proteomics – Cornell Center for Enervating NeuroImmune Disease

Dysregulation of EV protein cargo in ME/CFS females in response to maximal exercise

Exertion intolerance and post-exertional malaise are defining features of ME/CFS. Work from our Center aims to uncover the molecular disruption that occurs during and around these features. Thus, through the lens of extracellular vesicles (EVs), a publication from co-lead authors Ludovic Giloteaux and Katherine Glass provides novel insights into these topics.

The study involved the isolation of EVs, membrane-bound non-replicating particles released from cells, from the plasma of 18 ME/CFS and 17 healthy control female participants. The cargo of EVs are relevant in that they provide signals that could uncover elements of dysfunction in ME/CFS. Specifically, protein cargo from EVs were examined in this publication before and after a cardiopulmonary exercise test (i.e., around the induction of PEM).

Figure 1 from the publication provides an overview of the study’s experimental design

The EV proteome in response to exercise was clearly different in ME/CFS when compared to sedentary controls. These divergent responses were associated with different molecular pathways. Uncovered protein differences between the cohorts also point to contrasting tissues and cell types. Additionally, there were several proteins associated with a worsening of muscle pain (e.g., THBS1 and TPM4), PEM (e.g., NEXN), and fatigue (e.g., CLU) post exercise in ME/CFS. Although it is difficult to directly associate changes with the EV proteome to distinct cell types, this publication brings forth avenues to interrogate further.

If you are interested in more information regarding this publication, the entire paper is freely available in the Journal of Extracellular Vesicles.

New insights into ME/CFS using a multiomic approach

A new open access publication in the Journal of Translational Medicine describes the work by Giloteaux et al. to uncover ways to detect the disease ME/CFS. Ludovic Giloteaux and Jiayin Li, joint first authors, took a collaborative approach to improve our understanding of ME/CFS. Giloteaux isolated extracellular vesicles from the plasma of 98 Chronic Fatigue Initiative individuals (49 ME/CFS and 49 controls) to study their signaling molecules (i.e., cytokines). Then he worked with Jiayin Li and David Ruppert, statisticians at Cornell, and using data generated by Columbia University investigators, the group combined plasma cytokine, EV cytokine, plasma proteomic, and demographic datasets to explore new ways to approach ME/CFS.

One of the key findings from the publication is the 86% accuracy in differentiating between people with ME/CFS and health controls. Giloteaux et al. leveraged multiple datasets to achieve this goal. The paper also outlines interesting correlations between various biological molecules and clinical surveys that measure disease severity. For example, higher levels of pro-inflammatory molecules (e.g., CSF2 & TNFa) were correlated with greater physical and fatigue symptoms in people with ME/CFS.

The publication is open access so see the website for more information. Additionally, the EV cytokine data is available on mapMECFS.

Plasma proteomics publication shows disrupted cell-to-cell signaling

We are proud to announce our latest plasma proteomics publication is available as open access in Proteomes.

This manuscript takes a look at 4,790 circulating plasma proteins from 20 ME/CFS women compared to 20 healthy women, over an unprecedented range, for ME/CFS, of 9 orders of magnitude.

Pathway analysis uncovered disrupted cell-to-cell communication, specifically in the ephrin-Eph signaling pathway. This pathway is crucial for many aspects of our body’s homeostasis, including development, physiology, and disease regulation.

Additionally, the paper outlines promising results for the development of a diagnostic test using protein ratios.

First author, Arnaud Germain, PhD, outlines these findings in a video abstract below.

Transcripts for the video are available:
– English
– German

Chinese, French, and Spanish subtitles for the video abstract are available. See video settings to select an option.