Integrative multi-omic analysis reveals conserved cell-projection deficits in human Down syndrome brains
Open accessAbstract
Down syndrome (DS) is the most common genetic cause of cognitive disability. However, it is largely unclear how triplication of a small gene subset may impinge on diverse aspects of DS brain physiopathology. Here, we took a multi-omic approach and simultaneously analyzed by RNA-seq and proteomics the expression signatures of two diverse regions of human postmortem DS brains. We found that the overexpression of triplicated genes triggered global expression dysregulation, differentially affecting transcripts, miRNAs, and proteins involved in both known and novel biological candidate pathways. Among the latter, we observed an alteration in RNA splicing, specifically modulating the expression of genes involved in cytoskeleton and axonal dynamics in DS brains. Accordingly, we found an alteration in axonal polarization in neurons from DS human iPSCs and mice. Thus, our study provides an integrated multilayer expression database capable of identifying new potential targets to aid in designing future clinical interventions for DS.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
These data are consistent with a previous report comparing bulk proteomics and bulk RNAseq in brain samples from young individuals with DS compared with euploid-matched controls, which also reported a discordance between transcript and protein levels.
“These data are consistent with a previous report comparing bulk proteomics and bulk RNAseq in brain samples from young individuals with DS compared with euploid-matched controls, which also reported a discordance between transcript and protein levels [56].”
From Apolipoprotein E abundance is elevated in the brains of individuals with Down syndrome–Alzheimer’s disease (2025) · cites this paper as [56]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredHomeostatic processes can regulate the abundance of proteins, such that a raised level of a transcript does not always increase the abundance of the corresponding protein.
“Homeostatic processes can regulate the abundance of proteins, such that the raised level of a transcript does not always increase the abundance of the corresponding protein [8, 56, 59].”
From Apolipoprotein E abundance is elevated in the brains of individuals with Down syndrome–Alzheimer’s disease (2025) · cites this paper as [56]
The Results (page unavailable) show that mRNA and corresponding protein changes often differ in direction, supporting the point that a raised transcript level need not mean more protein. The Discussion (page unavailable) suggests posttranscriptional or posttranslational regulation, but does not establish that the processes responsible are homeostatic. (LLM, conf=0.82)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Part of this paper’s reference list could not be split into single entries.
A survey of best practices for RNA-seq data analysis
Conesa, A., Madrigal, P., Tarazona, S., Gomez-Cabrero, D., Cervera, A., McPherson, A., Szcześniak, M. W., Gaffney, D. J., Elo, L. L., Zhang, X., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0881-8 · Cited by 2,794 · see citing works
Transcriptomic Analysis of Autistic Brain Reveals Convergent Molecular Pathology
Voineagu, I., Wang, X., Johnston, P., Lowe, J. K., Tian, Y., Horvath, S., Mill, J., Cantor, R. M., Blencowe, B. J., & Geschwind, D. H. (2011) · Nature · doi:10.1038/nature10110 · Cited by 1,874 · see citing works
Kulak, N. A., Pichler, G., Paron, I., Nagaraj, N., & Mann, M. (2014) · Nature Methods · doi:10.1038/nmeth.2834 · Cited by 1,719 · see citing works
A compendium of RNA-binding motifs for decoding gene regulation
Ray, D., Kazan, H., Cook, K. B., Weirauch, M. T., Najafabadi, H. S., Li, X., Gueroussov, S., Albu, M., Zheng, H., Yang, A., et al. (2013) · Nature · doi:10.1038/nature12311 · Cited by 1,480 · see citing works
Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder
Gandal, M. J., Zhang, P., Hadjimichael, E., Walker, R. L., Chen, C., Liu, S., Won, H., van Bakel, H., Varghese, M., Wang, Y., et al. (2018) · Science · doi:10.1126/science.aat8127 · Cited by 1,247 · see citing works
mRNAs, proteins and the emerging principles of gene expression control
Buccitelli, C., & Selbach, M. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-020-0258-4 · Cited by 1,243 · see citing works
Context-dependent control of alternative splicing by RNA-binding proteins
Fu, X.-D., & Ares, M. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3778 · Cited by 1,044 · see citing works
Cell type- and brain region-resolved mouse brain proteome
Sharma, K., Schmitt, S., Bergner, C. G., Tyanova, S., Kannaiyan, N., Manrique-Hoyos, N., Kongi, K., Cantuti, L., Hanisch, U.-K., Philips, M.-A., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.4160 · Cited by 871 · see citing works
A Highly Conserved Program of Neuronal Microexons Is Misregulated in Autistic Brains
Irimia, M., Weatheritt, R. J., Ellis, J. D., Parikshak, N. N., Gonatopoulos-Pournatzis, T., Babor, M., Quesnel-Vallières, M., Tapial, J., Raj, B., O’Hanlon, D., et al. (2014) · Cell · doi:10.1016/j.cell.2014.11.035 · Cited by 693 · see citing works
Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism
Parikshak, N. N., Swarup, V., Belgard, T. G., Irimia, M., Ramaswami, G., Gandal, M. J., Hartl, C., Leppa, V., Ubieta, L. D. L. T., Huang, J., et al. (2016) · Nature · doi:10.1038/nature20612 · Cited by 685 · see citing works
Genetic identification of brain cell types underlying schizophrenia
Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Skene, N. G., Bryois, J., Bakken, T. E., Breen, G., Crowley, J. J., Gaspar, H. A., Giusti-Rodriguez, P., Hodge, R. D., Miller, J. A., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0129-5 · Cited by 651 · see citing works
Single-nucleus and single-cell transcriptomes compared in matched cortical cell types
Bakken, T. E., Hodge, R. D., Miller, J. A., Yao, Z., Nguyen, T. N., Aevermann, B., Barkan, E., Bertagnolli, D., Casper, T., Dee, N., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0209648 · Cited by 608 · see citing works
Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders
Parikshak, N. N., Gandal, M. J., & Geschwind, D. H. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3934 · Cited by 430 · see citing works
Dementia in Down syndrome: unique insights for Alzheimer disease research
Lott, I. T., & Head, E. (2019) · Nature Reviews Neurology · doi:10.1038/s41582-018-0132-6 · Cited by 383 · see citing works
Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans
Thrupp, N., Sala Frigerio, C., Wolfs, L., Skene, N. G., Fattorelli, N., Poovathingal, S., Fourne, Y., Matthews, P. M., Theys, T., Mancuso, R., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108189 · Cited by 329 · see citing works
PSD-95 is post-transcriptionally repressed during early neural development by PTBP1 and PTBP2
Zheng, S., Gray, E. E., Chawla, G., Porse, B. T., O'Dell, T. J., & Black, D. L. (2012) · Nature Neuroscience · doi:10.1038/nn.3026 · Cited by 254 · see citing works
Kahlem, P., Sultan, M., Herwig, R., Steinfath, M., Balzereit, D., Eppens, B., Saran, N. G., Pletcher, M. T., South, S. T., Stetten, G., et al. (2004) · Genome Research · doi:10.1101/gr.1951304 · Cited by 249 · see citing works
Gene expression profiling in the adult Down syndrome brain
Lockstone, H. E., Harris, L. W., Swatton, J. E., Wayland, M. T., Holland, A. J., & Bahn, S. (2007) · Genomics · doi:10.1016/j.ygeno.2007.08.005 · Cited by 240 · see citing works
Olmos-Serrano, J. L., Kang, H. J., Tyler, W. A., Silbereis, J. C., Cheng, F., Zhu, Y., Pletikos, M., Jankovic-Rapan, L., Cramer, N. P., Galdzicki, Z., et al. (2016) · Neuron · doi:10.1016/j.neuron.2016.01.042 · Cited by 239 · see citing works
Carlesimo, G. A., Marotta, L., & Vicari, S. (1997) · Neuropsychologia · doi:10.1016/s0028-3932(96)00055-3 · Cited by 211 · see citing works
Jin, M., Xu, R., Wang, L., Alam, M. M., Ma, Z., Zhu, S., Martini, A. C., Jadali, A., Bernabucci, M., Xie, P., et al. (2022) · Cell Stem Cell · doi:10.1016/j.stem.2022.06.007 · Cited by 174 · see citing works
Pinto, B., Morelli, G., Rastogi, M., Savardi, A., Fumagalli, A., Petretto, A., Bartolucci, M., Varea, E., Catelani, T., Contestabile, A., et al. (2020) · Neuron · doi:10.1016/j.neuron.2020.09.010 · Cited by 135 · see citing works
Self-citationHuo, H.-Q., Qu, Z.-Y., Yuan, F., Ma, L., Yao, L., Xu, M., Hu, Y., Ji, J., Bhattacharyya, A., Zhang, S.-C., et al. (2018) · Stem Cell Reports · doi:10.1016/j.stemcr.2018.02.001 · Cited by 122 · see citing works
Altered cell and RNA isoform diversity in aging Down syndrome brains
Palmer, C. R., Liu, C. S., Romanow, W. J., Lee, M.-H., & Chun, J. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2114326118 · Cited by 89 · see citing works
Gonzales, P. K., Roberts, C. M., Fonte, V., Jacobsen, C., Stein, G. H., & Link, C. D. (2018) · PLOS ONE · doi:10.1371/journal.pone.0194581 · Cited by 45 · see citing works
Altered patterning of trisomy 21 interneuron progenitors
Giffin-Rao, Y., Sheng, J., Strand, B., Xu, K., Huang, L., Medo, M., Risgaard, K. A., Dantinne, S., Mohan, S., Keshan, A., et al. (2022) · Stem Cell Reports · doi:10.1016/j.stemcr.2022.05.001 · Cited by 38 · see citing works
Laan, L., Klar, J., Sobol, M., Hoeber, J., Shahsavani, M., Kele, M., Fatima, A., Zakaria, M., Annerén, G., Falk, A., et al. (2020) · Clinical Epigenetics · doi:10.1186/s13148-019-0803-1 · Cited by 33 · see citing works
Flagged first, then by citations
Research works printed but not linked: 117
- [2]2. Vicari, S. ∙ Bellucci, S. ∙ Carlesimo, G.A. Implicit and explicit memory: a functional dissociation in persons with Down syndrome Neuropsychologia. 2000; 38:240-251 Crossref Scopus (112) PubMed Google Scholar
- [3]3. Roizen, N.J. ∙ Patterson, D. Down’s syndrome Lancet. 2003; 361:1281-1289 Full Text Full Text (PDF) Scopus (958) PubMed Google Scholar
- [4]4. Sherman, S.L. ∙ Allen, E.G. ∙ Bean, L.H. ... Epidemiology of Down syndrome Ment. Retard. Dev. Disabil. Res. Rev. 2007; 13:221-227 Crossref Scopus (349) PubMed Google Scholar
- [5]5. Contestabile, A. ∙ Benfenati, F. ∙ Gasparini, L. Communication breaks-Down: from neurodevelopment defects to cognitive disabilities in Down syndrome Prog. Neurobiol. 2010; 91:1-22 Crossref Scopus (0) PubMed Google Scholar
- [6]6. Rachidi, M. ∙ Lopes, C. Mental retardation and human Chromosome 21 gene overdosage: from functional genomics and molecular mechanisms towards prevention and treatment of the neuropathogenesis of Down syndrome Clelland, J.D. (Editor) Genomics, Proteomics, and the Nervous System Springer, 2011; 21-86 Crossref Google Scholar
- [7]7. Kazemi, M. ∙ Salehi, M. ∙ Kheirollahi, M. Down syndrome: current status, challenges and future perspectives Int. J. Mol. Cell. Med. 2016; 5:125-133 PubMed Google Scholar
- [8]8. Mégarbané, A. ∙ Ravel, A. ∙ Mircher, C. ... The 50th anniversary of the discovery of trisomy 21: The past, present, and future of research and treatment of Down syndrome Genet. Med. 2009; 11:611-616 Full Text Full Text (PDF) Scopus (176) PubMed Google Scholar
- [9]9. Letourneau, A. ∙ Santoni, F.A. ∙ Bonilla, X. ... Domains of genome-wide gene expression dysregulation in Down’s syndrome Nature. 2014; 508:345-350 Crossref Scopus (227) PubMed Google Scholar
- [10]10. Wiseman, F.K. ∙ Alford, K.A. ∙ Tybulewicz, V.L.J. ... Down syndrome--recent progress and future prospects Hum. Mol. Genet. 2009; 18:R75-R83 Crossref Scopus (194) PubMed Google Scholar
- [11]11. Herault, Y. ∙ Delabar, J.M. ∙ Fisher, E.M.C. ... Rodent models in Down syndrome research: impact and future opportunities Dis. Model. Mech. 2017; 10:1165-1186 Crossref Scopus (129) PubMed Google Scholar
- [12]12. Vacca, R.A. ∙ Bawari, S. ∙ Valenti, D. ... Down syndrome: neurobiological alterations and therapeutic targets Neurosci. Biobehav. Rev. 2019; 98:234-255 Crossref Scopus (62) PubMed Google Scholar
- [13]13. Rueda, N. ∙ Flórez, J. ∙ Dierssen, M. ... Translational validity and implications of pharmacotherapies in preclinical models of Down syndrome Prog. Brain Res. 2020; 251:245-268 Crossref Scopus (20) PubMed Google Scholar
- [14]14. Sommer, C.A. ∙ Pavarino-Bertelli, E.C. ∙ Goloni-Bertollo, E.M. ... Identification of dysregulated genes in lymphocytes from children with Down syndrome Genome. 2008; 51:19-29 Crossref Scopus (37) PubMed Google Scholar
- [15]15. Guedj, F. ∙ Pennings, J.L.A. ∙ Massingham, L.J. ... An integrated human/murine transcriptome and pathway approach to identify prenatal treatments for Down syndrome Sci. Rep. 2016; 6, 32353 Crossref Scopus (42) PubMed Google Scholar
- [16]16. Liu, Y. ∙ Borel, C. ∙ Li, L. ... Systematic proteome and proteostasis profiling in human trisomy 21 fibroblast cells Nat. Commun. 2017; 8, 1212 Crossref Scopus (84) Google Scholar
- [17]17. Liu, H. ∙ Wang, H. ∙ Zhu, H. ... Preliminary study of protein changes in trisomy 21 fetus by proteomics analysis in amniocyte Prenat. Diagn. 2018; 38:435-444 Crossref Scopus (3) PubMed Google Scholar
- [18]18. Stamoulis, G. ∙ Garieri, M. ∙ Makrythanasis, P. ... Single cell transcriptome in aneuploidies reveals mechanisms of gene dosage imbalance Nat. Commun. 2019; 10, 4495 Crossref Scopus (26) PubMed Google Scholar
- [19]19. Waugh, K.A. ∙ Araya, P. ∙ Pandey, A. ... Mass cytometry reveals global immune remodeling with multi-lineage hypersensitivity to Type I interferon in Down syndrome Cell Rep. 2019; 29:1893-1908.e4 Full Text Full Text (PDF) Scopus (68) PubMed Google Scholar
- [20]20. Lanzillotta, C. ∙ Greco, V. ∙ Valentini, D. ... Proteomics study of peripheral blood mononuclear cells in Down syndrome children Antioxidants (Basel). 2020; 9, 1112 Crossref Scopus (5) Google Scholar
- [23]23. Sobol, M. ∙ Klar, J. ∙ Laan, L. ... Transcriptome and proteome profiling of neural induced pluripotent stem cells from individuals with Down syndrome disclose dynamic dysregulations of key pathways and cellular functions Mol. Neurobiol. 2019; 56:7113-7127 Crossref Scopus (40) PubMed Google Scholar
- [29]29. Liu, Y. ∙ Aebersold, R. The interdependence of transcript and protein abundance: new data–new complexities Mol. Syst. Biol. 2016; 12, 856 Crossref Scopus (21) Google Scholar
- [31]31. Pan, Q. ∙ Shai, O. ∙ Lee, L.J. ... Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing Nat. Genet. 2008; 40:1413-1415 Crossref Scopus (2846) PubMed Google Scholar
- [32]32. Wang, E.T. ∙ Sandberg, R. ∙ Luo, S. ... Alternative isoform regulation in human tissue transcriptomes Nature. 2008; 456:470-476 Crossref Scopus (3908) PubMed Google Scholar
- [33]33. Darmanis, S. ∙ Sloan, S.A. ∙ Zhang, Y. ... A survey of human brain transcriptome diversity at the single cell level Proc. Natl. Acad. Sci. USA. 2015; 112:7285-7290 Crossref Scopus (895) PubMed Google Scholar
- [34]34. Lake, B.B. ∙ Ai, R. ∙ Kaeser, G.E. ... Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain Science. 2016; 352:1586-1590 Crossref Scopus (594) PubMed Google Scholar
- [35]35. Lake, B.B. ∙ Chen, S. ∙ Sos, B.C. ... Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain Nat. Biotechnol. 2018; 36:70-80 Crossref Scopus (562) PubMed Google Scholar
- [37]37. Macosko, E.Z. ∙ Basu, A. ∙ Satija, R. ... Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets Cell. 2015; 161:1202-1214 Full Text Full Text (PDF) Scopus (4617) PubMed Google Scholar
- [38]38. Usoskin, D. ∙ Furlan, A. ∙ Islam, S. ... Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing Nat. Neurosci. 2015; 18:145-153 Crossref Scopus (1423) PubMed Google Scholar
- [39]39. Zeisel, A. ∙ Muñoz-Manchado, A.B. ∙ Codeluppi, S. ... Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq Science. 2015; 347:1138-1142 Crossref Scopus (2104) PubMed Google Scholar
- [40]40. Tasic, B. ∙ Yao, Z. ∙ Graybuck, L.T. ... Shared and distinct transcriptomic cell types across neocortical areas Nature. 2018; 563:72-78 Crossref Scopus (909) PubMed Google Scholar
- [42]42. Créau, N. Molecular and cellular alterations in Down syndrome: toward the identification of targets for therapeutics Neural Plast. 2012; 2012, 171639 Crossref Scopus (25) PubMed Google Scholar
- [43]43. Zhang, Z. ∙ Stolrow, H.G. ∙ Christensen, B.C. ... Down syndrome altered cell composition in blood, brain, and buccal swab samples profiled by DNA-methylation-based cell-type deconvolution Cells. 2023; 12, 1168 Crossref Scopus (5) Google Scholar
- [44]44. Contestabile, A. ∙ Magara, S. ∙ Cancedda, L. The GABAergic hypothesis for cognitive disabilities in Down syndrome Front. Cell. Neurosci. 2017; 11, 54 Crossref Scopus (91) PubMed Google Scholar
- [46]46. Weaver, C.L. ∙ Espinoza, M. ∙ Kress, Y. ... Conformational change as one of the earliest alterations of tau in Alzheimer’s disease Neurobiol. Aging. 2000; 21:719-727 Crossref Scopus (275) PubMed Google Scholar
- [47]47. Koss, D.J. ∙ Jones, G. ∙ Cranston, A. ... Soluble pre-fibrillar tau and β-amyloid species emerge in early human Alzheimer’s disease and track disease progression and cognitive decline Acta Neuropathol. 2016; 132:875-895 Crossref Scopus (92) PubMed Google Scholar
- [48]48. Oddo, S. ∙ Caccamo, A. ∙ Shepherd, J.D. ... Triple-transgenic model of Alzheimer’s disease with plaques and tangles: intracellular Abeta and synaptic dysfunction Neuron. 2003; 39:409-421 Full Text Full Text (PDF) Scopus (3451) PubMed Google Scholar
- [49]49. Johnson, E.C.B. ∙ Carter, E.K. ∙ Dammer, E.B. ... Large-scale deep multi-layer analysis of Alzheimer’s disease brain reveals strong proteomic disease-related changes not observed at the RNA level Nat. Neurosci. 2022; 25:213-225 Crossref Scopus (166) PubMed Google Scholar
- [50]50. Lewis, B.P. ∙ Burge, C.B. ∙ Bartel, D.P. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets Cell. 2005; 120:15-20 Full Text Full Text (PDF) Scopus (10308) PubMed Google Scholar
- [51]51. Agarwal, V. ∙ Bell, G.W. ∙ Nam, J.-W. ... Predicting effective microRNA target sites in mammalian mRNAs eLife. 2015; 4, e05005 Crossref Scopus (5300) PubMed Google Scholar
- [52]52. Ule, J. ∙ Blencowe, B.J. Alternative splicing regulatory networks: functions, mechanisms, and evolution Mol. Cell. 2019; 76:329-345 Full Text Full Text (PDF) Scopus (399) PubMed Google Scholar
- [53]53. Tapial, J. ∙ Ha, K.C.H. ∙ Sterne-Weiler, T. ... An atlas of alternative splicing profiles and functional associations reveals new regulatory programs and genes that simultaneously express multiple major isoforms Genome Res. 2017; 27:1759-1768 Crossref Scopus (239) PubMed Google Scholar
- [54]54. Schafer, S. ∙ Miao, K. ∙ Benson, C.C. ... Alternative splicing signatures in RNA-seq data: percent spliced in (PSI) Curr. Protoc. Hum. Genet. 2015; 87:11.16.1-11.16.14 Crossref Scopus (97) PubMed Google Scholar
- [56]56. Raj, B. ∙ Blencowe, B.J. Alternative splicing in the mammalian nervous system: recent insights into mechanisms and functional roles Neuron. 2015; 87:14-27 Full Text Full Text (PDF) Scopus (325) PubMed Google Scholar
- [57]57. Capponi, S. ∙ Stöffler, N. ∙ Irimia, M. ... Neuronal-specific microexon splicing of TAF1 mRNA is directly regulated by SRRM4/nSR100 RNA Biol. 2020; 17:62-74 Crossref Scopus (14) PubMed Google Scholar
- [58]58. Gonatopoulos-Pournatzis, T. ∙ Niibori, R. ∙ Salter, E.W. ... Autism-misregulated eIF4G microexons control synaptic translation and higher order cognitive functions Mol. Cell. 2020; 77:1176-1192.e16 Full Text Full Text (PDF) Scopus (64) PubMed Google Scholar
- [59]59. Lin, L. ∙ Zhang, M. ∙ Stoilov, P. ... Developmental attenuation of neuronal apoptosis by neural-specific splicing of Bak1 microexon Neuron. 2020; 107:1180-1196.e8 Full Text Full Text (PDF) Scopus (42) PubMed Google Scholar
- [60]60. Monteuuis, G. ∙ Wong, J.J.L. ∙ Bailey, C.G. ... The changing paradigm of intron retention: regulation, ramifications and recipes Nucleic Acids Res. 2019; 47:11497-11513 Crossref Scopus (94) PubMed Google Scholar
- [62]62. Gerstberger, S. ∙ Hafner, M. ∙ Tuschl, T. A census of human RNA-binding proteins Nat. Rev. Genet. 2014; 15:829-845 Crossref Scopus (1360) PubMed Google Scholar
- [63]63. Duchon, A. ∙ Raveau, M. ∙ Chevalier, C. ... Identification of the translocation breakpoints in the Ts65Dn and Ts1Cje mouse lines: relevance for modeling Down syndrome Mamm. Genome. 2011; 22:674-684 Crossref Scopus (167) PubMed Google Scholar
- [64]64. Weick, J.P. ∙ Held, D.L. ∙ Bonadurer, G.F. ... Deficits in human trisomy 21 iPSCs and neurons Proc. Natl. Acad. Sci. USA. 2013; 110:9962-9967 Crossref Scopus (151) PubMed Google Scholar
- [65]65. Cheng, P.L. ∙ Poo, M.M. Early events in axon/dendrite polarization Annu. Rev. Neurosci. 2012; 35:181-201 Crossref Scopus (84) PubMed Google Scholar
- [67]67. Yu, T. ∙ Li, Z. ∙ Jia, Z. ... A mouse model of Down syndrome trisomic for all human chromosome 21 syntenic regions Hum. Mol. Genet. 2010; 19:2780-2791 Crossref Scopus (180) PubMed Google Scholar
- [68]68. Di Domenico, F. ∙ Coccia, R. ∙ Cocciolo, A. ... Impairment of proteostasis network in Down syndrome prior to the development of Alzheimer’s disease neuropathology: redox proteomics analysis of human brain Biochim. Biophys. Acta. 2013; 1832:1249-1259 Crossref Scopus (106) PubMed Google Scholar
- [69]69. Pelechano, V. ∙ Wei, W. ∙ Steinmetz, L.M. Extensive transcriptional heterogeneity revealed by isoform profiling Nature. 2013; 497:127-131 Crossref Scopus (320) PubMed Google Scholar
- [70]70. Melé, M. ∙ Ferreira, P.G. ∙ Reverter, F. ... Human genomics. The human transcriptome across tissues and individuals Science. 2015; 348:660-665 Crossref Scopus (888) PubMed Google Scholar
- [72]72. Goeldner, C. ∙ Kishnani, P.S. ∙ Skotko, B.G. ... A randomized, double-blind, placebo-controlled phase II trial to explore the effects of a GABAA-α5 NAM (basmisanil) on intellectual disability associated with Down syndrome J. Neurodev. Disord. 2022; 14, 10 Crossref Scopus (16) PubMed Google Scholar
- [74]74. Powers, R.K. ∙ Culp-Hill, R. ∙ Ludwig, M.P. ... Trisomy 21 activates the kynurenine pathway via increased dosage of interferon receptors Nat. Commun. 2019; 10, 4766 Crossref Scopus (62) PubMed Google Scholar
- [77]77. Seyfried, N.T. ∙ Dammer, E.B. ∙ Swarup, V. ... A multi-network approach identifies protein-specific co-expression in asymptomatic and symptomatic Alzheimer’s disease Cell Syst. 2017; 4:60-72.e4 Full Text Full Text (PDF) Scopus (275) PubMed Google Scholar
- [78]78. Poulopoulos, A. ∙ Murphy, A.J. ∙ Ozkan, A. ... Subcellular transcriptomes and proteomes of developing axon projections in the cerebral cortex Nature. 2019; 565:356-360 Crossref Scopus (103) PubMed Google Scholar
- [79]79. Wang, D. ∙ Eraslan, B. ∙ Wieland, T. ... A deep proteome and transcriptome abundance atlas of 29 healthy human tissues Mol. Syst. Biol. 2019; 15, e8503 Crossref Scopus (418) Google Scholar
- [80]80. Mangleburg, C.G. ∙ Wu, T. ∙ Yalamanchili, H.K. ... Integrated analysis of the aging brain transcriptome and proteome in tauopathy Mol. Neurodegener. 2020; 15, 56 Crossref Scopus (18) PubMed Google Scholar
- [81]81. Liu, Y. ∙ Beyer, A. ∙ Aebersold, R. On the dependency of cellular protein levels on mRNA abundance Cell. 2016; 165:535-550 Full Text Full Text (PDF) Scopus (1865) PubMed Google Scholar
- [82]82. Meissner, F. ∙ Geddes-McAlister, J. ∙ Mann, M. ... The emerging role of mass spectrometry-based proteomics in drug discovery Nat. Rev. Drug Discov. 2022; 21:637-654 Crossref Scopus (122) PubMed Google Scholar
- [83]83. Bartel, D.P. MicroRNAs: genomics, biogenesis, mechanism, and function Cell. 2004; 116:281-297 Full Text Full Text (PDF) Scopus (31464) PubMed Google Scholar
- [84]84. Vuong, C.K. ∙ Black, D.L. ∙ Zheng, S. The neurogenetics of alternative splicing Nat. Rev. Neurosci. 2016; 17:265-281 Crossref Scopus (224) PubMed Google Scholar
- [85]85. Da Cruz, S. ∙ Cleveland, D.W. Understanding the role of TDP-43 and FUS/TLS in ALS and beyond Curr. Opin. Neurobiol. 2011; 21:904-919 Crossref Scopus (285) PubMed Google Scholar
- [86]86. Fromer, M. ∙ Roussos, P. ∙ Sieberts, S.K. ... Gene expression elucidates functional impact of polygenic risk for schizophrenia Nat. Neurosci. 2016; 19:1442-1453 Crossref Scopus (711) PubMed Google Scholar
- [88]88. Jaffe, A.E. ∙ Straub, R.E. ∙ Shin, J.H. ... Developmental and genetic regulation of the human cortex transcriptome illuminate schizophrenia pathogenesis Nat. Neurosci. 2018; 21:1117-1125 Crossref Scopus (224) PubMed Google Scholar
- [89]89. Furlanis, E. ∙ Traunmüller, L. ∙ Fucile, G. ... Landscape of ribosome-engaged transcript isoforms reveals extensive neuronal-cell-class-specific alternative splicing programs Nat. Neurosci. 2019; 22:1709-1717 Crossref Scopus (74) PubMed Google Scholar
- [92]92. Toiber, D. ∙ Azkona, G. ∙ Ben-Ari, S. ... Engineering DYRK1A overdosage yields Down syndrome-characteristic cortical splicing aberrations Neurobiol. Dis. 2010; 40:348-359 Crossref Scopus (51) PubMed Google Scholar
- [93]93. Vilardell, M. ∙ Rasche, A. ∙ Thormann, A. ... Meta-analysis of heterogeneous Down syndrome data reveals consistent genome-wide dosage effects related to neurological processes BMC Genomics. 2011; 12, 229 Crossref Scopus (95) PubMed Google Scholar
- [94]94. Kuhn, D.E. ∙ Martin, M.M. ∙ Feldman, D.S. ... Experimental validation of miRNA targets Methods. 2008; 44:47-54 Crossref Scopus (321) PubMed Google Scholar
- [96]96. Muskens, I.S. ∙ Li, S. ∙ Jackson, T. ... The genome-wide impact of trisomy 21 on DNA methylation and its implications for hematopoiesis Nat. Commun. 2021; 12, 821 Crossref Scopus (33) PubMed Google Scholar
- [97]97. Zhang, X. ∙ Chen, M.H. ∙ Wu, X. ... Cell-type-specific alternative splicing governs cell fate in the developing cerebral cortex Cell. 2016; 166:1147-1162.e15 Full Text Full Text (PDF) Scopus (222) PubMed Google Scholar
- [100]100. Zhang, M. ∙ Ergin, V. ∙ Lin, L. ... Axonogenesis is Coordinated by Neuron-Specific Alternative Splicing Programming and Splicing Regulator PTBP2 Neuron. 2019; 101:690-706.e10 Full Text Full Text (PDF) Scopus (54) PubMed Google Scholar
- [101]101. Benavides-Piccione, R. ∙ Ballesteros-Yáñez, I. ∙ de Lagrán, M.M. ... On dendrites in Down syndrome and DS murine models: a spiny way to learn Prog. Neurobiol. 2004; 74:111-126 Crossref Scopus (111) PubMed Google Scholar
- [102]102. Cramer, N. ∙ Galdzicki, Z. From abnormal hippocampal synaptic plasticity in Down syndrome mouse models to cognitive disability in Down syndrome Neural Plast. 2012; 2012, 101542 Crossref Scopus (47) PubMed Google Scholar
- [103]103. Haas, M.A. ∙ Bell, D. ∙ Slender, A. ... Alterations to dendritic spine morphology, but not dendrite patterning, of cortical projection neurons in Tc1 and Ts1Rhr mouse models of Down syndrome PLoS One. 2013; 8, e78561 Crossref Scopus (38) Google Scholar
- [104]104. Engle, E.C. Human genetic disorders of axon guidance Cold Spring Harbor Perspect. Biol. 2010; 2, a001784 Crossref Scopus (161) PubMed Google Scholar
- [105]105. Nakai, N. ∙ Takumi, T. ∙ Nakai, J. ... Common defects of spine dynamics and circuit function in neurodevelopmental disorders: A systematic review of findings from in vivo optical imaging of mouse models Front. Neurosci. 2018; 12, 412 Crossref Scopus (36) PubMed Google Scholar
- [106]106. Lo, L.H.-Y. ∙ Lai, K.-O. Dysregulation of protein synthesis and dendritic spine morphogenesis in ASD: studies in human pluripotent stem cells Mol. Autism. 2020; 11, 40 Crossref Scopus (23) PubMed Google Scholar
- [107]107. Bahn, S. ∙ Mimmack, M. ∙ Ryan, M. ... Neuronal target genes of the neuron-restrictive silencer factor in neurospheres derived from fetuses with Down’s syndrome: a gene expression study Lancet. 2002; 359:310-315 Full Text Full Text (PDF) Scopus (184) PubMed Google Scholar
- [108]108. Guo, Y. ∙ Liu, Z. ∙ Chen, Y.K. ... Neurons with multiple axons have functional axon initial segments Neurosci. Bull. 2017; 33:641-652 Crossref Scopus (13) PubMed Google Scholar
- [110]110. Cox, J. ∙ Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification Nat. Biotechnol. 2008; 26:1367-1372 Crossref Scopus (10738) PubMed Google Scholar
- [111]111. Tyanova, S. ∙ Temu, T. ∙ Cox, J. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics Nat. Protoc. 2016; 11:2301-2319 Crossref Scopus (2786) PubMed Google Scholar
- [112]112. Dobin, A. ∙ Davis, C.A. ∙ Schlesinger, F. ... STAR: ultrafast universal RNA-seq aligner Bioinformatics Oxf. Engl. 2013; 29:15-21 Crossref Scopus (26779) PubMed Google Scholar
- [113]113. Huang, D.W. ∙ Sherman, B.T. ∙ Lempicki, R.A. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists Nucleic Acids Res. 2009; 37:1-13 Crossref Scopus (11188) PubMed Google Scholar
- [114]114. Shannon, P. ∙ Markiel, A. ∙ Ozier, O. ... Cytoscape: A software environment for integrated models of biomolecular interaction networks Genome Res. 2003; 13:2498-2504 Crossref Scopus (32316) PubMed Google Scholar
- [115]115. Li, B. ∙ Dewey, C.N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome BMC Bioinformatics. 2011; 12, 323 Crossref Scopus (13883) Google Scholar
- [116]116. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads EMBnet.journal. 2011; 17:10-12 Crossref Google Scholar
- [117]117. Lu, Y. ∙ Baras, A.S. ∙ Halushka, M.K. miRge 2.0 for comprehensive analysis of microRNA sequencing data BMC Bioinformatics. 2018; 19, 275 Crossref Scopus (52) Google Scholar
- [118]118. Kozomara, A. ∙ Birgaoanu, M. ∙ Griffiths-Jones, S. miRBase: from microRNA sequences to function Nucleic Acids Res. 2019; 47:D155-D162 Crossref Scopus (2694) PubMed Google Scholar
- [119]119. Robinson, M.D. ∙ McCarthy, D.J. ∙ Smyth, G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data Bioinformatics Oxf. Engl. 2010; 26:139-140 Crossref Scopus (26556) PubMed Google Scholar
- [120]120. Skene, N.G. ∙ Grant, S.G.N. Identification of vulnerable cell types in major brain disorders using single cell transcriptomes and expression weighted cell type enrichment Front. Neurosci. 2016; 10, 16 Crossref Scopus (168) PubMed Google Scholar
- [121]121. Yu, G. ∙ Wang, L. ∙ Han, Y. ... clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS A Journal of Integrative Biology. 2012; 16:284-287 Crossref Scopus (18309) PubMed Google Scholar
- [122]122. Gu, Z. ∙ Eils, R. ∙ Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data Bioinformatics. 2016; 32:2847-2849 Crossref Scopus (4724) PubMed Google Scholar
- [123]123. Barter, R.L. ∙ Yu, B. Superheat: an R package for creating beautiful and extendable heatmaps for visualizing complex data J. Comput. Graph. Stat. 2018; 27:910-922 Crossref Scopus (124) PubMed Google Scholar
- [124]124. Langfelder, P. ∙ Horvath, S. WGCNA: an R package for weighted correlation network analysis BMC Bioinformatics. 2008; 9, 559 Crossref Scopus (14566) PubMed Google Scholar
- [125]125. Leng, N. ∙ Dawson, J.A. ∙ Thomson, J.A. ... EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments Bioinformatics. 2013; 29:1035-1043 Crossref Scopus (914) PubMed Google Scholar
- [126]126. Lawrence, M. ∙ Gentleman, R. ∙ Carey, V. rtracklayer: an R package for interfacing with genome browsers Bioinformatics Oxf. Engl. 2009; 25:1841-1842 Crossref Scopus (456) PubMed Google Scholar
- [127]127. Yin, T. ∙ Cook, D. ∙ Lawrence, M. ggbio: an R package for extending the grammar of graphics for genomic data Genome Biol. 2012; 13, R77 Crossref Scopus (226) PubMed Google Scholar
- [128]128. Gel, B. ∙ Serra, E. karyoploteR: an R/Bioconductor package to plot customizable genomes displaying arbitrary data Bioinformatics. 2017; 33:3088-3090 Crossref Scopus (354) PubMed Google Scholar
- [129]129. Gel, B. ∙ Díez-Villanueva, A. ∙ Serra, E. ... regioneR: an R/Bioconductor package for the association analysis of genomic regions based on permutation tests Bioinformatics. 2016; 32:289-291 Crossref Scopus (295) PubMed Google Scholar
- [130]130. Wiseman, F.K. ∙ Al-Janabi, T. ∙ Hardy, J. ... A genetic cause of Alzheimer disease: mechanistic insights from Down syndrome Nat. Rev. Neurosci. 2015; 16:564-574 Crossref Scopus (373) PubMed Google Scholar
- [131]131. Reeves, R.H. ∙ Irving, N.G. ∙ Moran, T.H. ... A mouse model for Down syndrome exhibits learning and behaviour deficits Nat. Genet. 1995; 11:177-184 Crossref Scopus (789) PubMed Google Scholar
- [132]132. Reinholdt, L.G. ∙ Ding, Y. ∙ Gilbert, G.J. ... Molecular characterization of the translocation breakpoints in the Down syndrome mouse model Ts65Dn Mamm. Genome. 2011; 22:685-691 Crossref Scopus (146) PubMed Google Scholar
- [133]133. Parrini, M. ∙ Naskar, S. ∙ Alberti, M. ... Restoring neuronal chloride homeostasis with anti-NKCC1 gene therapy rescues cognitive deficits in a mouse model of Down syndrome Mol. Ther. 2021; 29:3072-3092 Full Text Full Text (PDF) Scopus (17) PubMed Google Scholar
- [134]134. Savardi, A. ∙ Borgogno, M. ∙ Narducci, R. ... Discovery of a small molecule drug candidate for selective NKCC1 inhibition in brain disorders Chem. 2020; 6:2073-2096 Full Text Full Text (PDF) Scopus (46) PubMed Google Scholar
- [136]136. Ewels, P. ∙ Magnusson, M. ∙ Lundin, S. ... MultiQC: summarize analysis results for multiple tools and samples in a single report Bioinformatics. 2016; 32:3047-3048 Crossref Scopus (3624) PubMed Google Scholar
- [137]137. Yates, A.D. ∙ Achuthan, P. ∙ Akanni, W. ... Ensembl 2020 Nucleic Acids Res. 2020; 48:D682-D688 Crossref Scopus (1119) PubMed Google Scholar
- [139]139. Robinson, M.D. ∙ Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data Genome Biol. 2010; 11, R25 Crossref Scopus (4948) PubMed Google Scholar
- [140]140. Ritchie, M.E. ∙ Phipson, B. ∙ Wu, D. ... limma powers differential expression analyses for RNA-sequencing and microarray studies Nucleic Acids Res. 2015; 43, e47 Crossref Scopus (21508) PubMed Google Scholar
- [142]142. Gel, B. ∙ Serra, E. karyoploteR: an R/Bioconductor package to plot customizable genomes displaying arbitrary data Bioinformatics. 2017; 33:3088-3090 Crossref Scopus (354) PubMed Google Scholar
- [144]144. Batth, T.S. ∙ Tollenaere, M.X. ∙ Rüther, P. ... Protein Aggregation Capture on Microparticles Enables Multipurpose Proteomics Sample Preparation Mol. Cell. Proteom. 2019; 18:1027-1035 Full Text Full Text (PDF) Scopus (160) PubMed Google Scholar
- [146]146. L.P.Bouvrette, B. ∙ Bovaird, S. ∙ Blanchette, M. ... oRNAment: a database of putative RNA binding protein target sites in the transcriptomes of model species Nucl. Acids Res. 2019; 48:D166-D173 Crossref Scopus (56) Google Scholar
- [147]147. Grant, C.E. ∙ Bailey, T.L. ∙ Noble, W.S. FIMO: scanning for occurrences of a given motif Bioinformatics. 2011; 27:1017-1018 Crossref Scopus (2522) PubMed Google Scholar
- [148]148. Bailey, T.L. ∙ Johnson, J. ∙ Grant, C.E. ... The MEME suite Nucleic Acids Res. 2015; 43:W39-W49 Crossref Scopus (2260) PubMed Google Scholar
Cited by
Sorted by citations
Farrell, C., Buhidma, Y., Mumford, P., Heywood, W. E., Hällqvist, J., Flores-Aguilar, L., Andrews, E. J., Rahimzadah, N., Taso, O. S., Doran, E., et al. (2025) · Acta Neuropathologica · doi:10.1007/s00401-025-02889-0 · Cited by 9 · see citing works
“Homeostatic processes can regulate the abundance of proteins, such that the raised level of a transcript does not always increase the abundance of the corresponding protein [8, 56, 59].”
- Authors
- Rastogi, M., Bartolucci, M., Nanni, M., Aloisio, M., Vozzi, D., Petretto, A., Contestabile, A., & Cancedda, L.
- Affiliations
- Italian Institute of Technology (IT)
- Istituto Giannina Gaslini (IT)
- Corresponding author
- Contestabile, Andrea
- Cancedda, Laura
- ORCID
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2024-08
- Volume
- 112
- Issue
- 15
- Pages
- 2503-2523.e10
- ISSN
- 0896-6273 (ISSN-L)
- OpenAlex
- W4399107398
- Cited by
- 26
- References
- 27
- Citations by year
- 2026: 12 · 2025: 14
- FWCI
- 10.77
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 371 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- ArTS Archivio della ricerca di Trieste (University of Trieste https://www.units.it/) (submittedVersion)
- Funders
- European Research Council (725563)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Down syndrome and intellectual disability research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Neurodevelopmental Disorders
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Keywords
- Down syndrome, gene overexpression, RNA splicing, cytoskeleton, human induced pluripotent stem cells, postmortem brain, microRNA, cognitive disability, therapeutic target
- MeSH terms
- Multiomics, Animals, Axons, Brain, Down Syndrome, Female, Humans, Male, Neurons, RNA Splicing, MicroRNAs, Proteomics, Mice, Induced Pluripotent Stem Cells
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
