P17
DECIPHERING THE EARLY BOVINE HOST RESPONSE AFTER Brucella
melitensis INFECTION
ROSSETTI
1
CA *
2
K ,
3
S ,
3
J ,
DRAKE
LAWHON
NUNES
GULL
4
4
3
EVERTS R , LEWIN H and ADAMS LG
3
T ,
KHARE
3
S ,
1 Inst. de Patobiología, CICVyA-CNIA, INTA (Argentina); 2 Seralogix Inc (USA); 3 Texas A&M University (USA);
4 University of Illinois (USA) * E-mail: crossetti@cnia.inta.gov.ar
AIM OF THE STUDY: Characterize the early host response to Brucella melitensis infection at the transcriptomic level
MATERIALS & METHODS
PERINATAL CALF ILEAL LOOP MODEL
SAMPLES PROCESSING
Control
Loops
Experimental
Processing
Inoculation
- Four clinically healthy 3 to 4-week old brucellosisfree male calves;
-Five 6 to 8 cm lenght loops were intralumenal
inoculated with 3x109 CFU of B. melitensis 16M
(infected loops) and other five were injected with 3
ml of sterile media (control loops);
-One infected and one control loops were
collected at 5 time points (0.25, 0.5, 1, 2, 4h p.i.)
and samples processed for host gene expression
profiling;
-Calf were euthanatized at the end of
procedure and the carcass incinerated.
the
MICROARRAY DATA ANALYSIS
Microarray data analysis and modeling was performed
using
an
integrated
platform
termed
the
BioSignature Discovery System (BioSignatureDS™)
(Seralogix, LLC, Austin, TX). This approach for
genomic data analysis and modeling at the system
biology level offers an integrated view of biological
mechanisms
and
networks
of
interactions.
Specifically for the analysis reported herein, the
tools were used to:
1) Determine significant gene modulations via a zscore sliding window thresholding technique and
fold change;
-cDNA from experimental RNA samples (Cy5)
were co-hybridized against cDNA generated from
the bovine reference RNA sample (Cy3) to a
custom 13K bovine 70mer oligoarray;
-Slides were scanned and the genes represented
on the arrays, adjusted for background and
normalized to internal controls.
2) Conduct biological system level analysis employing
Bayesian network models for scoring and ranking of
metabolic pathways, signaling pathways and gene
ontology (GO) groups;
3) Conduct Bayesian candidate mechanistic gene
analysis to identify genes within the network models
that are most responsible for causing pathway and
GO group perturbations.
RESULTS
GENES DIFFERENTIALLY EXPRESSED
IMMUNE- and INFECTIOUS DISEASERELATED PATHWAYS HEAT MAPS
1800
Genes UP reg
Num ber of host genes D E
1600
NETWORK MODEL FOR PATHWAY
SIGNIFICANTLY ALTERED AND
MECHANISTIC GENES
Genes D OWN reg
1400
1200
1000
800
600
400
200
0
0 .2 5
0 .5
1
2
4
Ti m e post -i nfect i on ( h)
-B. melitensis infection induces a progressive
host gene expression modification with the
highest activity at one hour p.i.;
-2918 genes (2284 up vs 634 dw) showed
differential expression (DE) in the first 4 h p.i.
compare to control loops;
-1587 genes (54%) (1088 up- and 499 dwregulated) were differentially expressed at
only 1 time point.
Dynamic Bayesian Network (DBN) model with mechanistic
genes (concentric yellow rings) and heat map of gene scores
for the Cytokine inflammatory response signaling pathway
CONCLUSIONS
- Microarray data analysis indicated an increase host gene expression at the onset of the infection, with later normalization;
- The great majority of the 219 metabolic and signaling pathways scored were induced over time;
- Mechanistic genes overlapped in different pathways, illustrating the importance of the cross-talk via inter-pathway interaction.
Acknowledgements: This study was supported by US-DHS:FAZD, NIH and NIAID grants