Skip to main content
Log in

Genome-wide DNA methylation analysis related to ALS patient progression and survival

  • Original Communication
  • Published:
Journal of Neurology Aims and scope Submit manuscript

Abstract

Background

Epigenetics contributes to the pathogenesis of amyotrophic lateral sclerosis (ALS). We aimed to characterize the DNA methylation profiles associated with clinical heterogeneity in disease progression and survival among patients.

Methods

We included a cohort of 41 patients with sporadic ALS, with a median follow-up of 86.9 months, and 27 rigorously matched healthy controls. Blood-based genome-wide DNA methylation analysis was conducted.

Results

A total of 948 progression rate-associated differentially methylated positions, 298 progression rate-associated differentially methylated regions (R-DMRs), 590 survival time-associated DMPs, and 197 survival time-associated DMRs (S-DMRs) were identified, using complementary grouping strategies. Enrichment analysis of differentially methylated genes highlighted the involvement of synapses and axons in ALS progression and survival. Clinical analysis revealed a positive correlation between the average methylation levels of the R-DMR in PRDM8 and disease progression rate (r = 0.479, p = 0.002). Conversely, there was an inverse correlation between the average methylation levels of the R-DMR in ANKRD33 and disease progression rate (r = − 0.476, p = 0.002). In addition, patients with higher methylation levels within the S-DMR of ZNF696 experienced longer survival (p = 0.016), while those with elevated methylation levels in the S-DMR of RAI1 had shorter survival (p = 0.006).

Conclusion

DNA methylation holds promise as a potential biomarker for tracking disease progression and predicting survival outcome and also offers targets for precision medicine.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

Availability of data and materials

Data and materials are available from the corresponding author on reasonable request.

Abbreviations

ABLIM1:

Actin-binding LIM protein 1

AGRN:

Agrin

ALS:

Amyotrophic lateral sclerosis

sALS:

Sporadic amyotrophic lateral sclerosis

ALSFRS-R:

Revised-Amyotrophic Lateral Sclerosis Functional Rating Scale

ANKRD33:

Ankyrin repeat domain 33

BRSK2:

BR serine/threonine kinase 2

CTTN:

Cortactin

C9orf72:

Chromosome 9 open reading frame 72

DLG2:

Discs large MAGUK scaffold protein 2

DNM1:

Dynamin 1

DSS:

Dispersion shrinkage for sequencing data

FUS:

Fused in sarcoma

GO:

Gene ontology

HC:

Healthy controls

ITGB1:

Integrin subunit beta 1

LYN:

LYN proto-oncogene

MC-seq:

Methylation capture bisulfite sequencing

PIEZO2:

Piezo-type mechanosensitive ion channel component 2

PLXND1:

Plexin D1

PPI:

Protein–protein interaction

PRDM8:

PR/SET domain 8

PTK2:

Protein tyrosine kinase 2

RAI1:

Retinoic acid induced 1

R-DMPs:

Progression rate-associated differentially methylated positions

R-DMRs:

Progression rate-associated differentially methylated regions

S-DMPs:

Survival time-associated differentially methylated positions

S-DMRs:

Survival time-associated differentially methylated regions

ZNF696:

Zinc finger protein 696

References

  1. Feldman EL, Goutman SA, Petri S et al (2022) Amyotrophic lateral sclerosis. Lancet 400:1363

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Zarei S, Carr K, Reiley L et al (2015) A comprehensive review of amyotrophic lateral sclerosis. Surg Neurol Int 6:171

    PubMed  PubMed Central  Google Scholar 

  3. Li C, Liu J, Lin J, Shang H (2022) COVID-19 and risk of neurodegenerative disorders: a Mendelian randomization study. Transl Psychiatry 12:283

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Yang T, Wei Q, Li C et al (2022) Spatial-temporal pattern of propagation in amyotrophic lateral sclerosis and effect on survival: a cohort study. Eur J Neurol 29:3177–3186

    PubMed  Google Scholar 

  5. Hussain N (2012) Epigenetic influences that modulate infant growth, development, and disease. Antioxid Redox Signal 17:224–236

    CAS  PubMed  Google Scholar 

  6. Machnik M, Oleksiewicz U (2020) Dynamic signatures of the epigenome: friend or foe? Cells 9:653

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Hillary RF, McCartney DL, Smith HM et al (2023) Blood-based epigenome-wide analyses of 19 common disease states: a longitudinal, population-based linked cohort study of 18,413 Scottish individuals. PLoS Med 20:e1004247

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Hop PJ, Zwamborn RAJ, Hannon E et al (2022) Genome-wide study of DNA methylation shows alterations in metabolic, inflammatory, and cholesterol pathways in ALS. Sci Transl Med 14:0264

    Google Scholar 

  9. Ruf WP, Hannon E, Freischmidt A et al (2022) Methylome analysis of ALS patients and presymptomatic mutation carriers in blood cells. Neurobiol Aging 116:16–24

    CAS  PubMed  Google Scholar 

  10. Cai Z, Jia X, Liu M, Yang X, Cui L (2022) Epigenome-wide DNA methylation study of whole blood in patients with sporadic amyotrophic lateral sclerosis. Chin Med J (Engl) 135:1466–1473

    CAS  PubMed  Google Scholar 

  11. Martin LJ, Adams DA, Niedzwiecki MV, Wong M (2022) Aberrant DNA and RNA methylation occur in spinal cord and skeletal muscle of human SOD1 mouse models of ALS and in human ALS: targeting DNA methylation is therapeutic. Cells 11:3448

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Appleby-Mallinder C, Schaber E, Kirby J et al (2021) TDP43 proteinopathy is associated with aberrant DNA methylation in human amyotrophic lateral sclerosis. Neuropathol Appl Neurobiol 47:61–72

    CAS  PubMed  Google Scholar 

  13. Cedarbaum JM, Stambler N, Malta E et al (1999) The ALSFRS-R: a revised ALS functional rating scale that incorporates assessments of respiratory function. BDNF ALS Study Group (Phase III). J Neurol Sci 169:13–21

    CAS  PubMed  Google Scholar 

  14. Wang J, C. P, M. N, inventors; Agilent SureSelectXT Methyl-Seq applications with low-input DNA and smaller capture libraries. https://www.agilent.com/cs/library/applications/5991-7838EN.pdf2017. Accessed 24 June 2023

  15. Shu C, Zhang X, Aouizerat BE, Xu K (2020) Comparison of methylation capture sequencing and Infinium MethylationEPIC array in peripheral blood mononuclear cells. Epigenet Chromatin 13:51

    CAS  Google Scholar 

  16. Krueger F, Andrews SR (2011) Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics 27:1571–1572

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Ryan DP, Ehninger D (2014) Bison: bisulfite alignment on nodes of a cluster. BMC Bioinform 15:337

    Google Scholar 

  18. Akalin A, Kormaksson M, Li S et al (2012) methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome Biol 13:R87

    PubMed  PubMed Central  Google Scholar 

  19. Teh AL, Pan H, Lin X et al (2016) Comparison of Methyl-capture Sequencing vs. Infinium 450K methylation array for methylome analysis in clinical samples. Epigenetics 11:36–48

    PubMed  PubMed Central  Google Scholar 

  20. Park Y, Wu H (2016) Differential methylation analysis for BS-seq data under general experimental design. Bioinformatics 32:1446–1453

    CAS  PubMed  Google Scholar 

  21. Wu T, Hu E, Xu S et al (2021) clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation (Camb) 2:100141

    CAS  PubMed  Google Scholar 

  22. Szklarczyk D, Kirsch R, Koutrouli M et al (2023) The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucl Acids Res 51:D638–D646

    CAS  PubMed  Google Scholar 

  23. McIntyre JC, Titlow WB, McClintock TS (2010) Axon growth and guidance genes identify nascent, immature, and mature olfactory sensory neurons. J Neurosci Res 88:3243–3256

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Orozco D, Edbauer D (2013) FUS-mediated alternative splicing in the nervous system: consequences for ALS and FTLD. J Mol Med (Berl) 91:1343–1354

    PubMed  Google Scholar 

  25. Wang Y, Wan B, Li D et al (2012) BRSK2 is regulated by ER stress in protein level and involved in ER stress-induced apoptosis. Biochem Biophys Res Commun 423:813–818

    CAS  PubMed  Google Scholar 

  26. Nothling J, Abrahams N, Toikumo S et al (2021) Genome-wide differentially methylated genes associated with posttraumatic stress disorder and longitudinal change in methylation in rape survivors. Transl Psychiatry 11:594

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Cypris O, Eipel M, Franzen J et al (2020) PRDM8 reveals aberrant DNA methylation in aging syndromes and is relevant for hematopoietic and neuronal differentiation. Clin Epigenet 12:125

    CAS  Google Scholar 

  28. Maierhofer A, Flunkert J, Oshima J et al (2019) Epigenetic signatures of Werner syndrome occur early in life and are distinct from normal epigenetic aging processes. Aging Cell 18:e12995

    PubMed  PubMed Central  Google Scholar 

  29. Jiang L, Penney KL, Giovannucci E, Kraft P, Wilson KM (2018) A genome-wide association study of energy intake and expenditure. PLoS ONE 13:e0201555

    PubMed  PubMed Central  Google Scholar 

  30. Ludolph A, Dupuis L, Kasarskis E, Steyn F, Ngo S, McDermott C (2023) Nutritional and metabolic factors in amyotrophic lateral sclerosis. Nat Rev Neurol 19:511

    PubMed  Google Scholar 

  31. Woo SH, Lukacs V, de Nooij JC et al (2015) Piezo2 is the principal mechanotransduction channel for proprioception. Nat Neurosci 18:1756–1762

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Sonkodi B (2023) Miswired proprioception in amyotrophic lateral sclerosis in relation to pain sensation (and in delayed onset muscle soreness)—is piezo2 channelopathy a principal transcription activator in proprioceptive terminals besides being the potential primary damage? Life (Basel) 13:657

    CAS  PubMed  Google Scholar 

  33. Chang YT, Kowalczyk M, Fogerson PM et al (2022) Loss of Rai1 enhances hippocampal excitability and epileptogenesis in mouse models of Smith-Magenis syndrome. Proc Natl Acad Sci USA 119:e2210122119

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Fogarty MJ, Noakes PG, Bellingham MC (2015) Motor cortex layer V pyramidal neurons exhibit dendritic regression, spine loss, and increased synaptic excitation in the presymptomatic hSOD1(G93A) mouse model of amyotrophic lateral sclerosis. J Neurosci 35:643–647

    PubMed  PubMed Central  Google Scholar 

  35. Fogarty MJ, Klenowski PM, Lee JD et al (2016) Cortical synaptic and dendritic spine abnormalities in a presymptomatic TDP-43 model of amyotrophic lateral sclerosis. Sci Rep 6:37968

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Gulino R (2023) Synaptic dysfunction and plasticity in amyotrophic lateral sclerosis. Int J Mol Sci 24:4613

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Verma M, Lizama BN, Chu CT (2022) Excitotoxicity, calcium and mitochondria: a triad in synaptic neurodegeneration. Transl Neurodegener 11:3

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Fogarty MJ (2019) Amyotrophic lateral sclerosis as a synaptopathy. Neural Regen Res 14:189–192

    PubMed  PubMed Central  Google Scholar 

  39. Traynelis SF, Wollmuth LP, McBain CJ et al (2010) Glutamate receptor ion channels: structure, regulation, and function. Pharmacol Rev 62:405–496

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Yang S, Park JH, Lu HC (2023) Axonal energy metabolism, and the effects in aging and neurodegenerative diseases. Mol Neurodegener 18:49

    PubMed  PubMed Central  Google Scholar 

  41. Okamoto K, Hirai S, Shoji M, Senoh Y, Yamazaki T (1990) Axonal swellings in the corticospinal tracts in amyotrophic lateral sclerosis. Acta Neuropathol 80:222–226

    CAS  PubMed  Google Scholar 

  42. Chia R, Chio A, Traynor BJ (2018) Novel genes associated with amyotrophic lateral sclerosis: diagnostic and clinical implications. Lancet Neurol 17:94–102

    CAS  PubMed  Google Scholar 

  43. Jones PA (2012) Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat Rev Genet 13:484–492

    CAS  PubMed  Google Scholar 

  44. Zhang L, Silva TC, Young JI et al (2020) Epigenome-wide meta-analysis of DNA methylation differences in prefrontal cortex implicates the immune processes in Alzheimer’s disease. Nat Commun 11:6114

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Chuang YH, Paul KC, Bronstein JM, Bordelon Y, Horvath S, Ritz B (2017) Parkinson’s disease is associated with DNA methylation levels in human blood and saliva. Genome Med 9:76

    PubMed  PubMed Central  Google Scholar 

  46. Wei QQ, Chen YP, Chen XP et al (2018) Prognostic nomogram associated with longer survival in amyotrophic lateral sclerosis patients. Aging Dis 9:965–975

    PubMed  PubMed Central  Google Scholar 

  47. Braun PR, Han S, Hing B et al (2019) Genome-wide DNA methylation comparison between live human brain and peripheral tissues within individuals. Transl Psychiatry 9:47

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank the patients and their families for their participation in the study.

Funding

This study was supported by the National Natural Science Foundation of China (Grant nos. 82371430 and 82101485) and Sichuan Science and Technology Program (Grant no. 2022ZDZX0023).

Author information

Authors and Affiliations

Authors

Contributions

TMY and HFS conceived and designed the study. TMY, CYL, QQW, YFC, JXH, JYL, YX, QRJ, and SCW collected the data. TMY, CYL, and DJP contributed to the statistical analysis. TMY wrote the first draft of the manuscript. TMY, HFS, CYL, and QQW contributed to the writing of the final version of the manuscript. DJP provided critical review. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Huifang Shang.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflict of interest.

Ethics approval and consent to participate

Ethics approval for the study was approved by the institutional ethics committee of Sichuan University. Written informed consent was obtained from each participant or their primary caregiver.

Consent for publication

Not applicable.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 311 KB)

Supplementary file2 (XLSX 1204 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, T., Li, C., Wei, Q. et al. Genome-wide DNA methylation analysis related to ALS patient progression and survival. J Neurol 271, 2672–2683 (2024). https://doi.org/10.1007/s00415-024-12222-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00415-024-12222-6

Keywords

Navigation