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On May 6, 2014, the Ontario Genomics Institute (OGI) and its Scintelligence division hosted a one day life sciences and mining workshop in Sudbury, Ontario. The workshop featured speakers discussing opportunities around the application of life sciences and genomics approaches in environmental assessment, monitoring and remediation. More than 40 workshop participants from mining companies and environmental firms, academia, industry associations and funding agencies discussed how to apply these technologies to the mining industry. As a result, discussions are on-going in terms of potential new collaborations, and ways to move forward with the application of the life sciences in mining.
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Mehrdad Hajibabaei Biodiversity Institute of Ontario
University of Guelph, Canada
Create a biodiversity map using DNA information Use such a map to monitor species/ecosystems over time and space
Biodiversity is…
Science of unknowns
Known biodiversity: 1.9 million species of plants and animals
Estimated biodiversity: 10 million to
100 million species
Microbiome
Macrobiome
25 parataxonomists 200+ years accumulated experience.
ACG, Costa Rica: large-scale biodiversity inventory
Taxonomic identification
Smithsonian Institution, Washington DC
John Burns
Lawton et al. 1998: Single site in Cameroon Birds: 50 scientist-hours Nematodes: 6000 scientist-hours Barlow et al. 2007 & Gardner et al. 2008: 5 tropical sites 15 taxonomic groups: 18200 person-hours
Challenges inherent in studies including a greater variety of species
Science 14 December 2012
Duhamel, Quebe
Wetlands of Louisiana
After Alberta oil sands open pit mine. Photo: DJ Baird
Before Athabasca River tributary, N Alberta. Photo: DJ Baird
Nunavut
British Columbia Alberta
Northwest Territory
Environmental impact of rapid development in remote regions
Canadian Aquatic Biomonitoring Network
Monitoring >1000 Species Across Canada Differential sensitivity of these species to environmental change
Early Warning System
The reference condition approach
Reference sites
The reference condition approach
Test sites Reference model
Identification Bottleneck
Low capacity (<1000 samples/yr) Low throughput (~12 months) Low resolution (often only to Family-level) Low frequency (~ every 3 years) Labour intensive
A DNA Based ID System for Species
DNA Barcoding
Typical cell has many
Mitochondria (green dots)
mtDNA
CO1
DNA Barcode
Mitochondrial Genome
Miotchondria contain DNA suitable for species identification
Hajibabaei et al. 2006
650 bases of DNA Barcode gene
DNA barcodes distinguish species of tropical Lepidoptera
98% resolution ~5000 individuals in over 500 species
Barcode coverage 10 March 2014
Extract DNA PCR Amplify Sequence
DNA barcoding single specimen workflow
Barcoding “mixed samples” using Next Generation Sequencing
Target DNA barcode libraries
Specimen: mixed environmental sample
Sequence data for selected markers/DNA
barcodes
Spe
cies
iden
titie
s an
d se
quen
ce c
ount
s
NGS (e.g. 454, MiSeq)
Extract e-DNA and amplify DNA barcode genes
Bioinformatics
Environmental barcoding using Next Generation Sequencing
DNA Metasystematics
Soil A Soil B
DNA Metasystematics
Biomonitoring 2.0
Bennett Dam Alberta Oil Sands
ooping Cranes, Wood Buffalo National Park, Canada. Photo: S Rosolen
Objectives 1. Resolving Biomonitoring 1.0 bottlenecks: sampling, sorting, identifying bioindicator species 2. Increasing information content of biomonitoring a. Species level vs. family/genus level for
bioindicators a. Biomonitoring beyond bioindicators: multi-
habitat; comprehensive taxonomic coverage 3. Large-scale pilot in an area of national
importance (e.g. Oil Sands)
BIO 1.0
BIO 2.0
Rocher wetland, Peace-Athabasca Delta
SOIL BENTHOS
AIR WATER
Logistic Challenges
Comparative analysis of environmental DNA barcodes
Biomonitoring
41
Biomonitoring 1.0 vs. Biomonitoring 2.0
Ian King Wendy Monk
Bio
mon
itorin
g 2.
0: N
GS
Dat
a
Bio
mon
itorin
g 1.
0: M
orph
olog
y
nMDS clustering of benthic NGS data
King et al. in prep
Wetlands bio-variability analysis
Phylogenetic profile/diversity Plants and Algae
Hajibabaei et al. in prep
Socio-economic Uptake
26 May
BIO-SAG 49
Ring of Fire Baseline Environmental Data Collection
From taxonomic diversity to functional diversity
Taxonomic barcodes Functional barcodes
Functional Barcodes Measuring Responses to Environmental Stressors
1. NGS comparative transcriptome analysis covering a wide range of organisms commonly used in ecotoxicology exposed to a certain chemical/mixture
2. Target fundamentally conserved cell machinery (e.g., transcription, translation) as taxonomically wide-ranging markers for environmental change (early warning system)
Gina Capretta
Acknowledgments
Team
Funding Genome Canada Ontario Genomics Institute Environment Canada Parks Canada MRI (Ontario) NSERC Canada Foundation for Innovation
Shadi Shokralla (Research Associate) Joel Gibson (Postdoc & PM) Ian King (Postdoc) Terri Porter (Postdoc, McMaster) Jennifer Spall (Graduate student) Lisa Ledger (Graduate student) Nicole Fahner (Graduate student) Michael Wright (Graduate student) Gina Capretta (Graduate student) Katie McGee (Graduate student) Stephanie Boilard (Lab tech) Rafal Dobosz (Bioinformatician)