2. 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)

3. 2 Outline Objectives What is ARD? Why Microbes? In-situ Microbial Community Assessment Choice of tools Approach: other methods vs PLFA analysis Results:  PLFA  BIOLOG  Exoenzymes  Viable Counts  Correlations Conclusions

4. 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

5. 4 Results when the mineral pyrite (FeS 2 ) is exposed to air and water, resulting in the formation of sulfuric acid and iron hydroxide FeS 2 + 3.75 O 2 + 3.5 H 2 O  Fe(OH) 3 + 2 H 2 SO 4 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)?

6. 5 Yellow Boy

7. 6 Why Microbes? Need to measure across different trophic levelsNeed to measure across different trophic levels Suitable for sediments and waterSuitable for sediments and water Rapid - less labour intensiveRapid - less labour intensive Logistics of repeat samplingLogistics of repeat sampling Response time / sub-lethal effectsResponse time / sub-lethal effects Public perceptionPublic perception

8. 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 PCR using rDNA primers, Separate with DGGE, sequence for identification and phylogenetic relationship. Great specificity Lipids: Extract, concentrate, structural analysis Quantitative, Insight into: viable biomass, community composition, Nutritional-physiological status, evidence for metabolic activity In-situ Microbial Community Assessment

9. 8 Tools Selected Chemical »PLFA - Primary »Polyhydroxyalkanoates (PHA’s) »Isoprenoid Quinones Growth »BIOLOG ® »Agar Plates Activity »Exoenzymes

10. 9 Lipid Biomarker Analysis

11. 10 What are Phospholipids? Phospholipids are essential components of the microbial cell membrane

12. 11 Structure of the lipid bi-layer

13. 12 Membrane Liability (turnover) VIABLENON-VIABLE OO || H 2 COC || C O CH | | H 2 C O P O CH 2 CN + H 3 || | O O-O- O H 2 C O H || O Polar lipid, ~ PLFA Neutral lipid, ~DGFA phospholipase cell death Rapid turnover  Provides biomarkers for viable biomass

14. 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

15. 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

16. 15 Lipid Extraction

17. 16 GC-MS analysis

18. 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?

19. 18 Functional Group Approach

20. 19 BIOLOG ® (Carbon Utilisation Assay)

21. 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)

22. 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

23. 22 Microbial Exoenzymes’ Activity Utilisation of different components of organic matter by three classes of exoenzymes whose activity was investigated and their corresponding functional groups

24. 23 Brukunga Mine Site The Brukunga pyrite mine site is located north of Nairne in the Adelaide Hills of South Australia

25. 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

26. 25 Statistical Analysis of Biological (PLFA) and Chemical Data (Water and Sediments)

27. 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

28. 27 Spring 98: PCA’s and RDA’s of Water and Sediments Sediments PCA Water PCA RDA with PLFA data

29. 28 Summer 99: PCA’s and RDA’s of Water and Sediments Water PCA Sediments PCARDA

30. 29 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

31. 30 Summer 99Spring 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

32. 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

33. 32 Total PLFA as a Measure of the Total Microbial Biomass

34. 33 Replication and Reproducibility Absolute Abundance (nmole PLFA/ g Dry wt) Relative Abundance (mole %)

35. 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

36. 35 Total PLFA / Total Microbial Biomass – Spring vs Summer

37. 36 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

38. 37 High biomass levels in Summer 1999 appear to correlate with the unique and unusually high concentrations of total soluble Fe and SO 4 2- along MDC sites Total Biomass - Summary

39. 38 The Microbial Community Structure Nov. 98Feb. 99 Sep. 00 Jul. 99 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 Jan. 02

40. 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

41. 40 Correlation With Other Methods

42. 41 PLFA vs Macroinvertebrates 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

43. 42 Total PLFA vs Viable Count 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)

44. 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

45. 44 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

46. 45 Multivariate analyses of the data produced through the Biolog TM 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

47. 46 Total PLFA vs Microbial Enzymatic Activities phosphataseβ-glucosidaseaminopeptidase

48. 47 Total PLFA vs Microbial Enzymatic Activities Rum Jungle Mine, Australian Northern Territories

49. 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

50. 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

51. 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

52. 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

53. 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