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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)
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.
Fluorochrome
16S rRNA gene
PCR primers
Intergenic Spacer
Variable Length
23S rRNA gene
Fragment Size
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
PCR
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
San Pedro Ocean Time
Series 4 year dataset
Flow cytometric counts
% area from ARISA
R2=0.86
Note: we use
a highly
standardized
assay, with
eukaryotes
removed, and
measured
amounts of
DNA
Fingerprint % area is remarkably proportional to counts.
Also, SAR11 % clones are close to % cells.
Brown, Hewson, Schwalbach & Fuhrman, Envir. Microbiol 2005
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
Easily determined difference
What is an ARISA OTU? Phylogenetic resolution is about
98% 16S rRNA similarity - comparable to “species” level
Brown et al, Env Microbiol 2005
Near-surface
SAR11 subclades as
determined by ITS
sequences and
lengths
Temporal Variability in Bacterioplankton Communities
How fast do communities change?
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
45 km
Relative stability over days at one location (open water, Catalina)
Percent of Total
Abundant taxa vary little
16
14
12
SAR 11
10
8
6
Actinobact
4
2
0
6/24
6/25
6/26
2.0
date
6/27
6/28
6/29
Rarer taxa can vary more
1.5
715
935
626
0.5
0.6
6/25
6/26
6/27
6/28
6/29
6/30
Rarest detectable taxa
837
651
1.0
927
532
0.5
572
0.0
6/23
945
6/24
6/25
6/26
6/27
6/28
6/29
6/30
Not just “noise” in
measurement
2.5
2.0
426
553
694
756
1.0
829
0.5
907
0.4
0.0
6/23
1.6
883
1.4
1004
1.2
616
1171
478
541
Prochlorococcus
1.5
975
1222
g
0.5
719
850
568
1.5
1031
6/24
726
763
646
742
0.7
g
2.0
488
559
0.8
Graphs: all OTU over 6 days
592
1.0
0.0
6/23
2.5
1015
a
g
668
665
686
620
680
661
437
538
422
750
703
913
6/24
6/25
6/26
6/27
6/28
6/29
6/30
691
.
895
CFB
548
966
1.0
788
SAR 11
0.8
768
0.3
696
0.6
773
0.2
485
0.4
876
0.1
403
0.2
807
0.0
6/23
796
6/24
6/25
6/26
6/27
6/28
6/29
6/30
519
0.0
6/23
707
6/24
6/25
6/26
6/27
6/28
6/29
6/30
988
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
Predictable Annual Bacterial Community Reassembly
Arbitrarily selected taxa
Multiple
Regression
Time Series
20
1.0
4
10
0.5
2
0
0.0
0
10
-0.5
-2
-20
-1.0
-4
-3 -2 -1 0
5
1.0
0
-5
0.5
0.0
-0.5
-10
10
1.0
10
5
0.5
5
0
0.0
0
-5
-0.5
-5
20
30
40
50
Time (months, 0 = August 2000)
Fuhrman et al., PNAS 2006
-1.0
0
3
-5
-1.0
10
2
0
10
-10
0
1
5
DFA Score
Autocorrelation
DFA Scores
Common taxa
Abundant taxa
Discriminant Function
Anaysis
-4
10
20
30
Lag (months)
40
50
-10
- 10
-2
-5
0
0
2
5
10
DFA (Predicted)
with Shahid Naeem
171 taxa followed by ARISA over 4.5 years
DFA scores reflect quantitative distribution of taxa via ARISA
DFA showed some subsets of bacterial taxa
could predict the month of sampling with
100% accuracy.
Significant
Parameters in
MRA
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.
temperature,
salinity,
nitrite,
nitrate,
silicate,
oxygen,
bacterial and
viral abundances,
bacterial
production via
leucine and
thymidine
incorporation,
chlorophyll,
phaeopigments
ARISA richness
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.
Abundance
Statistic
Discriminant
Function
Analyses
(DFA)
Time Series
Analyses
(TSA)
OTU analysed
sample size (n)
1
2
Dominant
3
OTU
4
5
Percent correct
Percent dispersion
1
4
5
6
10
20
Lag
(months)
Temp.
Oxygen
Salinity
Bacteria
Biotic
Virus
ChlA
Ecosyst. Phaeo
Funct. Leu
TDR
NO2
NO3
Nutrients
SiO3
PO4
Biodiv. # OTUs
Abiotic
Multiple
Regression
Analyses
(MRA)
2
R
p-value
Commonness
OTU OTU OTU Freq.
>1.6% >1.1% >0.2% >75%
Arbitrary
ALL
Freq. Freq. Freq.
OTUs OTUs OTUs
OTUs
>50% >33% >10% 399-528 531-657 660-844 849-1183
16
19
62
34
63
83
133
44
43
43
41
171
719
675
681
402
687
85
47
739
704
687
633
477
624
600
417
98
61
739
687
699
704
704
739
519
734
687
64
44
687
666
534
1040
799
89
46
477
408
516
534
513
98
58
447
444
441
465
492
94
52
546
531
555
621
570
100
40
699
769
687
690
739
70
50
919
914
447
444
441
465
492
93
46
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
57
51
57
46
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
19
59
X
X
X
X
X
X
X
X
0.54
<0.001
0.42
<0.01
X
X
X
X
X
X
X
X
0.41
0.48
0.56
0.39
0.72
0.22
0.28
0.71
<0.01 <0.001 <0.01 <0.001 <0.001 <0.05 <0.001 <0.001
0.12
<0.05
0.2
<0.05
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
SeaWiFS
•Global survey of bacterioplankton at numerous sites in 3
ocean basins, under Arctic ice cap, and near Antarctica
Global Diversity Measurements via ARISA
Assemblages clearly vary
“Things change”
Coral Sea
Arctic Ocean
New Caledonia
Philippines
Deception Is, Antarctica
Suva Harbor, Fiji
Gerlache Strait, Antarctica
Great Barrier Reef
Villefranche (Med)
Barbados
Singapore
Weddell Sea
Long Island NY
Norwegian Sea
Catalina Island
Bacterioplankton Biogeography
p<0.005
Highly significant
(p<0.005) as linear
regression, rank
correlation, or with
potential outliers
removed
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
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.
Deep-Sea (5003000 m depth)
patterns differ
with locations
and depth.
Cause(s)
unknown
Pacific
*
Pacific
*
North Atlantic 1000m
depth samples were in
vicinity of Amazon
Plume
Hewson et al. 2006
Limnol. Oceanogr.
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 wholecommunity fingerprinting approach
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
P3
Light
DAPI
Dark
Cell Abundances
Monitored over time
24hr
14:10hr
cycle
Mesocosms
(2x20L)
Collect Cells
After 5-10 days
DNA Extraction
Bacterial Community Composition
rDNA
16s
ITS
23s
ITS Clone Library Construction
PCR
rDNA
PCR
16s
ITS
23s
Clone & Sequence
DNA
16S-ITS-23S
ARISA
ABI 377XL
ABI 377XL
Delineate 98% 16s rDNA
Automated Ribosomal Intergenic Spacer Analysis
Database of
ARISA OTU
Identities
Schwalbach et al Aquat Microb Ecol 2005
Light Removal Experiments, 5-10 days darkness
20
Magnitude of change
(n-fold difference)
15
#
10
of
5
OTU
Histogram summarizing
magnitude of change in individual
taxa, light vs dark treatments
0
-10 -8 -6
-4 -2
0
2
4
6
8
10
Most taxa were NOT affected by
light removal
Dark preference Light preference
Cyano/Plastids
Cyanobacteria & Phytoplankton
Sar11
exhibited consistent preference for
Sar86
CFB
light treatments
Roseobacter
Sar116
Sar406
Actinobacter
Fibrobacter
Marinobacter
Verrucomicrobia
-15
Mixed Responses, mostly dark
preference, in ALL OTHER
“phototrophic” groups (e.g. SAR11,
SAR86, CFB, Roseobacter)
-10
-5
0
5
10
15
Number of taxa displaying response, ALL experiments
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.
Even the one
pure culture
that contains
proteorhodopsin
grows no better
in the light than
in the dark
Pelagibacter, in
SAR11 cluster
“The Pelagibacter proteorhodopsin functions as a lightdependent 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
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
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 slowgrowing 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
Metagenomics
BIAS? Missing
rRNA genes
from largeinsert library
All
BLAST hits%
SAR11
SSU
rRNA
GenesPresence/absence
DeLong et al.
Science, 2006