Open Access
Issue |
4open
Volume 1, 2018
|
|
---|---|---|
Article Number | 4 | |
Number of page(s) | 15 | |
Section | Life Sciences - Medicine | |
DOI | https://doi.org/10.1051/fopen/2018004 | |
Published online | 22 August 2018 |
- Park SK (2011), Genomic approaches for the understanding of aging in model organisms. BMB Rep 44, 291–297. [CrossRef] [Google Scholar]
- Morris JC, McManus DQ (1991), The neurology of aging: normal versus pathologic change. Geriatrics 46, 51–54. [Google Scholar]
- Weinert BT, Timiras PS (2003), Invited review: theories of aging. J Appl Physiol (Bethesda, Md.: 1985) 95, 1706–1716. [CrossRef] [Google Scholar]
- Capri M, Salvioli S, Sevini F, et al. (2006), The genetics of human longevity. Ann New York Acad Sci 1067, 252–263. [CrossRef] [Google Scholar]
- Kirkwood TBL (2002), Evolution of ageing. Mech Ageing Dev 123, 737–745. [CrossRef] [PubMed] [Google Scholar]
- Seviour EG, Lin SY (2010), The DNA damage response: balancing the scale between cancer and ageing. Aging. [Google Scholar]
- Liu L, Rando TA (2011), Manifestations and mechanisms of stem cell aging. J Cell Biol 193, 257. [CrossRef] [PubMed] [Google Scholar]
- Charville GW, Rando TA (2011), Stem cell ageing and non-random chromosome segregation, Philosophical Transactions of the Royal Society of London. Series B. Biol Sci 366, 85–93. [CrossRef] [Google Scholar]
- Kuilman T, Michaloglou C, Mooi WJ, Peeper DS (2010), The essence of senescence. Genes Dev 24, 2463–2479. [CrossRef] [PubMed] [Google Scholar]
- Uddin RK, Singh SM (2013), Hippocampal gene expression meta-analysis identifies aging and age-associated spatial learning impairment (ASLI) genes and pathways. PLoS ONE 8, e69768. [CrossRef] [PubMed] [Google Scholar]
- Rung J, Brazma A (2013), Reuse of public genome-wide gene expression data. Nat Rev Genet 14, 89–99. [CrossRef] [PubMed] [Google Scholar]
- Kanehisa M, Goto S (2000), KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28, 27–30. [CrossRef] [PubMed] [Google Scholar]
- Zhang B, Kirov S, Snoddy J (2005), WebGestalt: an integrated system for exploring gene sets in various biological contexts. Nucleic Acids Res 33, W741–W748. [CrossRef] [PubMed] [Google Scholar]
- Ramasamy A, Mondry A, Holmes CC, Altman DG (2008), Key issues in conducting a meta-analysis of gene expression microarray datasets. PLoS Med 5, e184. [CrossRef] [PubMed] [Google Scholar]
- Rhodes DR, Yu J, Shanker K, et al. (2004), Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proc Natl Acad Sci USA 101, 9309–9314. [CrossRef] [Google Scholar]
- Rodwell GEJ, Sonu R, Zahn JM, et al. (2004), A transcriptional profile of aging in the human kidney. PLoS Biol 2, e427. [CrossRef] [PubMed] [Google Scholar]
- Edwards MG, Anderson RM, Yuan M, Kendziorski CM, Weindruch R, Prolla TA (2007), Gene expression profiling of aging reveals activation of a p53-mediated transcriptional program. BMC Genomics 8, 80. [CrossRef] [PubMed] [Google Scholar]
- Pan F, Chiu CH, Pulapura S, et al. (2007), Gene Aging Nexus: a web database and data mining platform for microarray data on aging. Nucleic Acids Res 35, D756–D759. [CrossRef] [PubMed] [Google Scholar]
- Weindruch R, Kayo T, Lee CK, Prolla TA (2002), Gene expression profiling of aging using DNA microarrays. Mech Ageing Dev 123, 177–193. [CrossRef] [PubMed] [Google Scholar]
- McElwee JJ, Schuster E, Blanc E, et al. (2007), Evolutionary conservation of regulated longevity assurance mechanisms. Genome Biol 8, R132. [CrossRef] [PubMed] [Google Scholar]
- Barrett T, Suzek TO, Troup DB, et al. (2005), NCBI GEO: mining millions of expression profiles—database and tools. Nucleic Acids Res 33, D562–D566. [CrossRef] [PubMed] [Google Scholar]
- Brazma APH, Sarkans U, Shojatalab M, et al. (2003), ArrayExpress − a public repository for microarray gene expression data at the EBI. Nucleic Acids Res 31, 68–71. [CrossRef] [PubMed] [Google Scholar]
- Berchtold NC, Cribbs DH, Coleman PD, et al. (2008), Gene expression changes in the course of normal brain aging are sexually dimorphic. Proc Natl Acad Sci USA 105, 15605–15610. [CrossRef] [Google Scholar]
- Phillips BE, Williams JP, Gustafsson T, et al. (2013), Molecular networks of human muscle adaptation to exercise and age. PLoS Genet 9, e1003389. [CrossRef] [PubMed] [Google Scholar]
- Pang WW, Price EA, Sahoo D, et al. (2011), Human bone marrow hematopoietic stem cells are increased in frequency and myeloid-biased with age. Proc Natl Acad Sci USA 108, 20012–20017. [CrossRef] [Google Scholar]
- Keller K, Engelhardt M (2013), Strength and muscle mass loss with aging process. Age strength loss, Muscles, Ligaments Tendons J 3, 346–350. [Google Scholar]
- Dönertaş HM, İzgi H, Kamacıoğlu A, He Z, Khaitovich P, Somel M. (2017), Gene expression reversal toward pre-adult levels in the aging human brain and age-related loss of cellular identity. Sci Rep 7, 5894. [CrossRef] [PubMed] [Google Scholar]
- Somel M, Guo S, Fu N, et al. (2010), MicroRNA, mRNA, and protein expression link development and aging in human and macaque brain. Genome Res 20, 1207–1218. [CrossRef] [PubMed] [Google Scholar]
- Bengtsson H, Simpson K, Bullard J, Hansen K (2008), aroma.affymetrix: a generic framework in R for analyzing small to very large Affymetrix data sets in bounded memory, methods. Tech Rep 745, 1–9. [Google Scholar]
- Heber S, Sick B (2006), Quality assessment of Affymetrix GeneChip data. Omics. J Integr Biol 10, 358–368. [CrossRef] [Google Scholar]
- Smyth GK (2004), Linear models and empirical bayes methods for assessing differential expression in microarray experiments, Stat Appl Genet Mol Biol 3, 1–25. [CrossRef] [Google Scholar]
- Yoav B, Yosef H (1995), Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Statist Soc Series B 57, 289–300. [Google Scholar]
- Bairoch A (2000), The ENZYME database in 2000. Nucleic Acids Res 28, 304–305. [CrossRef] [PubMed] [Google Scholar]
- Apweiler R, Bairoch A, Wu CH, et al. (2004), UniProt: the Universal Protein knowledgebase. Nucleic Acids Res 32, D115–D119. [CrossRef] [PubMed] [Google Scholar]
- Goldberg T, Hecht M, Hamp T, et al. (2014), LocTree3 prediction of localization. Nucleic Acids Res 42, W350–W355. [CrossRef] [PubMed] [Google Scholar]
- Martin A, Ochagavia ME, Rabasa LC, Miranda J, Fernandez-de-Cossio J, Bringas R (2010), BisoGenet: a new tool for gene network building, visualization and analysis. BMC Bioinform 11, 91. [CrossRef] [Google Scholar]
- Huang DW, Sherman BT, Lempicki RA (2009), Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nature Protoc 4, 44–57. [Google Scholar]
- Dennis G, Sherman BT, Hosack DA, et al. (2003), DAVID: database for annotation, visualization, and integrated discovery. Genome Biol 4, P3. [CrossRef] [PubMed] [Google Scholar]
- Gusnanto A, Calza S, Pawitan Y (2007), Identification of differentially expressed genes and false discovery rate in microarray studies, Curr Opin Lipidol 18, 187–193. Review [CrossRef] [PubMed] [Google Scholar]
- Pawitan Y, Michiels S, Koscielny S, Gusnanto A (2005), Ploner A. False discovery rate, sensitivity and sample size for microarray studies. Bioinform 21, 3017–3024. [CrossRef] [Google Scholar]
- Chen J, Bardes EE, Aronow BJ, Jegga AG (2009), ToppGene Suite for gene list enrichment analysis and candidate gene prioritization, Nucleic Acids Res 37, W305–W311. [CrossRef] [PubMed] [Google Scholar]
- Hong F, Breitling R (2008), A comparison of meta-analysis methods for detecting differentially expressed genes in microarray experiments. Bioinform 24, 374–382. [CrossRef] [Google Scholar]
- Tarca AL, Draghici S, Khatri P, et al. (2009), A novel signaling pathway impact analysis. Bioinform 25, 75–82. [CrossRef] [Google Scholar]
- Marchler-Bauer A, Lu S, Anderson JB, et al. (2011), CDD: a Conserved Domain Database for the functional annotation of proteins. Nucleic Acids Res 39, D225–D229. [CrossRef] [PubMed] [Google Scholar]
- Park SK, Prolla TA (2005), Lessons learned from gene expression profile studies of aging and caloric restriction. Ageing Res Rev, 4, 55–65. [CrossRef] [PubMed] [Google Scholar]
- Montecino-Rodriguez E, Berent-Maoz B, Dorshkind K (2013), Causes, consequences, and reversal of immune system aging. J Clin Investig, 123, 958–965. [CrossRef] [Google Scholar]
- Shvarts A, Brummelkamp TR, Scheeren F, et al. (2002), A senescence rescue screen identifies BCL6 as an inhibitor of anti-proliferative p19ARF-p53 signaling. Genes Dev 16, 681–686. [CrossRef] [PubMed] [Google Scholar]
- Jia D, Hasso SM, Chan J, et al. (2013), Transcriptional repression of VEGF by ZNF24: mechanistic studies and vascular consequences in vivo. Blood 121, 707–715. [CrossRef] [Google Scholar]
- Pei W, Bellows CG, Jia Y, Heersche JNM (2006), Effect of age on progesterone receptor expression, and osteoprogenitor proliferation and differentiation in female rat vertebral cell populations. J Endocrinol 190, 261–270. [CrossRef] [PubMed] [Google Scholar]
- Yang L, Kowalski J, Yacono P, et al. (2006), Endothelial cell cortactin coordinates intercellular adhesion molecule-1 clustering and actin cytoskeleton remodeling during polymorphonuclear leukocyte adhesion and transmigration. J Immunol (Baltimore, Md.: 1950) 177, 6440–6449. [CrossRef] [Google Scholar]
- Rao KM, Cohen HJ (1991), Actin cytoskeletal network in aging and cancer. Mutat Res 256, 139–148. [CrossRef] [PubMed] [Google Scholar]
- Zhou M, Wu R, Dong W, Leong J, Wang P (2010), Accelerated apoptosis contributes to aging-related hyperinflammation in endotoxemia. Int J Mol Med 25, 929–935. [PubMed] [Google Scholar]
- Giampietri C, Petrungaro S, Coluccia P, et al. (2010), c-Flip overexpression affects satellite cell proliferation and promotes skeletal muscle aging. Cell Death Dis 1, e38. [CrossRef] [PubMed] [Google Scholar]
- Jinwal UK, Koren J, Borysov SI, et al. (2010), The Hsp90 cochaperone, FKBP51, increases Tau stability and polymerizes microtubules. J Neurosci: Off J Soc Neurosci 30, 591–599. [CrossRef] [Google Scholar]
- Conti AC, Maas JW, Muglia LM, et al. (2007), Distinct regional and subcellular localization of adenylyl cyclases type 1 and 8 in mouse brain. Neuroscience 146, 713–729. [CrossRef] [PubMed] [Google Scholar]
- Jaskelioff M, Muller FL, Paik JH, et al. (2011), Telomerase reactivation reverses tissue degeneration in aged telomerase-deficient mice. Nature 469, 102–106. [CrossRef] [PubMed] [Google Scholar]
- Park SC, Yeo EJ, Han JA, et al. (1999), Aging process is accompanied by increase of transglutaminase C. J Gerontol Ser A, Biol Sci Med Sci 54, B78–B83. [CrossRef] [Google Scholar]
- Wu Q, Jackson D (2018), Detection of MAPK3/6 phosphorylation during hypersensitive response (HR)-associated programmed cell death in plants. Methods Mol Biol 1743, 153–161. [CrossRef] [PubMed] [Google Scholar]
- Hayashi Y, Toyomasu Y, Saravanaperumal SA, et al. (2017), Hyperglycemia increases interstitial cells of Cajal via MAPK1 and MAPK3 signaling to ETV1 and KIT, leading to rapid gastric emptying. Gastroenterology 153, 521–535. [CrossRef] [PubMed] [Google Scholar]
- Liu Y, Yang L, Yin J, et al. (2018), MicroRNA-15b deteriorates hypoxia/reoxygenation-induced cardiomyocyte apoptosis by downregulating Bcl-2 and MAPK3. J Investig Med 66, 39–45. [CrossRef] [Google Scholar]
- Seoane S, Montero JC, Ocaña A, Pandiella A (2016), Breast cancer dissemination promoted by a neuregulin-collagenase 3 signalling node. Oncogene 35, 2756–2765. Epub 2015 Sep 14. [CrossRef] [PubMed] [Google Scholar]
- Araldi RP, Módolo DG, de Sá Júnior PL, et al. (2016), Genetics and metabolic deregulation following cancer initiation: a world to explore. Biomed Pharmacother 82, 449–458. Epub 2016 Jun 1. Review. [CrossRef] [PubMed] [Google Scholar]
- Sobolik T, Su YJ, Wells S, Ayers GD, Cook RS (2014), Richmond A. CXCR4 drives the metastatic phenotype in breast cancer through induction of CXCR2 and activation of MEK and PI3K pathways. Mol Biol Cell 25, 566–582. [CrossRef] [PubMed] [Google Scholar]
- Salzer U, Kubicek M, Prohaska R (1999), Isolation, molecular characterization, and tissue-specific expression of ECP-51 and ECP-54 (TIP49), two homologous, interacting erythroid cytosolic proteins. Biochim Biophys Acta 1446, 365–370. [CrossRef] [PubMed] [Google Scholar]
- Bauer A, Chauvet S, Huber O, et al. (2000), Pontin52 and Reptin52 function as antagonistic regulators of Beta-catenin signalling activity. EMBO Journal 19, 6121–6130. [CrossRef] [Google Scholar]
- Cho S-G, Bhoumik A, Broday L, Ivanov V, Rosenstein B, Ronai Z (2001), TIP49b, a regulator of activating transcription factor 2 response to stress and DNA damage. Molecular and cellular biology 21, 8398–8413. [CrossRef] [PubMed] [Google Scholar]
- Zhao YX, Wang YS, Cai QQ, Wang JQ, Yao WT (2015), Up-regulation of HDAC9 promotes cell proliferation through suppressing p53 transcription in osteosarcoma. Int J Clin Exp Med 8, 11818–11823. [Google Scholar]
- Solana R, Pawelec G, Tarazona R (2006), Aging and innate immunity. Immunity 24, 491–494. [CrossRef] [PubMed] [Google Scholar]
- Kawai T, Akira S (2006), TLR signaling. Cell Death Differ 13, 816–825. [CrossRef] [PubMed] [Google Scholar]
- Fulop T, Larbi A, Douziech N, et al. (2004), Signal transduction and functional changes in neutrophils with aging. Aging Cell, 3, 217–226. [CrossRef] [PubMed] [Google Scholar]
- Lee LYL, Liang X, Höök M, Brown EL (2004), Identification and characterization of the C3 binding domain of the Staphylococcus aureus extracellular fibrinogen-binding protein (Efb). J Biological Chem 279, 50710–50716. [CrossRef] [Google Scholar]
- Welle S, Brooks AI, Delehanty JM, Needler N, Thornton CA (2003), Gene expression profile of aging in human muscle, Physiol Genomics 14, 149–159. [CrossRef] [PubMed] [Google Scholar]
- Vadasz Z, Haj T, Kessel A, Toubi E (2013), Age-related autoimmunity. BMC Med 11, 94 [CrossRef] [PubMed] [Google Scholar]
- Peferoen LA, Vogel DY, Ummenthum K, et al. (2015), Activation status of human microglia is dependent on lesion formation stage and remyelination in multiple sclerosis. J Neuropathol Exp Neurol 74, 48–63. [CrossRef] [PubMed] [Google Scholar]
- Savill J, Dransfield I, Gregory C, Haslett C (2002), A blast from the past: clearance of apoptotic cells regulates immune responses. Nat Rev Immunol 2, 965–975. [CrossRef] [PubMed] [Google Scholar]
- Ponnappan S, Ponnappan U (2011), Aging and immune function: molecular mechanisms to interventions. Antioxid Redox Signal 14, 1551–1585. [CrossRef] [PubMed] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.