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?

Amplified Ribosomal Intergenic Spacer Analysis (ARISA)

Start with DNA extracted from a mixed community.PCR spans rRNA operon, 16S to 23S genes. One tagged

primer.

Shows exact sizes. Each peak = “Operational Taxonomic Unit.”Data based, not gel-based. Ref: Fisher and Triplett 1999

PCR

Fragment analysis. Smallest detectable peak ~0.1% of total

PCR primersFluorochrome

16S rRNA gene 23S rRNA geneIntergenic SpacerVariable Length

For microbial community fingerprints with high phylogenetic resolution

16S-23S clone libraries to identify most peaks: Brown, Hewson, Schwalbach & Fuhrman, Envir. Microbiol 2005

Flu

ore

scen

ce

Fragment Size

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.

Quantitation from PCR-based Fingerprinting?

Real comparison: Prochlorococcus, ARISA vs flow cytometry counts

Fingerprint % area is remarkably proportional to counts. Also, SAR11 % clones are close to % cells.

% area from ARISA

Flow cytometric counts

San Pedro Ocean Time Series 4 year dataset

R2=0.86

Brown, Hewson, Schwalbach & Fuhrman, Envir. Microbiol 2005

Note: we use a highly standardized assay, with eukaryotes removed, and measured amounts of DNA

Replicate 20L samples have very similar ARISA fingerprints

7 samples from each of 2 North Pacific Gyre Stations

Hewson et al. Aquat Microb Ecol 2006

Compares OTU proportions OTU Presence/absence only

What is an ARISA OTU? Phylogenetic resolution is about 98% 16S rRNA similarity - comparable to “species” level

Easi

ly d

ete

rmin

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diff

ere

nce

Brown et al, Env Microbiol 2005

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

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

San Pedro Ocean Time Series

Temporal Variability in Bacterioplankton CommunitiesHow fast do communities change?

45 km

02468

10121416

6/24 6/25 6/26 6/27 6/28 6/29

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otal

668665686620680661437538422750703

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Abundant taxa vary little

Rarer taxa can vary more Not just “noise” inmeasurement

Graphs: all OTU over 6 days

date

Prochlorococcus

CFB

SAR 11

SAR 11

Relative stability over days at one location (open water, Catalina)

Rarest detectable taxa .

Actinobact

Brown et al. 2005

Monthly observations at SPOTS over 4 years showed some taxa clearly had repeatable seasonal patterns.

How about the bacterial community in general?

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Lag (months)Time (months, 0 = August 2000) DFA (Predicted)

DF

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Discriminant FunctionAnaysis

Time SeriesMultiple

Fuhrman et al., PNAS 2006 with Shahid Naeem

171 taxa followed by ARISA over 4.5 yearsDFA scores reflect quantitative distribution of taxa via ARISA

Predictable Annual Bacterial Community Reassembly

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

OTU>1.6%

OTU>1.1%

OTU>0.2%

Freq.>75%

Freq.>50%

Freq.>33%

Freq.>10%

OTUs399-528

OTUs531-657

OTUs660-844

OTUs 849-1183

Statistic 16 19 62 34 63 83 133 44 43 43 41 171

1 719 739 633 739 704 687 477 447 546 699 919 4472 675 704 477 687 739 666 408 444 531 769 914 4443 681 687 624 699 519 534 516 441 555 687 4414 402 600 704 734 1040 534 465 621 690 4655 687 417 687 799 513 492 570 739 492

85 57 98 57 64 89 98 94 100 70 19 9347 51 61 46 44 46 58 52 40 50 59 46

1 X X X X X X X X X X X4 X X X X X5 X X X X X X X X X6 X X X X X X

10 X X X X20 X

Temp. X X X X X XOxygen X X X X X X X X XSalinity X X X XBacteria XVirus X XChlA X X X X X XPhaeo X X X X XLeu XTDR X X XNO2 X X X X X X

NO3 X X

SiO3 X X X

PO4 X XBiodiv. # OTUs X X X

0.41 0.48 0.56 0.39 0.72 0.22 0.28 0.71 0.12 0.54 0.42 0.2<0.01 <0.001 <0.01 <0.001 <0.001 <0.05 <0.001 <0.001 <0.05 <0.001 <0.01 <0.05

Commonness Arbitrary

p-value

Nutrients

ALLOTU analysed

R2

Multiple Regression Analyses (MRA)

Lag (months)

Abiotic

Biotic 

Ecosyst. Funct. 

Time Series Analyses (TSA)

Percent correctPercent dispersion

sample size (n)

Discriminant Function Analyses (DFA)

Dominant OTU

Abundance

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

Biogeography on a Global Scale

•Global survey of bacterioplankton at numerous sites in 3 ocean basins, under Arctic ice cap, and near Antarctica

SeaWiFS

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

Global Diversity Measurements via ARISAAssemblages clearly vary “Things

change”

Bacterioplankton BiogeographyLATITUDINAL 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

Regional Diversity PatternsBacterial Community Similarity (via ARISA) vs Distance

NEAR-SURFACE samples

Hewson et al 2006 Mar. Ecol. Prog. Ser. “Mixing” curve between Pacific and Indian Basins?

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 PlumeHewson et al. 2006 Limnol. Oceanogr.

*

*

Pacific

Pacific

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

Go beyond just observing nature - EXPERIMENTATION

EXPERIMENTAL TEST of Significance of Phototrophy.Light Removal Experiments – focus on Bacterial Groups that are supposed to have Proteorhodopsin

P3

P1 P2

110km

Dark24hr

Collect CellsAfter 5-10 days

DAPI Cell AbundancesMonitored over time

ITS Clone Library ConstructionBacterial Community Composition

PCR

Automated Ribosomal Intergenic Spacer Analysis

16s ITS 23s

rDNA

DNA Extraction

DNA

ABI 377XL

PCR16s ITS 23s

rDNA

ABI 377XL

Clone & Sequence

16S-ITS-23S

Database of ARISA OTU

Identities

ARISADelineate 98% 16s rDNA

Incubate bacteria in Light or Dark for 5-10 days

Light14:10hr cycle

Mesocosms(2x20L)

-15 -10 -5 0 5 10 15

Light Removal Experiments, 5-10 days darkness

Histogram summarizing magnitude of change in individual taxa, light vs dark treatments

Most taxa were NOT affected by light removal

0

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Magnitude of change(n-fold difference)

Number of taxa displaying response, ALL experiments

# of OTU

Cyano/PlastidsSar11Sar86

CFBRoseobacter

Sar116Sar406

ActinobacterFibrobacter

MarinobacterVerrucomicrobia

Cyanobacteria & Phytoplankton exhibited consistent preference for light treatments

Mixed Responses, mostly dark preference, in ALL OTHER “phototrophic” groups (e.g. SAR11, SAR86, CFB, Roseobacter)

Light preferenceDark preference

Schwalbach et al Aquat Microb Ecol 2005

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.

“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

Even the one pure culture that contains proteorhodopsin grows no better in the light than in the dark

Pelagibacter, in SAR11 cluster

AcknowledgementsNSF, esp. Microbial Observatories Program

USC Wrigley InstituteDave CaronMark BrownIan Hewson

Mike SchwalbachJosh Steele Anand Patel

Shahid NaeemTony MichaelsDoug Capone

Ximena HernandezR/V Kilo MoanaR/V Seawatch

Ajit SubramaniamBurt Jones

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?

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

DeLong et al. Science, 2006

SSUrRNAGenes-Presence/absence

AllBLAST hits-%

SA

R11

MetagenomicsBIAS? Missing rRNA genes from large-insert library