3.35. 5.44. 5.69. 5.7. 5.3. 4.75. 4.3. 4.84. 2.82. 3.02. 2.82. 2.75. 2.82. 2.48. 3.35. 2.61. 2.42. 3.34. 2.52. Depth. pH. [Arsenic]. Type. Tailings. Sediment. Forest.
Microbial Ecology of Potential Bioleaching Bacteria from High Arsenic Tailings in Sudbury, Canada Maxime Rivest , Graeme Spiers , Peter Beckett , Alexandre Poulain , Nadia Mykytczuk 1. Department of biology, University of Ottawa 2. Living with the Lake Center, University of Laurentian 1
2
Re-mining tailings. . . bioleaching as a sustainable option
2
1
2
Forest
DNA extraction, sequencing and bioinformatics
SentktokmisterkDNA forkBarcodingkandk sequencing
Relative Abundance
0.75
Extracted
• Massive tonnage of tailings material. • Often high reactivity, acid generating potential, toxic metal leachate • Many abandonned mine sites with no associated liability
Qiime
0.50
0.25 WithinkQiimeksampleskwere...
Acidobacteria
GAL15
Spirochaetes
Actinobacteria
Gemmatimonadetes
SR1
AD3
GOUTA4
Tenericutes
Armatimonadetes
MVP−21
TM6
Bacteroidetes
Nitrospirae
TM7
Chlorobi
NKB19
Verrucomicrobia
Chloroflexi
OD1
WPS−2
Cyanobacteria
OP11
WS1
Elusimicrobia
OP3
WS4
FCPU426
PAUC34f
WS5
Fibrobacteres
Planctomycetes
ZB3
Firmicutes
Proteobacteria
0.00
Sample
...denoised,kcheckedkforkchimera,kremovedksingletonkand Forest
pickedkOTUskusingkGOLDksilvakreferencekdatabase. Forkanalysis:kUnifrac,kRkandkQiimekwerekused.
● ● ● ●
●
● ●
●
●
●
● ●
●
● ● ●
●●
●
Wetland
Acidiphilium
Alicyclobacillus
Acidithiobacillus
Leptospirillum
Acidobacterium
Sulfobacillus
Thiomonas
0.2
0.0
●
●
●
● ●
Genus 0.4
5.0 EC .10 9 EC .0.10 9.2 0.3 0 De lta De 0.20 lta 20 . W1 40 .0. W1 10 .20 W1 .30 .50 .60 EC 1 EC .0.10 1.2 EC 0.30 1.5 EC 0.60 5.2 EC 0.30 5.5 0 FB .60 0.0 .1 FB 0.2 0 0 .30 FB 0 FB .50.6 0 1 FB 00.0. 10 10 FB 0.20. 3 10 0.5 0 0 .60 FB 2 FB 0.0.1 0 20 FB .20.3 0 20 .5 FB 0.60 5 FB 0.0.1 0 50 FB .20.3 0 50 .50 .60 W1 28 W1 6
500
●
Tailings
EC
● ●
Sediment
0.6
Relative Abundance
Passive extraction methods including bioleaching can effectively extract remaining low grade metals while also decreasing the oxidative potential associated with pyritic minerals. In tailings materials rich in more toxic metals, such as arsenic, bioleaching rates could potentially be hindered through cell toxicity.
●
Sample 400
●
●
●
OTU observed
● ● 300
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
● ●
●
Figure 4: (Top)Microbial diversity between sites represented by relative
● ●
●
●
●
abundance of OTUs by sample type. Only OTUs which represent >1% of
●
●
●
the total sequences for a given sample were plotted. OTUs are grouped at
●
200
100
the phylum level and each line segment represents the relative abundance ●
●
●
●
● 0
10000
●●
20000
30000
0
●
● ●
●
Arsenic (mg/kg)
● 25
50
75
100
of a single OTU within each phylum. (Bottom)Genus-level distribution of
● ●● ●
3
4
Iron (g/kg)
5
−100
pH
0
100
200
300
400
Typeylegend Tailings
Type Sampling Depth pH point FB100 bottom 2.52 0.88
Sediment Forest Wetland
0.40 0.62
0.35 0.29 0.99
0.45
0.44
0.45 0.47 0.74 1.0 1.0
0.97 0.88
0.57 0.99
0.73 0.34 0.83 0.59
0.44
0.62 0.97 meanypHykyysd)yymeanyarsenicykyysd)yy 3.08yk0.73)yyyyyyyyyyyy13283yk11865)
key Fe-/S-oxidizing genus and their relative abundance in sample sites.
500
Redox potential (mV)
Figure 2: Linear regression plots showing the relationship between the number of OTUs (subsampled at 3000 sequences) and the 5 environmental variables. The correlation coefficients are: -0.28 for arsenic, 0.05 for iron, 0.71 for pH, 0.70 for red-ox potential, and 0.27 for silver.
Figure 1: Map of the abandoned tailings at Long Lake in Sudbury, Ontario, Canada. Red squares mark sample sites. At each site, samples were taken from the top (0-10 cm), middle (20-30cm, and deep (50-60cm) depths. Samples were classified as tailings, forest, wetland, or sediment (stream or lake deltas).
Wetland
Phylum
Tailings are a sizable problem for the mining industry for several reasons:
...Characterize bacterial diversity occurring along environmental gradients present within and around 100 year old, high-arsenic tailings.
Tailings
1.00
Sampleskwerekcollected
Our goal was to. . .
Sediment
0.38 0.58
4.80yk0.79)yyyyyyyyyyyy10020yk6811)
[Arsenic]
kmg/kg)
6357
EC1
bottom 3.34
23354
FB50
bottom 2.42
1120
FB100
top
2.61
12408
FB50
middle
3.35
581
FB100
middle
2.48
26995
FB20
bottom 2.82
35812
EC1
middle
2.75
16552
EC5
middle
2.82
18633
EC1
top
3.02
8868
FB50
top
2.82
21841
FB20
top
4.84
113
FB20
middle
4.3
46
Delta
top
4.75
8037
W1
top
5.3
8882
W1
middle
5.7
7458
Delta
middle
5.69
12124
W1
bottom 5.44
10946
EC5
bottom 3.35
16931
EC9
top
3.8
3581
EC9
middle
3.75
27984
EC5
top
4.08
5746
FB0
top
4.78
6589
W128
top
5.65
15705
W16
top
5.37
217
FB0
bottom 5.05
FB0
middle
4.5
6054
From the data presented here we can learn the following: • pH and redox potential are much more correlated with bacterial diversity than iron and arsenic concentrations. • pH and redox potential are positively correlated with diversity while diversity is negatively correlated with arsenic although not as significantly as predicted. • Bacterial assemblages are affected mainly by ecosystem types and pH. High arsenic concentrations and sample depth do not appear to affect bacterial diversity, even in the highest As concentrations • Tailings materials are unevenly dominated by Fe- and S-ox bacteria most commonly, Acidithiobacillus and Leptospirillum spp. • These Fe- S-oxidizing OTUs do not appear to be affected by the higher arsenic concentrations All my thanks to Renate M. Vanderhorst, Trisha Williams, Autumn Watkinson, Dr Poulain’s lab, Dr. Mykytczuk’s lab, Dr. John Gunn.
10032
Figure 3: Unweighted Jackknifed Unifrac clustering tree. 1000 permutation were executed and 1000 sequences were kept per permutation. The label on the nodes represent the significance value. Sample characteristics including total arsenic concentrations are also shown.
Take home message
References 1 Johnson DB, Hallberg KB. (2005) Acid mine drainage remediation options: A review. 490 Sci Total
Environ 338(1):3-14. 2 Cowan, W.R., Robertson, J. (1999) Mine Rehabilitation in Ontario, Canada: Ten Years of 329 Progress. 3 Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, E.K., et al. 326 (2010b) QIIME allows analysis of high-throughput community sequencing data. Nature 327 Methods. 4 Lozupone, Hamady and Knight (2006>) UniFrac - An Online Tool for Comparing Microbial Community Diversity in a Phylogenetic Context.BMC Bioinformatics, 7:371.
