1. The document discusses two complimentary methods, DNA-based DGGE and lipid biomarker analysis, for assessing soil microbial communities. 2. It provides examples of how these methods have been used to study microbial community structure and diversity across different soil environments and conditions. 3. Specifically, it summarizes two studies that used these methods: one found evidence of "microbial diversity patchiness" correlated with vegetation in Negev desert soils, while the other found bacterial community succession along a desert rainfall gradient.

1. 1Choice of methods for soil microbial community analysisEric Ben-DavidEnvironment Division, Australian Nuclear Science and Technology Organisation (ANSTO)School of Biotechnology and Biomolecular Sciences, University of New South Wales (UNSW)

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3. 3Why Soil Microbes are Important? Soil microbes play pivotal roles in various biogeochemical cycles (BGC) and are responsible for the cycling of organic compounds. Soil microorganisms also influence above-ground ecosystems by contributing to plant nutrition, plant health, soil structure and soil fertility

4. 4What do we know about them? Why we do not know nothing about 95-99% of microbes?

5. Most looks similar under light microscope – difficult to group by simple shape criteria

6. Problematic to find suitable growing conditions for different microbes

7. Some will grow slowly, some will not grow in lab

8. Those, who grow easily, may not represent the major fraction of the studied community5In-situ Microbial Community AssessmentTwo Complimentary Biomarker Methods:DNA: Recover from surface, Amplify with PCRusing rDNA primers , Separate with DGGE, sequence for identification and phylogenetic relationship. Great specificityLipids:Extract, concentrate, structural analysisQuantitative, Insight into: viable biomass, community composition, Nutritional-physiological status, evidence for metabolic activity

9. 6The DGGE Technique Denaturing gradient gel electrophoresis (DGGE) is a nucleic acid based (DNA or RNA) technique which can be used to profile and identify dominant members of the microbial community based on their genetic fingerprint.

10. 7How does it work? Microbial biomass is collected and DNA/RNA are extracted• 16S rRNA genes are PCR amplified and observed on an agarose gel – Separation based on size• The identity of the PCR products (i.e., that of the organisms in the environmental sample) is then determined by sequencing of DGGE bandsResults of sequencing are than subject to phylogenetic analyses:– Who are the environmental bacteria most similar to?– What is the level of this similarity

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12. 9Examples of DGGE analysesLeft: An example of samples obtained from pure cultures• Right: An example from a “real” mixed microbial communities

13. 10Lipid Biomarker Analysis

14. 11What are Phospholipids? Phospholipids are essential components of the microbial cell membrane12Structure of the lipid bi-layer

15. 13Phospholipids have a polar head group and two hydrocarbon tails.saturated fatty acid->←unsaturated fatty acid

16. 14Membrane Liability (turnover)Rapid turnover  Provides biomarkers for viable biomassVIABLENON-VIABLEOO || ||H2COCH2COCOOphospholipase||||||cell deathC O CHC O CH|O|||H2 C O HH2 C O P O CH2CN+ H3|Neutral lipid, ~DGFAO-Polar lipid, ~ PLFA

17. 15PLFA AnalysisDistribution of lipids can be very species specific. Bacteria typically contain odd chain and branched fatty acids as well as cyclopropane and α- or β- derivativesConsequently, profiles based on the composition of phospholipid-linked fatty acids (PLFA) can be used to indicate community structure of bacteria and eucarya but not archaea (because they do NOT have fatty acids in their phospholipids).

18. 16There are many classes of fatty acids.They are designated according to:1. The total number of C atoms 2. Degree of unsaturation (double bonds)3. Position of the double bonds 4. Branching patterns

19. 17Examples16:0 = 16 carbons, no double bonds

20. 18:25 = 18 carbons, 2 double bonds at the 5th position from the aliphatic end

21. a15:0 = 15 carbons, no double bonds with anteiso branching18Some ecologically important patterns have been recognized:Ratio of i15:0 and a15:0 PLFA to 16:0 PLFA is a useful index of the proportion of bacteria and eucarya in the community. Also ratios of trans and cis isomers of saturated to unsaturated fatty acids may indicate physiological conditions of organisms or environmental stress.

22. 19CO2 x AM:amb, -AMamb, +AMele, -AMele, +AMCommunity fingerprintPrinciple Components Analysis (PCA) and cluster analysis can then be used to group microbial communities based upon their similarities:

23. 20Some fatty acids arebiomarkersBacteria = i14:0, i15:0, a115:0, 18:17c, cy19:0

24. Algae = 20:53, 18:33

25. Fungi = 18:26

26. Actinomycetes = 10Me17:0, 10Me18:0

27. Sulfate reducers = i17:1, 10Me16:0

28. Methanotrophs = 16:18c, 18:18c21Experimental ApproachLipids can be quantitatively extracted using simple methodsThe PLFAs are separated from other lipids using column chromatographyThe PLFAs are converted to fatty acid methyl esters (FAMEs) and quantified using GC-MSThe relative abundance of each FAME is calculated

29. 22Lipid Extraction

30. 23GC-MS analysisGas-phase ions are separated according to mass/charge ratio and sequentially detected

31. 24How 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 PLFAsWe group together suites of microbes that share biochemical characteristics. ie. eukaryotes vs prokaryotes

32. 25Example 1Patchiness of microbial community structure in Negev desert soils Question:Does the vegetation patchiness in desert landscapes is also being reflected in the microbial community structure of two sites from two climatic zones in the Negev, Israel?

33. 26Multivariate analysis (PCA) of the PLFA dataAVDATSAYERET SHAKEDZygophyllum dumosum (Zd)Hammada scoparia (Hs)Intershrub patches (ISPA) Noaea mucronata (Nm) Thymelaea hirsute (Th) Intershrub patches (ISPS)

34. 27Redundancy analysis to correlate between PLFA and soil chemistry dataAVDATSAYERET SHAKED

35. 28Conclusionsmultivariate statistics suggest the occurrence of “microbial diversity patchiness” in the Negev desert Gram -ve anaerobe indicators (Cy17:0, Cy19:0) dominated the ISP while the Gram +ve indicators (i15:0, a15:0 and i16:0) were associated with SUC samples Halophyte plants may have a distinct effect influence on the community structure Nitrate, EC and OM have a significant bearing on microbial community structure

36. 29EXAMPLE 2Microbial community succession along a desert rainfall gradient

37. 30BSC have a significant role in desert ecosystems:Influencing overland runoff production, soil moisture content,water infiltration and holding capacityPreventing soil erosion by water or wind, and are responsible for the stabilization of sand dunesImprove soil fertility by production of organic carbon and nitrogen

38. 31QuestionDoes the succesional stage of BSC, as affected by the rainfall gradient, will affect the microbial biomass and community structure and therefore, the ecosystem functioning?32Study sitesBSC samples were collected during winter 2007 from three different sites along the Israeli-Egyptian border comprising a rainfall gradient:Northern point N62 (150-170 mm),N85 (110-120 mm), Southern point N115 (70-90 mm)

39. 33Geomorphological and biophysiological parameters of the biological soil crusts along the rainfall gradient

40. 34PCA ordination of PLFA relative abundance datafrom the three sitesSite 62 and site 115 formed separate clusters