Introduction
Methanol is the most abundant oxygenated volatile organic compound (OVOC) in
the background troposphere where it acts as a climate-active gas,
influencing the oxidative capacity of the atmosphere, concentrations of
ozone and hydroxyl radicals (Carpenter et al., 2012). Methanol has been
shown to be ubiquitous in waters of the Atlantic Ocean ranging between
< 27 and 361 nM (Williams et al., 2004; Beale et al., 2013; Yang et
al., 2013, 2014). Our knowledge of the sources and sinks of
methanol is limited and often lacks consensus. For example, recent eddy
covariance flux estimates demonstrated a consistent flux of atmospheric
methanol into the surface waters of a meridional transect of the Atlantic
Ocean (Yang et al., 2013). However, along a similar transect, 12 months earlier,
Beale et al. (2013) calculated that the Atlantic Ocean
represents an overall source of methanol to the atmosphere (3 Tg yr-1),
which was largely attributable to an efflux from the North Atlantic Gyre;
where surface concentrations were as high as 361 nM. Wet deposition from
rainwater has also recently been suggested to represent a supply of methanol
to the ocean (Felix et al., 2014).
Although in situ marine photochemical production of methanol has previously been
found to be insignificant (Dixon et al., 2013), there is thought
to be a substantial unidentified biological source of methanol in seawater
(Dixon et al., 2011a). Biological production by phytoplankton and
during the breakdown of marine algal cells are possible sources (Sieburth and Keller, 1989; Nightingale, 1991; Heikes
et al., 2002). Recent
laboratory culture experiments suggest that methanol is produced by a wide
variety of phytoplankton including cyanobacteria (Prochlorococcus marinus, Synechococcus sp. and Trichodesmium erythraeum) and Eukarya
(Emiliania huxleyi, Phaeodactylum tricornutum, Nannochloropsis oculata, and Dunaliella tertiolecta; Mincer and Aicher, 2016; Halsey et al., 2017). The mechanisms of
in situ methanol production and their regulation remains largely unknown, although
Halsey et al. (2017) reported light-dependent rates of methanol production
in cultures of the marine green flagellate Dunaliella tertiolecta (cell size of 10–12 µm).
Methylotrophic bacteria are capable of utilising one-carbon compounds
including methanol as their sole source of energy (methanol dissimilation)
and carbon (methanol assimilation). Methylotrophs are widespread in
terrestrial and aquatic systems (Kolb, 2009), but research into these
bacteria in marine environments is still at an early stage. Traditionally,
methylotrophs were thought to utilise methanol dehydrogenase (MDH, encoded by
mxaF; McDonald and Murrell, 1997) to metabolise methanol to formaldehyde,
with further oxidation to CO2 or incorporation of carbon into biomass
(Chistoserdova et al., 2009; Chistoserdova, 2011). However,
recent progress in this field has resulted in the discovery of the xoxF gene,
encoding an alternative MDH (Wilson et al., 2008) and seemingly
present in all known gram-negative methylotrophs to date
(Chistoserdova et al., 2009; Chistoserdova, 2011). The
presence of methylotrophs in seawater has been confirmed using a range of
molecular approaches including functional gene primers, stable isotope
probing and metaproteomics (Neufeld et al., 2007, 2008; Dixon et al., 2013; Grob et al., 2015; Taubert et al., 2015). There are
also bacterial cells that utilise methanol and other C1 compounds for
the production of energy but not biomass, e.g. SAR11 for which
Sun et al. (2011) proposed the new term “methylovores”,
distinct from true methylotrophs which use C1 compounds as sources of
carbon and energy.
Limited studies of microbial methanol assimilation in the Atlantic Ocean have
previously shown rates up to 0.42 nmol L-1 h-1 in recently
upwelled coastal waters of the Mauritanian upwelling (Dixon et al., 2013).
However, open ocean waters of the Atlantic were substantially lower ranging
between 0.002 and 0.028 nmol L-1 h-1 (Dixon et al.,
2013). Microbial methanol dissimilation rates are generally up to 1000-fold
higher than rates of assimilation; ranging between 0.70 and 11.2 nmol L-1 h-1 compared to between < 0.001 and 0.026 nmol L-1 h-1, respectively, for coastal waters
(Dixon et al., 2011b; Sargeant et al., 2016). Methanol dissimilation
rates ranging between 0.08 and 6.1 nmol L-1 h-1 have also been
found in open ocean Atlantic waters (Dixon et al., 2011a).
However, despite the ubiquity of methanol in seawater, the spatial extent or
quantification of microbial methanol utilisation for energy production on a
basin scale has not been previously investigated. Therefore, the objective of
this research was to simultaneously characterise the spatial variability in
microbial methanol dissimilation rates (at depths to 200 m) and in microbial
community groups throughout contrasting biogeochemical regions of the
Atlantic Ocean. This study represents the first basin-wide approach to
investigating methanol as a source of reducing power and energy for microbes.
Materials and methods
Sampling strategy
Sampling was carried out during an Atlantic Meridional Transect (AMT) voyage
(http://www.amt-uk.org, last access: 21 August 2018). The research cruise (JC039, RRS James Cook, 13/10/09–01/12/09) departed from Falmouth, UK (50.15∘ N,
05.07∘ W), and arrived in Punta Arenas, Chile (53.14∘ S,
70.92∘ W). Water samples were collected daily from pre-dawn (97 %,
33 %, 14 % and 1 % photosynthetically active radiation, PAR, equivalent
depths and 200 m) and solar noon (97 %) conductivity, temperature and depth (CTD) casts. The PAR equivalent depths were
5, 10–31, 15–54 and 38–127 m
for the 97 %, 33 %, 14 % and 1 % light levels, respectively, and typically varied
with oceanic province. The pre-dawn and solar noon sampling periods were
approximately 45–65 nautical miles apart (sampling locations are shown in
Fig. 1). The Atlantic Ocean was divided into five oceanic provinces,
following the approach of Dixon et al. (2013), according broadly
to chlorophyll a concentrations (< 0.15 mg m-3 gyre regions,
> 0.15 mg m-3 temperate or upwelling regions; Fig. 1) with
the northern gyre subdivided into the northern subtropical gyre (NSG) and
the northern tropical gyre (NTG). Measurements of the concentration of methanol
in seawater (Beale et al., 2013) and of methanol assimilation rates (Dixon et al., 2013) made during this transect have been reported previously.
Remotely sensed MODIS-Aqua chlorophyll a composite image of the
Atlantic Ocean from November 2009 (image courtesy of NEODAAS). White squares
represent sampling points and ovals indicate samples within different
oceanic provinces, labelled with province names NT (northern temperate), NSG
(northern subtropical gyre), NTG (northern tropical gyre), EQU (equatorial
upwelling), SG (southern gyre) and ST (southern temperate).
Microbial methanol uptake
The oxidation of methanol to CO2 (dissimilation) was determined using
14C-labelled methanol (American Radiolabeled Chemicals Inc, Saint
Louis, MO, USA) seawater incubations as previously described in
Dixon et al. (2011b). Seawater samples of 1 mL were incubated with
∼10 nM (final concentration) 14C-labelled methanol to
measure rates of microbial methanol dissimilation. Seawater methanol
concentrations ranged between 48 and 361 nM (Beale et al., 2013), thus the
radiotracer additions represent 3 %–21 % of in situ concentrations in Atlantic
waters. Incubations were conducted in triplicate, with “killed” controls (5 % trichloroacetic acid, TCA, final concentration), at in situ temperatures and
in the dark. Incubation temperatures were determined by the sea surface
temperature recorded by the corresponding CTD casts. Sample counts of
14CO2, captured in the precipitate as Sr14CO3 (nCi mL-1 h-1, where Ci is the unit Curie, 1 Ci =3.7×1010 Bq), were divided by the total 14CH3OH added to
the sample (nCi mL-1) to calculate the apparent rate constants, k
(h-1).
The incorporation of methanol carbon into microbial biomass (assimilation)
was determined using sample volumes of 320 mL to increase the total sample
counts (Dixon et al., 2011b) following procedures outlined in Dixon et al. (2011b, 2013).
Filter sample counts were divided by the total
14CH3OH added to the sample (nCi mL-1) to calculate the
apparent rate constants, k (h-1).
Methanol uptake rates in nmol L-1 h-1 were calculated by dividing the sample counts
(nCi L-1 h-1) by the specific activity of 14C-labelled methanol (57.1 Ci mol-1)
following the approach of Dixon et al. (2013). Evaluation of control samples suggests that
≤0.3 % of the added 14CH3OH is recovered on the filters
and ≤2 % in the resultant precipitate for methanol assimilation and
dissimilation, respectively.
Bacterial leucine incorporation
Rates of bacterial leucine incorporation were measured using the
incorporation of 3H-leucine into bacterial protein in seawater samples
using the method described by Smith and Azam (1992). A final concentration
of 25 nM (6.8 µL) of 3H-leucine (calculated using the specific
activity of 161 Ci mmol-1, concentrations 1 mCi mL-1, American
Radiolabeled Chemicals Inc, Saint Louis, MO, USA) was incubated with 1.7 mL seawater samples. Incubations were conducted in triplicate with “killed”
controls (5 % TCA, final concentrations), at in situ temperature and in the
dark.
Bacterial community composition
Seawater samples of approximately 20 litres were collected for bacterial
DNA analysis from 97 %, 33 %, 1 % and < 1 % (200 m) PAR equivalent
depths during pre-dawn CTD casts only. Samples were filtered through 0.22 µm Sterivex polyethersulfone filters (Millipore, Watford, UK) using a
peristaltic pump. Filters were incubated with 1.6 mL of RNA Later (Life
Technologies, to preserve samples during shipment) overnight at 4 ∘C, after which the RNA Later was removed. Filters were stored immediately at
-80 ∘C before being shipped back to the UK on dry ice and
subsequently stored at -20 ∘C.
Bacterial DNA was extracted from filters using a modified
phenol : chloroform : isoamyl alcohol extraction method as previously described
in Neufeld et al. (2007). Extracted DNA was cleaned using
Amicon ultra-0.5 centrifugal filter devices (Millipore) to remove any RNA
Later residue. The 16S rRNA gene primers 341F (Muyzer et al., 1993)
and 907R (Muyzer et al., 1998) were used for PCR amplification
(32 cycles) with an annealing temperature of 55 ∘C. Purification
of PCR products from agarose gels was conducted using the QIAquick gel
extraction kit (Qiagen, Crawley, UK) before being sent to Molecular Research
LP (MR DNA, http://www.mrdnalab.com, last access: 21 August 2018) for 454 pyrosequencing
using the GS-FLX platform.
The 16S rRNA gene sequences were depleted of barcodes and primers, and then
sequences less than 200 bp, with ambiguous bases or with homopolymer runs
exceeding 6 bp, were removed. Sequences were de-noised and chimeras removed.
After the removal of singleton sequences, operational taxonomic units (OTUs)
were defined at 97 % 16S rRNA gene identity using Quantitative Insights
Into Microbial Ecology (QIIME, http://qiime.org, last access: 21 Auust 2018; Caporaso et al., 2010). The OTUs were assigned
taxonomically using BLASTn (Basic Local Alignment Search Tool, NCBI) against
the Silva database (http://www.arb-silva.de, last access: 21 August 2018). Sequences were
randomly resampled to the lowest number of sequences per sample (386
sequences per DNA sample) to standardise the sequencing effort.
Discussion
Basin-scale variability in biological methanol uptake
Maximum rates of methanol dissimilation in the Atlantic Ocean were recorded
in the NSG province at 33 % PAR light depth (25 m,
1.68 nmol L-1 h-1, Figs. 2 and 4a). An overview of the
variation in rates of methanol dissimilation to CO2 throughout the top
200 m of the water column in the Atlantic Ocean is shown in Fig. 4a, which
illustrates subsurface maxima in northerly latitudes. However, no
statistically significant differences were found between rates of methanol
dissimilation in the euphotic zone (97 %–1 % PAR) compared to the aphotic
zone (samples from 200 m) in the NSG (tNSG=2.63, t20=2.85 for
P < 0.01), NTG (tNTG=0.02, t12=3.05 for P < 0.01),
EQU (tEQU=1.01, t18=2.88 for P < 0.01) and SG regions
(tSG=0.88, t19=2.88 for P < 0.01). This is consistent with a
previous study in the north-east Atlantic Ocean, which similarly reported no
significant variability in methanol dissimilation rates with depth (Dixon and
Nightingale, 2012). Nevertheless, greater variability with depth was observed
for methanol dissimilation rates from the northern gyre (FNSG=3.22
where F3,17=3.20, P < =0.05 and FNTG=5.14 where F2,10=4.10,
P < 0.05). Variability in rates from the euphotic zone were found to
be significantly higher than those from 200 m in northern (tNT=3.17,
t20=2.85 for P < 0.01) and southern temperate regions
(tST=5.03, t10=3.17 for P < 0.01).
Although the highest rates of methanol dissimilation were determined in the
NSG, these values were approximately 7 times lower than the maxima
determined during a seasonal study of the temperate western English Channel
(0.5–11.2 nmol L-1 h-1; Sargeant et al., 2016). Rates
determined in the temperate waters of the South Atlantic (0.11–0.45 nmol L-1 h-1) are most comparable to the lowest rates determined during
late spring and early summer of ∼0.50 nmol L-1 h-1 in temperate northern coastal waters (Sargeant
et al., 2016). The seasonal study in the western English Channel showed
maximum rates of up to 11.2 nmol L-1 h-1 during autumn and
winter months (Sargeant et al., 2016). The differences in methanol
dissimilation rates between the temperate waters of the North Atlantic (0.83±0.42 nmol L-1 h-1) and South Atlantic (0.27±0.13 nmol L-1 h-1) may therefore reflect seasonal differences between
hemispheres, i.e. sampling in the NT region occurred during late autumn
compared to late spring in the ST region.
Methanol assimilation rates were generally 2 orders of magnitude lower than
dissimilation rates, reaching a maximum of 0.028 nmol L-1 h-1 in
the top 200m throughout the Atlantic Ocean (Fig. 4b). Rates of methanol
assimilation exhibited subsurface maxima (at 33 % PAR equivalent depth)
which were particularly evident just north of the Equator (EQU) and in the
northern gyre (NSG) of 0.015±0.004 nmol L-1 h-1. These
subsurface rates were on average higher than surface values (0.004±0.004 nmol L-1 h-1). Results are similar to findings by Dixon and
Nightingale (2012) who also demonstrated subsurface maxima between 20 and 30 m in the north-east Atlantic.
The methanol assimilation rates are shown for
direct comparison to dissimilation, but have been previously discussed in
more detail in Dixon et al. (2013).
Bacterial community and productivity
In contrast to microbial methanol dissimilation, rates of bacterial leucine
incorporation were lowest in the northern oligotrophic gyre (NSG 5.2±2.3 pmol L-1 h-1, NTG 7.8±2.3 pmol L-1 h-1)
reflecting lower microbial activity in these regions of the Atlantic.
Surface microbial methanol dissimilation rates exhibited a statistically
significant inverse correlation with bacterial leucine incorporation, (r=-0.351, n=36, P≤0.05). This is consistent with findings from a
seasonal study in the western English Channel, where surface rates of
methanol dissimilation were also inversely correlated to bacterial
production (Sargeant et al., 2016). For all the depth data, a negative
correlation was also found in the NTG, EQU and SG regions (r=-0.372, n=52, P≤0.01), but NT and NSG areas showed methanol dissimilation rates
independent of BLI. The productivity of heterotrophic bacteria is generally
associated with the concentrations of phytoplankton-derived dissolved
organic matter (DOM), e.g. proteins, lipids and carbohydrates which are
utilised as sources of energy and carbon (Ogawa and Tanoue, 2003; Nagata, 2008; Benner and Herndl, 2011). Results from this present study
indicate that in regions of low heterotrophic bacterial production, i.e. in
the North Atlantic Gyre (minimum rate of bacterial leucine incorporation
of 3 pmol L-1 h-1) rates of methanol dissimilation were relatively
higher. In oligotrophic regions, phytoplankton-derived DOM is scarce,
suggesting that those bacteria able to metabolise methanol are using the
carbon from methanol as an alternative source of energy (and to a lesser
extent carbon).
Nonmetric multidimensional scale (MDS) plots of (a) a
Bray–Curtis similarity matrix of the 16S rRNA gene sequences of the
bacterial community, (b) a Euclidean distance matrix of environmental
parameters (salinity, temperature, chl a, primary productivity, inorganic
nutrients, flow cytometry cell numbers, BLI) and (c) a Euclidean distance
matrix of rates of methanol dissimilation. Dashed lines highlight
significant sample groupings. Plots were generated using PRIMER-E (www.primer-e.com, last access: 21 August 2018).
(a) and (b) represent samples from 200 m, i.e. 0 % PAR.
Although the bacterial community 16S rRNA gene sequence data did not display
any clear patterns with changing biogeochemical province (in contrast to
microbial methanol dissimilation rates), the bacterial community was shown
to be depth-stratified throughout the Atlantic Ocean (Fig. 6a). A nonmetric
multi-dimensional scale (MDS) plot of a Bray–Curtis similarity matrix of 16S
rRNA gene sequences (Fig. 6a) found bacterial community samples to cluster
into three distinct groupings possibly reflecting light levels: sunlit (97 %
and 33 % PAR), minimal light (1 % PAR) and dark (200 m). Bacterial
community samples from the same PAR equivalent depths were found to group
together regardless of biogeochemical province. A larger cluster formed of
samples from 97 % and 33 % PAR is likely to be formed of bacterial
communities originating from the well-mixed surface layer of the water
column, accounting for their similarity in composition. When all
environmental parameters were considered together (including bacterial
numbers and BLI) a Euclidean distance matrix nonmetric MDS also
demonstrated photic waters (97 %–1 % PAR) clustered together, and were
significantly different to dark waters from 200 m (Fig. 6b). However, no
significant differences were observed between rates of methanol
dissimilation determined from the euphotic zone (samples from 97 % to 1 % PAR
equivalent depths) compared to the aphotic zone (samples from 200 m of depth)
for gyre and equatorial regions (NSG tNSG=2.63 (t20=2.85 for
P < 0.01), NTG tNTG=0.02 (t12=3.05 for P < 0.01), EQU tEQU=1.01 (t18=2.88 for P < 0.01) and SG
tSG=0.88 (t19=2.88 for P < 0.01)) although, clear
differences between provinces were evident (Fig. 6c). This is consistent
with results from Dixon and Nightingale (2012) who also found no
significant variation of methanol dissimilation with depth in the north-east
Atlantic Ocean. These data suggest that light levels do not have a strong
role to play in microbial methanol dissimilation in waters of the Atlantic,
despite the overall bacterial community showing strong variability with
depth (or incident light). Depth-stratification of microbial communities has
been observed previously by Carlson et al. (2004), DeLong et al. (2006) and between euphotic and aphotic
zones in the north-western Sargasso Sea (Carlson et al.,
2004). Heywood et al. (2006) suggested that the physical
separation of nutrient-poor surface waters in gyre regions from mixing with
more nutrient-rich waters below a defined pycnocline, in combination with
differing levels of light availability, could partially explain changes in
bacterial community composition throughout the water column. Therefore,
these results could indicate that methanol dissimilation is limited to
specific microbial groups that are present relatively uniformly between the
surface and 200 m, although more depth variability is shown north of
25∘ N where rates of methanol dissimilation are the highest and most
variable.
Summary of rates of methanol uptake (dissimilation and assimilation),
methanol concentrations, bacterial leucine incorporation (BLI) and production (BP),
numbers of heterotrophic bacteria (BN), Prochlorococcus (Pros) and Synechococcus (Syns).
Values given are average ± standard deviation (range). NA denotes that data are not available.
Atlantic province
Overall
NT
NSG
NTG
EQU
SG
ST
Methanol dissimilation
0.45±0.42
0.69±0.35
0.99±0.41
0.18±0.04
0.11±0.03
0.24±0.12
0.20±0.05
(nmol L-1 h-1)
(0.01–1.68)
(0.22–1.50)
(0.15–1.68)
(0.10–0.25)
(0.07–0.17)
(0.01–0.45)
(0.11–0.27)
Methanol assimilation (×10-2)
0.51±0.54
0.54±0.53
0.53±0.56
NA
0.67±0.66
0.19±0.16
NA
(nmol L-1 h-1)
(0.00–2.24)
(0.00–2.23)
(0.17–1.51)
(0.00–2.24)
(0.00–0.57)
BLI (pmol L-1 h-1)
9.4±8.9
7.7±4.0
9.7±14.2
8.0±4.3
13.7±7.9
8.2±9.5
NA
(0.5–60.2)
(0.9–14.2)
(1.0–60.2)
(2.0–17.0)
(0.6–26.4)
(0.5–41.5)
aBP(TCF) (ng C L-1 h-1)
14.6±13.8
11.9±6.1
15.0±21.9
12.4±6.6
21.2±12.2
12.7±14.8
NA
(0.8–96.1)
(1.5–22.0)
(1.5–96.1)
(3.2–26.3)
(1.0–41.0)
(0.8–64.3)
bBP (ECF) (ng C L-1 h-1)
4.8±4.6
3.9±2.0
4.9±7.2
4.1±2.2
7.0±4.0
4.2±4.9
NA
(0.3–31.6)
(0.5–7.2)
(0.5–31.6)
(1.0–8.7)
(0.3–13.5)
(0.3–21.1)
Numbers of heterotrophic bacteria
6.5±6.3
NA
NA
5.8±2.0
8.8±10.3
5.4±4.4
NA
(×105 cells mL-1)
(1.4–82.6)
(1.6–9.8)
(1.4–82.6)
(1.5–35.8)
Numbers of Prochlorococcus sp.
1.12±4.62
0.91±0.07
0.89±0.71
1.52±1.23
1.67±0.2
1.20±0.01
0.35±0.22
(×105 cells mL-1)
(0.0–4.62)
(0.0–2.56)
(0.0–4.21)
(0.0–4.19)
(0.0–4.62)
(0.0–2.45)
(0.0–2.33)
Numbers of Synechococcus sp.
1.64±31.4
1.96±3.61
0.18±0.21
0.15±0.17
1.34±2.69
0.14±0.13
8.30±10.3
(×104 cells mL-1)
(0.0–31.4)
(0.0–12.7)
(0.0–0.93)
(0.0–0.73)
(0.0–12.8)
(0.0–0.79)
(0.02–31.4)
cMethanol (nM)
143±82
110±126
203±38
193±46
148±37
110±33
132
(38–420)
(38–420)
(154–281)
(148–278)
(117–241)
(58–176)
a Theoretical conversion factor (TCF) 1.55 kg C mol leu-1. b Empirical conversion factor (ECF)
0.51 kg C mol leu-1. c From Beale et al. (2013).
Methanol dissimilation and SAR11
SAR11 cells have been shown to utilise methanol, but only as a source of
energy (Sun et al., 2011). The numbers of SAR11 16S rRNA gene sequences
exhibited a statistically significant correlation with rates of microbial
methanol dissimilation throughout the Atlantic basin (r=0.477, n=20, P < 0.05), where the number of SAR11 16S rRNA gene
sequences explained approximately half of the spatial variability in rates of
methanol dissimilation. It should be noted that this
correlation has been made with amplicon ratios, relating to the relative
success of SAR11 in the community, rather than with SAR11 cell numbers
specifically. In culture, SAR11 cells (strain HTCC1062) have previously been
shown to utilise methanol as a source of energy at a rate of ∼5×10-20 moles cell-1 h-1 (Sun et al., 2011), which equates
to 2 nmol L-1 h-1 (using a culture cell abundance of 4×107 cells mL-1; Sun et al., 2011). SAR11 cells dominate (59±4 %) the low nucleic acid (LNA) fraction of bacterioplankton consistently
across the Atlantic Ocean, where typically numbers of LNAs range between
0.2 and 1.0 ×109 cells L-1 (Mary et al., 2006a). Thus estimates of
in situ SAR11 numbers range between 0.12 and 0.59 ×109 cells L-1. This is consistent with estimates from the Sargasso Sea of
∼0.1×109 cells L-1 (where they are reported to
contribute ∼25 % of total prokaryotic abundance of 0.4×106 cells mL-1; Malmstrom et al., 2004). Thus, we estimate that
SAR11 cells of the Atlantic Ocean could be oxidising methanol at rates
between 5 and 29.5 pmol L-1 h-1, which could account for between 0.3 % and 59 % of the rates of methanol dissimilation in surface Atlantic waters.
A seasonal investigation in the western English Channel reported bacterial
numbers ranging between 2.0 and 15.8 ×105 cells mL-1 (Sargeant et al.,
2016) which agrees well with data from Mary et al. (2006b) (2.0–16.0 ×105 cells mL-1). Assuming that SAR11 contribute between 9 % and 20 % of total
bacterioplankton (Mary et al., 2006b) suggests SAR11 numbers range between
0.18–3.16 ×105 cells mL-1 at this coastal site. Using the above
estimate of ∼5×10-20 moles cell-1 h-1 for
rates of methanol dissimilation in cultured SAR11 cells suggests that SAR11
could oxidise methanol at rates ranging between 0.9 and 15.8 pmol L-1 h-1 in temperate coastal regions. This equates to < 0.01 %–2.3 %
of microbial community methanol dissimilation rates
(0.7–11.2 nmol L-1 h-1; Sargeant et al., 2016). Therefore, we
suggest that cells of the SAR11 clade are more likely to make a larger
contribution to marine microbial methanol dissimilation in open ocean
environments, where alternative sources of carbon are more limited relative
to temperate coastal waters. More work is required to add clarity and
understanding to the role that SAR11 cells play in marine community methanol
dissimilation.
Previously, methylotrophic bacteria such as Methylophaga sp., Methylococcaceae sp. and Hyphomicrobium sp. have been
identified, using mxaF functional gene primers (which encode for the classical
methanol dehydrogenase), from the same DNA samples analysed for 16S rRNA
genes in this study, from the upper water column of Atlantic Ocean provinces
(Dixon et al., 2013). Although numerically very rare (1–11 16S
rRNA gene sequences per sample), 16S rRNA gene sequences identified as
Methylophaga spp., Methylophaga sp. DMS021 (EU001861) and uncultured Methylophaga sp. (EU031899), were found in
each of the Atlantic Ocean provinces in this study (at 97 % PAR or 200 m
of depth), consistent with previous identification of Methlophaga spp. in these Atlantic
provinces using mxaF gene cloning (Dixon et al., 2013). More recently the
xoxF gene, which encodes an alternative methanol dehydrogenase, has also been
found to be widespread in coastal marine environments (Taubert et al.,
2015). SAR11 bacteria are thought to contain an Fe alcohol dehydrogenase,
which although not specific for methanol, can oxidise methanol (and other
short chain alcohols) to formaldehyde which is then thought to be converted
to CO2 by a tetrahydrofolate-linked C1 transfer pathway to produce
energy (Sun et al., 2011). Thus it seems likely that both methylotrophic
bacteria possessing mxaF and/or xoxF, together with microbes such as SAR11
(Sun et al., 2011), are largely responsible for the
turnover of methanol in seawater.
Members of Betaproteobacteria, OM43, have been shown to be potentially important obligate
methylotrophs, with cultivated cells of strain HTCC2181 dissimilating 3.5
times more methanol than was assimilated (Halsey et al., 2012). OM43 were
not successfully identified in the 16S rRNA sequences in this study, which
could be an artefact of the relatively low sequence coverage (386 sequences
per sample) leading to this taxon not being detectable. During a previous
coastal study, also analysing 16S rRNA pyrosequence data, in the western
English Channel (Sargeant et al., 2016) only a single sequence of the OM43
clade, HTCC2181, was identified. This is a limitation of this type of
environmental sequencing effort and should be a consideration in planning
any future projects aiming to understand microbial function through process
measurements alongside the generation of metagenomic datasets.
Marine methanol cycling
Data from this study substantially add to the measurements of microbial
methanol dissimilation rates in seawater. This extended spatial coverage
clearly demonstrates that methanol dissimilation is a widespread microbial
process taking place in light and dark environments throughout the Atlantic
Ocean. Dissimilation rates are typically 2 orders of magnitude greater
than assimilation rates across most of the Atlantic Basin. These data
suggest that methanol is an important source of energy for microbes. This is
particularly true in the northern oligotrophic waters of the Atlantic Ocean,
where corresponding in situ methanol concentrations range between 148 and 281 nM (Table 1).
What is not clear is the source of methanol in open ocean waters, which
is suspected to be biological in nature (Dixon et al., 2011a).
Although direct flux estimates suggest that the atmosphere could also act as
a source to the ocean (Yang et al., 2013), the magnitude of this flux is
insufficient to support the observed rates of microbial methanol consumed by
bacteria, and hence is suspected to be a minor contribution (Dixon et al.,
2011a). Recent culture studies indicate that Prochlorococcus sp., Synechococcus sp. and Trichodesmium sp. could produce
methanol (Mincer and Aicher, 2016; Halsey et al., 2017), but in situ production
mechanisms are unknown. Further work is needed to fully elucidate and
quantify the sources of methanol in marine waters.