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      Autophagy and Alzheimer’s Disease: From Molecular Mechanisms to Therapeutic Implications

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          Alzheimer’s disease (AD) is the most common cause of progressive dementia in the elderly. It is characterized by a progressive and irreversible loss of cognitive abilities and formation of senile plaques, composed mainly of amyloid β (Aβ), and neurofibrillary tangles (NFTs), composed of tau protein, in the hippocampus and cortex of afflicted humans. In brains of AD patients the metabolism of Aβ is dysregulated, which leads to the accumulation and aggregation of Aβ. Metabolism of Aβ and tau proteins is crucially influenced by autophagy. Autophagy is a lysosome-dependent, homeostatic process, in which organelles and proteins are degraded and recycled into energy. Thus, dysfunction of autophagy is suggested to lead to the accretion of noxious proteins in the AD brain. In the present review, we describe the process of autophagy and its importance in AD. Additionally, we discuss mechanisms and genes linking autophagy and AD, i.e., the mTOR pathway, neuroinflammation, endocannabinoid system, ATG7, BCL2, BECN1, CDK5, CLU, CTSD, FOXO1, GFAP, ITPR1, MAPT, PSEN1, SNCA, UBQLN1, and UCHL1. We also present pharmacological agents acting via modulation of autophagy that may show promise in AD therapy. This review updates our knowledge on autophagy mechanisms proposing novel therapeutic targets for the treatment of AD.

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          Most cited references 262

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          Proteomics. Tissue-based map of the human proteome.

          Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. Copyright © 2015, American Association for the Advancement of Science.
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            LC3, a mammalian homologue of yeast Apg8p, is localized in autophagosome membranes after processing.

            Little is known about the protein constituents of autophagosome membranes in mammalian cells. Here we demonstrate that the rat microtubule-associated protein 1 light chain 3 (LC3), a homologue of Apg8p essential for autophagy in yeast, is associated to the autophagosome membranes after processing. Two forms of LC3, called LC3-I and -II, were produced post-translationally in various cells. LC3-I is cytosolic, whereas LC3-II is membrane bound. The autophagic vacuole fraction prepared from starved rat liver was enriched with LC3-II. Immunoelectron microscopy on LC3 revealed specific labelling of autophagosome membranes in addition to the cytoplasmic labelling. LC3-II was present both inside and outside of autophagosomes. Mutational analyses suggest that LC3-I is formed by the removal of the C-terminal 22 amino acids from newly synthesized LC3, followed by the conversion of a fraction of LC3-I into LC3-II. The amount of LC3-II is correlated with the extent of autophagosome formation. LC3-II is the first mammalian protein identified that specifically associates with autophagosome membranes.
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              The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible

              A system-wide understanding of cellular function requires knowledge of all functional interactions between the expressed proteins. The STRING database aims to collect and integrate this information, by consolidating known and predicted protein–protein association data for a large number of organisms. The associations in STRING include direct (physical) interactions, as well as indirect (functional) interactions, as long as both are specific and biologically meaningful. Apart from collecting and reassessing available experimental data on protein–protein interactions, and importing known pathways and protein complexes from curated databases, interaction predictions are derived from the following sources: (i) systematic co-expression analysis, (ii) detection of shared selective signals across genomes, (iii) automated text-mining of the scientific literature and (iv) computational transfer of interaction knowledge between organisms based on gene orthology. In the latest version 10.5 of STRING, the biggest changes are concerned with data dissemination: the web frontend has been completely redesigned to reduce dependency on outdated browser technologies, and the database can now also be queried from inside the popular Cytoscape software framework. Further improvements include automated background analysis of user inputs for functional enrichments, and streamlined download options. The STRING resource is available online, at

                Author and article information

                Front Aging Neurosci
                Front Aging Neurosci
                Front. Aging Neurosci.
                Frontiers in Aging Neuroscience
                Frontiers Media S.A.
                30 January 2018
                : 10
                1Department of Pharmacy, Southeast University , Dhaka, Bangladesh
                2Department of Experimental Embryology, Institute of Genetics and Animal Breeding, Polish Academy of Sciences , Magdalenka, Poland
                3Department of Molecular Biology and Biochemical Pharmacology, Institute of Molecular Biology “Roumen Tsanev”, Bulgarian Academy of Sciences , Sofia, Bulgaria
                4Department of Clinical Psychology, Tottori University Graduate School of Medical Sciences , Tottori, Japan
                5Department of Molecular Biology, Institute of Genetics and Animal Breeding, Polish Academy of Sciences , Magdalenka, Poland
                6Department of Pharmacognosy, University of Vienna , Vienna, Austria
                7Department of Pharmacology, Federal University of São Paulo , São Paulo, Brazil
                8Department of Pharmacology, Suez Canal University , Ismailia, Egypt
                9Department of Ophthalmology and Micro-technology, Yokohama City University , Yokohama, Japan
                Author notes

                Edited by: Mohammad Amjad Kamal, King Abdulaziz University, Saudi Arabia

                Reviewed by: Charles Harrington, University of Aberdeen, United Kingdom; Caterina Scuderi, Sapienza Università di Roma, Italy; Agustina Alaimo, Universidad de Buenos Aires, Argentina

                *Correspondence: Adrian M. Stankiewicz, adrianstankiewicz85@ Atanas G. Atanasov, a.atanasov@
                Copyright © 2018 Uddin, Stachowiak, Mamun, Tzvetkov, Takeda, Atanasov, Bergantin, Abdel-Daim and Stankiewicz.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                Figures: 2, Tables: 0, Equations: 0, References: 264, Pages: 18, Words: 0


                tau, autophagy, alzheimer’s disease, amyloid beta


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