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Necroptosis activation in Alzheimer's disease

Nature Neuroscience volume 20, pages 1236–1246 (2017)Cite this article

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Abstract

Alzheimer's disease (AD) is characterized by severe neuronal loss; however, the mechanisms by which neurons die remain elusive. Necroptosis, a programmed form of necrosis, is executed by the mixed lineage kinase domain-like (MLKL) protein, which is triggered by receptor-interactive protein kinases (RIPK) 1 and 3. We found that necroptosis was activated in postmortem human AD brains, positively correlated with Braak stage, and inversely correlated with brain weight and cognitive scores. In addition, we found that the set of genes regulated by RIPK1 overlapped significantly with multiple independent AD transcriptomic signatures, indicating that RIPK1 activity could explain a substantial portion of transcriptomic changes in AD. Furthermore, we observed that lowering necroptosis activation reduced cell loss in a mouse model of AD. We anticipate that our findings will spur a new area of research in the AD field focused on developing new therapeutic strategies aimed at blocking its activation.

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Figure 1: Increase in necroptosis markers in AD human brains.
Figure 2: Necrosome formation in AD.
Figure 3: Necroptosis activation is linked to reduced brain weight.
Figure 4: The activation of RIPK1 and MLKL correlates with Braak stage.
Figure 5: Regulation of AD transcriptome and risk-associated genes by RIPK1.
Figure 6: Necroptosis activation exacerbates cognitive deficits in APP/PS1 mice.
Figure 7: Constitutively active MLKL induces a higher degree of neuronal death in APP/PS1 than NonTg mice.
Figure 8: Necroptosis contributes to neuronal death in 5xFAD mice.

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References

  1. Alzheimer's Association. 2015 Alzheimer's disease facts and figures. Alzheimers Dement. 11, 332–384 (2015).

  2. Liu-Seifert, H. et al. Cognitive and functional decline and their relationship in patients with mild Alzheimer's dementia. J. Alzheimers Dis. 43, 949–955 (2015).

    Article  CAS  PubMed  Google Scholar 

  3. Zahodne, L.B., Manly, J.J., MacKay-Brandt, A. & Stern, Y. Cognitive declines precede and predict functional declines in aging and Alzheimer's disease. PLoS One 8, e73645 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Querfurth, H.W. & LaFerla, F.M. Alzheimer's disease. N. Engl. J. Med. 362, 329–344 (2010).

    Article  CAS  PubMed  Google Scholar 

  5. Khan, S.S. & Bloom, G.S. Tau: the center of a signaling nexus in Alzheimer's disease. Front. Neurosci. 10, 31 (2016).

    PubMed  PubMed Central  Google Scholar 

  6. Serrano-Pozo, A., Frosch, M.P., Masliah, E. & Hyman, B.T. Neuropathological alterations in Alzheimer disease. Cold Spring Harb. Perspect. Med. 1, a006189 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Caselli, R.J., Beach, T.G., Yaari, R. & Reiman, E.M. Alzheimer's disease a century later. J. Clin. Psychiatry 67, 1784–1800 (2006).

    Article  CAS  PubMed  Google Scholar 

  8. Lalaoui, N., Lindqvist, L.M., Sandow, J.J. & Ekert, P.G. The molecular relationships between apoptosis, autophagy and necroptosis. Semin. Cell Dev. Biol. 39, 63–69 (2015).

    Article  CAS  PubMed  Google Scholar 

  9. Hanson, B. Necroptosis: a new way of dying? Cancer Biol. Ther. 17, 899–910 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Linkermann, A. & Green, D.R. Necroptosis. N. Engl. J. Med. 370, 455–465 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Cho, Y.S. et al. Phosphorylation-driven assembly of the RIP1-RIP3 complex regulates programmed necrosis and virus-induced inflammation. Cell 137, 1112–1123 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Vandenabeele, P., Galluzzi, L., Vanden Berghe, T. & Kroemer, G. Molecular mechanisms of necroptosis: an ordered cellular explosion. Nat. Rev. Mol. Cell Biol. 11, 700–714 (2010).

    Article  CAS  PubMed  Google Scholar 

  13. Ofengeim, D. et al. Activation of necroptosis in multiple sclerosis. Cell Rep. 10, 1836–1849 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Re, D.B. et al. Necroptosis drives motor neuron death in models of both sporadic and familial ALS. Neuron 81, 1001–1008 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Ito, Y. et al. RIPK1 mediates axonal degeneration by promoting inflammation and necroptosis in ALS. Science 353, 603–608 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Li, J. et al. The RIP1/RIP3 necrosome forms a functional amyloid signaling complex required for programmed necrosis. Cell 150, 339–350 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Costes, S.V. et al. Automatic and quantitative measurement of protein-protein colocalization in live cells. Biophys. J. 86, 3993–4003 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Liang, W.S. et al. Gene expression profiles in anatomically and functionally distinct regions of the normal aged human brain. Physiol. Genomics 28, 311–322 (2007).

    Article  CAS  PubMed  Google Scholar 

  19. Liang, W.S. et al. Alzheimer's disease is associated with reduced expression of energy metabolism genes in posterior cingulate neurons. Proc. Natl. Acad. Sci. USA 105, 4441–4446 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Braak, H. & Braak, E. Staging of Alzheimer's disease-related neurofibrillary changes. Neurobiol. Aging 16, 271–278, discussion 278–284 (1995).

    Article  CAS  PubMed  Google Scholar 

  21. Giacobini, E. & Gold, G. Alzheimer disease therapy—moving from amyloid-β to tau. Nat. Rev. Neurol. 9, 677–686 (2013).

    Article  CAS  PubMed  Google Scholar 

  22. Arevalo-Rodriguez, I. et al. Mini-Mental State Examination (MMSE) for the detection of Alzheimer's disease and other dementias in people with mild cognitive impairment (MCI). Cochrane Database Syst. Rev. (3): CD010783 (2015).

  23. Millstein, J., Zhang, B., Zhu, J. & Schadt, E.E. Disentangling molecular relationships with a causal inference test. BMC Genet. 10, 23 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Blalock, E.M. et al. Incipient Alzheimer's disease: microarray correlation analyses reveal major transcriptional and tumor suppressor responses. Proc. Natl. Acad. Sci. USA 101, 2173–2178 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Hitomi, J. et al. Identification of a molecular signaling network that regulates a cellular necrotic cell death pathway. Cell 135, 1311–1323 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Liu, Q. et al. Akt and mTOR mediate programmed necrosis in neurons. Cell Death Dis. 5, e1084 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Oakley, H. et al. Intraneuronal beta-amyloid aggregates, neurodegeneration, and neuron loss in transgenic mice with five familial Alzheimer's disease mutations: potential factors in amyloid plaque formation. J. Neurosci. 26, 10129–10140 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Jankowsky, J.L. et al. Mutant presenilins specifically elevate the levels of the 42 residue beta-amyloid peptide in vivo: evidence for augmentation of a 42-specific gamma secretase. Hum. Mol. Genet. 13, 159–170 (2004).

    Article  CAS  PubMed  Google Scholar 

  29. Rodriguez, D.A. et al. Characterization of RIPK3-mediated phosphorylation of the activation loop of MLKL during necroptosis. Cell Death Differ. 23, 76–88 (2016).

    Article  CAS  PubMed  Google Scholar 

  30. Dickerson, B.C. et al. Alzheimer-signature MRI biomarker predicts AD dementia in cognitively normal adults. Neurology 76, 1395–1402 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Popescu, B.O. & Ankarcrona, M. Mechanisms of cell death in Alzheimer's disease: role of presenilins. J. Alzheimers Dis. 6, 123–128 (2004).

    Article  CAS  PubMed  Google Scholar 

  32. Bancher, C., Lassmann, H., Breitschopf, H. & Jellinger, K.A. Mechanisms of cell death in Alzheimer's disease. J. Neural Transm. Suppl. 50, 141–152 (1997).

    Article  CAS  PubMed  Google Scholar 

  33. Zhu, X., Raina, A.K., Perry, G. & Smith, M.A. Apoptosis in Alzheimer disease: a mathematical improbability. Curr. Alzheimer Res. 3, 393–396 (2006).

    Article  CAS  PubMed  Google Scholar 

  34. Niikura, T., Tajima, H. & Kita, Y. Neuronal cell death in Alzheimer's disease and a neuroprotective factor, humanin. Curr. Neuropharmacol. 4, 139–147 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Cotman, C.W. & Su, J.H. Mechanisms of neuronal death in Alzheimer's disease. Brain Pathol. 6, 493–506 (1996).

    Article  CAS  PubMed  Google Scholar 

  36. Abraham, M.C. & Shaham, S. Death without caspases, caspases without death. Trends Cell Biol. 14, 184–193 (2004).

    Article  CAS  PubMed  Google Scholar 

  37. Snigdha, S., Smith, E.D., Prieto, G.A. & Cotman, C.W. Caspase-3 activation as a bifurcation point between plasticity and cell death. Neurosci. Bull. 28, 14–24 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Stadelmann, C., Brück, W., Bancher, C., Jellinger, K. & Lassmann, H. Alzheimer disease: DNA fragmentation indicates increased neuronal vulnerability, but not apoptosis. J. Neuropathol. Exp. Neurol. 57, 456–464 (1998).

    Article  CAS  PubMed  Google Scholar 

  39. Perry, G., Nunomura, A., Lucassen, P., Lassmann, H. & Smith, M.A. Apoptosis and Alzheimer's disease. Science 282, 1268–1269 (1998).

    Article  CAS  PubMed  Google Scholar 

  40. Vanden Berghe, T., Kaiser, W.J., Bertrand, M.J. & Vandenabeele, P. Molecular crosstalk between apoptosis, necroptosis, and survival signaling. Mol. Cell. Oncol. 2, e975093 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Zheng, C., Zhou, X.W. & Wang, J.Z. The dual roles of cytokines in Alzheimer's disease: update on interleukins, TNF-α, TGF-β and IFN-γ. Transl. Neurodegener. 5, 7 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Dillon, C.P. et al. RIPK1 blocks early postnatal lethality mediated by caspase-8 and RIPK3. Cell 157, 1189–1202 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Kaiser, W.J. et al. RIP1 suppresses innate immune necrotic as well as apoptotic cell death during mammalian parturition. Proc. Natl. Acad. Sci. USA 111, 7753–7758 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Dondelinger, Y. et al. MLKL compromises plasma membrane integrity by binding to phosphatidylinositol phosphates. Cell Rep. 7, 971–981 (2014).

    Article  CAS  PubMed  Google Scholar 

  45. Su, L. et al. A plug release mechanism for membrane permeation by MLKL. Structure 22, 1489–1500 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Wang, H. et al. Mixed lineage kinase domain-like protein MLKL causes necrotic membrane disruption upon phosphorylation by RIP3. Mol. Cell 54, 133–146 (2014).

    Article  CAS  PubMed  Google Scholar 

  47. Nelson, P.T. et al. Correlation of Alzheimer disease neuropathologic changes with cognitive status: a review of the literature. J. Neuropathol. Exp. Neurol. 71, 362–381 (2012).

    Article  PubMed  Google Scholar 

  48. Beach, T.G. et al. The Sun Health Research Institute Brain Donation Program: description and experience, 1987–2007. Cell Tissue Bank. 9, 229–245 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Caccamo, A., Ferreira, E., Branca, C. & Oddo, S. p62 improves AD-like pathology by increasing autophagy. Mol. Psychiatry 22, 865–873 (2017).

    Article  CAS  PubMed  Google Scholar 

  50. Kauffmann, A., Gentleman, R. & Huber, W. arrayQualityMetrics—a bioconductor package for quality assessment of microarray data. Bioinformatics 25, 415–416 (2009).

    Article  CAS  PubMed  Google Scholar 

  51. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

    Google Scholar 

  52. Du, P., Kibbe, W.A. & Lin, S.M. lumi: a pipeline for processing Illumina microarray. Bioinformatics 24, 1547–1548 (2008).

    Article  CAS  PubMed  Google Scholar 

  53. Irizarry, R.A. et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249–264 (2003).

    Article  PubMed  Google Scholar 

  54. Ritchie, M.E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Kersey, P.J. et al. Ensembl Genomes 2016: more genomes, more complexity. Nucleic Acids Res. 44, D1, D574–D580 (2016).

    Article  CAS  Google Scholar 

  56. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  Google Scholar 

  57. Liao, Y., Smyth, G.K. & Shi, W. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 41, e108 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Liao, Y., Smyth, G.K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    CAS  PubMed  Google Scholar 

  59. Robinson, M.D., McCarthy, D.J. & Smyth, G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  60. Law, C.W., Chen, Y., Shi, W. & Smyth, G.K. voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Van der Auwera, G.A. et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinformatics 43, 1–33 (2013).

    Google Scholar 

  63. Howie, B., Marchini, J. & Stephens, M. Genotype imputation with thousands of genomes. G3 (Bethesda) 1, 457–470 (2011).

    Article  Google Scholar 

  64. Howie, B.N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. Auton, A. et al. Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  PubMed  CAS  Google Scholar 

  66. Shabalin, A.A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28, 1353–1358 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Mirra, S.S. et al. The Consortium to Establish a Registry for Alzheimer's Disease (CERAD). Part II. Standardization of the neuropathologic assessment of Alzheimer's disease. Neurology 41, 479–486 (1991).

    Article  CAS  PubMed  Google Scholar 

  68. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Stenson, P.D. et al. The Human Gene Mutation Database: 2008 update. Genome Med. 1, 13 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Hamosh, A., Scott, A.F., Amberger, J., Valle, D. & McKusick, V.A. Online Mendelian Inheritance in Man (OMIM). Hum. Mutat. 15, 57–61 (2000).

    Article  CAS  PubMed  Google Scholar 

  71. Landrum, M.J. et al. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res. 44, D1, D862–D868 (2016).

    Article  CAS  Google Scholar 

  72. Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42, D1001–D1006 (2014).

    Article  CAS  PubMed  Google Scholar 

  73. Rouillard, A.D. et al. The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins. Database (Oxford) https://dx.doi.org/10.1093/database/baw100 (2016).

  74. Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. circlize Implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).

    Article  CAS  PubMed  Google Scholar 

  75. Velazquez, R., Shaw, D.M., Caccamo, A. & Oddo, S. Pim1 inhibition as a novel therapeutic strategy for Alzheimer's disease. Mol. Neurodegener. 11, 52 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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Acknowledgements

We thank E. Reiman for discussion and assistance, P. Coleman for kindly providing access to his expressing data set. We thank A. Rodin and A. Tran for contributing to the editing of the manuscript. We thank D. Green for kindly providing the MLKL constructs. We are grateful to the Banner Sun Health Research Institute Brain and Body Donation Program of Sun City, Arizona for providing the human tissue. Data for the RIPK1 causal regulatory gene network were generated from postmortem brain tissue collected through the Mount Sinai VA Medical Center Brain Bank and were provided by E. Schadt. The computational resources and staff expertise provided by the Department of Scientific Computing at the Icahn School of Medicine at Mount Sinai also contributed to this study. This work was supported by grants from the Arizona Alzheimer's Consortium and the US National Institutes of Health (R01 AG037637) to S.O., and R01 NS083801 and P50 AG016573 to K.N.G. The Brain and Body Donation Program is supported by the US National Institute of Neurological Disorders and Stroke (U24 NS072026 National Brain and Tissue Resource for Parkinson's Disease and Related Disorders), the National Institute on Aging (P30 AG19610 Arizona Alzheimer's Disease Core Center), the Arizona Department of Health Services (contract 211002, Arizona Alzheimer's Research Center), the Arizona Biomedical Research Commission (contracts 4001, 0011, 05-901 and 1001 to the Arizona Parkinson's Disease Consortium), and the Michael J. Fox Foundation for Parkinson's Research.

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Author notes

  1. Antonella Caccamo and Caterina Branca: These authors contributed equally to this work.

Authors and Affiliations

  1. Arizona State University-Banner Neurodegenerative Disease Research Center at the Biodesign Institute, Arizona State University, Tempe, Arizona, USA

    Antonella Caccamo, Caterina Branca, Eric Ferreira, Ramona Belfiore, Wendy Winslow & Salvatore Oddo

  2. Translational Genomics Research Institute, Phoenix, Arizona, USA

    Ignazio S Piras, Matthew J Huentelman & Winnie S Liang

  3. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA

    Ben Readhead & Joel T Dudley

  4. Department of Neurobiology and Behavior, University of California, Irvine, Irvine, California, USA

    Elizabeth E Spangenberg & Kim N Green

  5. Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy

    Ramona Belfiore

  6. School of Life Sciences, Arizona State University, Tempe, Arizona, USA

    Salvatore Oddo

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  1. Antonella Caccamo
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Contributions

A.C. and C.B. designed and performed the experiments and analyzed the data. I.S.P. and M.J.H. performed the statistical analyses. E.F. performed the confocal imaging and quantification. W.S.L. generated the expression data from the microarray analyses used to generate the RIPK1 causal regulatory network. B.R. and J.T.D. generated the RIPK1 causal regulatory network and performed the associated gene set analysis. E.E.S. and K.N.G. performed the experiments on 5xFAD mice. R.B. performed the colocalization experiments described in Supplementary Figure 5. W.W. performed the co-immunoprecipitation experiments. S.O. conceptualized and designed the experiments, analyzed the data, and wrote the manuscript. All of the authors contributed to the preparation of the manuscript.

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Correspondence to Salvatore Oddo.

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Integrated supplementary information

Supplementary Figure 1 Levels of necroptotic markers in TBS and Triton fractions

(a) Representative western blots of TBS and Triton extracts from AD and CTL patients probed with the indicated antibodies. (b-d) Quantitative analyses of the western blots. RIPK1 was not detected in the TBS fraction. For all the proteins measured, no changes were detected between the two groups. For RIPK1 triton [t(21) = 0.840, P = 0.409]; for RIPK3 TBS [t(21) = 0.402, P = 0.691]; for RIPK3 triton [t(21) = 0.357, P = 0.724]; for MLKL TBS [t(21) = 0.176, P = 0.862], for MLKL triton [t(21) = 0.065, P = 0.9491]. Data were normalized to β-actin, used as a loading control. Data are presented as box plots and were analyzed by unpaired t-test. n = 11 CTL cases and n = 12 AD cases. In the box plots, the center line represents the median value, the limits represent the 25th and 75th percentile, and the whiskers represent the minimum and maximum value of the distribution.

Supplementary Figure 2 Full blots of RIPK1 and MLKL western blots in human cases

(a-b) Proteins extracted with RIPA and UREA buffer from CTL and AD cases. Blots were probed with the indicated antibodies. The levels of the protein of interest (RIPK1 or MLKL) were normalized to β-actin for every sample, then all samples were expressed as a ratio with respect to the average of the CTL samples in the same blot. Doing so, the AD samples are expressed as a percentage change over the CTL samples. (c) Proteins from CTL and AD cases were immunoprecipitated with a RIPK1 antibody and probed with an MLKL antibody. The black arrows point to the MLKL band, the black arrowheads point to the IgG. (d) Proteins from CTL and AD cases were run in not reducing conditions and probed with an MLKL antibody. The gray arrows point to the MLKL dimers, the gray arrowheads point to the MLKL monomers. The levels of MLKL dimers were normalized to the levels of MLKL monomers for every samples. Then all the samples in the same blot were expressed as ratio with respect to the average of the CTL sample in the same blot. Doing so, the AD samples are expressed as percentage change over the CTL samples.

Supplementary Figure 3 Increased pMLKL in AD colocalize with phosphorylated tau

(a) Representative confocal images from CTL and AD cases stained with a different pMLKL antibody than the one used for Fig. 2. (b) The graph shows the quantitative analyses of the pMLKL immunoreactivity tissue [t(28) = 4.561; P < 0.0001]. (c) Representative confocal images from CTL and AD cases stained with the indicated antibodies. Statistical evaluation by Mander’s correlation, followed by Costes randomization test indicates that in CTL cases, 36.04 ± 1.5% of pMLKL immunoreactivity was located in the membrane [R(13) = 0.319 and Costes P = 0.96]. In AD cases, 52.90 ± 3.2% of pMLKL immunoreactivity was located in the membrane [R(13) = 0.365 and Costes P = 0.97]. (d) The graph shows the quantitative analyses of the colocalized pMLKL and cadherin pixels [t(28) = 6.991; P < 0.0001]. (e) Sections from AD patients were stained with the indicated antibodies. Statistical evaluation by Mander’s correlation followed by Costes randomization test indicates that 39.17 ± 3.1% of pMLKL immunoreactivity co-localized with CP13 [R(13) = 0.4155 and Costes p = 0.99]. These data confirm the data shown in Fig. 4, using a different pMLKL antibody. For all the data shown here, n = 15 CTL cases and n = 15 AD cases. Data in panels b and d were analyzed by unpaired t-test and are presented as box plots. In the box plots, the center line represents the median value, the limits represent the 25th and 75th percentile, and the whiskers represent the minimum and maximum value of the distribution.

Supplementary Figure 4 Caspase-3/pMLKL colocalization is similar between CTL and AD cases

(a) Microphotographs of CTL (n = 15 cases) and AD (n = 15 cases) brains stained with the indicated antibodies. (b), Quantitative analysis of the sections, which was obtained by Mander’s correlation followed by Costes randomization test indicates that 42.10 ± 1.9% of pMLKL immunoreactivity co-localized with Caspase-3 [R(13) = 0.6159 and Costes P = 0.965] for CTL, and 45.53 ± 2.3% [R(13) = 0.6075 and Costes P = 0.970] for AD. There was no significant difference between the two groups [t(28) = 0.319; P = 0.752). Data are presented as box plots and were analyzed by unpaired t-test. In the box plots, the center line represents the median value, the limits represent the 25th and 75th percentile, and the whiskers represent the minimum and maximum value of the distribution.

Supplementary Figure 5 pMLKL is mainly found in neurons

(a-c) Microphotographs of AD brain (n = 15) sections co-labeled with the indicated antibodies. Statistical evaluation by Mander’s correlation followed by Costes randomization test indicates that 60.22 ± 3.3% of pMLKL immunoreactivity co-localized with NeuN [R(13) = 0.583 and Costes P = 0.99], 11.14 ± 1.4% co-localized with GFAP [R(13) = 0.179 and Costes P = 0.63], and 28.00 ± 2.6% co-localized with Iba1 [R(13) = 0.578 and Costes P = 0.96].

Supplementary Figure 6 Schematic representation of the experimental design used to perform the causal inference testing.

Gene regulatory network for RIPK1 based inferred from postmortem brain tissue samples. Causal inference testing was used to determine directed regulatory links between RIPK1 and its correlated genes.

Supplementary Figure 7 Full blots showing levels of necroptotic markers in 5xFAD and APP/PS1 mice

(a) Proteins from 5xFAD mice (n = 5 mice) and littermate controls (NT; n = 7 mice), and APP/PS1 mice (n = 8) and littermate controls (WT; n = 8), were probed with the indicated antibodies. (b-d) Quantitative analyses of the blots show an increase of the kinases in the 5xFAD mice compared to control [t(10) = 2.423, P = 0.036 for RIPK1; t(10) = 2.910, P = 0.016 for MLKL; t(10) = 3.076, P = 0.012 for pMLKL]. No differences were found in the APP/PS1 mice compared to their control [t(14) = 1.245, P = 0.234 for RIPK1; t(14) = 0.807, P = 0.433 for MLKL]. (e-f) Representative confocal microphotographs of sections from 5xFAD and APP/PS1 stained with Fluro-Jade. Quantification of western blots were obtained by normalizing the protein of interest to β-actin (used as a loading control). Data are presented as box plots and were analyzed by unpaired t-test. In the box plots, the center line represents the median value, the limits represent the 25th and 75th percentile, and the whiskers represent the minimum and maximum value of the distribution.

Supplementary Figure 8 Increasing necroptosis does not change Aβ and tau pathology

(a-b) Representative hippocampal sections from APP/PS1 mice injected with the AAV expressing GFP or MLKL. Sections were stained with an Aβ42-specific antibody (n = 14 mice per group). (c-f) The graphs show soluble and insoluble Aβ40 and Aβ42 levels measured by sandwich ELISA. Panel c, t(26) = 1.197, P = 0.242. Panel d, t(26) = 0.4513, P = 0.655. Panel e, t(26) = 0.6518, P = 0.520. Panel f, t(26) = 0.8277, P = 0.415. Data are presented as box plots, and were analyzed by unpaired t-test (n = 14 mice per group). (g-j) Western blots of proteins extracted from APP/PS1-GFP (n = 7 mice) and APP/PS1-MLKL mice (n = 8 mice). Blots were probed with the indicated antibodies. The levels of Tau-5, which recognize total mouse tau, were similar between the two groups [t(13) = 0.484; P = 0.637]. The CP13 antibody, which is raised against tau phosphorylated at Ser202, recognized two bands of ~60 and ~50 kDa. Statistical analyses of both bands indicated that CP13 levels were similar between the two groups [t(13) = 0.48; P = 0.64] for both bands. Data are presented as box plots, and were analyzed by unpaired t-test. In the box plots, the center line represents the median value, the limits represent the 25th and 75th percentile, and the whiskers represent the minimum and maximum value of the distribution.

Supplementary Figure 9 Full blots for necrostatin treatments

(a-b) Proteins extracted from APP/PS1 and wild type primary neurons used for NeuN staining. Quantitative analyses are shown in Fig. 8b. (c-d) Proteins extracted from APP/PS1 and wild type primary neurons transfected with AAV-GFP. Quantitative analyses are shown in Fig. 8d. (e) Proteins extracted from 5xFAD and wild type mice. Quantitative analyses are shown in Fig. 8g. Blots were probed with the indicated antibodies.

Supplementary Figure 10 Validation of the RIPK1, RIPK3, MLKL, and pMLKL antibodies

(a) Proteins extracted from wild type cells, RIPK1 knockout cells (as a negative control), RIPK1 knockout cells transfected with a RIPK1 expressing plasmid (as a positive control), CTL and AD human cases, wild type and 5xFAD mice were probed with the RIPK1 antibody. The expected band of 73 kDa (arrow) was not present in the knockout cells. (b) Proteins extracted from wild type mice, RIPK3 knockout mice (as a negative control), wild type cells transfected with a RIPK3 expressing plasmid (as a positive control), CTL and AD human cases, wild type and 5xFAD mice, were probed with the RIPK3 antibody. The expected band of 55 kDa (arrow) was not present in the RIPK3 knockout mice and was present in the cells transfected with the RIPK3-expressing plasmid. The RIPK3 band in the positive control ran a little slower as the plasmid had a GFP tag to its C-terminal. (c) To validate the MLKL antibody, we loaded on a gel proteins extracted from wild type cells, MLKL knockout cells (as a negative control), MLKL knockout cells transfected with a MLKL-expressing plasmid (as a positive control), CTL and AD human cases, non-transgenic and 5xFAD mice. The expected band of 51 kDa (arrow) was not present in the knockout cells, but it was present when these cells were transfected with a MLKL plasmid. (d) To validate the phospho-specific MLKL antibody, we loaded on a gel protein extracted from MLKL knockout and wild type cells. As a positive control, cells were treated with 1 ng/mL TNFα and 50 μM Caspase inhibitor Z-VAD-FMK to induce necroptosis. (e) To validate the RIPK1 and MLKL antibodies for immunohistochemistry, we used HAP1 cells where the respective genes were knocked out. As a control (WT) we used the parental cell line. To validate the RIPK3 antibody for immunohistochemistry, we used mouse primary fibroblasts isolated from RIPK3 knockout and wild type mice. (f) To validate the pMLKL antibody for immunohistochemistry, we used HAP-1 cells and induced necroptosis activation.

Supplementary information

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Supplementary Figures 1–10 and Supplementary Tables 1–5 (PDF 2333 kb)

Supplementary Methods Checklist (PDF 513 kb)

Supplementary Data Set 1

Gene Expression File 1 (XLSX 18 kb)

Supplementary Data Set 2

Gene Expression File 2 (XLSX 315 kb)

Supplementary Data Set 3

Gene Expression File 3 (XLSX 503 kb)

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Caccamo, A., Branca, C., Piras, I. et al. Necroptosis activation in Alzheimer's disease. Nat Neurosci 20, 1236–1246 (2017). https://doi.org/10.1038/nn.4608

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