Editorial: The Power of a Modular Approach to Transcriptional Analysis

In this issue of Arthritis & Rheumatology, Chiche and colleagues provide additional insight into the complexity of interferon (IFN) signatures in the peripheral blood of lupus patients (1). Their data illustrate the power of combining correlated gene expression information with biologic associations, in this case based on empirically derived modules of gene expression and their post hoc annotations. These methods are beginning to provide more user-friendly approaches to analyzing and communicating results for monitoring immune function in the context of disease, vaccination, and therapy. Over the last decade, the development of increasingly comprehensive platforms for global analysis of messenger RNA (mRNA) expression at reasonable cost has spawned a huge number of studies that utilize gene expression profiling to understand human disease. A PubMed search for studies using gene expression profiling in humans yields tens of thousands of results, including more than 300 publications on systemic lupus erythematosus (SLE) and 500 publications on rheumatoid arthritis. A substantial fraction of these studies were carried out on mixed cell populations, most commonly peripheral blood mononuclear cells (PBMCs) or whole blood. Early studies documented the importance of fresh preparation of RNA, since thousands of genes can change expression in PBMCs after just a few hours and even more so after overnight shipping (2). This problem has been solved by the use of blood collections using PAXgene or Tempus tubes that immediately stabilize RNA. Nevertheless, the computational challenges of making sense of large-scale analog data related to mRNA transcript levels are substantial. A variety of exploratory statistical approaches to large-scale data have been employed, which are generally agnostic with regard to the biologic functions of the genes being analyzed. As a result, one of the biggest problems in the field has been the lack of validated results. Small sample sizes, together with statistical noise and an inability to combine data across experiments or laboratories because of standardization problems (batch effects), have led to isolated reports of results which could not later be reproduced. Studies of rheumatic diseases in particular have been plagued by this problem. However, an exception to this is the peripheral blood IFN signature in lupus, which has been well replicated in numerous studies since the first observations more than a decade ago (3–5). In 2008, Chaussabel et al reported on an approach to gene expression analysis that was based on an empirical examination of how groups of genes behave across a variety of disease conditions in PBMCs (6). Expression profiling was carried out on 239 patients with 1 of 8 disorders: juvenile rheumatoid arthritis, systemic lupus, type 1 diabetes mellitus, metastatic melanoma, Escherichia coli infection, Staphylococcus aureus infection, influenza A infection, and acute liver transplant rejection. These disorders all involve some degree of inflammation or immune stimulation, and thus, it was reasonable to expect some reflection of these states in the circulating peripheral blood cells. Remarkably, the authors observed clear “modules” of gene expression, most commonly involving hundreds of transcripts moving in a common pattern across samples from the 8 data sets. Upon inspection of the genes in these modular groupings, many modules could be reasonably designated as belonging to known cell types, such as B cells, T cells, myeloid lineage, etc., as well as to known pathways, such as IFN-inducible genes or ribosomeor major histocompatibility complex–related genes. Many modules did not lend themselves to easy categorization and were thus designated “undetermined”; a total of 28 distinct modules were identified. Not surprisingly, the IFN-associated module was strongly up-regulated in the pediatric lupus patients in that study (6). The study by Chiche et al takes advantage of an Peter K. Gregersen, MD, Michaela Oswald, PhD: Feinstein Institute for Medical Research, Manhasset, New York. Address correspondence to Peter K. Gregersen, MD, Robert S. Boas Center for Genomics and Human Genetics, Feinstein Institute for Medical Research, 350 Community Drive, Manhasset, NY 11030. E-mail: peterg@nshs.edu. Submitted for publication February 24, 2014; accepted in revised form March 11, 2014.