ASU TVDC Technical Report
Kathryn F. Sykes and Stephen A. Johnston
Completed Milestones: 25, 26, 28* and 32, 33, 34, 35,
36
Active Milestones: UNM 29*/ASU28*, ASU 37/38
Currently Inactive Milestones: 30
Slide 1
MILESTONE 28
Build SCHU S4
proteome
Gray: (sub)milestone title
Yellow: Re-opened
Build ORF expression
library corresponding
to proteome
Generate complete
protein-fragment library
Array protein-fragments
for T cell stimulation
assays
Re-opened
Re-opened
Re-opened
Slide 2
Generation of polypeptide
antigens for UNM T cell assays
SOP for detecting T cell
stimulation with synthetic
(IVTT) antigens
Production of
antigens (ASU)
Production
Assay
development
(UNM)
T cell proliferation
IFNg ELISpot assay
Screening
(UNM)
Identification of
stimulatory proteins &
peptides
Confirmation
Assay optimization
using ivt proteins
Slide 3
Testing new protein capture
beads
• In our initial test of the small vs. large
diameter capture beads, immune cells
from LVS-immunized NHP were used for
the IFN-gamma ELISPOT assays.
• We determined that background was low
using either bead.
Slide 4
ELIspot: two bead comparison
Slide 5
Experimental Design:
IFNg ELISpot plate layout
ELIspot Plate Layout: two bead
data
IglC
SFV053
1 uM
MyONE
M280
MyONE
M280
0.2 uM
MyONE
M280
MyONE
M280
0.04 uM
MyONE
M280
MyONE
M280
FF LVS neat
FF LVS 1:5
FF SCHU S4 neat
FF SCHU S4 1:5
Slide 6
Preparing for Specificity Test for
Improved Capture Beads
• Objective: Repeat large scale synthesis of IglC
1 and ASFV053 (negative control) on both
small MyOne (1micron) and large M280 (2.8
micron)beads.
• Last month these preps were tested against
LVS-immunized NHP immune cells.
• These antigens will next be evaluated at UNM
using immune cells from IglC1-immunized
rats.
Slide 7
New Preps of IglC1
and ASFV053 on the
two bead formats is
complete
0.5xIVT-rx ASFV053
BSA, ug
AMPure PureLink 0.5 1 2
-250
- 150
- 100
- 75
- 50
- 37
33 kDa
- 25
- 20
M-280
MyOne
M-280
- 15
MyOne
These bead-bound
antigens (17ug of
each) have been sent
to UNM
- 10
Precision Plus Standard (Bio-Rad)
kDa
Evaluation of new PCR purification system
for LEE templates prior to IVTT reactions
AMPure is
better
Slide 9
Production of LEE Templates
for IVTT
• Assessing quality and quantity of stored
2008 LEE templates and oligo primers
• A 2006 prep of genomic FTT was used
to evaluate the stored primers.
• Even amplifications that did not work
well originally (QC neg) work well now,
with titrated amounts of template.
Slide 10
New Amplification of the QC positive vs. negative templates
in order to evaluate stored FTT primers
MILESTONE 37/38
Generate and purify mg
quantities of 12
selected
FTT proteins
Gray: (sub)milestone title
Green: open
Transcriptome
Ag discovery
Proteome
Ag discovery
Live vector-based
Ag discovery
Active
Active
Active
Slide 12
MILESTONE 35 (cont’d)
Array hybridations with mouse RNAs
from virulent Schu 4 infection
& RT PCR confirmation of candidates
Virulent Schu 4 Samples
Gray: (sub )milestone title
Red: completed
Green: in progress
RT-PCR Confirmations
Completed
Initial samples
Dose-Response of Infection
Multi datatype analysis
Phase 1 – Protein array, expression (qRT-PCR
and array), western, ELIspot
Slide 13
MILESTONE 36
Final integration of expression data
and informatics analysis
Locus_Tag Gene
Protein_Length
hypothetical protein
193
rpsP
30S ribosomal protein S16
82
FTT1526c
idh
isocitrate dehydrogenase
747
serC
phosphoserine aminotransferase
350
FTT1038c
rpsU3 30S ribosomal protein S21
65
FTT1506
hypothetical protein
192
Decreasing
FTT0150
FTT0560c
Increasing
Flat
FTT0103c
Product
Gray: (sub )milestone title
Red: completed
Green: in progress
FTT0145
rpoC DNA-directed RNA polymerase, beta subunit
FTT0698
rpsO 30S ribosomal protein S15
88
FTT1137c
hypothetical protein
86
1417
Slide 14
UNM/DVC/NIAID 5/24/10
• Rick Lyons : UNM is in the discovery phase of correlates primarily
in rats (Fisher 344) and cynomolgus macaque primates.
• Data:
o Functional: Elispot (IFN gamma) detecting responses from lymph nodes from
LVS vaccinated cynos vs. FT protein library synthesized by ASU
o Transcriptome: FTT genes expressed during first 24 hrs of host infection in mice
and rats.
o Host antibody response to LVS vaccination and/or SCHU S4 challenge: Felgner
arrays run on UNM TVDC sera from rats, mice, humans and NHP
o Western data: DVC kindly ran UNM LVS immunized rat sera on their FTT 2D
protein Western blots
• UNM TVDC is looking across species and across different assays,
for correlates and potential vaccine candidates.
Multidata type analysis
• Presented by Phillip Stafford, PhD
• 5/24/2010
Multidatatype analysis
Many methods exist for analysis of multiple datatypes.
1) Data fusion (low level): combines data from multiple sources to produce new data
limitation: difficult, produces non-linear biases, may amplify the inherent error
2) Data mining (high level): extracts patterns in data without hypotheses or direction (unsupervised)
limitation: unsupervised mining requires high precision and nearly zero data bias to identify patterns
2a) Literature-based data mining: uses discovered knowledge to query existing data which may be novel or
different from literature
limitation: only previously discovered facts can be used
Multidatatype analysis
Many methods exist for analysis of multiple datatypes.
3) Hypothesis testing (high level, supervised): extracts conclusions dataset by dataset, resulting in interpretations
across a broad swatch of biology
limitation: when using high stringency, important information can be excluded
4) Hypothesis projection (high level, supervised): extracts conclusions using hypotheses, and projects results
onto other datasets
limitation: caution when interpreting the results, an expert in each field must review the interpretation
5) Binary clustering, decision tree: examines each dataset for a valid ‘yes/no’ answer, then combines answers
into a final interpretation
limitation decision tree: must have a solid set of answerable questions before beginning
limitation binary clustering: must be able to transform data to binary reliably and sensibly before clustering
limitation binary clustering: results may not make biological sense
Example:
1) FTT list from Western data for Rat provides proteins that have high binding to protected sera
2) This list is then projected onto Protein Array data, a similar datatype but orthogonal
3) The consensus of these data are projected onto Expression data to detect the relative abundance of transcripts
during infection
4) These data are then cross-referenced to literature-based data (pathogenicity island)
Western ‘hits’
Protein array - rat
2
1
Protein array - rat
Overlap between
literature, western,
ELISpot genes
Western data
3
4
Transcriptome data