Sex- and age-dependent human transcriptome variability: implications for chronic heart failure

Proc Natl Acad Sci U S A. 2003 Mar 4;100(5):2754-9. doi: 10.1073/pnas.0436564100. Epub 2003 Feb 24.

Abstract

Heart failure (HF) is the end result of progressive and diverse biological adaptations within the diseased myocardium. We used cDNA microarrays and quantitative PCR to examine the transcriptomes of 38 left ventricles from failing and nonfailing human myocardium. After identification of a pool of putative HF-responsive candidate genes by microarrays on seven nonfailing and eight failing hearts, we used quantitative PCR and a general linear statistical model in a larger sample set (n = 34) to validate and examine the role of contributing biological variables (age and sex). We find that most HF-candidate genes (transcription factors, Cebpb, Npat; signaling molecules, Map2k3, Map4k5; extracellular matrix proteins, Lum, Cola1; and metabolic enzymes, Mars) demonstrated significant changes in gene expression; however, the majority of differences among samples depended on variables such as sex and age, and not on HF alone. Some HF-responsive gene products also demonstrated highly significant changes in expression as a function of age and/or sex, but independent of HF (Ngp1, Cd163, and Npat). These results emphasize the need to account for biological variables (HF, sex and age interactions) to elucidate genomic correlates that trigger molecular pathways responsible for the progression of HF syndromes.

Publication types

  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Adult
  • Age Factors
  • Aged
  • Algorithms
  • Cardiomyopathy, Dilated / genetics*
  • Cardiomyopathy, Dilated / metabolism
  • Cardiomyopathy, Dilated / pathology*
  • DNA, Complementary / metabolism
  • Female
  • Heart Ventricles / metabolism
  • Humans
  • Male
  • Middle Aged
  • Myocardium / metabolism
  • Oligonucleotide Array Sequence Analysis
  • Polymerase Chain Reaction
  • RNA, Messenger / metabolism*
  • Sex Factors
  • Signal Transduction
  • Statistics as Topic
  • Transcription, Genetic*

Substances

  • DNA, Complementary
  • RNA, Messenger