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Cited by 130MicroRNA in disease regulationChromosomal and Genetic VariationsPlant Molecular Biology Research

MicroRNA Expression and Regulation in Human, Chimpanzee, and Macaque Brains

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Hu, Hai Yang, Guo, Song, Xi, Jiang, Yan, Zheng, Fu, Ning, Zhang, Xiaoyu, Menzel, Corinna, Liang, Hongyu +6 more·2011·PLoS Genetics
DOI 10.1371/journal.pgen.1002327PMC PMC3192836
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Abstract

Among other factors, changes in gene expression on the human evolutionary lineage have been suggested to play an important role in the establishment of human-specific phenotypes. However, the molecular mechanisms underlying these expression changes are largely unknown. Here, we have explored the role of microRNA (miRNA) in the regulation of gene expression divergence among adult humans, chimpanzees, and rhesus macaques, in two brain regions: prefrontal cortex and cerebellum. Using a combination of high-throughput sequencing, miRNA microarrays, and Q-PCR, we have shown that up to 11% of the 325 expressed miRNA diverged significantly between humans and chimpanzees and up to 31% between humans and macaques. Measuring mRNA and protein expression in human and chimpanzee brains, we found a significant inverse relationship between the miRNA and the target genes expression divergence, explaining 2%-4% of mRNA and 4%-6% of protein expression differences. Notably, miRNA showing human-specific expression localize in neurons and target genes that are involved in neural functions. Enrichment in neural functions, as well as miRNA-driven regulation on the human evolutionary lineage, was further confirmed by experimental validation of predicted miRNA targets in two neuroblastoma cell lines. Finally, we identified a signature of positive selection in the upstream region of one of the five miRNA with human-specific expression, miR-34c-5p. This suggests that miR-34c-5p expression change took place after the split of the human and the Neanderthal lineages and had adaptive significance. Taken together these results indicate that changes in miRNA expression might have contributed to evolution of human cognitive functions.

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  • Approximately 300 out of ~2,500 total human miRNAs were detected in human brain tissues.

    “Because miRNAs have strong tissue-specific expression patterns, only approximately 300 miRNAs out of ~2,500 total human miRNAs were detected in human brain tissues, and about 100 miRNAs account for most miRNAs in the brain38,39.”

    From Intra- and Inter-individual Variability of microRNA Levels in Human Cerebrospinal Fluid: Critical Implications for Biomarker Discovery (2017) · cites this paper as [39]

    The Materials and Methods section refers to 325 human miRNAs expressed in the brain, which supports the approximate figure. However, the Results/Discussion section reports detecting 413 miRNAs in the human prefrontal cortex or cerebellum, and the cited passages do not establish a total of approximately 2,500 human miRNAs. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • About 100 miRNAs account for most miRNAs in the brain.

    “Because miRNAs have strong tissue-specific expression patterns, only approximately 300 miRNAs out of ~2,500 total human miRNAs were detected in human brain tissues, and about 100 miRNAs account for most miRNAs in the brain38,39.”

    From Intra- and Inter-individual Variability of microRNA Levels in Human Cerebrospinal Fluid: Critical Implications for Biomarker Discovery (2017) · cites this paper as [39]

    The Results/Discussion section on high-throughput sequencing reports 413 miRNAs detected in human prefrontal cortex or cerebellum, but does not say how many account for most brain miRNA expression. Searches for “top 100 miRNA” and for miRNA abundance likewise found no passage supporting the claim about approximately 100 miRNAs. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • miRNAs have strong tissue-specific expression patterns.

    “Because miRNAs have strong tissue-specific expression patterns, only approximately 300 miRNAs out of ~2,500 total human miRNAs were detected in human brain tissues, and about 100 miRNAs account for most miRNAs in the brain38,39.”

    From Intra- and Inter-individual Variability of microRNA Levels in Human Cerebrospinal Fluid: Critical Implications for Biomarker Discovery (2017) · cites this paper as [39]

    The Materials and Methods section reports 325 human miRNAs expressed in brain, but it does not compare their expression across tissues. Searches for tissue-specific miRNA expression found no passage in our copy establishing the claimed strong tissue-specific patterns. (LLM, conf=0.72)

    Experimental — accuracy not yet measured

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References 69 · Cited by 130

Research works printed but not linked: 63

  1. [2]Enard W Khaitovich P Klose J Zollner S Heissig F 2002 Intra- and interspecific variation in primate gene expression patterns. Science 296 340 343 11951044
  2. [3]Caceres M Lachuer J Zapala MA Redmond JC Kudo L 2003 Elevated gene expression levels distinguish human from non-human primate brains. Proc Natl Acad Sci U S A 100 13030 13035 14557539 PMC240739
  3. [4]Gilad Y Oshlack A Smyth GK Speed TP White KP 2006 Expression profiling in primates reveals a rapid evolution of human transcription factors. Nature 440 242 245 16525476
  4. [5]Nowick K Gernat T Almaas E Stubbs L 2009 Differences in human and chimpanzee gene expression patterns define an evolving network of transcription factors in brain. Proc Natl Acad Sci U S A 106 22358 22363 20007773 PMC2799715
  5. [6]Bartel DP 2004 MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116 281 297 14744438
  6. [7]Ambros V 2003 MicroRNA pathways in flies and worms: growth, death, fat, stress, and timing. Cell 113 673 676 12809598
  7. [8]Winter J Jung S Keller S Gregory RI Diederichs S 2009 Many roads to maturity: microRNA biogenesis pathways and their regulation. Nat Cell Biol 11 228 234 19255566
  8. [9]Lewis BP Shih IH Jones-Rhoades MW Bartel DP Burge CB 2003 Prediction of mammalian microRNA targets. Cell 115 787 798 14697198
  9. [10]Lewis BP Burge CB Bartel DP 2005 Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120 15 20 15652477
  10. [11]Nilsen TW 2007 Mechanisms of microRNA-mediated gene regulation in animal cells. Trends Genet 23 243 249 17368621
  11. [12]Huang JC Babak T Corson TW Chua G Khan S 2007 Using expression profiling data to identify human microRNA targets. Nat Methods 4 1045 1049 18026111
  12. [13]He L Hannon GJ 2004 MicroRNAs: small RNAs with a big role in gene regulation. Nat Rev Genet 5 522 531 15211354
  13. [14]Zhao Y Srivastava D 2007 A developmental view of microRNA function. Trends Biochem Sci 32 189 197 17350266
  14. [15]Bushati N Cohen SM 2007 microRNA functions. Annu Rev Cell Dev Biol 23 175 205 17506695
  15. [16]Semendeferi K Armstrong E Schleicher A Zilles K Van Hoesen GW 2001 Prefrontal cortex in humans and apes: a comparative study of area 10. Am J Phys Anthropol 114 224 241 11241188
  16. [17]Rilling JK Insel TR 1999 The primate neocortex in comparative perspective using magnetic resonance imaging. J Hum Evol 37 191 223 10444351
  17. [18]Wood JN Grafman J 2003 Human prefrontal cortex: processing and representational perspectives. Nat Rev Neurosci 4 139 147 12563285
  18. [19]Rhead B Karolchik D Kuhn RM Hinrichs AS Zweig AS 2010 The UCSC Genome Browser database: update 2010. Nucleic Acids Res 38 D613 619 19906737 PMC2808870
  19. [20]Kent WJ 2002 BLAT--the BLAST-like alignment tool. Genome Res 12 656 664 11932250 PMC187518
  20. [21]Altschul SF Gish W Miller W Myers EW Lipman DJ 1990 Basic local alignment search tool. J Mol Biol 215 403 410 2231712
  21. [22]Larkin MA Blackshields G Brown NP Chenna R McGettigan PA 2007 Clustal W and Clustal X version 2.0. Bioinformatics 23 2947 2948 17846036
  22. [23]Robinson MD McCarthy DJ Smyth GK edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26 139 140 19910308 PMC2796818
  23. [24]Khaitovich P Muetzel B She X Lachmann M Hellmann I 2004 Regional patterns of gene expression in human and chimpanzee brains. Genome Res 14 1462 1473 15289471 PMC509255
  24. [25]Grimson A Farh KK Johnston WK Garrett-Engele P Lim LP 2007 MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol Cell 27 91 105 17612493 PMC3800283
  25. [28]Place RF Li LC Pookot D Noonan EJ Dahiya R 2008 MicroRNA-373 induces expression of genes with complementary promoter sequences. Proc Natl Acad Sci U S A 105 1608 1613 18227514 PMC2234192
  26. [29]Vasudevan S Tong Y Steitz JA 2007 Switching from repression to activation: microRNAs can up-regulate translation. Science 318 1931 1934 18048652
  27. [30]Tu K Yu H Hua YJ Li YY Liu L 2009 Combinatorial network of primary and secondary microRNA-driven regulatory mechanisms. Nucleic Acids Res 37 5969 5980 19671526 PMC2764428
  28. [32]Huang da W Sherman BT Lempicki RA 2009 Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4 44 57 19131956
  29. [33]Papadopoulos GL Alexiou P Maragkakis M Reczko M Hatzigeorgiou AG 2009 DIANA-mirPath: Integrating human and mouse microRNAs in pathways. Bioinformatics 25 1991 1993 19435746
  30. [34]Cooke SF Bliss TV 2006 Plasticity in the human central nervous system. Brain 129 1659 1673 16672292
  31. [35]Bliss TV Collingridge GL 1993 A synaptic model of memory: long-term potentiation in the hippocampus. Nature 361 31 39 8421494
  32. [36]Liu C Teng ZQ Santistevan NJ Szulwach KE Guo W Epigenetic regulation of miR-184 by MBD1 governs neural stem cell proliferation and differentiation. Cell Stem Cell 6 433 444 20452318 PMC2867837
  33. [37]Korzhevskii DE Otellin VA 2005 Immunocytochemical detection of astrocytes in brain slices in combination with Nissl staining. Neurosci Behav Physiol 35 639 641 16342622
  34. [38]Pilati N Barker M Panteleimonitis S Donga R Hamann M 2008 A rapid method combining Golgi and Nissl staining to study neuronal morphology and cytoarchitecture. J Histochem Cytochem 56 539 550 18285350 PMC2386766
  35. [39]Krings M Stone A Schmitz RW Krainitzki H Stoneking M 1997 Neandertal DNA sequences and the origin of modern humans. Cell 90 19 30 9230299
  36. [40]Green RE Krause J Briggs AW Maricic T Stenzel U A draft sequence of the Neandertal genome. Science 328 710 722 20448178 PMC5100745
  37. [41]Lujambio A Calin GA Villanueva A Ropero S Sanchez-Cespedes M 2008 A microRNA DNA methylation signature for human cancer metastasis. Proc Natl Acad Sci U S A 105 13556 13561 18768788 PMC2528872
  38. [42]Toyota M Suzuki H Sasaki Y Maruyama R Imai K 2008 Epigenetic silencing of microRNA-34b/c and B-cell translocation gene 4 is associated with CpG island methylation in colorectal cancer. Cancer Res 68 4123 4132 18519671
  39. [43]Cannell IG Bushell M Regulation of Myc by miR-34c: A mechanism to prevent genomic instability? Cell Cycle 9 2726 2730 20603603
  40. [44]Minones-Moyano E Porta S Escaramis G Rabionet R Iraola S MicroRNA profiling of Parkinson's disease brains identifies early downregulation of miR-34b/c which modulate mitochondrial function. Hum Mol Genet 21558425
  41. [45]Lopez-Romero P Gonzalez MA Callejas S Dopazo A Irizarry RA 2010 Processing of Agilent microRNA array data. BMC Res Notes 3 18 20205787 PMC2823597
  42. [46]Fu X Fu N Guo S Yan Z Xu Y 2009 Estimating accuracy of RNA-Seq and microarrays with proteomics. BMC Genomics 10 161 19371429 PMC2676304
  43. [47]Langmead B Trapnell C Pop M Salzberg SL 2009 Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10 R25 19261174 PMC2690996
  44. [48]Clark TA Schweitzer AC Chen TX Staples MK Lu G 2007 Discovery of tissue-specific exons using comprehensive human exon microarrays. Genome Biol 8 R64 17456239 PMC1896007
  45. [49]Friedlander MR Adamidi C Han T Lebedeva S Isenbarger TA 2009 High-resolution profiling and discovery of planarian small RNAs. Proc Natl Acad Sci U S A 106 11546 11551 19564616 PMC2703670
  46. [50]Hu HY Yan Z Xu Y Hu H Menzel C 2009 Sequence features associated with microRNA strand selection in humans and flies. BMC Genomics 10 413 19732433 PMC2751786
  47. [51]Li R Yu C Li Y Lam TW Yiu SM 2009 SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics 25 1966 1967 19497933
  48. [52]Griffiths-Jones S Saini HK van Dongen S Enright AJ 2008 miRBase: tools for microRNA genomics. Nucleic Acids Res 36 D154 158 17991681 PMC2238936
  49. [53]Griffiths-Jones S Grocock RJ van Dongen S Bateman A Enright AJ 2006 miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res 34 D140 144 16381832 PMC1347474
  50. [54]Griffiths-Jones S 2004 The microRNA Registry. Nucleic Acids Res 32 D109 111 14681370 PMC308757
  51. [55]Mi H Dong Q Muruganujan A Gaudet P Lewis S PANTHER version 7: improved phylogenetic trees, orthologs and collaboration with the Gene Ontology Consortium. Nucleic Acids Res 38 D204 210 20015972 PMC2808919
  52. [56]Krek A Grun D Poy MN Wolf R Rosenberg L 2005 Combinatorial microRNA target predictions. Nat Genet 37 495 500 15806104
  53. [57]Betel D Koppal A Agius P Sander C Leslie C Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biol 11 R90 20799968 PMC2945792
  54. [58]Wang X El Naqa IM 2008 Prediction of both conserved and nonconserved microRNA targets in animals. Bioinformatics 24 325 332 18048393
  55. [59]Maragkakis M Alexiou P Papadopoulos GL Reczko M Dalamagas T 2009 Accurate microRNA target prediction correlates with protein repression levels. BMC Bioinformatics 10 295 19765283 PMC2752464
  56. [60]Bandyopadhyay S Mitra R 2009 TargetMiner: microRNA target prediction with systematic identification of tissue-specific negative examples. Bioinformatics 25 2625 2631 19692556
  57. [61]Wen J Parker BJ Jacobsen A Krogh A MicroRNA transfection and AGO-bound CLIP-seq data sets reveal distinct determinants of miRNA action. RNA 17 820 834 21389147 PMC3078732
  58. [62]Saito T Saetrom PA two-step site and mRNA-level model for predicting microRNA targets. BMC Bioinformatics 11 612 21194446 PMC3019229
  59. [63]Silahtaroglu AN Nolting D Dyrskjot L Berezikov E Moller M 2007 Detection of microRNAs in frozen tissue sections by fluorescence in situ hybridization using locked nucleic acid probes and tyramide signal amplification. Nat Protoc 2 2520 2528 17947994
  60. [64]Jankowsky JL Derrick BE Patterson PH 2000 Cytokine responses to LTP induction in the rat hippocampus: a comparison of in vitro and in vivo techniques. Learn Mem 7 400 412 11112799 PMC311345
  61. [65]Wu X Watson M 2009 CORNA: testing gene lists for regulation by microRNAs. Bioinformatics 25 832 833 19181683 PMC2654799
  62. [66]Celniker SE Dillon LA Gerstein MB Gunsalus KC Henikoff S 2009 Unlocking the secrets of the genome. Nature 459 927 930 19536255 PMC2843545
  63. [68]Crawford GE Holt IE Whittle J Webb BD Tai D 2006 Genome-wide mapping of DNase hypersensitive sites using massively parallel signature sequencing (MPSS). Genome Res 16 123 131 16344561 PMC1356136

Authors
Hu, H. Y., Guo, S., Xi, J., Yan, Z., Fu, N., Zhang, X., Menzel, C., Liang, H., Yang, H., Zhao, M., Zeng, R., Chen, W., Pääbo, S., & Khaitovich, P.
Affiliations
  • Chinese Academy of Sciences (CN)
  • Northeast Forestry University (CN)
  • Max Planck Institute for Molecular Genetics (DE)
  • Max Delbrück Center (DE)
  • Max Planck Institute for Evolutionary Anthropology (DE)
Corresponding author
  • Zeng, Rong
  • Khaitovich, Philipp
ORCID
Venue
PLoS Genetics
Publisher
Public Library of Science (PLoS)
Published
2011-10-13
Volume
7
Issue
10
Pages
e1002327
ISSN
  • 1553-7390 (ISSN-L)
  • 1553-7404
DOI
10.1371/journal.pgen.1002327
PMC
PMC3192836
OpenAlex
W1995744844
Cited by
130
References
69
Citations by year
2026: 4 · 2025: 4 · 2024: 2 · 2023: 4 · 2021: 3 · 2020: 6 · 2019: 5 · 2018: 5 · 2017: 14 · 2016: 10 · 2015: 14 · 2014: 26 · 2013: 17 · 2012: 16
FWCI
6.42
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS Genetics (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • MDC Repository (Max-Delbrueck-Center for Molecular Medicine) (acceptedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Max Planck Institute for Plasma Physics (submittedVersion)
  • Figshare (submittedVersion, cc-by)
  • MPG.PuRe (Max Planck Society) (submittedVersion, cc-by)
Funders
  • Chinese Academy of Sciences (KSCX2-YW-R)
  • Ministry of Science and Technology of the People's Republic of China (2006CB910700, 2011CB910601)
  • Max-Planck-Gesellschaft
  • Universität Zürich
  • Directorate for Biological Sciences
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › MicroRNA in disease regulation
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Chromosomal and Genetic Variations
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Plant Molecular Biology Research
Keywords
microRNA, human evolution, gene expression divergence, prefrontal cortex, cerebellum, chimpanzees, rhesus monkeys, miRNA target genes, miR-34c-5p, positive selection, human cognitive evolution, neuroblastoma cell lines
MeSH terms
Animals, Brain, Cell Line, Cerebellum, Pan troglodytes, Cognition, Gene Expression Regulation, Humans, Macaca, Neurons, Phenotype, Phylogeny, Selection, Genetic, Species Specificity, Gene Expression, Prefrontal Cortex, MicroRNAs, Microarray Analysis

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