New Papers Drawn from CFS Dataset
Underscore the Power of Partnerships

By Suzanne Vernon, PhD

Suzanne Vernon, PhD, is the Association’s scientific director. To learn more about our scientific director, read “Meet Suzanne Vernon” (PDF format).

During the fall of 2005—when I was still at the Centers for Disease Control and Prevention (CDC)—25 investigators from around the world participated in the CFS Computational Challenge (C3), working together on what is arguably one of the richest and most comprehensive CFS clinical datasets, drawn from a two-day in-hospital study in Wichita, Kansas. Three years later, the dataset and collaborative concept initiated with C3 continues to yield insight into CFS.

Following C3, the CDC approved the sharing of the Wichita clinical dataset with both the CAMDA 2006 meeting at Duke University and the CAMDA 2007 meeting in Barcelona—scientific forums where biologists, statisticians, computer scientists and mathematicians from around the world compete with the best algorithms and software tools to assess different techniques used in genomics. This provided more opportunity to partner with a diverse and expert scientific group for some fresh perspectives, new tools and approaches to decipher CFS. A UCLA postdoctoral fellow won the 2006 CAMDA competition, and her work using a genomic approach to describe immune cell dysfunction in CFS is currently being considered for publication.

In fact, over the past three years, sharing the Wichita clinical dataset and partnering with expert extramural scientists has resulted in 20 publications. In addition to these 20 publications, the CDC's CFS Research Program has published at least 15 papers on the Wichita clinical study.

Meanwhile, two papers from participants of the CAMDA 2007 meeting have just been published in the journal Genomics: one by Jim Fuite, PhD, and colleagues, describing immune cell communication differences in CFS and the other by Madhuchhanda Bhattacharjee, PhD, and colleagues, identifying genes, gene activity and protein markers of CFS.

The Genomics paper by Fuite and his colleagues is titled “Neuroendocrine and Immune Network Re-modeling in Chronic Fatigue Syndrome: An Exploratory Analysis.” Using the Wichita clinical data and some mathematical techniques to identify patterns and communication networks, the authors are able to demonstrate that people with CFS have immune gene activity that resembles chronic inflammation. This altered immune function, in turn, affects neuroendocrine function in people with CFS. By examining data from several body systems at the same time, this is the first comprehensive assessment that demonstrates severe disturbances across multiple physiologic systems. These physiologic disturbances likely underpin CFS symptoms.

The other paper, by Bhattacharjee and his colleagues, titled “Bayesian Biomarker Identification Based on Marker-expression Proteomics Data,” used probability theory (Bayesian methods) to identify markers specific to fatigue that are also possibly exclusive to CFS. While this paper is more about the analysis technique than CFS, the findings are indeed remarkable. It identifies genes that have been found important in other CFS studies (e.g., the glucocorticoid receptor) and chromosomes with genes that are differentially active in CFS.

Lastly and importantly, these authors are the first to publish findings from the comprehensive proteomic analysis conducted on serum from the 227 participants in the Wichita clinical study. By relating the genetic data to the activity of genes and then to the production of proteins, Bhattacharjee identifies several regions of the genome that are associated with CFS. This is important because science already understands some functions of these genomic regions, helping to implicate possible biologic perturbations that may either be the cause of CFS or the mechanism that sustains the illness.

These two papers are excellent examples of the power of scientific partnerships and of sharing scientific data. There’s a tremendous amount of biologic proof wrapped into the 35 publications that have been published from the Wichita clinical dataset so far. These papers have advanced our understanding of CFS, have documented biologic explanations for CFS symptoms and should be used to develop models that will allow us to identify and objectively diagnosis CFS subtypes and to tailor management and treatment accordingly.

This reinforces the CFIDS Association's belief that the time is ripe for collaborative partnerships, research networks and leveraging existing CFS data to get the best value and fastest progress in CFS research.

Bhattacharjee M, Botting CH, Sillanpää MJ. Bayesian biomarker identification based on marker-expression proteomics data. Genomics. 2008 Aug 14. [Epub ahead of print]

Fuite J, Vernon SD, Broderick G. Neuroendocrine and immune network re-modeling in chronic fatigue syndrome: an exploratory analysis. Genomics. 2008 Sep 4. [Epub ahead of print]

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