Biallelic variants in HPDL, encoding 4-hydroxyphenylpyruvate dioxygenase-like protein, lead to an infantile neurodegenerative condition



Dioxygenases are oxidoreductase enzymes with roles in metabolic pathways necessary for aerobic life. 4-hydroxyphenylpyruvate dioxygenase-like protein (HPDL), encoded by HPDL, is an orphan paralogue of 4-hydroxyphenylpyruvate dioxygenase (HPD), an iron-dependent dioxygenase involved in tyrosine catabolism. The function and association of HPDL with human diseases remain unknown.


We applied exome sequencing in a cohort of over 10,000 individuals with neurodevelopmental diseases. Effects of HPDL loss were investigated in vitro and in vivo, and through mass spectrometry analysis. Evolutionary analysis was performed to investigate the potential functional separation of HPDL from HPD.


We identified biallelic variants in HPDL in eight families displaying recessive inheritance. Knockout mice closely phenocopied humans and showed evidence of apoptosis in multiple cellular lineages within the cerebral cortex. HPDL is a single-exonic gene that likely arose from a retrotransposition event at the base of the tetrapod lineage, and unlike HPD, HPDL is mitochondria-localized. Metabolic profiling of HPDL mutant cells and mice showed no evidence of altered tyrosine metabolites, but rather notable accumulations in other metabolic pathways.


The mitochondrial localization, along with its disrupted metabolic profile, suggests HPDL loss in humans links to a unique neurometabolic mitochondrial infantile neurodegenerative condition.

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Fig. 1: Variants in HPDL in eight independent consanguineous families lead to microcephaly and brain atrophy.
Fig. 2: HPD and HPDL display different subcellular localization patterns.
Fig. 3: Hpdl knockout (KO) mice display early lethality, smaller brain sizes, and cellular apoptosis.
Fig. 4: HPD and HPDL evolutionary analysis reveals two separate, yet significant, evolutionary events leading to the emergence of vertebrate HPDL with novel function.

Data availability

Data are available in a public, open access repository. The exome sequencing from individuals from the UCSD study site have been deposited in the Database of Genotypes and Phenotypes under accession numbers phs001272.v1.p1 and phs000744.v4.p2.


  1. 1.

    Brassier A, Ottolenghi C, Boddaert N, et al. Prenatal symptoms and diagnosis of inherited metabolic diseases. Arch Pediatr. 2012;19:959–969.

    CAS  Article  Google Scholar 

  2. 2.

    Tarailo-Graovac M, Shyr C, Ross CJ, et al. Exome sequencing and the management of neurometabolic disorders. N Engl J Med. 2016;374:2246–2255.

    CAS  Article  Google Scholar 

  3. 3.

    BoAli AY, Alfadhel M, Tabarki B. Neurometabolic disorders and congenital malformations of the central nervous system. Neurosciences (Riyadh). 2018;23:97–103.

    Article  Google Scholar 

  4. 4.

    Willemsen MA, Harting I, Wevers RA. Neurometabolic disorders: five new things. Neurol Clin Pract. 2016;6:348–357.

    Article  Google Scholar 

  5. 5.

    Abu-Omar MM, Loaiza A, Hontzeas N. Reaction mechanisms of mononuclear non-heme iron oxygenases. Chem Rev. 2005;105:2227–2252.

    CAS  Article  Google Scholar 

  6. 6.

    Burlina A, Zacchello F, Dionisi-Vici C, et al. New clinical phenotype of branched-chain acyl-CoA oxidation defect. Lancet. 1991;338:1522–1523.

    CAS  Article  Google Scholar 

  7. 7.

    Boissel S, Reish O, Proulx K, et al. Loss-of-function mutation in the dioxygenase-encoding FTO gene causes severe growth retardation and multiple malformations. Am J Hum Genet. 2009;85:106–111.

    CAS  Article  Google Scholar 

  8. 8.

    Fernández-Cañón JM, Granadino B, Beltrán-Valero de Bernabé D, et al. The molecular basis of alkaptonuria. Nat Genet. 1996;14:19–24.

    Article  Google Scholar 

  9. 9.

    Ferreira P, Shin I, Sosova I, et al. Hypertryptophanemia due to tryptophan 2,3-dioxygenase deficiency. Mol Genet Metab. 2017;120:317–324.

    CAS  Article  Google Scholar 

  10. 10.

    Bruick RK, McKnight SL. A conserved family of prolyl-4-hydroxylases that modify HIF. Science. 2001;294:1337–1340.

    CAS  Article  Google Scholar 

  11. 11.

    Moran GR. 4-Hydroxyphenylpyruvate dioxygenase. Arch Biochem Biophys. 2005;433:117–128.

    CAS  Article  Google Scholar 

  12. 12.

    Rüetschi U, Cerone R, Pérez-Cerda C, et al. Mutations in the 4-hydroxyphenylpyruvate dioxygenase gene (HPD) in patients with tyrosinemia type III. Hum Genet. 2000;106:654–662.

    Article  Google Scholar 

  13. 13.

    Kubo S, Kiwaki K, Awata H, et al. In vivo correction with recombinant adenovirus of 4-hydroxyphenylpyruvic acid dioxygenase deficiencies in strain III mice. Hum Gene Ther. 1997;8:65–71.

    CAS  Article  Google Scholar 

  14. 14.

    Szymanska E, Sredzinska M, Ciara E, et al. Tyrosinemia type III in an asymptomatic girl. Mol Genet Metab Rep. 2015;5:48–50.

    Article  Google Scholar 

  15. 15.

    Tomoeda K, Awata H, Matsuura T, et al. Mutations in the 4-hydroxyphenylpyruvic acid dioxygenase gene are responsible for tyrosinemia type III and hawkinsinuria. Mol Genet Metab. 2000;71:506–510.

    CAS  Article  Google Scholar 

  16. 16.

    Item CB, Mihalek I, Lichtarge O, et al. Manifestation of hawkinsinuria in a patient compound heterozygous for hawkinsinuria and tyrosinemia III. Mol Genet Metab. 2007;91:379–383.

    CAS  Article  Google Scholar 

  17. 17.

    Barroso F, Correia J, Bandeira A, et al. Tyrosinemia type III: a case report of siblings and literature review. Rev Paul Pediatr. 2020;38:e2018158.

    Article  Google Scholar 

  18. 18.

    Haijes HA, Willemsen M, Van der Ham M, et al. Direct infusion based metabolomics identifies metabolic disease in patients’ dried blood spots and plasma. Metabolites. 2019;9:12.

    Article  Google Scholar 

  19. 19.

    Pei J, Grishin NV. PROMALS3D: multiple protein sequence alignment enhanced with evolutionary and three-dimensional structural information. Methods Mol Biol. 2014;1079:263–271.

    Article  Google Scholar 

  20. 20.

    Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–1313.

    CAS  Article  Google Scholar 

  21. 21.

    Letunic I, Bork P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 2016;44:W242–245.

    CAS  Article  Google Scholar 

  22. 22.

    Yang Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol Biol Evol. 2007;24:1586–1591.

    CAS  Article  Google Scholar 

  23. 23.

    Abascal F, Zardoya R, Telford MJ. TranslatorX: multiple alignment of nucleotide sequences guided by amino acid translations. Nucleic Acids Res. 2010;38:W7–13.

    CAS  Article  Google Scholar 

  24. 24.

    Charif D, Thioulouse J, Lobry JR, Perriere G. Online synonymous codon usage analyses with the ade4 and seqinR packages. Bioinformatics. 2005;21:545–547.

    CAS  Article  Google Scholar 

  25. 25.

    Karczewski KJ, Francioli LC, Tiao G, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020;581:434–443.

    CAS  Article  Google Scholar 

  26. 26.

    GTEx Consortium. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 2015;348:648–660.

    Article  Google Scholar 

  27. 27.

    Zhang Y, Sloan SA, Clarke LE, et al. Purification and characterization of progenitor and mature human astrocytes reveals transcriptional and functional differences with mouse. Neuron. 2016;89:37–53.

    CAS  Article  Google Scholar 

  28. 28.

    Saunders A, Macosko EZ, Wysoker A, et al. Molecular diversity and specializations among the cells of the adult mouse brain. Cell. 2018;174:1015–.e1016.

    CAS  Article  Google Scholar 

  29. 29.

    Vanlandewijck M, He L, Mäe MA, et al. A molecular atlas of cell types and zonation in the brain vasculature. Nature. 2018;554:475–480.

    CAS  Article  Google Scholar 

  30. 30.

    Thul PJ, Akesson L, Wiking M, et al. A subcellular map of the human proteome. Science. 2017;356:eaal3321.

    Article  Google Scholar 

  31. 31.

    Fukasawa Y, Tsuji J, Fu SC, Tomii K, Horton P, Imai K. MitoFates: improved prediction of mitochondrial targeting sequences and their cleavage sites. Mol Cell Proteomics. 2015;14:1113–1126.

    CAS  Article  Google Scholar 

  32. 32.

    Roise D, Theiler F, Horvath SJ, et al. Amphiphilicity is essential for mitochondrial presequence function. Embo j. 1988;7:649–653.

    CAS  Article  Google Scholar 

  33. 33.

    Uhlén M, Fagerberg L, Hallström BM, et al. Proteomics. Tissue-based map of the human proteome. Science. 2015;347:1260419.

    Article  Google Scholar 

  34. 34.

    Natt E, Kida K, Odievre M, Di Rocco M, Scherer G. Point mutations in the tyrosine aminotransferase gene in tyrosinemia type II. Proc Natl Acad Sci U S A. 1992;89:9297–9301.

    CAS  Article  Google Scholar 

  35. 35.

    Phaneuf D, Lambert M, Laframboise R, Mitchell G, Lettre F, Tanguay RM. Type 1 hereditary tyrosinemia. Evidence for molecular heterogeneity and identification of a causal mutation in a French Canadian patient. J Clin Invest. 1992;90:1185–1192.

    CAS  Article  Google Scholar 

  36. 36.

    Tiranti V, Viscomi C, Hildebrandt T, et al. Loss of ETHE1, a mitochondrial dioxygenase, causes fatal sulfide toxicity in ethylmalonic encephalopathy. Nat Med. 2009;15:200–205.

    CAS  Article  Google Scholar 

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The authors thank the patients and their families for participation in this study. This work was supported by NIH U01 MH108898, R01NS048453, R01 NS098004, the Simons Foundation Autism Research Initiative (SFARI), and Qatar National Research Foundation 6–1463 (J.G.G.); NIH S10OD020025 and R01ES027595 (M.J.); the Deutsche Forschungsgemeinschaft (DFG) under Germany´s Excellence Strategy (EXC-2189 project ID: 390939984) and a European Research Council (ERC) Consolidator Grant (648235, N.W.); and supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre (D.M.). S.G.G. is supported by the Ruth L. Kirschstein Institutional National Research Service Award (T32 GM008666) from the National Institute on Deafness and Other Communication Disorders and by award F31HD095602 from the NIH Eunice Kennedy Shriver National Institute of Child Health and Human Development. We thank the Rady Children’s Institute for Genomic Medicine, Broad Institute (U54HG003067 to E. Lander and UM1HG008900 to D. MacArthur), the Yale Center for Mendelian Disorders (U54HG006504 to R. Lifton and M. Gunel) for sequencing support, and the Matchmaker Exchange. We also thank UCSD Mouse Transgenic Core and UCSD Neuroscience Microscopy Core (P30 NS047101). We acknowledge M. Gerstein, S. Mane, A.B. Ekici, S. Uebe, E.S. Cauley, and UCSD IGM Genetics Center for sequencing support and analysis; the Yale Biomedical High-Performance Computing Center for data analysis and storage; the Yale Program on Neurogenetics; and the Yale Center for Human Genetics. We thank Pradipta Ghosh for sharing CACO-2 cells.

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Correspondence to Joseph G. Gleeson MD.

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Ghosh, S.G., Lee, S., Fabunan, R. et al. Biallelic variants in HPDL, encoding 4-hydroxyphenylpyruvate dioxygenase-like protein, lead to an infantile neurodegenerative condition. Genet Med (2020).

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  • HPDL
  • HPD
  • 4-hydroxyphenylpyruvate dioxygenase-like protein
  • oxidoreductase
  • neurodegenerative disease