1. Analysis of Microbial Communities in Soils Kate M. Scow Dept. of Land, Air and Water Resources University of California, Davis

2. Outline I. Soil microbial diversity II. Overview of approaches for characterizing soil microbial communities III. Analysis of phospholipid fatty acids What factors control community composition? IV. Analysis of nucleic acids (DNA) by fingerprinting Relationship between microbial community composition and plant species V. Emerging technologies VI. Future directions

3. Soil is extraordinarily diverse Most diverse environment? 1000-5000 “species” per g of soil; most (95-99%) have not been cultured. Why so diverse? EPhysical/heterogeneity of soil creating many gradients in factors that affect microbes ECapacity of soil to entomb and preserve microbial life EVariety of growth substrates--mineral and organic

4. Members of the soil community and their scales. 1 um 50 um 1000 um ROOTS FAUNA FUNGI BACTERIA PROTOZOA VIRUSES SAND SILT CLAY Types and scales of organisms in soil Adapted from Munns, 1998

5. Actinomycetes coils on rotting corn cob. DECOMPOSING ORGANIC MATTER

6. Close-up of actinomycetes on corn cob.

7. Filaments colonizing a rotting corn cob.

8. Relative sizes of bacteria and fungi on oats

9. Group of rod-shaped bacterial cells in chains and coils.

10. Close-up of bacterial cells on surface of an aggregate.

11. A nematode wrapped around a plant fiber.

12. The “lasso” of a nematode-trapping fungus.

13. Backside view of mite.

14. Mite caught foraging in decomposing corn cob.

15. Close-up of mouth part on mite.

16. Entering the C cycle: mite (deceased) devoured by hyphae

17. HOW DO WE MEASURE MICROORGANISMS IN SOIL? We can count, measure activity, analyze cellular constituents 1. COUNTS : GIVES NUMBERS; DISTINGUISHES MAJOR GROUPS; e.g. by plate counts, microscope 2. ACTIVITY : REFLECTS “WORK” COMMUNITY CAN DO: ACTUAL OR POTENTIAL; e.g., respiration, nitrification.

18. 3. CELLULAR CONSTITUENTS (guts) C, N, P: AMOUNT OF NUTRIENTS CONTAINED IN MICROBIAL TISSUES; e.g., fumigation extraction LIPIDS GIVE COMMUNITY FINGERPRINT AND DISTINGUISH DIFFERENT GROUPS; e.g., PLFA analysis DNA GIVES COMMUNITY FINGERPRINT OR DISTINGUISH INDIVIDUAL SPECIES (pathogens, inoculants); e.g., DNA probes, fingerprint methods.

19. Bacterial Cell Structures Pilus Flagellum Nucleoid Cell Wall Cell Membrane Ribosomes Inclusion Sylvia, 1999

20. FATTY ACID ANALYSIS

21. Phospholipids Variety of PLFAs especially in bacteria Cell membrane

22. Fatty Acid Analysis MIDI Technical Note #101 1. Phospholipid Fatty Acid (PLFA) analysis targets membrane components of living organisms more selective, cleaner chromatography 2. Total Fatty Acid Methyl Ester (FAME) analysis includes all fatty acids such as storage compounds,dead organic matter Or soil sample ~3 - 8 g surface soil

23. Peat Soil Silty clay loam (surface) Silty clay loam (subsurface) Examples of PLFA gas chromatography data. 1. Each peak is a PLFA. Certain peaks are unique to specific microbial groups 2. Mass of all peaks gives estimate of biomass. 3. All peaks together make fingerprint of that community. 5 10 20

24. Microbial Communities of Agricultural Soils 1. Do agricultural soils differ in their microbial communities? 2. What factors most strongly influence a community’s composition, e.g, crop? soil texture? season? management? Method: phospholipid fatty acid analysis (PLFA)

25. Sustainable Agriculture Farming Systems Project (SAFS)--Originated 1988 ROTATION SEQUENCE TOMATO--SAFFLOWER--CORN--WHEAT/BEAN MANAGEMENT SYSTEMS CONVENTIONAL 4-YR: Four year rotation using mineral fertilizer and pesticides as needed (also CONV 2-YR w/tomato-wheat/bean rotation). LOW-INPUT: Four year rotation using cover crops supplemented with mineral fertilizer and pesticides as needed. ORGANIC: Four year rotation using cover crops and manure and no synthetic pesticides or chemicals.

26. ARE THERE DIFFERENCES AMONG MICROBIAL COMMUNITIES? 1. Microbial biomass and activity usually greater in organic than conventional systems ORGANIC = LOW INPUT CONV 4 = CONV 2 2. Composition differs among farming systems: low input is intermediate between organic and conventional organic and low input become more similar after cover crop input conventional and low input become more similar after side-dressing

27. ARE THERE DIFFERENCES AMONG MICROBIAL COMMUNITIES? 3. Differences associated with farming systems are smaller than differences associated with crop (e.g., organic and conv. tomatoes are more similar to each other than are organic corn and safflower communities). Composition differs among crops: Corn and tomatoes are most similar to one another Safflower and wheat/beans are each uniquely different from corn and tomatoes

28. ARE THERE DIFFERENCES AMONG MICROBIAL COMMUNITIES? (cont) 4. Changes over the season are greater than differences between systems and crops. 5. There is cyclic pattern to community composition at annual scale. E.g., winter vs spring vs summer patterns. 6. Differences between microbial communities at other locations and SAFS are greater than differences within the SAFS communities. (but there are similarities across locations, e.g. Davis and Fresno, with same soil types and crops)

29. Each circle contains organic, low input and conventional soils, which move together over time, though still main- taining differences from one another. Tomatoes, 1995

31. NUCLEIC ACID BASED METHODS DNA Fingerprinting

32. Variations in DNA sequences for specific regions of the 16S or 18S rRNA are used to identify organisms

33. Universal Phylogenetic Tree (Sylvia et al, 1999) 3 DOMAINS Work of Carl Woese ARCHAEA BACTERIA EUCARYA Animals, plants and fungi (oh my)

34. Extracting nucleic acids from soil

35. DNA FINGERPRINTING Gel 1 2 3 Environmental DNA samples 1. DNA extracted from environmental samples 2. Specific sequences amplified by universal or spec. primers via PCR (e.g., all bacteria, individual strains) 3. Bands of individual strains separated via electrophoresis by: a)sequence: DGGE b)size: ITS with bacterial primers, T-RFLP 4. Band roughly = species or strain  Compare banding patterns between samples - similarity - diversity  Look for specific band (i.e. sequence)  Clone and sequence bands: Who is it?

36. PCR

37. Objective 1. Develop method to amplify and compare DNA fingerprint patterns (of microbial populations) in potential sources of dust and in respirable dust. Objective 2. Determine if soil sources can be differentiated based on their fingerprints. 47 agricultural soil samples and adjacent land uses. Objective 3. Compare fingerprints of dust generated under controlled conditions to their source soils. CA Air Resources Board Project

38. (Source: www.arb.ca.gov)

39. 1000 bp 500 bp 20 Base Pair DNA Ladder Cotton Tomato Almond Bacterial ITS Fingerprints ITS Intergenic Transcribed Spacer Region (targets between 16S and 23S ribosomal DNA sequence) Analysis ~1 g surface soil

40. Grapes Safflower Cotton-silt loam Cotton-sandy loam Tomatoes Cotton-clay loam Various crops on clay soils Almonds Soil samples from top 10 cm of ag fields in San Joaquin Valley Bacterial ITS fingerprints of soils

41. Environmental variables most strongly correlated with patterns of community composition were: Soil electrical conductivity Soil texture Inorganic C Nitrogen content Not organic C, pH Soil samples grouped in same groupings based on soil reflectance properties (spectral analysis)

42. T-RFLP Terminal Restriction Enzyme Fluorescent Length Pattern

43. T-RFLP Patterns of Groundwater Collected Up and Down Gradient From a Bioreactive Barrier. Digitized data From Tiedje et al

44. CONCLUSIONS Methods rapidly evolving (and going extinct!) to explore soil diversity: environmental microarrays are on horizon ; emphasis on massive, fast throughput approaches MUST archive samples--frozen in -80 ideal Enormous challenge is data analysis Major thrust is to link “who is it” with “what does it do” (structure to function)

45. Krassi Hristova Mamie Inoue Geoff Elliott Mary Ann Bruns Jim Rowe Margaret Edwards Ken Graham Binyam Gebreyssus Jenn Macalady Mara Johnson Erica Lundquist Debby Bossio Nirmala Gunapala Kit Batten Shira Bell Yutaka Okano Postdocs, Grad Students, Technicians and Undergrads involved…... Nymph Chan, John Leung, Ryogo Sugitani, Lavina Loveless, Sarah Adamson, Angela Maroney