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DECIPHERING THE EARLY BOVINE HOST RESPONSE AFTER Brucella melitensis INFECTION ROSSETTI CA 1 * DRAKE K 2, LAWHON S 3, NUNES J 3, GULL T 3, KHARE S 3, EVERTS.

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Presentation on theme: "DECIPHERING THE EARLY BOVINE HOST RESPONSE AFTER Brucella melitensis INFECTION ROSSETTI CA 1 * DRAKE K 2, LAWHON S 3, NUNES J 3, GULL T 3, KHARE S 3, EVERTS."— Presentation transcript:

1 DECIPHERING THE EARLY BOVINE HOST RESPONSE AFTER Brucella melitensis INFECTION ROSSETTI CA 1 * DRAKE K 2, LAWHON S 3, NUNES J 3, GULL T 3, KHARE S 3, EVERTS R 4, LEWIN H 4 and ADAMS LG 3 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 MATERIALS & METHODS - 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 RESULTS AIM OF THE STUDY: Characterize the early host response to Brucella melitensis infection at the transcriptomic level PERINATAL CALF ILEAL LOOP MODEL SAMPLES PROCESSING 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 z- score sliding window thresholding technique and fold change; 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. - Four clinically healthy 3 to 4-week old brucellosis- free male calves; -Five 6 to 8 cm lenght loops were intralumenal inoculated with 3x10 9 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 the procedure and the carcass incinerated. -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. ExperimentalControl GENES DIFFERENTIALLY EXPRESSED NETWORK MODEL FOR PATHWAY SIGNIFICANTLY ALTERED AND MECHANISTIC GENES IMMUNE- and INFECTIOUS DISEASE- RELATED PATHWAYS HEAT MAPS -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 dw- regulated) 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 P17 LoopsInoculation Processing CONCLUSIONS


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