Phospholipid Fatty Acid Analysis to measure the Impact of acid rock drainage on microbial communities in sediment and comparison with other measures,

1. 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. BIOLOG

4. Exoenzymes

5. Viable Counts

6. CorrelationsConclusions

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

10. 5 Yellow Boy

11. 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

12. 7 In-situ Microbial Community Assessment 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

13. 8 ToolsSelected Chemical PLFA - Primary Polyhydroxyalkanoates (PHA’s) Isoprenoid Quinones Growth BIOLOG® Agar Plates Activity Exoenzymes

14. 9 Lipid Biomarker Analysis

17. 13 PLFA Analysis 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

18. 14 Experimental Approach 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

19. 15 Lipid Extraction

20. 16 GC-MS analysis

21. 17 How Can We Analyse the Microbial Community Structure? 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

22. 18 Functional Group Approach

23. 19 BIOLOG® (Carbon Utilisation Assay)

24. 20 BIOLOG® (Carbon Utilisation Assay) 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

25. 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

26. 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

27. 23 Brukunga Mine Site The Brukunga pyrite mine site is located north of Nairne in the Adelaide Hills of South Australia

28. 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

29. 25 Statistical Analysis of Biological (PLFA) and Chemical Data (Water and Sediments)

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

32. 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

34. 28 Summer 99: PCA’s and RDA’s of Water and Sediments Water PCA Sediments PCA RDA

39. 33 Replication and Reproducibility Absolute Abundance (nmole PLFA/ g Dry wt) Relative Abundance (mole %)

40. 34 Replication and Reproducibility - Summary 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

41. 35 Total PLFA / Total Microbial Biomass – Spring vs Summer

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

45. 38 The Microbial Community Structure Nov. 98 Feb. 99 Jul. 99 Jan. 02 Sep. 00 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

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

49. 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

50. 42 Total PLFA vs Viable Count (b) (a) 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

53. 45 Total PLFA vs BIOLOG - Summary 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

54. 46 Total PLFA vs Microbial Enzymatic Activities phosphatase β-glucosidase aminopeptidase

55. 47 Total PLFA vs Microbial Enzymatic Activities Rum Jungle Mine, Australian Northern Territories

56. 48 Microbial Enzymatic Activities - Summary 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.

57. 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

58. 50 GeneralConclusions 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

59. 51 GeneralConclusions 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

60. 52 Acknowledgments Many thanks to: Dr. Peter Holden (ANSTO) Dr. John Foster (UNSW) Dr. David Stone (ANSTO) Dr. John Ferris (ANSTO) Rob Russel Karyn Wilde