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Record W3000780619 · doi:10.1016/j.ebiom.2020.102627

Convergent systems-based approaches identify a role for OCIAD1 in Alzheimer's disease

2020· letter· en· W3000780619 on OpenAlexaff
Haley Geertsma, Maxime W.C. Rousseaux

Bibliographic record

VenueEBioMedicine · 2020
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsOntario Brain InstituteUniversity of Ottawa
FundersParkinson's Foundation
KeywordsAlzheimer's diseaseDiseaseMedicineComputational biologyBioinformaticsBiologyPathology

Abstract

fetched live from OpenAlex

Alzheimer's disease (AD) affects approximately 30–35 million individuals worldwide, posing a tremendous medical and fiscal burden on society. With life expectancy on the rise, the World Health Organization estimates that the number of people living with AD is predicted to triple by 2050. While its pathogenesis remains elusive, targeting Amyloid β (Aβ) pathology – a hallmark of the disease – in individuals with AD has been a primary focus for industry [[1]Nalivaeva N.N. Turner A.J. Targeting amyloid clearance in Alzheimer's disease as a therapeutic strategy.Br J Pharmacol. 2019; 176: 3447-3463Crossref PubMed Scopus (85) Google Scholar]. Recent failures of clinical trials targeting Aβ have increased the need for a better understanding of AD pathogenesis, particularly its earliest events. Though it is well appreciated that Aβ accumulates in the brains of individuals with AD, the initial consequences of hyperamyloidosis, one of the earliest pathological changes of this disease, remains poorly understood [[2]Jack Jr., C.R. Holtzman D.M. Biomarker modeling of Alzheimer's disease.Neuron. 2013; 80: 1347-1358Summary Full Text Full Text PDF PubMed Scopus (607) Google Scholar]. It is plausible that a better understanding of the earliest phenomena downstream of hyperamyloidosis will help elucidate disease pathogenesis and give rise to novel therapeutic avenues. There are increasing numbers of transcriptomic and proteomic datasets from both human AD cases and mouse models that can be mined, through bioinformatics followed by functional validation in orthogonal cellular platforms, to yield novel insights into the disease pathogenesis. In this issue of EBioMedicine, Li and colleagues contribute to these datasets, devise a pipeline for the identification of novel AD-centric pathways through the integration of multiple “omics” datasets, and elucidate a novel role for OCIAD1 (Ovarian Cancer Immunoreactive Antigen Domain Containing 1) in AD [[3]Li X. Wang L. Cykowski M. He T. Liu T. Chakranarayan J. et al.OCIAD1 contributes to neurodegeneration in Alzheimer's disease by inducing mitochondria dysfunction, neuronal vulnerability and synaptic damages.E Bio Med. 2020; (In Press)https://doi.org/10.1016/j.ebiom.2019.11.030Summary Full Text Full Text PDF Scopus (7) Google Scholar]. To begin, they identify key regulatory mechanisms downstream of amyloidosis by performing proteomic assays on two established mouse models of AD, while concurrently comparing gene expression profiles of vulnerable (entorhinal cortex and hippocampus) to less vulnerable (visual cortex) brain regions of sporadic AD patients. The convergence of these datasets yields three factors associated with disease development, one of which, OCIAD1, is upregulated in disease states. In exploring the relationship between OCIAD1 and disease development, Li et al. find that decreasing GSK3β, a key kinase in AD [[4]Llorens-Martin M. Jurado J. Hernandez F. Avila J. GSK-3beta, a pivotal kinase in Alzheimer disease.Front Mol Neurosci. 2014; 7: 46PubMed Google Scholar], also decreases OCIAD1 levels in cellular models and that, conversely, elevated OCIAD1 levels exacerbate multiple cellular stress responses. Additionally, by examining the protein-protein interaction networks of OCIAD1, they delineate a relationship between BCL-2, OCIAD1, and BAX in mitochondrial-associated neurodegeneration. Together, the authors conclude that OCIAD1 is a novel neurodegeneration-associated factor in the early stages of AD. Several steps remain before moving this target forward for pre-clinical trials. First, given that OCIAD1 is ubiquitously expressed throughout the body [[5]Uhlen M. Fagerberg L. Hallstrom B.M. et al.Proteomics. Tissue-based map of the human proteome.Science. 2015; 3471260419Crossref PubMed Scopus (7202) Google Scholar], a careful look at the consequences of its loss in model organisms may shed important light on its native function. Earlier this year, a report suggested that mice lacking Asrij (the mouse OCIAD1 ortholog), are viable and fertile, though progressively accumulate hematological deficits over time [[6]Sinha S. Dwivedi T.R. Yengkhom R. et al.Asrij/OCIAD1 suppresses CSN5-mediated p53 degradation and maintains mouse hematopoietic stem cell quiescence.Blood. 2019; 133: 2385-2400Crossref PubMed Scopus (18) Google Scholar]. Thus, inhibition of OCIAD1 in humans may require careful regulation in the context of its tissue locale, particularly in the context of an aging population. To wit, a first step forward will be to test whether decreasing Asrij is sufficient to mitigate neurodegenerative phenotypes in mouse models of AD. Second, given that a treatment targeting OCIAD1 would be chronic in nature, traditional pharmacology (as opposed to antibody- or antisense oligonucleotide-based approaches) would be a promising approach. As such, it will be critical to identify the specific pathological mechanism of OCIAD1 in order to inhibit its toxicity in AD. Given its potential role in scaffolding proteins as well as regulated mitochondrial metabolism, this may prove to be difficult [7Wang C. Michener C.M. Belinson J.L. Vaziri S. Ganapathi R. Sengupta S. Role of the 18:1 lysophosphatidic acid-ovarian cancer immunoreactive antigen domain containing 1 (OCIAD1)-integrin axis in generating late-stage ovarian cancer.Mol Cancer Ther. 2010; 9: 1709-1718Crossref PubMed Scopus (17) Google Scholar, 8Sinha A. Khadilkar R.J. S V.K. Roychowdhury Sinha A. Inamdar M.S. Conserved regulation of the JAK/stat pathway by the endosomal protein Asrij maintains stem cell potency.Cell Rep. 2013; 4: 649-658Summary Full Text Full Text PDF PubMed Scopus (36) Google Scholar, 9Shetty D.K. Kalamkar K.P. Inamdar M.S. OCIAD1 controls electron transport chain complex i activity to regulate energy metabolism in human pluripotent stem cells.Stem Cell Rep. 2018; 11: 128-141Summary Full Text Full Text PDF PubMed Scopus (15) Google Scholar]. Alternatively, targeting the regulators of OCIAD1 or its downstream effectors may be additional routes for intervention; though the efficacy of inhibiting targets such as GSK3β while maintaining specificity remains a challenge. An additional avenue of future investigation will be the use of OCIAD1 as a potential biomarker for AD. Li and colleagues found that OCIAD1 protein levels in the hippocampus correlate with increased pathological staging. To expand on this finding, it will be important to mine the growing body of AD sample/tissue repositories (e.g. ADNI, the Alzheimer's Disease Neuroimaging Initiative [[10]Petersen R.C. Aisen P.S. Beckett L.A. et al.Alzheimer's disease neuroimaging initiative (ADNI): clinical characterization.Neurology. 2010; 74: 201-209Crossref PubMed Scopus (1108) Google Scholar]) to determine if OCIAD1 is found in blood or cerebral spinal fluid and whether changes in its levels correlate with disease status. Using OCIAD1 as an early disease biomarker could help track disease progression and lead to an earlier marker of disease onset to aid in symptom management and future disease-modifying treatment for individuals living with AD. None declared. OCIAD1 contributes to neurodegeneration in Alzheimer's disease by inducing mitochondria dysfunction, neuronal vulnerability and synaptic damagesOur findings suggest that OCIAD1 contributes to neurodegeneration in AD by impairing mitochondria function, and subsequently leading to neuronal vulnerability, and synaptic damages. Full-Text PDF Open Access

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.054
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.046
GPT teacher head0.276
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2020
Admission routes1
Has abstractyes

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