# Cell-Based Blood Biomarkers for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Results

#### 2.1. ME/CFS and Patient Blood Samples Can be Distinguished by the Viability in Culture of Frozen Peripheral Blood Lymphocytes

#### 2.2. Immortalised Lymphocytes from ME/CFS and Patient Blood Samples Can be Distinguished by Mitochondrial and Cellular Respiratory Dysfunction

_{m}), the rate of O

_{2}consumption (OCR) by ATP synthesis and the proton leak (as fractions of the basal respiration rate), the maximum OCR by uncoupled mitochondria, the uncoupled activity of Complex I and the non-mitochondrial OCR (a surrogate measure of overall metabolic rate). We used these parameters in discriminant analysis and multiple logistic regression to determine their efficacy in distinguishing ME/CFS and control patients. Of these measures, all but ΔΨ

_{m}are obtained from the same respirometry experiments. For this reason, it was worthwhile determining if ΔΨ

_{m}provided significant additional discriminatory power in the tests. We therefore compared the results using the five respiration measures with and without ΔΨ

_{m}. The results (Table 2, Figure 3) showed there was only a small extra benefit in using the assay of ΔΨ

_{m}in addition to the respirometry—the “confusion” matrices revealed slightly higher test specificity when the ΔΨ

_{m}was included, but the sensitivities were identical and ROC curves were not significantly different.

#### 2.3. Immortalised Lymphocytes from ME/CFS and Patient Blood Samples Can be Distinguished by the Phosphorylation State of 4E-BP1, a TORC1 Kinase Substrate

#### 2.4. Combining Measures of Frozen Lymphocyte Death Rate, Lymphoblast Mitochondrial Dysfunction and Lymphoblast TORC1 Signalling Discriminates ME/CFS and Patient Blood Samples with High Accuracy

#### 2.5. A protocol that Combines a Screening Test Using Lymphocyte Death Rates and Confirmatory Tests of Respiratory Function and TORC1 Activity

## 3. Discussion

## 4. Materials and Methods

#### 4.1. Participant Cohort

#### 4.2. PBMC Isolation from Blood Sample

^{6}cells were set aside and resuspended in 5 mL Roswell Park Memorial Institute (RPMI) 1640 without L-glutamine (Life Technologies) supplemented with 1X Glutamax (Life Technologies, Carlsbad, California, United States), 10% fetal bovine serum (FBS) and 1% Penicillin/Streptomycin. Excess lymphocytes were separated into aliquots of 5 × 10

^{6}cells, harvested and resuspended in 250 µL of Recovery™ Cell Culture Freezing Medium (Life Technologies, Carlsbad, California, United States) and stored at 0 °C.

#### 4.3. Immortalisation of Lymphocytes

^{6}cells in 5 mL RPMI 1640. Per well, 150 µL of the mix was seeded in a 96-well U-bottom plate, then incubated for one hour within a humidified 5% CO

_{2}incubator at 37 °C. A final concentration of 500 ng/mL Cyclosporin A (Sigma, St. Louis, MO, USA) was then added to each well. Cultures were fed weekly by replacing half of the medium with the same formulation, without disturbing the cells. This process was repeated over a period of approximately three weeks until the cells were confluent and growing rapidly, after which the lymphoblast cultures were processed as described in the following section.

#### 4.4. Lymphoblast Cultures

_{2}incubator at 37 °C. Lymphoblast storage in Recovery™ Cell Culture Freezing Medium at −80 °C has been previously described in detail [32].

#### 4.5. Viable Cell Counts

^{6}cells/mL and kept in a humidified 5% CO

_{2}incubator at 37 °C over the course of the experiment. Each well was mixed gently by pipette before sampling to ensure counting of a homogeneous cell suspension.

#### 4.6. Mitochondrial Stress Test (Seahorse Respirometry)

^{5}viable PBMCs or lymphoblasts per well were measured using the Seahorse XFe24 Extracellular Flux Analyser with Seahorse XF24 FluxPaks (Agilent Technologies, Chicopee, Massachusetts, USA). Immortalised lymphoblasts were cultured in 3 mL growth medium per well in 6-well Costar plates prior to Seahorse experiments. Seahorse assays were carried out as previously described in detail [39]. Oxygen consumption rates (OCR in pmol/min) were measured (basal OCR) prior to and after successive injection of 1 µM oligomycin (ATP synthase inhibitor), 1 µM CCCP (carbonyl cyanide m-chlorophenyl hydrazone, an uncoupling protonophore), 1 µM rotenone (Complex I inhibitor) and 5 µM antimycin A (Complex III inhibitor). From the resulting data, we determined the OCR associated with respiratory ATP synthesis (oligomycin-sensitive), the maximum OCR in CCCP-uncoupled mitochondria and the rotenone-sensitive OCR attributable to uncoupled Complex I activity, the antimycin-sensitive Complex II/III activity, the OCR by mitochondrial functions (e.g., protein import) other than ATP synthesis that are Δψm-driven (so-called ‘proton leak’), non-respiratory oxygen consumption (e.g., by cellular and mitochondrial oxygenases and oxidases), and the respiratory ‘spare-capacity’ (excess capacity of the respiratory electron transport chain that is not being used in basal respiration).

#### 4.7. 4E-BP1 Phosphorylation Levels (TORC1 Activity)

^{5}cells/mL and plated in four replicates at 5 × 10

^{4}cells/well in a 96-well plate. Cells were incubated at 5% CO

_{2}/ 37 °C for 2 h, with two of the replicates subjected to TOR inhibition by 0.5 µM TORIN2. Lysis buffer was added to each well as per manufacturer instructions and the plate mixed on an orbital shaker for 40 min before plating each sample into a 384 well white plate (Corning, New York, USA)—incorporating various controls and antibody mix (anti- 4E-BP1 antibody labelled with d2 acceptor, and anti-phospho-4E-BP1 antibody labelled with Eu

^{3+}-cryptate donor) according to manufacturer instructions. After a 2 h incubation at room temperature the plate was scanned by the Clariostar plate reader (BMG, Ortenberg, Germany) and the ratio of the FRET signal from anti-phospho-4E-BP1 antibody to the donor fluorescence signal from anti-4E-BP1 antibody was measured. Internal normalisation control lymphoblasts were included within each assay in case of between-experiment variation.

#### 4.8. Quantification and Statistical Analysis of Biochemical Assays

_{2}consumption rate (OCR) attributable to ATP synthesis by Complex V and the use of the proton gradient in other mitochondrial membrane transport processes (the proton leak), maximum CCCP-uncoupled OCR, the maximum uncoupled Complex I activity and the nonmitochondrial OCR. The outcomes of both the linear discriminant analysis and the logistic regression were expressed as a “confusion matrix”, showing a cross tabulation of the actual source of the sample (ME/CFS or control) and the classification produced by the method in question. From this, the error rates (false positives and false negatives) were calculated.

## 5. Conclusions

^{nd}stage in which mitochondrial respiratory function and TORC1 signaling activity are measured in lymphoblastoid cell lines derived from the frozen lymphocytes. The combined tests may provide a highly reliable cell-based blood testing protocol to aid in ME/CFS diagnosis.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Appendix A

**Table A1.**Confusion matrix for linear discriminant analysis of PBMC death rate after 24, 48 and 72 h in culture medium.

Method | Clinical Group | Actual Count | Test Class | % Error | Statistics | ||
---|---|---|---|---|---|---|---|

ME/CFS | Control | ||||||

Linear discriminant analysis | ME/CFS | 57 | 45 | 12 | 21.0 | χ^{2} | 26.2 |

Control | 33 | 7 | 26 | 21.2 | p | 3.0 × 10^{−7} | |

TOTAL | 90 | 52 | 38 | 21.1 | Fisher p = 1.25 × 10^{−7} | ||

Logistic regression | ME/CFS | 57 | 47 | 10 | 17.5 | χ^{2} | 27.4 |

Control | 33 | 8 | 25 | 24.2 | p | 1.6 × 10^{−7} | |

TOTAL | 90 | 65 | 25 | 20.0 | Fisher p = 7.2 × 10^{−8} |

**Table A2.**Confusion matrix for logistic regression of PBMC and lymphoblast ME/CFS biomarkers using a randomly selected training and test subcohorts.

Subcohort | Clinical Group | Actual Count | Test Class | % Error | Statistics | ||
---|---|---|---|---|---|---|---|

ME/CFS | Control | ||||||

PBMC cell death after 48 h in culture | |||||||

Training | ME/CFS | 46 | 41 | 5 | 10.9 | χ^{2} | 17.0 |

Control | 25 | 10 | 15 | 40.0 | p | 3.8 × 10^{−5} | |

TOTAL | 71 | 51 | 20 | 21.2 | Fisher p = 2.1 × 10^{−5} | ||

Test | ME/CFS | 11 | 11 | 0 | 0 | χ^{2} | 6.39 |

Control | 8 | 3 | 5 | 37.5 | p | 0.012 | |

TOTAL | 19 | 14 | 5 | 15.8 | Fisher p = 4.8 × 10^{−3} | ||

Lymphoblast Seahorse respirometry | |||||||

Training | ME/CFS | 37 | 34 | 3 | 8.1 | χ^{2} | 16.6 |

Control | 17 | 6 | 11 | 35.3 | p | 4.6 × 10^{−5} | |

TOTAL | 54 | 40 | 14 | 16.6 | Fisher p = 3.1 × 10^{−5} | ||

Test | ME/CFS | 12 | 10 | 2 | 16.7 | χ^{2} | 4.22 |

Control | 3 | 0 | 3 | 0.0 | p | 0.04 | |

TOTAL | 15 | 10 | 5 | 13.3 | Fisher p = 0.022 | ||

Lymphoblast TORC1 activity | |||||||

Training | ME/CFS | 34 | 30 | 4 | 11.8 | χ^{2} | 10.65 |

Control | 19 | 8 | 11 | 42.1 | P | 1.1 × 10^{−3} | |

TOTAL | 53 | 38 | 15 | 22.6 | Fisher p = 9.1 × 10^{−4} | ||

Test | ME/CFS | 10 | 9 | 1 | 10.0 | χ^{2} | 1.59 |

Control | 3 | 1 | 2 | 33.3 | p | 0.21 | |

TOTAL | 13 | 10 | 3 | 15.3 | Fisher p = 0.11 | ||

Combined tests | |||||||

Training | ME/CFS | 22 | 21 | 1 | 4.5 | χ^{2} | 20.9 |

Control | 11 | 1 | 10 | 9.1 | p | 4.9 × 10^{−6} | |

TOTAL | 33 | 22 | 11 | 6.1 | Fisher p = 1.3 × 10^{−6} | ||

Test | ME/CFS | 7 | 6 | 1 | 14.3 | χ^{2} | 3.35 |

Control | 3 | 0 | 3 | 0.0 | p | 0.067 | |

TOTAL | 10 | 6 | 4 | 10.0 | Fisher p = 0.033 |

## References

- Falk Hvidberg, M.; Brinth, L.S.; Olesen, A.V.; Petersen, K.D.; Ehlers, L. The health-related quality of life for patients with myalgic encephalomyelitis / chronic fatigue syndrome (ME/CFS). Plos ONE
**2015**, 10, e0132421. [Google Scholar] [CrossRef] [PubMed] - Fukuda, K.; Straus, S.E.; Hickie, I.; Sharpe, M.C.; Dobbins, J.G.; Komaroff, A. The chronic fatigue syndrome: A comprehensive approach to its definition and study. International chronic fatigue syndrome study group. Ann. Intern. Med.
**1994**, 121, 953–959. [Google Scholar] [CrossRef] - Carruthers, B.M.; van de Sande, M.I.; De Meirleir, K.L.; Klimas, N.G.; Broderick, G.; Mitchell, T.; Staines, D.; Powles, A.C.; Speight, N.; Vallings, R.; et al. Myalgic encephalomyelitis: International Consensus Criteria. J. Intern. Med.
**2011**, 270, 327–338. [Google Scholar] [CrossRef] [Green Version] - Carruthers, B.M.; Jain, A.K.; De Meirleir, K.L.; Peterson, D.L.; Klimas, N.G.; Lerner, A.M.; Bested, A.C.; Flor-Henry, P.; Joshi, P.; Powles, A.C.; et al. Myalgic encephalomyelitis/chronic fatigue syndrome: Clinical working case definition, diagnostic and treatment protocols. J. Chronic. Fatigue. Syndr.
**2003**, 11, 7–36. [Google Scholar] [CrossRef] - Missailidis, D.; Annesley, S.J.; Fisher, P.R. Pathological mechanisms underlying myalgic encephalomyelitis/chronic fatigue syndrome. Diagnostics
**2019**, 9, 80. [Google Scholar] [CrossRef] [Green Version] - Nacul, L.C.; Mudie, K.; Kingdon, C.C.; Clark, T.G.; Lacerda, E.M. Hand grip strength as a clinical biomarker for ME/CFS and disease severity. Front. Neurol.
**2018**, 9, 992. [Google Scholar] [CrossRef] [PubMed] - Richardson, A.M.; Lewis, D.P.; Kita, B.; Ludlow, H.; Groome, N.P.; Hedger, M.P.; de Kretser, D.M.; Lidbury, B.A. Weighting of orthostatic intolerance time measurements with standing difficulty score stratifies ME/CFS symptom severity and analyte detection. J. Transl. Med.
**2018**, 16, 97. [Google Scholar] [CrossRef] [Green Version] - Brenu, E.W.; van Driel, M.L.; Staines, D.R.; Ashton, K.J.; Ramos, S.B.; Keane, J.; Klimas, N.G.; Marshall-Gradisnik, S.M. Immunological abnormalities as potential biomarkers in chronic fatigue syndrome/myalgic encephalomyelitis. J. Transl. Med.
**2011**, 9, 81. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Lidbury, B.A.; Kita, B.; Lewis, D.P.; Hayward, S.; Ludlow, H.; Hedger, M.P.; de Kretser, D.M. Activin B is a novel biomarker for chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) diagnosis: A cross sectional study. J. Transl. Med.
**2017**, 15, 60. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Lidbury, B.A.; Kita, B.; Richardson, A.M.; Lewis, D.P.; Privitera, E.; Hayward, S.; de Kretser, D.; Hedger, M. Rethinking me/cfs diagnostic reference intervals via machine learning, and the utility of activin B for defining symptom severity. Diagnostics
**2019**, 9, 79. [Google Scholar] [CrossRef] [Green Version] - Montoya, J.G.; Holmes, T.H.; Anderson, J.N.; Maecker, H.T.; Rosenberg-Hasson, Y.; Valencia, I.J.; Chu, L.; Younger, J.W.; Tato, C.M.; Davis, M.M. Cytokine signature associated with disease severity in chronic fatigue syndrome patients. Proc. Natl. Acad. Sci. USA
**2017**, 114, E7150–E7158. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Moneghetti, K.J.; Skhiri, M.; Contrepois, K.; Kobayashi, Y.; Maecker, H.; Davis, M.; Snyder, M.; Haddad, F.; Montoya, J.G. Value of circulating cytokine profiling during submaximal exercise testing in myalgic encephalomyelitis/chronic fatigue syndrome. Sci. Rep.
**2018**, 8, 2779. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Landi, A.; Broadhurst, D.; Vernon, S.D.; Tyrrell, D.L.; Houghton, M. Reductions in circulating levels of IL-16, IL-7 and VEGF-A in myalgic encephalomyelitis/chronic fatigue syndrome. Cytokine
**2016**, 78, 27–36. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Hornig, M.; Montoya, J.G.; Klimas, N.G.; Levine, S.; Felsenstein, D.; Bateman, L.; Peterson, D.L.; Gottschalk, C.G.; Schultz, A.F.; Che, X.; et al. Distinct plasma immune signatures in ME/CFS are present early in the course of illness. Sci. Adv.
**2015**, 1, 1. [Google Scholar] [CrossRef] [Green Version] - Peterson, D.; Brenu, E.W.; Gottschalk, G.; Ramos, S.; Nguyen, T.; Staines, D.; Marshall-Gradisnik, S. Cytokines in the cerebrospinal fluids of patients with chronic fatigue syndrome/myalgic encephalomyelitis. Mediat. Inflamm.
**2015**, 2015, 929720. [Google Scholar] [CrossRef] - Hornig, M.; Gottschalk, G.; Peterson, D.L.; Knox, K.K.; Schultz, A.F.; Eddy, M.L.; Che, X.; Lipkin, W.I. Cytokine network analysis of cerebrospinal fluid in myalgic encephalomyelitis/chronic fatigue syndrome. Mol. Psychiatry
**2016**, 21, 261–269. [Google Scholar] [CrossRef] - Mensah, F.K.F.; Bansal, A.S.; Ford, B.; Cambridge, G. Chronic fatigue syndrome and the immune system: Where are we now? Neurophysiol. Clin.
**2017**, 47, 131–138. [Google Scholar] [CrossRef] [Green Version] - Yang, T.; Yang, Y.; Wang, D.; Li, C.; Qu, Y.; Guo, J.; Shi, T.; Bo, W.; Sun, Z.; Asakawa, T. The clinical value of cytokines in chronic fatigue syndrome. J. Transl. Med.
**2019**, 17, 213. [Google Scholar] [CrossRef] - Roerink, M.E.; van der Schaaf, M.E.; Hawinkels, L.; Raijmakers, R.P.H.; Knoop, H.; Joosten, L.A.B.; van der Meer, J.W.M. Pitfalls in cytokine measurements - plasma TGF-beta1 in chronic fatigue syndrome. Neth. J. Med.
**2018**, 76, 310–313. [Google Scholar] - Yamano, E.; Sugimoto, M.; Hirayama, A.; Kume, S.; Yamato, M.; Jin, G.; Tajima, S.; Goda, N.; Iwai, K.; Fukuda, S.; et al. Index markers of chronic fatigue syndrome with dysfunction of TCA and urea cycles. Sci. Rep.
**2016**, 6, 34990. [Google Scholar] [CrossRef] - Naviaux, R.K.; Naviaux, J.C.; Li, K.; Bright, A.T.; Alaynick, W.A.; Wang, L.; Baxter, A.; Nathan, N.; Anderson, W.; Gordon, E. Metabolic features of chronic fatigue syndrome. Proc. Natl. Acad. Sci. USA
**2016**, 113, E5472–E5480. [Google Scholar] [CrossRef] [Green Version] - Germain, A.; Ruppert, D.; Levine, S.M.; Hanson, M.R. Metabolic profiling of a myalgic encephalomyelitis/chronic fatigue syndrome discovery cohort reveals disturbances in fatty acid and lipid metabolism. Mol. Biosyst.
**2017**, 13, 371–379. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Germain, A.; Ruppert, D.; Levine, S.M.; Hanson, M.R. Prospective biomarkers from plasma metabolomics of myalgic encephalomyelitis/chronic fatigue syndrome implicate redox imbalance in disease symptomatology. Metabolites
**2018**, 8, 90. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Armstrong, C.W.; McGregor, N.R.; Sheedy, J.R.; Buttfield, I.; Butt, H.L.; Gooley, P.R. NMR metabolic profiling of serum identifies amino acid disturbances in chronic fatigue syndrome. Clin. Chim. Acta
**2012**, 413, 1525–1531. [Google Scholar] [CrossRef] [PubMed] - Armstrong, C.W.; McGregor, N.R.; Butt, H.L.; Gooley, P.R. Metabolism in chronic fatigue syndrome. Adv. Clin. Chem.
**2014**, 66, 121–172. [Google Scholar] - Armstrong, C.W.; McGregor, N.R.; Lewis, D.; Butt, H.; Gooley, P.R. Metabolic profiling reveals anomalous energy metabolism and oxidative stress pathways in chronic fatigue syndrome patients. Metabolomics
**2015**, 11, 1626–1639. [Google Scholar] [CrossRef] - Fluge, O.; Mella, O.; Bruland, O.; Risa, K.; Dyrstad, S.E.; Alme, K.; Rekeland, I.G.; Sapkota, D.; Rosland, G.V.; Fossa, A.; et al. Metabolic profiling indicates impaired pyruvate dehydrogenase function in myalgic encephalopathy/chronic fatigue syndrome. JCI Insight
**2016**, 1, e89376. [Google Scholar] [CrossRef] [Green Version] - Santos Ferreira, D.L.; Maple, H.J.; Goodwin, M.; Brand, J.S.; Yip, V.; Min, J.L.; Groom, A.; Lawlor, D.A.; Ring, S. The effect of pre-analytical conditions on blood metabolomics in epidemiological studies. Metabolites
**2019**, 9, 90. [Google Scholar] [CrossRef] [Green Version] - Ghini, V.; Quaglio, D.; Luchinat, C.; Turano, P. NMR for sample quality assessment in metabolomics. N Biotechnol.
**2019**, 52, 25–34. [Google Scholar] [CrossRef] - Nacul, L.; de Barros, B.; Kingdon, C.C.; Cliff, J.M.; Clark, T.G.; Mudie, K.; Dockrell, H.M.; Lacerda, E.M. Evidence of clinical pathology abnormalities in people with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) from an analytic cross-sectional study. Diagnostics
**2019**, 9, 41. [Google Scholar] [CrossRef] [Green Version] - Esfandyarpour, R.; Kashi, A.; Nemat-Gorgani, M.; Wilhelmy, J.; Davis, R.W. A nanoelectronics-blood-based diagnostic biomarker for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Proc. Natl. Acad. Sci. USA
**2019**, 116, 10250–10257. [Google Scholar] [CrossRef] [Green Version] - Missailidis, D.; Annesley, S.J.; Allan, C.Y.; Sanislav, O.; Lidbury, B.A.; Lewis, D.P.; Fisher, P.R. An isolated Complex V inefficiency and dysregulated mitochondrial function in immortalized lymphocytes from ME/CFS patients. Int. J. Mol. Sci.
**2020**, 21, 1074. [Google Scholar] [CrossRef] [Green Version] - Valeri, C.R.; Pivacek, L.E. Effects of the temperature, the duration of frozen storage, and the freezing container on in vitro measurements in human peripheral blood mononuclear cells. Transfusion
**1996**, 36, 303–308. [Google Scholar] [CrossRef] - Hughes, A.J.; Daniel, S.E.; Ben-Shlomo, Y.; Lees, A.J. The accuracy of diagnosis of parkinsonian syndromes in a specialist movement disorder service. Brain
**2002**, 125, 861–870. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Joutsa, J.; Gardberg, M.; Roytta, M.; Kaasinen, V. Diagnostic accuracy of parkinsonism syndromes by general neurologists. Parkinsonism Relat. Disord.
**2014**, 20, 840–844. [Google Scholar] [CrossRef] [PubMed] - Robin, X.; Turck, N.; Hainard, A.; Tiberti, N.; Lisacek, F.; Sanchez, J.C.; Muller, M. pROC: An open-source package for R and S
^{+}to analyze and compare ROC curves. BMC Bioinform.**2011**, 12, 77. [Google Scholar] [CrossRef] - Antonogeorgos, G.; Panagiotakos, D.B.; Priftis, K.N.; Tzonou, A. Logistic regression and linear discriminant analyses in evaluating factors associated with asthma prevalence among 10- to 12-years-old children: Divergence and similarity of the two statistical methods. Int. J. Pediatr.
**2009**, 2009, 952042. [Google Scholar] [CrossRef] - Perdomo-Celis, F.; Salgado, D.M.; Castaneda, D.M.; Narvaez, C.F. Viability and functionality of cryopreserved peripheral blood mononuclear cells in pediatric dengue. Clin Vaccine Immunol.
**2016**, 23, 417–426. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Annesley, S.J.; Lay, S.T.; De Piazza, S.W.; Sanislav, O.; Hammersley, E.; Allan, C.Y.; Francione, L.M.; Bui, M.Q.; Chen, Z.P.; Ngoei, K.R.; et al. Immortalized Parkinson’s disease lymphocytes have enhanced mitochondrial respiratory activity. Dis. Model. Mech.
**2016**, 9, 1295–1305. [Google Scholar] [CrossRef] [Green Version] - Wolvetang, E.J.; Johnson, K.L.; Krauer, K.; Ralph, S.J.; Linnane, A.W. Mitochondrial respiratory chain inhibitors induce apoptosis. FEBS Lett.
**1994**, 339, 40–44. [Google Scholar] [CrossRef] [Green Version] - Yang, J.; Diaz, N.; Adelsberger, J.; Zhou, X.; Stevens, R.; Rupert, A.; Metcalf, J.A.; Baseler, M.; Barbon, C.; Imamichi, T.; et al. The effects of storage temperature on PBMC gene expression. BMC Immunol.
**2016**, 17, 6. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Weinberg, A.; Zhang, L.; Brown, D.; Erice, A.; Polsky, B.; Hirsch, M.S.; Owens, S.; Lamb, K. Viability and functional activity of cryopreserved mononuclear cells. Clin. Diagn. Lab. Immunol.
**2000**, 7, 714–716. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Stroncek, D.F.; Xing, L.; Chau, Q.; Zia, N.; McKelvy, A.; Pracht, L.; Sabatino, M.; Jin, P. Stability of cryopreserved white blood cells (WBCs) prepared for donor WBC infusions. Transfusion
**2011**, 51, 2647–2655. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Team, R.C. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2019. [Google Scholar]
- Fox, J. The R Commander: A basic-statistics graphical user interface to R. J. Stat. Software
**2005**, 14, 1–42. [Google Scholar] [CrossRef] [Green Version] - Kanda, Y. Investigation of the freely available easy-to-use software ’EZR’ for medical statistics. Bone Marrow Transplant
**2013**, 48, 452–458. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Williams, G.J. Data mining with Rattle and R. Use R! Springer: London, UK, 2011. [Google Scholar]

**Figure 1.**Time in frozen storage has no effect on the viability of lymphocytes after recovery and incubation in culture medium for 48 h. Some individuals were sampled on more than one occasion and some samples were tested at more than one storage time point using separately frozen aliquots. The sample sizes indicated (n) are the number of frozen aliquots that were tested from the number of individuals shown (i). Since the storage time had no effect in either ME/CFS patient or control samples, multiple samples tested from the same individual were averaged for subsequent analysis. The fraction of dead cells was greater in ME/CFS lymphocytes. Significance probabilities shown are from pairwise t tests of the difference in means.

**Figure 2.**Logistic regression and ROC analysis of the percentage of dead lymphocytes after 48 h post-storage culture. (

**a**) Box plot showing the distribution of the propensity score in logistic regression of the sample type against the fraction of dead lymphocytes observed after recovery from frozen storage and 48 h culture. The resulting regression coefficients are as indicated. The boxes show the median and the 25

^{th}and 75

^{th}percentiles, so that the height of the box is the interquartile range (IQR). The whiskers extend to the most extreme observations (largest and smallest) falling within ± 1.5 × IQR of the box. The ME/CFS and control sample sizes were 57 and 33 individuals, respectively. The Mann–Whitney significance probability tests the hypothesis that the scores in ME/CFS samples are greater than in control samples. Scores greater than 0.5 lead to classification of a sample as ME/CFS in the “confusion matrix”. Other relevant statistics showing that the logistic regression model provided a good fit to the data were: Hesmer–Lemeshow goodness of fit = 0.11; Pseudo R

^{2}= 0.59; χ

^{2}p = 1.9 × 10

^{−7}. (

**b**) ROC analysis of the propensity score, plotting sensitivity (proportion of true positives) against specificity (proportion of true negatives) with 95% confidence limits (cyan shading). The fractional area under the curve (AUC) is shown with 95% confidence limits. The “best” threshold for the propensity score (0.59) is shown, together with the specificity (0.76) and sensitivity (0.84) at that threshold (in parentheses).

**Figure 3.**Comparison of ROC curves of propensity scores in logistic regression of five key parameters of lymphoblast respiration with or without measures of the mitochondrial membrane potential. The significance probability of a bootstrap test of the difference between the ROC curves (Robin et al., 2011) is indicated [36]. The addition of the extra laboratory assay (for mitochondrial membrane potential, ΔΨ

_{m}) did not significantly improve the diagnostic value of the test.

**Figure 4.**Logistic regression and ROC analysis of lymphoblast respiration as an ME/CFS biomarker. (

**a**) Box plot showing the distribution of the propensity score from logistic regression of participant type against 5 key respiratory parameters. The resulting regression coefficients are as indicated. The boxes show the median and the 25

^{th}and 75

^{th}percentiles, so that the height of the box is the interquartile range (IQR). The whiskers extend to the most extreme observations (largest and smallest) falling within ± 1.5 × IQR of the box. The ME/CFS and control sample sizes were 49 and 20 individuals, respectively. The Mann–Whitney significance probability tests the hypothesis that the scores in ME/CFS samples are greater than in control samples. Each point represents a single individual. Scores greater than 0.5 lead to classification of a sample as ME/CFS in the “confusion matrix”. Other relevant statistics showing that the logistic regression model provided a good fit to the data were: Hesmer–Lemeshow goodness of fit = 0.14; Pseudo R

^{2}= 0.56; χ

^{2}p = 6.7 × 10

^{−4}. (

**b**) ROC analysis of the propensity score, plotting sensitivity (proportion of true positives) against specificity (proportion of true negatives) with 95% confidence limits (cyan shading). The fractional area under the curve (AUC) is shown with 95% confidence limits. The “best” threshold for the propensity score (0.59) is shown, together with the specificity (0.70) and sensitivity (0.90) at that threshold (in parentheses).

**Figure 5.**Logistic regression and ROC analysis of lymphoblast TORC1 activity. (

**a**) Box plot showing the distribution of the propensity score from logistic regression of participant type against the logarithm of the relative TORC1 activity, measured as phosphorylation state of 4E-BP1 (a specific cellular target of TORC1). The resulting regression coefficients are as indicated. The boxes show the median and the 25

^{th}and 75

^{th}percentiles, so that the height of the box is the interquartile range (IQR). The whiskers extend to the most extreme observations (largest and smallest) falling within ± 1.5 × IQR of the box. The ME/CFS and control sample sizes were 44 and 22 individuals, respectively. The Mann–Whitney significance probability tests the hypothesis that the scores in ME/CFS samples are greater than in control samples. Each point represents a single individual. Scores greater than 0.5 lead to classification of a sample as ME/CFS in the “confusion matrix”. Other relevant statistics showing that the logistic regression model provided a good fit to the data were: Hesmer–Lemeshow goodness of fit = 0.53; Pseudo R

^{2}= 0.60; χ

^{2}p = 1.9 × 10

^{−7}. (

**b**) ROC analysis of the propensity score, plotting sensitivity (proportion of true positives) against specificity (proportion of true negatives) with 95% confidence limits (cyan shading). The fractional area under the curve (AUC) is shown with 95% confidence limits. The “best” threshold for the propensity score (0.59) is shown, together with the specificity (0.77) and sensitivity (0.89) at that threshold (in parentheses).

**Figure 6.**Logistic regression and ROC analysis of combined tests for lymphocyte death in culture, lymphoblast respiration and lymphoblast TORC1 activity. (

**a**) Box plot showing the distribution of the propensity score from logistic regression of the percentage of dead lymphocytes after 48 h in culture, 5 key parameters of lymphoblast respiration and the TORC1 activity. The resulting regression coefficients were as indicated. The boxes show the median and the 25

^{th}and 75

^{th}percentiles, so that the height of the box is the interquartile range (IQR). The whiskers extend to the most extreme observations (largest and smallest) falling within ± 1.5 × IQR of the box. The Mann–Whitney significance probability tests the hypothesis, that the scores in ME/CFS samples are greater than in control samples. Each point represents a single individual. Scores greater than 0.5 lead to classification of a sample as ME/CFS in the “confusion matrix”. Other relevant statistics showing that the logistic regression model provided a good fit to the data were: Hesmer–Lemeshow goodness of fit = 0.77; Pseudo R

^{2}= 0.87; χ

^{2}p = 2.9 × 10

^{−6}. (

**b**) ROC analysis of the propensity score, plotting sensitivity (proportion of true positives) against specificity (proportion of true negatives) with 95% confidence limits (cyan shading). The fractional area under the curve (AUC) is shown with 95% confidence limits. The “best” threshold for the propensity score (0.61) is shown, together with the specificity (1.0) and sensitivity (0.97) at that threshold (in parentheses).

**Figure 7.**Box plots showing the distribution of logistic regression propensity scores for individuals from whom the fraction of dead lymphocytes after 48 h in culture would have tested as “ME/CFS suspected” or “ME/CFS not suspected”. NA refers to individuals for whom we have respirometry or TORC1 activity but not lymphocyte death rates. Propensity scores from (

**a**) five key parameters of lymphoblast respiration, and (

**b**) the TORC1 activity are plotted for each individual. In panel (

**c**) the propensity scores from all three tests combined are shown. The regression coefficients were as indicated in Figure 4, Figure 5 and Figure 6, respectively. The boxes show the median and the 25

^{th}and 75

^{th}percentiles, so that the height of the box is the interquartile range (IQR). The whiskers extend to the most extreme observations (largest and smallest) falling within ± 1.5 × IQR of the box. Each point represents a single individual. Scores greater than 0.5 lead to classification of a sample as ME/CFS in the corresponding “confusion matrix”.

Method | Clinical Group | Actual Count | Test Class | % Error | Statistics | |||
---|---|---|---|---|---|---|---|---|

ME/CFS | Control | |||||||

Linear discriminant analysis | ME/CFS | 57 | 52 | 5 | 8.8 | χ^{2} | 25.5 | |

Control | 33 | 13 | 20 | 39.4 | p | 4.5 × 10^{−7} | ||

TOTAL | 90 | 65 | 25 | 20.0 | Fisher p = 2.2 × 10^{−7} | |||

Logistic regression | ME/CFS | 57 | 52 | 5 | 8.8 | χ^{2} | 25.5 | |

Control | 33 | 13 | 20 | 39.4 | p | 4.5 × 10^{−7} | ||

TOTAL | 90 | 65 | 25 | 20.0 | Fisher p = 2.2 × 10^{−7} |

**Table 2.**“Confusion matrix” analysis of lymphoblast respiratory function and mitochondrial membrane potential (ΔΨ

_{m}).

Method | Clinical Group | Actual Count | Test Class | % Error | Statistics | ||
---|---|---|---|---|---|---|---|

ME/CFS | Control | ||||||

Respiratory function + ΔΨ_{m} | |||||||

Linear discriminant analysis | ME/CFS | 49 | 46 | 3 | 6.1 | χ^{2} | 27.9 |

Control | 20 | 6 | 14 | 30.0 | p | 1.3 × 10^{−7} | |

TOTAL | 69 | 52 | 17 | 13.0 | Fisher p = 1.2 × 10^{−7} | ||

Logistic regression | ME/CFS | 49 | 46 | 3 | 6.1 | χ^{2} | 24.4 |

Control | 20 | 7 | 13 | 35.0 | p | 7.7 × 10^{−7} | |

TOTAL | 69 | 54 | 15 | 14.5 | Fisher p = 7.7 × 10^{−7} | ||

Respiratory function only | |||||||

Linear discriminant analysis | ME/CFS | 49 | 46 | 3 | 6.1 | χ^{2} | 12.4 |

Control | 20 | 11 | 9 | 55.0 | p | 4.4 × 10^{−4} | |

TOTAL | 69 | 57 | 12 | 20.2 | Fisher p = 3.8 × 10^{−4} | ||

Logistic regression | ME/CFS | 49 | 46 | 3 | 6.1 | χ^{2} | 15.1 |

Control | 20 | 10 | 10 | 50.0 | p | 1.0 × 10^{−4} | |

TOTAL | 69 | 53 | 16 | 18.8 | Fisher p = 9.3 × 10^{−5} |

Method | Clinical Group | Actual Count | Test Class | % Error | Statistics | ||
---|---|---|---|---|---|---|---|

ME/CFS | Control | ||||||

Linear discriminant analysis | ME/CFS | 44 | 40 | 4 | 9.1 | χ^{2} | 16.6 |

Control | 22 | 9 | 13 | 40.9 | p | 4.5 × 10^{−5} | |

TOTAL | 66 | 49 | 17 | 19.7 | Fisher p = 2.9 × 10^{−5} | ||

Logistic regression | ME/CFS | 44 | 40 | 4 | 9.1 | χ^{2} | 16.6 |

Control | 22 | 9 | 13 | 40.9 | p | 4.5 × 10^{−5} | |

TOTAL | 66 | 49 | 17 | 19.7 | Fisher p = 2.9 × 10^{−5} |

Method | Clinical Group | Actual Count | Test Class | % Error | Statistics | ||
---|---|---|---|---|---|---|---|

ME/CFS | Control | ||||||

Linear discriminant analysis | ME/CFS | 29 | 28 | 1 | 3.4 | χ^{2} | 31.7 |

Control | 14 | 1 | 13 | 7.1 | p | 1.8 × 10^{−8} | |

TOTAL | 43 | 29 | 14 | 4.7 | Fisher p = 5.2 × 10^{−9} | ||

Logistic regression | ME/CFS | 29 | 28 | 1 | 3.4 | χ^{2} | 31.7 |

Control | 14 | 1 | 13 | 7.1 | p | 1.8 × 10^{−8} | |

TOTAL | 43 | 29 | 14 | 4.7 | Fisher p = 5.2 × 10^{−9} |

**Table 5.**“Confusion matrix” analysis of combined tests for participants whose lymphocytes had been tested for the rate of cell death in culture.

Method. | Clinical Group | Actual Count | Test Class | % Error | Statistics | ||
---|---|---|---|---|---|---|---|

ME/CFS | Control | ||||||

“ME/CFS suspected” | |||||||

Combined test | ME/CFS | 27 | 27 | 0 | 0 | χ^{2} | 3.00 |

Control | 2 | 1 | 1 | 50 | p | 0.083 | |

TOTAL | 29 | 28 | 1 | 3.4 | Fisher p = 0.069 | ||

“ME/CFS not suspected” | |||||||

Combined test | ME/CFS | 2 | 1* | 1 | 50 | χ^{2} | 1.12 |

Control | 12 | 0 | 12 | 0 | p | 0.29 | |

TOTAL | 14 | 1 | 13 | 7.1 | Fisher p = 0.14 |

**Table 6.**“Confusion matrix” analysis of respirometry and TORC1 activity tests for participants whose lymphocytes had not been tested for the rate of cell death in culture.

Method | Clinical Group | Actual Count | Test Class | % Error | Statistics | ||
---|---|---|---|---|---|---|---|

ME/CFS | Control | ||||||

Respirometry test | ME/CFS | 16 | 15 | 1 | 6.3 | χ^{2} | 5.96 |

Control | 6 | 2 | 4 | 33.3 | p | 0.015 | |

TOTAL | 22 | 18 | 4 | 13.6 | Fisher p = 9.3 × 10^{−3} | ||

TORC1 activity test | ME/CFS | 15 | 14 | 1 | 6.7 | χ^{2} | 0.352 |

Control | 8 | 6 | 2 | 75.0 | p | 0.553 | |

TOTAL | 23 | 20 | 3 | 30.4 | Fisher p = 0.269 |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Missailidis, D.; Sanislav, O.; Allan, C.Y.; Annesley, S.J.; Fisher, P.R.
Cell-Based Blood Biomarkers for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. *Int. J. Mol. Sci.* **2020**, *21*, 1142.
https://doi.org/10.3390/ijms21031142

**AMA Style**

Missailidis D, Sanislav O, Allan CY, Annesley SJ, Fisher PR.
Cell-Based Blood Biomarkers for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. *International Journal of Molecular Sciences*. 2020; 21(3):1142.
https://doi.org/10.3390/ijms21031142

**Chicago/Turabian Style**

Missailidis, Daniel, Oana Sanislav, Claire Y. Allan, Sarah J. Annesley, and Paul R. Fisher.
2020. "Cell-Based Blood Biomarkers for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome" *International Journal of Molecular Sciences* 21, no. 3: 1142.
https://doi.org/10.3390/ijms21031142