Previous Article | Next Article 
Applied and Environmental Microbiology, April 2000, p. 1479-1488, Vol. 66, No. 4
0099-2240/00/$04.00+0
Copyright © 2000, American Society for Microbiology. All rights reserved.
Sediment Microbial Community Structure and Mercury
Methylation in Mercury-Polluted Clear Lake, California
J. L.
Macalady,1,*
E. E.
Mack,2,
D. C.
Nelson,2 and
K.
M.
Scow1
Department of Land, Air and Water
Resources1 and Department of
Microbiology,2 University of California,
Davis, California 95616
Received 14 October 1999/Accepted 25 January 2000
 |
ABSTRACT |
Spatial and temporal variations in sediment microbial community
structure in a eutrophic lake polluted with inorganic mercury were
identified using polar lipid fatty acid (PLFA) analysis. Microbial
community structure was strongly related to mercury methylation
potential, sediment organic carbon content, and lake location. Pore
water sulfate, total mercury concentrations, and organic matter C/N
ratios showed no relationships with microbial community structure.
Seasonal changes and changes potentially attributable to temperature
regulation of bacterial membranes were detectable but were less
important influences on sediment PLFA composition than were differences
due to lake sampling location. Analysis of biomarker PLFAs
characteristic of Desulfobacter and Desulfovibrio groups of sulfate-reducing bacteria suggests
that Desulfobacter-like organisms are important mercury
methylators in the sediments, especially in the Lower Arm of Clear Lake.
| |
INTRODUCTION |
The mercury cycle is a complex
biogeochemical system involving both biotic and abiotic transformations
(48). The production of methylmercury
(CH3Hg+) is of particular interest because
methylmercury is more toxic and mobile than the precursor
Hg2+ ion and because methylmercury bioaccumulates in food
chains. Because mercury cannot be broken down to an innocuous
by-product, remediation of mercury-contaminated sites is dependent upon
gaining an understanding of the factors that make mercury bioavailable and mobile. Controls on mercury methylation in natural environments such as lakes are not well understood. Abiotic mercury methylation in
natural environments appears to be of minor importance (1). In contrast, microbial mercury methylation has been shown to occur in a
variety of marine, estuarine, and lacustrine environments. Microorganisms from diverse taxonomic groups have been shown to methylate mercury in laboratory studies (40). However,
several previous studies have indicated that sulfate-reducing bacteria are the primary mercury methylators in freshwater and estuarine anoxic
sediments (10, 11, 18, 19). Factors influencing microbial
methylmercury production include microbial community composition,
mercury availability, carbon availability, and the abundance of
electron acceptors such as sulfate.
Clear Lake is a mercury-polluted, eutrophic lake located in the
northern Coast Ranges, California (Fig.
1). Preliminary studies conducted at
Clear Lake suggested that mercury methylation potential (methylation of
added Hg2+) in the sediments is only partially attributable
to sulfate-reducing bacteria (28). The current study focuses
on identifying spatial and temporal variations in sediment microbial
community structure and relationships between microbial community
composition, mercury methylation potential, and other sediment
characteristics in Clear Lake. In environments such as soils and
sediments, high microbial diversity and the difficulty in culturing
native organisms make culture-based methods inadequate or inefficient
for differentiating microbial communities. Within such environments,
lipid analysis has become an important ecological tool (4, 9,
16). We used polar lipid fatty acid (PLFA) analysis to describe
microbial communities in Clear Lake in two distinct ways. First, we
used sediment PLFA composition to identify spatial and temporal
variation in sediment microbial communities within Clear Lake. Using
multivariate statistical methods, we then tested relationships between
PLFA composition and sediment characteristics (potential explanatory variables). These analyses indicate which measured environmental factors are the most important controls on microbial community structure and also identify relationships between microbial community structure and mercury methylation potential. Second, we quantified specific PLFA biomarkers as indicators of microbial groups. Because sulfate-reducing bacteria have been implicated in environmental mercury
methylation in previous studies, we compiled a database of published
information about the PLFA composition of sulfate-reducing bacteria to
identify biomarkers for sulfate-reducing bacterial groups in Clear Lake
sediments.
View larger version (43K):
[in this window]
[in a new window]
|
FIG. 1.
Map of Clear Lake. Sample locations are shown with water
depths and sediment total mercury and methylmercury concentrations per
gram (dry weight) of sediment (0 to 4 cm) (43). These
concentrations represent typical total Hg and methylmercury
distributions in the lake (see reference 41). The OA
site is closest to the source of mercury contamination (Sulfur Bank
Mine). The LA site is closest to the lake outlet and experiences the
bulk of water movement through Clear Lake. The UA site is farthest from
the Sulfur Bank Mine and receives the major stream inflows into the
lake.
|
|
Our results show that mercury methylation potential, sediment organic
carbon, and lake sample location are strongly related to changes in
sediment microbial community structure. Based on analysis of PLFA
biomarkers for sulfate-reducing bacterial groups, Desulfobacter species dominated sulfate reducer populations
at all lake locations and also made up a greater portion of the
microbial biomass at sites with higher mercury methylation potential.
| |
MATERIALS AND METHODS |
Sediment collection.
Sediment samples were collected in
April and July of 1997. One sample location was chosen in each of three
major arms of the lake (Fig. 1). Samples were collected from a boat
with an Eckman dredge. In April, bulk surficial (0 to 4 cm) sediments
were collected from three separate dredges per lake location. In July,
a similar protocol was used, except that each dredge was subcored
twice. The two subcores from each dredge were sectioned into 0- to 4- and 4- to 8-cm depth intervals and homogenized, resulting in one 0- to
4-cm and one 4- to 8-cm sample per dredge. Samples were transported on
ice and were stored at 2°C until processed (<24 h).
C and N elemental analyses.
A portion of each sediment
sample was freeze-dried, pulverized in a steel ball mill, and analyzed
for total C and N using an elemental analyzer (Fisons NA 1500 Series 2).
Mercury methylation potential in sediment slurries.
Mercury
methylation potential assays were carried out in anoxic sediment
slurries made from sectioned cores. Sediments (0 to 4 cm) were slurried
with surficial water from the Upper Arm (UA) site amended to 125 ppm of
Hg2+ (as HgCl2) in the ratio of 1 part sediment
to 2 parts (wt/vol) water and sparged with O2-free
N2. One slurry made from each pair of sections was frozen
immediately for an initial methylmercury measurement, and the other
slurry was incubated at in situ temperature in the dark, static
(unshaken), for 5 days. Incubations were ended by freezing the
slurries. Sediment slurries were analyzed for methylmercury at Battelle
Marine Science Laboratory (Sequim, Wash.) using the method of Bloom
(2a). Extracts were derivatized with sodium tetraethyl
borate to form volatile ethyl-mercury compounds, which were
subsequently separated using gas chromatography and detected by cold
vapor atomic fluorescence (2, 27).
Because inorganic Hg additions to the sediment slurries were roughly 4 to 60 times ambient levels, the resulting mercury methylation
rates are
considered "potential" rates representative of the activity
of a
stimulated population. The Hg amendments employed have been
shown
previously (E. E. Mack, unpublished data) to saturate methylation
potential at the three study sites. The effect of added inorganic
mercury on microbial community composition was tested by measuring
sediment PLFA profiles before and after incubations (methods described
below).
PLFA analyses.
Sediment samples were frozen (
80°C)
within 24 h of collection, lyophilized, homogenized, and stored
frozen (20°C) until analysis. Triplicate (April 1997) or duplicate
(July 1997) 1-g subsamples were extracted with a one-phase solvent
extractant using a modification of the method of Bligh and Dyer
(5). Polar lipids (including phospholipids) were separated
from neutral lipids and glycolipids using solid-phase extraction
columns (0.50 g of Si; Supelco, Inc., Bellefonte, Pa.). The polar lipid
fraction was subjected to mild alkaline methanolysis, and the resulting fatty acid methyl esters (FAMEs) were extracted with two aliquots of
hexane. The hexane was evaporated under N2 at room
temperature, and FAMEs derived from polar lipids were redissolved in
hexane containing an internal standard (19:0 FAME). Samples were
analyzed by capillary gas chromatography (Hewlett-Packard 6890) using a 25-m Ultra-2 column (J&W Scientific). Peaks were identified using 33 bacterial FAME standards and MIDI peak identification software (Microbial ID, Inc., Newark, Del.). Peak identifications were confirmed
by capillary gas chromatography-mass spectrometry (GC-MS). The GC-MS
system consisted of a Finnigan MAT GCQ system (ion trap) operated in
positive ionization mode with electron ionization (70 eV). FAME isomers
were separated on a 30-m capillary column (DB-5MS; 0.25-mm internal
diameter; film thickness, 0.25 µm; J&W Scientific). Double bond
positions in monounsaturated fatty acids were confirmed by GC-MS
analysis of their dimethyldisulfide adducts (30).
Fatty acids are described using the nomenclature "number of
carbons:number of unsaturations," followed by double bond locations
referenced from the omega (
), or aliphatic, end of the molecule.
For
example, "18:1
7" denotes an 18-carbon, monoenoic fatty acid
with
a double bond at carbon 7. Unsaturated fatty acids with unknown
double
bond locations are followed by a capital letter. Other
conventions are
listed as follows: cy, cyclopropyl group; i or
a (or br), iso- or
anteiso-branched (or unspecified branching);
c or t,
cis- or
trans- double bond configuration; 10me, methyl-branched
at
C-10 (from carboxylic end); 2OH or 3OH, hydroxy substitution
at C-2 or
C-3 (from carboxylic end); unk,
unknown.
Statistical analyses.
Fatty acid data were analyzed using
four statistical ordination methods designed to explore the structure
of large multivariate data sets. Ordination methods have the common
goal of arranging sample points in space such that points which fall
close together have similar values for variables. We used several
ordination methods in order to assure a robust interpretation of
differences in PLFA composition among samples (22). The
first ordination method, principal component analysis (PCA), is
analogous to multiple linear regression and is widely used to summarize
multivariate data sets of many types (6, 12). Fatty acid
data for PCAs were converted to mole percentages in order to eliminate
the effect of differences in total PLFA abundance (microbial biomass)
among lake locations. Peaks included in the analyses accounted for more than 95% of named peaks in each sample. PCA ordinations were
calculated using correlation (rather than covariance) matrices
(22). Some recent reports in the statistical literature
suggest that compositional data sets (such as PLFA abundances expressed
in mole percent) are subject to artifacts when analyzed using PCA
(21, 39, 42). As a result of this concern, we also analyzed
PLFA results using correspondence analysis (CA) (17). One
potential advantage of CA over PCA is that ordinations are the same
regardless of whether nanomole or mole percent (compositional) data are
used (21). PLFA data for CA ordinations were input as
nanomoles per gram. CAs were based on the same set of fatty acid peaks
as were PCA analyses.
PCA and CA ordination methods are unconstrained, in the sense that no
information about potential environmental driving forces
(explanatory
variables) is included in the calculations. Only
relationships among
samples and variables (fatty acids) are considered.
Constrained, or
direct gradient analysis, forms of both PCA and
CA show the
relationship of samples and variables (fatty acids)
to measured
environmental gradients. Redundancy analysis (RDA)
is the direct
gradient analysis form of PCA (
36). Canonical
correspondence
analysis (CCA) is the direct gradient analysis
form of CA (
32,
45). Both RDA and CCA allow the significance
of measured
environmental gradients to be tested explicitly using
a Monte Carlo
simulation. The Monte Carlo test returns a
P value
associated with the effect of the environmental variable on the
PLFA
composition of the
samples.
Rules for interpreting ordination plots are different for PCA and RDA
than for CA and CCA (
24). PCAs and RDAs in our study
indicated relationships among samples and fatty acids very similar
to
those indicated by CA and CCA. For simplicity, we report ordination
results only for CA and CCA. All ordination analyses were performed
using Canoco software (Microcomputer Power, Inc., Ithaca, N.Y.).
Lab
replicate PLFA analyses were averaged before ordination
analyses.
Other statistical analyses were performed using the general linear
models function of SAS (SAS Institute, Inc., Cary, N.C.).
PLFA content
and mercury methylation potential of surficial sediments
were regressed
against the average carbon content (three replicate
dredge samples) at
each site. The effect of bottom water temperature
on potentially
temperature-sensitive fatty acid ratios was tested
using one-way
analyses of variance (by sample date) in which both
depth intervals
from July samples were included and treated as
a single group of
observations. Methylmercury concentrations in
methylation potential
assays were compared using two-tailed Student's
t tests.
| |
RESULTS |
Physical and chemical characteristics of Clear Lake sediments.
Sediment pH at all of the Clear Lake sampling sites was slightly
alkaline on both sample dates (7.7 to 7.8). Pore water sulfate concentrations in the top 5 cm ranged from 30 to 110 µM at the Oaks
Arm (OA) site, 50 to 100 µM at the Lower Arm (LA) site, and 40 to 150 µM at the UA site over a 14-month period (28). Lake bottom
temperatures were 13°C in April 1997 and 25°C in July 1997. These
temperatures are characteristic of temperatures recorded at the same
locations over a 3-year period (41).
The total nanomoles of PLFA in Clear Lake sediment samples was
correlated with percent carbon in sediments (Fig.
2). The LA
sediment samples consistently
had the highest organic carbon contents,
followed by the OA samples.
Carbon contents for replicate dredge
samples had very small
coefficients of variation (<2.3%). C-to-N
ratios at all sites ranged
between 7.7 and 8.5 and did not show
any significant relationship with
lake location, sediment depth,
or sampling date (data not shown).
View larger version (16K):
[in this window]
[in a new window]
|
FIG. 2.
Percent carbon and total PLFA content of surficial (0- to 4-cm) Clear Lake sediments. Three replicate dredge samples from each
lake location on April and July sampling dates are shown. Carbon
contents for replicate dredge samples had very small coefficients of
variation (<2.3%).
|
|
PLFA composition of Clear Lake sediments.
A set of 37 consistently quantified fatty acid peaks (out of 71 detected) was
selected for statistical analysis. The 37 fatty acids included in the
analyses sum to greater than 95% of the total nanomoles of PLFA in
each sample and had coefficients of variation below 10% in lab
replicates. Minor peaks not reproducibly quantified due to detection
limits were omitted. Lab replicate PLFA analyses were averaged before
ordination analyses.
In both indirect (CA and PCA) and direct (CCA and RDA) gradient
analyses, LA and OA samples were tightly grouped except for
two
outliers. These outliers (LAB2 and OAT3) contain especially
high
amounts of 20:4 and 18:2 polyunsaturated fatty acids. These
polyunsaturates are characteristic of eukaryotes and may be associated
with invertebrates in Clear Lake sediments. We removed several
chironomids from LA sediments and extracted their polar lipids
using
the procedure described for sediments. The chironomids had
a PLFA
composition typical for eukaryotic organisms (
20) and
contained primarily saturated and unsaturated even, straight-chain
fatty acids. The polyunsaturated fatty acids 20:4 and 18:2 represented
23% of total chironomid PLFA. The two outlier samples have been
omitted from the ordination shown in Fig.
3.
View larger version (17K):
[in this window]
[in a new window]
|
FIG. 3.
(A) CA ordination of PLFA abundance data for Clear Lake
sediment samples (outliers LAB2 and OAT3 omitted). The plot shows 89%
of the total variability in the data set. Each point represents the
average of duplicate or triplicate PLFA analyses. Separate dredges are
represented by independent points and are identified by numbers 1 through 3. July samples are additionally identified by either "B"
(bottom; 4- to 8-cm depth) or "T" (top; 0- to 4-cm depth). Samples
which lie close together have similar fatty acid compositions.
Ellipsoids drawn on the plot are meant to aid in identifying samples
from the same location and sample date (dashed line, April; solid line,
July). (B) CA ordination scores for PLFA variables (outlier samples
LAB2 and OAT3 omitted). The expected abundance of each fatty acid
decreases with distance from its location in the plot. Fatty acids
which lie close to samples in panel A above when the plot origins are
superimposed are likely to have a high relative abundance in those
samples. For example, fatty acid i14:0 is likely to have the highest
abundance in samples from the LA.
|
|
Samples from the same lake location grouped together regardless of
sediment depth, and samples from different lake locations
consistently
separated along the first (most important) ordination
axis (Fig.
3A).
Both PCA and CA ordinations explained greater
than 50% of the total
variation in PLFA composition on the first
axis. Relationships between
community PLFA composition and lake
location, sampling date, and
sediment carbon content were determined
using direct gradient analyses
(CCA and RDA) with Monte Carlo
simulations. Lake location was a highly
significant explanatory
variable (
P < 0.001). Sediment
percent carbon was also highly
significant (
P < 0.001)
and produced ordinations almost identical
to those which included lake
location as an environmental variable.
Sampling date was not
significant as an explanatory variable (
P > 0.05).
When samples from a given lake location were analyzed
in separate
ordinations, sampling date was significant for the
UA site
(
P < 0.001) but not significant for the LA and OA
sites
(
P > 0.05).
Effect of temperature on sediment PLFA composition.
The in
situ temperature of Clear Lake bottom water increased from 13°C in
April to 25°C in July 1997. Since temperature adaptations of
phospholipid membranes may have contributed to changes in PLFA composition, we looked for shifts in fatty acids which are expected to
play a role in regulating phospholipid membrane fluidity. Many bacteria
respond to increased temperature by decreasing the amount of fatty acid
unsaturation in their membranes. Other strategies for decreasing
membrane fluidity in response to elevated temperature include
increasing the proportion of branched-chain fatty acids or increasing
the ratio of iso- to anteiso-branched fatty acids (25).
Ratios of saturated to monounsaturated isomers in 16- or 18-carbon
fatty acids were slightly higher in July samples at all lake locations,
as expected for an increase in temperature (Fig.
4). Ratios of i17:0/a17:0 and i15:0/a15:0
were also slightly higher in July samples, consistent with a membrane
adaptation to higher temperature (Fig. 4). Amounts of branched versus
unbranched fatty acids (mole percent) changed very little across lake
locations and sample dates and did not show a consistent trend with
temperature (data not shown).
View larger version (58K):
[in this window]
[in a new window]
|
FIG. 4.
Effect of temperature on Clear Lake sediment PLFA
composition. Fatty acid ratios shown are expected to increase with an
increase in temperature. July samples (higher temperature) are
identified with crosshatched bars. Error bars are 1 standard deviation
(n = 3). Samples with different lowercase letters are
significantly different at the 95% confidence interval in one-way
analyses of variance.
|
|
Mercury methylation potential in Clear Lake sediments.
Initial
sediment methylmercury concentrations at the three lake sites were not
significantly different (0.2 < P < 0.9).
Therefore, differences in methylation potential derive from differences
in methylmercury concentrations observed after 5 days with added mercury (Fig. 5). In spite of small
sample sizes (n = 2 dredges for each site/incubation
time), methylation potential at the LA site is significantly greater
than at the other two sites (P < 0.05). Methylation
potential at the three sites was correlated with sediment percent
carbon (r2 = 0.82, P < 0.02) but was
uncorrelated with total PLFA (r2 = 0.48).
CCA ordination of the sediment PLFA composition with mercury
methylation potential as an explanatory variable (Fig. 6) produced sample and PLFA biplots
remarkably similar to those produced by including environmental
variables for percent carbon or lake location. Mercury methylation
potential was a highly significant explanatory variable (P < 0.001). The addition of 125 ppm of Hg2+ to sediment
slurries did not change microbial community structure or total biomass
based on PLFA analysis of the sediments before and after 5-day mercury
methylation potential incubations (data not shown).
View larger version (29K):
[in this window]
[in a new window]
|
FIG. 5.
Mercury methylation potential in surficial (0- to 4-cm)
Clear Lake sediments. Methylmercury concentrations at initial
(t = 0 days) and final (t = 5 days)
incubation times are shown. Error bars are ±1 standard deviation
(n = 2 dredge samples). Within each of the two panels
of the graph, bars with different letters are significantly different
(P < 0.05, two-tailed Student's t test).
|
|
View larger version (14K):
[in this window]
[in a new window]
|
FIG. 6.
(A) CCA ordination of PLFA abundance data for Clear Lake
sediment samples (0- to 4 cm). The plot shows 86% of the total
variability in the data set. Mercury methylation values (nanograms per
gram [dry weight] of sediment) were divided by total nanomoles of
PLFA (microbial biomass). Since only one environmental variable was
included in the analysis (Hg methylation potential), it is 100%
correlated with the first CCA axis. (B) CCA ordination scores for PLFA
variables. As for CA ordinations, the expected abundance of each fatty
acid decreases with distance from its location in the plot, and fatty
acids which lie close to samples in panel A above when the plot origins
are superimposed are likely to have a high relative abundance in those
samples.
|
|
Published data on PLFA composition of sulfate-reducing
bacteria.
PLFA biomarkers for sulfate-reducing bacterial groups
have been identified by previous researchers and are shown in Table 1. Some of these biomarkers were
determined for a small subset of isolates and may not be present in, or
exclusive to, all members of the groups which they are reported to
represent. To provide a more comprehensive comparison of the reported
biomarkers with published PLFA abundances in sulfate-reducing bacterial
isolates, we compiled available information into a database containing
100 strains (3, 7, 13-15, 26, 34, 43, 46). PLFA abundances in the database are expressed in mole percentages.
Desulfovibrio (n = 52),
Desulfobacter (n = 16), and
Desulfobulbus (n = 6) were the most commonly
reported genera. Twelve genera have at least one representative in the
database. An "other" category includes "Spirillum" and "fat
vibrio" (14), Thermosulfobacterium commune (46), isolates 3801 and 3794 (15), and two
examples of Geobacter metallireducens (26).
Sulfate-reducing archaea are not included in the database.
The published PLFA analyses were of variable quality and resolution. In
order to include some data sets we would otherwise
have discarded due
to poor gas chromatography peak resolution
or differences in analytical
methods, we grouped some structurally
similar fatty acids into summed
aggregates. For example, iso-
and anteiso-15:1 isomers were summed and
reported as "br15:1."
Although some detailed information is lost
using this approach,
it allowed us to compare the largest possible
number of isolates.
Fatty acids of 13 carbons or less in chain length
were not reported
in some published analyses. In addition,
OH-substituted fatty
acids derived from polar lipids were sometimes
reported jointly
with lipopolysaccharide OH fatty acids, making it
difficult to
compare OH-substituted fatty acid abundances among
different studies.
As a result of these analytical limitations in
previous studies,
fatty acids 11:0, 12:0, a13:0, i13:0, 13:0, and
OH-substituted
fatty acids were omitted from ordination analyses of the
compiled
database.
CA and PCA ordinations of the pure culture PLFA database showed three
clusters of isolates containing
Desulfobacter,
Desulfobulbus,
and
Desulfovibrio isolates
(Fig.
7). Relationships between isolates
in the ordination plots reflect their taxonomic identity, as suggested
by previous studies comparing the PLFA compositions of 25 (
26)
or 40 (
46) strains of sulfate-reducing
bacteria.
Desulfovibrio isolates in the database are
characterized by high relative amounts
of branched saturated and
branched monounsaturated fatty acids.
Four
Desulfovibrio
isolates contained the polyunsaturated fatty
acid 18:2 (<1 mol%),
which was not detected in any other isolates
in the database.
Desulfobacter isolates were associated with high
relative
amounts of methyl-branched and cyclopropyl fatty acids,
especially
10me16:0, cy17:0, and cy19:0. The fatty acid 10me18:0
was detected in a
single isolate (0.5 mol%, marine
Desulfobacter sp. strain
3ac10 [
14]).
Desulfobulbus isolates in the
database
were characterized by a high abundance of unbranched fatty
acids
(17:1, 15:1, and 15:0). Other genera are represented by very few
strains in the database and fell between or within the
Desulfovibrio,
Desulfobulbus, or
Desulfobacter groups on ordination plots.
View larger version (21K):
[in this window]
[in a new window]
|
FIG. 7.
(A) CA ordination of PLFA abundance data for
sulfate-reducing bacterial isolates. Samples which are plotted close
together have similar PLFA compositions. Strains included in the
"other" category (H) are given in the text. Ellipsoids drawn on the
plot are meant to aid in identifying isolates of the same genus. (B) CA
ordination scores for PLFA variables in the pure culture database. The
expected abundance of each fatty acid decreases with distance from its
location in the plot. Fatty acids which are plotted close to samples in
panel A when plot origins are superimposed are likely to have a high
relative abundance in those samples.
|
|
Average contents of biomarker PLFAs for
Desulfovibrio
(br17:1),
Desulfobacter (10me16:0), and
Desulfobulbus (17:1) in selected
genera from the database
are shown in Table
2. Biomarker fatty
acids for
Desulfovibrio (br17:1) were detected in 48 out of
52
Desulfovibrio PLFA analyses but were also detected in six
isolates
assigned to other genera including
Desulfomonas
(
46),
Desulfomicrobium (
46),
Desulfococcus (
26),
Desulfotomaculum (
26),
Desulfuromonas (
26), and
Desulfosarcina (
26). The
Desulfobacter
biomarker
(10me16:0) was detected in 10 out of 11
Desulfobacter PLFA profiles
but was also detected in three
isolates assigned to
Desulfotomaculum (
26),
Geobacter (
26), or
Desulfobacterium
(
46). The
Desulfobulbus biomarkers (17:1) were
detected in all
Desulfobulbus PLFA analyses
but were also
detected in numerous isolates from at least nine
other genera (Table
2).
| |
DISCUSSION |
Microbial community structure in Clear Lake sediments is
significantly related to a gradient in organic carbon among the lake sample sites. In contrast, inorganic mercury concentration appeared to
have no effect on community structure, either in microcosm mercury
methylation potential assays or in lake sediments with differing total
Hg concentrations (Fig. 1). Pore water sulfate concentrations and
sediment organic matter C/N ratios were not significantly different
among lake locations and therefore cannot be responsible for the
differences in community composition that we observed. Sediment PLFA
patterns did not change significantly between 0- to 4- and 4- to 8-cm
depths, likely due to intense sediment bioturbation by chironomid
larvae and oligochaete worms (41).
Mercury methylation potential was positively correlated with sediment
percent carbon, highlighting the importance of heterotrophic biological
activity for driving mercury methylation in Clear Lake sediments. A
positive correlation between mercury methylation and percent carbon in
sediments has also been reported in previous studies (8, 23,
31). Sediment percent carbon was also positively correlated with
mercury methylation potential expressed per nanomole of PLFA (microbial
biomass). This correlation is explained by the observed changes in
microbial community structure from the LA site (high percent C) to the
UA site (low percent C) and suggests that high-carbon sites host
microbial communities with higher mercury methylation activity per unit
of microbial biomass.
Previous studies have proposed the use of PLFA biomarkers for
distinguishing sulfate-reducing bacterial groups from each other (Table
1) (14, 35, 46). Ordination analyses of the compiled fatty
acid database for pure cultures of sulfate-reducing bacteria generally
support the use of previously published biomarkers for grouping
pure cultures. Other studies have used biomarker PLFAs to identify the
presence and abundance of sulfate-reducing bacterial groups in natural
environments (9, 33, 44, 47). These studies assumed that
other bacteria in environmental samples did not contain significant
amounts of the sulfate reducer biomarkers. Biomarkers for
Desulfobulbus species consist of straight-chain saturated
and monounsaturated PLFAs (15:0, 15:1, and 17:1) which are known to be
widely distributed among bacterial taxa. As a result, this group is not
likely to be easily studied in environmental samples using PLFA
analysis. In contrast, biomarkers for Desulfovibrio (br17:1)
and Desulfobacter (10me-PLFA) are more narrowly distributed or are present in small amounts in bacteria other than sulfate reducers
(37), suggesting that these two genera are likely to be the
major sources of their respective biomarker fatty acids in
environmental samples. The fatty acid 10me16:0 is a trace component of
some high-G+C gram-positive strains but is a major component of
Desulfobacter strains. Likewise, branched monounsaturates
comprise a small percentage (<10%) of fatty acids in a few high-G+C
gram-positive groups but usually represent a large percentage (>30%)
of Desulfovibrio fatty acids. The small number of available
PLFA profiles for sulfate-reducing isolates in genera other than
Desulfovibrio and Desulfobacter limits
interpretation of environmental PLFA profiles with respect to these organisms.
Although the PLFA database for environmental bacterial isolates is
limited, putative sulfate reducer biomarkers for
Desulfovibrio and Desulfobacter groups are
potentially very useful for environments such as anoxic sediments
dominated by the activity of sulfate-reducing bacteria. Interpretation
of Clear Lake sediment PLFA data based on sulfate reducer biomarkers
leads to several hypotheses which can be tested in future studies. We
assumed that an average cell contains 7.31 × 10
8
nmol of PLFA (29). Based on the pure culture data in Table 2, the biomarker fatty acid 10me16:0 makes up roughly 25% of Desulfobacter PLFAs. Similarly, br17:1 fatty acids are
approximately 11% of Desulfovibrio PLFAs. Based on these
conversion factors, the bacterial community at the LA site has 91%
Desulfobacter (1.4 × 1010 cells/g of
sediment [dry weight]), 2% Desulfovibrio (2.7 × 108 cells/g), and 7% other organisms. In contrast, the UA
site has 33% Desulfobacter (2.2 × 109
cells/g), 3% Desulfovibrio (2.2 × 108
cells/g), and 64% other organisms. The OA site has a community composition intermediate between those of the LA and UA sites. It is
unlikely that sulfate-reducing bacteria can account for as much as 91%
(LA) of the sediment bacterial biomass, since they depend on
fermentation products produced from complex organic matter by large
populations of other heterotrophic bacteria. However, the estimates
above are useful as comparative measures of community structure at
different lake locations. Sulfate-reducing bacteria constitute a larger
portion of the sediment community at the LA site, which also had higher
mercury methylation potential than the other two sample sites. The
positive correlation between sulfate-reducing bacterial biomass and
mercury methylation is interesting in light of previous work at the
same sample locations, which showed that sulfate reduction and mercury
methylation potential are highly correlated at the LA site but only
weakly correlated at the OA and UA sites (28). Together,
these results suggest that sulfate reducers are important for mercury
methylation in Clear Lake sediments, but other unknown mercury
methylators may also be important, especially outside the LA.
Desulfobacter populations (or sulfate-reducing bacteria with
similar PLFA composition) were much more abundant than
Desulfovibrio populations at all lake sampling sites and
were also more abundant at sites with higher mercury methylation
potential per unit of microbial biomass. Desulfovibrio
populations were most abundant at the UA site, which had the lowest
mercury methylation potential per unit of biomass. Results from Clear
Lake are consistent with those from a previous microcosm study
(38), which reported that mercury methylation potential in
freshwater lake sediments was highly correlated with the abundance of
PLFA 10me16:0 (Desulfobacter biomarker). In an estuarine
sediment studied using oligonucleotide probes (11), the peak
in mercury methylation occurred at the same sediment depth as peaks in
rRNA from several groups of sulfate-reducing bacteria including members
of the family Desulfovibrionaceae and Desulfobacter species. Since there is no evidence that
sulfate-reducing bacterial species have different mercury methylating
abilities, the observed correlation between mercury methylation
potential and Desulfobacter biomarker PLFA freshwater
environments may simply reflect the dominance of
Desulfobacter species among sulfate-reducing bacterial
populations in lacustrine sediments studied to date.
Our results suggest that further eutrophication of Clear Lake may
increase mercury methylation, either by altering sediment microbial
communities or by increasing sediment organic carbon loading.
Sulfate-rich waters from mining pits adjacent to the OA of Clear Lake
appear to be a major source of sulfur in the lake (41) and
could contribute to increased production of methylmercury. Future
mercury remediation studies in the Clear Lake basin should consider
sulfur inputs and cycling. However, the lack of a strong relationship
between sulfate reduction and mercury methylation at two lake sample
sites also suggests that other bacterial groups which methylate mercury
are potentially important in the UA and OA of Clear Lake.
| |
ACKNOWLEDGMENTS |
This work was supported by the U.S. Environmental Protection
Agency Center for Ecological Health Research, the NIEHS Superfund Basic
Research Program (2P42 ES04699), and the USEPA Office of Exploratory
Research (grant no. R825433 to T. H. Suchanek and P. J. Richerson).
We are grateful for the assistance of Tom Suchanek and the staff at the
Clear Lake Environmental Research Center.
| |
FOOTNOTES |
*
Corresponding author. Mailing address: Department of
Land, Air and Water Resources, University of California, One Shields Avenue, Davis, CA 95616. Phone: (530) 752-0146. Fax: (530) 752-1552. E-mail: jlmacalady{at}ucdavis.edu.
Present address: U.S. Environmental Protection Agency, National
Exposure Research Laboratory
Ecosystem Research Division, Athens, GA 30605.
| |
REFERENCES |
| 1.
|
Berman, M., and R. Bartha.
1986.
Levels of chemical vs. biological methylation of mercury in sediments.
Bull. Environ. Contam. Toxicol.
36:401-404[CrossRef][Medline].
|
| 2.
|
Bloom, N.
1989.
Determination of picogram levels of methylmercury by aqueous phase ethylation, followed by cryogenic gas chromatography with cold vapour atomic fluorescence detection.
Can. J. Fish. Aquat. Sci.
46:1131-1140.
|
| 2a.
|
Bloom, N. S.,
J. A. Coleman, and L. Barber.
1997.
Artifact formation of methyl mercury during aqueous distillation and alternative techniques for the extraction of methyl mercury from environmental samples.
Fresenius J. Anal. Chem.
358:371-377[CrossRef].
|
| 3.
|
Boon, J. J.,
J. W. de Leeuw,
G. J. van der Hoek, and J. H. Vosjan.
1977.
Significance and taxonomic value of iso and anteiso monoenoic fatty acids and branched  -hydroxy acids in Desulfovibrio desulfuricans.
J. Bacteriol.
129:1183-1191[Abstract/Free Full Text].
|
| 4.
|
Boschker, H. T. S.,
S. C. Nold,
P. Wellsbury,
D. Bos,
W. De Graaf,
R. Pel,
R. J. Parkes, and T. E. Cappenberg.
1998.
Direct linking of microbial populations to specific biogeochemical processes by 13C-labelling of biomarkers.
Nature (London)
392:801-805[CrossRef].
|
| 5.
|
Bossio, D. A., and K. M. Scow.
1995.
Impacts of carbon and flooding on the metabolic diversity of microbial communities in soils.
Appl. Environ. Microbiol.
61:4043-4050[Abstract].
|
| 6.
|
Brereton, R. G.
1990.
Chemometrics: applications of mathematics and statistics to laboratory systems.
Ellis Horwood, New York, N.Y.
|
| 7.
|
Brink, D. E.,
I. Vance, and D. C. White.
1994.
Detection of Desulfobacter in oil field environments by non-radioactive DNA probes.
Appl. Microbiol. Biotechnol.
42:469-475[CrossRef].
|
| 8.
|
Callister, S. M., and M. R. Winfrey.
1986.
Microbial methylation of mercury in Upper Wisconsin River [USA] sediments.
Water Air Soil Pollut.
29:453-465.
|
| 9.
|
Coleman, M. L.,
D. B. Hedrick,
D. R. Lovley,
D. C. White, and K. Pye.
1993.
Reduction of iron-III in sediments by sulphate-reducing bacteria.
Nature (London)
361:436-438[CrossRef].
|
| 10.
|
Compeau, G. C., and R. Bartha.
1985.
Sulfate-reducing bacteria: principal methylators of mercury in anoxic estuarine sediment.
Appl. Environ. Microbiol.
50:498-502[Abstract/Free Full Text].
|
| 11.
|
Devereux, R.,
M. R. Winfrey,
J. Winfrey, and D. A. Stahl.
1996.
Depth profile of sulfate-reducing bacterial ribosomal RNA and mercury methylation in an estuarine sediment.
FEMS Microbiol. Ecol.
20:23-31.
|
| 12.
|
Digby, P. G. N., and R. A. Kempton.
1987.
Multivariate analysis of ecological communities.
Chapman and Hall, New York, N.Y.
|
| 13.
|
Dowling, N. J. E.,
P. D. Nichols, and D. C. White.
1988.
Phospholipid fatty acid and IR spectroscopic analysis of a sulfate-reducing consortium.
FEMS Microbiol. Ecol.
53:325-334[CrossRef].
|
| 14.
|
Dowling, N. J. E.,
F. Widdel, and D. C. White.
1986.
Phospholipid ester-linked fatty acid biomarkers of acetate-oxidizing sulfate-reducers and other sulfide-forming bacteria.
J. Gen. Microbiol.
132:1815-1826.
|
| 15.
|
Edlund, A.,
P. D. Nichols,
R. Roffey, and D. C. White.
1985.
Extractable and lipopolysaccharide fatty acid and hydroxy fatty acid profiles from Desulfovibrio species.
J. Lipid Res.
26:982-988[Abstract].
|
| 16.
|
Findlay, R. H.
1996.
The use of phospholipid fatty acids to determine microbial community structure, p. 1-17.
, section 4.1.4. In A. D. Akkermans et al. (ed.), Molecular microbial ecology manual. Kluwer Academic Publishers, Dordrecht, The Netherlands.
|
| 17.
|
Gauch, H. G., Jr.,
R. H. Whittaker, and T. R. Wentworth.
1977.
A comparative study of reciprocal averaging and other ordination techniques.
J. Ecol.
65:157-174[CrossRef].
|
| 18.
|
Gilmour, C. C., and E. A. Henry.
1991.
Mercury methylation in aquatic systems affected by acid deposition.
Environ. Pollut.
71:131-170[CrossRef][Medline].
|
| 19.
|
Gilmour, C. C.,
E. A. Henry, and R. Mitchell.
1992.
Sulfate stimulation of mercury methylation in freshwater sediments.
Environ. Sci. Technol.
26:2281-2287[CrossRef].
|
| 20.
|
Harwood, J. L., and N. J. Russell.
1984.
Lipids in plants and microbes.
George Allen & Unwin, London, United Kingdom.
|
| 21.
|
Jackson, D. A.
1997.
Compositional data in community ecology: the paradigm or peril of proportions?
Ecology
78:929-940[CrossRef].
|
| 22.
|
Jackson, D. A.
1993.
Multivariate analysis of benthic invertebrate communities: the implication of choosing particular data standardizations, measures of association, and ordination methods.
Hydrobiologia
268:9-26.
|
| 23.
|
Jackson, T. A.
1986.
Methyl mercury levels in a polluted prairie river-lake system: seasonal and site-specific variations, and the dominant influence of trophic conditions.
Can. J. Fish. Aquat. Sci.
43:1873-1887.
|
| 24.
|
Jongman, R. H. G.,
C. J. F. ter Braak, and O. F. R. van Tongeren (ed.).
1995.
Data analysis in community and landscape ecology.
Cambridge University Press, Cambridge, United Kingdom.
|
| 25.
|
Kaneda, T.
1991.
Iso-fatty and anteiso-fatty acids in bacteriabiosynthesis, function, and taxonomic significance.
Microbiol. Rev.
55:288-302[Abstract/Free Full Text].
|
| 26.
|
Kohring, L. L.,
D. B. Ringelberg,
R. Devereux,
D. A. Stahl,
M. W. Mittelman, and D. C. White.
1994.
Comparison of phylogenetic relationships based on phospholipid fatty acid profiles and ribosomal RNA sequence similarities among dissimilatory sulfate-reducing bacteria.
FEMS Microbiol. Lett.
119:303-308[CrossRef][Medline].
|
| 27.
|
Liang, L.,
N. S. Bloom, and M. Horvat.
1994.
Simultaneous determination of mercury speciation in biological materials by GC-CVAFS after ethylation and room-temperature precollection.
Clin. Chem.
40:602-607[Abstract/Free Full Text].
|
| 28.
|
Mack, E. E.
1998.
Ph.D. dissertation.
University of California, Davis.
|
| 29.
|
Neidhardt, F. C.,
J. L. Ingraham, and M. Schaechter.
1990.
Physiology of the bacterial cell.
Sinauer Associates, Inc., Sunderland, Mass.
|
| 30.
|
Nichols, P. D.,
J. B. Guckert, and D. C. White.
1986.
Determination of monounsaturated fatty acid double-bond position and geometry for microbial monocultures and complex consortia by capillary gas chromatography-mass spectrometry of their dimethyldisulfide adducts.
J. Microbiol. Methods
5:49-56.
|
| 31.
|
Pak, K. R., and R. Bartha.
1998.
Mercury methylation and demethylation in anoxic lake sediments and by strictly anaerobic bacteria.
Appl. Environ. Microbiol.
64:1013-1017[Abstract/Free Full Text].
|
| 32.
|
Palmer, M. W.
1993.
Putting things in even better order: the advantages of canonical correspondence analysis.
Ecology
74:2215-2230[CrossRef].
|
| 33.
|
Parkes, R. J.
1987.
Analysis of microbial communities within sediments using biomarkers, p. 147-178.
In
M. Fletcher, T. R. G. Gray, and J. G. Jones (ed.), Ecology of microbial communities. Cambridge University Press, Cambridge, United Kingdom.
|
| 34.
|
Parkes, R. J., and A. G. Calder.
1985.
The cellular fatty acids of three strains of Desulfobulbus, a propionate-utilizing sulphate-reducing bacterium.
FEMS Microbiol. Ecol.
31:361-363.
|
| 35.
|
Parkes, R. J.,
N. J. E. Dowling,
D. C. White,
R. A. Herbert, and G. R. Gibson.
1993.
Characterization of sulphate-reducing bacterial populations within marine and estuarine sediments with different rates of sulphate reduction.
FEMS Microbiol. Ecol.
102:235-250[CrossRef].
|
| 36.
|
Rao, C. R.
1964.
The use and interpretation of principal component analysis in applied research.
Sankhya Ser. A
26:329-358.
|
| 37.
|
Ratledge, C., and S. G. Wilkinson (ed.).
1989.
Microbial lipids.
Academic Press Limited, San Diego, Calif.
|
| 38.
|
Regnell, O.,
A. Tunlid,
G. Ewald, and O. Sangfors.
1996.
Methyl mercury production in freshwater microcosms affected by dissolved oxygen levels: role of cobalamin and microbial community composition.
Can. J. Fish. Aquat. Sci.
53:1535-1545[CrossRef].
|
| 39.
|
Reyment, R. A.
1988.
Compositional data analysis.
Terra
100:29-34.
|
| 40.
|
Robinson, J. B., and O. H. Tuovinen.
1984.
Mechanisms of microbial resistance and detoxification of mercury and organomercury compounds: physiological, biochemical and genetic analyses.
Microbiol. Rev.
48:95-124[Free Full Text].
|
| 41.
|
Suchanek, T. H.,
P. J. Richerson,
L. H. Mullen,
L. L. Brister,
J. C. Becker,
A. E. Maxson, and D. G. Slotton.
1997.
Interim final report, Sulphur Bank Mercury Mine Superfund site.
U.S. Environmental Protection Agency Region IX Superfund Program, San Francisco, Calif.
|
| 42.
|
Tangri, D., and R. V. S. Wright.
1993.
Multivariate analysis of compositional data: applied comparisons favour standard principal components analysis over Aitchison's loglinear contrast method.
Archaeometry
35:103-115.
|
| 43.
|
Taylor, J., and R. J. Parkes.
1983.
The cellular fatty acids of the sulphate-reducing bacteria, Desulfobacter sp., Desulfobulbus sp. and Desulfovibrio desulfuricans.
J. Gen. Microbiol.
129:3303-3309.
|
| 44.
|
Taylor, J., and R. J. Parkes.
1985.
Identifying different populations of sulfate-reducing bacteria within marine sediment systems, using fatty acid biomarkers.
J. Gen. Microbiol.
131:631-642.
|
| 45.
|
Ter Braak, C. J. F.
1986.
Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis.
Ecology (Tempe)
67:1167-1179.
|
| 46.
|
Vainshtein, M.,
H. Hippe, and R. M. Kroppenstedt.
1992.
Cellular fatty acid composition of Desulfovibrio species and its use in classification of sulfate-reducing bacteria.
Syst. Appl. Microbiol.
15:554-566.
|
| 47.
|
White, D. C.,
W. M. Davis,
J. S. Nickels,
J. D. King, and R. J. Bobbie.
1979.
Determination of the sedimentary microbial biomass by extractable lipid phosphate.
Oecologia
40:51-61[CrossRef].
|
| 48.
|
Winfrey, M. R., and J. W. M. Rudd.
1990.
Environmental factors affecting the formation of methylmercury in low pH lakes.
Environ. Toxicol. Chem.
9:853-870.
|
Applied and Environmental Microbiology, April 2000, p. 1479-1488, Vol. 66, No. 4
0099-2240/00/$04.00+0
Copyright © 2000, American Society for Microbiology. All rights reserved.
This article has been cited by other articles:
-
Fleming, E. J., Mack, E. E., Green, P. G., Nelson, D. C.
(2006). Mercury Methylation from Unexpected Sources: Molybdate-Inhibited Freshwater Sediments and an Iron-Reducing Bacterium. Appl. Environ. Microbiol.
72: 457-464
[Abstract]
[Full Text]
-
Acha, D., Iniguez, V., Roulet, M., Guimaraes, J. R. D., Luna, R., Alanoca, L., Sanchez, S.
(2005). Sulfate-Reducing Bacteria in Floating Macrophyte Rhizospheres from an Amazonian Floodplain Lake in Bolivia and Their Association with Hg Methylation. Appl. Environ. Microbiol.
71: 7531-7535
[Abstract]
[Full Text]
-
Siciliano, S. D., Sangster, A., Daughney, C. J., Loseto, L., Germida, J. J., Rencz, A. N., O'Driscoll, N. J., Lean, D. R. S.
(2003). Are Methylmercury Concentrations in the Wetlands of Kejimkujik National Park, Nova Scotia, Canada, Dependent on Geology?. J. Environ. Qual.
32: 2085-2094
[Abstract]
[Full Text]
-
Ekstrom, E. B., Morel, F. M. M., Benoit, J. M.
(2003). Mercury Methylation Independent of the Acetyl-Coenzyme A Pathway in Sulfate-Reducing Bacteria. Appl. Environ. Microbiol.
69: 5414-5422
[Abstract]
[Full Text]
Top
Abstract
Introduction
