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Age-related changes of gene expression in the neocortex: Preliminary data on RNA-Seq of the transcriptome in three functionally distinct cortical areas

Published online by Cambridge University Press:  15 October 2012

Oksana Yu. Naumova ,
Dean Palejev ,
Natalia V. Vlasova ,
Maria Lee ,
Sergei Yu. Rychkov ,
Olga N. Babich ,
Flora M. Vaccarino  and
Elena L. Grigorenko
Oksana Yu. Naumova
Affiliation:
Yale University Vavilov Institute of General Genetics RAS
Dean Palejev
Affiliation:
Yale University
Natalia V. Vlasova
Affiliation:
Medical Academy for Continuous Education
Maria Lee
Affiliation:
Yale University
Sergei Yu. Rychkov
Affiliation:
Vavilov Institute of General Genetics RAS
Olga N. Babich
Affiliation:
Medical Academy for Continuous Education
Flora M. Vaccarino
Affiliation:
Yale University
Elena L. Grigorenko*
Affiliation:
Yale University Moscow State University Columbia University
*
Address correspondence and reprints requests to: Elena L. Grigorenko, Child Study Center, Yale University, 230 South Frontage Road, New Haven, CT 06519–1124; E-mail: elena.grigorenko@yale.edu.
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Abstract

The study of gene expression (i.e., the study of the transcriptome) in different cells and tissues allows us to understand the molecular mechanisms of their differentiation, development and functioning. In this article, we describe some studies of gene-expression profiling for the purposes of understanding developmental (age-related) changes in the brain using different technologies (e.g., DNA-Microarray) and the new and increasingly popular RNA-Seq. We focus on advancements in studies of gene expression in the human brain, which have provided data on the structure and age-related variability of the transcriptome in the brain. We present data on RNA-Seq of the transcriptome in three distinct areas of the neocortex from different ages: mature and elderly individuals. We report that most age-related transcriptional changes affect cellular signaling systems, and, as a result, the transmission of nerve impulses. In general, the results demonstrate the high potential of RNA-Seq for the study of distinctive features of gene expression among cortical areas and the changes in expression through normal and atypical development of the central nervous system.

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Articles
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Development and Psychopathology , Volume 24 , Issue 4: Contributions of the Genetic/Genomic Sciences to Developmental Psychopathology , November 2012 , pp. 1427 - 1442
Copyright
Copyright © Cambridge University Press 2012

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References

Albert, M. S., & Stafford, J. L. (1988). Computed tomography studies. In Albert, M. S. & Moss, M. B. (Eds.), Geriatric neuropsychology. New York: Guilford Press.Google Scholar
Amaral, P. P., Clark, M. B., Gascoigne, D. K., Dinger, M. E., & Mattick, J. S. (2011). lncRNAdb: A reference database for long noncoding RNAs. Nucleic Acids Research, 39, D146–D151.Google Scholar
Amenta, F., Zaccheo, D., & Collier, W. L. (1991). Neurotransmitters, neuroreceptors and aging. Mechanisms of Ageing and Development, 61, 249–273.Google Scholar
Anders, S., & Huber, W. (2010). Differential expression analysis for sequence count data. Genome Biology, 11, R106.CrossRefGoogle ScholarPubMed
Asmann, Y. W., Klee, E. W., Thompson, E. A., Perez, E. A., Middha, S., Oberg, A. L., et al. (2009). 3′ tag digital gene expression profiling of human brain and universal reference RNA using Illumina Genome Analyzer. BMC Genomics, 10, 531.CrossRefGoogle ScholarPubMed
Bakker, S. L., de Leeuw, F. E., de Groot, J. C., Hofman, A., Koudstaal, P. J., & Breteler, M. M. (1999). Cerebral vasomotor reactivity and cerebral white matter lesions in the elderly. Neurology, 52, 578–583.Google Scholar
Band, G. P. H., Ridderinkhof, K. R., & Segalowitz, S. (2002). Explaining of neurocognitive aging: Is one factor enough? Brain and Cognition, 49, 259–267.CrossRefGoogle Scholar
Barresi, B. A., Nicholas, M., Tabor Connor, L., Obler, L. K., & Albert, M. L. (2000). Semantic degradation and lexical access in age-related naming failures. Aging, Neuropsychology, and Cognition, 7, 169–178.Google Scholar
Barresi, B. A., Obler, L. K., Au, R., & Albert, M. L. (1999). Language-related factors influencing naming in adulthood. In Hamilton, H. (Ed.), Language and communication in old age and language: Multidisciplinary perspectives (pp. 77–90). New York: Garland Publishing.Google Scholar
Barton, A. J. L., Pearson, R. C. A., Najlerahim, A., & Harrison, P. J. (1993). Pre- and postmortem influences on brain RNA. Journal of Neurochemistry, 61, 1–11.Google Scholar
Borovecki, F., Lovrecic, L., Zhou, J., Jeong, H., Then, F., Rosas, H. D., et al. (2005). Genome-wide expression profiling of human blood reveals biomarkers for Huntington's disease. Proceedings of the National Academy of Sciences, 102, 11023–11028.Google Scholar
Buechel, H. M., Popovic, J., Searcy, J. L., Porter, N. M., Thibault, O., & Blalock, E. M. (2011). Deep sleep and parietal cortex gene expression changes are related to cognitive deficits with age. PLoS One, 6, e18387.Google Scholar
Burke, S. N., & Barnes, C. A. (2006). Neural plasticity in the ageing brain. Nature Reviews Neuroscience, 7, 30–40.CrossRefGoogle ScholarPubMed
Catts, V. S., Catts, S. V., Fernandez, H. R., Taylor, J. M., Coulson, E. J., & Lutze-Mann, L. H. (2005). A microarray study of post-mortem mRNA degradation in mouse brain tissue. Molecular Brain Research, 138, 164–177.CrossRefGoogle ScholarPubMed
Chodroff, R. A., Goodstadt, L., Sirey, T. M., Oliver, P. L., Davies, K. E., Green, E. D., et al. (2010). Long noncoding RNA genes: Conservation of sequence and brain expression among diverse amniotes. Genome Biology, 11, R72.CrossRefGoogle ScholarPubMed
Cipriano, C., Malavolta, M., Costarelli, L., Giacconi, R., Muti, E., Gasparini, N., et al. (2006). Polymorphisms in MT1a gene coding region are associated with longevity in Italian Central female population. Biogerontology, 7, 357–365.Google Scholar
Colantuoni, C., Lipska, B. K., Ye, T., Hyde, T. M., Tao, R., Leek, J. T., et al. (2011). Temporal dynamics and genetic control of transcription in the human prefrontal cortex. Nature, 478, 519–524.CrossRefGoogle ScholarPubMed
Colantuoni, C., Purcell, A. E., Bouton, C. M. L., & Pevsner, J. (2000). High throughput analysis of gene expression in the human brain. Journal of Neuroscience Research, 59, 1–10.Google Scholar
Deary, I. J., Leaper, S., Murray, A. D., Staff, R. T., & Whalley, L. J. (2003). Cerebral white matter abnormalities and lifetime cognitive change: A 67-year follow-up of the Scottish Mental Survey of 1932. Psychology & Aging, 18, 140–148.Google Scholar
Dekaban, A. S., & Sadowsky, D. (1978). Changes in brain weights during the span of human life: Relation of brain weights to body heights and body weights. Annals of Neurology, 4, 345–356.Google Scholar
de la Grange, P., Gratadou, L., Delord, M., Dutertre, M., & Auboeuf, D. (2010). Splicing factor and exon profiling across human tissues. Nucleic Acids Research, 38, 2825–2838.CrossRefGoogle ScholarPubMed
Diaz, E. (2009). From microarrays to mechanisms of brain development and function. Biochemical & Biophysical Research Communications, 385, 129–131.CrossRefGoogle ScholarPubMed
Gilfillan, G. D., Selmer, K. K., Roxrud, I., Smith, R., Kyllerman, M., Eiklid, K., et al. (2008). SLC9A6 mutations cause X-linked mental retardation, microcephaly, epilepsy, and ataxia, a phenotype mimicking Angelman syndrome. American Journal of Human Genetics, 82, 1003–1010.Google Scholar
Harrison, P. J., Heath, P. R., Eastwood, S. L., Burnet, P. W. J., McDonald, B., & Pearson, R. C. A. (1995). The relative importance of premortem acidosis and postmortem interval for human brain gene expression studies: Selective mRNA vulnerability and comparison with their encoded proteins. Neuroscience Letters, 200, 151–154.Google Scholar
Harrow, J., Denoeud, F., Frankish, A., Reymond, A., Chen, C., Chrast, J., et al. (2006). GENCODE: Producing a reference annotation for ENCODE. Genome Biology, 7, S4.Google Scholar
Hof, P. R., & Morrison, J. H. (2004). The aging brain: Morphomolecular senescence of cortical circuits. Trends in Neurosciences, 27, 607–613.Google Scholar
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009). Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Research, 37, 1–13.Google Scholar
International Human Genome Sequencing Consortium. (2004). Finishing the euchromatic sequence of the human genome. Nature, 431, 931–945.Google Scholar
Irizarry, R. A., Warren, D., Spencer, F., Kim, I. F., Biswal, S., Frank, B. C., et al. (2005). Multiple-laboratory comparison of microarray platforms. Nature Methods, 2, 345–350.CrossRefGoogle ScholarPubMed
Johnson, M. B., Kawasawa, Y. I., Mason, C. E., Krsnik, Z., Coppola, G., Bogdanovic, D., et al. (2009). Functional and evolutionary insights into human brain development through global transcriptome analysis. Neuron, 62, 494–509.CrossRefGoogle ScholarPubMed
Kabaso, D., Coskren, P. J., Henry, B. I., Hof, P. R., & Wearn, S. L. (2009). The electronic structure of pyramidal neurons contributing to prefrontal cortical circuits in macaque monkeys is significantly altered in aging. Cerebral Cortex, 19, 2248–2268.CrossRefGoogle Scholar
Kaiser, L. G., Schuff, N., Cashdollar, N., & Weiner, M. W. (2005). Age-related glutamate and glutamine concentration changes in normal human brain: 1H MR spectroscopy study at 4T. Neurobiology of Aging, 26, 665–672.Google Scholar
Kang, H. J., Kawasawa, Y. I., Cheng, F., Zhu, Y., Xu, X., Li, M., et al. (2011). Spatio-temporal transcriptome of the human brain. Nature, 478, 483–489.Google Scholar
Kawasaki, E. S. (2006). The end of the microarray Tower of Babel: Will universal standards lead the way? Journal of Biomolecular Techniques, 17, 200–206.Google Scholar
Khaitovich, P., Muetzel, B., She, X., Lachmann, M., Hellmann, I., Dietzsch, J., et al. (2004). Regional patterns of gene expression in human and chimpanzee brains. Genome Research, 14, 1462–1473.CrossRefGoogle ScholarPubMed
Kinter, J., Zeis, T., & Schaeren-Wiemers, N. (2008). RNA profiling of MS brain tissues. International MS Journal, 15, 51–58.Google Scholar
Kluger, A., Gianutsos, J. G., Golomb, J., Ferris, S. H., George, A. E., Franssen, E., et al. (1997). Patterns of motor impairement in normal aging, mild cognitive decline, and early Alzheimer's disease. Journal of Gerontology, 52B, 28–39.CrossRefGoogle ScholarPubMed
Kolb, B., & Whishaw, I. (1988). Brain plasticity and behaviour. Annual Review of Psychology, 49, 43–64.Google Scholar
Kuss, A. W., & Chen, W. (2008). MicroRNAs in brain function and disease. Current Neurology and Neuroscience Reports, 8, 190–197.CrossRefGoogle ScholarPubMed
Lambert, N., Lambot, M. A., Bilheu, A., Albert, V., Englert, Y., Libert, F., et al. (2011). Genes expressed in specific areas of the human fetal cerebral cortex display distinct patterns of evolution. PLoS One, 6, e17753.Google Scholar
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S.L. (2009). Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biology, 10, R25.Google Scholar
Landgraf, P., Rusu, M., Sheridan, R., Sewer, A., Iovino, N., Aravin, A., et al. (2007). A mammalian microRNA expression atlas based on small RNA library sequencing. Cell, 129, 1401–1414.Google Scholar
Liu, S., Lin, L., Jiang, P., Wang, D., & Xing, Y. (2011). A comparison of RNA-Seq and high-density exon array for detecting differential gene expression between closely related spicies. Nucleic Acids Research, 39, 78–588.Google Scholar
Lockhart, D.J., & Barlow, C. (2001). DNA arrays and gene expression analysis in the brain. In Chin, H. R. & Moldin, S. O. (Eds.), Methods in genomic neuroscience (pp. 109–140). New York: CRC Press.Google Scholar
Lu, T., Pan, Y., Kao, S-Y., Li, C., Kohane, I., Chan, J., et al. (2004). Gene regulation and DNA damage in the ageing human brain. Nature, 429, 883–891.Google Scholar
Magnusson, K. R. (1998). The aging of NMDA receptor complex. Frontiers in Bioscience, 3, e70.Google Scholar
Malmgren, R. (2000). Epidemiology of aging. In Coffey, C. E. & Cummings, J. L. (Eds.), Textbook of geriatric neuropsychiatry (pp. 17–31). Washington, DC: American Psychiatric Press.Google Scholar
Marioni, J. C., Mason, C. E., Mane, S.M., Stephens, M., & Gilad, Y. (2008). RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays. Genome Research, 18, 1509–1517.Google Scholar
Masliah, E., Mallory, M., Hansen, L., DeTeresa, R., & Terry, R. D. (1993). Quantitative synaptic alterations in the human neocortex during normal aging. Neurology, 43, 192–197.Google Scholar
McShea, A., Marlatt, M. W., Lee, H. G., Tarkowsky, S. M., Smit, M., & Smith, M. A. (2006). The application of microarray technology to neuropathology: Cutting edge tool with clinical diagnostics potential or too much information? Journal of Neuropathology and Experimental Neurology, 65, 1031–1039.CrossRefGoogle ScholarPubMed
Mexal, S., Berger, R., Adams, C.E., Ross, R.G., Freedman, R., & Leonard, S. (2006). Brain pH has a significant impact on human postmortem hippocampal gene expression profiles. Brain Research, 1106, 1–11.Google Scholar
Miller, D. B., & O'Callaghan, J. P. (2003). Effect of aging and stress on hippocampal structure and function. Metabolism, 52, 17–21.CrossRefGoogle ScholarPubMed
Miller, R. M., & Federoff, H. J. (2006). Microarrays in Parkinson's disease: A systematic approach. Journal of the American Society for Experimental NeuroTherapeutics, 3, 319–326.Google Scholar
Mirnics, K., & Pevsner, J. (2004). Progress in the use of microarray technology to study the neurobiology of disease. Nature Neuroscience, 7, 434–439.Google Scholar
Molyneaux, B. J., Arlotta, P., Menezes, J. R., & Macklis, J. D. (2007). Neuronal subtype specification in the cerebral cortex. Nature Reviews Neuroscience, 8, 427–437.CrossRefGoogle ScholarPubMed
Moreau, M. P., Bruse, S. E., David-Rus, R., Buyske, S., & Brzustowicz, L. M. (2011). Altered microRNA expression profiles in postmortem brain samples from individuals with schizophrenia and bipolar disorder. Biological Psychiatry, 69, 188–193.Google Scholar
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008). Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods, 5, 621–628.Google Scholar
Mudge, J., Miller, N. A., Khrebtukova, I., Lindquist, I. E., May, G. D., Huntley, J. J., et al. (2007). Genomic convergence analysis of Schizophrenia: mRNA sequencing reveals altered synaptic vesicular transport in post-mortem cerebellum. PLoS One, 3, e3625.Google Scholar
Munakata, M., Watanabe, M., Otsuki, T., Nakama, H., Arima, K., Itoh, M., et al. (2007) Altered distribution of KCC2 in cortical dysplasia in patients with intractable epilepsy. Epilepsia, 48, 837–844.CrossRefGoogle ScholarPubMed
Myers, A. J., Gibbs, J. R., Webster, J. A., Rohrer, K., Zhao, A., Marlowe, L., et al. (2007). A survey of genetic human cortical gene expression. Nature Genetics, 39, 1494–1499.Google Scholar
Nadler, J. J., Zou, F., Huang, H., Moy, S. S., Lauder, J., Crawley, J. N., et al. (2006). Large-scale gene expression differences across brain regions and inbred strains correlate with a behavioral phenotype. Genetics, 174, 1229–1336.Google Scholar
Nicholas, M., Connor, L. T., Obler, L. K., & Albert, M. L. (1998). Aging, language, and language disorders. In Sarno, M. T. (Ed.), Acquired aphasia (pp. 413–449). San Diego, CA: Academic Press.Google Scholar
Nicholas, M., Obler, L. K., Albert, M. L., & Goodglass, H. (1985). Lexical retrieval in helathy aging. Cortex, 21, 595–606.Google Scholar
Ota, M., Yasuno, F., Ito, H., Seki, C., Kozaki, S., Asada, T., et al. (2006). Age-related decline of dopamine synthesis in the living human brain measured by positron emission tomography with L-[β-11 C]DOPA. Life Sciences, 79, 730–736.Google Scholar
Peters, A., Sethares, C., & Luebke, J. I. (2008). Synapses are lost during aging in the primate prefrontal cortex. Neuroscience, 152, 970–981.Google Scholar
Ponjavic, J., Oliver, P. L., Lunter, G., & Ponting, C. P. (2009). Genomic and transcriptional co-localization of protein-coding and long non-coding RNA pairs in the developing brain. PLoS Genetics, 5, e1000617.Google Scholar
Rabbit, P. (1997). Methodology of frontal and executive function. Hove: Taylor & Francis.Google Scholar
Ramskold, D., Wang, E. T., Burge, C. B., & Sandberg, R. (2009). An abundance of ubiquitously expressed genes revealed by tissue transcriptome sequence data. PLoS Computational Biology, 5, e1000598.Google Scholar
Raz, N., Rodrigue, K. M., Head, D., Kennedy, K. M., & Acker, J. D. (2004). Differential aging of the medial temporal lobe: a study of a five-year change. Neurology, 62, 433–438.Google Scholar
Roth, R. B., Hevezi, P., Lee, J., Willhite, D., Lechner, S. M., Foster, A. C., et al. (2006). Gene expression analyses reveal molecular relationships among 20 regions of the human CNS. Neurogenetics, 7, 67–80.Google Scholar
Sequeira, A., Morgan, L., Walsh, D. M., Cartagena, P. M., Choudary, P., Li, J., et al. (2012). Gene expression changes in the prefrontal cortex, anterior cingulate cortex and nucleus accumbens of mood disorders subjects that committed suicide. PLoS One, 7, e35367.CrossRefGoogle ScholarPubMed
Scherzer, C. R. (2009). Chipping away at diagnostics for neurodegenerative diseases. Neurobiology of Disease, 35, 148–156.Google Scholar
Skullerud, K. (1985). Variations in the size of the human brain. Influence of age, sex, body length, body mass index, alcoholism, Alzheimer changes, and cerebral atherosclerosis. Acta Neurologica Scandinavica Supplementum, 102, 1–94.Google Scholar
Somel, M., Franz, H., Yan, Z., Lorenc, A., Guo, S., Giger, T., et al. (2009). Transcriptional neoteny in the human brain. Proceedings of the National Academy of Sciences, 106, 5743–5757.Google Scholar
Somel, M., Guo, S., Fu, N., Yan, Z., Hu, H. Y., Xu, Y., et al. (2010). MicroRNA, mRNA, and protein expression link development and aging in human and macaque brain. Genome Research, 20, 1207–1218.Google Scholar
Somel, M., Khaitovich, P., Bahn, S., Paabo, S., & Lachmann, M. (2006). Gene expression becomes heterogeneous with age. Current Biology, 16, R359–R360.CrossRefGoogle ScholarPubMed
St Laurent, G., Faghihi, M. A., & Wahlestedt, C. (2009). Non-coding RNA transcripts: Sensors of neuronal stress, modulators of synaptic plasticity, and agents of change in the onset of Alzheimer's disease. Neuroscience Letters, 466, 81–88.Google Scholar
Strand, A. D., Aragaki, A. K., Baquet, Z. C., Hodges, A., Cunningham, P., Holmans, P., et al. (2007). Conservation of regional gene expression in mouse and human brain. PLoS Genetics, 3, e59.Google Scholar
Stuart-Hamilton, I. (2006). The psychology of ageing: An introduction (4th ed.). London: Jessica Kingsley.Google Scholar
Swindell, W. R. (2011). Metallothionein and the biology of aging. Ageing Research Reviews, 10, 132–145.Google Scholar
Terry, R. D., DeTeresa, R., & Hansen, L. A. (1987). Neocortical cell counts in normal human adult aging. Annals of Neurology, 21, 530–539.Google Scholar
‘t Hoen, P. A., Ariyurek, Y., Thygesen, H. H., Vreugdenhil, E., Vossen, R. H., de Menezes, R. X., et al. (2008). Deep sequencing-based expression analysis shows major advances in robustness, resolution and inter-lab portability over five microarray platforms. Nucleic Acids Research, 36, e141.Google Scholar
ToescuE., C. E., C. (2007). Altered calcium homeostasis in old neurons. In Riddle, D. R. (Ed.), Brain aging: Models, methods, and mechanisms. Boca Raton, FL: CRC Press.Google Scholar
Trapnell, C., Pachter, L., & Salzberg, S. L. (2009). TopHat: Discovering splice junctions with RNA-Seq. Bioinformatics, 25, 1105–1111.Google Scholar
Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A. M., Kwan, G., van Baren, M. J., et al. (2010). Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature Biotechnology, 28, 511–515.Google Scholar
van der Brug, M., Nalls, M. A., & Cookson, M. R. (2010). Deep sequencing of coding and non-coding RNA in the CNS. Brain Research, 1338, 146–154.Google Scholar
Von Bernhardi, R., Tichauer, J. E., & Eugenín, J. (2010). Aging-dependent changes of microglial cells and their relevance for neurodegenerative disorders. Journal Neurochemistry, 112, 1099–1114.Google Scholar
Wang, Z., Gerstein, M., & Snyder, M. (2009). RNA-Seq: A revolutionary tool for transcriptomics. Nature Reviews Genetics, 10, 57–63.Google Scholar
Wang, E. T., Sandberg, R., Luo, S., Khrebtukova, I., Zhang, L., Mayr, C., et al. (2008). Alternative isoform regulation in human tissue transcriptomes. Nature, 456, 470–476.CrossRefGoogle ScholarPubMed
Wang, J., Williams, R. W., & Manly, K. F. (2003). WebQTL: Web-based complex trait analysis. Neuroinformatics, 1, 299–308.Google Scholar
Woodruff-Pak, D. S. (1997). The neuropsychology of aging. Oxford: Blackwell.Google Scholar
Wong, D. F., Wagner, H. N. Jr., Dannals, R. F., Links, J. M., Frost, J. J., Ravert, H. T., et al. (1984). Effects of age on dopamine and serotonin receptors measured by positron tomography in the living human brain. Science, 226, 1393–1396.CrossRefGoogle ScholarPubMed
Yang, X., Doser, T. A., Fang, C. X., Nunn, J. M., Janardhanan, R., Zhu, M., et al. (2006). Metallothionein prolongs survival and antagonizes senescence-associated cardiomyocyte diastolic dysfunction: Role of oxidative stress. FASEB Journal, 20, 1024–1026.Google Scholar
Ziats, M. N., & Rennert, O. M. (2011). Expression profiling of autism candidate genes during human brain development implicates central immune signaling pathways. PLoS ONE, 6, e24691.Google Scholar
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