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Coordinated transcriptional regulation patterns associated with infertility phenotypes in men
  1. Peter J I Ellis (pjie2{at}cam.ac.uk)
  1. University of Cambridge Department of Pathology, United Kingdom
    1. Robert A Furlong
    1. University of Cambridge Department of Pathology, United Kingdom
      1. Sarah J Conner
      1. Reproductive Biology and Genetics Group, Institute of Biomedical Research, University of Birmingham, United Kingdom
        1. Jackson Kirkman-Brown
        1. Centre for Human Reproductive Science, The Assisted Conception Unit, Birmingham Women's Hospital, United Kingdom
          1. Masoud Afnan
          1. Centre for Human Reproductive Science, The Assisted Conception Unit, Birmingham Women's Hospital, United Kingdom
            1. Christopher Barratt
            1. Reproductive Biology and Genetics Group, Institute of Biomedical Research, University of Birmingham, United Kingdom
              1. Darren K Griffin
              1. Department of Biosciences, University of Kent, United Kingdom
                1. Nabeel A Affara (na{at}mole.bio.cam.ac.uk)
                1. University of Cambridge Department of Pathology, United Kingdom

                  Abstract

                  Introduction: Microarray gene expression profiling is a powerful tool for global analysis of the transcriptional consequences of disease phenotypes. Understanding the genetic correlates of particular pathological states is important for more accurate diagnosis and screening of patients, and thus for suggesting appropriate avenues of treatment. As yet, there has been little research describing gene expression profiling of infertile and subfertile men, and thus the underlying transcriptional events involved in loss of spermatogenesis remain unclear. Here we present the results of an initial screen of 33 patients with differing spermatogenic phenotypes.

                  Methods: Oligonucleotide array expression profiling was performed on testis biopsies for 33 patients presenting for testicular sperm extraction (TESE). Significantly regulated genes were selected using a mixed model ANOVA. Principle components analysis and hierarchical clustering were used to interpret the resulting dataset with reference to the patients' history, clinical findings and histological composition of the biopsies.

                  Results Striking patterns of coordinated gene expression were found. The most significant contains multiple germ cell specific genes and corresponds to the degree of successful spermatogenesis in each patient, while a second pattern corresponds to inflammatory activity within the testis. Smaller-scale patterns were also observed, relating to unique features of the individual biopsies.

                  • germ cell
                  • infertility
                  • microarray
                  • spermatogenesis
                  • testis

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