Microbial biodegradation of Alaska North Slope crude 

oil and Corexit 9500 in the Arctic marine environment

Principal Investigators

Mary Beth Leigh1

, Sarah Hardy2

Graduate Students

Alexis Walker2

, Taylor Gofstein3

1

Institute of Arctic Biology, University of Alaska Fairbanks 2

College of Fisheries and Ocean Sciences, University of Alaska Fairbanks

3

Department of Chemistry and Biochemistry, University of Alaska Fairbanks

Final Report

OCS Study BOEM 2020-033

May 2020

 

 
Contact Information: 

Email: UAF-CMI@alaska.edu 

Phone: 907.474.6782 

Coastal Marine Institute 

College of Fisheries and Ocean Sciences 

University of Alaska Fairbanks 

P. O. Box 757220 

Fairbanks, AK 99775-7220 

This study was funded in part by the U.S. Department of the Interior, Bureau of Ocean Energy Management (BOEM) under 

cooperative agreement M17AC00005 between BOEM Alaska Outer Continental Shelf Region and the University of Alaska 

Fairbanks. This report, BOEM 2020-033 is available through the Coastal Marine Institute, select federal depository libraries, and 

electronically from https://www.boem.gov/BOEM- Newsroom/Library/ Publications/Alaska-Scientific-and-TechnicalPublications.aspx

The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the 

opinions or policies of the U.S. Government. Mention of trade names or commercial products does not constitute their 

endorsement by the U.S. Government. 
ii

TABLE OF CONTENTS

LIST OF FIGURES ....................................................................................................................... iv

LIST OF TABLES......................................................................................................................... iv

ABSTRACT.....................................................................................................................................v

INTRODUCTION ..........................................................................................................................1

Fate, effects, and interactions of oil and Corexit in seawater..........................................................1

Oil biodegradation in the benthos....................................................................................................4

Objectives and hypotheses ..............................................................................................................5

Biodegradation and interactions of oil and Corexit in Arctic seawater.....................................6

Biodegradation of oil in Arctic marine sediments.......................................................................7

Seawater-sediment comparison...................................................................................................7

METHODS ......................................................................................................................................8

Seawater incubations .......................................................................................................................8

Sediment incubations.......................................................................................................................9

Oil quantification ...........................................................................................................................11

Seawater incubations ................................................................................................................11

Sediment incubations.................................................................................................................11

Corexit component quantification in seawater ..............................................................................11

Molecular microbial analyses........................................................................................................12

Seawater microbial community structure analyses...................................................................12

Sediment microbial community structure analyses...................................................................12

Seawater microbial metatranscriptomics analyses...................................................................13

RESULTS AND DISCUSSION....................................................................................................14

Oil biodegradation in seawater ......................................................................................................14

Dispersant degradation in seawater ...............................................................................................16

Seawater microbial community structure in response to oil and Corexit ......................................21

Microbial metagenomics analyses of experimental seawater communities..................................26

Overview....................................................................................................................................26

Functional gene expression.......................................................................................................31

Proposed Corexit surfactant component degradation pathway................................................32

Taxonomic identity of putative biodegrading organisms and genes in seawater .....................33

Oil biodegradation in Arctic marine sediments.............................................................................36
iii

Sediment microbial community response to oil.............................................................................37

Distribution of putative oil-degrading taxa in Beaufort Sea sediments.........................................41

Comparison of putative oil-degrading taxa detected in seawater vs. sediments............................42

CONCLUSIONS............................................................................................................................43

ACKNOWLEDGMENTS .............................................................................................................45

STUDY PRODUCTS ....................................................................................................................45

Presentations..................................................................................................................................45

Publications....................................................................................................................................47

REFERENCES ..............................................................................................................................48
iv

LIST OF FIGURES

Figure 1. Map of the study area and sediment collection location................................................................9

Figure 2. Experimental design for sediment oil biodegradation incubation tests.......................................10

Figure 3. Loss of oil fractions over time in seawater incubations...............................................................14

Figure 4. Corexit surfactant component loss in seawater incubations........................................................18

Figure 5. GC/MS chromatogram of oil with Corexit from seawater incubation samples...........................21

Figure 6. Prokaryotic community structure in seawater incubations with oil +/- Corexit..........................22

Figure 7. Prokaryotic community structure based on 16S rRNA sequence analyses associated with 

different treatments in seawater incubations...............................................................................................22

Figure 8. Bray-Curtis NMDS of metatranscriptomes from seawater oil/Corexit mesocosms....................27

Figure 9. Normalized transcript counts originating from bacteria ..............................................................28

Figure 10. Normalized counts of RNA polymerase B (rpoB) ....................................................................28

Figure 11. Total reads of fatty acid degradation gene transcripts and chemical loss..................................29

Figure 12. Normalized counts of select metabolic gene transcripts............................................................30

Figure 13. Chemical structures of the Corexit surfactant components and proposed bacterial 

biodegradation pathway for the major surfactant components with comparison to petroleum hydrocarbon 

alkane degradation. .....................................................................................................................................33

Figure 14. Bray-Curtis NMDS plot of the phylogenetic derivation of RNA transcripts ............................34

Figure 15. Relative abundance of bacterial taxa associated with metabolic gene expression ....................35

Figure 16. Hierarchical cluster dendrogram and nMDS ordination of the prokaryotic community from 

sediment-oil incubation experiment............................................................................................................38

Figure 17. Area charts of top genus-level taxa over time with no-, fresh-, and weathered-oil treatments in 

sediment incubations...................................................................................................................................39

Figure 18. Distribution of putative oil-degraders found in Beaufort Sea marine sediments.......................42

LIST OF TABLES

Table 1. Mean and standard deviation of total (biotic+abiotic) and abiotic percent losses of crude oil 

compounds in Arctic seawater incubations.................................................................................................15

Table 2. Relationships of environmental parameters to the prokaryotic microbial community structure in 

Arctic seawater mesocosms determined using Mantel tests.......................................................................23

Table 3. Backward stepwise models selected for individual genera using the lowest corrected Akaike and 

Bayesian Information Criterion from Arctic seawater mesocosm microbial community data ...................25

Table 4. Mantel test statistics and associated p-values for the vector analysis of specific KEGG metabolic 

gene classes to the overall metatranscriptomic profile in Arctic seawater mesocosms..............................30

Table 5. Summary of genera associated with the expression of different genes or metabolic pathway 

transcripts in Arctic seawater mesocosms ..................................................................................................34
v

ABSTRACT

This project investigated the biodegradation of Alaska North Slope crude oil and Corexit 9500

(Corexit) in Arctic seawater and sediments to identify the microbes and genes actively involved 

in the biodegradation process. Results showed degradation of non-ionic surfactants in Corexit

occurred before biotic degradation of petroleum hydrocarbons, but Corexit did not suppress oil 

biodegradation. The presence of oil appeared to delay mineralization of the Corexit component 

dioctyl sodium sulfosuccinate (DOSS). Some taxa responded selectively to the presence of oil 

(Thalassolituus, Sedimentalea) or Corexit (Colwellia, Polaribacter, Moritella, Octadecabacter, 

and Amylibacter); other taxa (Oleispira, Pseudofulvibacter, and Roseobacter) responded to both, 

suggesting the ability to utilize compounds from oil and Corexit. Metatranscriptomics (gene 

expression) analyses of experimental seawater microbial communities revealed that oil and 

Corexit stimulated different metabolic gene expression profiles in the microbial community. 

However, communities exposed to either oil or Corexit both showed upregulation of the pathway 

for fatty acid degradation. These observations, and evidence of increased metabolic activity in 

treatments containing oil with Corexit, support the theory that dispersants stimulate oil-degrading 

activity by the microbial community. Expression of fatty acid degradation genes was upregulated 

in the presence of Corexit without oil, indicating the pathway in degradation. Thus, the authors

propose a degradation pathway that utilizes the fatty acid β-oxidation pathway to break down the 

non-ionic and DOSS surfactant components of Corexit.

Mesocosm incubation tests were performed with Arctic marine sediments to identify indigenous 

oil-degrading microbes and assess oil biodegradation potential, the latter of which was not 

complete at the time of this report. Mesocosm treatments included fresh and weathered crude oil 

under anaerobic and aerobic conditions. Molecular analyses revealed that oiled communities 

differed from those in non-oiled treatments, significant microbial community shifts occurred in 

oiled treatments under aerobic conditions, and distinct community structures emerged following 

exposure to fresh versus weathered oil. Oil-associated shifts were characterized by increases in 

the relative abundance of several taxa, implicating them in oil biodegradation. Cycloclasticus, 

Neptuniibacter, Oleispira, Roseobacter clade NAC11-7, and Zhongshania, known oil-degraders,

were only present in oiled treatments. Oil-degrading taxa identified in the Chukchi Sea sediment 

incubation studies were compared to taxa data from Beaufort Sea sediments. The majority were 

present in very low abundances, indicating a broad distribution of degradation potential in the 

Beaufort Sea benthos.

Oil stimulated different hydrocarbon-degrading organisms in seawater (Thalassolituus, 

Sedimentalea, and Pseudofulvibacter) than in sediment (Cytoclasticus, Neptuniibacter, and 

Zhongshania). Oleispira and Roseobacter were stimulated in both environments. This finding

highlights the diversity and ubiquity of hydrocarbon-degrading microbes and the strong 

influence of environmental parameters on potential post-spill microbial community succession,

even in the same geographic region.
1

INTRODUCTION

Rapid change is occurring in the Arctic marine environment due to climbing atmospheric 

temperatures, and projections state that the summer Arctic will be nearly ice-free by 2030 

(Overland & Wang, 2013). Historically ice-covered waters are becoming more accessible to 

human activities, increasing the likelihood of anthropogenic disturbance and contaminant 

exposure through oil and gas development, the expansion of commercial shipping, and other 

activities. Microbial biodegradation is the primary means by which oil is naturally removed from 

the marine environment (Braddock et al., 1995; McFarlin et al., 2014), so understanding oil 

biodegradation potential of the indigenous benthic microbial communities may help in accurately 

predicting the fate and effects of oil in the environment and developing spill response strategies.

Chemical dispersants, such as Corexit 9500 (Corexit), have been widely applied in temperate and 

sub-tropical regions as an oil spill response strategy for open water slicks, and Corexit is 

currently under consideration for use in Alaskan waters. Some early dispersant formulations 

were toxic to wildlife (Lessard & DeMarco, 2000), but newer formulations, like Corexit, were 

designed to be less toxic (Word et al., 2015). While Corexit is credited with protecting shorelines 

and facilitating biodegradation, concerns remain regarding its fate and potential ecological 

consequences from its use. 

Fate, effects, and interactions of oil and Corexit in seawater

The 2010 Gulf of Mexico Deepwater Horizon oil spill (DWH) initiated one of the most notable 

large-scale applications of chemical dispersants. Corexit and Corexit 9527 were used in 

unprecedented amounts to disperse surface oil and help prevent surfacing of wellhead oil 

(National Research Council, 2001). The spill prompted numerous studies investigating Corexit, 

particularly its effects on microbial hydrocarbon biodegradation (Atlas & Hazen, 2011; 

Gonzalez-Gaya et al., 2019; Hazen et al., 2016; Prince & Butler, 2014; Prince et al., 2013; 

McFarlin et al., 2014) and the fate of dispersant components (Prince et al., 2013; McFarlin et al., 

2014; Brakstad et al., 2014).

Biodegradation of oil may be limited by environmental factors such as the bioavailability of oil 

(Hazen et al., 2016) and access to nutrients (Atlas, 1981; Atlas & Bartha, 1972). Corexit aids in 

the formation of small oil droplets, which may increase the bioavailability of oil to indigenous 

microorganisms and increase access to nutrients for microbes as droplets disperse through the 

water column. The majority of studies of Corexit reported increased oil biodegradation in 

seawater when compared to non-dispersed oil (Prince & Butler, 2014) or physically-dispersed oil 

(Brakstad et al., 2014; Brakstad et al., 2018; McFarlin et al., 2014; Prince et al., 2013). However, 

at least two studies have reported that Corexit inhibited oil-degrading bacteria (Kleindienst et al., 

2015; Rahsepar et al., 2016). Biodegradation of oil, including the genes and pathways involved, 

has been characterized in a variety of environmental conditions (Das & Chandran, 2011; 
2

Harayama et al., 1999), but less is known about oil degraders in the Arctic marine environment 

or those associated with Corexit degradation. 

The interactions between oil and dispersants are not well understood in Arctic marine 

ecosystems, and the biochemical pathways and bacterial taxa involved in the biodegradation of 

dispersants are unknown. Combining oil and Corexit may affect their respective biodegradation 

processes in Arctic waters because preferential degradation, enrichment, and suppression of 

select microbes may change the fate of these chemicals when mixed. Several studies have 

reported increased rates of petroleum biodegradation with the addition of chemical dispersants 

using either enrichment cultures (Campo et al., 2013; Techtmann et al., 2017; Venosa & Holder, 

2007; Zahed et al., 2010) or indigenous microbial communities (Brakstad et al., 2014; Brakstad 

et al., 2018; McFarlin et al., 2017; Prince et al., 2013; Prince et al., 2014 ). However, contrasting 

research has shown dispersants negatively affecting oil biodegradation in indigenous 

(Kleindienst et al., 2015) and cultured (Rahsepar et al., 2016) seawater by suppressing the 

growth of oil-degrading bacteria. 

The degradation of crude oil and chemical dispersants in the Alaskan Arctic marine environment 

has been investigated primarily through laboratory studies. Mesocosm studies by McFarlin et al. 

(2014) demonstrated that biodegradation of low concentrations of crude oil (2.5 and 15 ppm) by 

indigenous microorganisms in Chukchi Sea surface waters occurred with and without the 

addition of Corexit, with the dispersant slightly enhancing biodegradation. McFarlin et al. (2017) 

also conducted a survey of the microbial community structure and genetic potential for 

hydrocarbon degradation in the Burger oil and gas lease area in the Chukchi Sea. They observed 

the presence of bacterial genera known to include oil degraders and oil biodegradation genes in 

surface waters and at depth. An investigation of the biodegradation of oil and Corexit incubated 

separately in seawater found distinct microbial community shifts and evidence of Corexit 

component degradation (McFarlin et al., 2018).

The major constituents of Corexit include the anionic surfactant dioctyl sodium sulfosuccinate 

(DOSS, 18% w/w) and the non-ionic surfactants Span 80 (4.4% w/w), Tween 80 (18% w/w), and 

Tween 85 (4.6% w/w) in a petroleum distillate solvent (Gray et al., 2014; Parker et al., 2014; 

Place et al., 2010; Place et al., 2014). Ethylhexyl sulfosuccinate (EHSS) has also been identified 

as an abiotic hydrolysis product of DOSS (Campo et al., 2013; Ramirez et al., 2013) and is 

present in detectable quantities (0.28% w/w) in Corexit 9500 formulations (Place et al., 2016).

Studies that have directly or indirectly measured Corexit loss suggest that some components may 

be readily biodegraded (Campo et al., 2013; Kleindiest et al., 2015; McFarlin et al., 2014; 

McFarlin et al., 2018; Techtmann et al., 2017). However, most of these studies limited their 

focus to DOSS or provided only generalized evidence of mineralization of Corexit as a whole 

(e.g., via CO2 evolution), so less is known about the fate of the individual non-ionic surfactant 

and solvent components. 
3

Following the application of Corexit at the DWH spill, in situ measurements showed persistence 

of DOSS (Kujawinski et al., 2011; Perkins et al., 2017; White et al., 2014), indicating that some 

components of Corexit may be recalcitrant to degradation. The biodegradation of DOSS and the 

non-ionic surfactants in Corexit was observed when Corexit was incubated in Arctic seawater 

mesocosms with and without the addition of oil (McFarlin et al., 2018). A Geochip assay of 

Arctic seawater showed an increase in the abundance of hydrocarbon degradation genes for both 

treatments relative to controls (McFarlin et al., 2018). These results suggest that Corexit 

biodegradation may share genes or pathways with oil biodegradation, possibly through the 

cleavage of long alkyl chains present in the surfactant components. However, no studies have 

identified the specific genes or metabolic pathways associated with Corexit biodegradation. 

While specific genes and metabolic pathways have not yet been identified for Corexit 

biodegradation, associated shifts in microbial community structure have been well documented. 

Different bacterial taxa have been shown to proliferate in the presence of Corexit alone (e.g.,

Moritell: McFarlin et al., 2018) or oil alone (e.g., Thalassolituus, Coulon et al., 2007; McKew et 

al., 2007 and Cytoclasticus, Brakstad et al., 2018; Coulon et al., 2007; Kleindienst et al., 2015; 

McKew et al., 2007; Tremblay et al., 2017). Taxa have also been shown to respond positively to 

the presence of either oil or Corexit under some experimental conditions (e.g., Colwellia, 

Brakstad et al., 2008; Bælum et al., 2012; Kleindienst et al., 2015, Oleispira, McFarlin et al., 

2018; Coulon et al., 2007, and Polaribacter, McFarlin et al., 2018; Prabagaran et al., 2007). 

Techtmann et al. (2017) found that chemically-dispersed oil enriched a mixture of taxa that were 

observed to proliferate in both oil-only and Corexit-only treatments, which suggests they 

biodegrade components of both oil and Corexit. It is possible that the same genes and pathways 

are employed to biodegrade components of both mixtures. Given the functional genetic diversity 

among species and strains within the same genus, it is difficult to address this question with 16S 

rRNA data. Studies have focused on identifying taxa that become enriched following an oil spill 

(e.g., Brakstad et al., 2015; Hazen et al., 2010; Kleindienst et al., 2015; McFarlin et al., 2018), 

but less is known about the effects on functional gene expression, which may yield insights into 

the specific roles performed by members of the consortium. 

In this study, the authors identified microbial taxa putatively involved in biodegradation in the 

Arctic marine environment and investigated the interactive effects of Alaska North Slope (ANS) 

crude oil and Corexit on the microbial community and sequential and co-degradation. We 

identified genes associated with biodegradation of Corexit or oil (alone and combined), 

identified organisms that expressed degradation genes, and constructed a putative pathway for 

the biodegradation of Corexit surfactant components. We predicted that some taxa only degrade 

oil or Corexit, while other taxa utilize the same pathways and alkane hydrocarbon-degradation 

genes to degrade both.
4

Oil biodegradation in the benthos

Following an oil spill, the cleanup effort generally focuses on treating visible oil slicks on the sea 

surface or shorelines through techniques like containment, recovery, in situ burning or 

dispersion, and washing or bioremediation of shorelines. Generally, less attention is afforded to 

sediments despite conservative estimates that 20-30% of total oil from a spill event reaches 

benthic marine sediments (Muschenheim & Lee, 2002). Oil in the benthos can persist for many 

years and impact the health of the marine ecosystem, including adverse effects on benthic and 

pelagic food webs. Oil can be toxic to a multitude of demersal fishes and invertebrates and 

persist in tissues of exposed organisms. For example, oil compounds remained in marine 

sediments and at toxic levels within the tissues of organisms such as Pacific Halibut, mussels, 

and clams for ten years following the Exxon-Valdez oil spill (Jewett et al., 2002). In the Arctic, 

benthic fishes and invertebrates compose much of the diet for the walruses, seals, and whales 

that are essential subsistence species for Alaska Native communities (Laidre et al., 2008). Thus, 

understanding the fate of oil in Arctic marine sediments is important for assessing the potential 

environmental and human health impacts of an oil spill in this ecosystem. 

Oil is initially buoyant in seawater; however, multiple mechanisms help transport it to the 

seafloor, including aggregate formation and weathering. Aggregate formation occurs through the 

adhesion of oil droplets to suspended particulate material, such as sediment and detritus, which 

increases the density of oil and promotes sedimentation to the benthos. Oil weathering processes 

occur via photooxidation, evaporation, biodegradation, dissolution, emulsification, or through a 

combination of these processes. Typically, the lighter and more volatile oil compounds are the 

first lost during the process of weathering, leaving the heavier and more metabolically 

recalcitrant hydrocarbons and other compounds. Weathered oil components are the most 

persistent in marine environments and include many of the polycyclic aromatic hydrocarbons 

(PAHs).

Oxygen availability can be limiting to biodegradation in sediments, but evidence of anaerobic 

petroleum biodegradation, such as the presence of anaerobic biodegradation metabolites, has 

been documented in contaminated sediments, including those from the DWH oil spill (Kimes et 

al., 2013). Marine sediment environments exhibit local oxic and anoxic pockets, even within the 

upper 1 cm layer of sediment (Rysgaard et al., 2004). Anoxic conditions can slow or stop oil 

biodegradation or provide the necessary conditions for anaerobic biodegradation of more 

recalcitrant, weathered oil compounds (Rysgaard et al., 2004). Due to the presence of more labile 

components, fresh oil is generally broken down through microbial processing much more 

quickly than weathered oil (Rysgaard et al., 2004). Once oil has been heavily weathered, and 

primarily recalcitrant chemical compounds remain, oil biodegradation is very limited. However, 

some anaerobic microbes, which require low or no oxygen environments, are capable of 

breaking down some of these recalcitrant compounds (Braddock et al., 1995; Rysgaard et al., 

2004).
5

Three studies assessed aspects of microbial biodegradation of oil in sediments from the North 

American Arctic region (Braddock et al., 2004; Dong et al., 2015; Sharma et al., 2016). 

Braddock et al. (2004) sought to establish a baseline for microbial communities in Arctic marine 

sediments and associated oil biodegradation potential in several sites from Barrow to Prudhoe 

Bay, Alaska. Their research provided estimates of the abundance of total and oil-degrading 

bacteria in the region using direct counts and culture-based methods (Most Probable Number–

MPN). They also examined the mineralization potential of native microbes on two specific 

chemical components of oil (hexadecane and phenanthrene) and assessed how sediments affect 

the bioavailability of adsorbed oil. They found that biodegradation of hexadecane and 

phenanthrene by indigenous microbes was relatively slow, and it was unlikely that the 

bioavailability of oil was hindered by adhesion to sediments. Dong et al. (2015) identified 

microbes that degrade specific PAH components of oil in deep-sea sediments from the Chukchi 

Plateau. They used cultivated oil-degraders and, due to the limited culturability of many 

organisms, this method can underestimate the microbial diversity present in the environment. 

The use of indigenous microbial communities is more likely to reflect rates observed for in situ

conditions (McFarlin et al., 2014). Seeking to inform bioremediation efforts for Arctic 

shorelines, Sharma at al. (2016) investigated the effects of salinity, temperature, and oil

concentration on biodegradation in intertidal sediments from Utqiaġvik (formerly Barrow). 

This study expanded on the work of Braddock et al. (2004) by providing high-resolution 

information on sediment microbial community composition and the identity of oil-degrading 

microbes using modern molecular methods. Comprehensive quantification of biodegradation of 

the major components of crude oil was accomplished using gas chromatography coupled to mass 

spectrometry (GC-MS). Using high-throughput DNA sequencing techniques, we tracked 

microbial community shifts in response to oil exposure and identified key taxonomic groups that 

increased in abundance during biodegradation of fresh and weathered oil, identifying them as 

putative oil degraders. Identifying active oil degraders in the Arctic facilitates better predictions 

of degradation potential throughout the region. Additionally, this study assessed oil 

biodegradation rates and the associated microbial communities involved in both oxic and anoxic 

conditions to better understand the optimal oxygen conditions required for oil breakdown and 

bioremediation efforts.

Objectives and hypotheses 

The goal of this project was to investigate the biodegradation of ANS crude oil in Arctic 

seawater and sediments to better predict the fate and effects of the chemical dispersant Corexit in 

such environmental conditions. We quantified the biodegradation of oil and Corexit components 

in Arctic seawater and applied in-depth analyses of microbial community structure and gene 

expression to gain a comprehensive perspective of microbial biodegradation. Mesocosm 

incubation experiments were conducted using Arctic marine sediments. Fresh and weathered 

crude oil were added under both oxic and anoxic conditions to assess aerobic and anaerobic 
6

biodegradation potential and to identify oil-degrading microbes in the benthos. Following the 

identification of putative oil-degrading bacteria in incubation tests using Chukchi marine 

sediments, we queried our existing sediment microbial community datasets from the Beaufort 

Sea region to assess the biodegradation potential present in indigenous sediment microbial 

communities. Our findings provide an in-depth analysis of the crude oil biodegradation potential 

in the Arctic marine environment.

Reporting on Objectives 1 and 2 includes new data from our previous CMI project that focused 

on the biodegradation of oil and Corexit (contract number M15AC00008).

Biodegradation and interactions of oil and Corexit in Arctic seawater

Objective 1. Quantify the extent of oil and Corexit biodegradation over time and the effects of 

their co-presence in Arctic seawater on their fate.

 Hypothesis: Some Corexit components will degrade more slowly than others, such as DOSS, 

which is suspected to be among the most persistent surfactant components based on prior 

literature.

 Hypothesis: Despite the predicted lability of some of Corexit’s components, their presence 

will not retard oil biodegradation through processes such as inhibition or competitive 

biodegradation in seawater.

Objective 2. Identify microorganisms important to biodegradation of oil and dispersant chemical 

components in Arctic seawater and their overlaps and/or potential interactions. 

 Hypothesis: Distinct microbial populations will be stimulated by the presence of crude oil or

Corexit, and the co-presence of oil and Corexit will stimulate both populations together. 

Although the majority of taxa may degrade either oil or Corexit, some individual taxa will be

capable of biodegrading components of both mixtures. 

Objective 3. Characterize the pathways and genes and taxonomic affiliations involved in the 

biodegradation of petroleum hydrocarbons and Corexit in Arctic seawater.

 Hypothesis: The presence of oil in seawater will result in a significantly increased expression 

of hydrocarbon degradation genes by indigenous microbes. Incubation with crude oil will

first stimulate increased expression of genes associated with labile petroleum components 

(e.g., alkanes and other aliphatics) and, as they become depleted due to biodegradation, 

expression of genes for more recalcitrant oil components (e.g., aromatics) will increase. 

 Hypothesis: Microorganisms that biodegrade components of both oil and Corexit utilize 

some of the same alkane-degradation genes and pathways since some Corexit components 

contain structurally similar hydrocarbon side-chains. 
7

Biodegradation of oil in Arctic marine sediments

Objective 4. Assess the capacity for biodegradation of fresh and weathered ANS crude oil by 

indigenous microorganisms in subtidal Arctic marine sediments under aerobic and anaerobic 

conditions.

 Hypothesis: Arctic sediment microbial communities are capable of biodegrading oil both 

aerobically and anaerobically, with aerobic biodegradation being more rapid. 

 Hypothesis: Oil biodegradation in Arctic sediments follows trends observed for oil 

biodegradation in other environments where increased oil weathering reduces biodegradation 

rates compared to fresh oil.

Objective 5. Identify microorganisms indigenous to Arctic marine sediments that are important 

to oil biodegradation. 

 Hypothesis: Oil-degrading microbes are ubiquitous; a variety of oil-degrading microbial taxa

will be present in Arctic marine sediments. 

 Hypothesis: Arctic benthic microorganisms are not well studied; our analyses of community 

shifts in response to oil will implicate species in oil biodegradation that were not previously 

reported to perform this function.

Objective 6. Utilize taxonomic information from sediment incubation studies to assess the 

distribution of putative hydrocarbon-degrading microbes more broadly in Arctic sediments. 

 Hypothesis: Putative oil-degrading microorganisms are widespread in the environment; oil 

degradation potential is broadly distributed in North American Arctic sediments.

Seawater-sediment comparison

Objective 7. Identify and compare microorganisms involved in biodegradation of oil in both 

Arctic seawater and sediments and use taxonomic information from sediment incubation studies 

to assess the distribution of putative hydrocarbon-degrading microbes more broadly in Arctic 

sediments. 

 Hypothesis: Indigenous microorganisms involved in oil biodegradation in Arctic sediments 

are more diverse than those reported for seawater from the same region, due to the higher 

biomass and associated diversity of microbes associated with sediments. 
8

METHODS

Seawater incubations

Temporal incubation series of Arctic seawater with crude oil, dispersant, or both were completed 

in August 2016. Arctic surface seawater was collected ~1 km offshore of Utqiaġvik, Alaska,

from the Chukchi Sea in August 2016. The 150 liters of collected seawater were stored at 5°C 

overnight and immediately transported by air to Fairbanks, Alaska, where it was aerated 

overnight at the temperature recorded at the time of collection (4°C) prior to the initiation of the 

incubation experiment. Before aliquoting seawater into mesocosms, it was supplemented with 16 

ppm of Bushnell-Haas media (McFarlin et al., 2014) to prevent potential nutrient limitations that 

may occur as an artifact of the small-scale incubations. The supplementation with Bushnell-Hass 

provided an additional measured 62 μM phosphate, 49 μM ammonia, 42 μM nitrate, and 

estimated 1.5 μM iron. Since these nutrient concentrations are much higher than those expected 

in Arctic regions (Codispoti et al., 2009; Pisareva et al., 2015), the oil and Corexit component 

degradation reported here represents a best-case scenario.

Mesocosm incubation experiments were performed in a cold room set to a temperature of 4°C 

and the lights set to a 19-hour day/5-hour night cycle to mimic the conditions at the sampling site 

at the time of collection. Mesocosms were constructed by aliquoting 800 mL of seawater into 

acid-washed and pre-autoclaved 1-L glass bottles containing Teflon-coated magnetic stir bars 

and treated with either 50 ppm ANS crude oil, 5 ppm Corexit (1:10 dispersant-to-oil ratio), both, 

or neither, the latter of which served as a negative control. The bottles were stirred at a low speed 

to allow movement of the oil slick at the surface while preventing the formation of a large 

vortex, and the lids were left slightly ajar to allow air exchange. Sterile controls consisting of 

autoclaved seawater were also used to account for abiotic losses of oil and Corexit from 

processes such as evaporation, volatilization, hydrolysis, and photooxidation. Treatments were 

replicated in triplicate and destructively harvested at 0, 5, 10, 20, and 30 days for crude oil and 

microbial analyses. 

There were no 5- or 20-day mesocosms for Corexit analyses constructed because of space 

limitations; however, an additional triplicate series of larger 6-L mesocosms were created and 

subsampled at high frequency to capture the relatively rapid degradation of the non-ionic 

surfactant components of Corexit as reported by McFarlin et al. (2018). Treatments for the 

subsampled series included live and sterile abiotic treatments of seawater amended with 5 ppm 

Corexit with or without 50 ppm ANS crude oil. These large-sized incubations were subsampled 

through a Teflon tube with a syringe at 0, 1, 2, 3, 4, 5, 6, 7, 10, 20, and 30 days. In addition to 

acid washing and autoclaving, all vessels used for the analysis of Corexit were baked at 400°C 

for 12 hours to remove any surfactant contaminants present as a result of manufacturing or other 

contamination sources.
9

Sediment incubations

Marine sediment and seawater samples for the sediment oil-incubation experiment were 

collected on the AMBON cruise in August 2017 at 45 m water depth via Van Veen grab for 

sediments and an intake hose for seawater (69.91oN, -166.05oW; Figure 1). The sediments and 

seawater were collected in sterile, acid-washed containers and kept at in situ temperature, 5oC

until shipped on ice to the University of Alaska Fairbanks, where they were stored in a cold room 

set at 5

oC. All collected seawater was vacuum filtered through 0.2-um filters, autoclaved, cooled 

to 5

oC, and spiked with a 1% resazurin solution (with a final concentration of 0.002% per bottle)

before the experiment. These measures were implemented to prevent the seawater microbial 

signal from interfering with the sediment microbial signal, to maintain in situ water properties,

and to allow determination of oxygenation states (oxic, suboxic, anoxic) for individual serum 

bottles.

Figure 1. Map of the study area and sediment collection location (~ 130 km offshore of Pt. Lay,

Alaska).

The ANS crude oil was obtained from Polar Tankers Inc., in Valdez, Alaska, on July 12, 2017. 

Upon acquisition of the ANS crude, half of the oil was sealed and stored in an amber glass bottle 

in a dark area of the cold room at 5oC for use as the fresh oil treatment. For use in the weathered 

oil treatment, half of the oil was dispensed into a large graduated cylinder, weighed, and set on a 

shaker table loosely capped with sterile foil until 20% of its mass was lost to volatilization 

(Prince et al., 2012). 

Serum bottles were washed with lab-grade detergent, acid-washed in a 10% HCL solution, and 

autoclaved to ensure sterility and the removal of any potential contaminants. Based upon similar 

incubation studies, we chose to set up each 100-mL incubation serum bottle with 40 mL of 

seawater and 8 mL of sediment, and oil treatments with 1 mL of oil dispensed via positive 

displacement pipette for a total oil concentration of 2% (Braddock et al., 2004; Ferguson et al.,
10

2017; Zanaroli, personal communication, June 28, 2017). Sediment was weighed ten times to 

establish an average mass of 12.2 g sediment per 8 mL. After homogenization of source marine 

sediments, a sterile stainless-steel funnel was used to transfer 12.2g of sediment to each serum 

bottle. Residual sediment was rinsed from the funnel into the serum bottle with 40 mL of 

resazurin-spiked seawater. Bottles for abiotic quantification of oil loss were autoclaved, reoxygenated, and cooled down to 5

oC. Fresh or weathered ANS crude oil (1 Ml) was dispensed to 

bottles, which were then homogenized. A total of 333 bottles were prepared; the aerobic 

treatment bottles remained loosely capped with sterile foil as to remain oxic and anaerobic 

treatment bottles were plugged with blue butyl rubber stoppers and crimped with aluminum seal 

caps to foster natural anoxic conditions. All serum bottles were encased in a foam-lined holder 

on a shaker table. 

The oxic incubation bottles were harvested every 12 days. With the letter T representing time in 

days, our coding system is as follows: T1 = 12 d, T2 = 24 d, T3 = 36 d, T4 = 48 d, and T5 = 60 

d, including a T0 harvest on the first day of the incubation. The bottles designated for anaerobic

degradation of crude oil went anoxic after 5 days, as indicated by resazurin dye. These samples 

were allowed to incubate for 26 months as anaerobic oil degradation typically occurs at much 

slower rates than aerobic degradation. The anaerobic incubations were analyzed at two time 

points, T0 and Tfinal. An overview schematic of the experimental design is depicted in Figure 2.

Bottles harvested for oil quantification were immediately frozen at -80°C, and samples for DNA 

extraction were transferred to DNA/RNA-ase-free 50 mL falcon tubes and immediately frozen at 

-80°C until extraction. Oil degradation rates and community composition shifts were observed at 

six points at 12-day intervals over 60 days. There were three replicates for each of the incubation 

treatments: control (no-oil), fresh oil, weathered oil, oxic, and anoxic. Oxic treatments were 

analyzed at time 0 (T0), 12 (T1), 24 (T2), 36 (T3), 48 (T4), and 60 (T5) days. Anoxic 

incubations were only analyzed at T0 at Tfinal (26 months).

Figure 2. Experimental design for sediment oil biodegradation incubation tests.
11

Oil quantification

Seawater incubations

Chemical extraction and analyses of petroleum hydrocarbons were performed on seawater 

incubations using gas chromatography mass spectrometry (GC/MS) based on the methods of 

Prince & Douglas (2005). Three 20-mL aliquots of dichloromethane were added to each 

seawater mesocosm and mixed with a magnetic stir bar for a short time. After stirring, the 

dichloromethane droplets re-coalesced under the water layer and were pipetted out and 

combined. The extracts were dried with anhydrous sodium sulfate and stored at -20°C until 

analysis. Hydrocarbon analysis was performed on an HP 5890/5973 GC/MSD in scan mode with 

all signals normalized to the internal marker compound 17α(H),21β(H)-Hopane, which is 

naturally present in oil and does not biodegrade under normal environmental conditions (Aeppli 

et al., 2014; Prince et al., 1994). Total Petroleum Hydrocarbons (TPH) were measured, as well as 

individual n-alkanes, branched alkanes, and PAHs using the respective primary and secondary 

ions to identify those compounds (Douglas et al., 1994).

Sediment incubations

Several steps were employed to quantify oil degradation in sediment incubation mesocosms. 

Designated serum bottles were inoculated with 300 uL of 1000-ppm D8-naphthalene/methanol 

surrogate and homogenized to ascertain oil extraction efficiency accurately. Oil was then 

extracted from each bottle by adding 12.2 of anhydrous sodium sulfide to curb emulsions and 

three 20 mL aliquots of dichloromethane (DCM). After each aliquot of DCM, the sample was 

immediately sealed and gently swirled on a shaker table for 20 minutes, left to settle for 5 

minutes, and ~20 mL of the DMC oil mixture was pipetted out and transferred to an acid-washed 

collection flask and tightly capped. Once all 60 mL of DMC-oil mixture was recovered, 2 g of 

anhydrous sodium sulfide was added to ensure the solution was devoid of water. The DCM-oil

solution was then transferred to a 40-mL amber VOA vial, and 1000 uL transferred to an amber 

GC vial. Both vials were stored at -20°C until analysis on the GC-MS. Once samples were run 

on the GC-MS, peak integration of chemical profiles were manually conducted using the Agilent

Chemstation software, and TPH concentrations were calculated using extraction efficiency, 

internal standard, and surrogate values.

Corexit component quantification in seawater

Dispersant components were analyzed by liquid chromatography tandem mass spectrometry 

(LC/MS/MS) in whole-bottle mesocosm and subsampled 6-L incubations for the following 

compounds using the methods previously described by McFarlin et al. (2018): DOSS, Tween 80 

and 85, and Span 80, all known constituents of Corexit (Place et al., 2016), and EHSS, a 

degradation metabolite of DOSS (Campo et al., 2013; Place et al., 2014). The Tweens could not 

be quantitated individually and were quantitated as the sum of the concentrations of both (Place 
12

et al., 2016). Percent recovery for this method ranged from 88-119%, and precision reported as 

the relative standard deviation ranged from 1.4-23%, depending on the analyte (Place et al., 

2016).

Molecular microbial analyses

Seawater microbial community structure analyses

Seawater from mesocosms was vacuum filtered on 0.22-μm filters to separate cells from the bulk 

solution and immediately frozen at -80°C until analysis. Microbial community DNA was 

extracted from frozen filters using a DNeasy Powerwater (Qiagen, Venlo, Netherlands) 

commercial extraction kit following the manufacturer’s protocol. To study the prokaryotic 

community structure, the V4 region of the 16S rRNA gene was amplified using indexed 515F 

(5’-GTGCCAGCMGCCGCGGTAA-3’) and 806RB (5’-GGACTACNVGGGTWTCTAAT-3’) 

primers (Apprill et al., 2015) and sequenced on an Illumina MiSeq using a 2 x 250 bp format. 

Sequences were filtered, trimmed, dereplicated into 100% similarity amplicon sequence variants 

(ASVs), and assigned taxonomy from the SILVA rRNA database (v. 132) using the dada2 

bioinformatics pipeline (Callahan et al., 2016; Callahan et al., 2017; Quast et al., 2012).

Microbial community data analyses were performed using the PC-ORD V6 statistical software 

package (PC-ORD v. 6.255 Beta. Gleneden Beach, OR: MjM Software Design; McCune et al., 

2002). Non-metric multidimensional scaling (NMDS) plots were used to visualize differences in 

community structure between treatments and over time (Clarke & Ainsworth, 1993). Statistical 

significances were determined using PERMANOVA (Anderson, 2001) and non-parametric 

multi-response permutation procedure (MRPP) tests (Mielke & Berry, 2001). Correlations and 

modeling of chemical data to community structure were performed using Mantel tests and 

backward step-wise model selection (Mantel, 1967). All statistical analyses were performed 

using a 95% confidence interval

Sediment microbial community structure analyses

16S ribosomal (rRNA) amplicon sequencing was conducted on lyophilized sediments to assess 

the diversity, structure, and composition of prokaryotes in the Beaufort Sea benthos. To prepare 

samples for sequencing, total genomic DNA was extracted from sediment samples using the 

Qiagen PowerSoil kit, revised forward (515FB) and reverse primers (806RB) from the Earth 

Microbiome Project (EMP) were used to amplify the V4 region of the 16S rRNA gene. Library 

preparation with iTru adapters for sequencing was completed following the standard protocol 

used by the EMP - a one-step PCR protocol with indexed primers (Apprill et al., 2015; Caporaso 

et al., 2012; Parada et al., 2016; Walters et al., 2016). The processed samples were sequenced on 

an Illumina MiSeq at the UAF Institute of Arctic Biology Genomics Core Lab.
13

The 16S rRNA amplicon sequences were de-multiplexed using the Mr. Demuxy package (Cock 

et al., 2009). Demultiplexed sequences were run with mothur v1.40.0 on a high performancecomputing cluster through UAF Research Computing Systems using a modified MiSeq standard 

operating procedure (Schloss et al., 2009). Operational taxonomic units (OTUs) were clustered at 

100% similarity using OptiClust option in mothur, taxonomy was assigned to OTUs using the 

SILVA 132 mothur formatted reference database with a bootstrap cutoff of 100%, and the 

samples in the resulting OTU table were converted to relative abundances (Edgar, 2018; 

Glöckner et al., 2017; Wang et al., 2007; Westcott & Schloss, 2017). The table comprising the 

relative abundances of OTUs identified for each sample was used to identify prokaryotes 

involved in biodegradation of oil in Arctic marine sediments. A combination of nMDS 

ordination, hierarchical clustering analysis, and area plots was used to assess patterns in 

microbial communities between different experimental treatments.

Seawater microbial metatranscriptomics analyses

Metatranscriptomics analyses were performed on seawater incubations to examine the gene 

expression profiles of the microbial communities and their shifts in response to exposure to oil, 

Corexit, or both. Due to the cost of sequencing, the metatranscriptomic analyses were performed 

only at 0-, 5-, and 30-day time points. RNA was extracted from filters using a Qiagen RNeasy 

Lipid tissue kit following the manufacturer’s protocol; this kit was selected to help ensure 

extracts were free of oil. RNA extracts were held at -80°C and shipped on dry ice to the Oregon 

State University Center for Genome Research and Biocomputing. There, they were quantified, 

eukaryote ribodepleted, normalized to all contain the same amount of RNA, pooled, and 

sequenced on an Illumina HiSeq 3000 using a 100-bp paired-end format. 

Sequences were processed using the MG-RAST pipeline (v.4.0.3) and analyzed against the 

RefSeq phylogenetic and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional 

databases (Glass & Meyer, 2011; Kanehisa et al., 2016; O’Leary et al., 2016). All rRNA 

sequences were removed from the dataset during processing to focus on metabolic mRNA 

transcripts. Sequencing efforts generated 2,092,023,528 sequences that passed quality-control 

filters across 27 individual samples for an average of 76,340,561 sequences per sample. 

Statistical analyses were performed using the PC-ORD V6 statistical software package (McCune 

et al., 2002), which included non-metric multidimensional scaling (NMDS) plots to visualize 

metatranscriptomes, PERMANOVA tests to evaluate the statistical significance of observed 

differences in the metatranscriptomes, Mantel tests to correlate the abundance of metabolism 

gene classes with the overall metatranscriptomes, and ANOVA and Tukey’s honest significant 

difference tests to determine if the relative abundances of genes were different between 

treatments at a given time point. All statistical tests were performed using a 95% confidence 

interval.
14

RESULTS AND DISCUSSION

Oil biodegradation in seawater

Biodegradation of crude oil and the influence of Corexit on oil degradation rates were assessed 

by quantifying the TPH (C10-C30), n-alkane, branched alkane, and PAH losses over time (Figure 

3). While the majority of the alkanes biodegraded within the first ten days, the remaining TPH 

and PAHs degraded much more slowly over the course of the 30-day incubation. There were no 

significant differences observed for any of the measured petroleum losses with the addition of 

Corexit. Evidence of biodegradation contributing to TPH loss was detected on day 10, which was 

the first time point in which live treatments experienced greater loss (20±3%) than abiotic 

treatments (7±4%) (p=0.0002; Figure 3a). By the end of the experiment, biotic TPH loss had 

reached 29 ± 4% and was significantly higher (p<0.0001) than the TPH loss in abiotic treatments 

(12±2%). Based on these findings, both biotic and abiotic processes are important in the early 

loss of crude oil in the Arctic, with biodegradation becoming significant between 5-10 days for 

TPH. 

Figure 3. Loss of oil fractions over time in seawater incubations. Mean (n=3) percent loss of a) 

total petroleum hydrocarbons (TPH), b) n-alkanes, c) branched alkanes, and d) polycyclic 

aromatic hydrocarbons (PAHs). The error bars denote one standard deviation from the mean.
15

The loss of individual petroleum compounds followed the well-documented pattern of 

degradation, with smaller, simpler, and saturated hydrocarbon compounds degrading more 

readily than larger, complex, and conjugated compounds, such that loss of n-alkanes>branched 

alkanes>PAHs (Table 1). When comparing the loss of the different hydrocarbon compound 

classes to TPH loss, the hydrocarbons remaining in live treatments at 30 days were largely 

composed of PAHs and other recalcitrant hydrocarbons present in the unresolved complex 

mixture. While linear and branched alkanes showed relatively rapid degradation by Arctic 

microorganisms, the slower degradation of the remaining oil components raises concern for the 

long-term effects in the environment due to the toxic and bioavailable nature of these compounds 

(Booth et al., 2007; Gardiner et al., 2013; Petersen et al., 2017; Thomas et al., 1995).

Table 1. Mean and standard deviation of total (biotic+abiotic) and abiotic percent losses of crude 

oil compounds in Arctic seawater incubations. 

Total Petroleum 

Hydrocarbons n-alkanes

Treatment Time 

(days)

Total 

loss

Abiotic 

loss

Total 

loss

Abiotic 

loss

Oil 5 12.1±0.9 8.3±1.3 23.9±3.1 14.7±4.1

10 17.4±1.0 4.7±4.1 83.4±2.0 23.2±2.5

20 22.8±4.9 10.7±4.3 87.6±4.7 29.9±3.7

30 26.3±2.8 11.6±0.6 91.4±3.6 25.8±0.4

Oil+Corexit 5 9.0±3.9 8.0±4.4 20.4±2.9 15.1±4.3

10 22.2±2.7 8.9±2.3 81.6±6.6 22.3±4.6

20 27.5±5.4 13.9±0.3 90.7±2.2 27.9±2.3

30 32.3±3.8 13.4±1.9 93.1±1.3 25.9±2.2

Branched alkanes Polycyclic Aromatic 

Hydrocarbons

Treatment Time 

(days)

Total 

loss

Abiotic

loss

Total 

loss

Abiotic loss

Oil 5 16.6±1.7 6.0±5.0 20.3±1.6 12.6±2.3

10 65.9±1.8 16.8±4.3 17.6±3.8 18.5±5.6

20 72.5±6.0 24.6±5.9 34.0±3.1 30.3±4.9

30 78.8±8.8 13.5±1.6 55.8±7.2 38.2±2.6

Oil+Corexit 5 12.8±1.9 5.7±7.1 12.2±3.8 12.4±3.5

10 66.2±2.4 11.6±1.8 17.1±2.0 27.5±10.3

20 76.5±1.8 17.1±2.3 39.9±9.5 38.2±5.3

30 79.7±4.7 14.5±4.1 54.1±2.8 38.0±2.0

These findings support previous studies reporting that crude oil biodegradation can occur in the 

Arctic marine environment but more slowly than in temperate regions. In a similarly designed 

experiment by McFarlin et al. (2014), TPH percent losses from 2.5 ppm oil in -1°C Alaskan 

Arctic seawater (obtained from the same location as this study) were 36% and 45% after 10 and 
16

28 days, respectively. Percent loss was slightly lower in this study; 20±3% after 10 days and 

29±4% after 30 days. The lower percentages of TPH loss observed here can be attributed to the 

higher concentrations of oil used (50 ppm in this study vs. 2.5 ppm by McFarlin et al., 2014) and,

potentially, to increased weathering of the stock oil supply over time. Despite these differences, 

both experiments show lower extents of oil loss than in temperate regions. For example, a 

similarly designed incubation of New Jersey seawater with 2.5 ppm of ANS crude oil at 8°C 

observed 51% and 69% TPH loss at 11 and 24 days, respectively (Prince et al., 2013).

While no differences in TPH, n-alkane, branched alkane, or PAH loss were observed with the 

addition of Corexit in this experiment, similar seawater incubation studies have noted some 

dispersant-associated effects. McFarlin et al. (2014) observed increased TPH loss with the 

addition of Corexit at 10 days (47% loss with Corexit, 36% loss without) and 28 days (54% TPH 

loss with Corexit, 45% loss without). Similarly, Prince et al. (2013) observed increased TPH loss 

with the addition of Corexit at 11 days (64% loss with Corexit, 51% without) and 24 days (77% 

loss with Corexit, 69% without). In those studies, oil and Corexit were pre-mixed prior to their 

addition to seawater, which may influence the effect of Corexit on oil degradation. In this study,

oil and Corexit were added separately. Studies have reported inhibition of oil degradation with 

the addition of Corexit (Kleindienst et al., 2015; Rahsepar et al., 2016); however, the 

experimental conditions varied considerably from this study (e.g., use of very low oil 

concentrations, oil fractions, and enrichment cultures), precluding direct comparisons. The 

incubation test methods applied here were not optimized to replicate a comparison of an oil slick 

to dispersed oil (see Prince & Butler, 2014). Instead, this study mimicked the relatively low 

concentrations of oil and dispersant associated with a dispersed oil plume to provide more

realistic estimates of whole-oil biodegradation following dispersion and assessment of the 

sequence and potential interactions of petroleum and dispersant component biodegradation. 

Dispersant degradation in seawater

Analysis of the surfactant constituents of Corexit revealed the rapid degradation of the non-ionic 

surfactants but was inconclusive for DOSS due to high analytical variability (Figure 4). Relative 

to the non-ionic surfactants, DOSS measurements showed substantial variation between 

replicates, compared to the other Corexit components. Concentrations of DOSS in individual 

samples were consistent with the previously reported accuracy for the analytical methods (Place 

et al., 2016), yet were variable even in the whole-bottle mesocosms. This variability occurring 

even when entire mesocosms were solvent extracted suggests that DOSS measurement variation 

may occur as an innate property of the compound, such as its surface-active behavior.

DOSS concentrations were comparable in both destructively harvested and subsampled 

incubations and did not show significant differences between treatments over time until 30 days 

(Figure 4a). By the end of the 30-day incubation, a 91±13% loss of DOSS was observed for 

Corexit alone and 48±41% loss for Corexit in the presence of oil. The loss of DOSS in Corexitonly treatments was significantly higher (p=0.0018) than the abiotic controls (0±23% loss), 
17

suggesting biodegradation as a mechanism for loss. However, in treatments containing both oil 

and Corexit together, there was no difference in DOSS degradation between the biotic and 

abiotic conditions. It was also noted that, during analysis, recovery and measurement of DOSS in 

treatments containing oil was more challenging than with Corexit alone, contributing to the 

observed variation. EHSS, a metabolite of DOSS, accumulated significantly more in treatments 

containing both crude oil and Corexit than in other treatments by day 6 (p<0.0001) in the largevolume subsampled incubations and remained high throughout the remainder of the experiment 

in both incubation types (Figure 4b). This increase in EHSS concentrations suggests that, 

although direct quantitation of DOSS itself was inconclusive due to variation in the data, DOSS 

transformation may be occurring. EHSS concentrations increased for treatments containing both 

crude oil and Corexit but did not increase for treatments containing only Corexit. The prolonged 

presence of EHSS and decreased DOSS loss suggests that DOSS mineralization beyond EHSS 

was delayed or incomplete in the presence of oil. 

In contrast, the non-ionic surfactants (Tween 80, Tween 85, Span 80) showed rapid losses with 

and without the presence of oil. Tween 80 and 85 concentrations (Figure 4c) had fallen below 

limits of quantitation (LOQ) in both biotic treatments by day 5 (60,000 ng/L) and Span 80 

(Figure 4d) dropped below LOQ (60,000 ng/L) by day 3, suggesting that rapid degradation of 

both components can occur in Arctic environments. Decreases of all non-ionic surfactants in 

abiotic treatments by 30 days were also observed, suggesting that abiotic transformation and loss 

of these analytes were occurring in addition to biotic processes. 
18

Figure 4. Corexit surfactant component loss in seawater incubations. Mean (n=3) concentrations 

in subsampled 6-L mesocosms over time of Corexit constituents or suspected metabolites a) 

DOSS, b) EHSS, c) Tweens 80 and 85, and d) Span 80. The error bars denote one standard 

deviation from the mean, and the horizontal lines in each panel represent the limit of quantitation 

for that analyte (200 ng/L for DOSS and EHSS, 60,000 ng/L for the Tweens and Span 80).

Previous studies of the fate of Corexit have primarily focused on the degradation of the most 

abundant constituent, DOSS. As in our study, large variations for DOSS measurements over time 

have been observed by others (Kleindienst et al., 2015; McFarlin et al., 2018), underscoring the 

difficulty of reliably quantifying it in experiments. McFarlin et al. (2018) performed Arctic 

seawater incubations at 2°C and observed 98% total loss and 21% abiotic loss of DOSS in 

offshore seawater at 28 days but only 35% total and 2% abiotic loss in nearshore seawater. In 

experiments using Gulf of Mexico seawater at 8°C, Kleindienst et al. (2015) found an 8% loss of 

DOSS for Corexit alone and ~30% loss of DOSS for Corexit with oil at four weeks. In an 
19

experiment using cultures isolated near the Macondo wellhead in the Gulf of Mexico, Campo et 

al. (2013) found that DOSS did not undergo substantial degradation within 28 days at 5°C; 

however, at 25°C, the cultures exhibited rapid and complete DOSS degradation within 14 days. 

Similar results were observed by Techtmann et al. (2017), who used the same cultures and 

experimental conditions, with the majority of degradation at 25°C occurring within 20 days, but 

no observable degradation at 5°C over the course of the 56-day experiment. Our current and 

previous studies (McFarlin et al., 2018) demonstrated little abiotic loss of DOSS in Arctic 

seawater, which is supported by evidence that DOSS does not undergo significant hydrolysis or 

photolytic degradation under simulated solar conditions (Glover et al., 2014; Perkins, 2017).

While some mesocosm and culture-based experiments have observed rapid degradation of 

DOSS, in situ measurements in the Gulf of Mexico following the DWH spill suggest that DOSS 

may be more recalcitrant in the environment. Water samples collected by Kujawinski et al. 

(2011), 64 days after dispersant applications had ceased for the DWH spill, demonstrated the 

persistence of DOSS, which was present in concentrations significantly higher than those that 

were predicted by dilution and transport. Additionally, White et al. (2014), observed DOSS 

concentrations ranging from 6-9,000 ng/g in coral communities six months after the spill and 1-

260 ng/g in beach sands 26-45 months after. The presence of DOSS in Gulf of Mexico sediments 

was confirmed as well as the discovery of DOSS in settling particles (Perkins et al., 2017). This 

disparity between the results of laboratory experiments and in situ measurements may be due to 

laboratory conditions not accurately replicating environmental conditions. Based on current 

findings, it remains unclear if DOSS degrades, either biotically or abiotically, to an appreciable 

extent in the environment when large quantities are applied following an oil spill.

In contrast to DOSS, the non-ionic surfactant components of Corexit (Tween 80, Tween 85, and 

Span 80) underwent rapid losses, falling below LOQs within 3-5 days. Based on initial 

concentrations, this represents a ≥93% loss for the Tweens at 5 days in biotic treatments and 30 

days for abiotic treatments. However, the extent of the loss of Span 80 below the LOQ was more 

uncertain, with the LOQ representing ≥53% loss based on initial concentrations. This LOQ was 

reached at 3 days in biotic treatments and 20 days in abiotic treatments. The rapid loss of the 

non-ionic surfactants demonstrated here has been observed in other seawater incubation 

experiments. Kleindienst et al. (2015) observed concentrations of Tweens and Span 80 falling 

below detection limits (20 μg/L for Tweens and 36 μg/L Span 80) within one week using Gulf of 

Mexico seawater at 8°C; this represented ~99.7% of the Tweens and ~87% of Span 80. McFarlin 

et al. (2018) also observed near complete (>97%) loss of Span 80 in 28 days at 2°C using both 

nearshore and offshore Arctic seawater. The loss of Span 80 was attributed to biodegradation 

since much lower losses were observed for abiotic treatments (46% offshore and 1% nearshore at 

28 days). The Tweens also underwent rapid loss (>99% total loss) for nearshore seawater at 10 

days, but more was attributed to abiotic loss (82%) (McFarlin et al., 2018). These findings 

suggest that the non-ionic surfactant components of Corexit are rapidly lost from the 
20

environment through both biotic and abiotic processes, though the pathways and mechanisms 

underlying this have yet to be determined.

During TPH analysis, an unexpected set of peaks was detected by GC/MS in treatments of oil 

with the addition of Corexit (not present in oil-only treatments). The compound represented by 

these peaks was identified based on its mass spectra as 1-(2-Butoxy-1-methylethoxy)propane-2-

ol (Figure 5), which is also known as dipropylene glycol n-butyl ether (DGBE). DGBE is an 

industrial chemical used as a solvent, chemical reaction intermediate, insecticide, and surfactant 

that has been identified as a solvent component of Corexit (Parker et al., 2014). Relative to the 

components of the crude oil, significant DGBE remained at the end of the incubation series. Due 

to its complex chemical structure, DGBE may be resistant to biodegradation in the presence of 

more labile compounds in crude oil and Corexit, such as n-alkanes and non-ionic surfactants. 

Loss of this compound could not be accurately quantitated from the analytical methods used 

here, which were intended to measure the loss of compounds present in oil. DGBE

concentrations likely had high variability in mesocosms due to the extremely small volumes of 

Corexit used in this experiment. However, it is noteworthy that DGBE was detectable in 

significant amounts relative to crude oil compounds by the end of the 30-day incubation, which 

is an important environmental consideration. While there have not been any mesocosm-based 

experiments performed to study DGBE degradation, in situ persistence was observed in 

nearshore water and sediment samples and offshore water samples four months after the DWH

spill (Operational Science Advisory Team, 2010). Although DGBE has low acute toxicity 

compared to oil, it has acute effects on the liver, and the implications of chronic exposure are 

unknown (Johnson et al., 2005; Organization for Economic Co-operation and Development, 

2003). This component of Corexit has not been studied as extensively as other constituents, and 

future study is warranted.
21

Figure 5. GC/MS chromatogram of oil+Corexit from seawater incubation samples. A peak (a) 

identified as dipropylene glycol n-butyl ether (DGBE) was not present in oil-only samples and 

remained present in significant amounts by the end of the 30-day incubation (b).

Seawater microbial community structure

Changes in the prokaryotic microbial community structure were evaluated by analyzing bacterial

and archaeal 16S rRNA gene amplicon sequences. Experimental treatments and time both had

significant effects on microbial community succession (Figure 6). Communities associated with 

the co-presence of oil and Corexit (oil+Corexit) treatment began with structures similar to 

Corexit-only treatments and became more similar to oil-only treatments as the incubation 

progressed (Figure 7). Generally, different taxa and individual ASVs responded to the presence 

of either oil or Corexit, although some taxa and ASVs responded to multiple treatments. 

Parameters correlated with community structure and the proliferation of specific taxa included

the presence of petroleum, Corexit, nitrogen compounds, dissolved oxygen, and pH (Figure 6, 

Table 2).
22

Figure 6. Prokaryotic community structure in seawater incubations with oil +/- Corexit. BrayCurtis NMDS ordination of the prokaryotic microbial community structure and correlating 

environmental parameters over time exposed to crude oil, Corexit, or oil+Corexit.

Figure 7. Prokaryotic community structure based on 16S rRNA sequence analyses associated 

with different treatments in seawater incubations. Mean (n=3) relative abundance of prokaryotic 

microbial community taxa at the genus level for experimental treatments over time.
23

Table 2. Relationships of environmental parameters to the prokaryotic microbial community 

structure in Arctic seawater mesocosms determined using Mantel tests. All relationships in the 

table below are significant (95% confidence interval). Moderate to strong correlations (r>0.5) are 

in bold. Note that the following parameters are strongly correlated with time: TPH (r=0.775, 

p<0.0001), n-alkanes (r=0.573, p<0.0001), branched alkanes (r=0.638, p<0.0001), PAHs 

(r=0.978, p<0.0001), and DOSS (r=0.801, p<0.0001).

Mantel 

correlation 

coefficient (r)

p-value

Mantel 

correlation 

coefficient (r)

p-value

All samples Oil-containing treatments

Time 0.597 0.0010 Time 0.774 0.0010

Si(OH)4 0.182 0.0070 TPH 0.758 0.0010

NO2 0.268 0.0010 n-Alkanes 0.681 0.0010

NH4 0.236 0.0020 Branched alkanes 0.728 0.0010

pH 0.347 0.0010 PAHs 0.669 0.0010

DO 0.101 0.0200 PO4 0.253 0.0250

Si(OH)4 0.281 0.0071

Day 5 NO3 0.315 0.0050

No significant (p<0.05) relationships observed NO2 0.396 0.0010

pH 0.620 0.0010

Day 10

NH4 0.372 0.0030 Corexit-containing treatments

pH 0.391 0.0090 Time 0.588 0.0010

DO 0.374 0.0170 EHSS -0.164 0.0160

DOSS 0.273 0.0320

Day 20 NO3 0.416 0.0010

NO2 0.345 0.0460 NO2 0.355 0.0330

pH 0.384 0.0020

Day 30 DO 0.205 0.0470

Si(OH)4 0.289 0.0330

NO3 0.233 0.0400

DO 0.619 0.0010

The community structures of all samples appear to cluster by treatment (Figure 6), with the 

oil+Corexit community representing a mixture of the oil-only and Corexit-only communities. An 

MRPP test demonstrated the observed clustering was statistically significant (p<0.0001) and all 

pairwise comparisons significant (p<0.032), indicating that the microbial community of each 

treatment was significantly different from one another. A PERMANOVA test of all treatments 

and time points found significant effects of treatment (p=0.0002), time (p=0.0002), and the 

interactions of both (p=0.0002). The influence of time as a significant factor driving the 

microbial community structure is indicative of succession. Comparisons were also made at each 

time point to isolate the influence of the treatments applied. MRPPs showed a significant 

difference between treatments (p<0.0004 for each time point), and all pairwise comparisons were 

significant (p<0.033) except for oil and oil+Corexit at day 10 (p=0.8920).
24

The communities associated with the oil+Corexit treatment contained a combination of the 

genera stimulated by the presence of oil or Corexit alone (Figure 7). At day 5, the oil+Corexit 

community was more similar to the Corexit-only treatments, and, after 10 days, it was more 

similar to the oil-only treatments. The chemical analyses mirror this trend, showing a rapid loss 

of the non-ionic surfactants in Corexit due to degradation within the first 5 days and oil not 

undergoing extensive degradation until after 10 days. The patterns of community change and 

chemical losses suggest that labile Corexit components are initially preferentially degraded over 

the less labile oil compounds (Kleindienst et al., 2015). However, this did not appear to impact 

oil degradation negatively. Previous studies also found a distinct separation of community 

structure between incubations containing oil or Corexit separately (Kleindienst et al., 2015; 

McFarlin et al., 2018; Techtmann et al., 2017), with an overlap in the oil+Corexit communities 

(Techtmann et al., 2017). 

Several taxa increased significantly (p<0.05) in relative abundance in response to Corexit 

compared to controls, including Colwellia, Polaribacter, Moritella, Octadecabacter, and 

Amylibacter (Figure 7). Taxa that experienced significant (p<0.05) increases in response to oil 

included Thalassolituus and Sedimentitalea. Taxa that significantly increased (p<0.05) in 

abundance in the presence of oil, Corexit, or oil+Corexit included Oleispira, Pseudofulvibacter,

and Roseobacter. Backward, step-wise model selection for individual genera revealed that the 

relative abundances of organisms stimulated by the presence of oil or Corexit were predicted by 

concentrations of those respective chemical components and nutrients (Table 3). Genera 

stimulated by both oil and Corexit were predicted by compounds from both, implicating these 

taxa as potential biodegradative organisms. For these taxa, individual ASVs were frequently 

shared across all experimental treatments and enriched relative to controls. ASVs that were 

identified to be unique to either oil or Corexit treatments by indicator species analysis were 

found to be present in treatments containing both, indicating that the addition of Corexit did not 

suppress the proliferation of oil-degrading bacteria.

Many of the taxa stimulated by oil or Corexit here have been observed in previous in situ

measurements and incubation studies from a variety of marine environments, including Oleispira 

(Bælum et al., 2012; Coulon et al., 2007; Hazen et al., 2010; Ribicic et al., 2018a), Colwellia 

(Bælum et al., 2012; Brakstad et al., 2008; Dubinsky et al., 2013; Kleindienst et al., 2015; 

McFarlin et al., 2018; Redmond & Valentine, 2012; Tremblay et al., 2017), Moritella (McFarlin 

et al., 2018), Octadecabacter (Brakstad et al., 2008), Thalassolituus (Coulon et al., 2007; Crisafi 

et al., 2016; Hazen et al., 2010; McKew et al., 2007; Tremblay et al., 2017), and Roseobacter 

(Coulon et al., 2007; McKew et al., 2007).
25

Table 3. Backward stepwise models selected for individual genera using the lowest corrected 

Akaike and Bayesian Information Criterion from Arctic seawater mesocosm microbial 

community data.

Final model R

2 Adjusted R2 P values

Amylibacter = DOSS+Time+(DOSS*Time) 0.65 0.59 Overall<0.0001

DOSS=0.0007

Time=0.0433

DOSS*Time=0.0189

Colwellia = PAHs 0.30 0.27 Overall=0.0051

Moritella = Treatment 0.71 0.69 Overall<0.0001

Octadecabacter = DOSS+(DOSS*PAHs) 0.88 0.85 Overall<0.0001

DOSS=0.0002

DOSS*PAHs<0.0001

Oleispira = n-alkanes+DOSS 0.95 0.94 Overall<0.0001

n-alkanes=0.0009

DOSS=0.0118

Polaribacter = DOSS+(DOSS*pH) 0.80 0.78 Overall<0.0001

DOSS<0.0001

DOSS*pH<0.0001

Pseudofulvibacter = EHSS+NO3+PO4 0.71 0.66 Overall<0.0001

EHSS<0.0001

NO3=0.0024

PO4=0.0427

Roseobacter = PAHs+DOSS+NH4 0.93 0.90 Overall<0.0001

PAHs<0.0001

DOSS<0.0001

NH4=0.0051

Sedimentalea = PAHs+EHSS+(PAHs*EHSS) 0.90 0.86 Overall=0.0002

PAHs<0.0001

EHSS=0.0024

PAHs*EHSS=0.0081

Thalassolituus = TPH+NO3+PO4 0.90 0.88 Overall<0.0001

TPH=0.0003

NO3<0.0001

PO4=0.0003

The response of Amylibacter to Corexit and Pseudofulvibacter (previously unclassified) to both 

oil and Corexit described here has not been reported elsewhere. While some taxa have been 

demonstrated to be stimulated only by oil (e.g., Thalassolituus, Coulon et al., 2007; McKew et 

al., 2007, and Cytoclasticus, Brakstad et al., 2018; Coulon et al., 2007; Kleindienst et al., 2015; 

McKew et al., 2007; Tremblay et al., 2017) or Corexit (e.g., Moritella, McFarlin et al., 2018), 

others have been shown to be stimulated by both (e.g., Colwellia, Bælum et al., 2012; Brakstad 
26

et al., 2008; Kleindienst et al., 2015; McFarlin et al., 2018; Tremblay et al., 2017, Oleispira, 

Bælum et al., 2012; Brakstad et al., 2008; Coulon et al., 2007; McFarlin et al., 2018, and

Polaribacter, Brakstad et al., 2018; McFarlin et al., 2018; Prabagaran et al., 2007). The variety 

of different responsive taxa observed may be due to the number of environmental conditions that 

can influence community succession. For example, Redmond & Valentine (2012) observed that 

the abundance of Colwellia in the Gulf of Mexico samples was inversely proportional to 

temperature. Techtmann et al. (2017) also observed a dramatic influence of temperature on 

community composition. Yakimov et al. (2007) noted that biogeography influences 

communities, with Cycloclasticus and Thalassolituus widely distributed and Oleispira more 

common at high latitudes. The sharing of phylogenetic units (oligotypes, OTUs, ASVs) across 

oil and Corexit treatments has been observed in temperate (Kleindienst et al., 2015) and Arctic 

regions (McFarlin et al., 2018), suggesting that some organisms may be capable of degrading 

both oil and Corexit. This agrees with findings (McFarlin et al., 2018) that the expression of 

some oil degradation genes (e.g., alkB) increased in response to exposure to oil or Corexit in

Arctic seawater incubations.

Microbial metagenomics analyses of seawater communities

Overview

The metatranscriptomes of seawater microbial communities exposed to crude oil, Corexit, and 

oil+Corexit were all significantly different from an unexposed community and to each other 

(Figure 8). Visualization of the metabolic metatranscriptomes with an NMDS plot and a 

PERMANOVA test showed significant grouping by the treatments applied (p=0.0002), time 

(p=0.002), and the interaction of both of these factors (p=0.0016; Figure 8), with all pair-wise 

comparisons significant (p<0.02). The total number of reads originating from bacteria was

significantly different between treatments, with reads from treatments containing Corexit 

significantly higher than oil and the control at day 5, and oil+Corexit reads significantly higher 

than Corexit alone (p=0.0002; Figure 9). RNA polymerase B (rpoB) expression was also 

significantly higher for Corexit and oil+Corexit treatments than the oil-only and the control at 

day 5 (p=0.0010; Figure 10), with reads for oil+Corexit higher than Corexit-only (although not 

statistically different, p>0.05), indicating that overall gene expression was significantly increased 

for treatments containing Corexit early in the incubation relative to oil-only and the control.
27

Figure 8. Bray-Curtis NMDS of metatranscriptomes from seawater oil/Corexit mesocosms. The 

NMDS shows the metatranscriptomes observed for each sample at 0, 5, and 30 days in Arctic 

seawater mesocosms amended with Corexit and/or oil or no treatment (control). A 

PERMANOVA test was performed for the effects of the treatments applied (p=0.0002), time 

(p=0.002), and the interaction of both of these factors (p=0.0016), with all pair-wise comparisons 

between different treatments significant (p<0.02).

The presence of Corexit appears to markedly increase microbial activity soon after application, 

particularly when present with oil (Figure 9, Figure 10). Stimulation of bacterial communities by

Corexit alone or with oil has been observed in other seawater incubation studies using

respirometry (McFarlin et al., 2014), cell counts (Hazen et al., 2010; Kleindienst et al., 2015;

Lindstrom & Braddock, 2002), and qPCR of the 16S rRNA gene (McFarlin et al., 2018), which

all increased in the presence of Corexit relative to controls. The observation that microbial 

communities become more active when incubated with oil+Corexit than oil alone agrees with the 

theory that Corexit can enhance oil biodegradation by making oil more bioavailable to 

microorganisms (Lee et al., 2013; Lessard & DeMarco, 2000; Prince, 2015). In this experiment,

the number of total and metabolic transcripts returned to basal levels by 30 days. However, this 

may be different in situ, where much larger water, oil, and dispersant volumes are involved. 
28

Figure 9. Normalized transcript counts originating from bacteria. The histogram above depicts 

the total numbers of reads for bacterial gene transcripts in Arctic seawater mesocosms amended 

with Corexit and/or oil or no treatment (control) at 0, 5, and 30 days incubation. Wald tests with 

Benjamini-Hochberg adjusted p-values were performed to assess the relative expression of gene 

transcripts between treatments, revealing p=0.0145 at 5 days. Error bars represent one standard 

deviation from the mean.

Figure 10. Normalized counts of RNA polymerase B (rpoB). The histogram above depicts the 

normalized counts of RNA polymerase B gene transcripts at 0, 5, and 30 days in Arctic seawater 

mesocosms amended with Corexit and/or oil or no treatment (control). Wald tests with 

Benjamini-Hochberg adjusted p-values were performed to assess the relative expression of gene 

transcripts between treatments, revealing p<0.0001 at 5 days. Error bars represent one standard 

deviation from the mean.
29

The trend of increased gene expression for treatments containing Corexit between days 0 and 5 

was observed not only for the total number of metabolic reads but also for the relative abundance 

of several specific classes of functional transcripts, including lipid, fatty acid (Figure 11), 

respiration, nitrogen, and sulfur metabolism (Figure 12). A vector analysis of these metabolic 

gene classes relative to the functional metatranscriptome of each sample (Figure 8) revealed 

strong correlations (Mantel correlation coefficient, r>0.70) associating energy metabolism genes,

including oxidative phosphorylation and photosynthesis, with the microbial metatranscriptomic 

structure (Table 4). Moderate correlations (0.45<r<0.70) to metatranscriptomes also occurred for 

lipids, fatty acids, nitrogen, and sulfur metabolism genes (Table 4). The correlations of these 

functional gene classes to the overall metatranscriptome suggest that energy metabolism and 

lipid genes are largely responsible for the differences observed in the metatranscriptomes of the 

different treatments. Interestingly, total reads of photosynthesis-related transcripts were 

significantly lower (p=0.0002) in all experimental treatments relative to the control, indicating 

that photosynthesis was depressed during post-spill metabolism, which has been reported for

oiled (González et al., 2009) and dispersed oil (Hsiao et al., 1978; Lewis & Pryor, 2013)

mesocosms, and in situ following the DWH spill (Mishra et al., 2012).

Figure 11. Total reads of fatty acid degradation gene transcripts and chemical loss. The figure 

above shows fatty acid degradation gene transcripts (bars) and the extent of chemical loss (lines) 

at 0, 5, and 30 days for four treatments: oil only, Corexit only, oil+Corexit, and the unamended 

control in Arctic seawater mesocosms.
30

Figure 12. Normalized counts of select metabolic gene transcripts. The barplot above shows the 

distribution of several metabolism gene transcripts by treatment at 0, 5, and 30 days in Arctic 

seawater mesocosms amended with Corexit and/or oil or no treatment (control). Error bars 

represent one standard deviation from the mean.

Table 4. Mantel test statistics and associated p-values for the vector analysis of specific KEGG 

metabolic gene classes to the overall metatranscriptomic profile in Arctic seawater mesocosms. 

 Mantel test statistic, r p-value

Time 0.336 0.0010

Lipids 0.539 0.0010

 Fatty acids 0.429 0.0010

 Glycerolipids 0.061 0.1460

 Glycerophospholipids 0.158 0.0320

Energy metabolism 0.776 0.0010

 Oxidative Phosphorylation 0.742 0.0010

 Photosynthesis 0.789 0.0010

 Carbon fixation 0.130 0.1131

 Nitrogen 0.489 0.0010

 Sulfur 0.530 0.0010
31

Functional gene expression

Microbial communities exposed to oil and/or Corexit had increased expression of fatty acid 

degradation genes relative to the control, but each substance stimulated different genes. The 

presence of oil resulted in increased expression of hydrocarbon degradation gene alkane 1-

monooxygenase (alkB), and the presence of Corexit resulted in increased expression of βoxidation gene acyl co-A dehydrogenase (fadE). The oil+Corexit treatments showed the 

expression of both, suggesting that different genes from the same pathway are responsible for oil 

and Corexit component degradation. 

The numbers of total lipid and fatty acid gene reads, and their relative abundance within the 

metatranscriptome were higher in substrate-amended treatments than the control at five days 

(p=0.0021 and p=0.0001) (Figure 11). Oil-only treatments had significantly (p=0.0001) higher 

total reads of alkane 1-monooxygenase (alkB; a key enzyme in hydrocarbon alkane degradation) 

than the Corexit-only and control treatments (Figure 11). The total alkB reads in the oil+Corexit 

treatment were significantly higher than in the oil-only treatment. The relative abundance of alkB

for oil-only treatments was also significantly higher (p<0.0001) than for other treatments at day 5 

(Figure 11). Functional gene analyses performed following the DWH oil spill showed that the

presence (Lu et al., 2012) or expression (Mason et al., 2012) of alkane degradation genes such as

alkB were enriched in contaminated waters, although it is unknown which genes were associated

with oil vs. Corexit. However, in one study, increased intensity and richness of alkB genes on a

GeoChip microarray assay was observed for Arctic seawater exposed to either oil or Corexit

alone relative to controls, indicating that this gene or pathway may be involved in the

degradation of both (McFarlin et al., 2018).

In contrast, acyl-CoA dehydrogenase (fadE), which catalyzes the first step of fatty acid βoxidation, had higher numbers of total reads and relative abundances in treatments containing 

Corexit compared to oil-only and the control at 5 days (p=0.0068 and p<0.0001, respectively). 

The oil+Corexit treatment was significantly higher than Corexit-only. Additionally, the total 

reads and relative abundances for the β-oxidation genes acetyl-CoA acetyltransferase (fadA) and 

3-hydroxyacyl-CoA dehydrogenase (fadB) were all significantly higher (p<0.0005) in treatments 

containing Corexit at day 5. The increased expression of all of these β-oxidation genes was 

associated with a significant loss of Corexit non-ionic surfactant components to below detection 

limits at day 5 (Figure 11). However, the total reads and relative abundances were not different 

between treatments at 30 days, likely due to the extensive loss of Corexit compounds by this 

time point (Figure 11). The presence or expression of β-oxidation genes such as fadE, fadA, and 

fadB was reported for waters exposed to dispersed oil in situ (Rivers et al., 2013) and in 

incubation experiments (Ribicic et al., 2018b), but it is unclear whether those findings were 

associated with the oil, dispersant, or both.
32

Proposed Corexit surfactant component degradation pathway 

Early in the incubation, gene expression in the oil-only treatment was dominated by alkB, which

catalyzes alkane degradation, whereas, Corexit-only and oil+Corexit treatments were dominated

by fadE, which catalyzes the initial step of β-oxidation of fatty acids (Figure 11). Additionally,

the subsequent β-oxidation step enzymes fadA and fadB showed increased expression in the

presence of Corexit. The oil+Corexit treatment appeared to be an amalgamation of the oil-only

and Corexit-only treatments, with the abundances of alkB and fadE genes falling between the

two groups. Based on the high abundances of these genes at the 5-day time point (relative to oilonly and the control), increased β-oxidation and subsequent mineralization of compounds was

likely occurring in the presence of Corexit and oil+Corexit. This agrees with previously reported

chemical loss data from this experiment that showed the majority of Corexit surfactant loss

occurred by 5 days, and the majority of oil loss occurred at 10 days (Figure 11). When

examining the constituents of Corexit, their chemical components contain multiple ester groups

(Figure 13A), which may lead to them being more labile than the alkanes present in crude oil due

to fewer steps required to prepare esters to undergo β-oxidation (Figure 13B).

The above finding led to the construction of a proposed pathway for the biodegradation of the

major surfactant components in Corexit (Figure 13B). The pathway involves hydrolysis of the

ester groups with either lipase or abiotic hydrolysis to form fatty acids, which can then undergo

β-oxidation beginning with fadE. The alcohols produced from the ester hydrolysis can also

undergo further oxidation to fatty acids to enter the β-oxidation pathway (Figure 13B). In support

of biotic ester hydrolysis, the lipid ester-hydrolyzing enzyme phospholipase A1 (pldA) was

found to have significantly higher numbers of reads (p=0.0002) in treatments containing Corexit,

with oil+Corexit reads higher than Corexit-only. Compared to the degradation of alkanes, which

must undergo several oxidation steps to be transformed into fatty acids to enter β-oxidation, this

proposed pathway for Corexit compounds funnels directly into the central metabolism that is

widespread in prokaryotes. The general use and prevalence of these enzymes in the environment 

may serve to explain the rapid degradation of some Corexit components and the variety of 

organisms observed in response to Corexit (Kleindienst et al., 2015; McFarlin et al., 2018; 

Techtmann et al., 2017).

Based on the previously described chemical data, which showed rapid degradation of the nonionic surfactants within 5 days, the community shifts and functional genes observed at 5 days are 

most likely associated with degradation of the remaining Corexit components, including early 

stages of DOSS degradation and/or biotransformation. However, the non-ionic components of 

Corexit also contain many ester functional groups, as does DOSS, which allows them to enter the 

proposed degradation pathway described here. 
33

Figure 13. (A) Chemical structures of the Corexit surfactant components (Place et al., 2016) and 

(B) proposed bacterial biodegradation pathway for the major surfactant components and 

comparison to petroleum hydrocarbon alkane degradation.

Taxonomic identity of putative biodegrading organisms and genes in seawater

The taxa previously described in this study as proliferating in response to oil and/or Corexit were 

also observed (Table 5) to simultaneously take part in several key metabolic and biogeochemical 

processes relevant to contaminant degradation, including fatty acid degradation, nitrogen 

cycling, and sulfur cycling. Based on the taxa that each metabolic transcript originated from, 

different organisms became metabolically active in response to oil and/or Corexit (Figures 14

and 15).
34

Table 5. Summary of genera associated with the expression of different genes or metabolic 

pathway transcripts in Arctic seawater mesocosms.

Gene or metabolic pathway Associated genera

Alkane 1-monooxygenase (alkB) Alcanivorax, Bukholderia, Marinobacter, Pseudomonas

Acyl Co-A dehydrogenase (fadE) Chromohalobacter, Marinobacter, Pseudoalteromonas, 

Shewanella

Lipases Aeromonas, Burkholderia, Colwellia, Rhodopirellula, 

Pseudomonas, Shewanella

Oxidative phosphorylation Alcanivorax, Chromohalobacter, Colwellia, Flavobacterium, 

Marinobacter, Pseudomonas, Shewanella

Nitrogen cycling Alcanivorax, Alteromonas, Colwellia, Marinobacter, 

Marinomonas, Pseudoalteromonas, Pseudomonas, 

Psychromonas

Sulfur cycling Colwellia, Marinobacter, Marinomonas, Pseudoalteromonas, 

Pseudomonas, Saccharophagus, Shewanella

Figure 14. Bray-Curtis NMDS plot of the phylogenetic derivation of RNA transcripts. The

NMDS plot above shows the phylogenetic derivation of RNA transcripts in Arctic seawater 

mesocosms amended with Corexit and/or oil or no treatment (control) at 0, 5, and 30 days 

incubation. A PERMANOVA test was performed to assess the effects of the treatments applied 

(p=0.0002), time (p=0.0002), and the interaction of both of these factors (p=0.0002). All pairwise comparisons between different treatments were significant (p<0.05).
35

Figure 15. Relative abundance of bacterial taxa associated with metabolic gene expression.

Bacterial taxa associated with gene expression in the Arctic seawater mesocosm treatments at 0, 

5, and 30 days incubation are identified to order.

A PERMANOVA test of taxonomic affiliation of transcripts showed there were significant 

effects of treatment (p=0.0002), time (p=0.0002), and the interaction of both (p=0.0002) on the 

bacterial phylogenetic community make-up represented by the origins of metabolic transcripts, 

with all pair-wise comparisons showing significant differences (p<0.05). This agrees with prior 

16S-rRNA-gene amplicon sequence analyses from this incubation experiment, which showed 

that the overall microbial community structure and relative abundance of specific taxa differed 

significantly between treatments, and oil+Corexit incubations resulted in communities 

representing a mixture of the distinct communities that emerged in response to oil-only and 

Corexit-only treatments. Functional gene expression was dominated by gammaproteobacteria 

and included the orders Oceanospirillales, Pseudomonadales, and Alteromonadales (Figure 15). 

Genera belonging to the Oceanospirillales and Pseudomonadales were both stimulated by the 

presence of oil, Corexit, and oil+Corexit at 5 days, including Alcanivorax, Bermanella, 

Marinomonas, and Pseudomonas. Some Alteromonadales genera were stimulated by the 

presence of either oil or Corexit (Alteromonas, Marinobacter, Saccarophagus, Shewanella); 

however, total Alteromonadales counts and some genera were only stimulated by the presence of 

Corexit (Colwellia, Psychromonas, Moritella). There was no indication that the addition of 

Corexit had a negative impact on the presence or abundance of sequences belonging to genera 

enriched in the presence of oil alone, as previously reported for the oil-degrading taxa 
36

Marinobacter and Cytoclasticus in Gulf of Mexico seawater mesocosm incubations (Kleindienst 

et al., 2015).

When examining the organisms associated with metabolic gene expression, the upregulation of 

biodegradation processes in response to oil and/or Corexit (lipid metabolism, oxidative 

phosphorylation, nutrient cycling) was performed by multiple taxa (Table 5), as observed in 

other studies (Brakstad et al., 2015; Dombrowski et al., 2016; Handley et al., 2017). The alkB

and fadE genes expressed originated from different genera, with the exception of Marinobacter, 

which expressed both. Broader metabolic functions were otherwise generally associated with the 

same groups of genera regardless of treatment (Table 5). 

The genus Colwellia garnered substantial attention due to its increased abundance in the 

aftermath of the 2010 DWH spill (Bælum et al., 2012; Dubinsky et al., 2013; Hazen et al., 2010; 

Kleindienst et al., 2015; Mason et al., 2012, 2014; Rivers et al., 2013), and there has been

speculation that Colwellia are stimulated by the presence of Corexit. Colwellia are facultatively 

anaerobic psychrophilic heterotrophs that typically found in deep marine environments (Deming 

& Junge, 2015). As a result of cold-environment adaptations, Colwellia possess unique 

metabolic capabilities such as the ability to degrade polyhydroxyalkanoate (PHA), cyanophycin, 

and chitin-like compounds (Deming & Junge, 2015; Methé et al., 2005). PHAs are a family of 

polyesters that can be degraded and used as a carbon source or energy reserve. The ability to 

degrade PHAs is linked with a significant capacity to produce and degrade fatty acids using fadE

and fadB genes (Methé et al., 2005). Additionally, Colwellia possess coenzyme F420, which is 

linked to polynitroaromatic compound degradation (i.e., 2,4 dinitrophenol), the ability to respond 

to reactive oxygen species, and enzyme structural plasticity. These characteristics give Colwellia

a metabolic advantage over their mesophilic counterparts (Methé et al., 2005), and this metabolic 

niche may link Colwellia to Corexit degradation. While not linked to fadE expression in this 

experiment, Colwellia were involved in the expression of lipase and oxidative phosphorylation 

transcripts as well as those for nitrogen and sulfur cycling (Table 5) in both Corexit and 

oil+Corexit treatments. Further studies are needed to gain a better understanding of the roles 

Colwellia and other genera play in both dispersant and dispersed oil biodegradation. The use of 

cultures or stable isotope probing (SIP) of Corexit components can confirm that the proposed 

pathway (Figure 13b) is being utilized for Corexit degradation and to determine the organisms 

involved in the different steps.

Oil biodegradation in Arctic marine sediments

The sediment incubation experiment resulted in 333 samples, each requiring extraction and GCMS analysis. Only half of the extractions were completed by the time of this report due to 

methodological issues; specifically, the formation of emulsions during methylene chloride 

extraction of the wet sediments. We subsequently modified our method by separating the water 

from the sediment for each sample (via certified glass serological pipette) and then extracting oil 
37

from the water and sediment fractions independently. The addition of 2 grams of anhydrous 

sodium sulfate per 1 gram of sediment to the sediment fraction dehydrated the sample such that 

an emulsion could not occur. Methylene chloride was then added, mixed, and removed three 

times for each fraction, both water and sediment, to extract crude oil from the entire sample. 

GC-MS analysis will proceed after all samples are extracted. Data from these analyses will be 

reported in forthcoming publications (see Study Products). The information will provide 

quantitation of the % loss of oil in sediments under aerobic and anaerobic incubation conditions 

over time, providing an indication of degradation rates in the benthos. Percent loss values will be 

determined for incubations containing live microbes as well as sterile controls, which will enable 

us to distinguish between losses due to abiotic processes such as weathering and those associated 

with biodegradation.

Sediment microbial community response to oil 

Hierarchical clustering analysis and nMDS ordination indicate that there were seven different 

clusters of prokaryotic communities represented in the sediment-oil incubation experiment 

(Figure 16). Cluster 1 contains all samples, weathered, fresh, and non-oiled sediments, from time 

zero, the start of the incubation experiments. Cluster 2 contains all oil-free samples after 12 days 

of incubation and in situ community samples collected immediately upon acquisition of 

sediments in the Chukchi Sea. Cluster 3 encompasses all but one of the fresh and weathered oil 

samples from the first destructive harvest (12 days) of the incubation experiment. Cluster 4 

contains all non-oiled samples between the second harvest (24 days) and the final harvest (60 

days). Cluster 5 is represented by all weathered oiled samples from the second and third harvest, 

24 and 36 days, respectively. Cluster 6 contains all but one fresh oiled sample from the second 

harvest through the end of the incubation, 24-60 days. Cluster 7 encompasses all weathered oil 

samples from the fourth and final harvests, 48-60 days into the incubation experiment. The 

nMDS indicates that differences between non-oiled samples and oiled are largely exhibited on 

the nMDS1 axis, and within-cluster differences are largely exhibited on the nMDS2 axis.
38

Figure 16. Hierarchical cluster dendrogram and nMDS ordination of the prokaryotic community 

from sediment-oil incubation experiment. The figure above depicts the results from the 

hierarchical cluster analysis on the left-hand side and nMDS plot on the right-hand side; both are 

representations of prokaryotic community structure across treatments. Shapes identify treatments 

and colors identify incubation times in days. The dotted lines on both the cluster dendrogram and 

nMDS delineate the non-oiled samples from the oiled samples. The * highlights the clustering of 

the non-oiled samples with the in situ samples after 12 days, indicating that after the initial 

disturbance of putting sediments in experimental bottles, it took up to 12 days for the microbial 

community to reflect that of the in situ community.

To explore differences in prokaryotic community structure that explain the clustering patterns, 

change in taxa abundance was measured from the time of collection in situ to the end of the

experiment for each treatment (no-oil, fresh-oil, and weathered-oil) (Figure 17). The 14 most 

abundant taxa observed in the study were identified to genus-level. Woesia occurred solely in nooil samples, but eight taxa were highly abundant in all treatments and are considered the core 

community in this study: Aquibacter, Candidatus Nitrosopumilus, Halioglobus, Lutibacter, 

Lutimonas, Maritmimonas, Persicirhabdus, and Psychromonas. Five taxa were abundant only in 

the oiled samples; Cycloclasticus, Neptuniibacter, and Oleispira in fresh and weathered 

treatments, Zhongshania in fresh-oil samples, and Roseobacter clade AC117 in weathered-oil 

samples. Though the relative abundances of these five taxa varied and exhibited different trends 

over time within each treatment, they are considered putative oil-degraders because of their 

select occurrence in oiled treatments.
39

Figure 17. Area charts of top genus-level taxa over time with no-, fresh-, and weathered-oil 

treatments in sediment incubations. The charts above show the most abundant taxa (upper 14) 

identified to genus-level represented in no-oil, fresh-oil, and weathered-oil samples over time. It 

is important to note that with area charts, the change in thickness of the color associated with 

individual taxa is a metric of change in relative abundance over time.

The first putative oil-degrading taxonomic group to increase in relative abundance after 12 days 

of incubation in both the fresh- and weathered-oil samples was the genus Oleispira. Oleispira 

spp. are among the most well-known marine oil-degrading microbes and have been implicated in 

aerobic oil biodegradation, primarily of branched and straight-chain alkanes, in both seawater 

and marine sediments (Guibert et al., 2012; Ribicic et al., 2018a; Yakimov et al., 2003, 2007). 

The early increase in abundance of Oleispira in the oiled samples is congruent with the typical 

succession of chemical compounds in oil. Branched and straight-chain alkanes (saturated 

hydrocarbons) are generally the first compounds to be biodegraded (Head et al., 2006). The 

matching trend in Oleispira in both fresh- and weathered-oil samples indicates that the 

weathered-oil still contained saturated hydrocarbons. In the fresh-oil samples, Oleispira 

decreased in abundance from day 12 to day 48 when it exhibited a second spike in relative 

abundance. However, in the weathered-oil samples, Oleispira showed a consistent decline in 

abundance following the initial spike. The second peak in fresh-oil samples may indicate

byproducts of oil degradation favorable to Oleispira that were not present in the weathered-oil 

samples.

The initial spike in Oleispira relative abundance was followed by an increase of Neptuniibacter

and Cycloclasticus at 24 days of incubation in both fresh- and weathered-oil samples. The 

increase in Neptuniibacter was smaller in the weathered oil samples, where the increase in 

Cycloclasticus was more pronounced, particularly over time. Some studies have indicated that 
40

specific strains of Neptuniibacter can degrade carbazole and phenanthrene (Ahmad et al., 2015; 

Dombrowski et al., 2016; Maeda et al., 2010; Nagashima et al., 2010). Carbazole and 

phenanthrene are both PAHs containing three rings, though carbazole is also a heterocyclic 

compound in which one of the carbons has been replaced with nitrogen (Head et al., 2006; Salam

et al., 2017). In the fresh-oil samples, Neptuniibacter reached its peak abundance at 36 days,

which coincided with a trough in Oleispira. Following the peak in Neptuniibacter abundance, 

Oleispira exhibited a second peak of higher abundances at day 48, indicating that degradation 

products of fresh-oil from Neptuniibacter may be labile or favorable to Oleispira. This trend was 

not observed in the weathered-oil samples, where Neptuniibacter had relatively stable abundance

throughout the remainder of the experiment. Meanwhile, Oleispira continued to decline, 

suggesting that similar compounds present in the fresh-oil samples were not available during the 

weathered oil incubation at day 48.

Cycloclasticus exhibited the most dramatic increase between days 12 and 36 in the fresh-oil 

samples and remained relatively stable through the end of the experiment. In the weathered-oil 

samples, however, Cycloclasticus continued to increase in abundance substantially from day 12 

to day 60 and showed no sign of plateauing. Bacteria belonging to the Cycloclasticus genus have 

been indicated physiologically, experimentally, and inferentially in the aerobic degradation of 

PAHs (Dyksterhouse et al., 1995; Kasai et al., 2002). Members of Cycloclasticus are considered 

the prevalent, aerobic PAH-degrader in marine sediments and Pacific Arctic deep-sea sediments. 

Dong et al. (2015) found it was one of the most abundant taxa in PAH-enriched marine 

sediments and exhibited utilization of naphthalene and phenanthrene as sole carbon sources.

Studies by Yakimov et al. (2003, 2007) indicated that Cycloclasticus specializes in degrading the 

crude oil compounds that remain after more labile components have been degraded.

The remaining putative oil-degrading taxa detected in this experiment were treatment-specific

and only present in high abundances in either fresh- or weathered-oil samples. Zhongshania, 

representative of the fresh-oil samples, gradually increased in abundance from day 12 to day 36,

spiked at day 48, and gradually declined. Li et al. (2011) proposed Zhongshania as a genus

isolated from land-fast ice and seawater from the Antarctic. Since then, several Zhongshania

species have been described within the genus, all of which were isolated from marine 

environments and one of which is capable of degrading straight- or branched-chain alkanes, 

otherwise known as aliphatic hydrocarbons (Li et al., 2011; Naysim et al., 2014). Zhongshania 

aliphaticivorans, isolated from oil-contaminated marine sediments, has been implicated as an 

aliphatic hydrocarbon degrader based on the presence of several alkane degradation genes within 

its genome, including alkane 1-monooxygenase and haloalkane dehalogenase (Jia et al., 2016; 

Naysim et al., 2014). This was substantiated by Ribicic et al. (2018a), who indicated that 

Zhongshania played a significant role in the initial alkane degradation of chemically dispersed 

oil in Norwegian seawater. Metagenome analysis also suggested that Zhongshania may be 

capable of degrading aromatics such as biphenyl, benzoate, catechol, and cyclopentanol (Jia et 
41

al., 2016). To our knowledge, Zhongshania has not yet been identified or isolated from Arctic 

marine sediments. Those OTUs affiliated with Zhongshania in this study matched equally at 

100% identity in the BLAST database to Zhongshania aliphaticivorans and an uncultured clone. 

The peak in abundance of Zhongshania occurred with the second peak in Oleispira abundance at 

day 48 in the experiment suggesting that oil degradation products generated by Cycloclasticus, 

Neptuniibacter, or both were favorable to Zhongshania as well as Oleispira. 

The Roseobacter NAC11-7 lineage, which is representative of the weathered-oil samples, began 

to increase in abundance starting at day 12 and exhibited a gradual increase until day 48. It

gradually declined until day 60. The increase in abundance of the Roseobacter lineage in 

weathered-oil incubations is subtle, yet apparent, as it was not found to be abundant in non-oiled 

or fresh-oil samples. The Roseobacter NAC11-7 lineage is one that is commonly associated with 

phytoplankton blooms and has only been identified and described from seawater (Hahnke et al., 

2013; Luo & Moran, 2014; Teeling et al., 2016; West et al., 2008). There are few studies 

pertaining to this lineage with respect to oil degradation; however, Brakstad et al. (2004)

reported that Roseobacter NAC11-7 was abundant during oil biodegradation in low-temperature 

(5.9-7.4°C) seawater. Additionally, Netzer et al. (2018) found that bacteria in the Roseobacter

NAC11-7 lineage were prevalent only in oiled seawater (versus non-oiled), with slightly higher 

abundances associated with oiled diatom aggregates. Based upon these and other studies 

correlating Roseobacter with oil degradation, genomic studies have investigated the metabolic 

capabilities of 24 representatives of the Roseobacter lineage and found different pathways 

pertaining to the aerobic degradation of aromatic compounds (Buchan et al., 2000; Buchan et al., 

2019; Giebel et al., 2016).

Distribution of putative oil-degrading taxa in Beaufort Sea sediments

A Beaufort Sea sediment microbial community survey dataset was queried to assess the 

distribution of putative oil-degrading microbes in that region (Objective 4). Putative oildegrading taxa identified in this study were plotted on a map via ArcGIS (Figure 18). All taxa 

except Neptuniibacter were found in low abundances in the Beaufort Sea sediments. 

Roseobacter NAC11-7 clade exhibited the highest abundances at more sites, followed by 

Oleispira, Cycloclasticus, and, lastly, Zhongshania, which was found at just one site.
42

Figure 18. Distribution of putative oil-degraders found in Beaufort Sea marine sediments.

This map shows where the same bacterial genera identified in the experiment conducted in this 

study on Chukchi Sea sediments were found in another study where a survey of prokaryotes was 

conducted on Beaufort Sea surface sediments. The inset shows the sampling region.

Comparison of putative oil-degrading taxa detected in seawater vs. sediments

A comparative analysis of putative oil-degrading bacteria identified in our seawater and sediment 

studies was conducted to determine if similar taxa potentially play important roles in 

biodegradation in the pelagic versus the benthic zones of the Chukchi Sea environment. The 

sediments and seawater samples used in our experiments did not originate from the same time or 

geographic location in the Chukchi Sea; seawater was collected in 2016 approximately 1 km 

offshore of Utqiagvik and sediments were collected in 2017 at 130 km offshore of Pt. Lay,

Alaska (Figure 1). Nonetheless, some similarities were detected among putative oil degraders. 

Members of the genera Oleispira and Roseobacter were found to increase in relative abundance 

in both seawater and sediment incubations, suggesting that they play an active role in the 

biodegradation of petroleum hydrocarbons in these settings. Putative oil-degraders Cytoclasticus

and Neptuniibacter were only detected in sediments, while Thalassolituus, Sedimentalea, and 

Pseudofulvibacter were only detected in seawater. Zhongshania was most abundant and 

responsive to oil in sediments but was detected in very low numbers in some seawater incubation 

samples. In our original hypothesis, we predicted that the diversity (taxonomic richness) of 

hydrocarbon-degrading microbes would be higher in sediment than seawater due to the much 

greater concentration of biomass in sediments. However, the richness of the most abundant

organisms stimulated by the presence of oil was found to be comparable, although some different 

taxa were observed to respond in each sample type. It is important to note that this finding 

focuses exclusively on aerobic incubation tests, and potential anaerobic oil degraders may differ 

taxonomically. Results from this comparative analysis underscore the ubiquity of hydrocarbon-
43

degraders in the marine environment, as well as the influence of specific environmental 

conditions (i.e., nutrients, oxygen, temperature, etc.) on microbial community succession 

following an oil spill.

CONCLUSIONS

The findings of this study are consistent with prior studies indicating that the indigenous 

microbial community in Arctic seawater has the potential to biodegrade crude oil and 

components of Corexit (McFarlin et al., 2014, 2018). This study is the first to closely examine 

the interactions between oil and Corexit degradation when co-present in Arctic seawater. It

provides no evidence that Corexit suppressed crude oil biodegradation or oil-degrading 

microorganisms, as reported by Kleindeinst et al. (2015). Our experiments revealed that, when 

oil and Corexit were present in seawater together, the non-ionic surfactants (Span 80, Tween 80, 

Tween 85) were rapidly degraded, followed by subsequent biodegradation of oil components. 

This apparent preferential degradation of non-ionic dispersant components did not result in 

significant reductions in oil loss when Corexit was present. This may be due to the reduction in 

oil droplet size and increased bioavailability generated by Corexit application outweighing the 

effects of competitive degradation, or due to the relatively small quantity of carbon contributed 

by Corexit compared to oil when applied in the 1:10 dispersant: oil ratio being insufficient to 

outcompete the utilization of petroleum hydrocarbons. 

Analytical variability when quantifying the Corexit component DOSS precluded definitive 

determination of its degradation extent during the study but indicated that it is more persistent 

than the non-ionic surfactants. DOSS appeared to undergo some biotransformation to EHSS

concurrent with the degradation of oil and other Corexit components. 

In Arctic seawater, some taxa responded only to the presence of oil (Thalassolituus, 

Sedimentalea) or Corexit (Colwellia, Polaribacter, Moritella, Octadecabacter, and Amylibacter) 

alone, implicating them as degraders of those mixtures. Some members of the taxa Oleispira, 

Pseudofulvibacter, and Roseobacter became enriched in response to both oil and Corexit (when 

provided separately or together), suggesting that these organisms are capable of utilizing 

components of both mixtures. 

Metatranscriptomics (gene expression) analyses of the seawater microbial communities revealed

that oil and Corexit stimulate different overall metabolic gene expression profiles in the 

microbial community. It also indicated that oil and Corexit share a common upregulation of the 

pathway for fatty acid degradation that shows a synergistic response when both oil and Corexit 

are present. These results and the evidence of increased overall metabolic activity when exposed 

to oil+Corexit, support the theory that dispersants stimulate existing microbial oil-degrading 

activity, possibly through mechanisms such as increasing the bioavailability of oil. 
44

The observation that fatty acid degradation gene expression was upregulated in the presence of 

Corexit alone indicates that this pathway plays a role in the degradation of some Corexit 

components. We propose a hypothetical degradation pathway for Corexit components that 

utilizes the fatty acid β-oxidation pathway to break down side-chains of the non-ionic and DOSS 

surfactant components of Corexit. Fatty acid oxidation is a common metabolic pathway in 

bacteria, so Corexit surfactant compounds may be readily metabolized, completely or in part, by 

the many different taxa that responded to the presence of Corexit. Because acid β-oxidation is 

also used in the biodegradation of alkanes (after several enzymatic transformations), it appears 

that both petroleum-derived alkanes and the hydrocarbon side-chains of some Corexit 

components share a common degradation pathway. Corexit components are able to proceed to 

the pathway more directly since the alkane activation steps are not needed. Colwellia-associated

genes, which were particularly prominent in seawater exposed to Corexit, were associated with 

several metabolic processes that were enriched in the presence of Corexit, indicating it may play 

a key role in its degradation. Further research is needed to confirm the use of the β-oxidation 

pathway in Corexit surfactant degradation and elucidate which taxa are directly involved in its 

degradation, which will bolster our understanding of the fate of chemical dispersants such as 

Corexit in the environment.

Molecular analyses of the sediment microbial community response to oil in aerobic incubations 

revealed that the core sediment microbial community remained present following oil exposure. 

However, significant community shifts occurred in the oiled treatments, with fresh and 

weathered oil treatments producing distinct shifts relative to each other and to non-oiled 

treatments. The oil-associated shifts were characterized by increases in the relative abundance of

several taxa, which implicates these taxa as oil biodegraders. The taxa present in oiled treatments 

but absent in non-oiled samples, Cycloclasticus, Neptuniibacter, Oleispira, Roseobacter clade 

NAC11-7, and Zhongshania, have all been previously linked to oil degradation. Overall the 

presence of these taxa supports our hypothesis that oil-degrading microbial taxa will be present 

in Arctic marine sediments, but refute our hypothesis that we might identify abundant taxa that 

were not previously reported to degrade oil. Among these taxa, there is variation in relative 

abundances between fresh and weathered-oil samples over time, indicating that certain taxa are 

likely linked to specific oil compounds present during different stages of oil degradation.

Following our identification of putative oil-degrading taxa in Chukchi Sea sediment incubation 

studies, we assessed the distribution of these taxa across a geographically distributed series of 

Beaufort Sea sediment samples. We found that the majority of putative oil-degrading taxa found 

in Chukchi marine sediments were also present in very low abundances in Beaufort Sea marine 

sediments. The presence of these microbes indicated that oil degradation potential exists in

Beaufort Sea sediments, supporting our hypothesis that putative oil-degraders are broadly 

distributed in North American Arctic sediments. This hypothesis was predicated on previous 

findings, which suggest that oil-degrading bacteria are ubiquitous and generally present in very 
45

low abundances in the natural environment, then becoming enriched following a spill event 

(Yakimov et al., 2007). 

The presence of oil stimulated different hydrocarbon-degrading organisms in seawater

(Thalassolituus, Sedimentalea, and Pseudofulvibacter), versus sediment (Cytoclasticus, 

Neptuniibacter, and Zhongshania), with some taxa being stimulated in both environments 

(Oleispira and Roseobacter). This further underlines both the diversity and ubiquity of 

hydrocarbon-degrading microbes and the strong influence of environmental parameters (i.e.,

temperature, nutrients, oxygen, biomass, etc.) on post-spill microbial community succession 

even in the same geographic region.

ACKNOWLEDGMENTS

We are grateful to the UAF Institute of Arctic Biology (IAB), UAF College of Fisheries and 

Ocean Sciences, and the Prince William Sound Oil Spill Recovery Institute for cost-share 

funding. Additional support was provided through a National Defense Science and Engineering 

Graduate (NDSEG) Fellowship. We also thank Dr. Katrin Iken and graduate student Lauren 

Sutton for collecting the Arctic marine sediments used in this study. Access to molecular and 

other critical instrumentation was made possible by the IAB Genomics Core Laboratory and 

coordinator Kyle Dillplaine, with support from IAB and an Institutional Development Award 

(IDeA) from the National Institute of General Medical Sciences of the National Institutes of 

Health under grant number 2P20GM103395. The content is solely the responsibility of the 

authors and does not necessarily reflect the official views of the NIH.

STUDY PRODUCTS

Presentations

2018 – CMI Annual Review (oral presentation) – Mary Beth Leigh

2019 – CMI Annual Review (oral presentation) – Alexis Walker

2020 – CMI Annual Review (oral presentation) – Mary Beth Leigh

Gofstein, T. R. & Leigh, M. B. Fate and Influence of Petroleum Contamination and Chemical 

Dispersants in Arctic Marine Environments. 2017 Week of the Arctic. Fairbanks, AK, May 9-10, 

2017. (oral presentation)

Gofstein, T. R. & Leigh, M. B. Fate and Influence of Petroleum Contamination and Chemical 

Dispersants in Arctic Marine Environments. UAF Environmental Chemistry Symposium. 

Fairbanks, AK, April 21, 2017. (oral presentation)

Gofstein, T. R. & Leigh, M. B. Fate and Influence of Petroleum Contamination and Chemical 

Dispersants in Arctic Marine Environments. UAF Biomedical Research Conference. Fairbanks, 

AK, May 18, 2017. (oral presentation)
46

Leigh, M. B., McFarlin, K., Gofstein, T. R., Schuette, U., Perkins, M., & Field, J. Investigating 

oil spill response products Corexit 9500 and Oil Spill Eater II: composition, fate, and effects on 

oil biodegradation in Alaskan waters. Invited oral presentation. Alaska Oil Spill Technology 

Symposium. Anchorage, AK, March 28-29, 2018. (oral presentation)

Gofstein, T.R. Fate and Effects of Petroleum Contamination and Chemical Dispersants in Arctic 

Marine Environments. UAF Annual Environmental Chemistry Symposium Fairbanks, AK, April 

7, 2018. Received Outstanding Graduate Presentation award. (oral presentation)

Leigh, M. B., McFarlin, K., Gofstein, T. R., Schuette, U., Perkins, M., & Field, J. Investigating 

oil spill response products Corexit 9500 and Oil Spill Eater II: composition, fate, and effects on 

oil biodegradation in Alaskan waters. Alaska Branch of the American Society for Microbiology 

Annual Meeting. Fairbanks, AK, September 29, 2018. (oral presentation)

Leigh, M. B., McFarlin, K., Gofstein, T. R., Walker, A. Schuette, U., Perkins, M., & Field, J. 

Fate and effects of oil and oil spill response chemicals in Alaskan marine environments. UAF 

Institute of Arctic Biology Life Sciences Seminar Series. Fairbanks, AK, November 9, 2018.

(oral presentation)

Leigh, M. B., McFarlin, K., Gofstein, T. R., Walker, A. Schuette, U., Perkins, M., & Field, J.

Fate and effects of oil and oil spill response chemicals in Alaskan marine environments. UAF 

Institute of Marine Sciences Seminar Series. Fairbanks, AK, November 14, 2018. (oral 

presentation)

Gofstein, T.R. & Leigh, M. B. Biodegradation of Crude Oil and Corexit EC9500A in Arctic 

Seawater. Fifth International Symposium on Bioremediation and Sustainable Environmental 

Technologies. Baltimore, MD, April 18, 2019. (oral presentation)

Leigh, M. B., McFarlin, K., Gofstein, T. R., Walker, A. Schuette, U., Perkins, M., & Field, J. 

Fate and effects of oil and oil spill response chemicals in Alaskan marine environments. 

University of Oklahoma Department of Microbiology and Plant Biology. Norman, OK, 

November 6, 2019. (oral presentation)

Gofstein, T. R., Perkins, M., Field, J. & Leigh, M. B. Biodegradation of Crude Oil and Corexit 

EC9500A in Arctic Seawater. Alaska Oil Spill Technology Symposium. Anchorage, AK, March 

28-29, 2018. (poster presentation)

Gofstein, Taylor R., Matthew Perkins, Jennifer Field, & Mary Beth Leigh. Biodegradation of 

Crude Oil and Corexit EC9500A in Arctic Seawater. Poster presentation. Alaska Branch of the 

American Society for Microbiology Annual Meeting. Anchorage, AK, October 18-19, 2019.

(poster presentation)
47

Publications

Gofstein, T., Perkins, M., Field, J. & Leigh, M. B. (Submitted May 2020) Sequential 

Degradation of Crude Oil and Corexit EC9500A in Arctic Seawater. Applied and Environmental 

Microbiology

Gofstein, T. R. & Leigh, M. B. (in prep.) Microbial metatranscriptome shifts suggest shared 

biodegradation pathways for Corexit 9500 components and crude oil in Arctic seawater. 

Walker, A., Hardy, S., & Leigh, M.B. (in prep.) Biodegradation of Alaska North Slope crude oil 

in Arctic marine sediments. 
48

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The Department of the Interior Mission

As the Nation’s principal conservation agency, the Department of the Interior has 

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includes fostering the sound use of our land and water resources, protecting our fish, 

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