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Miller B, Kim SJ, Mehta HH, Cao K, Kumagai H, Thumaty N, Leelaprachakul N, Braniff RG, Jiao H, Vaughan J, Diedrich J, Saghatelian A, Arpawong TE, Crimmins EM, Ertekin-Taner N, Tubi MA, Hare ET, Braskie MN, Décarie-Spain L, Kanoski SE, Grodstein F, Bennett DA, Zhao L, Toga AW, Wan J, Yen K, Cohen P. Mitochondrial DNA variation in Alzheimer's disease reveals a unique microprotein called SHMOOSE. Mol Psychiatry 2023; 28:1813-1826. [PMID: 36127429 PMCID: PMC10027624 DOI: 10.1038/s41380-022-01769-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 08/15/2022] [Accepted: 08/26/2022] [Indexed: 01/22/2023]
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.
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Affiliation(s)
- Brendan Miller
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
| | - Su-Jeong Kim
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Hemal H Mehta
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Kevin Cao
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Hiroshi Kumagai
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Neehar Thumaty
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Naphada Leelaprachakul
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Regina Gonzalez Braniff
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Henry Jiao
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Joan Vaughan
- Clayton Foundation Laboratories for Peptide Biology, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Jolene Diedrich
- Clayton Foundation Laboratories for Peptide Biology, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Alan Saghatelian
- Clayton Foundation Laboratories for Peptide Biology, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Thalida E Arpawong
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Eileen M Crimmins
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | | | - Meral A Tubi
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
- Department of Neurology, University of Southern California, Los Angeles, CA, USA
| | - Evan T Hare
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
- Department of Neurology, University of Southern California, Los Angeles, CA, USA
| | - Meredith N Braskie
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
- Department of Neurology, University of Southern California, Los Angeles, CA, USA
| | - Léa Décarie-Spain
- Human and Evolutionary Biology Section, Department of Biological Sciences, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA, USA
| | - Scott E Kanoski
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
- Human and Evolutionary Biology Section, Department of Biological Sciences, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA, USA
| | - Francine Grodstein
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Lu Zhao
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - Junxiang Wan
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Kelvin Yen
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Pinchas Cohen
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA.
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Linking Brain Structure, Activity, and Cognitive Function through Computation. eNeuro 2022; 9:ENEURO.0316-21.2022. [PMID: 35217544 PMCID: PMC8925650 DOI: 10.1523/eneuro.0316-21.2022] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 01/11/2022] [Accepted: 01/17/2022] [Indexed: 01/19/2023] Open
Abstract
Understanding the human brain is a “Grand Challenge” for 21st century research. Computational approaches enable large and complex datasets to be addressed efficiently, supported by artificial neural networks, modeling and simulation. Dynamic generative multiscale models, which enable the investigation of causation across scales and are guided by principles and theories of brain function, are instrumental for linking brain structure and function. An example of a resource enabling such an integrated approach to neuroscientific discovery is the BigBrain, which spatially anchors tissue models and data across different scales and ensures that multiscale models are supported by the data, making the bridge to both basic neuroscience and medicine. Research at the intersection of neuroscience, computing and robotics has the potential to advance neuro-inspired technologies by taking advantage of a growing body of insights into perception, plasticity and learning. To render data, tools and methods, theories, basic principles and concepts interoperable, the Human Brain Project (HBP) has launched EBRAINS, a digital neuroscience research infrastructure, which brings together a transdisciplinary community of researchers united by the quest to understand the brain, with fascinating insights and perspectives for societal benefits.
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Fully automated quality control of rigid and affine registrations of T1w and T2w MRI in big data using machine learning. Comput Biol Med 2021; 139:104997. [PMID: 34753079 DOI: 10.1016/j.compbiomed.2021.104997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 10/06/2021] [Accepted: 10/26/2021] [Indexed: 11/23/2022]
Abstract
BACKGROUND Magnetic resonance imaging (MRI)-based morphometry and relaxometry are proven methods for the structural assessment of the human brain in several neurological disorders. These procedures are generally based on T1-weighted (T1w) and/or T2-weighted (T2w) MRI scans, and rigid and affine registrations to a standard template(s) are essential steps in such studies. Therefore, a fully automatic quality control (QC) of these registrations is necessary in big data scenarios to ensure that they are suitable for subsequent processing. METHOD A supervised machine learning (ML) framework is proposed by computing similarity metrics such as normalized cross-correlation, normalized mutual information, and correlation ratio locally. We have used these as candidate features for cross-validation and testing of different ML classifiers. For 5-fold repeated stratified grid search cross-validation, 400 correctly aligned, 2000 randomly generated misaligned images were used from the human connectome project young adult (HCP-YA) dataset. To test the cross-validated models, the datasets from autism brain imaging data exchange (ABIDE I) and information eXtraction from images (IXI) were used. RESULTS The ensemble classifiers, random forest, and AdaBoost yielded best performance with F1-scores, balanced accuracies, and Matthews correlation coefficients in the range of 0.95-1.00 during cross-validation. The predictive accuracies reached 0.99 on the Test set #1 (ABIDE I), 0.99 without and 0.96 with noise on Test set #2 (IXI, stratified w.r.t scanner vendor and field strength). CONCLUSIONS The cross-validated and tested ML models could be used for QC of both T1w and T2w rigid and affine registrations in large-scale MRI studies.
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