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
ed
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
Per
cent
of T
otal
668665686620680661437538422750703
0.0
0.5
1.0
1.5
2.0
2.5
6/23 6/24 6/25 6/26 6/27 6/28 6/29 6/30
726
850
763
568
837
651
927
532
572
945
478
0.0
0.5
1.0
1.5
2.0
6/23 6/24 6/25 6/26 6/27 6/28 6/29 6/30
715
1015
935
592
626
488
559
646
742
1031
719
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
6/23 6/24 6/25 6/26 6/27 6/28 6/29 6/30
691
895
548
966
788
768
773
876
807
707
9880.0
0.1
0.2
0.3
0.4
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0.6
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0.8
6/23 6/24 6/25 6/26 6/27 6/28 6/29 6/30
975
883
1004
1222
616
1171
696
485
403
796
519
0.0
0.5
1.0
1.5
2.0
2.5
6/23 6/24 6/25 6/26 6/27 6/28 6/29 6/30
541
426
553
694
756
829
907
913
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?
-20
10
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10
20
-1.0
-0.5
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-3 -2 -1 0 1 2 3- 4
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Lag (months)Time (months, 0 = August 2000) DFA (Predicted)
DF
A S
core
Au
toco
rre
latio
n
DF
A S
core
s
Ab
un
dan
t ta
xa
Co
mm
on
taxa
Arb
itra
rily
sele
cte
d t
axa
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
5
10
15
20
-10 -8 -6 -4 -2 0 2 4 6 8 10
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