Category: Genetics

DNA Methylation Changes in Blood Cells of Fibromyalgia and Chronic Fatigue Syndrome Patients

Abstract:

Purpose: Fibromyalgia (FM) and Chronic Fatigue Syndrome (CFS) affect 0.4% and 1% of society, respectively, and the prevalence of these pain syndromes is increasing. To date, no strong association between these syndromes and the genetic background of affected individuals has been shown. Therefore, it is plausible that epigenetic changes might play a role in the development of these syndromes.

Patients and Methods: Three previous studies have attempted to elaborate the involvement of genome-wide methylation changes in blood cells in the development of fibromyalgia and chronic fatigue syndrome. These studies included 22 patients with fibromyalgia and 127 patients with CFS, and the results of the studies were largely discrepant. Contradicting results of those studies may be attributed to differences in the omics data analysis approaches used in each study. We reanalyzed the data collected in these studies using an updated and coherent data-analysis framework.

Results: Overall, the methylation changes that we observed overlapped with previous results only to some extent. However, the gene set enrichment analyses based on genes annotated to methylation changes identified in each of the analyzed datasets were surprisingly coherent and uniformly associated with the physiological processes that, when affected, may result in symptoms characteristic of fibromyalgia and chronic fatigue syndrome

Conclusion: Methylomes of the blood cells of patients with FM and CFS in three independent studies have shown methylation changes that appear to be implicated in the pathogenesis of these syndromes.

Source: Przybylowicz PK, Sokolowska KE, Rola H, Wojdacz TK. DNA Methylation Changes in Blood Cells of Fibromyalgia and Chronic Fatigue Syndrome Patients. J Pain Res. 2023;16:4025-4036 https://doi.org/10.2147/JPR.S439412 https://www.dovepress.com/dna-methylation-changes-in-blood-cells-of-fibromyalgia-and-chronic-fat-peer-reviewed-fulltext-article-JPR (Full text)

Long read sequencing characterises a novel structural variant, revealing underactive AKR1C1 with overactive AKR1C2 as a possible cause of unexplained severe fatigue

Abstract

Background: Causative genetic variants cannot yet be found for many disorders with a clear heritable component, including chronic fatigue disorders like myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). These conditions may involve genes in difficult-to-align genomic regions that are refractory to short read approaches. Structural variants in these regions can be particularly hard to detect or define with short reads, yet may account for a significant number of cases. Long read sequencing can overcome these difficulties but so far little data is available regarding the specific analytical challenges inherent in such regions, which need to be taken into account to ensure that variants are correctly identified.

Research into chronic fatigue disorders faces the additional challenge that the heterogeneous patient population likely encompasses multiple aetiologies with overlapping symptoms, rather than a single disease entity, such that each individual abnormality may lack statistical significance within a larger sample. Better delineation of patient subgroups is needed to target research and treatment.

Methods: We use nanopore sequencing in a case of unexplained severe fatigue to identify and fully characterise a large inversion in a highly homologous region spanning the AKR1C gene locus, which was indicated but could not be resolved by short-read sequencing. We then use GC-MS/MS serum steroid analysis to investigate the functional consequences.

Results: Several commonly used bioinformatics tools are confounded by the homology but a combined approach including visual inspection allows the variant to be accurately resolved. The DNA inversion appears to increase the expression of AKR1C2 while limiting AKR1C1 activity, resulting in a relative increase of inhibitory neurosteroids and impaired progesterone metabolism.

Conclusions: This study provides an example of how long read sequencing can improve diagnostic yield in research and clinical care, and highlights some of the analytical challenges presented by regions containing tandem arrays of genes. It also proposes a novel gene associated with a specific disease aetiology that may be an underlying cause of complex chronic fatigue and possibly other conditions too. It reveals biomarkers that could be assessed in a larger cohort, potentially identifying a subset of patients who might respond to treatments suggested by the aetiology.

Source: Julia Oakley, Martin Hill, Adam Giess, Mélanie Tanguy, Greg Elgar. Long read sequencing characterises a novel structural variant, revealing underactive AKR1C1 with overactive AKR1C2 as a possible cause of unexplained severe fatigue. ResearchSquare [Preprint] https://www.researchsquare.com/article/rs-3218228/v2 (Full text)

Genetic Risk Factors for Severe and Fatigue Dominant Long COVID and Commonalities with ME/CFS Identified by Combinatorial Analysis

Abstract:

Background Long COVID is a debilitating chronic condition that has affected over 100 million people globally. It is characterized by a diverse array of symptoms, including fatigue, cognitive dysfunction and respiratory problems. Studies have so far largely failed to identify genetic associations, the mechanisms behind the disease, or any common pathophysiology with other conditions such as ME/CFS that present with similar symptoms.

Methods We used a combinatorial analysis approach to identify combinations of genetic variants significantly associated with the development of long COVID and to examine the biological mechanisms underpinning its various symptoms. We compared two subpopulations of long COVID patients from Sano Genetics’ Long COVID GOLD study cohort, focusing on patients with severe or fatigue dominant phenotypes. We evaluated the genetic signatures previously identified in an ME/CFS population against this long COVID population to understand similarities with other fatigue disorders that may be triggered by a prior viral infection. Finally, we also compared the output of this long COVID analysis against known genetic associations in other chronic diseases, including a range of metabolic and neurological disorders, to understand the overlap of pathophysiological mechanisms.

Results Combinatorial analysis identified 73 genes that were highly associated with at least one of the long COVID populations included in this analysis. Of these, 9 genes have prior associations with acute COVID-19, and 14 were differentially expressed in a transcriptomic analysis of long COVID patients. A pathway enrichment analysis revealed that the biological pathways most significantly associated with the 73 long COVID genes were mainly aligned with neurological and cardiometabolic diseases.

Expanded genotype analysis suggests that specific SNX9 genotypes are a significant contributor to the risk of or protection against severe long COVID infection, but that the gene-disease relationship is context dependent and mediated by interactions with KLF15 and RYR3.

Comparison of the genes uniquely associated with the Severe and Fatigue Dominant long COVID patients revealed significant differences between the pathways enriched in each subgroup. The genes unique to Severe long COVID patients were associated with immune pathways such as myeloid differentiation and macrophage foam cells. Genes unique to the Fatigue Dominant subgroup were enriched in metabolic pathways such as MAPK/JNK signaling. We also identified overlap in the genes associated with Fatigue Dominant long COVID and ME/CFS, including several involved in circadian rhythm regulation and insulin regulation. Overall, 39 SNPs associated in this study with long COVID can be linked to 9 genes identified in a recent combinatorial analysis of ME/CFS patient from UK Biobank.

Among the 73 genes associated with long COVID, 42 are potentially tractable for novel drug discovery approaches, with 13 of these already targeted by drugs in clinical development pipelines. From this analysis for example, we identified TLR4 antagonists as repurposing candidates with potential to protect against long term cognitive impairment pathology caused by SARS-CoV-2. We are currently evaluating the repurposing potential of these drug targets for use in treating long COVID and/or ME/CFS.

Conclusion This study demonstrates the power of combinatorial analytics for stratifying heterogeneous populations in complex diseases that do not have simple monogenic etiologies. These results build upon the genetic findings from combinatorial analyses of severe acute COVID-19 patients and an ME/CFS population and we expect that access to additional independent, larger patient datasets will further improve the disease insights and validate potential treatment options in long COVID.

Source: Krystyna Taylor, Matthew Pearson, Sayoni Das, Jason Sardell, Karolina Chocian, Steve Gardners. Genetic Risk Factors for Severe and Fatigue Dominant Long COVID and Commonalities with ME/CFS Identified by Combinatorial Analysis. medRxiv 2023.07.13.23292611; doi: https://doi.org/10.1101/2023.07.13.23292611 https://www.medrxiv.org/content/10.1101/2023.07.13.23292611v1.full-text (Full text)

Sex-Dependent Transcriptional Changes in Response to Stress in Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Pilot Project

Abstract:

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex, multi-symptom illness characterized by debilitating fatigue and post-exertional malaise (PEM). Numerous studies have reported sex differences at the epidemiological, cellular, and molecular levels between male and female ME/CFS patients. To gain further insight into these sex-dependent changes, we evaluated differential gene expression by RNA-sequencing (RNA-Seq) in 33 ME/CFS patients (20 female, 13 male) and 34 matched healthy controls (20 female and 14 male) before, during, and after an exercise challenge intended to provoke PEM.
Our findings revealed that pathways related to immune-cell signaling (including IL-12) and natural killer cell cytotoxicity were activated as a result of exertion in the male ME/CFS cohort, while female ME/CFS patients did not show significant enough changes in gene expression to meet the criteria for the differential expression. Functional analysis during recovery from an exercise challenge showed that male ME/CFS patients had distinct changes in the regulation of specific cytokine signals (including IL-1β). Meanwhile, female ME/CFS patients had significant alterations in gene networks related to cell stress, response to herpes viruses, and NF-κβ signaling. The functional pathways and differentially expressed genes highlighted in this pilot project provide insight into the sex-specific pathophysiology of ME/CFS.
Source: Gamer J, Van Booven DJ, Zarnowski O, Arango S, Elias M, Kurian A, Joseph A, Perez M, Collado F, Klimas N, et al. Sex-Dependent Transcriptional Changes in Response to Stress in Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Pilot Project. International Journal of Molecular Sciences. 2023; 24(12):10255. https://doi.org/10.3390/ijms241210255 https://www.mdpi.com/1422-0067/24/12/10255 (Full text)

Exploring the Genetic Contribution to Oxidative Stress in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Abstract:

OBJECTIVES/GOALS: Strong evidence has implicated oxidative stress (OS) as a disease mechanism in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The study aim was to assess whether a C>T single nucleotide polymorphism (SNP) (rs1800668), which reduces the activity of glutathione peroxidase 1 (GPX1), is associated with brain OS in patients with ME/CFS.

METHODS/STUDY POPULATION: Study population: The study enrolled 20 patients with ME/CFS diagnosed according to Canadian Consensus Criteria, and 11 healthy control (HC) subjects. Genotyping: DNA was extracted from whole blood samples, amplified by PCR, and purified. Sanger sequencing was used for genotyping. 1H MRS: Proton magnetic resonance spectroscopy (1H MRS) was used to measure levels of glutathione (GSH) a primary tissue antioxidant and OS marker in a 3x3x2 cm3 occipital cortex (OCC) voxel. GSH spectra were recorded in 15 minutes with the standard J-editing technique. The resulting GSH peak area was normalized to tissue water level in the voxel. Statistical Analysis: T-tests were used to compare OCC GSH levels between ME/CFS and HC groups, and between the study’s genotype groups (group 1: CC, group 2: combined TC and TT).

RESULTS/ANTICIPATED RESULTS: Clinical characteristics: ME/CFS and HC groups were comparable on age and BMI but not on sex (p = 0.038). Genotype frequencies: Genotype frequencies in the ME/CFS group were 0.55 (CC), 0.25 (TC) and 0.2 (TT); and 0.636 (CC), 0.364 (TC), and 0 (TT) in the HC group. GSH levels: There was a trend-level lower mean OCC GSH in ME/CFS than in HC (0.0015 vs 0.0017; p = 0.076). GSH levels by genotype group interaction: Within the ME/CFS group but not in the combined ME/CFS and HC group or HC group alone, GSH levels were lower in the TC and TT genotypes than in CC genotypes (0.00143 vs 0.00164; p = 0.018).

DISCUSSION/SIGNIFICANCE: This study found that the presence of a C>T SNP in GPX1 is associated with lower mean GSH levels and, hence, brain oxidative stress, in ME/CFS patients. If validated in a larger cohort, this finding may support targeted antioxidant therapy based on their genotype as a potentially effective treatment for patients with ME/CFS.

Source: Hampilos, N., Germain, A., Mao, X., Hanson, M., & Shungu, D. (2023). 474 Exploring the Genetic Contribution to Oxidative Stress in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Journal of Clinical and Translational Science, 7(S1), 137-138. doi:10.1017/cts.2023.488. DOI: https://doi.org/10.1017/cts.2023.488

Autoimmune gene expression profiling of fingerstick whole blood in Chronic Fatigue Syndrome

Abstract:

Background: Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a debilitating condition that can lead to severe impairment of physical, psychological, cognitive, social, and occupational functions. The cause of ME/CFS remains incompletely understood. There is no clinical diagnostic test for ME/CFS. Although many therapies have been used off-label to manage symptoms of ME/CFS, there are limited, if any, specific therapies or cure for ME/CFS. In this study, we investigated the expression of genes specific to key immune functions, and viral infection status in ME/CFS patients with an aim of identifying biomarkers for characterization and/or treatment of the disease.

Methods: In 2021, one-hundred and sixty-six (166) patients diagnosed with ME/CFS and 83 healthy controls in the US participated in this study via a social media-based application (app). The patients and heathy volunteers consented to the study and provided self-collected finger-stick blood and first morning void urine samples from home. RNA from the fingerstick blood was tested using DxTerity’s 51-gene autoimmune RNA expression panel (AIP). In addition, DNA from the same fingerstick blood sample was extracted to detect viral load of 4 known ME/CFS associated viruses (HHV6, HHV7, CMV and EBV) using a real-time PCR method.

Results: Among the 166 ME/CFS participants in the study, approximately half (49%) of the ME/CFS patients reported being house-bound or bedridden due to severe symptoms of the disease. From the AIP testing, ME/CFS patients with severe, bedridden conditions displayed significant increases in gene expression of IKZF2, IKZF3, HSPA8, BACH2, ABCE1 and CD3D, as compared to patients with mild to moderate disease conditions. These six aforementioned genes were further upregulated in the 22 bedridden participants who suffer not only from ME/CFS but also from other autoimmune diseases. These genes are involved in T cell, B cell and autoimmunity functions. Furthermore, IKZF3 (Aiolos) and IKZF2 (Helios), and BACH2 have been implicated in other autoimmune diseases such as systemic lupus erythematosus (SLE) and Rheumatoid Arthritis (RA). Among the 240 participants tested with the viral assays, 9 samples showed positive results (including 1 EBV positive and 8 HHV6 positives).

Conclusions: Our study indicates that gene expression biomarkers may be used in identifying or differentiating subsets of ME/CFS patients having different levels of disease severity. These gene targets may also represent opportunities for new therapeutic modalities for the treatment of ME/CFS. The use of social media engaged patient recruitment and at-home sample collection represents a novel approach for conducting clinical research which saves cost, time and eliminates travel for office visits.

Source: Wang Z, Waldman MF, Basavanhally TJ, Jacobs AR, Lopez G, Perichon RY, Ma JJ, Mackenzie EM, Healy JB, Wang Y, Hersey SA. Autoimmune gene expression profiling of fingerstick whole blood in Chronic Fatigue Syndrome. J Transl Med. 2022 Oct 25;20(1):486. doi: 10.1186/s12967-022-03682-3. PMID: 36284352; PMCID: PMC9592873.  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9592873/ (Full study)

Genetic and epigenetic regulation of Catechol-O-methyltransferase in relation to inflammation in chronic fatigue syndrome and Fibromyalgia

Abstract:

Background: Catechol-O-methyltransferase (COMT) has been shown to influence clinical pain, descending modulation, and exercise-induced symptom worsening. COMT regulates nociceptive processing and inflammation, key pathophysiological features of Chronic Fatigue Syndrome and Fibromyalgia (CFS/FM). We aimed to determine the interactions between genetic and epigenetic mechanisms regulating COMT and its influence on inflammatory markers and symptoms in patients with CFS/FM.

Methods: A case-control study with repeated-measures design was used to reduce the chance of false positive and increase the power of our findings. Fifty-four participants (28 patients with CFS/FM and 26 controls) were assessed twice within 4 days. The assessment included clinical questionnaires, neurophysiological assessment (pain thresholds, temporal summation, and conditioned pain modulation), and blood withdrawal in order to assess rs4818, rs4633, and rs4680 COMT polymorphisms and perform haplotype estimation, DNA methylation in the COMT gene (both MB-COMT and S-COMT promoters), and cytokine expression (TNF-α, IFN-γ, IL-6, and TGF-β).

Results: COMT haplotypes were associated with DNA methylation in the S-COMT promoter, TGF-β expression, and symptoms. However, this was not specific for one condition. Significant between-group differences were found for increased DNA methylation in the MB-COMT promoter and decreased IFN-γ expression in patients.

Discussion: Our results are consistent with basic and clinical research, providing interesting insights into genetic-epigenetic regulatory mechanisms. MB-COMT DNA methylation might be an independent factor contributing to the pathophysiology of CFS/FM. Further research on DNA methylation in complex conditions such as CFS/FM is warranted. We recommend future research to employ a repeated-measure design to control for biomarkers variability and within-subject changes.

Source: Polli A, Hendrix J, Ickmans K, Bakusic J, Ghosh M, Monteyne D, Velkeniers B, Bekaert B, Nijs J, Godderis L. Genetic and epigenetic regulation of Catechol-O-methyltransferase in relation to inflammation in chronic fatigue syndrome and Fibromyalgia. J Transl Med. 2022 Oct 25;20(1):487. doi: 10.1186/s12967-022-03662-7. PMID: 36284330; PMCID: PMC9598022. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9598022/ (Full text)

Bioinformatics and systems biology approach to identify the pathogenetic link of Long COVID and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Abstract:

Background: The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global crisis. Although many people recover from COVID-19 infection, they are likely to develop persistent symptoms similar to those of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) after discharge. Those constellations of symptoms persist for months after infection, called Long COVID, which may lead to considerable financial burden and healthcare challenges. However, the mechanisms underlying Long COVID and ME/CFS remain unclear.

Methods: We collected the genes associated with Long COVID and ME/CFS in databases by restricted screening conditions and clinical sample datasets with limited filters. The common genes for Long COVID and ME/CFS were finally obtained by taking the intersection. We performed several advanced bioinformatics analyses based on common genes, including gene ontology and pathway enrichment analyses, protein–protein interaction (PPI) analysis, transcription factor (TF)–gene interaction network analysis, transcription factor–miRNA co-regulatory network analysis, and candidate drug analysis prediction.

Results: We found nine common genes between Long COVID and ME/CFS and gained a piece of detailed information on their biological functions and signaling pathways through enrichment analysis. Five hub proteins (IL-6, IL-1B, CD8A, TP53, and CXCL8) were collected by the PPI network. The TF–gene and TF–miRNA coregulatory networks were demonstrated by NetworkAnalyst. In the end, 10 potential chemical compounds were predicted.

Conclusion: This study revealed common gene interaction networks of Long COVID and ME/CFS and predicted potential therapeutic drugs for clinical practice. Our findings help to identify the potential biological mechanism between Long COVID and ME/CFS. However, more laboratory and multicenter evidence is required to explore greater mechanistic insight before clinical application in the future.

Source: Lv Y, Zhang T, Cai J, Huang C, Zhan S and Liu J. Bioinformatics and systems biology approach to identify the pathogenetic link of Long COVID and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Front. Immunol. 13:952987  https://www.frontiersin.org/articles/10.3389/fimmu.2022.952987/full (Full text)

Genetic risk factors for ME/CFS identified using combinatorial analysis

Abstract:

Background:Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating chronic disease that lacks known pathogenesis, distinctive diagnostic criteria, and effective treatment options. Understanding the genetic (and other) risk factors associated with the disease would begin to help to alleviate some of these issues for patients.

Methods: We applied both GWAS and the PrecisionLife combinatorial analytics platform to analyze ME/CFS cohorts from UK Biobank, including the Pain Questionnaire cohort, in a case-control design with 1,000 cycles of fully random permutation. Results from this study were supported by a series of replication and cohort comparison experiments, including use of disjoint Verbal Interview CFS, post-viral fatigue syndrome and fibromyalgia cohorts also derived from UK Biobank, and results compared for overlap and reproducibility.

Results: Combinatorial analysis revealed 199 SNPs mapping to 14 genes, that were significantly associated with 91% of the cases in the ME/CFS population. These SNPs were found to stratify by shared cases into 15 clusters (communities) made up of 84 high-order combinations of between 3-5 SNPs. p-values for these communities range from 2.3 × 10−10 to 1.6 × 10−72. Many of the genes identified are linked to the key cellular mechanisms hypothesized to underpin ME/CFS, including vulnerabilities to stress and/or infection, mitochondrial dysfunction, sleep disturbance and autoimmune development. We identified 3 of the critical SNPs replicated in the post-viral fatigue syndrome cohort and 2 SNPs replicated in the fibromyalgia cohort. We also noted similarities with genes associated with multiple sclerosis and long COVID, which share some symptoms and potentially a viral infection trigger with ME/CFS.

Conclusions: This study provides the first detailed genetic insights into the pathophysiological mechanisms underpinning ME/CFS and offers new approaches for better diagnosis and treatment of patients

Source: Sayoni Das, Krystyna Taylor, James Kozubek, Jason Sardell, Steve Gardner. Genetic Risk Factors for ME/CFS Identified using Combinatorial Analysis. medRxiv 2022.09.09.22279773; doi: https://doi.org/10.1101/2022.09.09.22279773  https://www.medrxiv.org/content/10.1101/2022.09.09.22279773v2.full-text (Full text)