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Multi-omic analysis of selectively vulnerable motor neuron subtypes implicates altered lipid metabolism in ALS

Nature Neuroscience volume 24, pages 1673–1685 (2021)Cite this article

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A Publisher Correction to this article was published on 21 December 2021

This article has been updated

Abstract

Amyotrophic lateral sclerosis (ALS) is a devastating disorder in which motor neurons degenerate, the causes of which remain unclear. In particular, the basis for selective vulnerability of spinal motor neurons (sMNs) and resistance of ocular motor neurons to degeneration in ALS has yet to be elucidated. Here, we applied comparative multi-omics analysis of human induced pluripotent stem cell-derived sMNs and ocular motor neurons to identify shared metabolic perturbations in inherited and sporadic ALS sMNs, revealing dysregulation in lipid metabolism and its related genes. Targeted metabolomics studies confirmed such findings in sMNs of 17 ALS (SOD1, C9ORF72, TDP43 (TARDBP) and sporadic) human induced pluripotent stem cell lines, identifying elevated levels of arachidonic acid. Pharmacological reduction of arachidonic acid levels was sufficient to reverse ALS-related phenotypes in both human sMNs and in vivo in Drosophila and SOD1G93A mouse models. Collectively, these findings pinpoint a catalytic step of lipid metabolism as a potential therapeutic target for ALS.

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Fig. 1: Differentiation of PHOX2B::GFP+ and HB9::GFP+ neurons.
Fig. 2: Genome-wide RNA-seq analysis reveals aberrant lipid metabolism after comparison between post-sorted HB9::GFP+ and PHOX2B::GFP+ cells in SOD1A4V and C9ORF72 ALS lines.
Fig. 3: Metabolomics analysis indicates upregulation of lipid metabolism in post-sorted HB9::GFP+ cells of SOD1A4V and C9ORF72 ALS lines.
Fig. 4: Metabolomics analysis in unsorted sMN differentiation confirms upregulation of lipid metabolism and provides lipid-related metabolic candidates in TDP43Q343R, C9ORF72, SOD1A4V and sporadic ALS lines.
Fig. 5: 5-LOX inhibitors rescue motor neuron degeneration in vitro.
Fig. 6: 5-LOX inhibitors rescue the phenotype of Drosophila model.
Fig. 7: Caffeic acid alleviates disease pathogenesis in SOD1G93A mice.

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Data availability

The raw bulk RNA-seq data (Figs. 2 and 3d, Extended Data Figs. 4 and 5 and Supplementary Fig. 4) are available at the Gene Expression Omnibus under accession codes GSE132972 and GSE173115. Source data are provided with this paper.

Code availability

The R code used to analyze RNA-seq datasets is available from the corresponding authors on reasonable request.

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References

  1. Taylor, J. P., Brown, R. H. Jr & Cleveland, D. W. Decoding ALS: from genes to mechanism. Nature 539, 197–206 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Tandan, R. & Bradley, W. G. Amyotrophic lateral sclerosis: part 1. Clinical features, pathology, and ethical issues in management. Ann. Neurol. 18, 271–280 (1985).

    Article  CAS  PubMed  Google Scholar 

  3. Saxena, S. & Caroni, P. Selective neuronal vulnerability in neurodegenerative diseases: from stressor thresholds to degeneration. Neuron 71, 35–48 (2011).

    Article  CAS  PubMed  Google Scholar 

  4. Fujimori, K. et al. Modeling sporadic ALS in iPSC-derived motor neurons identifies a potential therapeutic agent. Nat. Med. 24, 1579–1589 (2018).

    Article  PubMed  Google Scholar 

  5. Kiskinis, E. et al. Pathways disrupted in human ALS motor neurons identified through genetic correction of mutant SOD1. Cell Stem Cell 14, 781–795 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Klim, J. R. et al. ALS-implicated protein TDP-43 sustains levels of STMN2, a mediator of motor neuron growth and repair. Nat. Neurosci. 22, 167–179 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Melamed, Z. et al. Premature polyadenylation-mediated loss of stathmin-2 is a hallmark of TDP-43-dependent neurodegeneration. Nat. Neurosci. 22, 180–190 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Kaminski, H. J., Richmonds, C. R., Kusner, L. L. & Mitsumoto, H. Differential susceptibility of the ocular motor system to disease. Ann. N. Y. Acad. Sci. 956, 42–54 (2002).

    Article  PubMed  Google Scholar 

  9. Cleveland, D. W. & Rothstein, J. D. From Charcot to Lou Gehrig: deciphering selective motor neuron death in ALS. Nat. Rev. Neurosci. 2, 806–819 (2001).

    Article  Google Scholar 

  10. Kaplan, A. et al. Neuronal matrix metalloproteinase-9 is a determinant of selective neurodegeneration. Neuron 81, 333–348 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Allodi, I. et al. Differential neuronal vulnerability identifies IGF-2 as a protective factor in ALS. Sci. Rep. 6, 25960 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Mazzoni, E. O. et al. Synergistic binding of transcription factors to cell-specific enhancers programs motor neuron identity. Nat. Neurosci. 16, 1219–1227 (2013).

    Article  Google Scholar 

  13. Allodi, I. et al. Modeling motor neuron resilience in ALS using stem cells. Stem Cell Reports 12, 1329–1341 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Cutler, R. G., Pedersen, W. A., Camandola, S., Rothstein, J. D. & Mattson, M. P. Evidence that accumulation of ceramides and cholesterol esters mediates oxidative stress-induced death of motor neurons in amyotrophic lateral sclerosis. Ann. Neurol. 52, 448–457 (2002).

    Article  CAS  PubMed  Google Scholar 

  15. Theofilopoulos, S. et al. Cholestenoic acids regulate motor neuron survival via liver X receptors. J. Clin. Invest. 124, 4829–4842 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Pattyn, A., Hirsch, M., Goridis, C. & Brunet, J.-F. Control of hindbrain motor neuron differentiation by the homeobox gene Phox2b. Development 127, 1349–1358 (2000).

    Article  CAS  PubMed  Google Scholar 

  17. Prakash, N. et al. Nkx6-1 controls the identity and fate of red nucleus and oculomotor neurons in the mouse midbrain. Development 136, 2545–2555 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Hasan, K. B., Agarwala, S. & Ragsdale, C. W. PHOX2A regulation of oculomotor complex nucleogenesis. Development 137, 1205–1213 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Deng, Q. et al. Specific and integrated roles of Lmx1a, Lmx1b and Phox2a in ventral midbrain development. Development 138, 3399–3408 (2011).

    Article  CAS  PubMed  Google Scholar 

  20. Nakano, M. et al. Homozygous mutations in ARIX (PHOX2A) result in congenital fibrosis of the extraocular muscles type 2. Nat. Genet. 29, 315–320 (2001).

    Article  Google Scholar 

  21. Oh, Y. et al. Functional coupling with cardiac muscle promotes maturation of hPSC-derived sympathetic neurons. Cell Stem Cell 19, 95–106 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kriks, S. et al. Dopamine neurons derived from human ES cells efficiently engraft in animal models of Parkinson’s disease. Nature 480, 547–551 (2011).

    Article  PubMed Central  Google Scholar 

  23. Tang, M., Luo, S. X., Tang, V. & Huang, E. J. Temporal and spatial requirements of Smoothened in ventral midbrain neuronal development. Neural Dev. 8, 8 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Danielian, P. S. & McMahon, A. P. Engrailed-1 as a target of the Wnt-1 signalling pathway in vertebrate midbrain development. Nature 383, 332–334 (1996).

    Article  Google Scholar 

  25. Tsarovina, K. et al. Essential role of Gata transcription factors in sympathetic neuron development. Development 131, 4775–4786 (2004).

    Article  CAS  PubMed  Google Scholar 

  26. Thaler, J. et al. Active suppression of interneuron programs within developing motor neurons revealed by analysis of homeodomain factor HB9. Neuron 23, 675–687 (1999).

    Article  CAS  PubMed  Google Scholar 

  27. Song, M.-R. et al. Islet-to-LMO stoichiometries control the function of transcription complexes that specify motor neuron and V2a interneuron identity. Development 136, 2923–2932 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Lewcock, J. W., Genoud, N., Lettieri, K. & Pfaff, S. L. The ubiquitin ligase Phr1 regulates axon outgrowth through modulation of microtubule dynamics. Neuron 56, 604–620 (2007).

    Article  CAS  PubMed  Google Scholar 

  29. Guidato, S., Barrett, C. & Guthrie, S. Patterning of motor neurons by retinoic acid in the chick embryo hindbrain in vitro. Mol. Cell. Neurosci. 23, 81–95 (2003).

    Article  CAS  PubMed  Google Scholar 

  30. Calder, E. L. et al. Retinoic acid-mediated regulation of GLI3 enables efficient motoneuron derivation from human ESCs in the absence of extrinsic SHH activation. J. Neurosci. 35, 11462–11481 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Hedlund, E., Karlsson, M., Osborn, T., Ludwig, W. & Isacson, O. Global gene expression profiling of somatic motor neuron populations with different vulnerability identify molecules and pathways of degeneration and protection. Brain 133, 2313–2330 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Valbuena, G. N. et al. Metabolomic analysis reveals increased aerobic glycolysis and amino acid deficit in a cellular model of amyotrophic lateral sclerosis. Mol. Neurobiol. 53, 2222–2240 (2016).

    Article  CAS  PubMed  Google Scholar 

  33. Xia, J. & Wishart, D. S. Using MetaboAnalyst 3.0 for comprehensive metabolomics data analysis. Curr. Protoc. Bioinformatics 55, 14.10.1–14.10.91 (2016).

    Article  Google Scholar 

  34. Lawton, K. A. et al. Plasma metabolomic biomarker panel to distinguish patients with amyotrophic lateral sclerosis from disease mimics. Amyotroph. Lateral Scler. Frontotemporal Degener. 15, 362–370 (2014).

    Article  CAS  PubMed  Google Scholar 

  35. Jatana, M. et al. Inhibition of NF-κB activation by 5-lipoxygenase inhibitors protects brain against injury in a rat model of focal cerebral ischemia. J. Neuroinflammation 3, 12 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Häfner, A.-K., Kahnt, A. S. & Steinhilber, D. Beyond leukotriene formation–the noncanonical functions of 5-lipoxygenase. Prostaglandins Other Lipid Mediat. 142, 24–32 (2019).

    Article  PubMed  Google Scholar 

  37. Cao, Y., Pearman, A. T., Zimmerman, G. A., McIntyre, T. M. & Prescott, S. M. Intracellular unesterified arachidonic acid signals apoptosis. Proc. Natl Acad. Sci. USA 97, 11280–11285 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Rizzo, M. T. et al. Induction of apoptosis by arachidonic acid in chronic myeloid leukemia cells. Cancer Res. 59, 5047–5053 (1999).

    CAS  PubMed  Google Scholar 

  39. Yang, J. Q., Zhou, Q. X., Liu, B. Z. & He, B. C. Protection of mouse brain from aluminum‐induced damage by caffeic acid. CNS Neurosci. Ther. 14, 10–16 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Bishayee, K. & Khuda-Bukhsh, A. R. 5-lipoxygenase antagonist therapy: a new approach towards targeted cancer chemotherapy. Acta Biochim. Biophys. Sin. 45, 709–719 (2013).

    Article  CAS  PubMed  Google Scholar 

  41. Pergola, C. & Werz, O. 5-Lipoxygenase inhibitors: a review of recent developments and patents. Expert Opin. Therapeutic Pat. 20, 355–375 (2010).

    Article  CAS  Google Scholar 

  42. Xu, Z. et al. Expanded GGGGCC repeat RNA associated with amyotrophic lateral sclerosis and frontotemporal dementia causes neurodegeneration. Proc. Natl Acad. Sci. USA 110, 7778–7783 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Abdel-Khalik, J. et al. Defective cholesterol metabolism in amyotrophic lateral sclerosis. J. Lipid Res. 58, 267–278 (2017).

    Article  CAS  PubMed  Google Scholar 

  44. An, D. et al. Stem cell-derived cranial and spinal motor neurons reveal proteostatic differences between ALS resistant and sensitive motor neurons. eLife 8, e44423 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Kuehne, A. et al. Acute activation of oxidative pentose phosphate pathway as first-line response to oxidative stress in human skin cells. Mol. Cell 59, 359–371 (2015).

    Article  CAS  PubMed  Google Scholar 

  46. Patra, K. C. & Hay, N. The pentose phosphate pathway and cancer. Trends Biochem. Sci. 39, 347–354 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Lopez-Gonzalez, R. et al. Poly (GR) in C9ORF72-related ALS/FTD compromises mitochondrial function and increases oxidative stress and DNA damage in iPSC-derived motor neurons. Neuron 92, 383–391 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Andrus, P. K., Fleck, T. J., Gurney, M. E. & Hall, E. D. Protein oxidative damage in a transgenic mouse model of familial amyotrophic lateral sclerosis. J. Neurochem. 71, 2041–2048 (1998).

    Article  CAS  PubMed  Google Scholar 

  49. Dodge, J. C. et al. Glycosphingolipids are modulators of disease pathogenesis in amyotrophic lateral sclerosis. Proc. Natl Acad. Sci. USA 112, 8100–8105 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Dodge, J. C. et al. Neutral lipid cacostasis contributes to disease pathogenesis in amyotrophic lateral sclerosis. J. Neurosci. 40, 9137–9147 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Andrés‐Benito, P. et al. Lipid alterations in human frontal cortex in ALS‐FTLD‐TDP43 proteinopathy spectrum are partly related to peroxisome impairment. Neuropathol. Appl. Neurobiol. 47, 544–563 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Mohassel, P. et al. Childhood amyotrophic lateral sclerosis caused by excess sphingolipid synthesis. Nat. Med. 27, 1197–1204 (2021).

    Article  CAS  PubMed  Google Scholar 

  53. Das, U. N. Arachidonic acid in health and disease with focus on hypertension and diabetes mellitus: a review. J. Adv. Res. 11, 43–55 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Solomonov, Y., Hadad, N. & Levy, R. Reduction of cytosolic phospholipase A2α upregulation delays the onset of symptoms in SOD1G93A mouse model of amyotrophic lateral sclerosis. J. Neuroinflammation 13, 1–12 (2016).

    Article  Google Scholar 

  55. Tallima, H. & El Ridi, R. Arachidonic acid: physiological roles and potential health benefits—a review. J. Adv. Res. 11, 33–41 (2018).

    Article  CAS  PubMed  Google Scholar 

  56. Blasco, H. et al. Lipidomics reveals cerebrospinal-fluid signatures of ALS. Sci. Rep. 7, 1–10 (2017).

    Article  CAS  Google Scholar 

  57. Chaves-Filho, A. B. et al. Alterations in lipid metabolism of spinal cord linked to amyotrophic lateral sclerosis. Sci. Rep. 9, 1–14 (2019).

    Article  CAS  Google Scholar 

  58. West, M. et al. The arachidonic acid 5-lipoxygenase inhibitor nordihydroguaretic acid inhibits tumor necrosis factor-α activation of microglia and extends survival of G93A-SOD1 transgenic mice. J. Neurochem. 91, 133–143 (2004).

    Article  CAS  PubMed  Google Scholar 

  59. Ran, F. A. et al. Genome engineering using the CRISPR–Cas9 system. Nat. Protoc. 8, 2281–2308 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Hockemeyer, D. et al. Genetic engineering of human pluripotent cells using TALE nucleases. Nat. Biotechnol. 29, 731–734 (2011).

    Article  PubMed Central  Google Scholar 

  61. Mali, P. et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Gendron, T. F. et al. Poly (GP) proteins are a useful pharmacodynamic marker for C9ORF72-associated amyotrophic lateral sclerosis. Sci. Transl. Med. 9, eaai7866 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Almad, A. A. et al. Connexin 43 in astrocytes contributes to motor neuron toxicity in amyotrophic lateral sclerosis. Glia 64, 1154–1169 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Choi, I. Y. et al. Concordant but varied phenotypes among Duchenne muscular dystrophy patient-specific myoblasts derived using a human iPSC-based model. Cell Rep. 15, 2301–2312 (2016).

    Article  CAS  PubMed  Google Scholar 

  65. Thomson, J. A. et al. Embryonic stem cell lines derived from human blastocysts. Science 282, 1145–1147 (1998).

    Article  CAS  PubMed  Google Scholar 

  66. Riera, M. et al. Establishment and characterization of an iPSC line (FRIMOi001-A) derived from a retinitis pigmentosa patient carrying PDE6A mutations. Stem Cell Res. 35, 101385 (2019).

    Article  CAS  PubMed  Google Scholar 

  67. Coyne, A. N. et al. G4C2 repeat RNA initiates a POM121-mediated reduction in specific nucleoporins in C9orf72 ALS/FTD. Neuron 107, 1124–1140 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Lee, G. et al. Modelling pathogenesis and treatment of familial dysautonomia using patient-specific iPSCs. Nature 461, 402–406 (2009).

    Article  PubMed Central  Google Scholar 

  69. Chambers, S. M. et al. Highly efficient neural conversion of human ES and iPS cells by dual inhibition of SMAD signaling. Nat. Biotechnol. 27, 275–280 (2009).

    PubMed Central  Google Scholar 

  70. Qi, Y. et al. Combined small-molecule inhibition accelerates the derivation of functional cortical neurons from human pluripotent stem cells. Nat. Biotechnol. 35, 154–163 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Lee, H. et al. Slit and Semaphorin signaling governed by Islet transcription factors positions motor neuron somata within the neural tube. Exp. Neurol. 269, 17–27 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Pattyn, A., Morin, X., Cremer, H., Goridis, C. & Brunet, J.-F. Expression and interactions of the two closely related homeobox genes Phox2a and Phox2b during neurogenesis. Development 124, 4065–4075 (1997).

    Article  CAS  PubMed  Google Scholar 

  73. Ng, S.-Y. et al. Genome-wide RNA-seq of human motor neurons implicates selective ER stress activation in spinal muscular atrophy. Cell Stem Cell 17, 569–584 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  75. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  76. Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  77. McCarthy, D. J. & Smyth, G. K. Testing significance relative to a fold-change threshold is a TREAT. Bioinformatics 25, 765–771 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Subramanian, A. et al. Gene-set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Eoh, H. & Rhee, K. Y. Multifunctional essentiality of succinate metabolism in adaptation to hypoxia in Mycobacterium tuberculosis. Proc. Natl Acad. Sci. USA 110, 6554–6559 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Lee, S.-K., Jurata, L. W., Funahashi, J., Ruiz, E. C. & Pfaff, S. L. Analysis of embryonic motoneuron gene regulation: derepression of general activators function in concert with enhancer factors. Development 131, 3295–3306 (2004).

    Article  CAS  PubMed  Google Scholar 

  82. Bai, G. et al. Presenilin-dependent receptor processing is required for axon guidance. Cell 144, 106–118 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Ludolph, A. C. et al. Guidelines for preclinical animal research in ALS/MND: a consensus meeting. Amyotroph. Lateral Scler. 11, 38–45 (2010).

    Article  PubMed  Google Scholar 

  84. Kim, K.-T. et al. ISL1-based LIM complexes control Slit2 transcription in developing cranial motor neurons. Sci. Rep. 6, 36491 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Ritson, G. P. et al. TDP-43 mediates degeneration in a novel Drosophila model of disease caused by mutations in VCP/p97. J. Neurosci. 30, 7729–7739 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Xia, J. & Wishart, D. S. MetPA: a web-based metabolomics tool for pathway analysis and visualization. Bioinformatics 26, 2342–2344 (2010).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The authors thank H. Zhang at the Flow Cytometry Core Facility (JHSPH) and H. Hao and C. Talbot at the Deep Sequencing Core Facility (JHU) for RNA-seq data analysis. We also thank the Developmental Studies Hybridoma Bank for antibodies and K. Eggan (Harvard university) for sharing the isogenic control line. This work was supported by grants from the Donald E. & Delia B. Baxter Foundation (to H.E.), Department of Molecular Microbiology and Immunology KSOM, USC (to H.E.), Robert Packard Center for ALS Research (to G.L.), the (R01NS093213 to G.L.), the Global Research Development Center Program from the National Research Foundation (NRF) of Korea (2017K1A4A3014959 to G.L.), NIH/NINDS (1R01NS117604-01 to N.J.M., Basic Research Program of the NRF (2021R1A2C1007793 to Y.B.H) and the Medical Research Center program (2016R1A5A2007009 to Y.B.H.) from the NRF.

Author information

Author notes

  1. Irina Kovlyagina

    Present address: Institute of Physiological Chemistry, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany

  2. Kyung-tai Kim

    Present address: Jeonbuk Branch Institute, Korea Institute of Toxicology, Jeongeup, Republic of Korea

  3. Yohan Oh

    Present address: Department of Biomedical Science, Graduate School of Biomedical Science and Engineering, Hanyang University, Seoul, Republic of Korea

Authors and Affiliations

  1. Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Hojae Lee, Seong-Hyun Park, Shinwon Ha, Irina Kovlyagina, Seongjun Kim, Yohan Oh, Hyesoo Kim, Sung-Ung Kang & Gabsang Lee

  2. Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Hojae Lee, Sandeep Kumar Dubey, Kai Ruan, Seong-Hyun Park, Shinwon Ha, Hyesoo Kim, Sung-Ung Kang, Thomas E. Lloyd, Nicholas J. Maragakis & Gabsang Lee

  3. The Robert Packard Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Hojae Lee & Gabsang Lee

  4. Department of Microbiology and Immunology, Keck School of Medicine, University of Southern California, Los Angeles, Los Angeles, CA, USA

    Jae Jin Lee & Hyungjin Eoh

  5. Department of Biochemistry, College of Medicine, Dong-A University, Busan, Korea

    Na Young Park & Young Bin Hong

  6. Department of Translational Biomedical Sciences, Graduate School of Dong-A University, Busan, Korea

    Na Young Park & Young Bin Hong

  7. The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Sandeep Kumar Dubey, Thomas E. Lloyd & Gabsang Lee

  8. Department of Biology, San Diego State University, San Diego, CA, USA

    Taeyong Kim

  9. Department of Biochemistry and Molecular Biology, Ajou University School of Medicine, Suwon, Korea

    Su Bin Lim

  10. School of Life Sciences, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea

    Kyung-tai Kim & Mi-Ryoung Song

  11. Cellular and Molecular Medicine Program, School of Medicine, Johns Hopkins University, Baltimore, MD, USA

    Thomas E. Lloyd

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Contributions

H.L. performed the experiments, analyzed the data and wrote the manuscript with input from all authors. J.J.L., N.Y.P., S.D., T.K., K.R., S.B.L., S.-H.P., S.H., I.K., K.-t.K., S.K. and Y.O. performed the experiments and/or analyzed the data. Y.O., H.K., S.-U.K., M.-R.S., T.E.L., N.J.M., Y.B.H. and H.E. contributed to the design, supervised the study, and aided in analysis and interpretation of data. G.L. designed the study, supervised the study/experiments, data analysis and interpretation and wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Young Bin Hong, Hyungjin Eoh or Gabsang Lee.

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The authors declare no competing interests.

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Peer review information Nature Neuroscience thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Characterization of transcripts in hiPSC-derived PHOX2B::GFP+ oMN-like cells.

(A-C′) Characterization of post-sorted PHOX2B::GFP+ cells using ISL1, NKX6.1 and PHOX2B (red), and TUJ1 (green) antibodies. (D-I) qRT-PCR analysis shows enrichments of oMN specification transcripts (ISL1, PHOX2A, NKX6.1 and CHAT) and midbrain regional transcript (EN1), but not mDA specification transcript (NURR1) after sorting (D14) (n=10 for each group from 3 independent batches; technical replicates; n.s.: not significant, ****P<0.0001 (p-value:<0.0001), two-tailed; unpaired student’s t-test). (J) Heatmap presents characteristic marker expression of ES (OCT4::GFP), oMN-like (PHOX2B::GFP), sympathetic autonomic neuron (PHOX2B::GFP), mDA (unsorting) and sMN (unsorting) (n=3 for each group, n means independent batches of differentiation; technical replicates). (K-L) Different schematic protocols to optimize oMN-like cell differentiation (K) and FACS results of PHOX2B::GFP+ (L) (n=3 for each groups (3 independent differentiation) from 1 batch; technical replicates; n.s.: not significant, **P<0.01 (0.0029), two-tailed; unpaired student’s t-test). Scale bar: 100 μm, Error bars, mean ± SEM., PHX2B: PHOX2B.

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Extended Data Fig. 2 Generation of PHOX2B::GFP reporter line and oMN-like cell specification in SOD1A4V and C9ORF72 ALS lines.

(A-C′) Representative images and karyotype results of control hiPSC, SOD1A4V and C9ORF72 PHOX2B::GFP reporter lines. (D-E) Time course GFP expression of oMN-like differentiation in SOD1A4V and C9ORF72 by FACS analysis (SOD1A4V: n=3, C9ORF72: n=4 for each group; technical replicates; n means independent batches of differentiation). (F-K) Enrichment of transcripts in post-sorted ES-derived and both ALS-derived PHOX2B::GFP+ is comparable for oMN (ISL1, PHOX2A/B and NKX6.1), midbrain specification (EN1) and mDA specification (NURR1) by qRT-PCR analysis (D14) (at least n=3 for each group, n means independent batches of differentiation (independent sorted samples); technical replicates; n.s.: not significant, #P < 0.05 (0.0104), **P<0.01 (0.0097), two-tailed; unpaired student’s t-test). Scale bar: 100 μm, Error bars, mean ± SEM.

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Extended Data Fig. 3 Generation of HB9::GFP reporter in SOD1A4V and C9ORF72 ALS lines.

(A-B′) Wholemount GFP expression of Hb9 and Isl1 transgenic mouse at E11.5 embryo. Magnified view of images as indicated in (A′) and (B′). (C-D) Representative images and karyotypes of SOD1A4V and C9ORF72 HB9B::GFP reporter lines. (E) Illustration of HB9 gene targeting using CRISPR-Cas9 homologous recombination. (F) Schematic protocol of sMN cell differentiation. (G) Time course GFP expression of sMN differentiation in SOD1A4V / C9ORF72 and HB9 antibody-stained cells of control hiPSC line by FACS analysis (at least n=3 for each group, n means independent batches of differentiation; technical replicates; n.s.: not significant, *P<0.05, **P<0.01, ***P<0.001, two-tailed; unpaired student’s t-test; p-values are indicated in each graph). (H) Heatmap presents characteristic marker expression of ES (OCT4 and NANOG) and sMN (HB9, ISL1, LHX3, CHAT and FOXP1) in post-sorted OCT4::GFP+, SOD1A4V HB9::GFP+ and C9ORF72 HB9::GFP+ cells by qRT-PCR (n=3 for each group, n means independent batches of differentiation; technical replicates). (I) A heatmap presents the gene expression levels of different spinal axis region markers (HOXA2, A5, A7 and A10) and cell type specific makers (HB9 for sMN and PHOX2A and TBX20 for oMN-like) in post-sorted PHOX2B::GFP+ and HB9::GFP+ cells of SOD1A4V and C9ORF72 ALS hiPSCs by qRT-PCR (n=3 for each group, n means independent batches of differentiation; technical replicates). Scale bars: 2000 μm (A-B′) or 100 μm (C-D), Error bars: mean ± SEM.

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Extended Data Fig. 4 Validation of oMN and sMN population by comparing transcriptome profile with reference dataset.

(A-B) Heatmap shows differential expression levels of oMN- or sMN-specific genes in sorted HB9::GFP+ and PHOX2B::GFP+ of SOD1A4V and C9ORF72 ALS hiPSC lines (A), or reanalyzed mouse dataset from a previous literature (B). (C) Combined heatmap shows relative expression levels of oMN- or sMN-specific genes in sorted HB9::GFP+ and PHOX2B::GFP+ of SOD1A4V and C9ORF72 ALS hiPSC lines, and reanalyzed mouse dataset from a previous literature.

Extended Data Fig. 5 Transcriptome profiling reveals differences between PHOX2B::GFP+ and HB9::GFP+ cells in both SOD1A4V and C9ORF72 ALS lines.

(A) Illustration of transcriptome profiling of HB9::GFP+ versus PHOX2B::GFP+. (B-C) Volcano plots indicate a substantial transcriptomic difference between HB9::GFP and PHOX2B::GFP in both SOD1A4V and C9ORF72 ALS lines (n=3 for each group, n means independent batches of differentiation; technical replicates) (see Methods section for details). (D) Principal component analysis (PCA) plot represents distinct clustering between HB9::GFP and PHOX2B::GFP cell types-derived from both SOD1A4V and C9ORF72 ALS lines (n=3 for each group, n means independent batches of differentiation; technical replicates). (E-F) Gene set enrichment analysis (GSEA) plots show commonly over-represented GO terms of HB9::GFP+ cells compared to PHOX2B::GFP+ cells in both SOD1A4V and C9ORF72 ALS lines (n=3 for each group, n means independent batches of differentiation; technical replicates). (G) Combined dataset of the two ALS lines consistently shows the same over-represented GO terms as observed in single ALS line datasets (n=3 for each group, n means independent batches of differentiation; technical replicates). p-value of the enrichment analysis was calculated using the Hypergeometric test (phyper function) which is equivalent to one-tailed Fisher’s Exact test.

Extended Data Fig. 6 Verification of abnormal expression of lipid related transcripts in SOD1A4V and C9ORF72 ALS lines by qRT-PCR analysis.

(A) Heatmap shows enriched transcripts in sorted HB9::GFP+ of SOD1A4V and C9ORF72, but not sorted control and PHOX2B::GFP+. (B-J) Individual plot indicates altered expression transcripts in post-sorted HB9::GFP+ of SOD1A4V and C9ORF72 (*P<0.05, **P<0.01, ***P<0.001, ****P<0.0001 (p-value:<0.0001), n.s.: not significant, two-tailed; unpaired student’s t-test, at least n=3 for each group, n means independent batches of differentiation; technical replicates; p-values are indicated in each graph). Error bars: mean ± SEM.

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Extended Data Fig. 7 Common alteration of C21H26O3 in multiple ALS lines and direct comparision of altered metabolic metabolites by metabolomics analysis in isogenic control of SOD1A4V and SOD1A4V ALS hiPSC lines.

(A-B) Ion count values present commonly down-regulated C21H26O3 metabolic candidate in multiple ALS lines (A) and direct comparison of isogenic control of SOD1A4V and SOD1A4V lines (B) (*P<0.05, ***P<0.001, ****P<0.0001, n.s.: not significant, two-tailed; unpaired student’s t-test; p-values are indicated in each graph; at least n=3 for each lines; technical replicates; n=1 hESC control, n=3 hiPSC control, n=1 isogenic control hiPSC of SOD1A4V, n=6 C9ORF72, n=3 SOD1, n=3 TDP43, n=5 sporadic ALS hiPSC; biological replicates). (C-D) Heatmap shows the lists of selective metabolite candidate in isogenic control of SOD1A4V and SOD1A4V ALS hiPSC lines (n=6 for each group, n means 6 independent differentiation from 2 batch; technical replicates). Error bars: mean ± SEM.

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Extended Data Fig. 8 Caffeic acid exclusively rescues HB9::GFP+ cells in SOD1A4V and C9ORF72.

(A-B) Experimental modification of media (conditioned media) shows enhanced HB9::GFP+ degeneration of SOD1A4V and C9ORF72 at D19 to D25 (complete media: n=3, conditioned media: n=4 for each ALS lines; technical replicates; n means independent batches of differentiation) (*P<0.05, **P<0.01, ****P<0.0001, two-tailed; unpaired student’s t-test; p-values are indicated in each graph). (C-D) Results of compound tests in SOD1A4V and C9ORF72 HB9::GFP+ cells indicate comparable effects to control vehicle for each compound (R-Deprenyl hydrochloride, Ajamaline, Creatine and ISP-1) by FACS analysis (Dot indicates different wells from at least 3 batches, at least n=6; technical replicates; *P<0.05, n.s.: not significant, unpaired student’s t-test, two-tailed). (E-F) FACS results of HB9::GFP+ between control (non-treated) and mitomycin C treated (1 µg/ml, 1hr; D17 to D19 (2 days)) in C9ORF72 and SOD1A4V ALS hiPSC lines (Dot indicates different wells from at least 2 batches, at least n=6; technical replicates; n.s.: not significant; unpaired student’s t-test, two-tailed). (G-H) CA elevates the levels of HB9::GFP expression in the sMN culture of C9ORF72 and SOD1A4V ALS hiPSC lines after mitomycin C treatment (n=3 for each group; n means 3 independent differentiation from 1 batch; technical replicates; n.s.: not significant; *,#, P < 0.05; **,##, P < 0.01 (*:compare with none treated vehicle, #: compare with mitomycin C treated vehicle), two-tailed; unpaired student’s t-test; P-values are indicated in each graph). Error bars: mean ± SEM.

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Extended Data Fig. 9 Caffeic acid alleviates disease pathogenesis in SOD1G93A mice.

(A) Experimental scheme illustrating the CA administration and assessment of the efficacy. CA or vehicle (PBS with 10% ethanol) was administered to SOD1G93A mice from D60 to D120 of age (5 days per week). (B) Changes of body weight monitored weekly (n=24 for each SOD1G93A mice group; and n=20 for WT mice group). (C) Grip strength analysis. n=24 for each group. (D) The ratio of gastrocnemius muscle to body weight (mg/g) at the indicated time points. n=14 at 16 wks and n=10 at 20 wks for each group. (E) Neuromuscular junction visualized by α-bungarotoxin (α-BTX, green) and neurofilament H/synapsin (NF/Syn, red) in gastrocnemius muscle at 16 wks (Scale bar: 20 µm). (F) The ratio of innervated neuromuscular junction (NMJ). n=8 for each group. (G) Cresyl violet staining (Nissl staining) to visualize the pyramidal neuron (layer V) in motor cortex at 20 wks (Scale bar: 50 µm). (H) The number of pyramidal neurons and dysmorphic neurons in the layer V of motor cortex (n=10 for each group). (WT, wild-type mice; Ctrl, vehicle administered SOD1G93A mice; CA, caffeic acid (30 mg/kg) administered SOD1G93A mice; *,#, P < 0.05; **, P < 0.01;***, P < 0.001 and ****, P < 0.0001., n.s.: not significant, two-tailed; unpaired student’s t-test; p-values are indicated in each graph). Error bars: mean ± SEM.

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Extended Data Fig. 10 Caffeic acid rescues aberrant levels of arachidonic acid in the sMN culture of multiple ALS hiPC lines.

(A-F) Ion count value shows arachidonic acid level is down-regulated in post-treatment with 25 μg/ml CA at D11 to D17 of sMN differentiation of control and CA treated of ALS hiPSC lines (at least n=3 for each group; the cell line names are listed; technical replicates; biological replicates; *P<0.05, **P<0.01, ***P<0.001, two-tailed; unpaired student’s t-test). (G) Schematic model of this study. Error bars: mean ± SEM.

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Supplementary information

Supplementary Information

Supplementary Figs.1–5 and Supplementary Tables 1 and 2

Reporting Summary

Supplementary Data 1

Raw values for Supplementary Fig. 1.

Supplementary Data 2

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Supplementary Data 4

Raw values for Supplementary Fig. 5.

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Lee, H., Lee, J.J., Park, N.Y. et al. Multi-omic analysis of selectively vulnerable motor neuron subtypes implicates altered lipid metabolism in ALS. Nat Neurosci 24, 1673–1685 (2021). https://doi.org/10.1038/s41593-021-00944-z

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