Authors: C. Camprubí, A. Salas-Huetos, R. Aiese-Cigliano, A. Godo, M.C. Pons, G. Castellano, M. Grossmann, W. Sanseverino, J. I Martin-Subero, N. Garrido, J. Blanco


  • Genetics of Male Fertility Group, Unitat de Biologia Cel⋅lular (Facultat de Biociències), Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès) 08193, Spain
  • Sequentia Biotech, Edifici CRAG, Campus UAB, Bellaterra (Cerdanyola del Vallès) 08193, Spain
  • Unidad de Reproducción Asistida de Centro Médico Teknon, Barcelona 08022, Spain
  • Departament d’Anatomia Patològica, Farmacologia i Microbiologia, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona 08036, Spain
  • Laboratorio de Andrología y Banco de Semen, Instituto Universitario IVI Valencia, Valencia 46015, Spain

Publication: Reproductive BioMedicine Online

Date: September, 2016

Full paper: Spermatozoa from infertile patients exhibit differences of DNA methylation associated with spermatogenesis-related processes: an array-based analysis

The influence of aberrant sperm DNA methylation on the reproductive capacity of couples has been postulated as a cause of infertility. This study compared the DNA methylation of spermatozoa of 19 fertile donors and 42 infertile patients using the Illumina 450K array. Clustering analysis of methylation data arranged fertile and infertile patients into two groups. Bivariate clustering analysis identified a differential distribution of samples according to the characteristics of seminogram and age, suggesting a possible link between these parameters and specific methylation profiles. The study identified 696 differentially methylated cytosine-guanine dinucleotides (CpG) associated with 501 genes between fertile donors and infertile patients. Ontological enrichment analysis revealed 13 processes related to spermatogenesis. Data filtering identified a set of 17 differentially methylated genes, some of which had functions relating to spermatogenesis. A significant association was identified between RPS6KA2 hypermethylation and advanced age (P = 0.016); APCS hypermethylation and oligozoospermia (P = 0.041); JAM3/NCAPD3 hypermethylation and numerical chromosome sperm anomalies (P = 0.048); and ANK2 hypermethylation and lower pregnancy rate (P = 0.040). This description of a set of differentially methylated genes provides a framework for further investigation into the influence of such variation in male fertility in larger patient cohorts.