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Anthony Chariton
Environmental Genomics, Ecology and Ecotoxicology Lab (EGEEL) Macquarie UniversitySydney, Australia
Environmental assessment and monitoring of sedimentary environments
Acknowledgments
CSIRO: Sarah Stephenson, Dr Paul Greenfield, Dr Andy Steven, Dr Chris Hardy, Dr Matt Colloff, Dr Matthew Morgan, Geoff Carlin and Gary Fry.
Western Washington University (USA): Scarlett Graham and Wayne Landis.
USEPA: Kay Ho and team.
Metabarcoding.org: Simon Jarman, Eric Coissac, Frederic Boyer and Pierre Taberlet
Many thanks to: Sandra Arresta, Sonia Ferreira and Fredrik Oxelfelt
• Aims and objectives of a monitoring program• What are we sampling?• An example of using metabarcoding for environmental
assessment• Some approaches for providing data which is useful for
end-users (environmental managers and government)• What about shot-gun derived community data?
Overview
• Every study has its limitations!• There are always trade-offs!• You need to know what suits your study!
FullFull
Contaminants{hazard/bioavailability}
Bioaccumulation{biological exposure}
Ecotoxicology{toxicity/single species}
Ecology{structure/function}
Cu
in a
mph
ipod
, mg/
kg
Time, h
Sediment
Water
Algae
Exposure Recovery
Cu
in a
mph
ipod
, mg/
kg
Time, h
Sediment
Water
Algae
Exposure Recovery
104103102101100 105
1.0
0.5
0Prob
abili
ty o
f Bio
logi
cal E
ffect
s
Chemical Stressor Concentration
Transition zone
Threshold for effects (TE) High
probability of effects (PE)
104103102101100 105
1.0
0.5
0Prob
abili
ty o
f Bio
logi
cal E
ffect
s
Chemical Stressor Concentration
Transition zone
Threshold for effects (TE) High
probability of effects (PE)
10 mm10 mm
5 mm5 mm
Dissolved contaminant
Sediment-boundcontaminant
Uptake by filtration
Uptake by ingestion
Uptake dependent on assimilation efficiency (AE)
Efflux
≡OC-Cu ≡FeO-Cu{Organic carbon} {Iron}
Cu2S, ≡FeS2-Cu{sulfide phases}
{Dissolved copper}Cu2+,CuSO4,CuCO3,CuCl+,OC-Cu
Dynamic quasi non-equilibrium
Sulfate reduction to sulfide
Porewater-Cu
Metabarcoding is now an ecological line of evidence in Australia’s sediment quality guidelines and will also be incorporated into our water quality guidelines.
• Provide for scientifically sound, cost effective evaluations;• Protect sensitive, healthy, natural aquatic communities;• Support and strive for protection of chemical, physical, and
biological integrity (functional and structural attributes);
The aim is not to sample every organism, but to provide a representative and reproducible view of the system.
• Other attributes may include:• End-users (e.g. managers) must be able to understand and find
the information useful.• Defensible in a court of law (we cannot use quantifiable amplicon
data).
What are the aims of monitoring programs?
What are we sampling? (Metabarcoding)
Parasites
Non-metazoan
Small & cryptic
larvae
Biofilm
Eggs and cysts (dormant)
Gut contents
Deceased
Catchment vegetation
Presence
Absence
Deep-sea sediments (2500-3000 m)
The abyssal smorgasbord: a solution
for a hungry planet?
Monitoring contamination
• Use a new core for each sample.• Cores need to be prewashed in bleach, rinsed with MQ
water, wrapped/bagged.• New gloves and tools for each sample.• Field blanks of your matrix• Lab (MQ) blanks in the lab• All blanks need to undergo PCR, if amplified, SEQUENCE
IT, remove contaminants.
Pseudo-Presence Pseudo-Absence
Trial study: SE Queensland Estuaries
Aim: Use metabarcoding of 18S rDNA to examine the benthic composition along five estuaries of varying ecological integrity.
• Can metabarcoding discriminate between estuaries of different conditions?
• Identify whether metabarcoding derived biotic composition is correlated with nutrients and other physico-chemical variables.
• Can metabarcoding provide ‘useful’ ecological data for the monitoring of SE Queensland estuaries?
Chariton et al (2015). Environmental Pollution
Estuary Score
Noosa B+
Maroochydore C
Pine C-
Logan F
Currumbin C
Noosa (B+)
Logan (F)
• 2,966 OTUs
• Richness was significantly greater in Logan (the unhealthy river) than the other locations
Estuary Score
Noosa B+
Maroochydore C
Pine C-
Logan F
Currumbin C
Richness
0
200
400
600
800
1000
1200
1400
1600
1800
1 2 3 4 5 6
OTU
s
Ocean River
Noosa
Maroochydore
Pine
Currumbin
Logan
Traditional
Inputs from small streams and tributaries
2D Stress: 0.13
NoosaMaroochydoorePineCurrumbinLogan
All estuaries contained different compositions. The most different were the Noosa and the Logan.
Estuary Score
Noosa B+
Maroochydore C
Pine C-
Currumbin C
Logan F
INDICATOR ANALYSIS (indispecies in R)
• A = SPECIFITY the conditional probability of the OTU as an indicator of the group.
• B = FIDELITY probability of finding the OTU in samples belonging to this group.
• Stat (Indicator Value) is an index with a maximum value of 1.00 occurring when a taxa is restricted to one group (or combination of groups) and present in all samples.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Prop
ortio
n of I
ndict
or M
OTUs
Potential Indicator OTUs
Choanozoa
Basidiomycota
Streptophyta
Oomycota
Cryptophyta
Chytridiomycota
Amoebozoa
Heliozoa
Apicomplexa
Rotifera
Ochrophyta
Platyhelminthes
Apusozoa
Nematoda
Annelida
Ascomycota
Arthropoda
Chromoalveolata(uk)
Miscellaneous
Dinoflagellata
Foraminifera
Chlorophyta
Cercozoa
Ciliophora
Bacillariophyta
Estuary Indicator OTUS
Noosa (B+) Bacillariophyta mainly from Bacillariophyceae.
Maroochydore(C)
Chlorophyts, helizoans, cillophoransand bacillariophyts
Pine (C-) Cillophorans, bacillariophyt, nematodes and turbellarians.
Currumbin (C) Crustaceans.
Logan (F) Many unique taxonomic groups: Choanozoa, Chytridiomycota, Ascomycota and Ciliophorans, Some metazoans :Annelida and Rotifera.
P(total)
NOx
Turbidity
pH
Chlorophyll a
Oxygen(%)
Temperature
N (organic) TOC Conductivity
Secchi depth
Ammonia
N(total)
• Composition of the Logan was driven by nutrients and turbidity
•The Noosa and Maroochydore reflected natural changes along an estuary (fresh to marine).
When examine collectively, four variables were shown to significantly contribute to changes in composition:
Total Phosphorus (18.6 %) ; NOx (7.9 %); Turbidity (8.1 %) and pH (6.12 %)
Noosa
Maroochydore
Pine
Currumbin
Logan
Relationships between biota and environmental variables
Estuary
Respond positively along a gradientRespond negatively
along a gradient
Community tipping points
Negative response to Total Phosphorus (TP)
• 330 OTUs responded negatively• 40 % diatoms (Bacillariophyceae). Pronounced decline at 24 µg P L-1 (22-34 µg P L-1).
Positive response to Total Phosphorus465 OTUs responded positively.These included diatoms (Coscinodiscophyceae), Annelida, Gastrotricha, Rotaliida and Micronuclearia.
Most pronounced increase occurred at 100 µg P L-1 (100-290 µg P L-1)
Community threshold (or change point)TP = P 34 µg P L-1 (26-42 µg P L-1)
Australian Water Quality guideline value
Threshold Indicator Taxa Analysis (TITAN): Total Phosphorus
27 |
Lower
Middle
29 |
Stressors EffectsWater quality
endpoints
Biodiversity endpoints (metabarcoding)
Region within catchment: up
stream, middle, lower
(mouth)
Bayesian network relative risk method model (BN-RRM) (Graham, in prep, U. Western Washington).
30 |
Relatively unmodified Highly impacted
Dissolved Oxygen (Endpoint)
Chlorophyll a (Endpoint)
Risk Regions Dissolved OxygenObjective
Probability to achieve objective Relative risk
Noosa Lower 90 - 105% 74% MediumNoosa Middle 85 - 105% 81% LowNoosa Upper 85 - 105% 75% LowLogan Lower 85 - 105% 69% MediumLogan Middle 85 - 105% 16% High
Lower
Middle
Why do we want abundance data?
• Negative perception regarding presence/absence data
• Used for traditional ecological end-points: e.g. diversity
• The dominance /rarity of taxa can tell you a lot about a system
e.g. Key processes are generally performed by dominant taxa; examine how specific taxa respond (+ and -) along environmental gradients.
• Linear/non-linear relationships (e.g. predatory/prey, synchronous patterns, mutualism and mutual exclusion).
• WHAT DOES READ ABUNDANCE ACTUALLY MEAN AS A COMMUNITY MEASUREMENTS.
• May be it is time to stop tail chasing
Metagenomics via Shot-gun sequencing
Modified from Sharpton 2014
Unamplified DNA
Structural• Taxonomic/Phylogeneticdiversity• Genome diversity, novel genomes
FunctionalGene Prediction: Gene diversity and novel genesFunction annotation: Protein family and functional diversity
Compositional and potential functional
profile
Sequence random fragments
Methods
• Three estuaries (and one tributary) were sampled in south-east Queensland.
• Estuaries are of varying condition: Noosa (Good), Maroochydore (Moderate), Logan and its tributary the Albert (Very poor)
• Sixty-five metagenomes were sequenced: four replicates taken from five sites within each estuary plus the Albert (tributary of the Logan).
• 13 lanes of Illumina HiSeq2500 (150 PE, 550bp insert, TruSeq)
• 1 sample was resequenced on an entire lane.
• Composition data was produced using a K-mer (25 mer) approach.
• Amplicon sequencing: 18S (454 w/APDP); 16S (MiSeq/Qiime)
Comparison (same sample) between 1 and 1/5th lane
Single lane:• 2.93 x 108 reads• 253 (18S) taxonomically informative fragments• 1,267 (16S) taxonomically informative fragments
1/5th of lane:• 5.1 x 107 (17%) reads• 86 (18S) taxonomically informative fragments (34% of single lane)• 792 (16S) taxonomically informative fragments (64% of single lane)• Rare 18S/16S fragments (read count ≤3) which were not detected
when sequencing depth was reduced.
0.0000%
0.0010%
0.0020%
0.0030%
0.0040%
0.0050%
0.0060%
0.0070%
0.0080%
0.000%
0.005%
0.010%
0.015%
0.020%
0.025%
0.030%
0.035%
0.040%
AA1A
AA1B
AA1C
AA1D
LL1A
LL1B
LL1C
LL1E
LL2A
LL2B
LL2C
LL2D
LL3A
LL3B
LL3C
LL3D
LL3E
LL4A
LL4B
LL4C
LL4D
LL5A
LL5B
LL5C
LL5D
MM
2AM
M2B
MM
2CM
M2D
MM
3BM
M3C
MM
3DM
M3E
MM
4AM
M4B
MM
4CM
M4D
MM
5AM
M5B
MM
5CM
M5D
MM
6AM
M6B
MM
6DM
M6E
NN
2AN
N2B
NN
2DN
N2E
NN
3AN
N3B
NN
3CN
N3D
NN
4AN
N4B
NN
4DN
N4E
NN
5AN
N5B
NN
5CN
N5D
NN
6AN
N6B
NN
6CN
N6D
Prop
ortio
n of
frag
men
ts a
ssoc
iate
d w
ith 1
8S
Prop
ortio
n of
s fr
agm
enst
ass
ocia
ted
with
16S
Proportion of fragments associated with 16S and 18S
16S 18S
. ≈0.030% of the reads were 16S fragments. Reads and 16S fragments (r2=0.975, p<0.001).
≈0.002% of the reads were 18S fragments. Reads and 18S fragments (r2=0.4454, p<0.001)
Comparison between 18S derived from shot-gun and amplicon
• Shot-gun : Mean Richness 107 ± 5.43 S.E, fragment reads/sample 1,262 ± 148 S.E• Amplicon (454): Mean Richness 484 ± 20.7 S.E. reads/sample 11,2245 ± 582 S.E
0
200
400
600
800
1000
1200Ri
chne
ss
Site
Amplicon
Shotgun
N2
N2
N2N2
N3N3N3N3
N4N4
N4
N4
N5N5N5N5N6N6N6 N6
M2
M2M2M2
M3M3M3M3
M4M4M4M4
M5M5M5
M5
M6M6M6
M6L1L1L1L1L2L2L2L2
L3L3L3L3L3
L4L4L4L4L5L5L5L5
A1A1A1A1
18S amplicon (p/a)Albert
Albert
AlbertAlbert
L1 L1L1
L1
L2L2L2L2L3L3L3
L3L3L4
L4
L4L4
L5L5
L5L5
M2M2
M2M2M3M3M3M3
M4M4M4M4
M5M5M5M5
M6M6M6
M6
N2
N2N2N2
N3N3N3N3N4N4N4
N4
N5
N5
N5
N5
N6
N6N6
N6
18S shotgun(abundance)
AlbertAlbert
AlbertAlbert
L1L1
L1L1
L2 L2L2L2
L3L3
L3L3L3
L4L4
L4L4
L5
L5L5
L5
M2M2
M2M2M3M3M3M3
M4
M4M4M4M5M5 M5
M5
M6M6M6M6
N2N2N2N2N3N3N3N3
N4
N4N4
N4
N5
N5
N5
N5
N6
N6N6 N6
18S shotgun(p/a)
EstuaryAlbert
Logan
Maroochydore
Noosa
All approaches showed differences in compositions among all estuaries.
Similarities among and within estuaries was greatest with the shot-gun abundance data, far lower with 454 (p/a).
Strong separation between marine and estuarine sites was only observed using the 454 (p/a) data (marine sites in blue circles)
The composition conundrum: an important consideration when using proportional data
Sampling
• 40 million bacterial per gram of soil. Richness estimates vary between 2000 and 8.3 million per gram.
• This does not include eukaryotes, including fungi!!
• Unfeasible to sequence the complete metagenomes of the soil.
• Therefore we are sequencing a random sub-sample of the DNA extract!!!!
Total DNA pool from extracted sample
Potentially randomly sequenced sub-samples
•Abundant taxa (blue/green)•Medium (red)•Rare (yellow, purple and pink)
•Common taxa will more likely be captured, some rare maybe sampled, some won’t.•Richness, abundance of taxa, and total reads will vary among samples.
Sample A
Taxa True Abundance Relative Abundance
SampleA
SampleB
SampleA
SampleB
Cats 3
Dogs 2
Guineapig
0
Owl 0Sample A
Taxa True Abundance Relative Abundance
SampleA
SampleB
SampleA
SampleB
Cats 3 60%
Dogs 2 40%
Guineapig
0 0%
Owl 0 0%Sample A
Sample B
Taxa True Abundance Relative Abundance
SampleA
SampleB
SampleA
SampleB
Cats 3 3 60%
Dogs 2 2 40%
Guineapig
0 1 0%
Owl 0 1 0%Sample A Sample B
Taxa True Abundance Relative Abundance
SampleA
SampleB
SampleA
SampleB
Cats 3 3 60%
Dogs 2 2 40%
Guineapig
0 1 0% 14%
Owl 0 1 0% 14%Sample A Sample B
Taxa True Abundance Relative Abundance
SampleA
SampleB
SampleA
SampleB
Cats 3 3 60 % 43 %
Dogs 2 2 40 % 29 %
Guineapig
0 1 0 % 14 %
Owl 0 1 0 % 14 %Sample A Sample B
Summary
• The world is facing some serious environmental challenges.
• Science is critical for identifying issues, obtaining public support, predicting changes and developing protocols which underpin legislation.
• Metabarcoding has provided a step-change in the way we obtain ecological data.
• To aid the adoption of metabarcoding you must have a clear understanding of what the science can and cannot do!!!
• You must know what you are and what you are not sampling.
Summary
• Quality assurance and quality control is critical to minimise contamination and monitor the quality of the data.
• All methodologies and bioinformatic pipelines perform differently, you need to understand their strengths and weaknesses.
• You need to provide the information into a format the end-users can use and understand.
• Metabarcoding is only one line of ecological data!!
Final thoughts
