Common patterns of gene regulation associated with Cesarean section and the development of islet autoimmunity - indications of immune cell activation
M Laimighofer, R Lickert, R Fuerst, F J Theis, C Winkler, E Bonifacio, A-G Ziegler, J Krumsiek, M Laimighofer, R Lickert, R Fuerst, F J Theis, C Winkler, E Bonifacio, A-G Ziegler, J Krumsiek
Abstract
Birth by Cesarean section increases the risk of developing type 1 diabetes later in life. We aimed to elucidate common regulatory processes observed after Cesarean section and the development of islet autoimmunity, which precedes type 1 diabetes, by investigating the transcriptome of blood cells in the developing immune system. To investigate Cesarean section effects, we analyzed longitudinal gene expression profiles from peripheral blood mononuclear cells taken at several time points from children with increased familial and genetic risk for type 1 diabetes. For islet autoimmunity, we compared gene expression differences between children after initiation of islet autoimmunity and age-matched children who did not develop islet autoantibodies. Finally, we compared both results to identify common regulatory patterns. We identified the pentose phosphate pathway and pyrimidine metabolism - both involved in nucleotide synthesis and cell proliferation - to be differentially expressed in children born by Cesarean section and after islet autoimmunity. Comparison of global gene expression signatures showed that transcriptomic changes were systematically and significantly correlated between Cesarean section and islet autoimmunity. Moreover, signatures of both Cesarean section and islet autoimmunity correlated with transcriptional changes observed during activation of isolated CD4+ T lymphocytes. In conclusion, we identified shared molecular changes relating to immune cell activation in children born by Cesarean section and children who developed autoimmunity. Our results serve as a starting point for further investigations on how a type 1 diabetes risk factor impacts the young immune system at a molecular level.
Trial registration: ClinicalTrials.gov NCT01115621.
Conflict of interest statement
The authors declare no competing interests.
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References
- Donath MY, Halban PA. Decreased beta-cell mass in diabetes: significance, mechanisms and therapeutic implications. Diabetologia. 2004;47:581–589. doi: 10.1007/s00125-004-1336-4.
- Krischer JP, et al. The 6 year incidence of diabetes-associated autoantibodies in genetically at-risk children: the TEDDY study. Diabetologia. 2015;58:980–987. doi: 10.1007/s00125-015-3514-y.
- Giannopoulou EZ, et al. Islet autoantibody phenotypes and incidence in children at increased risk for type 1 diabetes. Diabetologia. 2015;58:2317–2323. doi: 10.1007/s00125-015-3672-y.
- Lipman TH, et al. Increasing incidence of type 1 diabetes in youth: twenty years of the Philadelphia Pediatric Diabetes Registry. Diabetes care. 2013;36:1597–1603. doi: 10.2337/dc12-0767.
- Kallionpaa H, et al. Innate immune activity is detected prior to seroconversion in children with HLA-conferred type 1 diabetes susceptibility. Diabetes. 2014;63:2402–2414. doi: 10.2337/db13-1775.
- Ferreira RC, et al. A type I interferon transcriptional signature precedes autoimmunity in children genetically at risk for type 1 diabetes. Diabetes. 2014;63:2538–2550. doi: 10.2337/db13-1777.
- Cardwell CR, et al. Caesarean section is associated with an increased risk of childhood-onset type 1 diabetes mellitus: a meta-analysis of observational studies. Diabetologia. 2008;51:726–735. doi: 10.1007/s00125-008-0941-z.
- Bonifacio E, Warncke K, Winkler C, Wallner M, Ziegler AG. Cesarean section and interferon-induced helicase gene polymorphisms combine to increase childhood type 1 diabetes risk. Diabetes. 2011;60:3300–3306. doi: 10.2337/db11-0729.
- Biasucci G, Benenati B, Morelli L, Bessi E, Boehm G. Cesarean delivery may affect the early biodiversity of intestinal bacteria. The Journal of nutrition. 2008;138:1796S–1800S. doi: 10.1093/jn/138.9.1796S.
- Caicedo RA, Schanler RJ, Li N, Neu J. The developing intestinal ecosystem: implications for the neonate. Pediatric research. 2005;58:625–628. doi: 10.1203/01.PDR.0000180533.09295.84.
- Neu J, Rushing J. Cesarean versus vaginal delivery: long-term infant outcomes and the hygiene hypothesis. Clinics in perinatology. 2011;38:321–331. doi: 10.1016/j.clp.2011.03.008.
- Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic acids research. 2000;28:27–30. doi: 10.1093/nar/28.1.27.
- Jiang P, Du W, Wu M. Regulation of the pentose phosphate pathway in cancer. Protein & cell. 2014;5:592–602. doi: 10.1007/s13238-014-0082-8.
- Lane AN, Fan TW. Regulation of mammalian nucleotide metabolism and biosynthesis. Nucleic acids research. 2015;43:2466–2485. doi: 10.1093/nar/gkv047.
- O’Neill LA, Kishton RJ, Rathmell J. A guide to immunometabolism for immunologists. Nature reviews. Immunology. 2016;16:553–565. doi: 10.1038/nri.2016.70.
- Breuer K, et al. InnateDB: systems biology of innate immunity and beyond–recent updates and continuing curation. Nucleic acids research. 2013;41:D1228–1233. doi: 10.1093/nar/gks1147.
- Maliga Z, et al. A genomic toolkit to investigate kinesin and myosin motor function in cells. Nature cell biology. 2013;15:325–334. doi: 10.1038/ncb2689.
- Abbas AR, et al. Immune response in silico (IRIS): immune-specific genes identified from a compendium of microarray expression data. Genes and immunity. 2005;6:319–331. doi: 10.1038/sj.gene.6364173.
- Irizarry RA, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003;4:249–264. doi: 10.1093/biostatistics/4.2.249.
- Wood, S. N. Generalized additive models: an introduction with R. (CRC press, 2017).
- Wood SN. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2011;73:3–36. doi: 10.1111/j.1467-9868.2010.00749.x.
- Barry WT, Nobel AB, Wright FA. Significance analysis of functional categories in gene expression studies: a structured permutation approach. Bioinformatics. 2005;21:1943–1949. doi: 10.1093/bioinformatics/bti260.
Source: PubMed