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Microbiome Characterization and Management

Microbiome Characterization and Management. Activity 2.2-2.4. Microbial colonization patterns associated with health & improved performance. Microbiome – WBT, Immune Response & Disease Resilience. Activity 2.2.

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Microbiome Characterization and Management

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  1. Microbiome Characterization and Management • Activity 2.2-2.4

  2. Microbial colonization patterns associated with health & improved performance

  3. Microbiome – WBT, Immune Response & Disease Resilience • Activity 2.2

  4. 2.2.1 Microbiome and blood transcriptome as predictors of Salmonella shedding Blood transcriptome is predictive of Salmonella shedding in pigs (Kommadath et al., BMC Genomics 2014, 15:452) Huang et al. (2011) PLoS ONE 6(12)

  5. qPCR validation of genes of interest in predicting Salmonella shedding • qPCR panel of genes highly correlated with RNAseq results • Maintained same prediction

  6. 2.2.1 Microbiome and blood transcriptome as predictors of Salmonella shedding 20 predicted high Salmonella challenge Blood transcript panel to predict shedding 20 predicted low Correlation between microbiota and blood transcriptome 107 pigs 12 predicted high IP challenge and immune assessment PND7 PND21 PND49 Faecal sampling at PND7, 21 and 49 for microbial composition analysis 12 predicted low

  7. Week 6 prediction for week 9 infection • Prediction at week 6 was no better than 50/50 • Predicted status changed between week 6 and week 9 for 13 of the 31 pigs

  8. Prediction of total shedding from week 9 samples Accuracy of prediction 11/15 = 73.3 % • LS: 5/7 = 71 % • PS: 6/8 = 75 %

  9. Antibiotic treatment altered the expression of Salmonella resistance predicting genes long after withdrawal CONTROL ANTIBIOTIC

  10. Testing consistency and effect of antibiotics • Treatment • 30 mg/kg 2x per day • AB = Amoxicillin • PL= Placebo PND35 PND49 PND21 PND0 PND42 PND14 piglets weaned into nursery 2 litters 2x/day Rx dose Piglet weights Blood sampling

  11. Gene Expression Week 7 * P < 0.10 ** P < 0.05 ** ** ** * * ** * ** ** * ** * ** **

  12. Prediction of Salmonella Shedding 4/6 antibiotic pigs predicted LS 2/6 antibiotic pigs predicted as PS 4/7 control pigs predicted as LS 3/7 control pigs predicted as PS • 8/13 pigs changed between week 5 and week 7

  13. Looking at immune characteristics with intraperitoneal challenge

  14. Fig. 2. Leukocyte infiltration after intraperitoneal injection of Heat-killed Salmonella typhimurium. Rapid leukocyte recruitment was observed as soon as 4 hour post challenge. 4h and 12h are significant different compared to negative control (C-) in each group (N=7). There are no significant difference between treatments. However, slightly increased numbers were detected at 4h post injection in antibiotic group.

  15. Fig. 4. Oral antibiotic administration upregulates NF-KB translocation in activated peritoneal leukocytes. Pigs were challenged with heat-killed Salmonella typhimurium and peritoneal leukocytes were recovered at 4 and 12h post challenge. FITC anti-NF-KB was used to measure the level of nuclear translocation. *p<0.05. n=6 Translocated No Translocated

  16. Work in progress • Complete blood gene panel prediction of IP challenged pigs • Complete microbiome analysis on IP challenged pigs

  17. 2.2.2 Microbiome, blood transcriptome and vaccine response Vaccinate Booster PND49 Blood samples for transcriptome Cell mediated and humoral immune response PND63 PND 28 30 34 80 conventional piglets Correlation between microbiota, blood transcriptome and vaccine response - Natural variation and differences between conventional and antibiotic free 40 Antibiotic-free piglets PND28 PND7 Faecal sampling at PND7, 28 for microbial composition analysis

  18. Antibody titers vary up to 100-fold

  19. Kinome profile clusters with vaccine response

  20. Transcriptome of vaccine response • D2 time-point analyzed • 245 genes differentially expressed between high and low responders

  21. 2.2.2 Vaccine study (work in progress) • Work to be completed • Completion of transcriptome analysis • Completion of microbiome analysis • Connection of datasets to identify possible relationships

  22. 2.2.3Microbiome in natural challenge model (1.2) Faecal samples from all 3600 pigs at entry and during challenge (bleeds 2, 3 and 4) 500 healthy pen mates 250 sick pigs Microbiome sequencing of entry samples from selected pigs

  23. International validation • Shared postdoc being hired with INRA • INRA has completed a very similar study to the vaccine study • Postdoc will work with Plastow/Willing at UofA and Rogel-Gaillard and Estellé at INRA on both datasets

  24. Manipulating the Microbiome • Activity 2.3

  25. Act 2.3 Feed management & Gut MicrobiomeInternational validation • Managing gut health and microbial population through diet • Feed additives to manage gut health • Affects of dietary manipulation on microbiome, metabolome and transcriptome • Can feed changes drive disease resilience phenotypes observed in other activities?

  26. 2.3.1 Effects of Maternal Supplementation on Piglet

  27. Proposed pilot study

  28. 2.3.2 Impact of undigested dietary carbohydrates and protein on gut microbiota Cannulated pigs – digestibility Ileum, faeces  microbial analysis Ileum, faeces and blood  metabolites 10 diets varying in amounts of typical feed ingredients Metabolites Ingredient Microbiota Growth Performance - Blood and intestinal transcriptome - Ileum and colon microbiota - Ileum, faeces and blood metabolites 5dietsvarying amounts of in high-low undigested protein and fermentable carbohydrate Diet effects extrapolated back to microbiota and transcriptome studies of resilience Progress & Next-6 months: • Animal trials on-going • Diets of interest will be established • Optimization of extraction and MS/MS protocols by metabolomics centre

  29. Update Details • First trial is underway • Modest dietary manipulations (benzoic acid) • Small delay with MS/MS equipment for metabolomic analysis

  30. Maternal Effects Gut Health • Activity 2.4

  31. Effects of maternal / litter environment • Birth weight (BW) has consequences that last throughout the pigs’ life • Gonad development • Muscle development • Gut development and immune status • There is a large variation in repeatability of a litter birth weight phenotype among sows • certain sows will consistently give birth to low BW litters (LBW) - others high BW litters (HBW) • GC Project builds on National Pork Board project to assess factors affecting Sow productivity

  32. Effects of birth weight on duodenal mucosal height 150 days birth High BW Low BW (Alvarenga et al., 2012)

  33. Effects of maternal / litter environment • Birth weight (BW) has consequences that last throughout the pigs’ life • Gonad development • Muscle development • Gut development and immune status • There is a large variation in repeatability of a litter birth weight phenotype among sows • certain sows will consistently give birth to low BW litters (LBW) - others high BW litters (HBW) • GC Project builds on National Pork Board project to assess factors affecting Sow productivity

  34. 2.4Maternal-Fetal Effects on Gut Health ~700 Sows > 3 parities SNP analysis 2400 Sows Litter birthweight (BW) data High – Low analysis Low BW Sows N = 40 sows High BW Sows N = 40 sows • 80 LITTERS (2 piglets/litter) • Microbiome analysis • SUBSET LITTERS (5 Sows/group) • Gut histology • Gut transcriptome • Gut contents - microbiome

  35. Variation Average Litter Birth Weight > 3 parities ~700 sows – Litter BW Phenotypes & DNA samples High BW Sows n = 40 Low BW Sows n = 40 - 500 phenotyped sows of interest being selected for SNP genotyping

  36. Litter of Origin Effects on the Gut WEANING High BW Sows n=40 sows Low BW Sows n=40 sows Fecal Swabs - Microbiome analysis 2 ♀ piglets/litter n=80 piglets 2 ♀ piglets/litter n=80 piglets Fecal Swabs - Microbiome analysis Subset 5 litters 2 piglets/litter - Euthanized Subset 5 litters 2 piglets/litter - Euthanized Gut Contents - Microbiome analysis Gut Tissues - Histology - Transcriptome • All samples collected & extractions completed • Microbiome & Transcriptome analysis to be initiated • Gut Histology to be initiated

  37. Microbiome Characterization and Management • Activity 2.2-2.4

  38. Resilience Phenotype Genetic makeup Immune Response RESILIENT Feeds and Feed Management Microbial Colonization Maternal/Litter Environment

  39. Application of Genomics to Improve Disease Resilience and Sustainability in Pork Production Other Project Related Activities

  40. Project Dissemination • 12 papers published • 12 oral and 7 poster presentations at national/international conferences, • 17 public press articles Research Team has been actively seeking additional funding and collaborations relayed to Genome Canada Application of Genomics to Improve Disease Resilience and Sustainability in Pork Production project.

  41. Project Start - Genome Canada Status Report (Aug 2015) • 97% of Co-funding secured • $178K outstanding Since Last ROC meeting • Team members have secured >$1.4 million in additional funding directly related to this project Project Co-Funding Update Additional funding secured fulfills the co-funding requirements for the Genome Canada Application of Genomics to Improve Disease Resilience and Sustainability in Pork Production project.

  42. New Tools to Enable Effective Genomic Selection for Disease Resilience. Swine Innovation Pork. PI– Plastow: $400KApril 1, 2016 (2-year funding extension) Phenomics for Genetic and Genome-Enabled Improvement of Resilience in Pigs. National Institute for Food and Agriculture (USDA).PI – Jack Dekkers (PI): USD$1M. January, 2017 (3-year project). Unofficial Notification of Award New Project Co-Funding

  43. Storage and Processing Request to Compute Canada • Anticipated number of RNA-Seq samples: 1056 • Long-term storage to be requested for sequencing data and key output files: • 12 TB • Compute power to be requested for read mapping and statistical analysis: • 3 core years • Other data types can be handled using default allocation (no additional resources required).

  44. Dr. Caroline GilbertCHU de Québec-Université Laval Collaboration established to conduct DRAA analysis on disease challenged animals from CDPQ. Replacing the late Dr. Jenny Phipps (Metaxos, Ottawa) on the project. • Dr. Matheus CostaUtrecht University, NL. Collaboration to develop a device to improve tissue viability of IVOC tissues. New Collaborations

  45. Improvement of Health and Welfare by Early Detection of Diseases Using Infrared Thermography.Quebec Agriculture Ministry (MAPAQ)) PI -Frédéric Fortin: $215,000. August, 2016 (15 month project). Establishing a foundation to harvest the potential of the microbiome in livestock species. ALMA. PI – Ben Willing: $656,900.September, 2016 (3-year project). Automated recording of feed/water intake & weight/ conformation.PI -Frédéric Fortin: : $150,000. Swine Innovation Porc. April 2014 (3-year project). Related Projects & Funding

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