transcriptional profiling as a key tool to improve marbling in beef cattle
TRANSCRIPT
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Cellular signaling pathway analysis of transcriptomics profiling as a key tool to improve marbling in beef cattle.
A possible approach for artificial meat quality improvement
Susan Steinbusch-Coort, PhD
Dept. Bioinformatics-BiGCaT, NUTRIM, Maastricht University [email protected]
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Marbling
• Intramuscular fat – the fat present within a cut of meat and between the muscle fibers.
• Breed, genotype, age, diet, husbandry and growth stages.
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Aim
To identify genes and biological pathways regulating marbling of muscle tissue in beef cattle.
Gene 1
Gene 2
Gene 3
Gene 4
Gene 5
Gene 6
Gene 7
Gene 8
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WikiPathways (www.wikipathways.org)
Community curated pathway database
o everybody can contribute and share pathways o everybody can edit and curate pathways o everybody can use the pathway collection o not just diagrams but fully annotated pathways o new findings can be added immediately
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Bos taurus collection at WikiPathways
282 Bt pathways
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Analysis workflow
Adapted from: Kutmon M, Evelo CT, Coort SL. (2014) A network biology workflow to study transcriptomics data of the diabetic liver. BMC Genomics. 15:971. doi: 10.1186/1471-2164-15-971.
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Transcriptomics data
Limousin - Low marbling
Hereford - High marbling
N=4 N=
N=4
Agilent Two-Color Mi Bovine (V2) 4 x 44K Gene Expression Microarray
T. Sadkowski et al “Transcriptional background of beef marbling – Novel genes implicated in intramuscular fat deposition,” Meat Sci., vol. 97, no. 1, pp. 32–41, 2014.
Semitendinosus muscle of 15-month-old bulls
Isolation RNA
Gene expression
Publicly available at ArrayExpress (accession number E-GEOD-46411).
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Differential expression
Differentially expressed genes comparing Hereford with Limousin
- Absolute log10 fold change > 0.11
- Absolute fold change of 1.3
- P-value < 0.05
28,753 genes measured and annotated
2,873 differentially expressed genes
1421 genes
1452 genes
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Over-representation analysis
PathVisio: a tool to edit and analyze biological pathways
Transcriptomics data
Pathway collection
Analysis collection from WikiPathways (www.wikipathways.org)
Differentially expressed
genes
Z-score per pathway
Over-representation analysis
Ranking of pathways based on z-score
Kutmon, M et al. PathVisio 3: An Extendable Pathway Analysis Toolbox. (Accepted for publication in PLOS Computational Biology)
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Pathway Positive(r) Measured(n) Total Z Score P-value Marbling
Hypertrophy Model 8 15 19 3.94 0.000 *
MAPK signaling pathway 34 124 167 3.51 0.002 *
Histone Modifications 13 35 43 3.41 0.000 -
IL-1 signaling pathway 15 41 54 3.17 0.003 *
P38 MAPK signaling pathway 10 27 36 2.98 0.006 *
Cardiac progenitor differentiation 11 33 54 2.72 0.006 -
TCR signaling pathway 19 68 89 2.70 0.011 *
MicroRNAs in cardiomyocyte hypertrophy
20 73 102 2.67 0.006 *
Mitochondrial gene expression 5 11 23 2.66 0.006 *
Physiological and pathological hypertrophy of the heart
7 19 26 2.47 0.012 *
Parkin-Ubiquitin proteasomal system pathway
13 44 64 2.45 0.022 -
Insulin Signaling 27 111 157 2.41 0.012 -
Transcriptional activation by NRF2
5 13 19 2.2 0.020 -
The highest ranked pathways based on Z score in Hereford vs Limousin
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Gene expression in Hereford vs Limousin visualized on the Hypertrophy model.
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Conclusions
• MAPK and P38 MAPK signaling, two pathways well known to affect lipid metabolism, were enriched in the high-marbling breed.
• The signaling pathways “Hypertrophy Model”, “MicroRNAs in cardiomyocyte hypertrophy” and “Physiological and pathological hypertrophy of the heart” that play a role in tissue development were affected.
• Immune response pathways - IL signaling, TCR signaling and Toll-like receptor signaling pathways – were changed.
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Future directions
• Integration of different types of omics data in pathway analysis, like proteomics and metabolomics.
• Effect of environmental factors on marbling and the underlying biological processes.
• Extending the analysis with network analysis to add a regulatory level.
• Analyzing cultured meat quality at a molecular level.
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Acknowledgements
Chris Evelo
Maastricht University (Maastricht – NL)
- Lars Eijssen - Egon Willighagen - Anwesha Bohler-Dutta - Elisa Cirillo - Nuno Nunes - Jonathan Mellius - Linda Rieswijk - Frederieke Ehrhart
Dept. Animal Science, University of Ferdowsi Mashhad (Mashhad – Iran)
Zahra Roudbari
Gladstone Institute (San Francisco - USA) - Alexander Pico - Samad Lotia - Kristina Hanspers
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Questions