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Alzheimer’s disease-associated U1 snRNP splicing dysfunction causes neuronal hyperexcitability and cognitive impairment

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Abstract

Recent proteome and transcriptome profiling of Alzheimer’s disease (AD) brains reveals RNA splicing dysfunction and U1 small nuclear ribonucleoprotein (snRNP) pathology containing U1-70K and its N-terminal 40-KDa fragment (N40K). Here we present a causative role of U1 snRNP dysfunction to neurodegeneration in primary neurons and transgenic mice (N40K-Tg), in which N40K expression exerts a dominant-negative effect to downregulate full-length U1-70K. N40K-Tg recapitulates N40K insolubility, erroneous splicing events, neuronal degeneration and cognitive impairment. Specifically, N40K-Tg shows the reduction of GABAergic synapse components (for example, the GABA receptor subunit of GABRA2) and concomitant postsynaptic hyperexcitability that is rescued by a GABA receptor agonist. Crossing of N40K-Tg and the 5xFAD amyloidosis model indicates that the RNA splicing defect synergizes with the amyloid cascade to remodel the brain transcriptome and proteome, deregulate synaptic proteins and accelerate cognitive decline. Thus, our results support the contribution of U1 snRNP-mediated splicing dysfunction to AD pathogenesis.

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Fig. 1: Tissue proteomics confirms U1 snRNP aggregation in AD brains whereas N40K shows detergent insolubility and dominant-negative effects to deplete U1-70K through proteasomal degradation in primary neurons.
Fig. 2: Comprehensive analysis of transcriptome and proteome reveals synaptic pathway in N40K-induced neuron death.
Fig. 3: Biochemical and cellular characterization of the N40K-Tg mouse model.
Fig. 4: N40K-Tg mice display neuron loss and cognitive impairment.
Fig. 5: N40K-Tg mice exhibit AD-related splicing defects enriched in synaptic function.
Fig. 6: N40K-Tg mice show GABRA2 reduction, postsynaptic hyperexcitation and LTP impairment.
Fig. 7: Synergistic effects of human N40K and amyloid pathway on synaptic deregulation and cognitive deficiency.

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Data availability

ROSMAP data can be download from this website (https://adknowledgeportal.synapse.org/) and mapped to human transcriptome (hg19). All mouse RNA-seq data were deposited in the GEO database under accession codes GSE115177 (mouse brain) and GSE196873 containing two sub-series IDs, GSE196871 (mouse brain) and GSE196872 (mouse neuronal culture) and mapped to the mouse transcriptome (mm9). The MS proteomics data were deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifiers PXD031587 (human aggregated proteome), PXD031581 (mouse aggregated proteome), PXD031546 (mouse neuronal culture) and PXD023395 and PXD031545 (mouse brain). Both hg19 and mm9 can be download from UCSC website (https://genome.ucsc.edu/index.html). Other data underlying the paper are provided as source data files or are available from the corresponding author upon reasonable request.

Code availability

The source codes are available at https://github.com/PengLabStJude/N40K_model.

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Acknowledgements

We thank all other laboratory and center members for discussion and technical support. We thank St Jude Shared Resources and Core Facilities, including the Animal Research Center, Transgenic/Gene Knockout, In Vivo Imaging and Therapeutics, Veterinary Pathology, Cytogenetics, Genome Sequencing, Proteomics and Metabolomics, Applied Bioinformatics and Cell and Tissue Imaging. We also thank J. Jankowsky for providing plasmids, I. Bayazitov for electrophysiology, F. Zheng for the discussion of behavior studies, A. Hemphill for western blots of replicates during revision and I. Chen for helpful discussion and guidance. This work was partially supported by National Institutes of Health grants R01AG047928 (J.P.), R01AG053987 (J.P.), RF1AG068581 (J.P.), RF1AG064909 (G.Y. and J.P.), U54NS110435 (J.P.), U19AG069701 (J.P.), R01MH095810 (S.S.Z.) and American Lebanese Syrian Associated Charities. The Banner Sun Health Research Institute Brain and Body Donation Program was supported by National Institutes of Health grants U24NS072026, P30AG072980, P30AG19610, the Arizona Department of Health Services, the Arizona Biomedical Research Commission and the Michael J. Fox Foundation for Parkinson’s Research.

Author information

Author notes

  1. Lauren N. Beloate

    Present address: Department of Biomedical Engineering and Electrical Engineering, Penn State University, State College, PA, USA

  2. Bing Bai

    Present address: Department of Laboratory Medicine, Center for Precision Medicine, Nanjing Drum Tower Hospital, Nanjing University Medical School, Nanjing, China

  3. Joseph Mertz

    Present address: GlaxoSmithKline, Rockville, MD, USA

  4. Xusheng Wang

    Present address: Department of Biology, University of North Dakota, Grand Forks, ND, USA

  5. Laurie R. Earls

    Present address: Department of Biological Sciences, Loyola University of New Orleans, New Orleans, LA, USA

  6. Richard J. Smeyne

    Present address: Department of Neuroscience, Thomas Jefferson University, Philadelphia, PA, USA

  7. Tudor Moldoveanu

    Present address: Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AK, USA

  8. Gang Yu

    Present address: Department of Neuroscience, Peter O’Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA

  9. These authors contributed equally: Ping-Chung Chen, Xian Han.

Authors and Affiliations

  1. Department of Structural Biology, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Ping-Chung Chen, Xian Han, Yingxue Fu, Huan Sun, Mingming Niu, Zhen Wang, Yun Jiao, Bing Bai, Joseph Mertz, Yuxin Li, Zhiping Wu, Danting Liu, Suresh Poudel, Ariana Mancieri, Tudor Moldoveanu & Junmin Peng

  2. Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Ping-Chung Chen, Xian Han, Yingxue Fu, Huan Sun, Mingming Niu, Zhen Wang, Yun Jiao, Brett J. W. Teubner, Donnie Eddins, Lauren N. Beloate, Bing Bai, Joseph Mertz, Yuxin Li, Zhiping Wu, Danting Liu, Suresh Poudel, Ariana Mancieri, Mary H. Patton, Laurie R. Earls, Elizabeth Stewart, Michael A. Dyer, Richard J. Smeyne, Stanislav S. Zakharenko & Junmin Peng

  3. Integrated Biomedical Sciences Program, University of Tennessee Health Science Center, Memphis, TN, USA

    Xian Han & Xusheng Wang

  4. Center for Proteomics and Metabolomics, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Timothy I. Shaw, Yingxue Fu, Yuxin Li, Ji-Hoon Cho, Suresh Poudel, Zuo-Fei Yuan & Junmin Peng

  5. Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Timothy I. Shaw & Gang Wu

  6. Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA

    Timothy I. Shaw

  7. Department of Chemical Biology and Therapeutics, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Jonathan Low, Hyeong-Min Lee & Taosheng Chen

  8. Department of Oncology, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Elizabeth Stewart

  9. Veterinary Pathology Core, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Peter Vogel

  10. Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Yawei Hui, Shibiao Wan & Gang Wu

  11. Department of Neurological Sciences, Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA

    David A. Bennett

  12. Banner Sun Health Research Institute, Sun City, AZ, USA

    Geidy E. Serrano & Thomas G. Beach

  13. Department of Neuroscience, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Gang Yu

Authors
  1. Ping-Chung Chen
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  2. Xian Han
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  3. Timothy I. Shaw
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  4. Yingxue Fu
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  5. Huan Sun
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  6. Mingming Niu
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  7. Zhen Wang
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  8. Yun Jiao
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  9. Brett J. W. Teubner
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  12. Bing Bai
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  14. Yuxin Li
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  15. Ji-Hoon Cho
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  16. Xusheng Wang
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  18. Danting Liu
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  20. Zuo-Fei Yuan
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  22. Jonathan Low
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  23. Hyeong-Min Lee
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  24. Mary H. Patton
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  27. Peter Vogel
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  28. Yawei Hui
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  29. Shibiao Wan
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  30. David A. Bennett
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  31. Geidy E. Serrano
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  32. Thomas G. Beach
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  33. Michael A. Dyer
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  34. Richard J. Smeyne
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  35. Tudor Moldoveanu
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  36. Taosheng Chen
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  37. Gang Wu
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  38. Stanislav S. Zakharenko
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  39. Gang Yu
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  40. Junmin Peng
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Contributions

J.P., P.-C.C., X.H. and G.Y. conceived the project. P.-C.C. generated the N40K-Tg mouse model. P.-C.C., X.H., H.S., M.N., Y.J., B.J.W.T., D.E., L.B., B.B., J.M., Z.W., D.L., J.L., H.-M.L., A.M., L.R., M.H.P., L.R.E., E.S. and P.V. performed the experiments. G.E.S., T.G.B. and D.A.B. characterized and provided human brain samples. X.H., P.-C.C., T.I.S., Y.F., Y.L., J.-H.C., X.W., S.P., Z.-F.Y., Y.H., S.W., M.A.D., R.J.S., T.M., T.C., G.W., S.S.Z., G.Y. and J.P. analyzed the data. X. H., P.-C.C. and J.P. wrote the manuscript.

Corresponding author

Correspondence to Junmin Peng.

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The authors declare no competing interests.

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Nature Aging thanks Benjamin Logsdon, Benjamin Wolozin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Deep TMT profiling of detergent-insoluble proteome in AD and quantitative analysis of western blotting.

a, Proteomic profiling of detergent-insoluble proteome of 10 control and 10 AD cases by TMT-LC/LC-MS/MS. A total of 8,917 proteins were identified, out of which 365 proteins were found to increase in AD detergent-insoluble proteome. b, Principal component analysis of the insoluble proteome in control and AD cases. c, Heatmap of selected top proteins including U1 snRNP components enriched in AD. d, Pathway enrichment analysis of the proteins enriched in the AD insoluble proteome (Fisher’s exact test and BH procedure to generate FDR values). e, Enriched protein-protein interaction module of spliceosome. f, Quantified data of western blotting (4 replicates) in Fig. 1d (Student’s t-test, two-tailed). g, Quantified data of western blotting (triplicates) in Fig. 1e to show that the N40K expression leads to the depletion of endogenous U1-70K in neurons (Student’s t-test, two-tailed). Data are shown as mean ± SEM. Full statistical information is in Source Data Statistics

Source data

Extended Data Fig. 2 Generation of multiple N40K-Tg lines with N40K expression and U1-70K downregulation.

a, The strategy for producing N40K-Tg lines by the injection of N40-expressing lentivirus. b, Chromosomal localization of Tg determined by fluorescence in situ hybridization (FISH) analysis. Left: Chr18 (green), N40K (red) in Tg396 line; Right: Chr10 (green), N40K (red) in Tg318 line. c, Western blotting of U1-70K and N40K in Tg396 and Tg318 Tg lines. N40K expression led to similar depletion of U1-70K protein in the hippocampus of both lines. d, Quantitation of relative N40K levels in cortex and hippocampus of Tg396 lines by western blotting. Titrated Tg proteins produce a linear response curve (R2 = 0.98). According to the curve equation, N40K is at a ~2-fold level of native U1-70K as in WT mice (Student’s t-test, two-tailed). Data are shown as mean ± SEM. In b, c, d, the assay was repeated three times. Full statistical information is in Source Data Statistics.

Source data

Extended Data Fig. 3 Proteomics analysis of insoluble fraction in WT and N40K-Tg mice.

a, Proteomic profiling of detergent-insoluble proteome of WT and N40K-Tg mice (12-month-old, WT n = 3, Tg n = 3). b, Principal component analysis of the insoluble proteome of WT and N40K. c, Heatmap of differentially expressed proteins in N40K-Tg insoluble fraction. d, Pathway enrichment analysis of differentially expressed proteins in N40K-Tg insoluble fraction (Fisher’s exact test) followed by FDR analysis by the BH procedure. e, Enriched PPI module of spliceosomal snRNP complex (Fisher’s exact test and the BH procedure, FDR < 0.05). Each dot represents a protein, whereas the interactions are indicated by connected lines.

Extended Data Fig. 4 N40K-Tg mice exhibit brain weight loss and brain volume reduction, but with normal locomotive activities.

a-c, N40K-Tg mice showed the reduction of brain weight and brain volume, whereas the body weight had no change at different ages (3-month-old: WT n = 14, Tg n = 10; 12-month-old: WT n = 14, Tg n = 11, Student’s t-test, two- tailed). d, Volume change of Tg318 mouse in cortex and hippocampus (Hipp) measured by MRI at different ages (3-month-old: WT n = 15, Tg n = 10; 12-month-old: WT n = 15, Tg n = 10, two-way ANOVA followed by Sidak’s multiple comparison test). e, Morris water maze task for Tg318 mice. Compared to WT control, Tg318 mice showed significant difference at day 3-6 (two-way ANOVA followed by Sidak’s multiple comparison test). f, Speed in the probe trial of Morris water maze at day 6 showed no significant difference between WT and Tg mice in Fig. 4g (12-month-old: WT n = 9, Tg n = 9, Student’s t-test, two-tailed). Data are shown as mean ± SEM, ns (not significant). Full statistical information is in Source Data Statistics.

Source data

Extended Data Fig. 5 Validation of splicing deficient transcripts of synaptic pathway in N40K-Tg mice.

a-b, Quantitation of intron accumulation at the selected region of Gabra2, Gng7, Kcnh1 and Camk1d. IGV software was used to display RNA read density with same scale range in each gene from RNA-seq (ex: exon; in: intron). Red boxes show selected regions for quantification. Intron reads % was analyzed by RNA-seq ((intron-exon junctions + introns)/(intron-exon junctions + introns + exons + exon-exon junctions)). Intron retention % was analyzed by RT-PCR ((intron-containing PCR band intensity)/(intron-containing PCR band intensity + exon-exon PCR band intensity)). Gabra2: intron 6 vs exon 6 and 7; Gng7: intron 2 vs exon 2 and 3, Kcnh1: intron 9 vs exon 9 and 10 and Camk1d: intron 5 vs exon 5 and 6. 12-month-old: WT n = 3, Tg n = 3. Scale bar, 10 kb. Data are shown as mean ± SEM. Statistical significance was analyzed by Student’s t-test, two-tailed. Data are shown as mean ± SEM. Full statistical information is in Source Data Statistics.

Source data

Extended Data Fig. 6 Profiling analysis of dTg and tau mice.

a, Representative co-immunofluorescence staining of 5xFAD and dTg brain slides with Thioflavin S for plaques and the U1-70K C-terminal antibody for U1-70K depletion. Scale bar, 100 µm. The immunostaining was repeated from three animals. b, PCA for RNA-seq and proteomics studies. c, Distribution of splicing deficiency scores of mapped transcripts. Statistical comparisons between different genotypes are shown (Kolmogorov–Smirnov test). d, Relative Aβ level in 5xFAD and dTg by the proteomics analysis (mean ± SEM, Student’s t-test, two-tailed, ns: not significant). e, Cell type enriched DE proteins in the dTg mice. The 979 DE proteins were overlapped with the cell type expression data from RNA-seq analysis99. f, Swimming speed of WT, N40K-Tg, 5xFAD, dTg in the Morris water maze experiment (mean ± SEM, one-way ANOVA, ns: not significant). g, RNA-seq analysis of WT and Tau (P301S) mice. h, The percentage of mapped intron reads in all transcripts from the cortices of WT (n = 5) and Tau P301S (n = 6) mice (mean ± SEM, Student’s t-test, two-tailed, ns: not significant). i, Distribution of splicing deficiency scores of mapped transcripts (Kolmogorov–Smirnov test). Full statistical information is in Source Data Statistics.

Source data

Extended Data Fig. 7 Proteomic comparison of the three mouse models and AD.

a, The workflow to examine the consistent changes in the three mouse models and human AD cases by overlapping differentially expressed proteins. b, DE protein numbers in the mouse models, human cases and the overlapped portions. c, Enriched pathways of the human-overlapped DE proteins in three genotypes (selected from supplementary table 19, Fisher’s exact test and the BH procedure to derive FDR, FDR cutoff of 0.05). d, Enriched protein-protein interaction modules (selected from supplementary table 20, Fisher’s exact test and the BH procedure to derive FDR). FDR cutoff is 0.05 except the Aβ binding module, which is selected due to biological significance.

Extended Data Fig. 8

Analysis of the role of TDP-43 in human cases and the mouse models. a, The weak correlation between the percentage of intron reads and the stages of TDP-43 pathology. Pearson correlation coefficient (r) is shown. b, The percentage of mapped intron reads in all transcripts from human cases of different TDP-43 stages (mean ± SEM, Student’s t-test, two-tailed, ns: not significant). As the sample size was small, we merged stages 0-1 and stages 2–3 in the analysis. c, Distribution of splicing deficiency scores of mapped transcripts (Kolmogorov–Smirnov test). d, Staining of plaques, Tau, TDP-43 and nuclei in a human AD brain sample (positive controls) and in the mouse models (cortex, ~12-month-old). Phosphorylated Tau and TDP-43 antibodies were used. The immunostaining was repeated from three human cases or animals. Full statistical information is in Source Data Statistics.

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Chen, PC., Han, X., Shaw, T.I. et al. Alzheimer’s disease-associated U1 snRNP splicing dysfunction causes neuronal hyperexcitability and cognitive impairment. Nat Aging 2, 923–940 (2022). https://doi.org/10.1038/s43587-022-00290-0

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