RNA-Seq Bayesian Network Exploration of Immune System in Bovine

Document Type: Research Paper

Authors

1 Department of Animal Science, Faculty of Animal and Food Science, Khuzestan Agricultural Sciences and Natural Resources University, Mollasani, Khuzestan, Iran

2 Department of Animal Science, Yasouj University, Yasouj, Iran

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

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

Abstract

Background: The stress is one of main factors effects on production system. Several factors (both genetic and environmental elements) regulate immune response to stress.
Objectives: 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.
Material 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 nuclear factor-kappa B (NF-κB) signalling, and to activate expression of NF-κB 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.

Keywords

Main Subjects


1.           Xiu L, Fu YB, Deng Y, Shi XJ, Bian ZY, Ruhan A, et al. Deep sequencing-based analysis of gene expression in bovine mammary epithelial cells after Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae infection. Genet Mol Res. 2015;14(4):16948-16965. doi: 10.4238/2015.December.15.1 pmid: 26681042

2.           Patel AK, Bhatt VD, Tripathi AK, Sajnani MR, Jakhesara SJ, Koringa PG, et al. Identification of novel splice variants in horn cancer by RNA-Seq analysis in Zebu cattle. Genomics. 2013;101(1):57-63. doi: 10.1016/j.ygeno.2012.10.001 pmid: 23063905

3.           Liu GF, Cheng HJ, You W, Song EL, Liu XM, Wan FC. Transcriptome profiling of muscle by RNA-Seq reveals significant differences in digital gene expression profiling between Angus and Luxi cattle. Animal Prod Sci. 2015;55(9):1172. doi: 10.1071/an14096

4.           O'Loughlin A, Lynn DJ, McGee M, Doyle S, McCabe M, Earley B. Transcriptomic analysis of the stress response to weaning at housing in bovine leukocytes using RNA-seq technology. BMC Genomics. 2012;13:250. doi: 10.1186/1471-2164-13-250 pmid: 22708644

5.           McLoughlin KE, Nalpas NC, Rue-Albrecht K, Browne JA, Magee DA, Killick KE, et al. RNA-seq Transcriptional Profiling of Peripheral Blood Leukocytes from Cattle Infected with Mycobacterium bovis. Front Immunol. 2014;5:396. doi: 10.3389/fimmu.2014.00396 pmid: 25206354

6.           Nemzek JA, Hodges AP, He Y. Bayesian network analysis of multi-compartmentalized immune responses in a murine model of sepsis and direct lung injury. BMC Res Notes. 2015;8:516. doi: 10.1186/s13104-015-1488-y pmid: 26423575

7.           Steeneveld W, van der Gaag L, Barkema H, Hogeveen H, editors. Bayesian networks for mastitis management on dairy farms. Proceedings of a meeting in Society for Veterinary Epidemiology and Preventive Medicine; 2009; London, UK.

8.           Ettema JF, Ostergaard S, Kristensen AR. Estimation of probability for the presence of claw and digital skin diseases by combining cow- and herd-level information using a Bayesian network. Prev Vet Med. 2009;92(1-2):89-98. doi: 10.1016/j.prevetmed.2009.08.014 pmid: 19747742

9.           Ghaderi-Zefrehei M, Dolatabady M, Rowghani E. Simple gene regulatory network of immune system candidate genes in dairy cattle. Res Opinions Animal Vet Sci. 2015;5(12):499-506.

10.        Thorne T. Approximate inference of gene regulatory network models from RNA-Seq time series data. BMC Bioinformatics. 2018;19(1):127. doi: 10.1186/s12859-018-2125-2 pmid: 29642837

11.        Mallard BA, Emam M, Paibomesai M, Thompson-Crispi K, Wagter-Lesperance L. Genetic selection of cattle for improved immunity and health. Jpn J Vet Res. 2015;63 Suppl 1:S37-44. pmid: 25872325

12.        Andrews S. FastQC: a quality control tool for high throughput sequence data UK: Bioinformatics; 2010 [cited 2018]. Available from: http://www.bioinformatics.babraham.ac.uk/projects/fastqc.

13.        Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114-2120. doi: 10.1093/bioinformatics/btu170 pmid: 24695404

14.        Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009;25(9):1105-1111. doi: 10.1093/bioinformatics/btp120 pmid: 19289445

15.        Anders S, Pyl PT, Huber W. HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31(2):166-169. doi: 10.1093/bioinformatics/btu638 pmid: 25260700

16.        Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139-140. doi: 10.1093/bioinformatics/btp616 pmid: 19910308

17.        Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. doi: 10.1093/nar/gkv007 pmid: 25605792

18.        Law CW, Chen Y, Shi W, Smyth GK. voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 2014;15(2):R29. doi: 10.1186/gb-2014-15-2-r29 pmid: 24485249

19.        Fraley C, Percival D. Model-Averaged [Formula: see text] Regularization using Markov Chain Monte Carlo Model Composition. J Stat Comput Simul. 2015;85(6):1090-1101. doi: 10.1080/00949655.2013.861839 pmid: 25642001

20.        Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498-2504. doi: 10.1101/gr.1239303 pmid: 14597658

21.        esyN - Easy Network Database 2019 [updated 2019; cited 2019]. Available from: http://www.esyn.org.

22.        Pathway Analysis - Upload Data 2019 [updated 2019; cited 2019]. Available from: https://www.innatedb.com/batchSearchInit.do.

23.        Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44-57. doi: 10.1038/nprot.2008.211 pmid: 19131956

24.        Bean DM, Heimbach J, Ficorella L, Micklem G, Oliver SG, Favrin G. esyN: network building, sharing and publishing. PLoS One. 2014;9(9):e106035. doi: 10.1371/journal.pone.0106035 pmid: 25181461

25.        Behdani E, Bakhtiarizadeh MR. Construction of an integrated gene regulatory network link to stress-related immune system in cattle. Genetica. 2017;145(4-5):441-454. doi: 10.1007/s10709-017-9980-z pmid: 28825201

26.        Pavlopoulos GA, Secrier M, Moschopoulos CN, Soldatos TG, Kossida S, Aerts J, et al. Using graph theory to analyze biological networks. BioData Min. 2011;4:10. doi: 10.1186/1756-0381-4-10 pmid: 21527005

27.        Yoon J, Blumer A, Lee K. An algorithm for modularity analysis of directed and weighted biological networks based on edge-betweenness centrality. Bioinformatics. 2006;22(24):3106-3108. doi: 10.1093/bioinformatics/btl533 pmid: 17060356

28.        Newman MEJ. A measure of betweenness centrality based on random walks. Soc Networks. 2005;27(1):39-54. doi: 10.1016/j.socnet.2004.11.009

29.        Zhang Y, Chiu S, Liang X, Gao F, Zhang Z, Liao S, et al. Rap1-mediated nuclear factor-kappaB (NF-kappaB) activity regulates the paracrine capacity of mesenchymal stem cells in heart repair following infarction. Cell Death Discov. 2015;1:15007. doi: 10.1038/cddiscovery.2015.7 pmid: 27551443

30.        Wong ET, Tergaonkar V. Roles of NF-kappaB in health and disease: mechanisms and therapeutic potential. Clin Sci (Lond). 2009;116(6):451-465. doi: 10.1042/CS20080502 pmid: 19200055

31.        Biswas SK, Bist P, Dhillon MK, Kajiji T, Del Fresno C, Yamamoto M, et al. Role for MyD88-independent, TRIF pathway in lipid A/TLR4-induced endotoxin tolerance. J Immunol. 2007;179(6):4083-4092. doi: 10.4049/jimmunol.179.6.4083 pmid: 17785847

32.        Stahn C, Buttgereit F. Genomic and nongenomic effects of glucocorticoids. Nat Clin Pract Rheumatol. 2008;4(10):525-533. doi: 10.1038/ncprheum0898 pmid: 18762788

33.        Arpin M, Chirivino D, Naba A, Zwaenepoel I. Emerging role for ERM proteins in cell adhesion and migration. Cell Adh Migr. 2011;5(2):199-206. doi: 10.4161/cam.5.2.15081 pmid: 21343695

34.        Fidalgo M, Guerrero A, Fraile M, Iglesias C, Pombo CM, Zalvide J. Adaptor protein cerebral cavernous malformation 3 (CCM3) mediates phosphorylation of the cytoskeletal proteins ezrin/radixin/moesin by mammalian Ste20-4 to protect cells from oxidative stress. J Biol Chem. 2012;287(14):11556-11565. doi: 10.1074/jbc.M111.320259 pmid: 22291017

35.        Schmid SR, Linder P. D-E-A-D protein family of putative RNA helicases. Mol Microbiol. 1992;6(3):283-291. doi: 10.1111/j.1365-2958.1992.tb01470.x pmid: 1552844

36.        Lin C, Yang L, Yang JJ, Huang Y, Liu ZR. ATPase/helicase activities of p68 RNA helicase are required for pre-mRNA splicing but not for assembly of the spliceosome. Mol Cell Biol. 2005;25(17):7484-7493. doi: 10.1128/MCB.25.17.7484-7493.2005 pmid: 16107697

37.        Wood KW, Chua P, Sutton D, Jackson JR. Centromere-associated protein E: a motor that puts the brakes on the mitotic checkpoint. Clin Cancer Res. 2008;14(23):7588-7592. doi: 10.1158/1078-0432.CCR-07-4443 pmid: 19047083

38.        Teymourian H, Mohajerani SA, Bagheri P, Seddighi A, Seddighi AS, Razavian I. Effect of Ondansetron on Postoperative Shivering After Craniotomy. World Neurosurg. 2015;84(6):1923-1928. doi: 10.1016/j.wneu.2015.08.034 pmid: 26342782

39.        Lenardo M, Chan KM, Hornung F, McFarland H, Siegel R, Wang J, et al. Mature T lymphocyte apoptosis--immune regulation in a dynamic and unpredictable antigenic environment. Annu Rev Immunol. 1999;17:221-253. doi: 10.1146/annurev.immunol.17.1.221 pmid: 10358758

40.        Tanaka TU. Bi-orienting chromosomes on the mitotic spindle. Curr Opin Cell Biol. 2002;14(3):365-371. doi: 10.1016/s0955-0674(02)00328-9 pmid: 12067660

41.        Wahl MC, Will CL, Luhrmann R. The spliceosome: design principles of a dynamic RNP machine. Cell. 2009;136(4):701-718. doi: 10.1016/j.cell.2009.02.009 pmid: 19239890

42.        Martinez NM, Pan Q, Cole BS, Yarosh CA, Babcock GA, Heyd F, et al. Alternative splicing networks regulated by signaling in human T cells. RNA. 2012;18(5):1029-1040. doi: 10.1261/rna.032243.112 pmid: 22454538

43.        Burke NC, Scaglia G, Boland HT, Swecker WS, Jr. Influence of two-stage weaning with subsequent transport on body weight, plasma lipid peroxidation, plasma selenium, and on leukocyte glutathione peroxidase and glutathione reductase activity in beef calves. Vet Immunol Immunopathol. 2009;127(3-4):365-370. doi: 10.1016/j.vetimm.2008.11.017 pmid: 19110316

44.        Wernicki A, Urban-Chmiel R, Kankofer M, Mikucki P, Puchalski A, Tokarzewski S. Evaluation of plasma cortisol and TBARS levels in calves after short-term transportation. Revue Méd Vét. 2006;157(1):30.

45.        Sancar A, Lindsey-Boltz LA, Unsal-Kacmaz K, Linn S. Molecular mechanisms of mammalian DNA repair and the DNA damage checkpoints. Annu Rev Biochem. 2004;73:39-85. doi: 10.1146/annurev.biochem.73.011303.073723 pmid: 15189136

46.        Zhang JY, Tao S, Kimmel R, Khavari PA. CDK4 regulation by TNFR1 and JNK is required for NF-kappaB-mediated epidermal growth control. J Cell Biol. 2005;168(4):561-566. doi: 10.1083/jcb.200411060 pmid: 15699216