1. Metagenomics and biogeochemistry
How do microorganism-driven geochemical cycles affect structure and function of ecosystems?
How do we assess structure and function of ecosystems?
How about starting by relating microbial assemblage composition to biogeochemical parameters and functions?
Can we find predictable relationships? Patterns and scales of variability?
Is metagenomics (e.g. shotgun or large-insert libraries) the best way to assess microbial assemblage composition for such studies?
Are there faster and cheaper ways that permit analysis of many samples?

2. Amplified Ribosomal Intergenic Spacer Analysis (ARISA)
For microbial community fingerprints with high phylogenetic resolution
Start with DNA extracted from a mixed community.
PCR spans rRNA operon, 16S to 23S genes. One tagged primer.
PCR primers
Fluorochrome
16S rRNA gene
23S rRNA gene
Intergenic Spacer
Variable Length
PCR
Fragment Size
Fluorescence
Fragment analysis. Smallest detectable peak ~0.1% of total
Shows exact sizes. Each peak = “Operational Taxonomic Unit.”
Data based, not gel-based Ref: Fisher and Triplett 1999
16S-23S clone libraries to identify most peaks:
Brown, Hewson, Schwalbach & Fuhrman, Envir. Microbiol 2005

3. 16S-ITS Clone Library permits ID from ARISA.
Example:
USC Microbial Observatory
512 clones cover 94% of ARISA peaks
Brown et al. 2005, Envir Microbiol.

4. Quantitation from PCR-based Fingerprinting?
Real comparison: Prochlorococcus, ARISA vs flow cytometry counts
San Pedro Ocean Time
Series 4 year dataset
Note: we use a highly standardized assay, with eukaryotes removed, and measured amounts of DNA
R2=0.86
Flow cytometric counts
% area from ARISA
Fingerprint % area is remarkably proportional to counts.
Also, SAR11 % clones are close to % cells.
Brown, Hewson, Schwalbach & Fuhrman, Envir. Microbiol 2005

5. 7 samples from each of 2 North Pacific Gyre Stations
Replicate 20L samples have very similar ARISA fingerprints
7 samples from each of 2 North Pacific Gyre Stations
Compares OTU proportions OTU Presence/absence only
Hewson et al. Aquat Microb Ecol 2006

6. What is an ARISA OTU? Phylogenetic resolution is about 98% 16S rRNA similarity - comparable to “species” level
Easily determined difference
Brown et al, Env Microbiol 2005

7. Near-surface SAR11 subclades as determined by ITS sequences and lengths

8. Temporal Variability in Bacterioplankton Communities
How fast do communities change?
San Pedro Ocean Time Series
USC Microbial Observatory
Measured Microbial and Oceanographic properties monthly since 2000, at depths to 880 m
Also, daily measurements near USC Wrigley Marine Science Center on Catalina - open water accessible daily by small boat
Follow taxa by ARISA to look for temporal diversity patterns
45 km

9. Relative stability over days at one location (open water, Catalina)
Abundant taxa vary little
Graphs: all OTU over 6 days g
SAR 11
Actinobact
date
Rarer taxa can vary more
Not just “noise” in
measurement a g
Prochlorococcus
Rarest detectable taxa .
CFB g
SAR 11

10. Monthly observations at SPOTS over 4 years showed some taxa clearly had repeatable seasonal patterns.
How about the bacterial community in general?
Brown et al. 2005

11. Predictable Annual Bacterial Community Reassembly
Fuhrman et al., PNAS with Shahid Naeem
171 taxa followed by ARISA over 4.5 years
DFA scores reflect quantitative distribution of taxa via ARISA

12. DFA showed some subsets of bacterial taxa could predict the month of sampling with 100% accuracy.
Multiple Regression with environmental parameters was highly significant (r2 ~0.7)– implies predictability of bacterial communities – even in an open marine system. Different subsets of taxa were predictable from different parameters – implies niches.
Highly repeatable and predictable patterns imply little functional redundancy, contrary to common expectation for bacteria. This refers to combinations of functions in a particular taxon.
Note- Not all taxa were included in the predictable subsets, but most were.
Significant Parameters in MRA
temperature, salinity, nitrite, nitrate, silicate, oxygen, bacterial and viral abundances, bacterial production via leucine and thymidine incorporation, chlorophyll, phaeopigments ARISA richness

13. The taxa that had significant multiple regression coefficients were affected by different parameters – many controlling factors, and different taxa controlled differently (niches).

14. Biogeography on a Global Scale
SeaWiFS
Global survey of bacterioplankton at numerous sites in 3 ocean basins, under Arctic ice cap, and near Antarctica

15. “Things change” Global Diversity Measurements via ARISA
Assemblages clearly vary
“Things change”
Weddell Sea
Singapore
Catalina Island
Great Barrier Reef
Long Island NY
Norwegian Sea
Suva Harbor, Fiji
Villefranche (Med)
Barbados
Gerlache Strait, Antarctica
Deception Is, Antarctica
Coral Sea
Arctic Ocean
New Caledonia
Philippines

16. Bacterioplankton Biogeography LATITUDINAL GRADIENT OF RICHNESS
ARISA measured the same way from 78 samples collected in all seasons and both hemispheres over 10 years (opportunistic sampling)
Diversity generally highest at low latitudes, lowest in polar environments – like animals and plants (in every general biology textbook)
Contrasts sharply with results reported for protists
p<0.005
Highly significant
(p<0.005) as linear regression, rank correlation, or with potential outliers removed

17. Regional Diversity Patterns
Bacterial Community Similarity (via ARISA) vs Distance
NEAR-SURFACE samples
“Mixing” curve between Pacific and Indian Basins?
Hewson et al 2006 Mar. Ecol. Prog. Ser.

18. Deep-Sea (500-3000 m depth) patterns differ with locations and depth.
Cause(s) unknown
North Atlantic 1000m depth samples were in vicinity of Amazon Plume
Pacific *
Pacific *
Hewson et al Limnol. Oceanogr.

19. Example - What does proteorhodopsin do?
Go beyond just observing nature - EXPERIMENTATION
Example - What does proteorhodopsin do?
Does it provide much energy, and help microbial growth, as many assume? Genomics alone can’t answer.
Schwalbach et al. (2005 Aquat. Microb. Ecol. 39: 235 ) did light/dark experiments with oceanic plankton.
Water collected from oligotrophic and mesotrophic Pacific Ocean locations, collected and stored in natural light or total darkness for 5-10 days.
Bacterial assemblages monitored by the ARISA whole-community fingerprinting approach

20. Database of ARISA OTU Identities
EXPERIMENTAL TEST of Significance of Phototrophy.
Light Removal Experiments – focus on Bacterial Groups that are supposed to have Proteorhodopsin
Incubate bacteria in Light or Dark for 5-10 days
P1 P2
110km
Light
14:10hr
cycle
Dark
24hr
DAPI
Cell Abundances
Monitored over time
Mesocosms
(2x20L)
Collect Cells
After 5-10 days P3
DNA Extraction
Bacterial Community Composition
ITS Clone Library Construction
16s
ITS
23s
rDNA
PCR
PCR
rDNA
16s
ITS
23s
Clone & Sequence
DNA
ABI 377XL
ARISA
Delineate 98% 16s rDNA
16S-ITS-23S
ABI 377XL
Database of ARISA OTU Identities
Automated Ribosomal Intergenic Spacer Analysis

21. Light Removal Experiments, 5-10 days darkness
Schwalbach et al Aquat Microb Ecol 2005
Light Removal Experiments, 5-10 days darkness
Magnitude of change
(n-fold difference)
Histogram summarizing magnitude of change in individual taxa, light vs dark treatments
Most taxa were NOT affected by light removal # of
OTU
Dark preference
Light preference
Cyano/Plastids
Sar11
Sar86
CFB
Roseobacter
Sar116
Sar406
Actinobacter
Fibrobacter
Marinobacter
Verrucomicrobia
Cyanobacteria & Phytoplankton exhibited consistent preference for light treatments
Mixed Responses, mostly dark preference, in ALL OTHER “phototrophic” groups (e.g. SAR11, SAR86, CFB, Roseobacter)
Number of taxa displaying response, ALL experiments

22. Conclusions of Schwalbach et al (2005) :
Most taxa (including presumed PR-containing and bacteriochlorophyll a – containing groups) do not decline significantly in extended darkness, unlike cyanobacteria.
In fact, most bacterial groups did no differently or much better in extended darkness than in normal light.
Suggests no clear direct benefit from light for most organisms.
But some organisms do benefit.

23. Pelagibacter, in SAR11 cluster
Even the one pure culture that contains proteorhodopsin grows no better in the light than in the dark
Pelagibacter, in SAR11 cluster
Oni et al Nature 2005
“The Pelagibacter proteorhodopsin functions as a light-dependent proton pump. The gene is expressed by cells grown in either diurnal light or in darkness, and there is no difference between the growth rates or cell yields of cultures grown in light or darkness.”
Giovannoni et al. Nature 2005

24. NSF, esp. Microbial Observatories Program
Acknowledgements
NSF, esp. Microbial Observatories Program
USC Wrigley Institute
Dave Caron
Mark Brown
Ian Hewson
Mike Schwalbach
Josh Steele
Anand Patel
Shahid Naeem
Tony Michaels
Doug Capone
Ximena Hernandez
R/V Kilo Moana
R/V Seawatch
Ajit Subramaniam
Burt Jones

25. Other Issues
Quantitation from Environmental Genomic Data
Accurate prediction of biogeochemical (or any other) function from genes. “Genome Rot,” Multifunctional genes, e.g. generic reductases. More important with slow-growing organisms and “streamlined” genomes?

26. Quantitation Issues/Problems
PCR Clone Libraries – Copy number bias mentioned yesterday.
Primer Choice/Bias, Extension Bias? Yes, but how bad?
Example – Marine Archaea compared to Bacteria. DISTANT
Fuhrman et al. (1992) used universal primers, found 5 of 7 clones from 500 m were Crenarchaeota. DeLong (1992) used archaeal primers with surface waters only, and RNA hybridization to compare to Bacteria. Archaea <2%.
Fuhrman and Davis (1997, univ. primers) Archaea were 1/3 of clones from 500 m – 3000 m, Atlantic and Pacific
FISH results – Fuhrman and Ouverney 1998, Archaea to 40% at 600 m in Pacific, 60% at 200 m in Mediterranean. Karner et al. (2001) – Archaea ~30% below ~ 200m at HOT over > 1 year.
Note – If QPCR shows doubling each cycle and if not at the saturation point, anything primed OK should quantify OK

27. DeLong et al. Science, 2006 Metagenomics
BIAS? Missing rRNA genes from large-insert library
All
BLAST hits- %
SSU rRNA
Genes-
Presence/absence
DeLong et al.
Science, 2006
SAR11