Whole blood transcriptomic analysis of beef cattle at arrival identifies potential predictive molecules and mechanisms that indicate animals that naturally resist bovine respiratory disease

Autoři: Matthew A. Scott aff001;  Amelia R. Woolums aff001;  Cyprianna E. Swiderski aff002;  Andy D. Perkins aff003;  Bindu Nanduri aff004;  David R. Smith aff001;  Brandi B. Karisch aff005;  William B. Epperson aff001;  John R. Blanton, Jr. aff005
Působiště autorů: Department of Pathobiology and Population Medicine, Mississippi State University, Mississippi State, MS, United States of America aff001;  Department of Clinical Sciences, Mississippi State University, Mississippi State, MS, United States of America aff002;  Department of Computer Science and Engineering, Mississippi State University, Mississippi State, MS, United States of America aff003;  Department of Basic Sciences, Mississippi State University College of Veterinary Medicine, Mississippi State University, Mississippi State, MS, United States of America aff004;  Department of Animal and Dairy Sciences, Mississippi State University, Mississippi State, MS, United States of America aff005
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0227507


Bovine respiratory disease (BRD) is a multifactorial disease complex and the leading infectious disease in post-weaned beef cattle. Clinical manifestations of BRD are recognized in beef calves within a high-risk setting, commonly associated with weaning, shipping, and novel feeding and housing environments. However, the understanding of complex host immune interactions and genomic mechanisms involved in BRD susceptibility remain elusive. Utilizing high-throughput RNA-sequencing, we contrasted the at-arrival blood transcriptomes of 6 beef cattle that ultimately developed BRD against 5 beef cattle that remained healthy within the same herd, differentiating BRD diagnosis from production metadata and treatment records. We identified 135 differentially expressed genes (DEGs) using the differential gene expression tools edgeR and DESeq2. Thirty-six of the DEGs shared between these two analysis platforms were prioritized for investigation of their relevance to infectious disease resistance using WebGestalt, STRING, and Reactome. Biological processes related to inflammatory response, immunological defense, lipoxin metabolism, and macrophage function were identified. Production of specialized pro-resolvin mediators (SPMs) and endogenous metabolism of angiotensinogen were increased in animals that resisted BRD. Protein-protein interaction modeling of gene products with significantly higher expression in cattle that naturally acquire BRD identified molecular processes involving microbial killing. Accordingly, identification of DEGs in whole blood at arrival revealed a clear distinction between calves that went on to develop BRD and those that resisted BRD. These results provide novel insight into host immune factors that are present at the time of arrival that confer protection from BRD.

Klíčová slova:

Beef – Blood – Cattle – Gene expression – Inflammation – Livestock care – Macrophages – Veterinary diseases


1. USDA. Part IV: Health and Health Management on U.S. Feedlots with a Capacity of 1,000 or More Head. Fort Collins, CO:; 2013.

2. Grissett G, White B, Larson R. Structured Literature Review of Responses of Cattle to Viral and Bacterial Pathogens Causing Bovine Respiratory Disease Complex. Journal of Veterinary Internal Medicine. 2015; 29(3): 770–780. doi: 10.1111/jvim.12597 25929158

3. Earley B, Sporer K, Gupta S. Invited review: Relationship between cattle transport, immunity and respiratory disease. Animal: An International Journal of Animal Bioscience. 2017; 11(3): 486–492.

4. Kishimoto M, Tsuchiaka S, Rahpaya S, Hasebe A, Otsu K, Sugimura S, et al. Development of a one-run real-time PCR detection system for pathogens associated with bovine respiratory disease complex. Journal of Veterinary Medical Science. 2017; 79(3): 517–523. doi: 10.1292/jvms.16-0489 28070089

5. Avra T, Abell K, Shane D, Theurer M, Larson R, White B. A retrospective analysis of risk factors associated with bovine respiratory disease treatment failure in feedlot cattle. Journal of Animal Science. 2017; 95(4): 1521–1527. doi: 10.2527/jas.2016.1254 28464093

6. Reinhardt C, Busby W, Corah L. elationship of various incoming cattle traits with feedlot performance and carcass traits. Journal of Animal Science. 2009; 87(9): 3030–3042. doi: 10.2527/jas.2008-1293 19465501

7. Taylor J, Fulton R, Lehenbauer T, Step D, Confer A. The epidemiology of bovine respiratory disease: What is the evidence for predisposing factors? Canadian Veterinary Journal. 2010; 51(10): 1095–1102. 21197200

8. Griffin D, Chengappa M, Kuszak J, McVey D. Bacterial Pathogens of the Bovine Respiratory Disease Complex. Veterinary Clinics of North America: Food Animal Practice. 2010; 26(2): 381–394. doi: 10.1016/j.cvfa.2010.04.004 20619191

9. Nicola I, Cerutti F, Grego E, Bertone I, Gianella P, D'Angelo A, et al. Characterization of the upper and lower respiratory tract microbiota in Piedmontese calves. Microbiome. 2017; 152(5).

10. Murray G, More S, Sammin D, Casey M, McElroy M, O'Neill R, et al. Pathogens, patterns of pneumonia, and epidemiologic risk factors associated with respiratory disease in recently weaned cattle in Ireland. Journal of Veterinary Diagnostic Investigation. 2017; 29(1): 20–34. doi: 10.1177/1040638716674757 28074713

11. O'Connor A, Coetzee J, da Silva N, Wang C. A mixed treatment comparison meta-analysis of antibiotic treatments for bovine respiratory disease. Preventative Veterinary Medicine. 2013; 110(2): 77–87.

12. Larson R, Step D. Evidence-based effectiveness of vaccination against Mannheimia haemolytica, Pasteurella multocida, and Histophilus somni in feedlot cattle for mitigating the incidence and effect of bovine respiratory disease complex. Veterinary Clinics of North America: Food Animal Practice. 2012; 28(1): 97–106. doi: 10.1016/j.cvfa.2011.12.005 22374120

13. McVey D. BRD research needs in the next 10–20 years. Animal Health Research Reviews. 2009; 10(2): 165–167. doi: 10.1017/S1466252309990247 20003656

14. Babcock A, White B, Dritz S, Thomson D, Renter D. Feedlot health and performance effects associated with the timing. Journal of Animal Science. 2009; 87: 314–327. doi: 10.2527/jas.2008-1201 18765846

15. Timsit E, Holman D, Hallewell J, Alexander T. The nasopharyngeal microbiota in feedlot cattle and its role in respiratory health. Animal Frontiers. 2016; 6: 44–50.

16. White B, Renter D. Bayesian estimation of the performance of using clinical observationsand harvest lung lesions for diagnosing bovine respiratory disease inpost-weaned beef calves. Journal of Veterinary Diagnostic Investigation. 2009; 21(4): 446–453. doi: 10.1177/104063870902100405 19564492

17. Neibergs H, Seabury C, Wojtowicz A, Wang Z, Scraggs E, Kiser J, et al. Susceptibility loci revealed for bovine respiratory disease complex in pre-weaned holstein calves. BMC Genomics. 2014; 15(1): 1164.

18. Lipkin E, Strillacci M, Eitam H, Yishay M, Schiavini F, Soller M, et al. The Use of Kosher Phenotyping for Mapping QTL Affecting Susceptibility to Bovine Respiratory Disease. PLoS One. 2016; 11(4): e0153423. doi: 10.1371/journal.pone.0153423 27077383

19. Neupane M, Kiser J, Team BRDCCAPR, Neibergs H. Gene set enrichment analysis of SNP data in dairy and beef cattle with bovine respiratory disease. Animal Genetics. 2018; 49(6): 527–538. doi: 10.1111/age.12718 30229962

20. Wray N, Yang J, Hayes B, Price A, Goddard M, Visscher P. Pitfalls of predicting complex traits from SNPs. Nature Reviews Genetics. 2013; 14(7): 507–515. doi: 10.1038/nrg3457 23774735

21. Korte A, Farlow A. The advantages and limitations of trait analysis with GWAS: a review. BMC Plant Methods. 2013; 9(29).

22. Fridley B, Biernacka J. Gene set analysis of SNP data: benefits, challenges, and future directions. European Journal of Human Genetics. 2011; 19(8): 837–843. doi: 10.1038/ejhg.2011.57 21487444

23. Klein C, Lohmann K, Ziegler A. The Promise and Limitations of Genome-wide Association Studies. Journal of the American Medical Association. 2012; 308(18): 1867–1868.

24. Wang X, Wu Z, Zhang X. Isoform abundance inference provides a more accurate estimation of gene expression levels in RNA-seq. Journal of Bioinformatics and Computational Biology. 2010;: 177–192. doi: 10.1142/s0219720010005178 21155027

25. Waghmare A, Basom R, Delrow J, Huang M, Garnace E, Stevens-Ayers T, et al. Whole Blood RNA-Seq Differentiates Hematopoietic Cell Transplant Recipients with Upper Versus Lower Respiratory Tract Rhinovirus Infection. Biology of Blood and Marrow Transplantation. 2019; 25(3): S372–S373.

26. Englert J, Cho M, Lamb A, Shumyatcher M, Barragan-Bradford D, Basil M, et al. Whole blood RNA sequencing reveals a unique transcriptomic profile in patients with ARDS following hematopoietic stem cell transplantation. Respiratory Research. 2019; 20(1): 15. doi: 10.1186/s12931-019-0981-6 30665420

27. Griffin C, Scott J, Karisch B, Woolums A, Blanton J Jr., Kaplan R, et al. A randomized controlled trial to test the effect of on-arrival vaccination and deworming on stocker cattle health and growth performance. Bovine Practitioner (Stillwater). 2018; 52(1): 26–33.

28. Step D, Krehbiel C, DePra H, Cranston J, Fulton R, Kirkpatrick J, et al. Effects of commingling beef calves from different sources and weaning protocols during a forty-two-day receiving period on performance and bovine respiratory disease. Journal of Animal Science. 2008; 86(11): 3146–3158. doi: 10.2527/jas.2008-0883 18567723

29. Woolums A, Karisch B, Frye J, Epperson W, Smith D, Blanton J, et al. Multidrug resistant Mannheimia haemolytica isolated from high-risk beef stocker cattle after antimicrobial metaphylaxis and treatment for bovine respiratory disease. Veterinary Microbiology. 2018; 221: 143–152. doi: 10.1016/j.vetmic.2018.06.005 29981701

30. Andrews S. FastQC Software. [Online].; 2018. Available from: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/.

31. Bolger A, Lohse M, Usadel B. Trimmomatic: A flexible trimmer for Illumina Sequence Data. Bioinformatics. 2014; 30(15): 2114–2120. doi: 10.1093/bioinformatics/btu170 24695404

32. USDA ARS. ARS-UCD1.2 (GenBank Assembly GCA_002263795.2). 2018.

33. Kim D, Langmead B, Salzberg S. HISAT: a fast spliced aligner with low memory requirements. Nature Methods. 2015; 12: 357–360. doi: 10.1038/nmeth.3317 25751142

34. Li H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics. 2011; 27(21): 2987–2993. doi: 10.1093/bioinformatics/btr509 21903627

35. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009; 25(16): 2078–2079. doi: 10.1093/bioinformatics/btp352 19505943

36. Pertea M, Pertea G, Antonescu C, Chang T, Mendell J, Salzberg S. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nature Biotechnology. 2015; 33: 290–295. doi: 10.1038/nbt.3122 25690850

37. Pertea M, Kim D, Pertea G, Leek J, Salzberg S. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nature Protocols. 2016; 11: 1650–1667. doi: 10.1038/nprot.2016.095 27560171

38. Pertea G, Kirchner R. GffCompare: Program for processing GTF/GFF files. [Online].; 2018. Available from: https://ccb.jhu.edu/software/stringtie/gffcompare.shtml.

39. Pertea G. stringtie/prepDE.py. [Online].; 2018. Available from: https://github.com/gpertea/stringtie/blob/master/prepDE.py.

40. Robinson M, McCarthy D, Smyth G. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010; 26(1): 139–140. doi: 10.1093/bioinformatics/btp616 19910308

41. McCarthy J, Chen Y, Smyth K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Research. 2012; 40(10): 4288–4297. doi: 10.1093/nar/gks042 22287627

42. Love M, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology. 2014; 15: 550. doi: 10.1186/s13059-014-0550-8 25516281

43. Robinson M, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biology. 2010; 11(3).

44. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biology. 2010; 11(10).

45. Maza E. In Papyro Comparison of TMM (edgeR), RLE (DESeq2), and MRN Normalization Methods for a Simple Two-Conditions-Without-Replicates RNA-Seq Experimental Design. Frontiers in Genetics. 2016; 7: 164. doi: 10.3389/fgene.2016.00164 27695478

46. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B. 1995; 57(1): 289–300.

47. Kolde R. pheatmap: Pretty Heatmaps. R package version 1.0.12. [Online].; 2019. Available from: https://CRAN.R-project.org/package=pheatmap.

48. Liao Y, Wang J, Jaehnig E, Shi Z, Zhang B. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Research. 2019; 47(W1): W199–W205. doi: 10.1093/nar/gkz401 31114916

49. Zhang B, Kirov S, Snoddy J. WebGestalt: an integrated system for exploring gene sets in various biological contexts. Nucleic Acids Research. 2005; 33: W741–W748. doi: 10.1093/nar/gki475 15980575

50. Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, et al. The Reactome Pathway Knowledgebase. Nucleic Acids Research. 2018; 46(1): 649–655.

51. Szklarczyk D, Gable A, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research. 2019; 47: 607–613. doi: 10.1093/nar/gky982

52. Andersen C, Jensen J, Ørntoft T. Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Research. 2004; 64(15): 5245–5250. doi: 10.1158/0008-5472.CAN-04-0496 15289330

53. Livak K, Schmittgen T. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001; 25(4): 402–408. doi: 10.1006/meth.2001.1262 11846609

54. Ye J, Coulouris G, Zaretskaya I, Cutcutache I, Rozen S, Madden T. Primer-BLAST: a tool to design target-specific primers for polymerase chain reaction. BMC Bioinformatics. 2012; 13(134): doi: 10.1186/1471-2105-13-134 22708584

55. Timsit E, Tison N, Booker C, Buczinski S. Association of lung lesions measured by thoracic ultrasonography at first diagnosis of bronchopneumonia with relapse rate and growth performance in feedlot cattle. Journal of Veterinary Internal Medicine. 2019; 33(3): 1540–1546. doi: 10.1111/jvim.15483 30901138

56. Fulton R, Confer A. Laboratory test descriptions for bovine respiratory disease diagnosis and their strengths and weaknesses: Gold standards for diagnosis, do they exist? Canadian Veterinary Journal. 2012; 53(7): 754–761. 23277642

57. Vähänikkilä N, Pohjanvirta T, Haapala V, Simojoki H, Soveri T, Browning G, et al. Characterisation of the course of Mycoplasma bovis infection in naturally infected dairy herds. Veterinary Microbiology. 2019; 231: 107–115. doi: 10.1016/j.vetmic.2019.03.007 30955796

58. White B, Amrine D, Goehl D. Determination of value of bovine respiratory disease control using a remote early disease identification system compared with conventional methods of metaphylaxis and visual observations. Journal of Animal Science. 2015; 93(8): 4115–4122. doi: 10.2527/jas.2015-9079 26440191

59. Seyednasrollah F, Laiho A, Elo L. Comparison of software packages for detecting differential expression in RNA-seq studies. Briefings in Bioinformatics. 2015; 16(1): 59–70. doi: 10.1093/bib/bbt086 24300110

60. Li X, Wu D, Cooper N, Rai S. Sample size calculations for the differential expression analysis of RNA-seq data using a negative binomial regression model. Statistical Applications in Genetics and Molecular Biology. 2019; 18(1).

61. Timsit E, Dendukuri N, Schiller I, Buczinski S. Diagnostic accuracy of clinical illness for bovine respiratory disease (BRD) diagnosis in beef cattle placed in feedlots: A systematic literature review and hierarchical Bayesian latent-class meta-analysis. Preventative Veterinary Medicine. 2016; 135: 67–73.

62. Fulton R. Bovine respiratory disease research (1983–2009). Animal Health Research Reviews. 2009; 10(2): 131–139. doi: 10.1017/S146625230999017X 20003649

63. Irsik M, Langemeier M, Schroeder T, Spire M, Roder J. Estimating the Effects of Animal Health on the Performance of Feedlot Cattle. The Bovine Practitioner. 2006; 40(3): 65–74.

64. Molfino A, Amabile M, Monti M, Muscaritoli M. Omega-3 Polyunsaturated Fatty Acids in Critical Illness: Anti-Inflammatory, Proresolving, or Both? Oxidative Medicine and Cellular Longevity. 2017; PMC5488236.

65. Basil M, Levy B. Specialized pro-resolving mediators: endogenous regulators of infection and inflammation. Nature Reviews Immunology. 2016; 16(1): 51–67. doi: 10.1038/nri.2015.4 26688348

66. Levy B, Clish C, Schmidt B, Gronert K, Serhan C. Lipid mediator class switching during acute inflammation: signals in resolution. Nature Immunology. 2001; 2(7): 612–619. doi: 10.1038/89759 11429545

67. Serhan C, Petasis N. Resolvins and protectins in inflammatory resolution. Chemical Reviews. 2011; 111(10): 5922–5943. doi: 10.1021/cr100396c 21766791

68. Profita M, Vignola A, Sala A, Mirabella A, Siena L, Pace E, et al. Interleukin-4 enhances 15-lipoxygenase activity and incorporation of 15(S)-HETE into cellular phospholipids in cultured pulmonary epithelial cells. American Journal of Respiratory Cell and Molecular Biology. 1999; 20(1): 61–68. doi: 10.1165/ajrcmb.20.1.3151 9870918

69. Profita M, Sala A, Riccobono L, Pace E, Paternò A, Zarini S, et al. 15(S)-HETE modulates LTB(4) production and neutrophil chemotaxis in chronic bronchitis. American Journal of Physiology: Cell Physiology. 2000; 279(4): C1249–1258. doi: 10.1152/ajpcell.2000.279.4.C1249 11003605

70. Guo H, Verhoek I, Prins G, van der Vlag R, van der Wouden P, van Merkerk R, et al. Novel 15-Lipoxygenase-1 Inhibitor Protects Macrophages from Lipopolysaccharide-Induced Cytotoxicity. Journal of Medicinal Chemistry. 2019; 62(9): 4624–4637. doi: 10.1021/acs.jmedchem.9b00212 30964295

71. David J, Barkema H, Guan L, Buck J. Gene-expression profiling of calves 6 and 9 months after inoculation with Mycobacterium avium subspecies paratuberculosis. Veterinary Research. 2014; 160: 107–117.

72. Malvisi M, Palazzo F, Morandi NLB, Williams J, Pagnacco G, Minozzi G. Responses of Bovine Innate Immunity to Mycobacterium avium subsp. paratuberculosis Infection Revealed by Changes in Gene Expression and Levels of MicroRNA. PLoS One. 2016; 10(e0164461): 11.

73. Yan M, Rerko R, Platzer P, Dawson D, Willis J, Tong M, et al. 15-Hydroxyprostaglandin dehydrogenase, a COX-2 oncogene antagonist, is a TGF-beta-induced suppressor of human gastrointestinal cancers. Proceedings of the National Academy of Sciences of the United States of America. 2004; 101(50): 17468–17473. doi: 10.1073/pnas.0406142101 15574495

74. Cho H, Huang L, Hamza A, Gao D, Zhan C, Tai H. Role of glutamine 148 of human 15-hydroxyprostaglandin dehydrogenase in catalytic oxidation of prostaglandin E2. Bioorganic and Medicinal Chemistry. 2006; 14(19): 6486–6491. doi: 10.1016/j.bmc.2006.06.030 16828555

75. Tai H, Ensor C, Tong M, Zhou H, Yan F. Prostaglandin catabolizing enzymes. Prostaglandins and Other Lipid Mediators. 2002; 68: 483–493. doi: 10.1016/s0090-6980(02)00050-3 12432938

76. Serhan C, Chiang N, Dalli J, Levy B. Lipid Mediators in the Resolution of Inflammation. Cold Spring Harbor Perspectives in Biology. 2014; 7(2): a016311. doi: 10.1101/cshperspect.a016311 25359497

77. Arita M, Clish C, Serhan C. The contributions of aspirin and microbial oxygenase to the biosynthesis of anti-inflammatory resolvins: novel oxygenase products from omega-3 polyunsaturated fatty acids. Biochemical and Biophysical Research Communications. 2005; 338(1): 149–157. doi: 10.1016/j.bbrc.2005.07.181 16112645

78. Kyriakopoulos A, Nagl M, Baliou S, Zoumpourlis V. Alleviating Promotion of Inflammation and Cancer Induced by Nonsteroidal Anti-Inflammatory Drugs. International Journal of Inflammation. 2017; 2017: 9632018. doi: 10.1155/2017/9632018 28573063

79. Duvall M, Levy B. DHA- and EPA-derived resolvins, protectins, and maresins in airway inflammation. European Journal of Pharmacology. 2016; 785: 144–155. doi: 10.1016/j.ejphar.2015.11.001 26546247

80. Wang B, Gong X, Wan J, Zhang L, Zhang Z, Li H, et al. Resolvin D1 protects mice from LPS-induced acute lung injury. Pulmonary Pharmacology and Therapeutics. 2011; 24(4): 434–441. doi: 10.1016/j.pupt.2011.04.001 21501693

81. Kebir D, Jozsef L, Pan W, Wang L, Petasis N, Serhan C, et al. 15-Epi-lipoxin A4 Inhibits Myeloperoxidase Signaling and Enhances Resolution of Acute Lung Injury. American Journal of Respiratory and Critical Care Medicine. 2009; 180(4): 311–319. doi: 10.1164/rccm.200810-1601OC 19483113

82. Walker J, Dichter E, Lacorte G, Kerner D, Spur B, Rodriguez A, et al. Lipoxin a4 increases survival by decreasing systemic inflammation and bacterial load in sepsis. Shock: Injury, Inflammation, and Sepsis: Laboratory and Clinical Approaches. 2011; 36(4): 410–416.

83. Pien H, Ville A, Pace S, Temml V, Garscha U, Raasch M, et al. Endogenous metabolites of vitamin E limit inflammation by targeting 5-lipoxygenase. Nature Communications. 2018; 9(3834): s41467-018–06158-5.

84. Loneragan G, Morely P, Wagner J, Mason G, Yost G, Thoren M, et al. Effects of feeding aspirin and supplemental vitamin E on plasma concentrations of 3-methylindole, 3-methyleneindolenine-adduct concentrations in blood and pulmonary tissues, lung lesions, and growth performance in feedlot cattle. American Journal of Veterinary Research. 2002; 63(12): 1641–1647. doi: 10.2460/ajvr.2002.63.1641 12492277

85. Carter J, Meredith G, Montelongo M, Gill D, Krehbiel C, Payton M, et al. Relationship of vitamin E supplementation and antimicrobial treatment with acute-phase protein responses in cattle affected by naturally acquired respiratory tract disease. American Journal of Veterinary Research. 2002; 63(8): 1111–1117. doi: 10.2460/ajvr.2002.63.1111 12171163

86. Silver R, Reid A, Mackins C, Askwith T, Schaefer U, Herzlinger D, et al. Mast cells: a unique source of renin. Proceedings of the National Academy of Science of the United States of America. 2004; 101(37): 13607–13612.

87. Masuda T, Tanaka H, Komai M, Nagao K, Ishizaki M, Kajiwara D, et al. Mast cells play a partial role in allergen-induced subepithelial fibrosis in a murine model of allergic asthma. Clinical and Experimental Allergy: Journal of the British Society for Allergy and Clinical Immunology. 2003; 33(5): 705–713.

88. Jippo T, Morii E, Ito A, Kitamura Y. Effect of anatomical distribution of mast cells on their defense function against bacterial infections: demonstration using partially mast cell-deficient tg/tg mice. Journal of Experimental Medicine. 2003; 197(11): 1417–1425. doi: 10.1084/jem.20022157 12771178

89. Pollock K, McNeil K, Mottram J, Lyons R, Brewer J, Scott P, et al. The Leishmania mexicana cysteine protease, CPB2.8, induces potent Th2 responses. Journal of Immunology. 2003; 170(4): 1746–1753.

90. Bouma B, Mosnier L. Thrombin activatable fibrinolysis inhibitor (TAFI)—how does thrombin regulate fibrinolysis? Annals of Medicine. 2006; 38(6): 378–388. doi: 10.1080/07853890600852898 17008302

91. Lyons P, Callaway M, Fricker L. Characterization of Carboxypeptidase A6, an Extracellular. Journal of Biological Chemistry. 2008; 283(11): 7054–7063. doi: 10.1074/jbc.M707680200 18178555

92. Pavón-Romero G, Pérez-Rubio G, Ramírez-Jiménez F, Ambrocio-Ortiz E, Bañuelos-Ortiz E, Alvarado-Franco N, et al. MS4A2-rs573790 Is Associated With Aspirin-Exacerbated Respiratory Disease: Replicative Study Using a Candidate Gene Strategy. Frontiers in Genetics. 2018; 9: 363. doi: 10.3389/fgene.2018.00363 30254660

93. Kruit A, Grutters J, Ruven H, Sato H, Izumi T, Nagai S, et al. Chymase gene (CMA1) polymorphisms in Dutch and Japanese sarcoidosis patients. Respiration: International Review of Thoracic Diseases. 2006; 73(5): 623–633. doi: 10.1159/000091190 16446531

94. Oki K, Kopf P, Campbell W, Lam M, Yamazaki T, Gomez-Sanchez C, et al. Angiotensin II and III metabolism and effects on steroid production in the HAC15 human adrenocortical cell line. Endocrinology. 2013; 154(1): 214–221. doi: 10.1210/en.2012-1557 23221601

95. Coble J, Grobe J, Johnson A, Sigmund C. Mechanisms of brain renin angiotensin system-induced drinking and blood pressure: importance of the subfornical organ. American Journal of Physiology: Regulatory, Integrative, and Comparative Physiology. 2015; 308(4): R238–R249.

96. The UniProt Consortium. UniProtKB—Q01362 (FCERB_HUMAN). Oxford: Database. Available from: https://www.genecards.org/cgi-bin/carddisp.pl?gene=MS4A2.

97. Cruse G, Kaur D, Leyland M, Bradding P. A novel FcεRIβ-chain truncation regulates human mast cell proliferation and survival. The FASEB Journal. 2010; 24(10): 4047–4057. doi: 10.1096/fj.10-158378 20554927

98. Metcalfe S. LIF in the regulation of T-cell fate and as a potential therapeutic. Genes and Immunity. 2011; 12: 157–168. doi: 10.1038/gene.2011.9 21368774

99. Cheong H, Kim L, Park B, Choi Y, Park H, Hong S, et al. Association analysis of interleukin 5 receptor alpha subunit (IL5RA) polymorphisms and asthma. Journal of Human Genetics. 2005; 50: 628–634. doi: 10.1007/s10038-005-0304-2 16217591

100. Meyer-Manlapat A, Segal D. Mast Cell Degranulation Inhibits Th1 and Promotes Th2 Responses. Journal of Allergy and Clinical Immunology. 2008; 121(2): S114.

101. Woolums A. Feedlot Acute Interstitial Pneumonia. Veterinary Clinics of North America: Food Animal Practice. 2015; 31(3): 381–389. doi: 10.1016/j.cvfa.2015.05.010 26253266

102. Brandenburg L, Konrad M, Wruck C, Koch T, Luucius R, Pufe T. Functional and physical interactions between formyl‐peptide‐receptors and scavenger receptor MARCO and their involvement in amyloid beta 1–42‐induced signal transduction in glial cells. Journal of Neurochemistry. 2010; 113(3): 749–760. doi: 10.1111/j.1471-4159.2010.06637.x 20141570

103. Böhm M, Grässel S. Role of Proopiomelanocortin-Derived Peptides and Their Receptors in the Osteoarticular System: From Basic to Translational Research. Endocrine Reviews. 2012; 33(4): 623–651. doi: 10.1210/er.2011-1016 22736674

104. Schaefer L, Babelova A, Kiss E, Hausser H, Baliova M, Krzyzankova M, et al. he matrix component biglycan is proinflammatory and signals through Toll-like receptors 4 and 2 in macrophages. Journal of Clinical Investigation. 2005; 115(8): 2223–2233. doi: 10.1172/JCI23755 16025156

105. Tufvesson E, Westergren-Thorsson G. Tumour necrosis factor-alpha interacts with biglycan and decorin. FEBS Letters. 2002; 530(1–3): 124–128. doi: 10.1016/s0014-5793(02)03439-7 12387878

106. Lu Y, Yeh W, Ohashi P. LPS/TLR4 signal transduction pathway. Cytokine. 2008; 42(2): 145–151. doi: 10.1016/j.cyto.2008.01.006 18304834

107. Kawai T, Akira S. Toll-like receptors and their crosstalk with other innate receptors in infection and immunity. Immunity. 2011; 34(5): 637–650. doi: 10.1016/j.immuni.2011.05.006 21616434

108. Kawai T, Akira S. TLR signaling. Cell Death and Differentiation. 2006; 13(5): 816–825. doi: 10.1038/sj.cdd.4401850 16410796

109. Akira S, Uematsu S, Takeuchi O. Pathogen recognition and innate immunity. Cell. 2006; 124(4): 783–801. doi: 10.1016/j.cell.2006.02.015 16497588

110. Song J, Duncan M, Guojie L, Chan C, Grady R, Stapleton A, et al. A Novel TLR4-Mediated Signaling Pathway Leading to IL-6 Responses in Human Bladder Epithelial Cells. PLoS Pathogens. 2007; 3(4): e60. doi: 10.1371/journal.ppat.0030060 17465679

Článek vyšel v časopise


2020 Číslo 1