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Bassoon proteinopathy drives neurodegeneration in multiple sclerosis

Nature Neuroscience volume 22, pages 887–896 (2019)Cite this article

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Abstract

Multiple sclerosis (MS) is characterized by inflammatory insults that drive neuroaxonal injury. However, knowledge about neuron-intrinsic responses to inflammation is limited. By leveraging neuron-specific messenger RNA profiling, we found that neuroinflammation leads to induction and toxic accumulation of the synaptic protein bassoon (Bsn) in the neuronal somata of mice and patients with MS. Neuronal overexpression of Bsn in flies resulted in reduction of lifespan, while genetic disruption of Bsn protected mice from inflammation-induced neuroaxonal injury. Notably, pharmacological proteasome activation boosted the clearance of accumulated Bsn and enhanced neuronal survival. Our study demonstrates that neuroinflammation initiates toxic protein accumulation in neuronal somata and advocates proteasome activation as a potential remedy.

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Fig. 1: Expression profiling of neurons during CNS inflammation.
Fig. 2: BSN accumulates in neuronal somata in EAE and MS.
Fig. 3: Bsn accumulation is neurotoxic.
Fig. 4: Somatic Bsn expression causes neurodegeneration and reduces lifespan in Drosophila.
Fig. 5: Bsn drives neuroaxonal injury and clinical disability in EAE.
Fig. 6: Pharmacological proteasome activation clears somatic Bsn accumulation and ameliorates neurodegeneration during CNS inflammation.
Fig. 7: Neuronal aggresome formation during inflammation is Bsn-dependent.

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Data availability

Data generated for this study are available through the Gene Expression Omnibus under accession number GSE104899.

Code availability

The routine for the single-cell data analysis can be assessed as an R Notebook file via github (github.com/INIMS/Bsn_scRNA-seq).

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Acknowledgements

We thank the UKE Mouse Pathology Facility for histopathology of EAE mice, N. Heintz for the Chat-bacTRAP mice, M. Heiman for advice on TRAP optimization, T. Dresbach for providing the Bsn DNA expression constructs, C.-L. Chien for providing the Map1a DNA expression construct and M. Binder and N. Akyüz for sequencing support. This work is supported by a Deutsche Forschungsgemeinschaft grant (grant no. FR 1720/11-1) and Oppenheim Förderpreis für Multiple Sklerose (Novartis) to M.A.F., as well as the Else Kröner-Fresenius-Stiftung (grant no. 2013_A217) to M.A.F. and K.E.D. D.M. is supported by the Swiss National Science Foundation (grant no. PP00P3_152928), Helmut Horten Foundation and Gebert-Rüf Foundation (grant no. GRS-049/13). U.T. and E.D.G. are supported by the Deutsche Forschungsgemeinschaft (grant no. CRC 854/B08).

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Author notes

  1. These authors contributed equally: Benjamin Schattling, Jan Broder Engler.

Authors and Affiliations

  1. Institut für Neuroimmunologie und Multiple Sklerose, Zentrum für Molekulare Neurobiologie Hamburg, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany

    Benjamin Schattling, Jan Broder Engler, Constantin Volkmann, Nicola Rothammer, Marcel S. Woo, Iris Winkler, Max Kaufmann, Sina C. Rosenkranz, Aparajita Bose, Simone Bauer, Simone Träger & Manuel A. Friese

  2. Zentrum für Molekulare Neurobiologie Hamburg, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany

    Meike Petersen, Katharine K. Miller, Kent E. Duncan & Peter Soba

  3. Leibniz-Institute für Neurobiologie, Magdeburg, Germany

    Anna Fejtova, Ulrich Thomas & Eckart D. Gundelfinger

  4. Psychiatrische und Psychotherapeutische Klinik, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany

    Anna Fejtova

  5. Institut für Neuropathologie, Universitätsmedizin Göttingen, Georg-August-Universität Göttingen, Göttingen, Germany

    Wolfgang Brück

  6. Transkriptomanalyselabor, Institut für Entwicklungsbiochemie, Universitätsmedizin Göttingen, Georg-August-Universität Göttingen, Göttingen, Germany

    Gabriela Salinas

  7. Center for Behavioral Brain Sciences and Medical Faculty, Otto von Guericke Universität, Magdeburg, Germany

    Eckart D. Gundelfinger

  8. Department of Pathology and Immunology, Service of Clinical Pathology, Geneva Faculty of Medicine, Geneva, Switzerland

    Doron Merkler

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  1. Benjamin Schattling
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Contributions

C.V. conducted the initial TRAP screening experiment. K.K.M. and K.E.D. helped optimizing the TRAP method. G.S. performed the RNA-seq. J.B.E. carried out the bioinformatics analysis. B.S., I.W., A.B. and S.C.R. performed the candidate validation by TRAP and histology. W.B. and D.M. conducted the human histopathology experiments. N.R. established the culture and sorting of N2a cells overexpressing Bsn. M.K. and J.B.E. performed the single-cell RNA-seq analysis. A.F., U.T. and E.D.G. generated the transgenic Bsn mice and flies and provided reagents and expertize. M.S.W., M.P. and P.S. performed the Drosophila experiments. B.S. performed EAE in Bsn knockout animals and together with J.B.E. in IU1-treated animals. B.S., S.T. and S.B. conducted the histopathological analysis in EAE. J.B.E., B.S. and M.A.F. designed the experiments and analyzed the data. J.B.E., B.S. and M.A.F. wrote the manuscript. M.A.F. conceived and supervised the study.

Corresponding author

Correspondence to Manuel A. Friese.

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Journal peer review information Nature Neuroscience thanks Joseph Dougherty, Hartmut Wekerle and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Integrated supplementary information

Supplementary Fig. 1 Chat-L10a-eGFP expression validation and sample similarity analysis.

a, Representative immunohistochemical stainings of astrocytes (GFAP), vessels (CD31), microglia (Iba1), immune cells (CD45), oligodendrocytes (CNPase) and neurons (NeuN) co-localized with eGFP in cervical spinal cord sections of Chat-L10a-eGFP mice. Experiment was repeated two times with similar results. Scale bar, 50 µm. b, Number of motor neurons in cervical spinal cord sections of healthy (n = 3) and acute EAE (n = 4) Chat-L10a-eGFP mice with quantification. Student’s t-test, one-tailed: n = 3 healthy versus n = 4 EAE mice, t(5) = 2.440, P = 0.0293. Bars show mean values plus SEM. Scale bar, 100 µm. c, Principal component analysis including the top 500 variable genes. Experimental groups: spinal cord (SC), inflamed spinal cord (iSC), motor neurons (MN), inflamed motor neurons (iMN). d, Heatmap of Eeuclidean sample distances with unsupervised hierarchical clustering. Colors of experimental groups as in (c). *P < 0.05; **P < 0.01.

Supplementary Fig. 2 Expression changes in inflamed spinal cords.

a, Comparison of inflamed spinal cords (iSC) versus healthy spinal cords (SC). Bar graph of differentially expressed (DE) genes. Red, up-regulated, Blue down-regulated. Darker tones of red and blue represent increasing absolute log2 fold changes (>1, >2, >4). Candidates were identified by DESeq2, negative binomial generalized linear models, adjusted for multiple comparisons by FDR: n = 5 biologically independent samples per group each pooled from 3 mice, log2 fold change > 1, FDR-adjusted P < 0.05. b, Percentage of DE genes annotated for the gene ontology (GO) term ‘immune system process’. c, Gene expression heatmap of top 20 up- and down-regulated genes. Genes annotated for ‘immune system process’ are marked in ocher on the left. d, Enrichment map of regulated biological process GO terms. Red, up-regulated. Blue, down-regulated. Node size, gene set size. Grey lines, gene set overlap.

Supplementary Fig. 3 Expression changes in inflamed motor neurons and candidate gene identification.

a, Comparison of inflamed motor neurons (iMN) versus healthy motor neurons (MN). Bar graph of differentially expressed (DE) genes. Red, up-regulated, Blue down-regulated. Darker tones of red and blue represent increasing absolute log2 fold changes (>1, >2, >4). Candidates were identified by DESeq2, negative binomial generalized linear models, adjusted for multiple comparisons by FDR: n = 5 biologically independent samples per group each pooled from 3 mice, log2 fold change > 1, FDR-adjusted P < 0.05. b, Percentage of DE genes annotated for the gene ontology (GO) term ‘immune system process’. c, Gene expression heatmap of top 20 up- and down-regulated genes. Genes annotated for ‘immune system process’ are marked in ocher on the left. d, Enrichment map of regulated biological process GO terms. Red, up-regulated. Blue, down-regulated. Node size, gene set size. Grey lines, gene set overlap. e, Venn diagrams summarizing candidate criteria. f, Bar graph of candidate genes. Red, up-regulated, Blue down-regulated. Darker tones of red and blue represent increasing absolute log2 fold changes (>1, >2, >4). g, Percentage of candidate genes annotated for the GO term ‘immune system process’.

Supplementary Fig. 4 Candidate genes drive GO enrichment analysis.

a–b, Heatmap of candidate gene expression in healthy motor neurons (MN) and inflamed motor neurons (iMN) belonging to the regulated gene ontology (GO) terms ‘mitochondrion’ and ‘ubiquitin-dependent protein catabolic process’.

Supplementary Fig. 5 Characterization of Bsn expression.

a, Analysis of BSN expression in human MS microarray data from Han, M. H. et al. J Exp Med 2012. b, Gene expression analysis of Bsn in an independent EAE TRAP cohort (n = 3 per group). MN, motor neurons. iMN, inflamed motor neurons. Student’s t-test, two-tailed: n = 4 MN versus n = 3 iMN samples each pooled from 3 mice, t(5) = 2.695, P = 0.0431. Bars show mean values plus SEM. c, Immunohistochemical stainings of Bsn with quantification in spinal cord sections of healthy and chronic EAE mice at day 30 post immunization. Co-staining for neurons (NeuN). Student’s t-test, two-tailed: n = 3 mice per group, t(4) = 4.062, P = 0.0153. Bars show mean values plus SEM. MFI, mean fluorescence intensity. Scale bar, 20 µm. d, Immunohistochemical staining of Bsn in spinal cord sections of animals immunized with complete Freund’s adjuvant but without MOG35–55 at day 15 post immunization. Co-staining for neurons (NeuN). Experiment was repeated two times with similar results. Scale bar, 20 µm. e, Immunohistochemical staining of Bsn in spinal cord sections of EAE animals at day 15 post immunization. Co-staining for neurons (NeuN). Experiment was repeated three times with similar results. Scale bar, 20 µm. f, Immunohistochemical stainings of synapsin 1/2 in cervical spinal cord sections of healthy and chronic EAE mice. Co-staining for neurons (NeuN) and bassoon (Bsn). Experiment was repeated two times with similar results. Scale bar, 20 µm. *P < 0.05.

Supplementary Fig. 6 Staining of BSN in MS tissue.

a, Immunohistochemical stainings of exemplary MS lesions showing demyelination (loss of myelin basic protein, MBP) and varying degrees of inflammatory activity (CD3+ T cells, CD68+ activated phagocytes). Scale bar, 100 µm. b, Immunohistochemical staining of BSN in comparison to control stainings without BSN primary antibody in MS tissue. Co-staining for neurons (MAP2). Scale bar, 50 µm. c, Quantification of immunohistochemical stainings for BSN in spinal cord sections of control individuals and MS patients. 5–10 high power fields from 4 MS patients (n = 27 power fields) and 3 controls (n = 16 power fields). Median with interquartile range. d, Analysis of mouse Bsn and human BSN amino acid sequence for disordered residues by PONDR VSL2 algorithm. Red value indicates the percentage of disordered residues.

Supplementary Fig. 7 Overexpression of eGFP-Bsn in N2a cells.

a, Somatic distribution of eGFP signal in N2a cells overexpressing eGFP, eGFP–Bsn or eGFP–Map1a. Experiment was repeated three times with similar results. Scale bar, 20 µm. b, Co-localization of punctate eGFP–Bsn accumulation with markers for Golgi (Golgin97), endosomes (Rab5), ER (PDI) and lysosomes (LAMP-2). Scale bar, 20 µm. c, Overlap of genes regulated in EAE motor neurons (EAE candidates) and eGFP–Bsn overexpressing N2a cells (Bsn overexpression). Statistical analysis was performed by hypergeometric testing. d, Overlap of GO terms regulated in EAE motor neurons (EAE candidates) and eGFP–Bsn overexpressing N2a cells (Bsn overexpression). Statistical analysis was performed by hypergeometric testing.

Supplementary Fig. 8 Neuroaxonal numbers and immune cell infiltration in Bsn−/−and IU1 treatment EAE.

a, Quantification of neurofilament-positive axons in the dorsal columns of cervical spinal cord sections of wildtype and Bsn−/− animals without EAE induction. Student’s t-test, two-tailed: n = 3 mice per group, t(4) = 1.165, P = 0.3086. Bars show mean values plus SEM. b, Quantification of NeuN-positive neurons in cervical spinal cord sections of wildtype and Bsn−/− animals without EAE induction. Student’s t-test, two-tailed: n = 3 mice per group, t(4) = 0.01243, P = 0.9907. Bars show mean values plus SEM. c, Gating strategy of CNS-infiltrating T cells. d, T cell subset composition in CNS infiltrates of wildtype and Bsn−/− EAE animals at day 15 after immunization. Two-way ANOVA with Sidak’s post hoc test: n = 3 mice per group, F(2,12) = 0.0060, P = 0.9940; Sidak’s post hoc test: CD8+ T cells, WT versus Bsn−/−: P = 0.9998; CD4+ T cells, WT versus Bsn−/−: P = 0.9998; CD4+Foxp3+ Treg, WT versus Bsn−/−: P > 0.9999. Bars show mean values plus SEM. e, T cell subset composition in CNS infiltrates of vehicle and IU1-treated EAE animals at day 15 after immunization. Two-way ANOVA with Sidak’s post hoc test: n = 3 mice per group, F(2,12) = 6.666, P = 0.0113; Sidak’s post hoc test: CD8+ T cells, vehicle-treated versus IU1-treated: P = 0.0778; CD4+ T cells, vehicle-treated versus IU1-treated: P = 0.0778; CD4+Foxp3+ Treg, vehicle-treated versus IU1-treated: P = 0.7514.Bars show mean values plus SEM.

Supplementary Fig. 9 Graphical summary.

To investigate the neuronal response to central nervous system (CNS) inflammation, ribosome-bound mRNA from spinal cord motor neurons was isolated by translating ribosome affinity purification (TRAP) comparing healthy mice to mice undergoing experimental autoimmune encephalomyelitis (EAE), the animal model of multiple sclerosis (MS). Samples were subjected to RNA-seq and regulated candidate genes and associated pathways were identified (top). Results indicate a cytoplasmic deposition of the strongly induced presynaptic protein bassoon (Bsn) in both EAE and MS that was accompanied by induction of protein catabolism and reduced energy metabolism. Bsn overexpression and genetic deletion established a toxic gain-of-function of Bsn that drives neurodegeneration during CNS inflammation. Pharmacological activation of the proteasome cleared Bsn deposits and enhanced neuronal survival (bottom).

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Schattling, B., Engler, J.B., Volkmann, C. et al. Bassoon proteinopathy drives neurodegeneration in multiple sclerosis. Nat Neurosci 22, 887–896 (2019). https://doi.org/10.1038/s41593-019-0385-4

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