Transcriptomic and epigenetic analyses reveal a gender difference in aging-associated inflammation: the Vitality 90+ study
- 425 Downloads
Aging is associated with a pro-inflammatory state, often referred to as inflammaging. The origin of the pro-inflammatory mediators and their role in the pathogenesis of the aging-associated diseases remain poorly understood. As aging is also associated with profound changes in the transcriptomic and epigenetic (e.g., DNA methylation) profiles of cells in the peripheral blood, we analyzed the correlation of these profiles with inflammaging using the “classical” marker interleukin-6 as an indicator. The analysis of the whole-genome peripheral blood mononuclear cell (PBMC) gene expression revealed 62 transcripts with expression levels that significantly correlated with the plasma interleukin-6 (IL-6) levels in men, whereas no correlations were observed in women. The Gene Ontology analysis of plasma IL-6-associated transcripts in men revealed processes that were linked to the inflammatory response. Additionally, an Ingenuity Pathway Analysis (IPA) pathway analysis identified Tec kinase signaling as an affected pathway and upstream regulator analysis predicted the activation of IL-10 transcript. DNA methylation was assessed using a HumanMethylation450 array. Seven genes with expression profiles that were associated with the plasma IL-6 levels in men were found to harbor CpG sites with methylation levels that were also associated with the IL-6 levels. Among these genes were IL1RN, CREB5, and FAIM3, which mapped to a network of inflammatory response genes. According to our results, inflammaging is manifested differently at the genomic level in nonagenarian men and women. Part of this difference seems to be of epigenetic origin. These differences point to the genomic regulation of inflammatory response and suggest that the gender-specific immune system dimorphism in older individuals could be accounted for, in part, by DNA methylation.
KeywordsAging Inflammaging IL-6 Epigenetics DNA methylation Gene expression
We would like to thank Sinikka Repo-Koskinen, Janette Hinkka, Katri Välimaa, and Sanna Tuominen for their skillful technical assistance.
Conflict of interest
TN was primarily responsible for writing the manuscript. JJ and TN and SM performed the experiments. LK processed the data and performed statistical analyses. AH and MJ were responsible for recruiting the study population. MH provided the reagents and materials for the study. TN, SM, LK, JJ, and MH contributed to the design of the study. All authors contributed to the writing of the manuscript.
- Baylis D, Bartlett DB, Patel HP, Roberts HC (2013) Understanding how we age: insights into inflammaging. Longev Healthspan 2:8-2395-2-8Google Scholar
- Eden E, Navon R, Steinfeld I, Lipson D, Yakhini Z (2009) GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics 10: 48-2105-10-48Google Scholar
- Francisco Cribari-neto, A.Z., 2010—last update, beta regression in R. [Homepage of Journal of Statistical Software 34(2), 1-24.], [Online]. Available: http://www.jstatsoft.org/v34/i02/
- Lin H, Joehanes R, Pilling LC, Dupuis J, Lunetta KL, Ying SX et al (2014) Whole blood gene expression and interleukin-6 levels. GenomicsGoogle Scholar
- Pidsley R, Y Wong CC, Volta M, Lunnon K, Mill J, Schalkwyk LC (2013) A data-driven approach to preprocessing Illumina 450K methylation array data. BMC Genomics 14, 293. doi: 10.1186/1471-2164-14-293