RNA-Seq Bayesian Network Exploration of Immune System in Bovine

Document Type: Research Paper


1 Department of Animal Sciences, faculty of Agriculture and Natural Resources, Ramin University, Khozestan, Iran

2 Department of Animal Sciences, faculty of Agriculture and Natural Resources, Yasouj University, Yasouj, Iran

3 Department of Agricultural Biotechnology, Faculty of Agricultural Sciences, University of Guilan, 4199613769, Rasht, Iran.

4 Department of Animal and Poultry Science, College of Aburaihan, University of Tehran, Tehran, Iran


Background: The stress is one of main factors effects on production system. Several factors (both genetic and environmental elements) regulate immune response to stress.
Objective: In order to determine the major immune system regulatory genes underlying stress responses, a learning Bayesian network approach for those regulatory genes was applied to RNA-Seq data from a bovine leukocyte model system.
Materials and Methods: The transcriptome dataset GSE37447 was used from GEO and a Bayesian network on differentially expressed genes was learned to investigate the gene regulatory network.
Results: Applying the method produced a strongly interconnected network with four genes (TERF2IP, PDCD10, DDX10 and CENPE) acting as nodes, suggesting these genes may be important in the transcriptome regulation program of stress response. Of these genes TERF2IP has been shown previously to regulate gene expression, act as a regulator of the NF-kappa-B signalling, and to activate expression of NF-kappa-B target genes; PDCD10 encodes a conserved protein associated with cell apoptosis; DDX10 encodes a DEAD box protein and is believed to be associated with cellular growth and division; and CENPE involves unstable spindle microtubule capture at kinetochores. Together these genes are involved in DNA damage of apoptosis, RNA splicing, DNA repairing, and regulating cell division in the bovine genome. The topology of the learned Bayesian gene network indicated that the genes had a minimal interrelationship with each other. This type of structure, using the publically available computational tool, was also observed on human orthologous genes of the differentially expressed genes.
Conclusions: Overall, the results might be used in transcriptomic-assisted selection and design of new drug targets to treat stress-related problems in bovines.


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