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Temporal Transcriptional Analysis of the Early in vivo Initial Interactions of both Brucella & Bovine Host Carlos A. Rossetti, Brian Kamery, Sara Lawhon, Jairo Nunes, Tamara Gull, Cristi Galindo, Sangeeta Khare, Robin Everts, Mitch Magee, Harris Lewin, Stephen Johnston, Harold Garner, Ken Drake & L. Garry Adams Department of Veterinary Pathobiology College of Veterinary Medicine Texas A&M University Brucellosis Research Conference 1 XII 07 Chicago Pathogenesis of initial infection Natural infections occur primarily through adhesion and penetration of mucous membranes - B. abortus & melitensis: alimentary tract -B. canis, B. ovis & B. suis: genital tract Macrophages, dendritic cells, & neutrophils phagocytose free Brucella in the submucosal interstitium Persistence of infection in reticuloendotelial system Metastasis to regional (primary) lymph nodes Objective The goal of this study was to analyze the transcriptome of host and Brucella during their early in vivo interactions in an effort to understand how this interaction modulates the outcome of the infectious process. METHODOLOGY • Four - 12 hour ligated ileal loop non-survival surgeries in 3week old male brucellosis-free beef calves under BSL3 condition • Abdominal wall incised under general anesthesia • Twenty one – 6 cm ligated ileal loops - 7 inoculated with 3 ml of Live 1x109 Wild Type (WT) Bmel 16M - 7 inoculated with 3 ml of Heat Inactivated (H-I)1x109 Bmel - 7 injected with 3 ml of media (control loops) METHODOLOGY • Three loops (1 WT, 1 H-I and 1 control) excised at 0.25, 0.5, 1, 2, 4, 8 & 12 h PI • Sampled for tissue associated bacteria, histopathology, TEM, SEM & RNA • Calves euthanatized at 12 h PI BACTERIOLOGICAL RESULTS Peyer's patches colonization CFU/g of tissue (log 10) 6.5 6.4 6.3 6.2 6.1 6 5.9 5.8 5.7 0.25 0.5 1 2 4 Time post-inoculation (h) 8 12 BACTERIOLOGICAL RESULTS • B. melitensis was recovered from – Systemic blood within 30 min PI – Mesenteric LN & liver at 12 h PI – Control & HI loops at 8 & 12 h PI Conclusions: Rapid penetration of B. melitensis through Peyer’s patch & metastasis via lymphatic vessels followed by systemic bacteremia & organ colonization Intracellular B. melitensis gene expression profile • 4 biological replicas of B. melitensis RNA were enriched & amplified from total RNA extracted from PP at 15 min to 4 h PI & co-hybridized against B. melitensis gDNA on 3.2 K B. melitensis oligo-arrays • Original total RNA from WT B. melitensis-infected PP (non enriched, non amplified) was also co-hybridized against B. melitensis gDNA to B. melitensis oligo-arrays - Oligospots with signal were considered non-specific & eliminated from all analyses to reduce false positive gene detection • The intracellular in vivo B. melitensis gene expression was compared to the gene expression in the inoculum (cultures of B. melitensis at late-log growth phase) In Vivo Intracellular B. melitensis expression profile 70 618 genes differentially expressed 60 Down reg - 365 up-regulated - 253 down-regulated 50 # of genes Up reg 40 30 20 10 0 A B C D E F G H I J K L M N O Protein biosynthesis Energy production and conversion Q R Unknown function COGs functional categories Amino acid transport and metabolism P Cell wall/ membrane biogenesis In Vivo intracellular profile of B. melitensis expression • B. melitensis had a common in vivo transcriptional profile in the first 4 h PI • 618 genes (19.3 % of B. melitensis genome) were identified as differentially expressed in at least 4 of 5 time points evaluated • Most of the functional categories were over expressed, except transcription, defense, motility, intracellular trafficking & secretion • 37.5% of the genes differentially expressed lacked functional annotation In vivo Bovine Peyer’s patch gene expression profile • Total RNA was extracted from 4 calves at 15 min to 4 h post-infection from WT, H-I and control loops (n=60) • RNA was co-hybridized against bovine reference RNA & 13K custom bovine arrays (UIUC) B. melitensis-infected Bovine Peyer’s patch transcriptional profile 70 Up-regulated Down-regulated 60 224 genes differentially expressed - 196 Up-regulated # of genes 50 15 m - 1 h PI 40 - 28 Down-regulated 30 20 10 0 A B C D E F G H I J GO biologica l proce sse s 50 45 1 h - 4 h PI 40 Down-reg - 459 Up-regulated - 704 Down-regulated 35 # of genes Up-reg 1163 genes differentially expressed 30 25 20 15 10 5 0 A B C D E F G H I J K L M N GO biological processes O P Q R S T U V W B. melitensis infected bovine Peyer’s patch transcriptional profile • Two different expression profiles were observed in the bovine Peyer’s patch during the first 4 h PI – Up-regulation of the transcriptome in the first hour PI (86%) – Down-regulation of the transcriptome between the 1 and the 4 h PI (63%) • Interesting findings – – – – Anti-chemo attractant PMN and monocyte response Pro-apoptotic response Pro-abortive transcriptional response Arresting of the cell cycle and inhibition of cell proliferation and differentiation H-I B. melitensis-inoculated bovine Peyer’s patch expression profile 45 140 genes differentially expressed Up-regulated 40 - 78 Up-regulated - 62 Down-regulated Dow n-regulated 35 # of genes 30 25 20 15 10 5 0 A B Inflammatory and immune response C D E GO biological processes F G Unknown function Mathematical modeling predictive analysis framework for mechanistic discovery • Data fusion – Prior biological knowledge (qualitative data) – Metadata – Data extracted from the actual experiment (quantitative data) Multi-conditional analysis Biosystem modeling and discovery Mathematical modeling predictive analysis framework for mechanistic discovery • Data fusion – Prior biological knowledge (qualitative data) – Metadata – Data extracted from the actual experiment (quantitative data) • Pathways & GO comparative analysis: Identify significantly expressed genes associated with known metabolic and regulatory pathways & map significant changed genes to GO categories Multi-conditional analysis Biosystem modeling and discovery B. melitensis bio-signature mechanistic candidate genes in vivo Dynamic Bayesian modeling: - 11 highly activated GO biological groups containing 78 mechanistic candidate genes in the first 4 h PI - 17 top pathways analyzed containing 119 mechanistic candidate genes B. melitensis bio-signature mechanistic candidate genes in vivo Dynamic Bayesian modeling: - 11 highly activated GO biological groups containing 78 mechanistic candidate genes in the first 4 h PI - 17 top pathways analyzed containing 119 mechanistic candidate genes Top Scoring GO Biological Groups Top Scoring Pathways 1. Amino acid biosynthesis 2. Adaptation to atypical conditions 3. Cell envelope 4. Regulation of transcription 5. Transcription 6. Transport: Cations and iron carrying compounds 1. Oxidative phosphorylation 2. Protein export 3. ABC transporters 4. Pyruvate metabolism 5. TCA cycle 6. Two component system Bovine bio-signature mechanistic candidate genes in vivo Dynamic Bayesian modeling: - 47 highly activated GO biological groups containing 52 mechanistic candidate genes in the first 4 h PI - 16 top pathways analyzed containing 37 mechanistic candidate genes Bovine bio-signature mechanistic candidate genes in vivo Dynamic Bayesian modeling: - 47 highly activated GO biological groups containing 52 mechanistic candidate genes in the first 4 h PI - 16 top pathways analyzed containing 37 mechanistic candidate genes Top Scoring GO Biological Groups 1. Positive regulation of TNFa biosynthesis 2. Proteolysis and peptidolysis 3. MHC II presentation 4. Inflammatory response 5. Coupled receptor protein signaling pathway 6. Regulation of cell cycle Top Scoring Pathways 1. NK cells mediated cytotoxicity 2. Cytokine-cytokine receptor interaction 3. MAPK signaling 4. Cell adhesion 5. Ca signaling pathway 6. Leukocyte transendotelial migration Conclusions • Brucella invade the host via intestinal Peyer’s patches followed • NO histopathological changes in early infected tissues • A common transcriptional profile was identified in B. melitensis in the first 4 h PI in vivo • Two different transcriptional profiles were observed in the bovine host early in the infection • This study provides specific genes & pathways to further elucidate how both host and Brucella interact in vivo during the early infectious process to the eventual benefit of the pathogen and to the detriment of the naïve host by metastasis & systemic distribution and organ colonization via blood and lymphatic vessels Future steps • Develop of modern software and modeling approaches that help connect Brucella effectors with host targets • Laser Capture Micro-dissection (LCM) analysis to study the temporal expression profile of both, Brucella and the host more precisely, providing an approach of how Brucella modify their transcriptome inside different cell types & how these cells respond to Brucella infection • Discovery of novel genes & important pathways critical to the host response in the pathogen virulence to determine potential targets for subsequent therapeutic and vaccine research Acknowledgements • • Dr. Adams’ lab - Tiffany Fausett - Josely Figueiredo - Tamara Gull - Doris Hunter - Sangeeta Khare - Sara Lawhon - Jairo Nunes - Alan Patranella - Roberta Pugh - Quynhtien Tran B. melitensis microarray printing • Microarray analysis - Dr. Cristi L. Galindo and Dr. Harold Garner (UTSWMS – Dallas) - Dr. Bryan Kamery and Dr. Ken Drake (Seralogix, Inc.) • Financial support: I.N.T.A.Fulbright Argentina scholarship, NIH/NIAID Western Regional Center of Excellence, & DHS – FAZD grants - Dr. Mitchell McGee and Dr. Stephen A. Johnston from the Center for Innovations in Medicine, A.S.U • Bovine microarray printing - Dr. Robin Everts and Dr. Harris Lewin from UIUC