'Humoral Immunity Profiling of Subjects with ME Using a Random Peptide Microanarray'


Myalgic encephalomyelitis (ME) is a complex, heterogeneous illness of unknown etiology. The search for biomarkers that can delineate cases from controls is one of the most active areas of ME research; however, little progress has been made in achieving this goal. In contrast to identifying biomarkers that are directly involved in the pathological process, an immunosignature identifies antibodies raised to proteins expressed during, and potentially involved in, the pathological process. Although these proteins might be unknown, it is possible to detect antibodies that react to these proteins using random peptide arrays. In the present study, we probe a custom 125,000 random 12-mer peptide microarray with sera from 21 ME cases and 21 controls from the USA and Europe and used these data to develop a diagnostic signature. We further used these peptide sequences to potentially uncover the naturally occurring candidate antigens to which these antibodies may specifically react with in vivo. Our analysis revealed a subset of 25 peptides that distinguished cases and controls with high specificity and sensitivity. Additionally, Basic Local Alignment Search Tool (BLAST) searches suggest that these peptides primarily represent human self-antigens and endogenous retroviral sequences and, to a minor extent, viral and bacterial pathogens.

AntibodyChronic fatigue syndromeImmunosignatureMyalgic encephalomyelitisPeptide array
Sahajpreet Singh and Phillip Stafford contributed equally to this work.


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The paper is here.

Authors & Institutions

Sahajpreet Singh 1
Phillip Stafford 2
Karen A. Schlauch 3 4
Richard R. Tillett4
Martin Gollery 5
Stephen Albert Johnston 2
Svetlana F. Khaiboullina 1 6
Kenny L. De Meirleir 1
Shanti Rawat 1
Tatjana Mijatovic 7
Krishnamurthy Subramanian 1
András Palotás 68 Email author
Vincent C. Lombardi 1 9 Email author
1.Nevada Center for Biomedical ResearchRenoUSA
2.The Biodesign Institute Center for Innovations in Medicine at Arizona State UniversityTempeUSA
3.Department of Biochemistry and Molecular BiologyUniversity of NevadaRenoUSA
4.Nevada INBRE Bioinformatics CoreUniversity of NevadaRenoUSA
5.Tahoe BioinformaticsRenoUSA
6.Kazan Federal UniversityKazanRussian Federation
7.R.E.D. LaboratoriesZellikBelgium
8.Asklepios-Med (private medical practice and research center)SzegedHungary
9.Department of PharmacologyUniversity of Nevada, Reno, School of MedicineRenoUSA
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From the discussion

"In this report, we present a “proof-of-concept” study where random peptide arrays show utility in delineating ME cases from healthy controls. The ultimate goal of this work is the development of a non-subjective clinical tool for diagnosing patients with ME. To this end, we utilized a random peptide array, which has previously produced IMS for other chronic and complicated diseases that are difficult to diagnose such as cancer, valley fever, and Alzheimer’s disease"

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