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Longitudinal modeling of human neuronal aging reveals the contribution of the RCAN1–TFEB pathway to Huntington’s disease neurodegeneration

Nature Aging volume 4, pages 95–109 (2024)Cite this article

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Abstract

Aging is a common risk factor in neurodegenerative disorders. Investigating neuronal aging in an isogenic background stands to facilitate analysis of the interplay between neuronal aging and neurodegeneration. Here we perform direct neuronal reprogramming of longitudinally collected human fibroblasts to reveal genetic pathways altered at different ages. Comparative transcriptome analysis of longitudinally aged striatal medium spiny neurons (MSNs) in Huntington’s disease identified pathways involving RCAN1, a negative regulator of calcineurin. Notably, RCAN1 protein increased with age in reprogrammed MSNs as well as in human postmortem striatum and RCAN1 knockdown rescued patient-derived MSNs of Huntington’s disease from degeneration. RCAN1 knockdown enhanced chromatin accessibility of genes involved in longevity and autophagy, mediated through enhanced calcineurin activity, leading to TFEB’s nuclear localization by dephosphorylation. Furthermore, G2-115, an analog of glibenclamide with autophagy-enhancing activities, reduced the RCAN1–calcineurin interaction, phenocopying the effect of RCAN1 knockdown. Our results demonstrate that targeting RCAN1 genetically or pharmacologically can increase neuronal resilience in Huntington’s disease.

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Fig. 1: Identification of RCAN1 as an age-associated factor in reprogrammed MSNs from longitudinally collected fibroblasts.
Fig. 2: RCAN1 KD protects HD-MSNs from degeneration and induces chromatin accessibility changes.
Fig. 3: RCAN1 KD- and CaN KD-induced chromatin changes.
Fig. 4: Enhancing TFEB function by RCAN1 KD via its nuclear localization.
Fig. 5: RCAN1 KD promotes neuronal resilience through enhancing TFEB nuclear localization.
Fig. 6: G2-115 promotes TFEB function by reducing RCAN1–CaN interaction and promoting TFEB nuclear localization.

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Data availability

RNA-seq data and ATAC-seq data presented in the current study will be available through the Gene Expression Omnibus (GEO) at NCBI with accession IDs GSE241430 and GSE210996. Raw data that support all findings of the study are available as Source data and supplementary tables provided with this paper. All other data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

We thank the Genome Technology Access Center at Washington University for deep-sequencing experiments. An image of the experimental scheme was created with BioRender.com. This study was supported by a Hereditary Disease Foundation (HDF) Grant, the Cure Alzheimer’s Fund (CAF), the CHDI Foundation Research Agreement, National Institute on Aging (NIA) Grant no. RF1AG056296, NIA grant no. R01AG078964, National Institute of Neurological Disorders and Stroke (NINDS) Grant no. R01NS107488, the Farrell Foundation Fund and the Mallinckrodt Scholar Award (A.S.Y.).

Author information

Author notes

  1. Seong Won Lee & Young Mi Oh

    Present address: Department of Biomedical Sciences, Mercer University School of Medicine, Columbus, GA, USA

  2. These authors contributed equally: Seong Won Lee, Young Mi Oh.

Authors and Affiliations

  1. Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA

    Seong Won Lee, Young Mi Oh, Yan Yang, Shawei Chen, Ilya Strunilin & Andrew S. Yoo

  2. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Matheus B. Victor

  3. Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA

    Sonika Dahiya

  4. Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA

    Roland E. Dolle

  5. Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA

    Stephen C. Pak, Gary A. Silverman & David H. Perlmutter

  6. Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA

    Andrew S. Yoo

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Contributions

S.W.L. and A.S.Y. conceived and developed the idea, designed the experiments and analyzed data. S.W.L. and Y.M.O. performed all experiments and analyses unless specified. M.B.V., S.C. and I.S. performed reduction-of-function testing and analysis of candidate HD modifiers. Y.Y. performed whole-cell recordings. S.D. provided human striatal section samples. R.E.D., S.C.P., G.A.S. and D.H.P. developed the G2 analog. S.W.L. and A.S.Y. wrote the manuscript. A.S.Y. supervised the overall project.

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Correspondence to Andrew S. Yoo.

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Extended data

Extended Data Fig. 1 Gene expression profiling in longitudinally collected fibroblasts and corresponding reprogrammed MSNs.

a-c, Whole-cell recording from reprogrammed MSNs from three independent longitudinal groups (individual I (a); II (b); III (c)) co-cultured with human astrocytes showing the inward/outward currents and multiple action potentials (APs). d, RT-qPCR analysis of DARPP-32 expression in longitudinally aged MSNs (n = 6, ****p < 0.0001, The sample size (n) corresponds to the number of biologically independent samples). Statistical significance was determined using two-tailed unpaired t-test and mean±s.e.m. e and f, Heatmap of Differentially Expressed Genes (DEGs) in fibroblasts (e) and MSNs (f) (FDR < 0.05, │FC│ ≥ 1.5). g, Venn diagram of the genes enriched in calcium signaling pathway from old HD-MSNs.

Source data

Extended Data Fig. 2 Age-associated RCAN1 in longitudinally aged MSN.

a, Upstream regulator analysis of up- or down-regulated genes in old fibroblasts and MSNs. b, Gene network of upstream regulators and DEGs. c, Representative immunoblotting (top) and quantification (bottom) of RCAN1 expression in six MSNs from 22, 29, 24 (young) and 53, 50, 60 (old) years old-individuals (young n = 3, old n = 3, **p = 0.0038). d, Quantification of RCAN1 mRNA from six independent fibroblasts and MSNs from three longitudinal individuals (I, II, and III) (Fibroblasts n = 12, MSNs n = 12). e, Representative immunoblotting (top, left) and quantification of relative RCAN1 expression normalized to GAPDH (bottom. left) in Young / Old-MSNs from three longitudinal individuals treated with cyclohexamide (CHX). Comparison of RCAN1 expression in CHX-treated MSNs (Young and Old) from three longitudinal individuals in the presence of DMSO, MG132 or, CQ (right) (n = 6). f. Representative immunoblotting (top) and quantification (bottom) of RCAN1 expression in age-matched control-Old-MSNs and HD-MSNs (n = 6). g. Representative Immunoblotting (top) and quantification (bottom) of HDAC3 expression in MSNs from three longitudinal individuals (n = 6, *p = 0.0467). h. Representative immunoblotting (top) and quantification (bottom) of RCAN1 expression in individual III’s MSNs (n = 2). Statistical significance was determined using two-tailed unpaired t-test (c,f,g) and one-way ANOVA with Tukey’s post hoc test (d,h). *p < 0.05, **p < 0.01, ns, not significant, and mean±s.e.m (c,d,f,g). The sample size (n) corresponds to the number of biologically independent samples (c-h).

Source data

Extended Data Fig. 3 Identification of modifier genes whose reduction protects HD-MSNs from degeneration.

a, Experimental scheme of genetic modifiers testing in HD-MSNs. b, Representative images (left) and quantification (right) of MAP2-, NCAM-, NEUN-, ACTL6B-, DARPP-32-, and GABA-positive cells from four independent HD-MSNs (HD.43, HD.40, HD.47, HD.45). An average of 300 cells per each were counted from three or more randomly chosen fields (n = 4). Scale bars represent 20 μm. c, High-content imaging of Sytox green dye accumulation in HD-MSNs (HD.46) in a 96-well format. Representative images of HD-MSNs in each well of a 96-well plate, immunostained with anti-GABA, TUBB3, and DARPP-32 antibodies (left). Example pictures for high content image analysis to measure cell death levels (right): Hoechst for whole cell population and Sytox-green for dead cells. d, Quantification of Sytox-positive cells from HD-MSNs (HD.46) and healthy control (Ctrl.17) at post-induction day 35 (n = 2). e, Quantification of Sytox-positive cells in HD-MSNs (HD.46) transduced with shRNAs of modifier genes. The genes whose reduction significantly lowered cell death levels were marked (red) within the pink area (± 10 % of cell death level from healthy control) compared to control shRNA. Statistical significance was determined using unpaired t-test and mean±s.e.m (n = 2, RCAN1: p = 0.0143); RTCA: p = 0.0198); UBE2D4: p = 0.0073). f, Representative image (left) and quantification (right) of Sytox-positive cells from three independent HD-MSNs (HD.46, HD.44, HD.43) transduced with shRNAs of each gene (n = 12). Scale bars represent 100 μm. Box-and-whiskers plot: The center line denotes the median value while the box contains the 25th to 75th percentiles of dataset. The whiskers mark minimal value to maximal value. ****p < 0.0001. g, Representative image (left) and quantification (right) of cells with HTT inclusion bodies (IBs) in HD-MSNs (HD.40) transduced with shRNAs of each gene. Cells were immunostained with anti-HTT and TUBB3 antibodies. An average of 100 cells per each were counted from four to six randomly chosen fields (n = 6, 4, 6, 4). Scale bars represent 10μm. Statistical significance was determined using unpaired t-test (e) and one-way ANOVA with Tukey’s post-hoc test (f,g); ****p < 0.0001, *p < 0.05, ns, not significant, and mean±s.e.m. The sample size (n) corresponds to the number of biologically independent samples (b,d,e,f,g).

Source data

Extended Data Fig. 4 Validation of reprogrammed neurons of rescuing or non-rescuing condition for ATAC-sequencing.

a, RCAN1 expression in fibroblasts transduced with shRCAN1 (top) or RCAN1 (middle) in a dose-dependent manner. RCAN1 expression in HD-MSNs (HD.43) transduced with shCtrl, shRCAN1, or RCAN1 (bottom). b, Representative image (top) and quantification (bottom) of DARPP-32-positive cells from four independent HD-MSNs transduced with shCtrl, shRCAN1, or shCaN (HD.43, HD.40, HD.47, HD.45). Cells were immunostained with anti-DARPP-32 and TUBB3 antibodies. An average of 183 cells of each were counted from three or more randomly chosen fields (n = 4). Scale bars represent 10 μM. c, RT-qPCR analysis of the expression of RCAN1 and CaN in (b) (n = 12, 12, 8, 8). Statistical significance was determined using one-way ANOVA with Tukey’s post-hoc test (b) and two-tailed unpaired t-test (c); ****p < 0.0001, ns, not significant, and mean±s.e.m (b,c). The sample size (n) corresponds to the number of biologically independent samples (b,c).

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Extended Data Fig. 5 RCAN1 promotes nuclear localization of TFEB for HD survival.

a, Expression of phosphor-TFEB in fibroblasts transduced with Control, TFEB wildtype, or phosphor-mutant (S142/211 A). b. Representative image (left) and quantification of nuclear TFEB from three independent HD-MSNs (HD.45, HD.45b, HD.47) transduced with shCtrl or shRCAN1. Cells were treated with DMSO or Cyclosporin A (CaN inhibitor) (n = 3). shCtrl versus shRCAN1 ***p = 0.0002, shRCAN1 versus shRCAN1+Cyclosporin A ***p = 0.0001. c, Representative image (left) and quantification of Sytox-positive cells (middle) from three independent HD-MSNs (HD.45, HD.45b, HD.47) transduced with shCtrl, shRCAN1, or shTFEB. Expression of RCAN1 and TFEB in HD-MSNs transduced with shCtrl, shRCAN1, or shTFEB (right) (n = 3). Statistical significance was determined using one-way ANOVA with Tukey’s post hoc test (b,c); ***p < 0.001, *p < 0.05, ns, not significant, and mean±s.e.m (b,c). The sample size (n) corresponds to the number of biologically independent samples (b,c).

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Extended Data Fig. 6 Neuroprotective role of G2-115 through reducing RCAN1-CaN interaction.

a, Immunoprecipitation analysis of RCAN1-transduced fibroblasts with anti-CaN antibody followed by immunoblotting with anti-RCAN1 antibody. Cells are treated with 0.5 μM of G2-115 and 60 μM of chloroquine (lysosome inhibitor). b, Immunoprecipitation analysis of RCAN1-transduced fibroblasts with anti-CaN followed by immunoblotting with anti-RCAN1 antibody. Cells were treated with DMSO or 0.5 μM of G2-115, 8 mM of metformin, and 500 nM of rapamycin. c, Experimental scheme of NanoBit binding assay (top). Binding assay of HEK293 cells transfected with RCAN1 fused to large Bit and CaN fused to small Bit. Cells were treated with 0.5, 1.0, 1.5, and 2.0 μM of G2-115 in a dose-dependent manner (bottom). (n = 3, The sample size (n) corresponds to the number of independent experiments). DMSO versus G2-115 1.0 μM 0.5 hr *p = 0.0379, 1.0 hr **p = 0.0091, 2.0 hr **p = 0.0082, DMSO versus G2-115 2.0 μM 0.5 hr **p = 0.0044, 1.0 hr *p = 0.0230, 2.0 hr **p = 0.0084. d, Quantification of CYTO-ID-positive cells from three independent HD-MSNs (HD.45, HD.45b, HD.47) treated with DMSO or G2-115. Cells were transduced with RCAN1 (n = 3). DMSO versus G2-115 **p = 0.0025, G2-115 versus G2-115 + RCAN1 cDNA **p = 0.0060. e, Representative image (left) and quantification (right) of nuclear TFEB from three-independent HD-MSNs (HD.45, HD.45b, HD.47) treated with DMSO or G2-115. Cells were immunostained with anti-TFEB and TUBB3 antibodies. An average of 107 cells per each were counted from three or more randomly chosen fields (n = 3, ****p < 0.0001). Scale bars represent 20 μm. f, Graphical work model to illustrate the function of RCAN1-CaN-TFEB cascade in Young/Old-MSNs (left) and the neuroprotective role of RCAN1 KD for HD survival. Statistical significance was determined using one-way ANOVA with Tukey’s post hoc test in (d) and two-tailed unpaired t-test (c,e); ****p < 0.0001, **p < 0.01, *p < 0.05, ns, not significant, and mean±s.e.m (c-e). The sample size (n) corresponds to the number of biologically independent samples (d,e).

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Supplementary information

Reporting Summary

Supplementary Table 1

Information on fibroblast and brain samples used in the study.

Supplementary Table 2

List of GeM-HD modifiers used in the study.

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Lee, S.W., Oh, Y.M., Victor, M.B. et al. Longitudinal modeling of human neuronal aging reveals the contribution of the RCAN1–TFEB pathway to Huntington’s disease neurodegeneration. Nat Aging 4, 95–109 (2024). https://doi.org/10.1038/s43587-023-00538-3

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