Beyond the antibodies: serum metabolomic profiling of myasthenia gravis

Serum metabolomic profile of myasthenia gravis and potential values as biomarkers in disease monitoring

OBJECTIVE

Serum metabolites from 19 myasthenia gravis (MG) patients and 15 normal controls were analyzed via untargeted metabolomics, including 6 pre/post-treatment paired MG patients, to assess the value of serum metabolites as biomarkers in monitoring MG.

METHOD

Differential metabolites between MG patients and normal controls were identified through liquid and gas chromatography-mass spectrometry simultaneously. Principal component analysis and orthogonal partial least squares-discriminant analysis were conducted to identify the differential metabolites. Candidate metabolites and pathways associated with MG were selected through a random forest machine learning model.

RESULT

A total of 310 differential metabolites were identified with a threshold of variable projected importance > 1 and P value < 0.05. Among these, 158 metabolites were upregulated and 152 were downregulated. The random forest machine learning model selected 5 metabolites as potential biomarkers associated with MG: lignoceric acid (AUC=0.944), uridine diphosphate-N-acetylglucosamine (AUC=0.951), arachidonic acid (AUC=0.951), beta-glycerophosphoric acid (AUC=0.933), and L-Asparagine (AUC=0.877). Further analysis using 6 paired MG patients pre- and post-immunosuppression treatment revealed 25 upregulated and 6 downregulated metabolites in post-treatment serum, which might be relevant to disease intervention. The significance remains elusive due to the limited number of patients.

CONCLUSION

A subset of differential metabolites was identified in the serum of MG patients, some of which changed with immunosuppressive therapy. Small molecule metabolites may serve as valuable biomarkers for disease monitoring in MG.

A mendelian randomization study investigating the causal relationships between 1400 serum metabolites and autoimmune diseases

Objective

This study aims to explore the causal relationships between 1400 serum metabolites (SMs) and five autoimmune diseases (Myasthenia gravis [MG], Multiple sclerosis [MS], Systemic lupus erythematosus [SLE], Type 1 diabetes mellitus [T1DM], and Ulcerative colitis [UC]) through Mendelian randomization analysis.

Method

Data on MG, MS, SLE, T1DM, and UC were obtained from the IEU OpenGWAS Project database, while information on 1400 SMs was extracted from GWAS summary statistics provided by Chen et al. Causal relationships were assessed using the inverse variance weighted (IVW), MR-Egger, Weighted Median (WME), and Simple median (SME) methods. The robustness of instrumental variables was verified through computation of the F-statistic. Heterogeneity was evaluated using Cochran's Q test and the leave-one-out (LOO) method. Horizontal pleiotropy was assessed using MR-Egger regression and MR-PRESSO.

Result

Following correction of the IVW P values using the False Discovery Rate (FDR) method, it was found that increased levels of 5-methyluridine (ribothymidine) (OR = 1.191, 95%CI 1.086-1.307, FDR-P = 0.000) and 2'-deoxyuridine (OR = 1.337, 95%CI 1.127-1.586, FDR-P = 0.001) were found to be correlated with a higher risk of MS. Conversely, the ratio of S-adenosylhomocysteine (SAH) to 5-methyluridine (ribothymidine) (OR = 0.771, 95%CI 0.649-0.916, FDR-P = 0.007) was linked to a decreased risk of MS. Levels of 1,2-dilinoleoyl-GPE (18:2/18:2) (OR = 0.877, 95%CI 0.791-0.974, FDR-P = 0.003) appear to be a protective factor for T1DM. No notable correlations between SMs and MG, SLE, or UC. The study detected no heterogeneity or horizontal pleiotropy.

Conclusion

Levels of 5-methyluridine (ribothymidine), 2'-deoxyuridine, and the ratio of S-adenosylhomocysteine (SAH) to 5-methyluridine (ribothymidine) can serve as predictors for MS. Similarly, 1,2-dilinoleoyl-GPE (18:2/18:2) levels can be used to predict T1DM. However, no significant causal relationships were found between SMs and MG, SLE, or UC. This observation holds significant clinical implications for crafting tailored preventive and therapeutic approaches for ADs.

Comparative Analysis of Serum Amino Acid Profiles in Patients with Myasthenia Gravis and Multiple Sclerosis

Background: Multiple sclerosis (MS) and myasthenia gravis (MG) are autoimmune diseases that attack the central nervous system (CNS) and the neuromuscular junction, respectively. As the common pathogenesis of both diseases is associated with an autoimmune background and the involvement of T and B lymphocytes, the overlapping of selected clinical symptoms may cause difficulties in the differential diagnosis of both diseases. Methods: The aim of the study was to use Liquid Chromatography-Electrospray Ionization-Mass Spectrometry (LC-ESI-MS/MS) in conjunction with multivariate statistical analyses to examine the changes in amino acid metabolic profiles between patients with MG, MS, and a control group. Results: Comparative analysis of amino acids (AA) between patients with MG, MS, and within the control group allowed for the identification of statistically significant differences in the amino acid profile. Comparing the patients (patients with MS and MG) with the control group, and after taking the results of multiple tests into account, it was observed that amino acids such as ARG, PRO, TRP, CIT were significantly different between the groups. When considering the comparison between the AA concentrations in MS and MG patients, we found three AAs that were significantly different in the MS and MG groups, after correcting for multiple testing (CIT, GABA, and AAA). Higher concentrations of amino acids that showed significant differences were observed in patients with myasthenia gravis. Conclusions: Our results have indicated AAs that may prove valuable for improving the diagnostics of MS and MG patients. To better assess the potential utility of these markers, their performance requires further validation in a larger study group and limitation of possible confounding factors, e.g., medications and diet.

Refocusing generalized myasthenia gravis: Patient burden, disease profiles, and the role of evolving therapy

BACKGROUND AND PURPOSE

Generalized myasthenia gravis (gMG) continues to present significant challenges for clinical management due to an unpredictable disease course, frequent disease fluctuations, and varying response to therapy. The recent availability of new pharmacologic therapies presents a valuable opportunity to reevaluate how this disease is classified, assessed, and managed and identify new ways to improve the clinical care of patients with gMG.

METHODS

Narrative review was made of publications identified via searches of PubMed and selected congresses (January 2000-September 2022).

RESULTS

New consensus definitions are required to ensure consistency, to better characterize patients, and to identify patients who will benefit from specific drugs and earlier use of these agents. There is a need for more frequent, standardized patient assessment to identify the cause of motor function deficits, provide a clearer picture of the disease burden and its impact on daily living and quality of life (QoL), and better support treatment decision-making. Novel approaches that target different components of the immune system will play a role in more precise treatment of patients with gMG, alongside the development of new algorithms to guide individualized patient management.

CONCLUSIONS

gMG has a physical, mental, and social impact, resulting in a considerable burden of disease and substantially decreased QoL, despite standard treatments. The availability of novel, targeted treatments that influence key pathological mediators of gMG, together with new biomarkers, offers the potential to optimize patient management and ultimately enables a greater number of patients to achieve minimal manifestation status and a reduced burden of disease.

Residual serum fibrinogen as a universal biomarker for all serotypes of Myasthenia gravis

Myasthenia Gravis (MG) is an autoimmune disease associated with severe neuromuscular weakness. Diagnostic confirmation of MG is typically delayed and secured in about 85% and 50% of patients with generalized and ocular MG, respectively with serum antibodies. We have identified a sensitive and specific diagnostic biomarker for various MG serotypes with quantitative proteomics. Serum proteomes of 18 individuals (MG patients, healthy controls (HC), Rheumatoid Arthritis (RA) were quantified in a pilot study and occurrence of high residual fibrinogen was validated by immunoblotting and further investigated by targeted mass spectrometry on the sera of 79 individuals (31 MG of various serotypes, 30 HC, 18 RA). Initial proteomic analysis identified high residual fibrinogen in MG patient sera which was then validated by antibody-based testing. Subsequently, a blinded study of independent samples showed 100% differentiation of MG patients from controls. A final serological quantification of 14 surrogate peptides derived from α-, β-, and γ-subunits of fibrinogen in 79 individuals revealed fibrinogen to be highly specific and 100% sensitive for MG (p < 0.00001), with a remarkable average higher abundance of > 1000-fold over control groups. Our unanticipated discovery of high levels of residual serum fibrinogen in all MG patients can secure rapid bedside diagnosis of MG.

Serum metabolomics of treatment response in myasthenia gravis

OBJECTIVE

High-dose prednisone use, lasting several months or longer, is the primary initial therapy for myasthenia gravis (MG). Upwards of a third of patients do not respond to treatment. Currently no biomarkers can predict clinical responsiveness to corticosteroid treatment. We conducted a discovery-based study to identify treatment responsive biomarkers in MG using sera obtained at study entry to the thymectomy clinical trial (MGTX), an NIH-sponsored randomized, controlled study of thymectomy plus prednisone versus prednisone alone.

METHODS

We applied ultra-performance liquid chromatography coupled with electro-spray quadrupole time of flight mass spectrometry to obtain comparative serum metabolomic and lipidomic profiles at study entry to correlate with treatment response at 6 months. Treatment response was assessed using validated outcome measures of minimal manifestation status (MMS), MG-Activities of Daily Living (MG-ADL), Quantitative MG (QMG) score, or a strictly defined composite measure of response.

RESULTS

Increased serum levels of phospholipids were associated with treatment response as assessed by QMG, MMS, and the Responders classification, but all measures showed limited overlap in metabolomic profiles, in particular the MG-ADL. A panel including histidine, free fatty acid (13:0), γ-cholestenol and guanosine was highly predictive of the strictly defined treatment response measure. The AUC in Responders' prediction for these markers was 0.90 irrespective of gender, age, thymectomy or baseline prednisone use. Pathway analysis suggests that xenobiotic metabolism could play a major role in treatment resistance. There was no association with outcome and gender, age, thymectomy or baseline prednisone use.

INTERPRETATION

We have defined a metabolomic and lipidomic profile that can now undergo validation as a treatment predictive marker for MG patients undergoing corticosteroid therapy. Metabolomic profiles of outcome measures had limited overlap consistent with their assessing distinct aspects of treatment response and supporting unique biological underpinning for each outcome measure. Interindividual variation in prednisone metabolism may be a determinate of how well patients respond to treatment.

Serum amino acid profiles in patients with myasthenia gravis

Myasthenia gravis (MG) is an autoimmune disease characterized by weakness and rapid fatigue. Diagnostic methods used for myasthenia gravis are not conclusive and satisfactory, therefore it is necessary to develop reliable tools to help diagnose myasthenia gravis as early as possible. The aim of the study was to use HPLC-MS in conjunction with multivariate statistical analyses to investigate changes in the amino acid metabolic profiles between myasthenia gravis patients compared and controls. In addition, the effect of treatment regimens and myasthenia gravis type, on the observed changes in amino acid metabolic profiles were assessed. Serum levels of 29 amino acids were determined in 2 groups of individuals-28 patients with myasthenia gravis and 53 control subjects (CS). The results of our study indicate that serum levels of several amino acids in patients with myasthenia gravis changed significantly compared to the control group. Statistical analysis revealed differences between amino acids concentration in patients with different therapeutic scheme. In conclusion, amino acids may be involved in mechanisms underlying myasthenia gravis pathogenesis as well as may be potential biomarkers in MG patients diagnosis. However, considering the multifactorial, heterogenous and complex nature of this disease, validation on a larger study sample in further research is required before application into diagnostic practice.

Identification of Potential Serum Protein Biomarkers in Thymoma with Myasthenia Gravis After Docetaxel Treatment

INTRODUCTION

Myasthenia gravis (MG) is a devastating acquired autoimmune disease that can seriously affect the patient's quality of life. It is also a common complication of thymoma. Previous studies have shown that docetaxel alleviates myasthenic symptoms in thymoma with MG (TMG). However, little is known about the protein expression profiles and biomarkers for efficacy after docetaxel treatment.

METHODS

We recruited 9 healthy controls and 30 patients with TMG for the serum proteomics study with data-independent acquisition (DIA) technology. We further recruited additional 30 patients for the key protein validation by enzyme-linked immunosorbent assay (ELISA).

RESULTS

We identified 43 proteins by trend analysis and analyzed the interaction between these proteins and MG pathogenic proteins from the DisGNET database and the correlation analysis with clinical data of patients with TMG. Among these, KRAS and SELP were screened out and validated. KRAS and SELP increased in patients with TMG and decreased significantly after docetaxel treatment.

CONCLUSIONS

Our study revealed that the serum proteins were differentially expressed after docetaxel treatment, suggesting their important role in patients with TMG, as well as the critical role of KRAS and SELP as biomarkers in evaluating the efficacy of docetaxel treatment.

Integrated proteomics and metabolomics analysis reveals hubs protein and network alterations in myasthenia gravis

Background: Thymoma-associated myasthenia gravis (TAMG) is a well-described subtype of Myasthenia gravis (MG). Nevertheless, the detailed proteins and bioprocess differentiating TAMG from TAMG (−) thymoma have remained unclear.

Methods: The proteomics and metabolomics were carried out on serum samples from thymoma group (n = 60, TNMG), TAMG (+) thymoma group (n = 70, TAMG (+)), and TAMG (−) thymomas group (n = 62, TAMG (−)), and controls (n = 159). groups. Proteomics and metabolomics analyses, including weighted gene co-expression network analysis (WGCNA), was conducted to detect the hub proteins and metabolomics processes that could differentiate TAMG (+) from TAMG (−) thymomas. MetaboAnalyst was used to examine the integration of proteomic and metabolomic analysis to differentiate TAMG (+) from TAMG (−) thymomas.

Results: The of module–trait correlation of WGCNA analysis identified KRT1, GSN, COL6A1, KRT10, FOLR2, KRT9, KRT2, TPI1, ARF3, LYZ, ADIPOQ, SEMA4B, IGKV1-27, MASP2, IGF2R was associated with TAMG (+) thymomas. In addition, organismal systems-immune system and metabolism-biosynthesis of other secondary metabolites were closely related to the mechanism of TAMG (+) pathogenesis.

Conclusion: Our integrated proteomics and metabolomics analysis supply a systems-level view of proteome changes in TAMG (+), TAMG (−) thymomas and exposes disease-associated protein network alterations involved in.

Immunoregulatory Cells in Myasthenia Gravis

Myasthenia gravis (MG) is a T cell-dependent, B-cell mediated autoimmune disease caused by antibodies against the nicotinic acetylcholine receptor or other components of the post-synaptic muscle endplate at the neuromuscular junction. These specific antibodies serve as excellent biomarkers for diagnosis, but do not adequately substitute for clinical evaluations to predict disease severity or treatment response. Several immunoregulatory cell populations are implicated in the pathogenesis of MG. The immunophenotype of these populations has been well-characterized in human peripheral blood. CD4⁺FoxP3⁺ regulatory T cells (Tregs) are functionally defective in MG, but there is a lack of consensus on whether they show numerical perturbations. Myeloid-derived suppressor cells (MDSCs) have also been explored in the context of MG. Adoptive transfer of CD4⁺FoxP3⁺ Tregs or MDSCs suppresses ongoing experimental autoimmune MG (EAMG), a rodent model of MG, suggesting a protective role of both populations in this disease. An imbalance between follicular Tregs and follicular T helper cells is found in untreated MG patients, correlating with disease manifestations. There is an inverse correlation between the frequency of circulating IL-10–producing B cells and clinical status in MG patients. Taken together, both functional and numerical defects in various populations of immunoregulatory cells in EAMG and human MG have been demonstrated, but how they relate to pathogenesis and whether these cells can serve as biomarkers of disease activity in humans deserve further exploration.

Metabolic adaptation orchestrates tissue context-dependent behavior in regulatory T cells

The diverse distribution and functions of regulatory T cells (Tregs) ensure tissue and immune homeostasis; however, it remains unclear which factors can guide distribution, local differentiation, and tissue context-specific behavior in Tregs. Although the emerging concept that Tregs could re-adjust their transcriptome based on their habitations is supported by recent findings, the underlying mechanisms that reprogram transcriptome in Tregs are unknown. In the past decade, metabolic machineries have been revealed as a new regulatory circuit, known as immunometabolic regulation, to orchestrate activation, differentiation, and functions in a variety of immune cells, including Tregs. Given that systemic and local alterations of nutrient availability and metabolite profile associate with perturbation of Treg abundance and functions, it highlights that immunometabolic regulation may be one of the mechanisms that orchestrate tissue context-specific regulation in Tregs. The understanding on how metabolic program instructs Tregs in peripheral tissues not only represents a critical opportunity to delineate a new avenue in Treg biology but also provides a unique window to harness Treg-targeting approaches for treating cancer and autoimmunity with minimizing side effects. This review will highlight the metabolic features on guiding Treg formation and function in a disease-oriented perspective and aim to pave the foundation for future studies.

Metabolomic profile overlap in prototypical autoimmune humoral disease: a comparison of myasthenia gravis and rheumatoid arthritis

INTRODUCTION

Myasthenia gravis (MG) and rheumatoid arthritis (RA) are examples of antibody-mediated chronic, progressive autoimmune diseases. Phenotypically dissimilar, MG and RA share common immunological features. However, the immunometabolomic features common to humoral autoimmune diseases remain largely unexplored.

OBJECTIVES

The aim of this study was to reveal and illustrate the metabolomic profile overlap found between these two diseases and describe the immunometabolomic significance.

METHODS

Metabolic analyses using acid- and dansyl-labelled was performed on serum from adult patients with seropositive MG (n = 46), RA (n = 23) and healthy controls (n = 49) presenting to the University of Alberta Hospital specialty clinics. Chemical isotope labelling liquid chromatography mass spectrometry (CIL LC-MS) methods were utilized to assess the serum metabolome in patients; 12C/13C-dansyl chloride (DnsCl) was used to label amine/phenol metabolites and 12C/13C-p-dimethylaminophenacyl bromide (DmPA) was used for carboxylic acids. Metabolites matching our criteria for significance were selected if they were present in both groups. Multivariate statistical analysis [including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA)] and biochemical pathway analysis was then conducted to gain understanding of the principal pathways involved in antibody-mediated pathogenesis.

RESULTS

We found 20 metabolites dysregulated in both MG and RA when compared to healthy controls. Most prominently, observed changes were related to pathways associated with phenylalanine metabolism, tyrosine metabolism, ubiquinone and other terpenoid-quinone biosynthesis, and pyruvate metabolism.

CONCLUSION

From these results it is evident that many metabolites are common to humoral disease and exhibit significant immunometabolomic properties. This observation may lead to an enhanced understanding of the metabolic underpinnings common to antibody-mediated autoimmune disease. Further, contextualizing these findings within a larger clinical and systems biology context could provide new insights into the pathogenesis and management of these diseases.