved in oil degradation are abundant and active in seawater containing oil with dispersant, regardless of the dominant bacterial genera observed in microbial community successions. Our results clearly show that Thalassolituus (summer) and Oleispira (winter) are important bacterial genera involved in the degradation of oil in the presence of dispersant.
Experimental Procedures
Sampling, microcosm setup and molecular analyses.
In the east coast, seawater samples used for microcosms were collected in the vicinity of two oil platforms (Hibernia and Terra Nova) and one natural gas production platform (Thebaud) in offshore Newfoundland/Labrador and Nova Scotia. In the west coast, microcosms were prepared from two stations from the Douglas Channel deep-water fjord at the northwest coast of Canada (FOC1A and KSK1A) and one station in Hecate Strait (HEC1A).
The experimental design for east coast microcosms has been reported in Tremblay et al., (2017). Briefly, seawater samples were collected using a Seabird Niskin rosette frame (24-10 L bottles) cast from a Canadian Coast Guard vessel. For microcosm preparation, the seawater (100 mL) was transferred to 250 mL baffled flasks. Bushnell-Haas (Difco, Becton Dickinson and Company) nutrients (2 mL) was added to each bottle. Oil (Terra Nova and Hibernia) or gas condensate (Thebaud) only or premixed dispersant (COREXIT EC ® 9500A) with oil or gas condensate were added to each flask/bottle. For each location - Hibernia, Terra Nova and Thebaud - three microcosms of each type (BH + oil, BH + oil and dispersant) with were sacrificed at either 5 days (summer) or 7 days (winter) for early microcosms and 42 days for late microcosms. Late microcosms also had a series of triplicates containing BH only and BH
with dispersant for each location.
West coast sampling and microcosm experiments are described in a submitted manuscript by our group (Schreiber et al., - Submitted). To provide context for the current article, excerpts from the Schreiber et al. manuscript describing relevant methodology are included in the next two paragraphs.
For the west coast, surface seawater samples (3–5 m depth) were collected using a Sea-bird carousel water sampler (Sea-Bird Scientific, Bellevue, WA, USA) equipped with Niskin bottles. Summer samples were collected in July of 2014 and 2015 from the CCGS John P. Tully at station FOC (53.736°N, 87 129.030°W) in the Douglas Channel and at station HEC
(52.821°N, 129.846°W) located in the Hecate Strait near the entrance to Douglas Channel. Winter samples were collected in March 2016 from station FOC and station KSK (53.480°N, 129.209°W) in Douglas Channel from the CCGS W.E. Ricker. Subsamples for molecular biology analyses of T=0 were collected in biological triplicates (each representing a separate Niskin bottle) by filtration of 2 l of seawater onto polyethersulfone membranes (pore size 0.22 μm; Millipore) immediately following sample collection. Filter membranes were flash frozen using liquid nitrogen and stored at -80 °C until processed for nucleic acid extraction. Microcosm experiments were initiated onboard ship as soon as samples were collected. For each sampling event, the contents of multiple Niskin bottle casts were combined to fill an acid-cleaned 20 L jerrican. This integrated seawater sample was divided into three jerricans that were used for preparing the individual flask microcosms. One hundred ml from these blended seawater replicates was transferred to 150 ml baffled flasks.
Becton Dickinson and Company, Mississauga, ON, Canada) to provide sufficient nutrients (N, P) for biodegradation. For each station, triplicate setups of dilbit with and without the dispersant Corexit 9500A (Nalco Energy Services, Burlington, ON, Canada) were prepared.
Sterile controls were set up concurrently using the same experimental conditions as for biotic microcosms, and consisted of sterile-filtered (pore size 0.1 μm) seawater amended with sterile (autoclaved twice for 30 minutes at 121°C) dilbit or dilbit/dispersant mixtures. Microcosms were generally set up with the dilbit blends Access Western Winter Blend (AWB) and Cold Lake Winter Blend (CLWB) provided by the Department of Fisheries and Oceans through the World Class Tanker Safety System (WCTSS) program. Additionally, the Cold Lake Summer Blend (CLSB) dilbit was used to prepare 2015 summer and 2016 winter microcosms from station FOC. Dilbit blends were artificially weathered prior to use by purging (24-48 hours) with a gentle stream of nitrogen. The change in mass was recorded to quantify the extent (%) of weathering. Dilbit required heating to 40°C to dispense and was added to a final concentration of 150 ppm. The dilbit-dispersant mixture was prepared at a dispersant-to-oil ratio of 1:20. Microcosms were incubated at the approximate temperature of surface seawater at the time of collection: 15°C for summer microcosms and 7-8°C for winter microcosms. Microcosms were continually mixed at 150 rpm using orbital shaker tables. Two parallel sets of microcosms were prepared for (i) molecular biology analyses and (ii) chemical analyses. In this study, only shotgun metagenomic sequencing data and results from the first set of microcosms are presented.
For each location - FOC1A, HEC1A and KSK1A - summer 2014 and winter 2016 microcosms were sacrificed at 3 and 42 days and summer 2015 microcosms at 3 and 28 days. The logistics associated with the Coast guard ship time and the availability of the laboratory
where incubation took place after the mission forced us to adjust the incubation period of our microcosms and the final sacrifice point to 28 days (instead of 42 days) during summer 2015.
The final concentrations of COREXIT EC ® 9500A in all microcosms were as follows: oil with dispersant microcosms were setup at 5.4 ppm (1:186,281 v/v); condensate with dispersant microcosms had 6.0 ppm (1:167,364 v/v); and dispersant only microcosms had 7.3 ppm
(1:137,461 v/v). Oil and condensate concentrations were 107.4 (1:20 v/v) and 119.5 ppm (1:20 v/v), respectively.
Bioinformatics and data analysis.
Metagenomic and metatranscriptomic libraries were prepared and sequenced on an Illumina HiSeq 2000 system on a 2 x 100 bp configuration for the east coast microcosms and HiSeq 2500 system on a 2 x 125 bp configuration for west coast microcosms. A total of 381 and 385 samples were submitted for metagenome (MG) and metatranscriptome (MT) sequencing for east and west coasts, respectively. Sequencing data (1076 Gb for metagenome and 1066 Gb for metatranscriptome) was processed through our metagenomic and metatranscriptomic bioinformatic pipelines as previously described in Tremblay et al., 2017 and in the supplementary methods. Briefly, reads were controlled for quality and co-assembled into contigs using Megahit (assembly statistics available in SI – Table S1) on a 3 terabyte of Random Access Memory compute node. Genes were predicted from contigs and quality controlled reads used for co-assembly input were mapped back onto contigs to evaluate contig and gene abundances. Read count summaries and insert sizes are provided for both metagenome (SI - Table S2) and metatranscriptome (SI - Table S3) sequencing libraries. Normalized Counts Per
Million (CPM) abundances for both metagenome and metatranscriptome data types were obtained using edgeR (Robinson et al., 2010). Metagenomic data was further processed into metagenome bins to gain insights on gene functions at the genome level.
For each station and season, oil with dispersant was compared with oil only microcosms for both early (3, 5 or 7 days) and late (28-42 days) sacrificed microcosms. The R software package edgeR was used to extract all genes having a log fold-change ≥ 1.5 and logCPM ≥ 1.5 and False Discovery Rate < 0.05. For the west coast microcosms, we combined the various oil blends with and without dispersant in the experimental design to assess significantly
differentially abundant genes for both metagenomic and metatranscriptomic datasets.
Availability of data
East coast raw sequence reads of the shotgun metagenomic and metatranscriptomic data were submitted to the NCBI Sequence Read Archive (SRA) under accession no. SRP079000 under Bio Project PRJNA329908. West coast raw sequence reads of both metagenomic and metatranscriptomic libraries can be found under accession no. SRP152554 under BioProject PRJNA450643.
List of abbreviations
DWH: Deep Water Horizon; PAH: Polycyclic Aromatic Hydrocarbon; CPM: Counts Per Million; rRNA: ribosomal RNA. KEGG ortholog (KO); MCP: Methyl-accepting chemotaxis protein. MG:
Acknowledgements
The NRC and COOGER teams thank Drs. Simon Courtenay (DFO; project
management), William Li (DFO; bacteria enumeration), Youyu Lu (DFO; project management), Haibo Niu (Dalhousie University; modelling work) and Yongsheng Wu (DFO; modelling work) and Mr. Rod Doane (DFO; administrative support and editing) for their contributions to this study. We are indebted to Susan Cobanli from the Bedford Institute of Oceanography, in
Dartmouth, Nova Scotia for her role in logistics and the preparation of the CCGS H. Hudson and CCGS W.E. Ricker field missions. We are also grateful to Cynthia Wright and Sophie
Johannessen from the Institute of Ocean Sciences, Fisheries and Oceans Canada, in Sydney, BC for their role in the preparation of the CCGS John P. Tully field missions. In addition, we acknowledge the excellent technical support of Jennifer Mason, Brian Robinson, Gary Wolfgeschaffen, Scott Ryan, Peter Thamer, Claire McIntyre, Graeme Soper and Sylvie
Sanschagrin. We also acknowledge Robert Dunphy (Hibernia Management Corporation), Trudy Wells (Suncor) and Megan Tuttle (ExxonMobil) for the representative samples of crude oil and gas condensate. We acknowledge Compute Canada for access to the Waterloo University High Performance Computing (HPC) infrastructure (Graham system). We thank Lars Schrieberfor comments and for manuscript review. We also thank the Canadian Coast Guard and specifically the crews of the CCGS H. Hudson, CCGS John P. Tully and CCGS W.E. Ricker for their
The east coast part of this study was funded by the Environmental Studies Research Funds (Natural Resources Canada). The west coast part of this study was supported by the World Class Tanker Safety System Program.
Authors' contributions
JT wrote software, analyzed data and wrote the manuscript. CWG planned the experimental design, analyzed data and edited the manuscript. NF planned, coordinated and participated in the field work, completed the microcosm experiments and edited the manuscript. ME performed DNA extraction and prepared the sequencing libraries. TLK and KL participated in the design of the study and manuscript editing. JW analyzed the data.
Conflict of interest
The authors declare that they have no competing interests.
Figures
Figure 1. Microbial community profiles of west (FOC1A, HEC1A, KSK1A) and east (Hibernia,
Terra Nova, Hibernia) coasts of known oil degrading microbial genera from all metagenome bins. Early phase microcosms were sacrificed at 3 or 5 days (west coast) and 5 or 7 days (east
coast). Late phase microcosms were sacrificed at 28 or 42 days (west coast) and 42 days (east coast). Abbreviations: SW=seawater; BH=Bushnell-Haas; AWBo=Access Western Blend; AWBoW=Access Western Winter Blend; CLBoS=Cold Lake Summer Blend; CLBoW=Cold Lake Winter Blend; D=Dispersant. “o” refers to oil substrate with no specific type for Hibernia and Terra Nova and to gas condensate for Thebaud. In the y-axis legends: Su=Summer, Wi=Winter followed by the year of sampling: either 2013, 2014, 2015 or 2016.
Figure 2. Taxonomic profiles of a selection of known oil degrading bacteria of all significantly
differentially abundant genes in early (3, 5 or 7 days) or late (28 or 42 days) oil/dispersant vs oil only microcosms. In the y-axis legends: Su=Summer, Wi=Winter followed by the year of sampling: either 2013, 2014, 2015 or 2016.
Figure 3. Metagenome (MG) and metatranscriptome (MT) abundance profiles of KEGG
pathways of differentially abundant genes (log2 fold-change > 1.5 and log2 CPM > 1.5) between
oil/disp vs oil only early (3 - 5 days) microcosms. Each predicted gene was assigned a KEGG ortholog (KO) (Blastp; e-value >= 1e-10). The number of significantly differentially abundant and expressed genes for each KEGG pathway is shown in the lower panel. The normalized gene abundances (upper panel) of each gene were then aggregated according to the corresponding pathway(s) assigned to each gene. Only a selection of relevant pathways are included in this figure. In the y-axis legends: Su=Summer, Wi=Winter followed by the year of sampling: either 2013, 2014, 2015 or 2016.
Figure 4. Taxonomic profiles of known oil degrading genes. Normalized abundance values of
differentially abundant genes (log2 fold-change > 1.5 and log2 CPM > 1.5) in oil/disp vs oil only
early microcosms were aggregated according to their gene function and colored by their taxonomic assignment. Positive values mean that genes were more abundant in oil/disp vs oil only microcosms. Negative values mean that genes were more abundant in oil only vs oil/disp microcosms. Left panel corresponds to a) Gene abundance/metagenomic and right panel to b) gene expression/metatranscriptomic. In the y-axis legends: Su=Summer, Wi=Winter followed by the year of sampling: either 2013, 2014, 2015 or 2016.
References
American Academy of Microbiology (2011) Microbes and oil spills.
Atlas, R.M. and Hazen, T.C. (2011) Oil biodegradation and bioremediation: a tale of the two worst spills in U.S. history. Environ. Sci. Technol. 45: 6709–6715.
Baelum, J., Borglin, S., Chakraborty, R., Fortney, J.L., Lamendella, R., Mason, O.U., et al. (2012) Deep-sea bacteria enriched by oil and dispersant from the Deepwater Horizon spill. Environ. Microbiol. 14: 2405–2416.
van Beilen, J.B., Funhoff, E.G., van Loon, A., Just, A., Kaysser, L., Bouza, M., et al. (2006) Cytochrome P450 alkane hydroxylases of the CYP153 family are common in alkane-degrading eubacteria lacking integral membrane alkane hydroxylases. Appl. Environ. Microbiol. 72: 59–65.
Bejarano, A.C., Levine, E., and Mearns, A.J. (2013) Effectiveness and potential ecological effects of offshore surface dispersant use during the Deepwater Horizon oil spill: a retrospective analysis of monitoring data. Environ. Monit. Assess. 185: 10281–10295. Berthe-Corti, L. and Nachtkamp, M. (2010) Bacterial Communities in
Hydrocarbon-Contaminated Marine Coastal Environments. In, Handbook of Hydrocarbon and Lipid Microbiology., pp. 2349–2359.
Biello, D. (2010) Slick Solution: How Microbes Will Clean Up the Deepwater Horizon Oil Spill. Scientific American.
Brakstad, O.G. and Bonaunet, K. (2006) Biodegradation of Petroleum Hydrocarbons in Seawater at Low Temperatures (0–5 °C) and Bacterial Communities Associated with Degradation. Biodegradation 17: 71–82.
Brakstad, O.G., Ribicic, D., Winkler, A., and Netzer, R. (2017) Biodegradation of dispersed oil in seawater is not inhibited by a commercial oil spill dispersant. Mar. Pollut. Bull. 129: 555– 561.
Brakstad, O.G., Throne-Holst, M., Netzer, R., Stoeckel, D.M., and Atlas, R.M. (2015) Microbial communities related to biodegradation of dispersed Macondo oil at low seawater
temperature with Norwegian coastal seawater. Microb. Biotechnol. 8: 989–998.
Committee on Oil in the Sea: Inputs, Fates, and Effects, Board, O.S., Board, M., Board, T.R., Division on Earth and Life Studies, and National Research Council (2003) Oil in the Sea III: Inputs, Fates, and Effects National Academies Press.
Committee on Understanding Oil Spill Dispersants: Efficacy and Effects, Board, O.S., Division on Earth and Life Studies, and National Research Council (2005) Oil Spill Dispersants: Efficacy and Effects National Academies Press.
Coulon, F., McKew, B.A., Osborn, A.M., McGenity, T.J., and Timmis, K.N. (2007) Effects of temperature and biostimulation on oil-degrading microbial communities in temperate estuarine waters. Environ. Microbiol. 9: 177–186.
Cui, Z., Lai, Q., Dong, C., and Shao, Z. (2008) Biodiversity of polycyclic aromatic hydrocarbon-degrading bacteria from deep sea sediments of the Middle Atlantic Ridge. Environ. Microbiol. 10: 2138–2149.
Dini-Andreote, F., Stegen, J.C., van Elsas, J.D., and Salles, J.F. (2015) Disentangling
mechanisms that mediate the balance between stochastic and deterministic processes in microbial succession. Proc. Natl. Acad. Sci. U. S. A. 112: E1326–32.
Dubinsky, E.A., Conrad, M.E., Chakraborty, R., Bill, M., Borglin, S.E., Hollibaugh, J.T., et al. (2013) Succession of hydrocarbon-degrading bacteria in the aftermath of the deepwater horizon oil spill in the gulf of Mexico. Environ. Sci. Technol. 47: 10860–10867.
Ferguson, R.M.W., Gontikaki, E., Anderson, J.A., and Witte, U. (2017) The Variable Influence of Dispersant on Degradation of Oil Hydrocarbons in Subarctic Deep-Sea Sediments at Low
Temperatures (0-5 °C). Sci. Rep. 7: 2253.
Fortunato, C.S., Larson, B., Butterfield, D.A., and Huber, J.A. (2018) Spatially distinct,
temporally stable microbial populations mediate biogeochemical cycling at and below the seafloor in hydrothermal vent fluids. Environ. Microbiol. 20: 769–784.
Garneau, M.-È., Michel, C., Meisterhans, G., Fortin, N., King, T.L., Greer, C.W., and Lee, K. (2016) Hydrocarbon biodegradation by Arctic sea-ice and sub-ice microbial communities during microcosm experiments, Northwest Passage (Nunavut, Canada). FEMS
Microbiology Ecology 92: fiw130.
Greer, C.W. (2010) Bacterial Diversity in Hydrocarbon-Polluted Rivers, Estuaries and Sediments. In, Handbook of Hydrocarbon and Lipid Microbiology., pp. 2329–2338. Gutierrez, T., Singleton, D.R., Berry, D., Yang, T., Aitken, M.D., and Teske, A. (2013)
Hydrocarbon-degrading bacteria enriched by the Deepwater Horizon oil spill identified by cultivation and DNA-SIP. ISME J. 7: 2091–2104.
Harayama, S., Kasai, Y., and Hara, A. (2004) Microbial communities in oil-contaminated seawater. Curr. Opin. Biotechnol. 15: 205–214.
Harris, C. and Chris, H. (1995) THE BRAER INCIDENT: SHETLAND ISLANDS, JANUARY 1993. International Oil Spill Conference Proceedings 1995: 813–819.
Hawley, A.K., Nobu, M.K., Wright, J.J., Durno, W.E., Morgan-Lang, C., Sage, B., et al. (2017) Diverse Marinimicrobia bacteria may mediate coupled biogeochemical cycles along eco-thermodynamic gradients. Nat. Commun. 8: 1507.
Hazen, T.C., Dubinsky, E.A., DeSantis, T.Z., Andersen, G.L., Piceno, Y.M., Singh, N., et al. (2010) Deep-sea oil plume enriches indigenous oil-degrading bacteria. Science 330: 204– 208.
Head, I.M., Jones, D.M., and Röling, W.F.M. (2006) Marine microorganisms make a meal of oil. Nat. Rev. Microbiol. 4: 173–182.
Henry, C. and Charlie, H. (2005) REVIEW OF DISPERSANT USE IN U.S. GULF OF MEXICO WATERS SINCE THE OIL POLLUTION ACT OF 1990. International Oil Spill Conference Proceedings 2005: 439–442.
Johannessen, S.C., Wright, C.A., and Spear, D.J. (2015) Seasonality and Physical Control of Water Properties and Sinking and Suspended Particles in Douglas Channel, British Columbia. ISSN:1488-5417 Fisheries and Oceans Canada.
Well Blowout across Gulf of Mexico Environments. Bioscience 64: 766–777.
Kessler, J.D., Valentine, D.L., Redmond, M.C., Du, M., Chan, E.W., Mendes, S.D., et al. (2011) A persistent oxygen anomaly reveals the fate of spilled methane in the deep Gulf of Mexico. Science 331: 312–315.
King, G.M., Kostka, J.E., Hazen, T.C., and Sobecky, P.A. (2015) Microbial responses to the Deepwater Horizon oil spill: from coastal wetlands to the deep sea. Ann. Rev. Mar. Sci. 7: 377–401.
Kleindienst, S., Paul, J.H., and Joye, S.B. (2015) Using dispersants after oil spills: impacts on the composition and activity of microbial communities. Nat. Rev. Microbiol. 13: 388–396. Kleindienst, S., Seidel, M., Ziervogel, K., Grim, S., Loftis, K., Harrison, S., et al. (2015) Chemical
dispersants can suppress the activity of natural oil-degrading microorganisms. Proc. Natl. Acad. Sci. U. S. A. 112: 14900–14905.
Kostka, J.E., Prakash, O., Overholt, W.A., Green, S.J., Freyer, G., Canion, A., et al. (2011) Hydrocarbon-degrading bacteria and the bacterial community response in gulf of Mexico beach sands impacted by the deepwater horizon oil spill. Appl. Environ. Microbiol. 77: 7962–7974.
Kostka, J.E., Teske, A.P., Joye, S.B., and Head, I.M. (2014) The metabolic pathways and environmental controls of hydrocarbon biodegradation in marine ecosystems. Front. Microbiol. 5: 471.
Kvenvolden, K.A. and Cooper, C.K. (2003) Natural seepage of crude oil into the marine environment. Geo-Mar. Lett. 23: 140–146.
Law, R.J. and Carole, K. (2004) The impact of the “Sea Empress ” oil spill. Aquat. Living Resour. 17: 389–394.
Lu, Z., Deng, Y., Van Nostrand, J.D., He, Z., Voordeckers, J., Zhou, A., et al. (2012) Microbial gene functions enriched in the Deepwater Horizon deep-sea oil plume. ISME J. 6: 451–460. Mason, O.U., Hazen, T.C., Borglin, S., Chain, P.S.G., Dubinsky, E.A., Fortney, J.L., et al. (2012)
Metagenome, metatranscriptome and single-cell sequencing reveal microbial response to Deepwater Horizon oil spill. ISME J. 6: 1715–1727.
McGenity, T.J., Folwell, B.D., McKew, B.A., and Sanni, G.O. (2012) Marine crude-oil biodegradation: a central role for interspecies interactions. Aquat. Biosyst. 8: 10.
McKew, B.A., Coulon, F., Yakimov, M.M., Denaro, R., Genovese, M., Smith, C.J., et al. (2007) Efficacy of intervention strategies for bioremediation of crude oil in marine systems and
effects on indigenous hydrocarbonoclastic bacteria. Environ. Microbiol. 9: 1562–1571. Nealson, K.H. and Craig Venter, J. (2007) Metagenomics and the global ocean survey: what’s in
it for us and why should we care? ISME J. 1: 185–187.
Niepceron, M., Portet-Koltalo, F., Merlin, C., Motelay-Massei, A., Barray, S., and Bodilis, J. (2010) Both Cycloclasticus spp. and Pseudomonas spp. as PAH-degrading bacteria in the Seine estuary (France). FEMS Microbiol. Ecol. 71: 137–147.
Oliveira, S.M.D., Häkkinen, A., Lloyd-Price, J., Tran, H., Kandavalli, V., and Ribeiro, A.S. (2016) Temperature-Dependent Model of Multi-step Transcription Initiation in Escherichia coli Based on Live Single-Cell Measurements. PLoS Comput. Biol. 12: e1005174.
Redmond, M.C. and Valentine, D.L. (2012) Natural gas and temperature structured a microbial community response to the Deepwater Horizon oil spill. Proc. Natl. Acad. Sci. U. S. A. 109: 20292–20297.
Ribicic, D., Netzer, R., Hazen, T.C., Techtmann, S.M., Drabløs, F., and Brakstad, O.G. (2018) Microbial community and metagenome dynamics during biodegradation of dispersed oil reveals potential key-players in cold Norwegian seawater. Mar. Pollut. Bull. 129: 370–378. Rivers, A.R., Sharma, S., Tringe, S.G., Martin, J., Joye, S.B., and Moran, M.A. (2013)
Transcriptional response of bathypelagic marine bacterioplankton to the Deepwater Horizon oil spill. ISME J. 7: 2315–2329.
Robinson, M.D., McCarthy, D.J., and Smyth, G.K. (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26: 139–140. Rojo, F. (2009) Degradation of alkanes by bacteria. Environ. Microbiol. 11: 2477–2490.
Röling, W.F.M., Milner, M.G., Jones, D.M., Lee, K., Daniel, F., Swannell, R.J.P., and Head, I.M. (2002) Robust hydrocarbon degradation and dynamics of bacterial communities during nutrient-enhanced oil spill bioremediation. Appl. Environ. Microbiol. 68: 5537–5548. Ron, E.Z. and Rosenberg, E. (2001) Natural roles of biosurfactants. Minireview. Environ.
Microbiol. 3: 229–236.
Smith, J.E. (1968) “Torrey Canyon” Pollution and Marine Life: A Report by the Plymouth Laboratory of the Marine Biological Association of the United Kingdom.
Steen, A., Alexis, S., and Abigail, F. (2008) FREQUENCY OF DISPERSANT USE WORLDWIDE. International Oil Spill Conference Proceedings 2008: 645–649.
dispersants enhance the activity of oil- and gas condensate-degrading marine bacteria. ISME J. 11: 2793–2808.
Valentine, D.L., Kessler, J.D., Redmond, M.C., Mendes, S.D., Heintz, M.B., Farwell, C., et al. (2010) Propane respiration jump-starts microbial response to a deep oil spill. Science 330: 208–211.
Wang, W. and Shao, Z. (2014) The long-chain alkane metabolism network of Alcanivorax dieselolei. Nat. Commun. 5: 5755.
Widdel, F., Knittel, K., and Galushko, A. (2010) Anaerobic Hydrocarbon-Degrading
Microorganisms: An Overview. In, Handbook of Hydrocarbon and Lipid Microbiology., pp. 1997–2021.
Xia, W., Li, J., Han, L., Liu, L., Yang, X., Hao, S., and Zhang, M. (2018) Influence of
Dispersant/Oil Ratio on the Bacterial Community Structure and Petroleum Hydrocarbon Biodegradation in Seawater. J. Coast. Res. 84: 77–81.
Yakimov, M.M. (2004) Thalassolituus oleivorans gen. nov., sp. nov., a novel marine bacterium that obligately utilizes hydrocarbons. Int. J. Syst. Evol. Microbiol. 54: 141–148.
Yakimov, M.M., Giuliano, L., Gentile, G., Crisafi, E., Chernikova, T.N., Abraham, W.-R., et al. (2003) Oleispira antarctica gen. nov., sp. nov., a novel hydrocarbonoclastic marine bacterium isolated from Antarctic coastal sea water. Int. J. Syst. Evol. Microbiol. 53: 779– 785.
Yakimov, M.M., Timmis, K.N., and Golyshin, P.N. (2007) Obligate oil-degrading marine bacteria. Curr. Opin. Biotechnol. 18: 257–266.
Yooseph, S., Nealson, K.H., Rusch, D.B., McCrow, J.P., Dupont, C.L., Kim, M., et al. (2010) Genomic and functional adaptation in surface ocean planktonic prokaryotes. Nature 468: 60–66.
