An update on CSIRO research into AGD
in Tasmania
Mathew T Cook, Richard Taylor, Ben Maynard, Paula Lima, Peter Kube,
Klara Verbyla and Nick Elliott
FOOD FUTURES FLAGSHIP
2 |
3 |
Environment – water sampling, traditionally
labour intensive and replication difficult
4 |
Semi-automatic sampling in situ
5 |
Results – Semi-automated water sampling
RNA analysis
40
35
DNA Analysis
Average Ct
30
25
20
15
10
5
0
10000
1000
100
10
Concentration (cells/L)
6 |
1
0
2nd Generation sampler
• Computer controlled – set and forget
• Capable of taking and archiving 24 samples
• Pumps water across filter and then ‘floods’
with appropriate buffer
• Will be deployed 2nd ½ of 2014
7 |
Non-destructive sampling of gills - swabs
8 |
RNA vs DNA results
GS 4
GS 2
GS 0
GS 3
Average Cycle Threshold Value per GS Score
GS 1
34
32
30
28
Ct Average
26
Lineær (Ct Average)
24
22
20
0
9 |
1
2
3
4
5
10 |
Development of Amoebae Bioassay
• Assay based on the observation that P.
perurans actively ‘takes up’ Neutral red dye
• Apply treatments to observe ‘uptake’
• Requires appropriate controls (+ve and –ve)
11 |
Indicative results
100
80
60
40
20
0
12 |
% viability of N. pemaquidensis after treatment with
candidate compound
Transcriptomic analysis
Compare genetic information of amoeba P. Perurans (infective) and closely
related species P. Pemaquidensis (non-infective) to identify differences.
N. perurans
N. pemaquidensis
Compare the two datasets to identify:
1.
2.
Seq01:
Seq02:
Seq03:
Seq04:
Seq05:
Seq06:
Seq07:
Seq08:
Seq09:
Seq10:
Seq11:
Seq12:
Seq13:
Seq14:
Seq15:
Seq16:
ACGGTAGGCTAGACTAGATATTAACG
CCTGAGTACCTGGACTAGATAC
GATGCGGTTACGTACGATCCATGGA
CATTTATTATATATACGCGCGCGA
TTTCGATAGGGGATATATTAACGCCG
GTAGGTAGGTGGAGGCCCGCAGACGC
GATAGACTCGCGCCGATATATAG
ATATATTTCCTAGATCGAGAGATAC
Seq01: ACGGTAGGCTAGACTAGATATTAACG
GATAGGTTAATTAATTTCCTATAT
Seq02: CCTGAGTACCTGGACTAGATAC
TGGATTGGATAGCGCGATAGATC
Seq03: GATGCGGTTACGTACGATCCATGGA
AAAAGTCGATAAGGCTAGAGCTAG
Seq04: CATTTATTATATATACGCGCGCGA
GGATATAGATATATCTAGATATC
Seq05: TTTCGATAGGGGATATATTAACGCCG
CGATATAGCCCTCTAGAGATACTTT
Seq06: GTAGGTAGGTGGAGGCCCGCAGACGC
GATACCCGCGATATATCAT
Seq07: GATAGACTCGCGCCGATATATAG
TAGATCCCCGAGATAGAGACT
Seq08: ATATATTTCCTAGATCGAGAGATAC
CACCATAGAAGACTGATCGAGATAG
Seq09: GATAGGTTAATTAATTTCCTATAT
Seq10: TGGATTGGATAGCGCGATAGATC
Seq11: AAAAGTCGATAAGGCTAGAGCTAG
Seq12: GGATATAGATATATCTAGATATC
Seq13: CGATATAGCCCTCTAGAGATACTTT
Seq14: GATACCCGCGATATATCAT
Seq15: TAGATCCCCGAGATAGAGACT
Seq16: CACCATAGAAGACTGATCGAGATAG
?
Transcripts (i.e., functionality) present in one dataset that are “missing” in
the other dataset?
Of those transcripts that are similar, how similar are they?
13 | Amoeba Transcriptome Analysis
Raw Read Support
ACGTTA
TTAACG
ACCGCA
TTAACG
GGCACG
TATACG
TACGTA
AACGTA
CCGGTA
ACGGTAGGCTAGACTAGATATTAACG
TACAGT
TACAGT
TACAGT
CCTGAGTACCTGGACTAGATAC
CATAGT
CATAGT
CATAGT
GATGCGGTTACGTACGATCCATGGA
AGTCAT
AGTCAT
AGTCAT
CATTTATTATATATACGCGCGCGA
GTACAT
GTACAT
GTACAT
TTTCGATAGGGGATATATTAACGCCG
CATGTA
CATGTA
CATGTA
GTAGGTAGGTGGAGGCCCGCAGACGC
ACGTTA
ACGTTA
ACGTTA
TTAACG
TTAACG
TTAACG
ATATATTTCCTAGATCGAGAGATAC
TACGTA
TACGTA
TACGTA
CCTGAGTACCTGGACTAGATAC
TACAGT
TACAGT
TACAGT
GTAGGTAGGTGGAGGCCCGCAGACGC
CATAGT
CATAGT
CATAGT
ACGGTAGGCTAGACTAGATATTAACG
AGTCAT
AGTCAT
AGTCAT
GTACAT
GTACAT
GTACAT
CATGTA
CATGTA
CATGTA
BLAST
GTAGGTAGGTGGAGGCCCGCAGACGC
ACGTTA
AGTCAT
CATGTA
TGTA
AGTCAT
CATGTA
ATGTA
GTAGGTACG
GTAGGTACGTGGAGGCCCGCAGACGC
GCCCGCAGACG
CATGTA
AGTCAT
TCAACG
G
ACGTTA
TACGTA
14 | Amoeba Transcriptome Analysis
GATAGACTCGCGCCGATATATAG
We can turn off genes in P. perurans
GSP for ‘Target’
1
2 3
4 5 6 7
Average Ct
cDNA
4
gDNA
5
6
7
time
Target
18S
Untreated
28.2
22.5
24hr
28.7
22.9
48hr
29.8
23.6
72hr
32
23.9
1week
31.7
24.9
NTC
32.5
34.4
16 |
Hybrids – can we unlock the ‘key’ to resistance
SxS
SxT
TxS
TxT
17 | AGD functional feed and hybrid trials | Dr Ben Maynard
18 | AGD functional feed and hybrid trials | Dr Ben Maynard
Hybrid vigour
• 63% hybrid
vigour observed
at AGD4
• “Best parent”
performance
19 | AGD functional feed and hybrid trials | Dr Ben Maynard
Breeding for AGD Resistance
Broodstock never go to sea
20 |
Gill Score is a consistent heritable trait
AGD1 versus AGD3
ͻ (heritability range is h2 = 0.14+0.02 to
0.40+0.03)
4.0
Gill score 3rd infection
• All infections have significant
genetic variation
3.5
3.0
2.5
2.0
1.5
• First infection has lower genetic
variation (h2 = 0.14)
• Subsequent infections have
stronger genetic expression,
especially when measured during
summer
21 |
1.0
0.5
0.0
2.0
2.5
3.0
3.5
4.0
Gill score 1st infection
4.5
AGD3 versus AGD4
Gill score 4rd infection
• Two distinct traits, one is
measured at first infection and
the other at all subsequent
infections
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1.0
1.5
2.0
2.5
3.0
3.5
Gill score 3rd infection
4.0
Selection is reducing the number of bathes
MONTH
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Jan
2005 YC
Founders
2006 YC
2007 YC
2008 YC
1st generation
selection
2009 YC
2010 YC
*
2nd generation
selection
2011 YC
Water temp.
11.5o
11.3o
11.6o
12.5o
14o
15.1o
16.5o
17.2o
16.1o
14.9o
13.6o
12.2o
11.5o
11.3o
11.6o
Marine grow-out period
AGD infections (1 to 6) and measurement events
Low score, prophylactic treatment
22 |
12.5o
14o
15.1o
16.5o
Selection is working – A deliberate ‘Bad’ family
AVERAGE FAMILY VALUES 2012 YC (SBP COHORT)
SBP
BAD AGD
Best AGD
families
family
family
EBV AGDac (Bath
interval) *
25%
-12%
49%
Mean AGD1 Gill score
1.4
1.8
1.2
Mean AGD2 Gill score
3.1
4.5
1.2
NB͗ Following GS2, Best AGD Family хϳ5% ч GS1
23 |
Within vs between family variation
AGD predictions before and after progeny
testing 2009YC
Genetic change after progeny testing
60%
y = 0.8618x
R² = 0.4568
50%
40%
30%
20%
10%
0%
-10%
-20%
-20%
-10%
0%
10%
20%
30%
40%
50%
Predicted genetic change before progeny testing
• Highlights the power of whole genome selection (WGS) to increase genetic
gains as the broodstock remain in freshwater (biosecurity)
24 |
Can SNP explain some of the AGD resistance
observed?
SNP #
1
2
3
25 |
LG
Ssa-07
Ssa-01
Ssa-19
%Variance
34.85
22.45
15.16
Summary
• Activities are focused on ‘HOST’, ‘PATHOGEN’ and ‘ENVIRONMENT’
• Focus is on delivering solutions/information to assist in alleviating
the issue
• Partnership with the end users (industry) is crucial
• AGD R&D needs to be dynamic to adjust to changing priorities
• International collaboration is key – no need to reinvent the wheel
26 |
The CSIRO AGD Team
Selective Breeding
Amoeba Biology
Mat Cook
Richard Taylor
Natasha Botwright
Paula Lima
AGD Resilience
Nick Elliott
Peter Kube
Richard Taylor
Mat Cook
Sonja Dominik
John Henshall
Scott Cooper
Richard Taylor
Peter Kube
Amoeba Detection
Mat Cook
Richard Taylor
Sharon Appleyard
Paula Lima
27 |
Dietary Intervention
Brett Glencross
Mat Cook
Ben Maynard
Richard Taylor
Funding bodies and Industry and Academic
collaborators
28 |
Thank you
CSIRO Food Futures Flagship
Mathew Cook
Stream Leader
t +61 7 3833 5993
e [email protected]
w www.csiro.au