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Dysregulation of mitochondrial and proteolysosomal genes in Parkinson’s disease myeloid cells

Nature Aging volume 1, pages 850–863 (2021)Cite this article

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Abstract

An increasing number of identified Parkinson’s disease (PD) risk loci contain genes highly expressed in innate immune cells, yet their role in pathology is not understood. We hypothesized that PD susceptibility genes modulate disease risk by influencing gene expression within immune cells. To address this, we generated transcriptomic profiles of monocytes from healthy subjects and 230 individuals with sporadic PD. We observed dysregulation of mitochondrial and proteasomal pathways. We also generated transcriptomic profiles of primary microglia from brains of 55 subjects and observed discordant transcriptomic signatures of mitochondrial genes in PD monocytes and microglia. We further identified 17 PD susceptibility genes whose expression, relative to each risk allele, was altered in monocytes. These findings reveal widespread transcriptomic alterations in PD monocytes, with some being distinct from microglia, and facilitate efforts to understand the roles of myeloid cells in PD as well as the development of biomarkers.

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Fig. 1: Overview of the study design.
Fig. 2: Transcriptomic analysis of PD-derived monocytes and age-matched controls.
Fig. 3: Coexpression networks in monocytes capture PD-specific processes.
Fig. 4: Single-cell profiling of CD14+ monocytes from PD and control subjects.
Fig. 5: Comparison of transcriptome profiles of PD monocytes and primary microglia.
Fig. 6: Parkinson’s disease susceptibility alleles alter gene expression in monocytes.

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Data availability

Processed read counts and full eQTL summary statistics are available from the Zenodo online data sharing portal at https://zenodo.org/record/4715907. Raw RNA-seq data and genotypes from the MyND cohort have been made available via dbGAP (study accession ID: phs002400.v1.p1) at https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs002400.v1.p1. RNA-seq data and genotypes of the PPMI cohort were obtained from the AMP-PD knowledge platform. For up-to-date information on the study, visit https://www.amp-pd.org.

Code availability

The code used for the primary analysis is available on GitHub at https://github.com/RajLabMSSM/MyND-Analysis. Any additional code used for analysis is available upon request from the corresponding author.

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Acknowledgements

We thank the study participants for providing blood samples and for their generous gifts of brain donation to the MyND study. We thank the Netherlands Brain Bank and the Neuropathology Brain Bank & Research Core at Mount Sinai for assistance in collecting human brain samples. We thank the Flow Cytometry Core and the Human Immune Monitoring Center at Icahn School of Medicine at Mount Sinai (ISMMS) for optimization of cell isolations; Genewiz Inc. for RNA-seq; C. Proukakis for feedback on the manuscript; S. Kim-Schulze for help in optimizing PBMC and monocyte isolation; Y.-C. Wang for help with processing of RNA-seq data; J. Fernandez-Lopez for help with data analysis; members of the Ronald Loeb Center for AD for helpful discussions; and research participants and employees of 23andMe who contributed to PD GWAS. Data used in the preparation of this article were obtained from the AMP-PD Knowledge Platform. AMP-PD—a public–private partnership—is managed by the US National Institutes of Health (NIH) and funded by Celgene, GSK, the Michael J. Fox Foundation for Parkinson’s Research, the National Institute of Neurological Disorders and Stroke, Pfizer, Sanofi and Verily. PPMI—a public–private partnership—is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners (names of all PPMI funding partners can be found at www.ppmi-info.org/fundingpartners). The PPMI Investigators did not participate in reviewing the data analysis or content of the manuscript. For up-to-date information on the study, visit www.ppmi-info.org. This work was supported in part through the computational resources and staff expertise provided by Scientific Computing at the ISMMS. T.R. was supported by grants from the Michael J. Fox Foundation (nos. 4899 and 16743) and NIH (nos. NINDS R01-NS116006, NINDS U01-NS120256, NIA R01-AG054005, NIA R21-AG063130 and NIA U01 P50-AG005138). R.S-P. is supported by grants from the NIH (nos. NINDS U01-NS107016 and NINDS U01-NS094148-01) and Bigglesworth Family Foundation. K.F. is supported by NIH (no. F32 AG056098). E.N. is supported by a fellowship from the Ramon Areces Foundation (Spain). The research reported in this paper was additionally supported by the Office of Research Infrastructure of the NIH under award nos. S10OD018522 and S10OD026880.

Author information

Author notes

  1. These authors contributed equally: Elisa Navarro, Evan Udine.

Authors and Affiliations

  1. Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Elisa Navarro, Evan Udine, Katia de Paiva Lopes, Madison Parks, Brian M. Schilder, Jack Humphrey, Ricardo A. Vialle, Maojuan Zhuang, Tamjeed Sikder, Charalambos Argyrou, Amanda Allan, Michael J. Chao, Kurt Farrell, Ana C. Pereira, Tim Ahfeldt, Alison M. Goate, John F. Crary & Towfique Raj

  2. Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Elisa Navarro, Evan Udine, Katia de Paiva Lopes, Madison Parks, Brian M. Schilder, Jack Humphrey, Ricardo A. Vialle, Maojuan Zhuang, Tamjeed Sikder, Charalambos Argyrou, Amanda Allan, Michael J. Chao, Kurt Farrell, Tim Ahfeldt, Alison M. Goate, John F. Crary & Towfique Raj

  3. Department of Genetics and Genomic Sciences & Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Elisa Navarro, Evan Udine, Katia de Paiva Lopes, Madison Parks, Brian M. Schilder, Jack Humphrey, Ricardo A. Vialle, Maojuan Zhuang, Charalambos Argyrou, Amanda Allan, Michael J. Chao, Alison M. Goate & Towfique Raj

  4. Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Elisa Navarro, Evan Udine, Katia de Paiva Lopes, Madison Parks, Brian M. Schilder, Jack Humphrey, Ricardo A. Vialle, Maojuan Zhuang, Charalambos Argyrou, Amanda Allan, Michael J. Chao, Sarah Simon, Deborah Raymond, Sonya Elango, Roberto A. Ortega, Vicki Shanker, Matthew Swan, Ruth H. Walker, Winona Tse, Ana C. Pereira, Tim Ahfeldt, Alison M. Goate, Susan Bressman, Rachel Saunders-Pullman & Towfique Raj

  5. The Marlene and Paolo Fresco Institute for Parkinson’s Disease and Movement Disorders, New York University Langone Health, New York, NY, USA

    Giulietta Riboldi, Brooklyn Henderson & Steven Frucht

  6. Universita degli Studi di Milano, Molecular and Translational Medicine, Milan, Italy

    Giulietta Riboldi

  7. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA

    Gijsje J. L. Snijders & Lotje de Witte

  8. Mental Illness Research, Education and Clinical Center (MIRECC), James J Peters VA Medical Center, NY, Bronx, USA

    Gijsje J. L. Snijders & Lotje de Witte

  9. Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Tamjeed Sikder, Kurt Farrell & John F. Crary

  10. Neuropathology Brain Bank & Research CoRE, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Tamjeed Sikder, Kurt Farrell & John F. Crary

  11. Department of Neurology, Mount Sinai Beth Israel, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Sarah Simon, Deborah Raymond, Sonya Elango, Roberto A. Ortega, Vicki Shanker, Matthew Swan, Susan Bressman & Rachel Saunders-Pullman

  12. Department of Geriatrics and Palliative Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Carolyn W. Zhu & Mary Sano

  13. Geriatric Research, Education and Clinical Centers, James J. Peters VA Medical Center, New York, NY, USA

    Carolyn W. Zhu & Mary Sano

  14. Alzheimer’s Disease Research Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Carolyn W. Zhu & Mary Sano

  15. Department of Neurology, Zucker School of Medicine at Hofstra Northwell, New York, NY, USA

    Ritesh Ramdhani

  16. Department of Neurology, James J. Peters VA Medical Center, Bronx, NY, USA

    Ruth H. Walker

Authors
  1. Elisa Navarro
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  2. Evan Udine
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  3. Katia de Paiva Lopes
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  4. Madison Parks
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  5. Giulietta Riboldi
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  6. Brian M. Schilder
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  7. Jack Humphrey
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  8. Gijsje J. L. Snijders
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  9. Ricardo A. Vialle
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  10. Maojuan Zhuang
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  11. Tamjeed Sikder
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  12. Charalambos Argyrou
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  13. Amanda Allan
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  14. Michael J. Chao
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  15. Kurt Farrell
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  16. Brooklyn Henderson
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  17. Sarah Simon
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  18. Deborah Raymond
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  19. Sonya Elango
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  20. Roberto A. Ortega
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  21. Vicki Shanker
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  22. Matthew Swan
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  23. Carolyn W. Zhu
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  24. Ritesh Ramdhani
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  25. Ruth H. Walker
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  26. Winona Tse
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  27. Mary Sano
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  28. Ana C. Pereira
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  29. Tim Ahfeldt
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  30. Alison M. Goate
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  31. Susan Bressman
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  32. John F. Crary
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  33. Lotje de Witte
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  34. Steven Frucht
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  35. Rachel Saunders-Pullman
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  36. Towfique Raj
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Contributions

T.R. conceived the study. T.R., E.N. and E.U. led the project, designed and performed the experiments and analysis and wrote the manuscript. K.d.P.L. performed coexpression network and microglial transcriptomic analysis. M.P. and E.N. optimized the experimental approach. M.P., M.Z. and A.A. performed experimental isolations. B.M.S. and J.H. performed single-cell and splicing analyses, respectively. T.S., C.A., B.H. and S.S. contributed to clinical coordination. R.A.V. contributed to data analyses. G.R. and S.F. were responsible for the implementation and recruitment of patients with movement disorder at the Fresco Institute for Parkinson’s and Movement Disorders at New York University (NYUMD) and Bendheim Parkinson and Movement Disorders Center at Mount Sinai (BPMD). M.J.C. contributed to genetic and population characterization. D.R., S.E., R.A.O., V.S., M.Swan., S.B. and R.S.-P. contributed to recruitment and clinical characterization of patients from Movement Disorder Center at Mount Sinai Beth Israel (MSBI). C.W.Z. and M.Sano. contributed to recruitment of donors from the Alzheimer’s Research Center (ADRC). A.C.P. implemented recruitment of donors from the Center for Cognitive Health (CCH). R.R., R.H.W. and W.T. helped with recruitment of patients from BPMD. T.A. and A.M.G. helped with intellectual discussion and interpretation of the results. K.F., R.H.W. and J.F.C. contributed to brain sample collection. G.J.L.S. and L.d.W. contributed to microglial isolation and characterization. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Towfique Raj.

Ethics declarations

Competing interests

A.M.G. served on the scientific advisory board for Denali Therapeutics from 2015 to 2018 and has served as a consultant to AbbVie, Biogen, Eisai, Illumina and GSK. R.S.-P. and S.B. have served as consultants to Denali Therapeutics. R.H.W. has served as consultant to Neurocine Biosciences, Inc. The other authors declare no competing interests.

Additional information

Peer review information Nature Aging thanks David Craig, Richard Youle and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Experimental flow outline and demographic/clinical information for subjects for monocytes isolation.

(A) Blood was collected from five independent clinics across New York City (ADRC, CCH, MSMD, BIMD, and NYUMD; details described in Methods) and transferred to the Icahn School of Medicine at Mount Sinai for monocyte sorting and RNA/DNA isolation. Samples were genotyped for common SNPs using Global Screening Array (GSA) and LRRK2, GBA and APOE were independently genotyped. RNA-seq was performed at Genewiz Inc. in three independent and randomized batches. DNA and RNA data was subjected to stringent QC, DNA data was imputed and ancestry was calculated. DNA and RNA were compared to the identification of miss-matches prior outlier identification. After QC, a total of 230 samples were used for subsequent analysis. (B) Demographic, (C) genotype and (D) clinical variables describing the 230 samples included in the study.

Extended Data Fig. 2 Differential expression analysis at the transcript level in PD and controls derived monocytes.

(A) MA plot showing the fold-change (log2 scale) at the transcript level in the y-axis and the mean of log2counts (x-axis), highlighting the DETs at FDR < 0.05 in red. (B) Volcano plot showing the fold-change (log2 scale) of transcripts between PD-monocytes (n = 135) and controls (n = 95) (x-axis) and their significance in the y-axis -log10 P-value scale). DETs at FDR < 0.05 are highlighted in red (upregulated) and blue (downregulated). Moderated t-statistic (two sided) is used for statistical test (see R package limma). (C) Pathway enrichment analysis for the upregulated (n = 230 independent samples) and (D) downregulated DETs using Biological processes from GSEA. Significance is represented in the x-axis (-log10 P-value scale of the q-value). Only the 20 most significant pathways (q-value < 0.05) with a minimum overlap of 5 genes are shown. Pathways are grouped and colored by biological related processes. n = 230 independent samples.

Extended Data Fig. 3 Differential expression analysis at the splicing level in PD and controls derived monocytes.

(A) Histogram reflecting the counts (y-axis) and the % of missingness (x-axis). (B) Volcano plot showing the delta PSI of genes with splicing events in PD-monocytes (n = 135) and controls (n = 95) (x-axis) and their significance in the y-axis (-log10 P-value scale). DSs at FDR < 0.05 are highlighted in red (delta PSI > 0) and blue (delta PSI < 0). Positive delta-PSI indicates that the long isoform is favored whereas negative delta-PSI indicates preference for the short isoform. Chi-squared is used for statistical test (C) Pathway enrichment analysis for the DSs at FDR < 0.0.5 (left panel) DSs + DEGs at FDR < 0.05 (right panel) using Biological processes from GSEA. Significance is represented in the x-axis (-log10 P-value scale of the q-value). Only the 20 most significant pathways (q-value < 0.05) with a minimum overlap of 5 genes are shown. Pathways are grouped and colored by biological related processes. n = 230 independent samples. (D) Examples of genes showing significant splicing events.

Extended Data Fig. 4 Module enrichment for biological pathways.

(A) Enrichment of modules (x-axis) containing co-expressed genes for specific biological pathways and curated gene sets (y-axis). Modules are represented by color names and are ordered by size. Enrichment for selected gene sets and GO biological processes (top panel). The size and color of the circles indicate the significance level (-log10 P-value). Enrichments for PD heritability, using stratified LD score regression (bottom panel). The size and color of circles indicate the enrichment value (from LD score) and significance level (-log10 P-value) of enrichment, respectively. Only modules that were significant at a nominal P-value < 0.05 are shown here. (B) Barchart showing Pearson correlation coefficient (r) (x-axis) of three modules (y-axis) significantly associated with PD (FDR < 0.05) determined by the module eigengene analysis. Numbers on the plot represent adjusted P-values, Two-sided Wilcoxon rank-signed test. n = 230 independent samples.

Extended Data Fig. 5 Monocyte subcluster characterization by single-cell analysis.

(A) Proportions of the 3 main monocyte sub-clusters using FACS (n = 11 controls and 11 PD). No statistical differences were obtained between groups. (B) Cell proportions of the 6 sub-clusters obtained by unsupervised clustering with monocle3 in scRNA-seq (n = 3 controls and 7 PD). No statistical differences were obtained between cases and controls in cell proportions. Cluster 1 corresponds to classical monocytes and cluster 2 to intermediate monocytes. (C) Top: UMAP colored by diagnosis (green = controls, yellow = PD). Bottom: UMAP colored by CD14 and FCGR3A (CD16) marker genes expression. (D) Histogram showing the variance (y-axis) explained by the 20 first PCA components (x-axis). (E) Histogram showing the frequency of the genes colored by diagnosis (green = control, yellow = PD). (F) Expression of mitochondrial genes by each cluster and divided by diagnosis. Boxplots: the line represents the median. The boxes extend from the 25th - 75th percentile and the lines extend 1.5 times the interquartile range. n = 10 independent donors.

Extended Data Fig. 6 Fresh microglia transcriptome analysis.

Microglia transcriptomic profiling was performed from 22 samples from 13 PD donors and 106 samples from 42 control donors. (A) Experimental workflow for the generation of human microglial transcriptomic profiles (B) Tables describing the samples included in the study (top: demographic information, middle: clinical information, bottom: brain regions). CC: Corpus Callosum; MFG: medial frontal gyrus; STG: Superior temporal gyrus; THA: thalamus; SVZ: subventricular zone; SN: substantia nigra (C) Heatmap for the expression of marker genes of different brain cell types (red: microglia, dark blue: astrocytes, green: neurons, light blue: oligodendrocytes). (D) Microglial isolation purity assessed by qPCR comparing the brain homogenate, and the positive and negative fractions after CD11b magnetic beads comparing microglial markers (P2RY12, CXCR1, TREM2) and astrocytic markers (GFAP, FGFR3). (E) Violin plot showing the % of variance (y-axis) explained by known covariates (x-axis) by variancePartition. Each dot represents a gene. (F) PCA after regressing out covariates colored by diagnosis (left panel), brain region (middle panel), postmortem interval (right panel). n = 128 samples from 55 independent donors. (G) Volcano plot showing the fold-change of genes (log2 scale) between PD-microglia (22 samples from 13 donors) and controls (106 samples from 42 donors) (x-axis) and their significance (y-axis, -log10 scale). Moderated t-statistic (two sided) is used for statistical test (see R package DREAM). (H) Expression of selected mitochondria-specific genes in microglia. Adjusted gene expression levels after normalization are shown. Boxplots: the line represents the median. The boxes extend from the 25th - 75th percentile and the lines extend 1.5 times the interquartile range. n = 128 samples from 55 independent donors.

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Navarro, E., Udine, E., Lopes, K.d.P. et al. Dysregulation of mitochondrial and proteolysosomal genes in Parkinson’s disease myeloid cells. Nat Aging 1, 850–863 (2021). https://doi.org/10.1038/s43587-021-00110-x

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