Abstract
Mitochondrial DNA variants have previously associated with disease, but the underlying mechanisms have been largely elusive. Here, we report that mitochondrial SNP rs2853499 associated with Alzheimer’s disease (AD), neuroimaging, and transcriptomics. We mapped rs2853499 to a novel mitochondrial small open reading frame called SHMOOSE with microprotein encoding potential. Indeed, we detected two unique SHMOOSE-derived peptide fragments in mitochondria by using mass spectrometry—the first unique mass spectrometry-based detection of a mitochondrial-encoded microprotein to date. Furthermore, cerebrospinal fluid (CSF) SHMOOSE levels in humans correlated with age, CSF tau, and brain white matter volume. We followed up on these genetic and biochemical findings by carrying out a series of functional experiments. SHMOOSE acted on the brain following intracerebroventricular administration, differentiated mitochondrial gene expression in multiple models, localized to mitochondria, bound the inner mitochondrial membrane protein mitofilin, and boosted mitochondrial oxygen consumption. Altogether, SHMOOSE has vast implications for the fields of neurobiology, Alzheimer’s disease, and microproteins.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Change history
20 January 2023
A Correction to this paper has been published: https://doi.org/10.1038/s41380-023-01956-w
References
Saghatelian A, Couso JP. Discovery and characterization of smORF-encoded bioactive polypeptides. Nat Chem Biol. 2015;11:909–16.
Martinez TF, Chu Q, Donaldson C, Tan D, Shokhirev MN, Saghatelian A. Accurate annotation of human protein-coding small open reading frames. Nat Chem Biol. 2020;16:458–68.
Mudge JM, Ruiz-Orera J, Prensner JR, Brunet MA, Calvet F, Jungreis I, et al. Standardized annotation of translated open reading frames. Nat Biotechnol. 2022;40:994–9.
Miller B, Kim SJ, Kumagai H, Mehta HH, Xiang W, Liu J, et al. Peptides derived from small mitochondrial open reading frames: Genomic, biological, and therapeutic implications. Exp Cell Res. 2020;2:112056.
Kim SJ, Guerrero N, Wassef G, Xiao J, Mehta HH, Cohen P, et al. The mitochondrial-derived peptide humanin activates the ERK1/2, AKT, and STAT3 signaling pathways and has age-dependent signaling differences in the hippocampus. Oncotarget 2016;7:46899–912.
Guo F, Jing W, Ma CG, Wu MN, Zhang JF, Li XY, et al. [Gly(14)]-humanin rescues long-term potentiation from amyloid beta protein-induced impairment in the rat hippocampal CA1 region in vivo. Synapse 2010;64:83–91.
Tajima H, Kawasumi M, Chiba T, Yamada M, Yamashita K, Nawa M, et al. A humanin derivative, S14G-HN, prevents amyloid-beta-induced memory impairment in mice. J Neurosci Res. 2005;79:714–23.
Ikonen M, Liu B, Hashimoto Y, Ma L, Lee KW, Niikura T, et al. Interaction between the Alzheimer’s survival peptide humanin and insulin-like growth factor-binding protein 3 regulates cell survival and apoptosis. Proc Natl Acad Sci USA. 2003;100:13042–7.
Tsukamoto E, Hashimoto Y, Kanekura K, Niikura T, Aiso S, Nishimoto I. Characterization of the toxic mechanism triggered by Alzheimer’s amyloid-beta peptides via p75 neurotrophin receptor in neuronal hybrid cells. J Neurosci Res. 2003;73:627–36.
Hashimoto Y, Niikura T, Tajima H, Yasukawa T, Sudo H, Ito Y, et al. A rescue factor abolishing neuronal cell death by a wide spectrum of familial Alzheimer’s disease genes and Abeta. Proc Natl Acad Sci USA. 2001;98:6336–41.
Yen K, Wan J, Mehta HH, Miller B, Christensen A, Levine ME, et al. Humanin prevents age-related cognitive decline in mice and is associated with improved cognitive age in humans. Sci Rep. 2018;8:1–10.
Zempo H, Kim SJ, Fuku N, Nishida Y, Higaki Y, Wan J, et al. A pro-diabetogenic mtDNA polymorphism in the mitochondrial-derived peptide, MOTS-c. Aging 2021;13:1692–717.
Miller B, Torres M, Jiang X, McKean-Cowdin R, Nousome D, Kim S-J, et al. A Mitochondrial Genome-Wide Association Study of Cataract in a Latino Population. Transl Vis Sci Technol. 2020;9:25–25.
Miller B, Arpawong TE, Jiao H, Kim S-J, Yen K, Mehta HH, et al. Comparing the utility of mitochondrial and nuclear DNA to adjust for genetic ancestry in association studies. Cells 2019;8:306.
Yonova-Doing E, Calabrese C, Gomez-Duran A, Schon K, Wei W, Karthikeyan S, et al. An atlas of mitochondrial DNA genotype-phenotype associations in the UK Biobank. Nat Genet. 2021;53:982–93.
Ridge PG, Wadsworth ME, Miller JB, Saykin AJ, Green RC, Alzheimer’s Disease Neuroimaging I. et al. Assembly of 809 whole mitochondrial genomes with clinical, imaging, and fluid biomarker phenotyping. Alzheimers Dement. 2018;14:514–9.
Miller B, Haghani A, Ailshire J, Arpawong TE. Human Population Genetics in Aging Studies for Molecular Biologists. Aging 2020;2144:67–76.
Zhang Z, Castello A. Principal components analysis in clinical studies. Ann Transl Med. 2017;5:351.
Lakatos A, Derbeneva O, Younes D, Keator D, Bakken T, Lvova M, et al. Association between mitochondrial DNA variations and Alzheimer’s disease in the ADNI cohort. Neurobiol Aging. 2010;31:1355–63.
Balduzzi S, Rucker G, Schwarzer G. How to perform a meta-analysis with R: a practical tutorial. Evid Based Ment Health. 2019;22:153–60.
Alfaro-Almagro F, Jenkinson M, Bangerter NK, Andersson JLR, Griffanti L, Douaud G, et al. Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage 2018;166:400–24.
Miller KL, Alfaro-Almagro F, Bangerter NK, Thomas DL, Yacoub E, Xu J, et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci. 2016;19:1523–36.
Reuter M, Rosas HD, Fischl B. Highly accurate inverse consistent registration: a robust approach. Neuroimage 2010;53:1181–96.
Segonne F, Dale AM, Busa E, Glessner M, Salat D, Hahn HK, et al. A hybrid approach to the skull stripping problem in MRI. Neuroimage 2004;22:1060–75.
Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 2002;33:341–55.
Fischl B, Salat DH, van der Kouwe AJ, Makris N, Segonne F, Quinn BT, et al. Sequence-independent segmentation of magnetic resonance images. Neuroimage. 2004;23:S69–84.
Sled JG, Zijdenbos AP, Evans AC. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging. 1998;17:87–97.
Segonne F, Pacheco J, Fischl B. Geometrically accurate topology-correction of cortical surfaces using nonseparating loops. IEEE Trans Med Imaging. 2007;26:518–29.
Fischl B, Dale AM. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci USA. 2000;97:11050–5.
Fischl B, Sereno MI, Tootell RB, Dale AM. High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum Brain Mapp. 1999;8:272–84.
Dinov I, Lozev K, Petrosyan P, Liu Z, Eggert P, Pierce J, et al. Neuroimaging study designs, computational analyses and data provenance using the LONI pipeline. PLoS One 2010;5:e13070.
Dinov ID, Van Horn JD, Lozev KM, Magsipoc R, Petrosyan P, Liu Z, et al. Efficient, Distributed and Interactive Neuroimaging Data Analysis Using the LONI Pipeline. Front Neuroinform. 2009;3:22.
Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 2018;562:203–9.
Miller B, Arpawong TE, Jiao H, Kim SJ, Yen K, Mehta HH, et al. Comparing the Utility of Mitochondrial and Nuclear DNA to Adjust for Genetic Ancestry in Association Studies. Cells 2019;8:306.
Worsley KJ, Evans AC, Marrett S, Neelin P. A three-dimensional statistical analysis for CBF activation studies in human brain. J Cereb Blood Flow Metab. 1992;12:900–18.
Zhao L, Batta I, Matloff W, O’Driscoll C, Hobel S, Toga AW. Neuroimaging PheWAS (Phenome-Wide Association Study): A Free Cloud-Computing Platform for Big-Data, Brain-Wide Imaging Association Studies. Neuroinformatics 2020;19:285–303.
Crimmins EM, Kim JK, Langa KM, Weir DR. Assessment of cognition using surveys and neuropsychological assessment: the Health and Retirement Study and the Aging, Demographics, and Memory Study. J Gerontol B Psychol Sci Soc Sci. 2011;66:i162–171.
Baek M, DiMaio F, Anishchenko I, Dauparas J, Ovchinnikov S, Lee GR, et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 2021;373:871–6.
Gulsevin A, Meiler J. Prediction of amphipathic helix-membrane interactions with Rosetta. PLoS Comput Biol. 2021;17:e1008818.
McFadden WM, Yanowitz JL. idpr: A package for profiling and analyzing Intrinsically Disordered Proteins in R. PLoS One. 2022;17:e0266929.
Erdos G, Pajkos M, Dosztanyi Z. IUPred3: prediction of protein disorder enhanced with unambiguous experimental annotation and visualization of evolutionary conservation. Nucleic Acids Res. 2021;49:W297–W303.
Manjón JV, Coupé P, Concha L, Buades A, Collins DL, Robles M. Diffusion weighted image denoising using overcomplete local PCA. PLoS One. 2013;8:e73021.
Tournier JD, Smith R, Raffelt D, Tabbara R, Dhollander T, Pietsch M, et al. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. Neuroimage 2019;202:116137.
Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. FSL. Neuroimage. 2012;62:782–90.
Avants BB, Epstein CL, Grossman M, Gee JC. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal. 2008;12:26–41.
Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage. 2011;54:2033–44.
Nir TM, Jahanshad N, Villalon-Reina JE, Isaev D, Zavaliangos-Petropulu A, Zhan L, et al. Fractional anisotropy derived from the diffusion tensor distribution function boosts power to detect Alzheimer’s disease deficits. Magn Reson Med. 2017;78:2322–33.
Lo Buono V, Palmeri R, Corallo F, Allone C, Pria D, Bramanti P, et al. Diffusion tensor imaging of white matter degeneration in early stage of Alzheimer’s disease: a review. Int J Neurosci. 2020;130:243–50.
Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen-Berg H, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 2004;23:S208–19.
Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE, et al. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 2006;31:1487–505.
Smith SM, Nichols TE. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage. 2009;44:83–98.
Miller SW, Trimmer PA, Parker WD Jr., Davis RE. Creation and characterization of mitochondrial DNA-depleted cell lines with “neuronal-like” properties. J Neurochem. 1996;67:1897–907.
He L, Diedrich J, Chu YY, Yates JR 3rd. Extracting Accurate Precursor Information for Tandem Mass Spectra by RawConverter. Anal Chem. 2015;87:11361–7.
Xu T, Park SK, Venable JD, Wohlschlegel JA, Diedrich JK, Cociorva D, et al. ProLuCID: An improved SEQUEST-like algorithm with enhanced sensitivity and specificity. J Proteom. 2015;129:16–24.
Tabb DL, McDonald WH, Yates JR 3rd. DTASelect and Contrast: tools for assembling and comparing protein identifications from shotgun proteomics. J Proteome Res. 2002;1:21–26.
Peng J, Elias JE, Thoreen CC, Licklider LJ, Gygi SP. Evaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) for large-scale protein analysis: the yeast proteome. J Proteome Res. 2003;2:43–50.
Gong Z, Su K, Cui L, Tas E, Zhang T, Dong HH, et al. Central effects of humanin on hepatic triglyceride secretion. Am J Physiol Endocrinol Metab. 2015;309:E283–292.
Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 2012;16:284–7.
Cox J, Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol. 2008;26:1367–72.
Choi M, Chang CY, Clough T, Broudy D, Killeen T, MacLean B, et al. MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments. Bioinformatics 2014;30:2524–6.
Webb-Robertson BJ, Wiberg HK, Matzke MM, Brown JN, Wang J, McDermott JE, et al. Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics. J Proteome Res. 2015;14:1993–2001.
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 2013;29:15–21.
Chandramohan R, Wu PY, Phan JH, Wang MD. Benchmarking RNA-Seq quantification tools. Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:647–50.
Ryan TM, Caine J, Mertens HD, Kirby N, Nigro J, Breheney K, et al. Ammonium hydroxide treatment of Abeta produces an aggregate free solution suitable for biophysical and cell culture characterization. PeerJ. 2013;1:e73.
Allen M, Carrasquillo MM, Funk C, Heavner BD, Zou F, Younkin CS, et al. Human whole genome genotype and transcriptome data for Alzheimer’s and other neurodegenerative diseases. Sci Data. 2016;3:160089.
Kalari KR, Nair AA, Bhavsar JD, O’Brien DR, Davila JI, Bockol MA, et al. MAP-RSeq: Mayo Analysis Pipeline for RNA sequencing. BMC Bioinforma. 2014;15:224.
Allen M, Wang X, Burgess JD, Watzlawik J, Serie DJ, Younkin CS, et al. Conserved brain myelination networks are altered in Alzheimer’s and other neurodegenerative diseases. Alzheimers Dement. 2018;14:352–66.
Stein CS, Jadiya P, Zhang X, McLendon JM, Abouassaly GM, Witmer NH, et al. Mitoregulin: A lncRNA-Encoded Microprotein that Supports Mitochondrial Supercomplexes and Respiratory Efficiency. Cell Rep. 2018;23:3710–20.e3718.
Ng B, Casazza W, Patrick E, Tasaki S, Novakovsky G, Felsky D, et al. Using Transcriptomic Hidden Variables to Infer Context-Specific Genotype Effects in the Brain. Am J Hum Genet. 2019;105:562–72.
Davatzikos C, Xu F, An Y, Fan Y, Resnick SM. Longitudinal progression of Alzheimer’s-like patterns of atrophy in normal older adults: the SPARE-AD index. Brain. 2009;132:2026–35.
Jack CR Jr., Knopman DS, Jagust WJ, Shaw LM, Aisen PS, Weiner MW, et al. Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol. 2010;9:119–28.
Grangeon L, Paquet C, Bombois S, Quillard-Muraine M, Martinaud O, Bourre B, et al. Differential Diagnosis of Dementia with High Levels of Cerebrospinal Fluid Tau Protein. J Alzheimers Dis. 2016;51:905–13.
Bartzokis G. Alzheimer’s disease as homeostatic responses to age-related myelin breakdown. Neurobiol Aging. 2011;32:1341–7.
Hudson G, Nalls M, Evans JR, Breen DP, Winder-Rhodes S, Morrison KE, et al. Two-stage association study and meta-analysis of mitochondrial DNA variants in Parkinson disease. Neurology 2013;80:2042–8.
McRae AF, Byrne EM, Zhao ZZ, Montgomery GW, Visscher PM. Power and SNP tagging in whole mitochondrial genome association studies. Genome Res. 2008;18:911–7.
Malhi RS, Eshleman JA, Greenberg JA, Weiss DA, Schultz Shook BA, Kaestle FA, et al. The structure of diversity within New World mitochondrial DNA haplogroups: implications for the prehistory of North America. Am J Hum Genet. 2002;70:905–19.
Ge Q, Jia D, Cen D, Qi Y, Shi C, Li J, et al. Micropeptide ASAP encoded by LINC00467 promotes colorectal cancer progression by directly modulating ATP synthase activity. J Clin Investig. 2021;131:e152911.
Zhang S, Reljic B, Liang C, Kerouanton B, Francisco JC, Peh JH, et al. Mitochondrial peptide BRAWNIN is essential for vertebrate respiratory complex III assembly. Nat Commun. 2020;11:1312.
Lee C, Zeng J, Drew BG, Sallam T, Martin-Montalvo A, Wan J, et al. The mitochondrial-derived peptide MOTS-c promotes metabolic homeostasis and reduces obesity and insulin resistance. Cell Metab. 2015;21:443–54.
Miller B, Kim SJ, Kumagai H, Yen K, Cohen P. Mitochondria-derived peptides in aging and healthspan. J Clin Investig. 2022;132:e158449.
Mercer TR, Neph S, Dinger ME, Crawford J, Smith MA, Shearwood AM, et al. The human mitochondrial transcriptome. Cell 2011;146:645–58.
Feng Y, Madungwe NB, Bopassa JC. Mitochondrial inner membrane protein, Mic60/mitofilin in mammalian organ protection. J Cell Physiol. 2019;234:3383–93.
Gieffers C, Korioth F, Heimann P, Ungermann C, Frey J. Mitofilin is a transmembrane protein of the inner mitochondrial membrane expressed as two isoforms. Exp Cell Res. 1997;232:395–9.
Wright BW, Yi Z, Weissman JS, Chen J. The dark proteome: translation from noncanonical open reading frames. Trends Cell Biol. 2022;32:243–58.
Acknowledgements
The study was supported by NIH grants P30AG10161, P30AG072975, R01AG15819, R01AG17917, U01AG61356, R01AG069698, RF1AG061834, R01AG068405, P30AG068345, P01AG055369, DK118402, F31 AG059356, and T32 AG00037; as well as The Quebec Research Funds Postdoctoral Fellowship, Royal Golden Jubilee Ph.D. Program, and Miriam and Merle Hinrich Mitochondrial DNA Research Fund. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
Author information
Authors and Affiliations
Consortia
Contributions
BM conceived and designed analyses, collected data, performed analyses, and wrote the paper. SK conceived experimental design. HM collected data. KC collected data. HK collected data. NT collected data. NL conceived experimental design. HJ contributed analysis tools. JV contributed experimental design and collected data. JD collected data. AS conceived experimental design. TA contributed analysis tools. EC contributed analysis tools. NE-T contributed analysis tools. MT carried out analyses. EH carried out analyses. MB conceived analysis design. LD-S carried out animal studies. SK conceived experimental design. FG collected data. DB contributed analysis tools. LZ carried out analyses. AT contributed analysis tools. JW designed experimental tools and carried out experiments. KY conceived analyses. PC conceived and designed analyses and contributed to manuscript preparation.
Corresponding author
Ethics declarations
Competing interests
Intellectual property related to SHMOOSE has been filed by the University of Southern California.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The original online version of this article was revised: In the by-line of this article, the following phrase was missing: Include the phrase “for the Alzheimer’s Disease Neuroimaging Initiative*.
In the by-line of this article, the following author was missing: Regina Gonzalez Braniff.
In the methods section of this article, the following text was missing: “Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD)”..
In the Acknowledgements section of this article the following text was missing: “Data collection and sharing for this project was funded by the Alzheimer’'s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California”.
In the sentence beginning “rs2853499 (henceforth referred to as SHMOOSE.D47N)…” in this article, the typo “from glutamine to aspartic acid” should read “from aspartic acid to asparagine”. The original article has been corrected.
Supplementary information
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.
About this article
Cite this article
Miller, B., Kim, SJ., Mehta, H.H. et al. Mitochondrial DNA variation in Alzheimer’s disease reveals a unique microprotein called SHMOOSE. Mol Psychiatry 28, 1813–1826 (2023). https://doi.org/10.1038/s41380-022-01769-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41380-022-01769-3
This article is cited by
-
A naturally occurring variant of SHLP2 is a protective factor in Parkinson’s disease
Molecular Psychiatry (2024)
-
Intermittent Theta Burst Stimulation Attenuates Cognitive Deficits and Alzheimer’s Disease-Type Pathologies via ISCA1-Mediated Mitochondrial Modulation in APP/PS1 Mice
Neuroscience Bulletin (2024)
-
The AMPK-related kinase NUAK1 controls cortical axons branching by locally modulating mitochondrial metabolic functions
Nature Communications (2024)
-
A small protein coded within the mitochondrial canonical gene nd4 regulates mitochondrial bioenergetics
BMC Biology (2023)
-
Mitochondrial dysfunction in microglia: a novel perspective for pathogenesis of Alzheimer’s disease
Journal of Neuroinflammation (2022)