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TRANSCRIPT
1
Novel alkane hydroxylase (alkB) gene diversity in sediments associated with 1
hydrocarbon seeps in the Timor Sea, Australia. 2
3
Kenneth Wasmund1,2
, Kathryn A. Burns1, D. Ipek Kurtböke
2, David G. Bourne
1* 4
5
1Australian Institute of Marine Science, PMB 3 Townsville, Australia, 4810 6
2Faculty of Science, Health and Education, University of the Sunshine Coast, 7
Maroochydore DC, Australia, 4558. 8
Corresponding Author: D. G. Bourne 9
Email: [email protected] 10
Copyright © 2009, American Society for Microbiology and/or the Listed Authors/Institutions. All Rights Reserved.Appl. Environ. Microbiol. doi:10.1128/AEM.01370-09 AEM Accepts, published online ahead of print on 9 October 2009
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Abstract 11
Hydrocarbon seeps provide inputs of petroleum hydrocarbons to widespread 12
areas of the Timor Sea. Alkanes constitute the largest proportion of chemical 13
components found in crude oils and therefore genes involved in the biodegradation of 14
these compounds may act as bioindicators for this ecosystems response to seepage. To 15
assess alkane biodegradation potential, the diversity and distribution of alkane 16
hydroxylase (alkB) genes in sediments of the Timor Sea was studied. AlkB protein 17
sequences derived from clone libraries identified sequences only distantly related to 18
previously identified AlkB sequences, suggesting the Timor Sea maybe a rich 19
reservoir for novel alkane hydroxylase enzymes. Most sequences clustered with AlkB 20
sequences previously identified from marine Gammaproteobacteria, though protein 21
sequence identities only averaged 73% (with a range of 60%-94% sequence 22
identities). AlkB sequence diversity was lower in deep water (> 400 m) samples off 23
the continental slope compared to shallow water (< 100 m) samples on the 24
continental shelf, but not significantly different in response to levels of alkanes. Real-25
time PCR assays targeting Timor Sea alkB genes were designed and used to quantify 26
alkB gene targets. No correlation was found between gene copy numbers and levels of 27
hydrocarbons measured in sediments using sensitive GC-MS techniques, probably 28
due to the very low-levels of hydrocarbons found in most sediment samples. 29
Interestingly however, copy numbers of alkB genes increased substantially in 30
sediments exposed directly to active seepage, even though only low or undetectable 31
concentrations of hydrocarbons were measured in these sediments in complementary 32
geochemical analyses due to efficient biodegradation. 33
Key Words: Hydrocarbon seeps, Timor Sea, alkB, alkanes, real-time PCR and clone 34
libraries. 35
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Introduction 36
Alkanes are saturated hydrocarbons that are widespread in marine 37
environments due to a variety of anthropogenic and natural sources. They constitute 38
the major fraction of hydrocarbon components found in crude oils and refined 39
petroleum, and are also produced by various marine organisms (e.g., zooplankton) as 40
cellular components (2, 45). Alkanes are considered as pollutants, with short chained 41
alkanes acting as solvents towards cellular membranes and other lipid components 42
(35), while longer chained alkanes may contribute to the formation of oil films and 43
slicks that may limit nutrient and oxygen exchange (22). Importantly, alkanes also 44
serve as important carbon and energy sources for some microorganisms. In marine 45
environments, alkanes succumb to various removal and dispersal processes such as 46
dissolution, photochemical oxidation, evaporation, adsorption and sedimentation. 47
However, the greatest removal pathway for alkanes in marine sediments is via 48
biodegradation by bacteria (14). This mechanism also mediates the transfer of oil-49
derived carbon to higher trophic levels (29, 38) and therefore these bacteria have an 50
important role in carbon cycling in environments subject to long term inputs of 51
hydrocarbons such as marine seep associated ecosystems. Alkane biodegradation is 52
mediated by a diverse range of marine bacteria using various electron acceptors, 53
although degradation generally proceeds at greater rates under aerobic conditions in 54
comparison to anaerobic processes that proceed relatively slowly (8, 27). 55
In the presence of oxygen, well characterized alkane oxidation pathways are 56
initiated by an activation step whereby oxygen is introduced to the alkane substrate 57
before further catabolic steps can proceed. A number of oxygen-dependent alkane 58
hydroxylase enzyme systems have been discovered that catalyse this initial step 59
including the soluble di-iron methane monooxygenases and the membrane-bound 60
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copper-containing methane monooxygenases, both of which act upon short-chain 61
alkanes (i.e., C1 up to C8). Integral membrane non-heme iron alkane hydroxylases (the 62
‘alk’ system) that are related to the well characterised AlkB of Pseudomonas putida 63
GPo1 (also known as P. oleovorans TF4-1 I), act upon longer chain alkanes (i.e., C5 64
to C16) (41). Other systems including alkane-hydroxylating cytochrome P450 65
enzymes and other enzyme systems known to exist based purely on chemical analyses 66
of metabolites formed during alkane degradation experiments are known (23, 26, 30), 67
however, knowledge pertaining to the enzymes and genes involved, as well as their 68
importance in the environment is limited. Only recently have genes involved in the 69
degradation of long chain alkanes (e.g., C32 and C36) been identified in Acinetobacter 70
sp. strain DSM 17874 (40), though there is no information about the presence or 71
importance of such enzymes in the environment. 72
Although various chemical and microbiological aspects of petroleum oil and 73
alkane biodegradation in marine systems have been relatively well studied, there is a 74
general lack of knowledge concerning the diversity or abundance of functional genes 75
involved. The biochemical and molecular aspects of alkB genes and the enzymes they 76
encode have been relatively well studied and this has enabled the development of 77
molecular tools for the study of alkB genes in the environment (20). Elevated levels of 78
hydrocarbons or the introduction of hydrocarbons to environments has been shown to 79
increase gene copy numbers, indicating the potential use of alkB genes as 80
bioindicators of oil pollution and/or biodegradation (17, 34, 37, 44). However, to date 81
only one study has used culture-independent molecular methods to examine the 82
diversity of alkB genes in a marine environment (21) and no studies have examined 83
hydrocarbon degrading genes where natural hydrocarbon seepage occurs. 84
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In this study, the diversity and relative abundance of alkB genes was examined 85
in sediments of the Timor Sea, a region where natural seeps are sources of widespread 86
petroleum hydrocarbons. It was hypothesized that (i) novel alkB genes may exist in 87
this unique tropical marine environment, (ii) that variations in gene diversity would be 88
found in sediments with different hydrocarbon levels, and (iii) that the abundance of 89
certain alkB gene types may reflect the levels of measured hydrocarbons in sediments 90
and therefore this assay could be used as a complimentary tool for monitoring 91
petroleum inputs into sediments of the Timor Sea. 92
93
Methods 94
Sample collection and processing 95
Samples were collected from the Timor Sea, Northwestern Australia, during 96
the RV Southern Surveyor cruise SS05/06 of June 2005. A heavily weighted Smith-97
MacIntyre (0.25 m2 surface area) grab sampler was used to obtain sediment grabs 98
with undisturbed surface sediments. Grabs were then sub-sampled with a sterile core 99
device (5 cm inner diameter and 30 cm in length) shipboard. Locations of sediment 100
grabs are indicated in Table 1 and Figure 1. All cores were sectioned into 1 cm 101
intervals and stored at –20oC shipboard, in liquid nitrogen (–180
oC) during transport 102
(5 days) and at –80oC in the laboratory prior to molecular analysis. Details of 103
analytical methods for measuring hydrocarbon content are reported previously (4). 104
105
DNA extraction 106
The DNA extraction protocol was based on previously described procedures 107
for the extraction of total DNA from soil samples (10, 46). Briefly, the sediment 108
slurry (0.5 g wet weight) was placed in a 1.5 ml tube and suspended in 1.35 ml of 109
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extraction buffer [100 mM Tris-HCl (pH 8.0), 100 mM sodium EDTA (pH 8.0), 100 110
mM sodium phosphate (pH 8.0), 1.5 M NaCl, 1% (w/v) CTAB to which 5 µl of fresh 111
Proteinase K (20 mg ml−1
) was added. The sample was placed horizontally on a 200 112
rpm shaker at 37°C for 30 min. After shaking, 150 µl of SDS [20% (w/v)] was added 113
and the tube was placed at 65°C for 2 h and mixed by inversion every 15 min. The 114
supernatant was decanted into a clean tube after centrifugation for 10 min at 6000 x g 115
and 25°C. The sediment pellet was again suspended in 450 µl of extraction buffer and 116
50 µl of SDS [20% (w/v)] was added, incubated at 65°C, centrifuged as before and the 117
supernatant added to the first aliquot. The crude extract was gently extracted with an 118
equal volume of chloroform [containing 4% (v/v) isoamyl alcohol to minimize 119
foaming] and centrifuged at 16 000 x g for 10 min at 25°C. The aqueous phase (upper 120
layer) was transferred to a clean tube and the DNA was precipitated by adding 0.6 vol. 121
of 2-propanol. The DNA was left to precipitate for 1 h at 20°C. The DNA was 122
pelleted by centrifugation at 16 000 x g for 30 min at 20°C. The DNA pellet was 123
rinsed with 500 µl of 70% (v/v) ethanol and air-dried for 20 min. DNA was 124
resuspended in 50 µl of deionized water and diluted 1:10 to facilitate PCR 125
amplification. DNA concentrations were determined using a NanoDrop ND1000 126
(NanoDrop Technologies) in triplicate. All DNA extracts were stored at –20°C. 127
128
PCR amplification of alkB genes 129
Partial alkB genes were amplified using the forward primer alkB-1f 130
(5′-AAYACNGCNCAYGARCTNGGNCAYAA-3’) and the reverse primer alkB-1r 131
(5′-GCRTGRTGRTCNGARTGNCGYTG-3’) (20). All PCR reactions (final volume 132
of 25 µl) contained 1 x Qiagen PCR Buffer (Qiagen), 1 U of HotStarTaq DNA 133
Polymerase (Qiagen), 200 uM of each dNTP, 25 pmoles of each primer, 0.5 µl of 134
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purified Bovine Serum Albumin (BSA) 10 ug µl-1
(New England Biolabs), 0.5 µl of 135
DNA template (~ 50 ng) and deionized water up to 25 µl. PCR cycling conditions 136
included an initial ‘enzyme activation’ step at 95°C for 15 min, followed by an 137
additional 39 cycles of 94°C for 1 min, annealing at 55°C for 1 min and extension at 138
72°C for 1 min. A final extension step of 72°C for 10 min was included to facilitate 139
‘A-tailing’ of PCR products for cloning. 140
141
Clone library construction, colony hybridization and DNA sequencing 142
PCR products from three replicate reactions for each sample were pooled and 143
subject to agarose gel electrophoresis. Bands of expected size were excised and 144
purified using a QIAquick Gel Extraction Kit (Qiagen) according to the manufacturers 145
instructions. Purified DNA was then cloned using a TOPO-TA Cloning Kit 146
(Invitrogen) according to the manufacturers instructions. Individual colonies were 147
suspended in 100 µl of sterile deionized water using a sterile toothpick, vortexed 148
briefly, allowed to stand for 60 min, vortexed again and this cell suspension used as 149
template for PCR using the M13 primers to confirm inserts. Colony hybridisation to 150
identify positive alkB inserts in clones was performed as previously described (20). 151
Generated PCR products were dried and sent to Macrogen Inc. (Seoul, South Korea) 152
for purification and sequencing using an ABI3730 XL automatic DNA sequencer 153
using the M13F vector specific primer. 154
155
Phylogenetic analysis of alkB genes 156
Retrieved alkB gene nucleotide sequences were initially checked using 157
Chromas Lite software version 2.01 (Technelysium) before being truncated to exclude 158
primer and vector sequence. Nucleotide sequences were translated into protein 159
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sequences using the Translate tool in the ExPASy (Expert Protein Analysis System) 160
proteomics server of the Swiss Institute
of Bioinformatics 161
(http://us.expasy.org/tools/dna.html). Deduced protein sequences from each library 162
were grouped into ‘Operational Protein Units’ (OPUs) using DOTUR software (32) 163
with a distance threshold of 0.20 (80% sequence similarity). This cut-off value was 164
selected after initial examination of phylogenetic trees which included all nucleotide 165
sequences generated by neighbour-joining analysis. The phylogenetic comparison 166
identified a general clustering of AlkB sequences at this level, i.e., when sequences 167
from distinct clusters were aligned using the BLAST (bl2seq) they generally exhibited 168
more than 80% sequence identity. This approximate distance threshold was also used 169
in a similar study of AlkB sequences in Antarctic sediments (21) and therefore allows 170
comparisons between studies. Deduced protein sequences were aligned using 171
ClustalX version 1.83 (39) with related reference sequences identified from BLAST 172
(1) searches, in addition to other sequences of various representative AlkB sequences 173
identified from the literature. Distance matrices were calculated using the PROTDIST 174
program in PHYLIP (11, 12). Phylogenetic trees were generated from distance 175
matrices using the neighbor-joining method (31) and Kimura substitution algorithm 176
(19) using PHYLIP. Bootstrapping with 1000 replicates was performed using SeqBoot 177
as integrated in PHYLIP. 178
179
Statistical analysis of clone library data 180
Rarefaction analysis (16), Ace, Chao1 nonparametric richness estimates (7), 181
Simpson Index of diversity (24) and Shannon-Weaver Index of diversity (33) were 182
generated using DOTUR software (32). Statistical evaluations were obtained using the 183
80% amino acid sequence similarity. 184
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185
Real-time PCR amplification of alkB genes 186
Real-time PCR assays were carried out using a Rotor-Gene 3000 real-time 187
DNA amplification system (Corbett Research) using a Platinum SYBR Green qPCR 188
SuperMix-UDG kit (Invitrogen). PCR reactions (final volume of 25 µl) contained 189
12.5 µl of 2 x Platinum SYBR Green qPCR SuperMix-UDG (Invitrogen), 4.0 mM 190
MgCl2, 5 pmoles of each primer, 0.5 µl of purified BSA (10 µg ul-1
; New England 191
Biolabs), 2.0 µl of DNA template (DNA extracts from 1:10 dilutions of crude extracts 192
were used and exhibited no PCR inhibition) and deionized water up to 25 µl. Primers 193
were designed to target specific clusters of abundant alkB genes detected in clone 194
libraries to avoid problems associated with aberrant PCR efficiencies associated with 195
the use of the highly degenerate primers used for the amplification of diverse alkB 196
genes. Two sets of primers targeting Clusters A and C (Figure 3) were designed after 197
examination of alignments from Clustal X. The design of primers suitable for real-198
time PCR application was aided by using the program NetPrimer 199
(http://www.premierbiosoft.com/netprimer/index.html). AlkB genes from 200
Cluster A were amplified using the forward primer 201
A-f (5′-TACGGGCACTTCGCGATTGA-3’) and the reverse primer A-r 202
(5′-CGCCCAGTTCGAMACGATGTG-3’). AlkB genes from Cluster C were 203
amplified using the forward primer C-f (5′-TCGTACTTGCCGTGCCTGTGTA-3’) 204
and the reverse primer C-r (5′-CGATCAGCGTCAGTTGAATCAC-3’). Real-time 205
PCR cycling conditions included an initial ‘UDG incubation’ step at 50°C for 2 min, 206
an ‘enzyme activation’ step at 95°C for 2 min, followed by 40 cycles of 95°C for 207
20 sec, annealing at 59°C for 20 sec and extension at 72°C for 40 sec. Acquisition of 208
fluorescence signal was performed during the 72°C extension step of each cycle. This 209
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was followed by a melt curve from 45°C to 95°C. In addition to melt curve analysis, 210
PCR products were checked on a 1% agarose gel to ensure they were the expected 211
size. 212
The DNA standards used in the real-time PCR assays consisted of serial 213
dilutions of purified PCR product derived from cloned alkB genes. Clones F44 and 214
G17 (see Figure 3) were used as standards for Cluster A and C, respectively. 215
Standards were PCR amplified directly from a colony using M13 vector specific 216
primers, checked using standard agarose gel electrophoresis, gel purified using a 217
QIAquick Gel Extraction Kit (Qiagen) according to the manufacturers instructions, 218
and DNA concentrations were determined using a Quant-iT PicoGreen dsDNA 219
quantitation kit (Molecular Probes) using fluorimetry. Measured concentrations of 220
purified PCR product were then converted to copies per microliter and the 221
concentration was adjusted to 1 x 109
copies µl-1
prior to performing serial dilutions. 222
A five point standard curve (1 x 105 to 1.0 x 10
1 copies per reaction) was run in 223
duplicate with each run, and each run was performed twice (totaling four replicates 224
per sample). Environmental samples and negative controls (no template DNA) were 225
included in each run and were also performed in duplicate. Data and copy numbers of 226
alkB targets in environmental samples were analysed using the Rotor-Gene software 227
version 6.1.71 (Corbett Research) following manufacturers guidelines. Final copy 228
numbers of alkB genes in environmental samples were calculated assuming 100% 229
DNA extraction efficiency and were expressed as copy numbers per gram of 230
sediment. 231
232
Genbank accession numbers 233
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Nucleic acid sequences determined in this study have been deposited in the 234
GenBank/EMBL/DDBJ databases. The accession numbers for sequences of the genes 235
are: GQ184383-GQ184421, GQ184423-GQ184432, GQ184434. 236
237
Results 238
Sample description and hydrocarbon chemistry 239
Samples were collected from shallow continental shelf waters associated with 240
the active Cornea seep area (Grabs D, E, and H), from waters north of the Cornea seep 241
area in a ‘paleo-riverbed’ (Grab G and F), and from deeper waters off the continental 242
slop (Grabs B, J, K L, and M) (Figure 1). Details of sample coordinates and 243
hydrocarbon concentrations are presented in Table 1. Concentrations of measured 244
total hydrocarbons (THC) were below detection limits in Cornea seep associated 245
sediments (Grabs D, E and H), while only 11.5 ng g-1
of total n-alkanes were 246
measured in sediments of Grab H that are presumed to be directly influenced by 247
hydrocarbons actively seeping from underlying sediments to the water column, as 248
detected by ship-board echo-sounder and towed video camera surveys. Concentrations 249
of polycyclic aromatic hydrocarbons (oil PAHs) at the Cornea seep area were among 250
the lowest in the Timor Sea, ranging from 3.2-62 ng g-1
. Relatively low to mid ranged 251
concentrations of THC (0.19-0.66 mg g-1
), total n-alkanes (40.5-57.8 ng g-1
) and oil 252
PAHs (5.2-5.4 ng g-1
) were measured in Grabs G and F taken from the paleo-riverbed 253
sediments to the north of the Cornea seep area. The highest concentrations of 254
petroleum hydrocarbons were measured in sediments (Grab B) to the west of the 255
Sahul Shoals, with relatively high THC (8.42 mg g-1
), total n-alkanes (344 ng g-1
) and 256
oil PAHs (144.4 ng g-1
). Relatively high concentrations of hydrocarbons in grabs from 257
the other deep water locations within the Cartier Trough (Grabs J, K, and L) were also 258
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found, with concentrations of THC (0.05-0.40 mg g-1
), total n-alkanes (45.8-210.3 ng 259
g-1
) and oil PAHs (19.9-57.1 ng g-1
) being measured. 260
261
Diversity of AlkB sequences 262
Rarefaction analysis (Figure 2) of a clone library constructed from deep-water 263
samples that possessed the highest measured hydrocarbon content (Grab B) displayed 264
similar AlkB deduced protein diversity when compared directly against a low 265
concentration site (Grab M). The highest AlkB diversity of all samples was found 266
within Grab G from the paleo-river to the north of the Cornea seep area and was only 267
slightly higher than diversity predicted in sediments closely associated with the largest 268
seep plume identified in the Timor Sea (Grab H) and those from another paleo-river 269
sample (Grab F). Rarefaction curves did not reach an asymptote for any for the 270
libraries (Figure 2), suggesting a greater diversity of AlkB sequences were present in 271
the samples than revealed by the sequencing effort. Ace and Chao estimators also 272
suggested more OPUs were present in the samples than were detected in the clone 273
libraries (Table 2). Rarefaction curves for libraries F, G and H (on the continental 274
shelf) indicated higher diversity than for libraries B and M (off the continental shelf) 275
(Figure 2). Both Shannon-Weaver and Simpson indices of diversity were consistent 276
with rarefaction analysis showing greater diversity of AlkB sequences in libraries G, 277
F, and H than in libraries B and M (Table 2). 278
279
Phylogenetic analysis of AlkB sequences 280
Phylogenetic analysis of AlkB deduced protein sequences revealed that all 281
Timor Sea sequences were not closely related to any previously identified AlkB 282
sequences present in the public databases (Figure 3). Overall, sequence similarity of 283
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Timor Sea AlkB sequences to sequences present in the public databases ranged from 284
60%-94%, with an average sequence similarity of only 73%. Most Timor Sea AlkB 285
sequences grouped in a large cluster of diverse sequences that were most closely 286
related to marine and mostly gammaproteobacterial derived AlkB sequences, although 287
a few soil derived sequences also clustered within this group. Two sequences 288
representing OPUs B4 and M61 affiliated with AlkB sequences derived from Gram-289
positive actinobacterial Nocardia sp. strain CF8 and Prauserella rugosa strains. Other 290
sequences belonging to OPUs G69, H12 and M60 also affiliated most closely with 291
other marine derived AlkB sequences. No trends in the clustering of sequences from 292
different sites were apparent (e.g., high hydrocarbon concentrations versus low 293
hydrocarbon concentrations, or deep water versus shallow water), and was most 294
evident by the fact that sequences from each site were well represented in Clusters A 295
and C, which were comprised of the most abundant sequences in each library. 296
Importantly, all deduced AlkB protein sequences included in this analysis revealed the 297
presence of two highly conserved regions (motif B, EHXXGHH and motif C, 298
NYXEHYG) identified as important for alkane hydroxylase activity (results not 299
shown) (36, 43). 300
301
Quantification of alkB genes using real-time PCR 302
Previously designed primers targeting alkB genes belonging to marine alkane 303
degrading bacteria Thalassolituus sp. and Alcanivorax borkumesus (5, 25) were 304
unable to produce positive amplification products when applied to Timor Sea 305
sediment samples. Therefore quantitative real-time PCR primers were designed 306
targeting the most abundant alkB gene sequences identified in Timor Sea clone 307
libraries. Two sets of primers targeting Clusters A and C (Figure 3) proved to be 308
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functional under real-time PCR conditions, i.e., produced acceptable amplification 309
efficiencies across all samples and standards, and were specific for the alkB genes 310
from which they were designed for, as tested by PCR using plasmid DNA from target 311
and non-target clones as template (results not shown). 312
When these primers were used for the evaluation of the relative abundance of 313
these gene groups in Timor Sea sediments, gene copy numbers were found to be 314
highest in sediments of Grab H sampled from the area of the largest active seep plume 315
in the Timor Sea. Approximately 2.9 x 105 and 1.1 x 10
5 copies of the alkB gene per g 316
of sediment were recovered for primer sets targeting Clusters A and C, respectively 317
(Figure 4). These gene copy numbers in sediments of Grab H were higher than gene 318
copy numbers identified in all other sediment samples. For example, other sediments 319
from the Cornea seep area (Grabs D and E) and sediments from the paleo-river to the 320
north of the Cornea seep area (Grabs G and F) displayed lower levels of alkB genes, 321
revealing no more than 2.3-3.3 x 104 alkB gene copies per g of sediment for both 322
Clusters A and C (Figure 4). Low or no copies of alkB genes were detected in all 323
deep water sediments with primers targeting Cluster A or were below detection limits 324
with primers targeting Cluster C (Figure 4). 325
326
Discussion 327
In this study, sediment samples were taken from various geographically 328
separated sites in the Timor Sea to explore and compare the diversity and abundance 329
of the alkB functional gene in relation to levels of hydrocarbons measured in 330
sediments. Phylogenetic analysis of Timor Sea AlkB sequences revealed a novel array 331
of AlkB sequence types. Essentially all AlkB OPUs were divergent to previously 332
characterized AlkB sequences with average amino acid sequence identities of only 333
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73% (range 60%-94%) to sequences available in public databases. These results 334
suggest that the Timor Sea harbors a unique suite of AlkB sequences probably with a 335
range of substrate specificities and/or induction patterns enabling the degradation of 336
various n-alkanes. Most sequences were related to AlkB sequences derived from 337
marine Proteobacteria (mostly gammaproteobacterial). Effectively all marine bacteria 338
so far implicated in the degradation of alkanes (elucidated predominately through 339
culture-dependent studies) belong to the Gammaproteobacteria (15), and therefore 340
these results independently support the notion that members of the 341
Gammaproteobacteria are responsible for the aerobic degradation of alkanes in 342
marine environments. Results from this study are in contrast to analysis of alkB genes 343
from soils which identified mostly Gram-positive derived genes (20) and suggest 344
markedly different alkane degrading bacteria exist in the marine environment in 345
comparison to terrestrial environments. 346
Interestingly, AlkB sequence diversity did not appear to be influenced by 347
levels of measured alkanes since gene diversity was not substantially different in 348
sediments with high concentrations of hydrocarbons versus sediments with low 349
concentrations, or in sediments in direct association with active seepage that are 350
presumably exposed to high concentrations of hydrocarbons. For instance, in libraries 351
from Grab B (the highest hydrocarbon levels measured in the Timor Sea) and Grab M 352
(very low hydrocarbon levels) that were taken from comparable depths and away 353
from any active seepage, differences in AlkB sequence diversity as assessed by 354
rarefaction analysis and diversity statistics of clone libraries, were minimal. In a study 355
of AlkB sequence diversity in samples influenced by anthropogenic activities 356
polluting Antarctic marine sediments, much lower AlkB sequence diversity was 357
identified in heavily contaminated in comparison to less-contaminated sediments (21), 358
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suggesting an enrichment of specific gene types with increasing alkane concentrations 359
and resulting lower diversity indices. Levels of alkanes in contaminated Antarctic 360
sediments were an order of magnitude higher in contaminated sediments (e.g., 5 µg g-
361
1 total n-alkanes) versus ‘control’ (e.g., 0.29 µg g
-1 total n-alkanes) sediments. These 362
Antarctic ‘control’ sediments had alkane concentrations comparable to the highest 363
levels measured in the Timor Sea, suggesting concentrations measured in Timor Sea 364
sediments may not have been sufficient to cause significant shifts in gene diversity as 365
was observed in the Antarctic study. 366
In addition, Ace and Chao indices predicted over three times higher diversity 367
in Timor Sea sediments than in Antarctic sediments. It is possible that since Timor 368
Sea sediments have been exposed to naturally seeping hydrocarbons since the 369
Pliocene era (28) that a greater diversification of AlkB sequences has occurred in this 370
ecosystem, in comparison to the historically pristine Antarctic environments. The high 371
diversity in the Timor Sea may be ‘constitutively’ present through out the region and 372
therefore the detectable diversity may be relatively unaltered when exposed to alkane 373
inputs. In contrast, Antarctic sediments which have been pristine in the past, have 374
apparently less diversity and therefore become dominated by certain AlkB sequence 375
types when exposed to anthropogenic sources. 376
The only notable differences in AlkB sequence diversity identified in this 377
study were between shallow water sites (i.e., Grabs F, G and H, on the continental 378
shelf, in < 100 m water) and deep water (i.e., Grabs B and M, off the continental 379
shelf, in> 400 m water). All shallow water samples generally displayed greater AlkB 380
sequences diversity as assessed by rarefaction analysis and statistical analyses of 381
clone libraries. Interestingly, no studies have investigated bacterial diversity in 382
sediments over depth gradients in the ocean. It is possible that bacterial diversity 383
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decreases with increasing depth, driven by factors such as decreasing amounts of 384
labile organic matter and therefore available substrates for nutrition, in addition to 385
increasing barometric pressures and decreasing temperatures. If such changes in 386
bacterial diversity do occur through depth, these may account for the reduction in the 387
AlkB sequence diversity at depth observed in this study. 388
Quantification of alkB genes belonging to Clusters A and C (Figure 3) using 389
real-time PCR identified significantly higher copy numbers in sediments associated 390
with an actively venting seep (Grab H) where hydrocarbons are seeping from the 391
underlying sediments and into the water column. Interestingly, hydrocarbon 392
concentrations in sediments exposed to active seepage (and in other sediments from 393
the Cornea seep area, i.e., Grabs D and E) were relatively low or even undetectable. 394
This suggests that rapid microbial degradation of hydrocarbons is occurring and that 395
this rapid degradation removes detectable quantities of hydrocarbons before they can 396
be measured by geochemical analyses. The quantification of alkB gene copy numbers 397
in sediments therefore provides an insight into the microbial response to the seepage 398
of hydrocarbons and acts as a useful complementary tool for understanding this 399
ecosystems response to hydrocarbons in addition to geochemical measurements that 400
suggest little or no exposure to hydrocarbons if used solely without other 401
complementary observations or data. Interestingly, no considerable increases in gene 402
copy numbers were detected in sediments that had high measured hydrocarbon 403
concentrations (i.e., Grab B which had the highest hydrocarbon concentrations 404
measured in the Timor Sea), in comparison to other samples from comparable water 405
depths that had low or undetectable hydrocarbon concentrations (e.g., Grab M). 406
Quantitative molecular methods targeting hydrocarbon degrading genes in 407
environmental samples have generally identified some correlations between 408
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hydrocarbon degrading gene copy numbers and hydrocarbon concentrations (6, 9), 409
although other studies have found only weak correlations (18). Within this study it is 410
likely that hydrocarbon concentrations in the Timor Sea (with the exception of 411
sediments exposed directly to active seepage, e.g., Grab H) may not be high enough to 412
invoke dramatic shifts on gene copy numbers such as occurs in study sites where such 413
correlations have been made. Quantitative mRNA-based approaches may be useful as 414
a more sensitive tool for monitoring subtle changes in gene copy numbers in marine 415
sediments exposed to low levels of hydrocarbons. The assay developed here however, 416
may be sensitive enough to be applied as a tool for monitoring, for example, the 417
effects of oil released from petroleum oil extraction operations in the Timor Sea, such 418
as those studied by Burns and Codi which monitored the spatial effects of oil 419
discharge from oil rigs in the Timor Sea (3). 420
Since the actual hydrocarbon concentrations measured in sediments did not 421
appear to have a significant influence on gene copy numbers, other factors appear 422
important in determining gene copy numbers in this oceanic environment. Water 423
depth in combination with fluxes of hydrocarbons from the water column to 424
sediments may be important in determining numbers of alkB genes in this ecosystem, 425
at least in sediments not exposed directly to active seepage (e.g., all grabs excluding 426
Grab H). For example, alkB gene copy numbers were very low or undetectable in 427
deep water (> 400 m) sediment samples (i.e., Grabs B, K, L, and M) off the 428
continental shelf and well away from any known active seepage, but easily detectable 429
in all samples (Grabs D, E, F, G and H) from shallow water sediments (c. 100 m) on 430
the continental shelf. Complementary research that measured fluxes of hydrocarbons 431
from the water column to sediments using a layered sediment trap approach revealed 432
that only a small flux of alkanes actually reach deeper waters of more than 400 m, 433
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e.g., up to 85% of n-alkanes are degraded while settling from 100 m to 400 m water 434
depth (unpublished results). Therefore it seems fluxes of alkanes to deep water 435
sediments are probably not high enough to sustain detectable numbers of alkB genes 436
using the real-time PCR assays applied here. 437
This study provides new insights into the diversity of alkane degrading 438
enzymes in the marine environment. The identification of novel AlkB sequence 439
diversity points to the existence of a diverse range of what are most likely 440
gammaproteobacterial species/gene-types capable of degrading alkanes in the Timor 441
Sea. Such AlkB enzymes are worthy of further investigation for biotechnological 442
applications, since these enzymes have various applications in the synthetic 443
production of various compounds such as secondary metabolites, pharmaceuticals and 444
agrochemical intermediates (42). High copy numbers of alkB genes were identified in 445
association with an actively venting seep, demonstrating enhanced alkane degrading 446
capacity at this site. However, despite high measured gene copy numbers this did not 447
equate to distinct changes in observed alkB gene diversity when compared to other 448
sampled sediments. Chronic exposure of sediments to hydrocarbon inputs appeared to 449
not alter alkB gene copy numbers within the sediments, though efficient and rapid 450
biodegradation of hydrocarbons was occurring, mediated by microbial communities 451
that are well adapted to the readily available carbon source. It is anticipated that future 452
examination of alkB gene transcripts could be used as a more sensitive tool for 453
assessing the response of alkB gene diversity and copy numbers to hydrocarbons in 454
this environment. 455
456
Acknowledgements 457
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We thank the crew and scientific support staff of the RV Southern Surveyor 458
during cruise SS05/06 to the Timor Sea for collection of samples during June of 2005. 459
A special thanks to Gregg Brunskill and Irena Zagorskis in aiding in sampling and 460
geochemical analyses. Tim Simmonds is thanked for help in preparation of the 461
manuscript figures. The author thanks the Australian Biological Resources Study 462
(ABRS) and Commonwealth Scientific and Industrial Research Organization 463
(CSIRO) for stipend and financial support. 464
465
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614
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Figure 1. Locations of sediment grab samples in the Timor Sea. Sediment grab 615
locations are indicated by open circles. Sediment grabs D, E and H are in close 616
proximity to each other and are represented by one open circle at the Cornea seep 617
area. 618
619
Figure 2. Rarefaction analysis of AlkB deduced protein sequences from the Timor 620
Sea. OPUs were defined using a 20% sequence similarity cut-off. 621
622
Figure 3. Phylogenetic tree based on deduced AlkB amino acid sequences retrieved 623
from this study relative to reference sequences from cultured bacteria and sequences 624
retrieved from other studies. Unique sequences from each library are presented in bold 625
type. Numbers presented in brackets after each sequence retrieved in this study 626
indicate the number of sequences from each OPU retrieved from each clone library. 627
Bootstrap values of ≥50% and ≥90% from 1000 resamplings are presented at nodes as 628
filled (•) and open circles (o), respectively. The tree was rooted with a xylene 629
monooxygenase subunit-1 amino acid sequence from Pseudomonas putida mt-2 (13). 630
The scale bar represents 10% sequence divergence. 631
632
Figure 4. Quantitative real-time PCR analysis of Timor Sea sediments targeting alkB 633
genes recovered in clone libraries. Error bars indicate standard deviations in gene 634
copy number measured from replicate PCRs. 635
636
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Table 1. Details of sediment grab locations and hydrocarbon content. 637
638
Sample
code Coordinates Depth
Geographical
description
Total
hydrocarbons
(mg g-1
)
Total n-alkanes
C11-C38 (ng g-1
)
Sum oil
PAHs
(ng/g)
B 11:26.61S
123:59.67E 482m
West of Sahul
shoals 8.42 344 144.4
D 13:39.31S
124:42.69E 89.2m
Cornea seep
area < D.L.
a < D.L. 6.2
E 13:39.15S
124:42.69E 88.4m
Cornea seep
area < D.L. < D.L. 3.2
F 13:24.71S
124:41.80E 107.2m North of seep 0.66 57.8 5.4
G 13:30.62S
124:42.48E 108.4m North of seep 0.19 40.5 5.2
H 13:39.27S
124:42.70E 90m
Cornea active
seep < D.L. 11.5 4.3
J 11:42.32S
125:01.61E 269m Cartier trough 0.05 45.8 19.9
K 11:30.65S
125:00.20E 414m Cartier trough 0.10 137.8 34.2
L 11:22.00S
125:00.00E 453m Cartier trough 0.40 210.3 57.1
M 11:12.50S
125:12.51E 430m Cartier trough < D.L. < D.L. 7.9
N 11:22.65S
126:52.60E
106.8m Darwin Shelf b 0.10 71.2 15.0
639
a Detection Levels (D.L.) were based on individual peak detection within a complex mixture. For these 640
summaries the estimate was < ~0.05 mg g-1
total hydrocarbons (4). 641
b Darwin Shelf was a station with no known active oil seepage nearby and was used as a ‘control’ site. 642
643
644
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Table 2. Diversity analysis of alkB deduced protein sequences. 645
Library No. of clones No. of OPUs ACE a Chao1
b 1 – D
c H′
d
B 57 8 31.5 27 0.77 1.82
F 40 10 33.6 28 0.84 2.11
G 41 13 51.8 50 0.86 2.28
H 56 14 49.8 36 0.84 2.23
M 52 8 37 48 0.75 1.67
646
a ACE richness estimate 647
b Chao1 richness estimate 648
c Simpson Index of diversity 649
d Shannon-Weaver Index of diversity 650
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1 0 %
M 2 5 ( 2 3 ) [ G Q 1 8 4 4 0 7 ]B 2 4 ( 2 7 ) [ G Q 1 8 4 4 2 7 ]G 1 ( 5 ) [ G Q 1 8 4 3 9 3 ]H 1 1 ( 2 0 ) [ G Q 1 8 4 4 2 8 ]F 4 4 ( 1 0 ) [ G Q 1 8 4 3 9 2 ]M 1 9 [ G Q 1 8 4 4 0 6 ]G 1 9 [ G Q 1 8 4 3 9 4 ]H 1 0 [ G Q 1 8 4 4 1 3 ]H 7 [ G Q 1 8 4 4 1 2 ]L i m n o b a c t e r s p . M E D 1 0 5 [ E D M 8 2 5 0 9 ]M a r i n o b a c t e r s p . E L B 1 7 [ E A Z 9 8 4 7 0 ]G 7 0 [ G Q 1 8 4 3 8 4 ]B 5 4 [ G Q 1 8 4 4 0 5 ]H 2 [ G Q 1 8 4 4 1 1 ]F 2 1 ( 2 ) [ G Q 1 8 4 3 8 5 ]G 8 ( 6 ) [ G Q 1 8 4 3 8 6 ]H 9 ( 9 ) [ G Q 1 8 4 4 3 2 ]g r a s s l a n d s o i l c l o n e a l k W 1 C 1 0 8 [ A B B 9 6 0 9 3 ]F 3 2 [ G Q 1 8 4 3 8 8 ]B 5 5 [ G Q 1 8 4 4 0 3 ]G 9 [ G Q 1 8 4 3 8 9 ]A l c a n i v o r a x b o r k u m e n s i s S K 2 [ B A C 9 8 3 6 5 ]M a r i n o b a c t e r a q u a e o l e i V T 8 [ A B M 1 7 5 4 1 ]O c e a n i c a u l i s a l e x a n d r i i H T C C 2 6 3 3 [ E A P 8 9 3 3 5 ]M 1 4 [ G Q 1 8 4 4 0 4 ]F 1 2 ( 1 4 ) [ G Q 1 8 4 3 9 5 ]B 2 6 ( 9 ) [ G Q 1 8 4 4 0 0 ]H 4 [ G Q 1 8 4 4 1 6 ]M 1 5 ( 1 3 ) [ G Q 1 8 4 4 0 1 ]G 1 7 ( 1 5 ) [ G Q 1 8 4 4 3 4 ]P a c i f i c s e d i m e n t c l o n e 9 E 7 [ C A M 5 8 0 7 8 ]F 3 1 [ G Q 1 8 4 3 9 6 ]P a c i f i c m e t a g e n o m e c l o n e 2 1 G 8 [ C A M 5 8 1 1 7 ]H 5 ( 1 0 ) [ G Q 1 8 4 4 3 0 ]F 5 0 [ G Q 1 8 4 3 9 7 ]F 2 6 ( 3 ) [ G Q 1 8 4 4 1 7 ]G 1 8 [ G Q 1 8 4 4 1 8 ]H 6 ( 3 ) [ G Q 1 8 4 4 1 9 ]F 3 3 [ G Q 1 8 4 3 9 8 ]G 4 6 [ G Q 1 8 4 3 9 9 ]B 2 2 ( 9 ) [ G Q 1 8 4 4 0 2 ]B 5 [ G Q 1 8 4 4 2 0 ]M 2 6 ( 1 1 ) [ G Q 1 8 4 4 2 1 ]H 1 3 ( 2 ) [ G Q 1 8 4 4 3 1 ]b a r l e y f i e l d s o i l c l o n e a l k G 2 C 3 1 K [ A B B 9 0 6 8 3 ]G 3 7 ( 3 ) [ G Q 1 8 4 3 8 7 ]H 1 [ G Q 1 8 4 4 0 9 ]B 6 6 [ G Q 1 8 4 4 2 4 ]G 3 2 [ G Q 1 8 4 3 9 0 ]M 4 0 [ G Q 1 8 4 4 2 3 ]H 3 [ G Q 1 8 4 4 1 0 ]H 1 4 [ G Q 1 8 4 4 1 5 ]H 8 [ G Q 1 8 4 4 1 4 ]F 3 [ G Q 1 8 4 3 8 3 ]R h o d o c o c c u s s p . Q 1 5 [ A A K 9 7 4 5 4 ]G o r d o n i a s p . T F 6 [ B A D 6 7 0 2 0 ]N o c a r d i a f a r c i n i c a I F M 1 0 1 5 2 [ B A D 5 9 4 6 9 ]M y c o b a c t e r i u m b o v i s A F 2 1 2 2 / 9 7 [ C A D 9 5 3 7 2 ]N o c a r d i a s p . C F 8 [ A A K 3 1 3 4 8 ]B 4 [ G Q 1 8 4 4 0 8 ]P r a u s e r e l l a r u g o s a [ C A B 5 1 0 2 4 ]M 6 1 [ G Q 1 8 4 4 2 6 ]R a l s t o n i a p i c k e t t i i 1 2 J [ A C D 2 9 2 1 0 ]B u r k h o l d e r i a c e p a c i a [ C A C 3 6 3 5 6 ]P s e u d o m o n a s f l u o r e s c e n s D S M 5 0 1 0 6 [ A A C 3 6 3 5 3 ]R h o d o c o c c u s s p . Q 1 5 B [ A A K 9 7 4 4 7 ]G 6 9 [ G Q 1 8 4 3 9 1 ]H 1 2 [ G Q 1 8 4 4 2 9 ]A n t a r c t i c s e d i m e n t c l o n e B P 7 9 E 1 [ A B O 6 1 7 9 8 ]A c i n e t o b a c t e r c a l c o a c e t i c u s [ C A B 5 1 0 2 0 ]P s e u d o m o n a s a e r u g i n o s a [ C A G 1 7 6 0 8 ]M i c r o s c i l l a m a r i n a A T C C 2 3 1 3 4 [ E A Y 2 9 3 0 7 ]M 6 0 [ G Q 1 8 4 4 2 5 ]L e g i o n e l l a p n e u m o p h i l a s t r . L e n s [ C A H 1 7 1 6 9 ]B d e l l o v i b r i o b a c t e r i o v o r u s H D 1 0 0 [ C A E 8 0 0 3 2 ]S i l i c i b a c t e r p o m e r o y i D S S C 3 [ A A V 9 6 6 7 5 ]P s e u d o m o n a s p u t i d a x y l P 2 1 3 9 5 [ B A A 0 9 6 6 2 ]Actinobacteria
ProteobacteriaClusterCClusterAA n t a r c t i c s e d i m e n t c l o n e B P 2 7 E 2 [ A B O 6 1 7 6 8 ]
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S a m p l e s i t eB D E F G H J K L M N106
105
104
103
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