NOVEL EXOSOME BIOMARKERS OF IRON PATHOLOGY IN AD
AD 中铁病理学的新型外泌体生物标志物
基本信息
- 批准号:10223789
- 负责人:
- 金额:$ 43.3万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-05-01 至 2023-04-30
- 项目状态:已结题
- 来源:
- 关键词:AddressAlzheimer&aposs DiseaseAlzheimer&aposs disease brainAlzheimer&aposs disease pathologyAmericanAmyloidAntibodiesAstrocytesAtlasesAutopsyBiological AssayBiological MarkersBiologyBiomedical EngineeringBloodBrainCarrier ProteinsCell DeathCell Surface ProteinsCell physiologyCell surfaceCellsCellular biologyCeruloplasminContralateralDataDevelopmentDiagnosisDiseaseDisease ProgressionElectron MicroscopyElectron energy loss spectroscopyExhibitsFerritinFreezingFutureGene ExpressionGlutathioneGoalsHippocampus (Brain)HomeostasisHumanImageImmuneInductively Coupled Plasma Mass SpectrometryInflammationInterventionInvestigationIronKnowledgeLeadLinkLipidsLiteratureMagnetic Resonance ImagingMass Spectrum AnalysisMeasurableMeasuresMetabolic PathwayMetalsMethodsMicroRNAsMicrogliaMolecularNerve DegenerationNeurodegenerative DisordersNeuronsNucleic AcidsOligodendrogliaOxidative StressPathologic ProcessesPathologyPensionsPharmacologic SubstancePlayPrevalenceProteinsPublishingRNARaman Spectrum AnalysisReproducibilityRoentgen RaysRoleSpecificitySpecimenSynchrotronsTFRC geneTechnologyTissuesTransferrinTranslatingTranslationsVesicleWestern BlottingWorkX ray microscopybasebrain cellcell typecostdisorder controldivalent metaleffective therapyefficacious treatmentexosomeextracellular vesicleshepcidinhuman tissueimage reconstructionin vivoinnovationinsightintercellular communicationiron metabolismiron oxidationmachine learning algorithmmetal transporting protein 1multidisciplinarymultimodalitynanoscaleneuropathologynext generation sequencingnovelnovel therapeuticsoxidationpopulation basedspecific biomarkerstau Proteinstau aggregationtranscriptome sequencing
项目摘要
PROJECT SUMMARY/ABSTRACT
Alzheimer’s disease (AD) afflicts millions of Americans, yet no effective treatments exist. Iron has been shown
to be involved in key AD pathologic processes, including amyloid and tau aggregation, inflammation, oxidative
stress, and cell death mechanisms. Despite this growing evidence, it is challenging to ascertain alterations in
iron metabolism in vivo, limiting potential translation to biomarkers and novel therapies. Exosomes are
nanometer-sized vesicles shed by cells to transport proteins, nucleic acids, metals, lipids or metabolites. While
exosomes reflect cellular processes and can reveal disease-related pathologies in human tissues and biofluids,
iron abnormalities in AD exosomes have not yet been investigated. We will address this knowledge gap through
state-of-the-art exosome isolation technology combined with advanced iron imaging, protein quantification and
next generation sequencing methods. Our goal is to investigate iron dysregulation in exosomes from post-
mortem AD brains, in order to unveil AD-specific biomarkers and facilitate the development of novel therapies.
The project aims are: (1) To determine whether the quantity, oxidation state, and cellular origin of exosomal
iron is altered in AD. Using MRI and synchrotron X-ray microscopy, we will quantify tissue iron content and
oxidation state in human AD and control hippocampal specimens. We will then use our novel exosome
isolation platform, ExoTIC, to isolate exosomes from regions of high hippocampal iron content in the same
specimens. Using antibodies that target cell-surface proteins, we will enrich the isolated exosomes based on
their cellular origin (e.g. neurons, microglia, etc.). We will quantify exosomal iron content from each cell type
using mass spectrometry (ICP-MS), and measure exosomal iron oxidation state using electron microscopy.
Taken together, we will determine whether iron content and oxidation state are altered in Alzheimer’s
exosomes compared to controls, in particular in exosomes originating in microglia, the brain’s immune cells.
(2) Detect dysregulation of iron-related proteins and RNAs in AD exosomes. Using Western blotting on the
enriched exosomes, we will determine whether levels of proteins that play a role in iron metabolism are altered
in AD compared to controls. Because exosomes are generally rich in microRNAs that are known to regulate
gene expression, we will use RNA-Seq to determine whether exosomal microRNAs regulating these same
iron-related proteins are also altered in AD. Machine learning algorithms will enable the creation of an atlas of
microRNAs linking iron, iron-related proteins, and neuropathology, which should provide a deeper
understanding of AD biology.
Characterization of exosome content in the AD brain should result in cell-specific signatures of iron
dysregulation associated with neurodegeneration. This approach may elucidate novel aspects of AD biology,
lead to novel assays to detect early AD, and facilitate a much-needed future therapy.
项目总结/摘要
阿尔茨海默病(AD)困扰着数百万美国人,但目前还没有有效的治疗方法。铁已被证明
参与关键的AD病理过程,包括淀粉样蛋白和tau蛋白聚集、炎症、氧化
压力和细胞死亡机制。尽管有越来越多的证据,但要确定
体内铁代谢,限制了生物标志物和新疗法的潜在转化。外来体是
由细胞脱落的纳米大小的囊泡,用于运输蛋白质、核酸、金属、脂质或代谢物。而
外来体反映细胞过程并可揭示人体组织和生物流体中的疾病相关病理,
尚未研究AD外泌体中的铁异常。我们将通过以下方式解决这一知识差距
最先进的外泌体分离技术结合先进的铁成像,蛋白质定量和
下一代测序方法。我们的目标是调查铁失调的外来体从后-
AD的大脑,以揭示AD特异性生物标志物,促进新疗法的开发。
该项目的目标是:(1)确定外泌体的数量、氧化状态和细胞来源
铁在AD中改变。使用MRI和同步加速器X射线显微镜,我们将量化组织铁含量,
人AD和对照海马样本中的氧化状态。然后我们将使用我们的新型外泌体
分离平台ExoTIC,以从相同的海马铁含量高的区域分离外泌体。
标本使用靶向细胞表面蛋白的抗体,我们将根据以下方法富集分离的外泌体:
它们的细胞起源(例如神经元、小胶质细胞等)。我们将定量每种细胞类型的外泌体铁含量
使用质谱法(ICP-MS),并使用电子显微镜测量外泌体铁氧化态。
总之,我们将确定是否铁含量和氧化状态改变阿尔茨海默氏症
外泌体与对照相比,特别是源自小胶质细胞(大脑的免疫细胞)的外泌体。
(2)检测AD外泌体中铁相关蛋白和RNA的失调。使用蛋白质印迹法,
富集的外泌体,我们将确定在铁代谢中发挥作用的蛋白质水平是否改变
与对照组相比。因为外泌体通常富含微RNA,
基因表达,我们将使用RNA-Seq来确定外泌体microRNA是否调节这些相同的基因表达,
铁相关蛋白在AD中也发生改变。机器学习算法将能够创建一个地图集,
连接铁,铁相关蛋白和神经病理学的microRNA,这应该提供更深入的研究。
了解AD生物学。
AD脑中外泌体含量的表征应导致铁的细胞特异性特征
与神经变性相关的失调。这种方法可以阐明AD生物学的新方面,
导致新的检测早期AD的测定,并促进急需的未来治疗。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Utkan Demirci其他文献
Utkan Demirci的其他文献
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{{ truncateString('Utkan Demirci', 18)}}的其他基金
CANARY CANCER RESEARCH EDUCATION SUMMER TRAINING (CANARY CREST) PROGRAM
加那利癌症研究教育夏季培训(Canary Crest)计划
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10462468 - 财政年份:2017
- 资助金额:
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10713237 - 财政年份:2017
- 资助金额:
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- 资助金额:
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9995434 - 财政年份:2017
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