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Analysis of the Cerebrospinal Fluid Proteome in Alzheimer's Disease
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
KTH Royal Inst Technol, Sch Biotechnol, Affin Prote, Sci Life Lab, Stockholm, Sweden..
Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Chemistry - BMC, Analytical Chemistry.
KTH Royal Inst Technol, Sch Biotechnol, Affin Prote, Sci Life Lab, Stockholm, Sweden..
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2016 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 11, no 3, e0150672Article in journal (Refereed) Published
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Text
Abstract [en]

Alzheimer's disease is a neurodegenerative disorder accounting for more than 50% of cases of dementia. Diagnosis of Alzheimer's disease relies on cognitive tests and analysis of amyloid beta, protein tau, and hyperphosphorylated tau in cerebrospinal fluid. Although these markers provide relatively high sensitivity and specificity for early disease detection, they are not suitable for monitor of disease progression. In the present study, we used label-free shotgun mass spectrometry to analyse the cerebrospinal fluid proteome of Alzheimer's disease patients and non-demented controls to identify potential biomarkers for Alzheimer's disease. We processed the data using five programs (DecyderMS, Maxquant, OpenMS, PEAKS, and Sieve) and compared their results by means of reproducibility and peptide identification, including three different normalization methods. After depletion of high abundant proteins we found that Alzheimer's disease patients had lower fraction of low-abundance proteins in cerebrospinal fluid compared to healthy controls (p<0.05). Consequently, global normalization was found to be less accurate compared to using spiked-in chicken ovalbumin for normalization. In addition, we determined that Sieve and OpenMS resulted in the highest reproducibility and PEAKS was the programs with the highest identification performance. Finally, we successfully verified significantly lower levels (p<0.05) of eight proteins (A2GL, APOM, C1QB, C1QC, C1S, FBLN3, PTPRZ, and SEZ6) in Alzheimer's disease compared to controls using an antibody-based detection method. These proteins are involved in different biological roles spanning from cell adhesion and migration, to regulation of the synapse and the immune system.

Place, publisher, year, edition, pages
2016. Vol. 11, no 3, e0150672
National Category
Neurology Geriatrics
Identifiers
URN: urn:nbn:se:uu:diva-283774DOI: 10.1371/journal.pone.0150672ISI: 000371990100049PubMedID: 26950848OAI: oai:DiVA.org:uu-283774DiVA: diva2:919656
Funder
Knut and Alice Wallenberg FoundationMarianne and Marcus Wallenberg FoundationThe Swedish Brain FoundationSwedish Research Council FormasSwedish Research Council, P29797-1Swedish Research Council, 621-2011-4423
Available from: 2016-04-14 Created: 2016-04-14 Last updated: 2017-11-30Bibliographically approved
In thesis
1. Proteomics Studies of Subjects with Alzheimer’s Disease and Chronic Pain
Open this publication in new window or tab >>Proteomics Studies of Subjects with Alzheimer’s Disease and Chronic Pain
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Alzheimer’s disease (AD) is a neurodegenerative disease and the major cause of dementia, affecting more than 50 million people worldwide. Chronic pain is long-lasting, persistent pain that affects more than 1.5 billion of the world population. Overlapping and heterogenous symptoms of AD and chronic pain conditions complicate their diagnosis, emphasizing the need for more specific biomarkers to improve the diagnosis and understand the disease mechanisms.

To characterize disease pathology of AD, we measured the protein changes in the temporal neocortex region of the brain of AD subjects using mass spectrometry (MS). We found proteins involved in exo-endocytic and extracellular vesicle functions displaying altered levels in the AD brain, potentially resulting in neuronal dysfunction and cell death in AD.

To detect novel biomarkers for AD, we used MS to analyze cerebrospinal fluid (CSF) of AD patients and found decreased levels of eight proteins compared to controls, potentially indicating abnormal activity of complement system in AD.

By integrating new proteomics markers with absolute levels of Aβ42, total tau (t-tau) and p-tau in CSF, we improved the prediction accuracy from 83% to 92% of early diagnosis of AD. We found increased levels of chitinase-3-like protein 1 (CH3L1) and decreased levels of neurosecretory protein VGF (VGF) in AD compared to controls.

By exploring the CSF proteome of neuropathic pain patients before and after successful spinal cord stimulation (SCS) treatment, we found altered levels of twelve proteins, involved in neuroprotection, synaptic plasticity, nociceptive signaling and immune regulation.

To detect biomarkers for diagnosing a chronic pain state known as fibromyalgia (FM), we analyzed the CSF of FM patients using MS. We found altered levels of four proteins, representing novel biomarkers for diagnosing FM. These proteins are involved in inflammatory mechanisms, energy metabolism and neuropeptide signaling.

Finally, to facilitate fast and robust large-scale omics data handling, we developed an e-infrastructure. We demonstrated that the e-infrastructure provides high scalability, flexibility and it can be applied in virtually any fields including proteomics. This thesis demonstrates that proteomics is a promising approach for gaining deeper insight into mechanisms of nervous system disorders and find biomarkers for diagnosis of such diseases.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2017. 82 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 1385
Keyword
Bioinformatics, microservices, biomarkers, Alzheimer's disease, chronic pain, fibromyalgia, neuropathic pain, spinal cord stimulation, cloud computing, proteomics, metabolomics, software, workflows, data analysis, mass spectrometry
National Category
Geriatrics Neurology Neurosciences
Research subject
Bioinformatics; Neurology; Geriatrics
Identifiers
urn:nbn:se:uu:diva-331748 (URN)978-91-513-0111-2 (ISBN)
Public defence
2017-12-05, Rosénsalen, Akademiska sjukhuset, Ing 95/96, nbv, Uppsala, 09:00 (English)
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Available from: 2017-11-14 Created: 2017-10-17 Last updated: 2017-11-14

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