1

Phospholipid Fatty Acid Analysis as a Measure of Impact of Acid Rock Drainage on Microbial Communities in Sediment

and Comparison With Other Measures

Eric Ben-David

Environment Division, Australian Nuclear Science and Technology Organisation (ANSTO)

School of Biotechnology and Biomolecular Sciences, University of New South Wales (UNSW)

2

Outline

• Objectives• What is ARD?• Why Microbes?• In-situ Microbial Community Assessment• Choice of tools• Approach: other methods vs PLFA analysis• Results:

PLFABIOLOG ExoenzymesViable CountsCorrelations

• Conclusions

3

Objectives

· To determine the ecological impact of ARD using PLFA analysis technique

· To determine the relationships between the microbial community structure and a gradient of water and sediments quality parameters

· To validate/compare the PLFA technique against other methods

4

• Results when the mineral pyrite (FeS2) is exposed to air and water, resulting in the formation of sulfuric acid and iron hydroxide

• FeS2 + 3.75 O2 + 3.5 H2O Û Fe(OH)3 + 2 H2SO4

• The products: acidity and iron, can devastate water resources by lowering the pH and coating stream bottoms with iron hydroxide, forming the familiar orange colored "yellow boy" common in areas with abandoned mine drainage.

What is Acid Rock Drainage (ARD)?

5

Yellow Boy

6

Why Microbes?

• Need to measure across different trophic levels

• Suitable for sediments and water

• Rapid - less labour intensive

• Logistics of repeat sampling

• Response time / sub-lethal effects

• Public perception

7

In the Environment < 1.0 to 0.1% of the in-situ microbial community is detected using Isolation and Classical Plate Count

Many non-culturable organisms can be infectious, isolation can take days, lose insight into community interactions & physiology

Two Complimentary Biomarker Methods:

DNA: Recover from surface, Amplify with PCRusing rDNA primers , Separate with DGGE, sequence for identification and phylogenetic relationship. Great specificity

Lipids: Extract, concentrate, structural analysisQuantitative, Insight into: viable biomass, community composition, Nutritional-physiological status, evidence for metabolic activity

In-situ Microbial Community Assessment

8

Tools Selected

• Chemical »PLFA - Primary»Polyhydroxyalkanoates (PHA’s)» Isoprenoid Quinones

• Growth»BIOLOG®

»Agar Plates• Activity

»Exoenzymes

9

Lipid Biomarker Analysis

10

What are Phospholipids?

• Phospholipids are essential components of the microbial cell membrane

11

Structure of the lipid bi-layer

12

Membrane Liability (turnover)

VIABLE NON-VIABLE

O O || ||

H2COC H2COC

| |C O CH C O CH

| |

H2 C O P O CH2CN+ H3

||

|

O

O-

||O

H2 C O H

||O

Polar lipid, ~ PLFA

Neutral lipid, ~DGFA

phospholipase

cell death

• Rapid turnover Provides biomarkers for viable biomass

13

• Sufficiently complex to provide biomarkers for

viable biomass, community composition

nutritional/physiological status

• Found in reasonably constant amounts in

bacterial cells as they occur in nature

PLFA Analysis

14

• Lipids can be quantitatively extracted using simple

methods

• The PLFAs are separated from other lipids using

column chromatography

• The PLFAs are converted to fatty acid methyl esters

(FAMEs) and quantified using GC-MS

• The relative abundance of each FAME is calculated

Experimental Approach

15Q uinones

O ptiona l:H PLC

N eutra l L ip ids

C hloroform E lua te

O ptiona l:Hydrolys is

D eriva tis a tionG C

G lycolip ids

Acetone E lua te

H ydrolysisD eriva tis a tion of O H-F A M E sInte rna l s tandards addition

G C /MS

G C ca libra tionusing B AME sta nda rds

Phospholip ids

Metha nol E lua te

S ilic ic Acid C olum n

Modified B ligh & D yer Extra ction

Sa m ple (40 g)

Lipid Extraction

16

GC-MS analysis

17

• Pure culture studies, mixed enrichment cultures and manipulative lab and field experiments established the link between groups of microbes and specific PLFAs

• We group together suites of microbes that share biochemical characteristics. ie. eukaryotes vs prokaryotes

How Can We Analyse the Microbial Community Structure?

18

Functional Group Approach

Functional Group Fatty Acid(s)

Microeukaryotes 16:4ω1, 16:3, 18:4ω3, 18:3ω3,20:3ω6, 20:4ω6, 20:5ω6, 22:6ω3

Aerobic prokaryotes and eukaryotes 16:1ω3, 16:1ω7, 17:1ω9, 18:1ω7c,18:1ω9, 18:1ω6

Gram-positive prokaryotes and 14:0, a15:0, i15:0, 15:0, i16:0, other anaerobic bacteria (except16:1ω3t)

16:1ω13t

Sulfate-reducing bacteria and other 16:0, 10Me16:0, a17:0, i17:0,aerobic prokaryotes cy17:0, 17:0, 18:0, cy19:0a Findlay et al

19

BIOLOG® (Carbon Utilisation Assay)

20

• BIOLOG plates are 96 well microplates containing multiple carbon substrate

• Each well contains a carbon substrate and a dye which produce a violet colour on oxidation of the substrate

• A measure of the functional ability is obtained with the quantification of the colour formation through absorbance measurement

BIOLOG® (Carbon Utilisation Assay)

21

Microbial Exoenzymes’ Activity

• In order to utilise macromolecules, microbes produce extracellular enzymes

• The enzymes hydrolyse organic material into monomeric compounds that can be transported across the cell membrane

• Exoenzymes’ activity can be measured using spectrofluorometric technique

• This enables the determination of microbial activity and productivity

22

Microbial Exoenzymes’ Activity

Macromolecular Organic Matter (dissolved and particulate)

polysaccharides proteins Porganic

Gluconases e.g. β-glucosidase

Peptidases e.g. leucine

aminopeptidase

Phosphatases

sugars Pi Amino acids

Microbial Growth & respiration

Heterotrophic microorganisms

(bacteria and fungi)

Heterotrophic bacteria

Phytoplankton, algae, bacteria, zooplankton, protozoans

Utilisation of different components of organic matter by three classes of exoenzymes whose activity was investigated and their corresponding functional groups

23

Brukunga Mine Site• The Brukunga pyrite mine

site is located north of Nairne in the Adelaide Hills of South Australia

24

Map of field sites in the Dawesley catchment

• ARD from the sulfide waste rock and tailing dam drain into Dawesly Creek

• Other insults to the system include:– treated sewage – Agricultural and

rural/ urban run-off

– dry-land salinity

25

Statistical Analysis of Biological (PLFA) and Chemical Data (Water

and Sediments)

26

PLFAs are treated as individual species rather than biomarkers of functional groups

Principal Component Analysis (PCA) can be used to summarise the large number of variables in the data set

RDA is a constrained ordination technique based on PCA which enables the assessment of the relationship between environmental data and the variation in the PLFAs’ profiles

the length of the arrow is a measure of fit(R) with the ordination diagram; The arrow points in the direction in which species abundance value increase at the largest rate

Multivariate Statistical Analysis

27

Spring 98: PCA’s and RDA’s of Water and Sediments

Sediments PCA

Water PCA

RDA with PLFA data

28

Summer 99: PCA’s and RDA’s of Water and Sediments

Water PCA

Sediments PCA RDA

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• multivariate analysis of PLFA relative abundance data clustered sample sites into distinct groups that corresponded with both the water and sediment based ordinations of sites

• The chemical and biological impacts of ARD downstream Brukunga Mine was limited to 14.5 km in the dry summer period but extended as far as 22 km downstream in other seasons. In contrast ARD impact downstream the post rehabilitated Rum Jungle Mine was limited to the first 3 km

Multivariate Analysis - Summary

30

Summer 99 Spring 98

Redundancy analysis biplot of PLFA relative abundance. In order to simplify the ordination, only PLFA species fit range ≥ 30% were labelled. Notation: ●, indicates a site located downstream of the mine; ▲, indicates a site located above the mine or along a tributary.

Specific PLFA Biomarkers of ARD

31

• PCA of PLFA’s relative abundance revealed a number of PLFA species which were most influential in discriminating between ARD polluted sites and the remaining sites

• These PLFA included the Gram -ve markers: 2OH12:0, 3OH12:0, 3OH14:0; the fungal marker: 18:2ω6 and Acidithiobacillus markers 10me16:1 and 10me18:1

Specific PLFA Biomarkers of ARD - Summary

32

Total PLFA as a Measure of the Total Microbial Biomass

33

0.00

2.00

4.00

6.00

8.00

10.00

12.00C

11:0

C12

:0

C13

:0

C14

:0

C15

:0

C16

:0

C17

:0

C18

:0

C19

:0

C20

:0

i15:

0

a15:

0

i16:

0

i17:

0

2OH

10:0

2OH

12:0

2OH

14:0

2OH

16:0

3OH

12:0

3OH

14:0

10M

e16:

0

10M

e17:

0

10M

e18:

0

10M

e16:

1w7

10M

e18:

1w9

16:1

w7

16:1

w7t

17:1

w7

18:1

w7

18:1

w11

18:1

w9

18:2

w6

18:3

w6

18:3

w3

18:4

w3

20:4

w6

20:5

w3

22:1

w9

Type of FAME

Con

cent

ratio

n (n

mol

/g)

P4 P6 R9

0.0

5.0

10.0

15.0

20.0

25.0

30.0

C11

:0

C12

:0

C13

:0

C14

:0

C15

:0

C16

:0

C17

:0

C18

:0

C19

:0

C20

:0

i15:

0

a15:

0

i16:

0

i17:

0

2OH

10:0

2OH

12:0

2OH

14:0

2OH

16:0

3OH

12:0

3OH

14:0

10M

e16:

0

10M

e17:

0

10M

e18:

0

10M

e16:

1w7

10M

e18:

1w9

16:1

w7

16:1

w7t

17:1

w7

18:1

w7

18:1

w11

18:1

w9

18:2

w6

18:3

w6

18:3

w3

18:4

w3

20:4

w6

20:5

w3

22:1

w9

Type of FAME

Rel

ativ

e C

once

ntra

tion

(mol

%)

P4 P6 R9

Replication and Reproducibility

Absolute Abundance (nmole PLFA/ g Dry wt)

Relative Abundance (mole %)

34

• The data provides us with a wealth of info.

• Absolute abundance is considerably higher in the reference sites

• Relative abundance indicates that the main variations in PLFA profiles are confined to specific fatty acids

Replication and Reproducibility - Summary

35

0

5

10

15

20

25

PB MS GS DB DBN MB BR NC

Site

To

tal

PL

FA

(n

mo

l/g

)

8.2

8.4

8.6

8.8

9

9.2

Lo

g n

um

ber o

f cells

Downstream DC

Total PLFA / Total Microbial Biomass – Spring vs Summer

0

10

20

30

40

50

60

2 3 MS 4 6 7 8u/s 8 12 14 16 17 19 20 9 15 18Site

Tota

l PLF

A (n

mol

/g)

7

7.5

8

8.5

9

9.5

10

Calculated biomass (log num

ber of cells)

Nov-98

Feb-99

Downstream DC Ref.

- 2

- 0.10.1

6.3

14.5

14.6

22

24

27

2833

2.5 180km

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• Total PLFA concentration, which is indicative of the total microbial biomass, showed marked spatial and seasonal variation during the five-year study period.

• Sites further downstream of the mine were characterised by lower biomass despite their improved water quality, compared with more proximal sites

• This elevated biomass was attributed principally to more favourable conditions for growth of acidophilic prokaryotes and eukaryotes immediately below the mine

Total Biomass - Summary

37

• High biomass levels in Summer 1999 appear to correlate with the unique and unusually high concentrations of total soluble Fe and SO4

2- along MDC sites

Total Biomass - Summary

0

2

4

6

8

2 4 6 14 16 17 19 20 9 15 18

Site

Fe

(mg

/L)

for

No

v-98

, Ju

n-9

9,

Jun

-01,

an

d J

an-0

2

0

100

200

300

400

500

Fe (m

g/L

) for F

eb-1999

Nov-98

Jun-99

Jun-01

Jan-02

Feb-99

Downstream DC Ref.

0

1500

3000

4500

6000

7500

2 4 6 14 16 17 19 9 15 18Site

SO

4 (m

g/L

)

Nov-98

Feb-99

Jun-99

Sep-00

Jun-01

Downstream DC Ref.

38

The Microbial Community StructureNov. 98 Feb. 99

Sep. 00

Jul. 99

0

10

20

30

40

50

I II III IV

Microbial group

Fu

nct

ion

al g

rou

p (

%)

Reference

MDC

LDC

0

10

20

30

40

50

I II III IV

Microbial groupF

un

ctio

nal

gro

up

(%

)

Reference

MDC

LDC

Temporal changes in the relative abundance of microbial functional groups in sediments (1998-2002): I, microeukaryotes; II, aerobic prokaryotes and eukaryotes; III, Gram-positive and other anaerobic bacteria; IV, SRB and other anaerobic prokaryotes. MDC, sites along middle Dawesley Creek (MS-DBN); LDC, sites along the lower part of Dawesley Creek (MB-BR); Reference, reference sites PB and NC

0

10

20

30

40

50

I II III IV

Microbial group

Fu

nct

ion

al g

rou

p (

%)

Reference

MDC

0

10

20

30

40

50

60

I II III IV

Microbial group

Fu

ncti

on

al

gro

up

(%

)

Reference

MDC

LDC

 

0

10

20

30

40

50

I II III IV

Microbial group

Fu

ncti

on

al

gro

up

(%

) Reference

MDC Jan. 02

39

• Composition: Gram -ve prokaryotes followed by lower proportions of Gram-positive prokaryotes and minute proportions of microeukaryotes and SRB.

• In addition, high proportions of PLFA biomarkers consistent with the presence of Acidithiobacillus sp. were found at sites immediately downstream of Brukunga Mine.

• The fungal markers were notably elevated just below Brukunga Mine compared with the reference sites

The Microbial Community Structure - Summary

40

Correlation With Other Methods

41

PLFA vs Macroinvertebrates

0

5

10

15

20

25

PB GS DB DBN MBC BR NCSite

To

tal

PL

FA

(n

mo

l/g

)

0

1

2

3

4

5

Ma

cro

inv

erte

bra

tes

(no

. of s

pe

cie

s/1

0)

Macroinvertebrates (average 1996-1998)total PLFA (nmol/g)Maroinvertebrates (Sep-98)Macroinvertebrates (Dec-98)

Downstream DC

Comparison of mean macroinvertebrates species richness at each site (September 1996 to December 1998 average, September 1998, and December 1998) with PLFA based cells estimate

42

0.1

1.1

2.1

3.1

4.1

5.1

6.1

2* 3* 4 7 8u/s 14 16 9* 15* 18*

Mill

ions

Site

Bio

mas

s es

timat

es (P

LFA

): ba

cter

ia/8

00 &

fung

i/60

0.1

2.1

4.1

6.1

Millions

(Fungi CFU

/ml)x 500 &

bacteria C

FU/m

lPLFA based estimate (Bacterial cells/800)18:2w6 (fungal cells/60)Culturable Fungi (x 500)Culturable Bacteria

Downstream DC Ref.

Total PLFA vs Viable Count

0.0E+00

3.0E+06

6.0E+06

9.0E+06

1.2E+07

2 3 4 5 6 7 8 9

pH

Bact

eria

(CFU

/ml)

0.0E+00

3.0E+03

6.0E+03

9.0E+03

1.2E+04

1.5E+04

Fungi (CFU/ml)

R2A RBG

Comparison of bacterial and fungal viable counts with PLFA based bacterial and fungal cell estimates (a); and (b) number of bacteria (CFU/ml) and fungi (CFU/ml) relative to pH

(b)(a)

43

• The results suggest that bacterial counts using the viable count method was about 2-3 orders of magnitude lower compared with the PLFA based biomass estimates throughout the study area.

• With viable counts, microbial cell numbers peaked at a near neutral pH. It is suggested, therefore, that the viable counts method fails to enumerate microorganisms with growth requirements that do not favour neutral pH, which may represent a significant component of the community structure.

Total PLFA vs Viable Count - Summary

44

0.0E+00

1.0E+09

2.0E+09

3.0E+09

4.0E+09

2* 3* 4 7 8u/s 14 16 9* 15* 18*

Site

Cal

cula

ted

bio

mas

s (P

LFA

)

0.05

0.45

0.85

1.25

Averag

e AW

CD

oxid72hrs assim72hrs

PLFAGN48hrs

Downstream DC Ref.

Total PLFA vs BIOLOG AWCD Values

Comparison of mean GN AWCD at 48 hours for GN microplates and YT AWCD at 72 hours for YT microplates with PLFA based cells estimate. Sites above mine and along external tributaries are indicated with an asterisk (*) character

45

• Multivariate analyses of the data produced through the BiologTM and PLFA analyses gave highly similar results

• The fact that two completely different methods were in good agreement with each other support the conclusion that the microbial community changed in response to ARD/salinity.

• Since one method provides structural data and the other functional data, the two methods are complementary.

• AWCD values were not correlated with the microbial biomass

• This was not surprising since the BIOLOG assay does not measure the activity of autotrophs or anaerobic microbes

Total PLFA vs BIOLOG - Summary

46

Total PLFA vs Microbial Enzymatic Activities

1.0E+07

5.1E+08

1.0E+09

1.5E+09

2.0E+09

2.5E+09

3.0E+09

3.5E+09

2* 3* 4 7 8u/s 8 14 16 9* 18*

Site

PLFA

bas

ed m

icro

bial

cel

ls

estim

ate

0

5

10

15

20

25

30

35

40

Exoglucanase (nmol M

UF released/m

in.g)

Cell numbers estimate Exoglucanase

Downstream DC Ref.

0

5

10

15

20

25

30

35

40

45

2* 4 9* 18*

SiteA

ctiv

ity (n

mol

/min

/g)

0.0E+00

5.0E+08

1.0E+09

1.5E+09

2.0E+09

2.5E+09

3.0E+09

PLFA

cells estimate

Phosphatase Leucine aminopeptidase

Glucosidase PLFA

0

20

40

60

80

100

120

2 3 4 5 6 7 8 9 10pH

% A

ctiv

ity

2

4

9

0

20

40

60

80

100

120

2 3 4 5 6 7 8 9 10pH

% A

ctiv

ity

2

4

6

9

18

0

20

40

60

80

100

120

2 3 4 5 6 7 8 9 10pH

% A

ctiv

ity

4

9

phosphatase β-glucosidase aminopeptidase

0.0E+00

5.0E+08

1.0E+09

1.5E+09

2.0E+09

2.5E+09

3.0E+09

2* 3* 4 7 9*

Site

Calc

ulat

ed m

icro

bial

bio

mas

s

0

2000

4000

6000

8000

10000

12000

DMS (area)

DMS

PLFA

47

Total PLFA vs Microbial Enzymatic Activities

0

20

40

60

80

100

120

140

EB

8A

AB

8B

FC

B

WO

EB

6

TC

P

EB

4U

EB

2

HS

EB

4S

LF

RB

0

2

4

6

8

10

12

14

16PhosphataseTot. PLFAB-Glucosidase

Rum Jungle Mine, Australian Northern Territories

48

• Advantage - useful measurement as it provides info. regarding the physiological status of mixed microbial communities relevant to biogeochemical cycling and ecosystem function

• Drawback - Unable to provide information about the community structure in terms of numbers, the types of microorganisms or the specific fraction of the total number engaged in respiration.

• Phosphatase and β-glucosidase from the ARD impacted sites had a lower pH optima (pH = 4) compared with the reference sites (pH = 5-6).

• This indicates that ARD impacted sediments contained a mixed microbial population composed of acidophilic, heterotrophic microorganisms, bacteria and/or fungi which were adapted to the acid conditions.

Microbial Enzymatic Activities - Summary

49

General Conclusions

• PLFA analysis was successfully applied to rapidly assess the toxicity of ARD affected sediments and to differentiate this response from the effect of other pollutants, viz increased nutrients and salinity

• PLFA profiling is sensitive enough to monitor even moderate levels of pollution (I.e. post rehabilitated East Branch of Rum Jungle)

• Particularly useful when the PLFA’s relative abundance was analysed by multivariate statistics

50

• The study demonstrated that monitoring and analysing sediment microbial communities under environmental perturbations requires an integrated and polyphasic approach using a range of techniques, both biological and chemical

• The results suggest that total microbial biomass may not correlate well with measures that rely on growth.

• Activity measures, however, may better predict the microbial biomass in moderately polluted ecosystems such as Rum Jungle

General Conclusions

51

• The “response” of the microbial community was a consequence of the specific component of the microbial community that each technique was able to detect

• Measures of total biomass may not be very useful for the assessment of heavy metal effect on the dynamics of microbial communities of ARD impacted sediments

General Conclusions

52

• Many thanks to:–Dr. Peter Holden (ANSTO)–Dr. John Foster (UNSW)–Dr. David Stone (ANSTO)–Dr. John Ferris (ANSTO)–Rob Russel –Karyn Wilde

Acknowledgments