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Analysis of gene expression in rheumatoid arthritis and related conditions offers insights into sex-bias, gene biotypes and co-expression patterns

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  • Alexander Platzer
  • Thomas Nussbaumer
  • Thomas Karonitsch
  • Josef S Smolen
  • Daniel Aletaha

Abstract

The era of next-generation sequencing has mounted the foundation of many gene expression studies. In rheumatoid arthritis research, this has led to the discovery of important candidate genes which offered novel insights into mechanisms and their possible roles in the cure of the disease. In the last years, data generation has outstripped data analysis and while many studies focused on specific aspects of the disease, a global picture of the disease is not yet accomplished. Here, we analyzed and compared a collection of gene expression information from healthy individuals and from patients suffering under different arthritis conditions from published studies containing the following clinical conditions: early and established rheumatoid arthritis, osteoarthritis and arthralgia. We show comprehensive overviews of this data collection and give new insights specifically on gene expression in the early stage, into sex-dependent gene expression, and we describe general differences in expression of different biotypes of genes. Many genes that are related to cytoskeleton changes (actin filament related genes) are differently expressed in early rheumatoid arthritis in comparison to healthy subjects; interestingly, eight of these genes reverse their expression ratio significantly between men and women compared early rheumatoid arthritis and healthy subjects. There are some slighter changes between men and woman between the conditions early and established rheumatoid arthritis. Another aspect are miRNAs and other gene biotypes which are not only promising candidates for diagnoses but also change their expression grossly in average at rheumatoid arthritis and arthralgia compared to the healthy condition. With a selection of intersecting genes, we were able to generate simple classification models to distinguish between healthy and rheumatoid arthritis as well as between early rheumatoid arthritis to other arthritides based on gene expression.

Suggested Citation

  • Alexander Platzer & Thomas Nussbaumer & Thomas Karonitsch & Josef S Smolen & Daniel Aletaha, 2019. "Analysis of gene expression in rheumatoid arthritis and related conditions offers insights into sex-bias, gene biotypes and co-expression patterns," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-23, July.
    • Handle: RePEc:plo:pone00:0219698
    DOI: 10.1371/journal.pone.0219698
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    References listed on IDEAS

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    2. Shinya Tasaki & Katsuya Suzuki & Yoshiaki Kassai & Masaru Takeshita & Atsuko Murota & Yasushi Kondo & Tatsuya Ando & Yusuke Nakayama & Yuumi Okuzono & Maiko Takiguchi & Rina Kurisu & Takahiro Miyazaki, 2018. "Multi-omics monitoring of drug response in rheumatoid arthritis in pursuit of molecular remission," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
    3. Fran Supek & Matko Bošnjak & Nives Škunca & Tomislav Šmuc, 2011. "REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-9, July.
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