Circulating cell-free RNA signatures for the characterization and diagnosis of myalgic encephalomyelitis/chronic fatigue syndrome, 2025, Gardella+

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Circulating cell-free RNA signatures for the characterization and diagnosis of myalgic encephalomyelitis/chronic fatigue syndrome

Anne E. Gardella, Daniel Eweis-LaBolle, Conor J. Loy, Emma D. Belcher, Joan S. Lenz, Carl J. Franconi, Sally Y. Scofield, Andrew Grimson, Maureen R. Hanson, Iwijn De Vlaminck

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Significance
Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating illness that affects millions of individuals worldwide. Despite the growing prevalence of ME/CFS, patients suffer from the unavailability of laboratory diagnostic tests.

Here, we establish cell-free RNA (cfRNA) as a minimally invasive bioanalyte for investigating circulating biomarkers and the pathobiology of ME/CFS. Using machine learning, we develop a cfRNA-based diagnostic model with high accuracy.

We find evidence of immune system dysfunction in patients, with elevated levels of immune cell-derived transcripts as well as chronic inflammatory signaling pathways.

These findings highlight the potential of circulating cfRNA to advance biomarker discovery and uncover disease mechanisms for ME/CFS.

Abstract
People living with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) experience heterogeneous and debilitating symptoms that lack sufficient biological explanation, compounded by the absence of accurate, noninvasive diagnostic tools.

To address these challenges, we explored circulating cell-free RNA (cfRNA) as a blood-borne bioanalyte to monitor ME/CFS. cfRNA is released into the bloodstream during cellular turnover and reflects dynamic changes in gene expression, cellular signaling, and tissue-specific processes.

We profiled cfRNA in plasma by RNA sequencing for 93 ME/CFS cases and 75 healthy sedentary controls, then applied machine learning to develop diagnostic models and advance our understanding of ME/CFS pathobiology.

A generalized linear model with least absolute shrinkage selector operator regression trained on condition-specific signatures achieved a test-set AUC of 0.81 and an accuracy of 77%.

Immune cfRNA deconvolution revealed differences in platelet-derived cfRNA between cases and controls, as well as elevated levels of plasmacytoid dendritic, monocyte, and T cell–derived cfRNA in ME/CFS. Biological network analysis further implicated immune dysfunction in ME/CFS, with signatures of cytokine signaling and T cell exhaustion.

These findings demonstrate the utility of RNA liquid biopsy as a minimally invasive tool for unraveling the complex biology behind chronic illnesses.

Web | PNAS | Paywall
 
Here is the press release from Cornell that Dr Hanson posted on X.

news.cornell.edu

Researchers identify key biomarkers for chronic fatigue syndrome | Cornell Chronicle

Researchers developed machine-learning models that can sift through cell-free RNA and identify key biomarkers for chronic fatigue syndrome, a debilitating disease that is difficult to confirm in patients because its symptoms can be easily confused with those of other illnesses.
news.cornell.edu news.cornell.edu
 
This might support a role for dendritic cells rather than macrophages in the JE et al hypothesis.
Cornell" said:
“We identified six cell types that were significantly different between ME/CFS cases and controls,” Gardella said. “The topmost elevated cell type in patients is the plasmacytoid dendritic cell. These are immune cells that are involved in producing type 1 interferons, which could indicate an overactive or prolonged antiviral immune response in patients.
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wigglethemouse said:
This might support a role for dendritic cells rather than macrophages in the JE et al hypothesis.
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If I’m remembering correctly from my old lab’s CITE-Seq data sets, Fc gamma receptors were lowly expressed on pDCs compared to other phagocytes, and I’m fairly certain the majority was FCgR2, which is broadly inhibitory.

Type I interferon (alpha/beta) production from pDCs is almost entirely triggered by TLR stimulation.

But that’s partially why I’m interested in which genes were picked up on in their cell deconvolution. If it’s primarily type I interferon-related genes, that would have been assigned to pDCs by any reference because pDCs are “professional producers” of type I interferon, but it doesn’t necessarily mean those transcripts are coming from pDCs.
 
jnmaciuch said:
If I’m remembering correctly from my old lab’s CITE-Seq data sets, Fc gamma receptors were lowly expressed on pDCs compared to other phagocytes, and I’m fairly certain the majority was FCgR2, which is broadly inhibitory.
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Thanks for the correction. They also identified monocytes again, and monocyte derived macrophages have high expression of FcGRI.
 
We implemented differential abundance analysis (DESeq2, Materials and Methods) and identified 743 unique features that differed between cases and controls [Benjamini–Hochberg (BH) adjusted P-values (P- adj) threshold of < 0.05 and a log2 fold change cutoff of ±0.5]. Of these, 608 features were elevated in cases, and 135 were elevated in controls. Genes such as IL18R1, CCR7, FCRL5, PRF1, and IFNLR1 were more abundant in cases, whereas genes such as CCL3, TGFB2, CD109, and COL1A1 were more abundant in controls (Fig. 2A).
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In addition, we observed changes in abundance of several mtRNA transcripts between cases and controls. Notably, levels of a mitochondrial ribosome assembly factor (MTG1) were significantly increased, while several transcripts (MTND1P23, MT-TL1, MT-TV, MT-TH, MT-TS2, MT-TT, MTATP8P1, MTND6P3, and MTRNR2L3) were significantly decreased. Many of these mtRNA transcripts are inversely correlated with MTG1 in cases (SI Appendix, Fig. S2).
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It says the data is available on GEO but the accession (link) is currently marked as private. The analysis code is on GitHub.
 
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INFLR1 is a surprising hit—as far as I know, interferon lambda signaling is almost exclusively in epithelial cells, particularly respiratory tract.

[Edit: though I suppose something other than interferon lambda might upregulate expression of that receptor]
 
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