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Choice of methods for soil microbial community analysis

Eric Ben-David

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

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

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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

Why Soil Microbes are Important?

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Why we do not know nothing about 95-99% of microbes? Most looks similar under light microscope –

difficult to group by simple shape criteria Problematic to find suitable growing conditions for

different microbes Some will grow slowly, some will not grow in lab Those, who grow easily, may not represent the

major fraction of the studied community

What do we know about them?

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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

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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.

The DGGE Technique

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• 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 bands• Results of sequencing are than subject to phylogenetic

analyses:– Who are the environmental bacteria most similar to?– What is the level of this similarity

How does it work?

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• Left: An example of samples obtained from pure cultures• Right: An example from a “real” mixed microbial communities

Examples of DGGE analyses

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Lipid Biomarker Analysis

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What are Phospholipids?

• Phospholipids are essential components of the microbial cell membrane

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Structure of the lipid bi-layer

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Phospholipids have a polar head group and two hydrocarbon tails.

saturated fatty acid→

←unsaturated fatty acid

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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

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PLFA Analysis

Distribution of lipids can be very species specific. Bacteria typically contain odd chain and branched fatty acids as well as cyclopropane and α- or β- derivatives

Consequently, 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).

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There 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

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Examples

• 16:0 = 16 carbons, no double bonds• 18:25 = 18 carbons, 2 double bonds at the

5th position from the aliphatic end• a15:0 = 15 carbons, no double bonds with

anteiso branching

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Some 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.

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CO2 x AM:

amb, -AMamb, +AMele, -AMele, +AM

Principle Components Analysis (PCA) and cluster analysis can then be used to group microbial communities based upon their similarities:

Community fingerprint

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Some fatty acids are biomarkers• Bacteria = i14:0, i15:0, a115:0, 18:17c, cy19:0• Algae = 20:53, 18:33• Fungi = 18:26• Actinomycetes = 10Me17:0, 10Me18:0• Sulfate reducers = i17:1, 10Me16:0• Methanotrophs = 16:18c, 18:18c

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• 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

22Q 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

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GC-MS analysis

Gas-phase ions are separated according to mass/charge ratio and sequentially detected

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• 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?

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Example 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?

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AVDAT SAYERET SHAKED

Multivariate analysis (PCA) of the PLFA data

Zygophyllum dumosum (Zd)Hammada scoparia (Hs) Intershrub patches (ISPA)

Noaea mucronata (Nm) Thymelaea hirsute (Th) Intershrub patches (ISPS)

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AVDAT SAYERET SHAKED

Redundancy analysis to correlate between PLFA and soil chemistry data

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Conclusions• multivariate 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

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EXAMPLE 2Microbial community succession along a desert rainfall gradient

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BSC have a significant role in desert ecosystems:

• Influencing overland runoff production, soil moisture content, water infiltration and holding capacity

• Preventing soil erosion by water or wind, and are responsible for the stabilization of sand dunes

• Improve soil fertility by production of organic carbon and nitrogen

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Question

• Does the succesional stage of BSC, as affected by the rainfall gradient, will affect the microbial biomass and community structure and therefore, the ecosystem functioning?

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Study sites

• BSC 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)

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SiteAverage Rainfall amount (mm)

Resistance to pressure (kg cm-2)

InfiltrationRate

(ml min-1)

Polysaccharides (mg g-1)

Protein (mg g-1)

Chlorophyll (a+b)

(mg cm-2)

115 70-90 1.5±0.6 b 11.0±1.0 a 53.4±15.8 c 1.1±0.7 c 0.1±0.1 b

85 110-120 2.5±0.7 ab 9.7±4.5 ab 158.9±44.1 b 3.5±1.7 b 0.8±0.3 a

62 150-170 3.0±0.8 a 7.2±2.7 b 405.6±172.9 a 8.2±2.1 a 0.9±0.2 a

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

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PCA ordination of PLFA relative abundance data from the three sites

• Site 62 and site 115 formed

separate clusters

• The samples of site 85 were

dispersed throughout the diagram

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Relative abundance of PLFA indicator groups

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62 85 115

SitePL

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Significantly higher cyanobacteria in site 115

Significantly higher G+ve in site 62

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115 85 62

DGGE patterns of the three sites

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Ward's cluster analysis of the DGGE banding patterns of the three sites; 62, 85, and 115

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DGGE band

Most similar sp. % similarity Accession number

2.5 Uncultured soil bacterium 87 EU861933

3.3 Phormidium sp. (cyanobacterium)

98 AM398777

6.1 Uncultured Firmicutes sp. 99 EF651204

6.2 Beta proteobacterium 97 AF336359

8.1 Oscillatoria sp. 94 AB074509

8.2 Uncultured soil bactrium 99 EF667395

11.1 Microcoleus vaginatus 99 EF667962

11.2 Microcoleus vaginatus 99 EF667962

12.1 Uncultured bacterium 95 AY647893

14.1 Pseudanabaenaceae cynanobacterium

94 EF654061

Phylogenetic distribution of prominent 16S rRNA gene sequences

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Conclusions• Both methods showed that the northern site

(62) microbial community was significantly different from the southern site (115).

• Site 115 was dominated by the resilient cyanobacteria Microcoleus vaginatus

• However, a shift to a more diverse population as seen in sites 85 and 62 may reflect development in the BSC succesional stage.

• Both methods correlated well with the geomorphological parameters

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• Many thanks to:• Prof Ali Nejidat• Dr Eli Tzaadi

Acknowledgments