The transcriptome profile of human trisomy 21 blood cells.
Blood Cells
/ metabolism
Carbon-Nitrogen Ligases
/ genetics
Chromosomes, Human, Pair 21
/ genetics
Down Syndrome
/ epidemiology
Energy Metabolism
/ genetics
Gene Expression Regulation
/ genetics
Genome, Human
/ genetics
Humans
Intellectual Disability
/ epidemiology
Mitochondria
/ genetics
Myxovirus Resistance Proteins
/ genetics
Phosphoribosylglycinamide Formyltransferase
/ genetics
RNA-Seq
Reduced Folate Carrier Protein
/ genetics
Software
Transcriptome
/ genetics
Blood cells
Down syndrome
Human chromosome 21
RNA sequencing
Transcriptome
Trisomy 21
Journal
Human genomics
ISSN: 1479-7364
Titre abrégé: Hum Genomics
Pays: England
ID NLM: 101202210
Informations de publication
Date de publication:
01 05 2021
01 05 2021
Historique:
received:
07
01
2021
accepted:
14
04
2021
entrez:
2
5
2021
pubmed:
3
5
2021
medline:
5
2
2022
Statut:
epublish
Résumé
Trisomy 21 (T21) is a genetic alteration characterised by the presence of an extra full or partial human chromosome 21 (Hsa21) leading to Down syndrome (DS), the most common form of intellectual disability (ID). It is broadly agreed that the presence of extra genetic material in T21 gives origin to an altered expression of genes located on Hsa21 leading to DS phenotype. The aim of this study was to analyse T21 and normal control blood cell gene expression profiles obtained by total RNA sequencing (RNA-Seq). The results were elaborated by the TRAM (Transcriptome Mapper) software which generated a differential transcriptome map between human T21 and normal control blood cells providing the gene expression ratios for 17,867 loci. The obtained gene expression profiles were validated through real-time reverse transcription polymerase chain reaction (RT-PCR) assay and compared with previously published data. A post-analysis through transcriptome mapping allowed the identification of the segmental (regional) variation of the expression level across the whole genome (segment-based analysis of expression). Interestingly, the most over-expressed genes encode for interferon-induced proteins, two of them (MX1 and MX2 genes) mapping on Hsa21 (21q22.3). The altered expression of genes involved in mitochondrial translation and energy production also emerged, followed by the altered expression of genes encoding for the folate cycle enzyme, GART, and the folate transporter, SLC19A1. The alteration of these pathways might be linked and involved in the manifestation of ID in DS.
Sections du résumé
BACKGROUND
Trisomy 21 (T21) is a genetic alteration characterised by the presence of an extra full or partial human chromosome 21 (Hsa21) leading to Down syndrome (DS), the most common form of intellectual disability (ID). It is broadly agreed that the presence of extra genetic material in T21 gives origin to an altered expression of genes located on Hsa21 leading to DS phenotype. The aim of this study was to analyse T21 and normal control blood cell gene expression profiles obtained by total RNA sequencing (RNA-Seq).
RESULTS
The results were elaborated by the TRAM (Transcriptome Mapper) software which generated a differential transcriptome map between human T21 and normal control blood cells providing the gene expression ratios for 17,867 loci. The obtained gene expression profiles were validated through real-time reverse transcription polymerase chain reaction (RT-PCR) assay and compared with previously published data. A post-analysis through transcriptome mapping allowed the identification of the segmental (regional) variation of the expression level across the whole genome (segment-based analysis of expression). Interestingly, the most over-expressed genes encode for interferon-induced proteins, two of them (MX1 and MX2 genes) mapping on Hsa21 (21q22.3). The altered expression of genes involved in mitochondrial translation and energy production also emerged, followed by the altered expression of genes encoding for the folate cycle enzyme, GART, and the folate transporter, SLC19A1.
CONCLUSIONS
The alteration of these pathways might be linked and involved in the manifestation of ID in DS.
Identifiants
pubmed: 33933170
doi: 10.1186/s40246-021-00325-4
pii: 10.1186/s40246-021-00325-4
pmc: PMC8088681
doi:
Substances chimiques
MX1 protein, human
0
MX2 protein, human
0
Myxovirus Resistance Proteins
0
Reduced Folate Carrier Protein
0
SLC19A1 protein, human
0
Phosphoribosylglycinamide Formyltransferase
EC 2.1.2.2
Carbon-Nitrogen Ligases
EC 6.3.-
GART protein, human
EC 6.3.4.13
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
25Références
Parker SE, Mai CT, Canfield MA, Rickard R, Wang Y, Meyer RE, et al. Updated national birth prevalence estimates for selected birth defects in the United States, 2004-2006. Birth Defects Res A Clin Mol Teratol. 2010;88(12):1008–16. https://doi.org/10.1002/bdra.20735 .
pubmed: 20878909
Strippoli P, Pelleri MC, Piovesan A, Caracausi M, Antonaros F, Vitale L. Genetics and genomics of Down syndrome. Int Rev Res Dev Disabil. 2019;56:1–39. https://doi.org/10.1016/bs.irrdd.2019.06.001 .
Lejeune J, Gauthier M, Turpin R. Human chromosomes in tissue cultures. Comptes rendus hebdomadaires des seances de l’Academie des sciences. 1959;248(4):602–3.
pubmed: 13629913
Pelleri MC, Cicchini E, Petersen MB, Tranebjaerg L, Mattina T, Magini P, et al. Partial trisomy 21 map: ten cases further supporting the highly restricted Down syndrome critical region (HR-DSCR) on human chromosome 21. Mol Genet Genomic Med. 2019;7:e797.
pubmed: 31237416
pmcid: 6687668
Piovesan A, Pelleri MC, Antonaros F, Strippoli P, Caracausi M, Vitale L. On the length, weight and GC content of the human genome. BMC Res Notes. 2019;12(1):106. https://doi.org/10.1186/s13104-019-4137-z .
pubmed: 30813969
pmcid: 6391780
Piovesan A, Antonaros F, Vitale L, Strippoli P, Pelleri MC, Caracausi M. Human protein-coding genes and gene feature statistics in 2019. BMC Res Notes. 2019;12(1):315. https://doi.org/10.1186/s13104-019-4343-8 .
pubmed: 31164174
pmcid: 6549324
Piovesan A, Caracausi M, Antonaros F, Pelleri MC, Vitale L. GeneBase 1.1: a tool to summarize data from NCBI gene datasets and its application to an update of human gene statistics. Database (Oxford). 2016;2016.
Piovesan A, Caracausi M, Ricci M, Strippoli P, Vitale L, Pelleri MC. Identification of minimal eukaryotic introns through GeneBase, a user-friendly tool for parsing the NCBI Gene databank. DNA Res. 2015;22(6):495–503. https://doi.org/10.1093/dnares/dsv028 .
pubmed: 26581719
pmcid: 4675715
Letourneau A, Santoni FA, Bonilla X, Sailani MR, Gonzalez D, Kind J, et al. Domains of genome-wide gene expression dysregulation in Down’s syndrome. Nature. 2014;508(7496):345–50. https://doi.org/10.1038/nature13200 .
pubmed: 24740065
Olmos-Serrano JL, Kang HJ, Tyler WA, Silbereis JC, Cheng F, Zhu Y, et al. Down syndrome developmental brain transcriptome reveals defective oligodendrocyte differentiation and myelination. Neuron. 2016;89(6):1208–22. https://doi.org/10.1016/j.neuron.2016.01.042 .
pubmed: 26924435
pmcid: 4795969
Pelleri MC, Cattani C, Vitale L, Antonaros F, Strippoli P, Locatelli C, et al. Integrated quantitative transcriptome maps of human trisomy 21 tissues and cells. Front Genet. 2018;9:125. https://doi.org/10.3389/fgene.2018.00125 .
pubmed: 29740474
pmcid: 5928158
Antonarakis SE. Down syndrome and the complexity of genome dosage imbalance. Nat Rev Genet. 2017;18(3):147–63. https://doi.org/10.1038/nrg.2016.154 .
pubmed: 28029161
Conrad B, Antonarakis SE. Gene duplication: a drive for phenotypic diversity and cause of human disease. Annu Rev Genomics Hum Genet. 2007;8(1):17–35. https://doi.org/10.1146/annurev.genom.8.021307.110233 .
pubmed: 17386002
Lejeune J. Biochemical investigations and trisomy 21 (author’s transl). Ann Genet. 1979;22(2):67–75.
pubmed: 227317
Pagano G, Castello G. Oxidative stress and mitochondrial dysfunction in Down syndrome. Adv Exp Med Biol. 2012;724:291–9. https://doi.org/10.1007/978-1-4614-0653-2_22 .
pubmed: 22411251
Duchon A, Herault Y. DYRK1A, a dosage-sensitive gene involved in neurodevelopmental disorders, is a target for drug development in Down syndrome. Front Behav Neurosci. 2016;10:104.
pubmed: 27375444
pmcid: 4891327
Patel A, Yamashita N, Ascano M, Bodmer D, Boehm E, Bodkin-Clarke C, et al. RCAN1 links impaired neurotrophin trafficking to aberrant development of the sympathetic nervous system in Down syndrome. Nat Commun. 2015;6(1):10119. https://doi.org/10.1038/ncomms10119 .
pubmed: 26658127
Guedj F, Pennings JL, Massingham LJ, Wick HC, Siegel AE, Tantravahi U, et al. An integrated human/murine transcriptome and pathway approach to identify prenatal treatments for Down syndrome. Sci Rep. 2016;6(1):32353. https://doi.org/10.1038/srep32353 .
pubmed: 27586445
pmcid: 5009456
Vilardell M, Rasche A, Thormann A, Maschke-Dutz E, Perez-Jurado LA, Lehrach H, et al. Meta-analysis of heterogeneous Down syndrome data reveals consistent genome-wide dosage effects related to neurological processes. BMC Genomics. 2011;12(1):229. https://doi.org/10.1186/1471-2164-12-229 .
pubmed: 21569303
pmcid: 3110572
Bhattacharyya R, Sanyal D, Bhattacharyya S. Diagnostic algorithm of Down syndrome by minor physical anomaly. Indian J Psychiatry. 2018;60(4):398–403. https://doi.org/10.4103/psychiatry.IndianJPsychiatry_401_17 .
pubmed: 30581204
pmcid: 6278205
Costa Ade F, Franco OL. Insights into RNA transcriptome profiling of cardiac tissue in obesity and hypertension conditions. J Cell Physiol. 2015;230(5):959–68. https://doi.org/10.1002/jcp.24807 .
pubmed: 25393239
Chen L, Sun F, Yang X, Jin Y, Shi M, Wang L, et al. Correlation between RNA-Seq and microarrays results using TCGA data. Gene. 2017;628:200–4. https://doi.org/10.1016/j.gene.2017.07.056 .
pubmed: 28734892
Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 2012;7(3):562–78. https://doi.org/10.1038/nprot.2012.016 .
pubmed: 22383036
pmcid: 3334321
Wang C, Gong B, Bushel PR, Thierry-Mieg J, Thierry-Mieg D, Xu J, et al. The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance. Nat Biotechnol. 2014;32(9):926–32. https://doi.org/10.1038/nbt.3001 .
pubmed: 25150839
pmcid: 4243706
Costa-Silva J, Domingues D, Lopes FM. RNA-Seq differential expression analysis: an extended review and a software tool. PLoS One. 2017;12(12):e0190152. https://doi.org/10.1371/journal.pone.0190152 .
pubmed: 29267363
pmcid: 5739479
Wang H, Guan Q, Nan Y, Ma Q, Zhong Y. Overexpression of human MX2 gene suppresses cell proliferation, migration, and invasion via ERK/P38/NF-κB pathway in glioblastoma cells. J Cell Biochem. 2019;120(11):18762–70. https://doi.org/10.1002/jcb.29189 .
pubmed: 31265172
Zhao S, Fung-Leung WP, Bittner A, Ngo K, Liu X. Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells. PLoS One. 2014;9(1):e78644. https://doi.org/10.1371/journal.pone.0078644 .
pubmed: 24454679
pmcid: 3894192
Timmons JA, Atherton PJ, Larsson O, Sood S, Blokhin IO, Brogan RJ, et al. A coding and non-coding transcriptomic perspective on the genomics of human metabolic disease. Nucleic Acids Res. 2018;46(15):7772–92. https://doi.org/10.1093/nar/gky570 .
pubmed: 29986096
pmcid: 6125682
Vitale L, Piovesan A, Antonaros F, Strippoli P, Pelleri MC, Caracausi M. A molecular view of the normal human thyroid structure and function reconstructed from its reference transcriptome map. BMC Genomics. 2017;18(1):739. https://doi.org/10.1186/s12864-017-4049-z .
pubmed: 28923001
pmcid: 5604164
Malone JH, Oliver B. Microarrays, deep sequencing and the true measure of the transcriptome. BMC Biol. 2011;9(1):34. https://doi.org/10.1186/1741-7007-9-34 .
pubmed: 21627854
pmcid: 3104486
Lenzi L, Facchin F, Piva F, Giulietti M, Pelleri MC, Frabetti F, et al. TRAM (Transcriptome Mapper): database-driven creation and analysis of transcriptome maps from multiple sources. BMC Genomics. 2011;12(1):121. https://doi.org/10.1186/1471-2164-12-121 .
pubmed: 21333005
pmcid: 3052188
Powers RK, Culp-Hill R, Ludwig MP, Smith KP, Waugh KA, Minter R, et al. Trisomy 21 activates the kynurenine pathway via increased dosage of interferon receptors. Nat Commun. 2019;10(1):4766. https://doi.org/10.1038/s41467-019-12739-9 .
pubmed: 31628327
pmcid: 6800452
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21. https://doi.org/10.1093/bioinformatics/bts635 .
pubmed: 23104886
Ameur A, Zaghlool A, Halvardson J, Wetterbom A, Gyllensten U, Cavelier L, et al. Total RNA sequencing reveals nascent transcription and widespread co-transcriptional splicing in the human brain. Nat Struct Mol Biol. 2011;18(12):1435–40. https://doi.org/10.1038/nsmb.2143 .
pubmed: 22056773
Lejeune J, Rethore MO, de Blois MC, Maunoury-Burolla C, Mir M, Nicolle L, et al. Metabolism of monocarbons and trisomy 21: sensitivity to methotrexate. Ann Genet. 1986;29(1):16–9.
pubmed: 2940958
Vitale L, Serpieri V, Lauriola M, Piovesan A, Antonaros F, Cicchini E, et al. Human trisomy 21 fibroblasts rescue methotrexate toxic effect after treatment with 5-methyl-tetrahydrofolate and 5-formyl-tetrahydrofolate. J Cell Physiol. 2019;234(9):15010–24. https://doi.org/10.1002/jcp.28140 .
Costa V, Angelini C, D’Apice L, Mutarelli M, Casamassimi A, Sommese L, et al. Massive-scale RNA-Seq analysis of non ribosomal transcriptome in human trisomy 21. PLoS One. 2011;6(4):e18493. https://doi.org/10.1371/journal.pone.0018493 .
pubmed: 21533138
pmcid: 3080369
Sullivan KD, Lewis HC, Hill AA, Pandey A, Jackson LP, Cabral JM, et al. Trisomy 21 consistently activates the interferon response. eLife. 2016;5. https://doi.org/10.7554/eLife.16220 .
Bordi M, Darji S, Sato Y, Mellén M, Berg MJ, Kumar A, et al. mTOR hyperactivation in Down syndrome underlies deficits in autophagy induction, autophagosome formation, and mitophagy. Cell Death Dis. 2019;10(8):563. https://doi.org/10.1038/s41419-019-1752-5 .
pubmed: 31332166
pmcid: 6646359
Stawowczyk M, Van Scoy S, Kumar KP, Reich NC. The interferon stimulated gene 54 promotes apoptosis. J Biol Chem. 2011;286(9):7257–66. https://doi.org/10.1074/jbc.M110.207068 .
pubmed: 21190939
Ciminski K, Pulvermüller J, Adam J, Schwemmle M. Human MxA is a potent interspecies barrier for the novel bat-derived influenza A-like virus H18N11. Emerg Microbes Infect. 2019;8(1):556–63. https://doi.org/10.1080/22221751.2019.1599301 .
pubmed: 30945621
pmcid: 6455144
John SP, Sun J, Carlson RJ, Cao B, Bradfield CJ, Song J, et al. IFIT1 exerts opposing regulatory effects on the inflammatory and interferon gene programs in LPS-activated human macrophages. Cell Rep. 2018;25(1):95–106.e6.
pubmed: 30282041
pmcid: 6492923
Ma H, Yang W, Zhang L, Liu S, Zhao M, Zhou G, et al. Interferon-alpha promotes immunosuppression through IFNAR1/STAT1 signalling in head and neck squamous cell carcinoma. Br J Cancer. 2019;120(3):317–30. https://doi.org/10.1038/s41416-018-0352-y .
pubmed: 30555157
Cao H, Krueger EW, Chen J, Drizyte-Miller K, Schulz ME, McNiven MA. The anti-viral dynamin family member MxB participates in mitochondrial integrity. Nat Commun. 2020;11(1):1048. https://doi.org/10.1038/s41467-020-14727-w .
pubmed: 32102993
pmcid: 7044337
Dumbrepatil AB, Zegalia KA, Sajja K, Kennedy RT, Marsh ENG. Targeting viperin to the mitochondrion inhibits the thiolase activity of the trifunctional enzyme complex. J Biol Chem. 2020;295(9):2839–49. https://doi.org/10.1074/jbc.RA119.011526 .
pubmed: 31980458
pmcid: 7049979
Ritchie C, Cordova AF, Hess GT, Bassik MC, Li L. SLC19A1 is an importer of the immunotransmitter cGAMP. Mol Cell. 2019;75(2):372–81.e5.
pubmed: 31126740
pmcid: 6711396
Verstegen RHJ, Kusters MAA. Inborn errors of adaptive immunity in Down syndrome. J Clin Immunol. 2020;40(6):791–806. https://doi.org/10.1007/s10875-020-00805-7 .
pubmed: 32638194
Caracausi M, Ghini V, Locatelli C, Mericio M, Piovesan A, Antonaros F, et al. Plasma and urinary metabolomic profiles of Down syndrome correlate with alteration of mitochondrial metabolism. Sci Rep. 2018;8(1):2977. https://doi.org/10.1038/s41598-018-20834-y .
pubmed: 29445163
pmcid: 5813015
Pecze L, Randi EB, Szabo C. Meta-analysis of metabolites involved in bioenergetic pathways reveals a pseudohypoxic state in Down syndrome. Mol Med. 2020;26(1):102.
pubmed: 33167881
pmcid: 7653803
Caracausi M, Piovesan A, Vitale L, Pelleri MC. Integrated transcriptome map highlights structural and functional aspects of the normal human heart. J Cell Physiol. 2017;232(4):759–70. https://doi.org/10.1002/jcp.25471 .
pubmed: 27345625
Lenzi L, Frabetti F, Facchin F, Casadei R, Vitale L, Canaider S, et al. UniGene Tabulator: a full parser for the UniGene format. Bioinformatics. 2006;22(20):2570–1. https://doi.org/10.1093/bioinformatics/btl425 .
pubmed: 16895929
Caracausi M, Rigon V, Piovesan A, Strippoli P, Vitale L, Pelleri MC. A quantitative transcriptome reference map of the normal human hippocampus. Hippocampus. 2016;26(1):13–26. https://doi.org/10.1002/hipo.22483 .
pubmed: 26108741
Chomczynski P, Sacchi N. Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction. Anal Biochem. 1987;162(1):156–9. https://doi.org/10.1016/0003-2697(87)90021-2 .
pubmed: 2440339
Cunningham F, Achuthan P, Akanni W, Allen J, Amode MR, Armean IM, et al. Ensembl 2019. Nucleic Acids Res. 2019;47(D1):D745–d51. https://doi.org/10.1093/nar/gky1113 .
pubmed: 30407521
Barrett T, Edgar R. Gene expression omnibus: microarray data storage, submission, retrieval, and analysis. Methods Enzymol. 2006;411:352–69. https://doi.org/10.1016/S0076-6879(06)11019-8 .
pubmed: 16939800
pmcid: 1619900
NCBI Resource Coordinators. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2018;46(D1):D8–d13. https://doi.org/10.1093/nar/gkx1095 .
Piovesan A, Antonaros F, Strippoli P, Vitale L, Pelleri MC, Caracausi M. Reference quantitative transcriptome dataset for adult Caenorhabditis elegans. Data Brief. 2019;25:104152. https://doi.org/10.1016/j.dib.2019.104152 .
pubmed: 31440537
pmcid: 6700341
Chen J, Bardes EE, Aronow BJ, Jegga AG. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 2009;37(Web Server issue):W305–11.
pubmed: 19465376
pmcid: 2703978
Engels WR. Contributing software to the internet: the Amplify program. Trends Biochem Sci. 1993;18(11):448–50. https://doi.org/10.1016/0968-0004(93)90148-G .
pubmed: 8291093
Sharrocks A. The design of primer for PCR. In: Griffin HG, Griffin AM, editors. PCR technology—current innovations. Boca Raton: CRC Press; 1994. p. 5–11.
Antonaros F, Olivucci G, Cicchini E, Ramacieri G, Pelleri MC, Vitale L, et al. MTHFR C677T polymorphism analysis: a simple, effective restriction enzyme-based method improving previous protocols. Mol Genet Genomic Med. 2019;7(5):e628. https://doi.org/10.1002/mgg3.628 .
pubmed: 30868767
pmcid: 6503068
Caracausi M, Vitale L, Pelleri MC, Piovesan A, Bruno S, Strippoli P. A quantitative transcriptome reference map of the normal human brain. Neurogenetics. 2014;15(4):267–87. https://doi.org/10.1007/s10048-014-0419-8 .
pubmed: 25185649
Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2
pubmed: 11846609