S41467-019-13144-Y.Pdf

ARTICLE

https://doi.org/10.1038/s41467-019-13144-y OPEN The landscape of multiscale transcriptomic networks and key regulators in Parkinson’s disease

Qian Wang1,2,3,4, Yuanxi Zhang1, Minghui Wang 2,3,4, Won-Min Song 2,3,4, Qi Shen2,3,4, Andrew McKenzie2,3,4, Insup Choi1, Xianxiao Zhou 2,3,4, Ping-Yue Pan1, Zhenyu Yue1* & Bin Zhang 2,3,4*

Genetic and genomic studies have advanced our knowledge of inherited Parkinson’s disease (PD), however, the etiology and pathophysiology of idiopathic PD remain unclear. Herein, we

1234567890():,; perform a meta-analysis of 8 PD postmortem brain transcriptome studies by employing a multiscale network biology approach to delineate the gene-gene regulatory structures in the substantia nigra and determine key regulators of the PD transcriptomic networks. We identify STMN2, which encodes a stathmin family protein and is down-regulated in PD brains, as a key regulator functionally connected to known PD risk genes. Our network analysis predicts a function of human STMN2 in synaptic trafficking, which is validated in Stmn2-knockdown mouse dopaminergic neurons. Stmn2 reduction in the mouse midbrain causes dopaminergic neuron degeneration, phosphorylated α-synuclein elevation, and locomotor deficits. Our integrative analysis not only begins to elucidate the global landscape of PD transcriptomic networks but also pinpoints potential key regulators of PD pathogenic pathways.

1 Department of Neurology and Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 10029, USA. 2 Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 10029, USA. 3 Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, New York, NY 10029, USA. 4 Icahn Institute of Genomics and Multi-scale Biology, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 10029, USA. *email: [email protected]; [email protected]

NATURE COMMUNICATIONS | (2019) 10:5234 | https://doi.org/10.1038/s41467-019-13144-y | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-13144-y

arkinson’s disease (PD) is a common neurodegenerative Results disorder characterized pathologically by the loss of dopa- An integrative multiscale network biology framework for PD. P + minergic (DA, or tyrosine hydroxylase positive, TH ) We employed an integrated network biology framework to sys- neurons in the substantia nigra (SN) and the presence of Lewy tematically model a large transcriptomic data set in PD (Sup- bodies and Lewy neurites in affected brain regions. Previous plementary Fig. 1a). By searching the Gene Expression Omnibus research has identified over 20 PD causal mutations in SNCA, (GEO, https://www.ncbi.nlm.nih.gov/geo/) database using Par- LRRK2, VPS35, PINK1, DJ-1, Parkin, FBXO7, DNAJC6, kinson’s Disease and human substantia nigra as key words, we ATP13A2, DCTN1, and SYNJ1 (for review, see ref. 1 and MDS collected gene expression data from eight human PD studies28–32 Taskforce database: www.mdsgene.org). Tremendous effort has (Supplementary Table 1). The curated gene expression data went been made to characterize the functions of these genes. For through log2 transformation, quantile normalization, and cor- example, SNCA has been reported to be localized at presynapse rection for known covariates by a linear regression model. The through lipid-raft binding2 and play a role in vesicular traffick- differentially expressed genes (DEGs) between the case and ing3. LRRK2 has been implicated in a variety of cellular processes, control groups were identified by a meta-analysis followed by including autophagy4, mitochondrial function5, and vesicular Benjamini–Hochberg (BH) correction. Gene ontology (GO) trafficking6. VPS35 is a core component of the retromer complex analysis was performed on the DEGs to identify dysregulated responsible for the retrograde transport of proteins in endosomes pathways in PD. The expression data from all the PD samples in to the trans-Golgi network7. These PD genes are involved in the collection were merged into a global PD expression data set multiple cellular pathways, including ubiquitin–proteasome (n = 83) by Z-score transformation, and the same process was degradations, chaperone activities and endosomal–lysosomal applied to the control samples (n = 70). The batch effect of these dynamics, as well as mitochondrial maintenance and mitophagy1. studies was removed by Z-score transformation as shown in However, genetic variants account for ~20% of PD cases8, Supplementary Fig. 1b. Subsequently, Multiscale Embedded Gene while the etiology of the majority or sporadic cases is largely co-Expression Network Analysis (MEGENA)20 was performed unclear. Idiopathic PD is believed to result from the complex on the global PD and control data sets separately. The co- interplay among multiple genes and environmental factors. expressed gene modules in the global PD network were then Exposure to particular pesticides is associated with increased risk characterized by their enrichment for the PD DEGs and the cell- of PD9, while caffeine consumption can reduce PD risk10.Thus, type specific markers33,34. In parallel, we constructed the module- large-scale epidemiological longitudinal data are needed to truly based Bayesian regulatory networks (BN)35,36 to infer the reg- evaluate the risks of PD. Furthermore, genome-wide association ulatory relationships among the genes in each gene module. Key studies (GWAS) have so far reported 41 PD-associated risk network hub genes in the top-ranked module were further loci in various cohorts11–13, yet translation of the findings prioritized based on differential expression and network neigh- into biological understanding remains a major challenge and borhood enrichment of the PD DEGs for experimental validation. requires better and innovative tools to dissect the molecular mechanisms of PD. The development of high-throughput molecular profiling PD DEGs are enriched for disease-associated pathways.We techniques has advanced the research of complex diseases with identified 946 DEGs between the PD and control groups in the detailed regulatory machineries hidden underneath. Gene net- meta-analysis of the eight curated data sets at 5% FDR and work analysis has been extensively utilized as an unbiased standard mean difference (SMD) >0.5 (Supplementary Data 1). approach to identify gene co-expression/co-regulation patterns in To investigate whether the DEGs identified were associated with higher organisms for discovery of novel pathways and gene tar- the clinical traits, we performed a separate meta-analysis to gets in various biological processes and complex human dis- identify DEGs by excluding GSE4903628, which was the only data eases14–23. For example, our previous study integrated large-scale set in our collection with a complete clinical assessment and a genetic and gene expression data as well as clinical and patho- sufficient number of samples for correlation analysis. This sepa- logical traits into multiscale network models of Alzheimer’s dis- rate analysis yielded 420 DEGs (Supplementary Data 2), and ease (AD)16, and several predicted key regulators of AD 1,633 genes correlated with Braak score (Supplementary Data 3) pathogenesis such as TYROBP, DTL/CDT2, and GJA1 were and there was a significant overlap between the negative Braak- subsequently validated in various model systems24–27. However, correlated genes (BCGs) and the downregulated DEGs (multiple- such network biology approaches have not been applied to PD testing corrected Fisher’s Exact Test (cFET) p = 2.7E-54, 5.7-fold research yet due to the lack of molecular profiling data from a enrichment (FE)) as well as between the positive BCGs and the large number of postmortem brain samples. As a result, little is upregulated DEGs (cFET p = 9.5E-05, 4.3 FE) (Supplementary known about the global as well as local structures of gene inter- Fig. 2a, b), suggesting the robustness of meta-analysis to identify actions and regulations in PD, thus hindering our understanding DEGs associated with disease progression. Therefore, we focused of idiopathic PD. on the 946 DEGs identified in the meta-analysis of the eight In this study, we develop multiscale gene network models of curated data sets for the downstream analysis. Gene Ontology PD based on an ensemble of all the existing human brain gene (GO) analysis (Supplementary Data 4) showed that the down- expression data sets in PD followed by a comprehensive func- regulated DEGs were enriched for synaptic function and tional validation of a top key regulator. We identify a neuron- metabolism-related GO terms, such as synaptic signaling, specific synaptic signaling gene module most associated with PD synaptic vesicle cycle, and organophosphate metabolic process, and pinpoint STMN2 as one top key regulator of the module. indicating that neuronal activity particularly neurotransmission Knockdown of Stmn2 in mouse DA neurons leads to impaired was impaired in PD, while the upregulated DEGs were associated synaptic vesicle (SV) endocytosis. Knockdown of Stmn2 in the with spinal cord development and embryonic digit morphogen- mouse SN further causes DA neuron loss, increased α-synuclein esis. These DEGs are largely consistent with the previously phosphorylation, and locomotor deficits. The network models not published meta-analyses in PD postmortem brains. Glaab and only shed a light on the global landscape of molecular interactions Schneider applied protein–protein interaction network (PPIN)- and regulations in PD but also reveal detailed circuits and based enrichment analysis to identify synaptic transmission and potential key regulators of PD pathogenic pathways for further dopamine metabolism as the top disrupted pathways in PD37.In experimental investigation. Oerton and Bender’s analysis, 21 of the 32 top-ranked

2 NATURE COMMUNICATIONS | (2019) 10:5234 | https://doi.org/10.1038/s41467-019-13144-y | www.nature.com/naturecommunications NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-13144-y ARTICLE downregulated genes and 2 of the 11 top-ranked upregulated infer the potential regulatory relationships among genes, as we genes were present in our meta-analysis with consistent regula- did in the previous study of AD16. Distinct from co-expression tion (cFET p = 5.9E-19, 11.6 FE and cFET p = 0.02, 12.3 FE, networks which are built upon correlations in the linear space, respectively)38. Zheng et al. showed that electron transport chain, BNs are able to capture nonlinear relationships due to the data oxidative phosphorylation and metabolic pathways were most discretization and modeling on categorical distributions. Thus, affected in PD32. Taken together, our meta-analysis provides a BN can be used in complement to MEGENA for candidate dis- PD DEG signature that extends the published work for further covery and prioritization. Network hubs identified in both the analysis. MEGENA network and the BN are likely to be more robust than those identified by a single network. We identified 33 and 45 key regulators of M4 by MEGENA and the key driver analysis (KDA) PD MEGENA modules show cell-type-specific features. A total of the M4-based BN, respectively. The two sets of regulators of 90 parent-child modules with module size >50 were identified shared 16 genes including 10 downregulated and 1 upregulated from the global PD data set using MEGENA (Fig. 1a). These genes in PD in comparison with the normal control. We then modules were then rank-ordered by their relevance to PD (Fig. 1b; rank-ordered these key regulators based on their enrichment of Supplementary Table 2) which was calculated from the enrichment the PD DEGs in their two-layer network neighborhoods. The for the above identified DEGs (Fig. 1c), PD GWAS hits down- network neighborhood of a given gene refers to the genes that are loaded from GWAS catalogue (https://www.ebi.ac.uk/gwas/)and N-layers (N = 1, 2, …) away from the gene in a given network. an independent set of PD genes curated from the Kyoto Ency- We identified RALYL, BASP1, ANKRD34C, STMN2, and clopedia of Genes and Genomes (KEGG hsa05012). Each module SYNGR3 as the top five key regulators of M4 by combining the was annotated by the most significantly associated GO term rankings from both the MEGENA network and the M4-based BN (Fig. 1e for major pathways; for the complete list, see Supple- (Supplementary Table 3). We hypothesize that perturbation of mentary Data 5). these key regulators of M4 could potentially influence the gene As the central nervous system (CNS) is composed of multiple networks and ultimately introduce PD-like phenotypes. To fur- types of cells with distinct contributions to PD pathogenesis and ther prioritize the key hub genes for experimental validation, we progression, we examined the cell-type specificity of the modules examined their differential expression in the human SN (Sup- using the gene signatures of six major brain cell types, including plementary Fig. 4) and in different cell types in the adult mouse neurons, astrocytes, microglia, endothelial cells, oligodendrocyte SN39. All of five top key regulators were downregulated in the precursor cells (OPC), and oligodendrocytes generated from the human SN with PD, while only Stmn2 and Syngr3 had a pre- human brain33,34 (Fig. 1d; Supplementary Data 6). The results ferable expression in the DA neurons over the other neuronal reflected the cell-type-specific functional modules of highly subtypes in the adult mouse SN (Supplementary Table 4). Syngr3 coordinated molecular dysregulations in PD. The module M4 homozygous knockout mice demonstrated a significant decrease was ranked as the top for its most significant enrichment of the in locomotor activity compared with their age- and sex-matched overall PD DEGs (cFET p = 7.9E-26, 2.3 FE) and downregulated controls (Supplementary Fig. 5e, data downloaded from https:// DEGs (cFET p = 2.7E-29, 2.7 FE) followed by its three child www.mousephenotype.org) and Syngr3 mediated presynaptic module branches M116-M562, M114-M557, and M117. M4 was dysfunction induced by Tau40, which was consistent with our associated with synaptic signaling (cFET p = 7.7E-05, 2.1 FE) and prediction that Syngr3 was involved in neurotransmitter transport enriched for neuron-specific markers (cFET p = 6.2E-28, 3.9 FE). (Supplementary Data 7). Therefore, we selected STMN2 for fur- The other branches of the top-ranked modules included the M2 ther functional investigation. branch (M30-M475 and M32) and the M6 branch (M160-M615- M1001 and M165-M628-M1010). The M2-M30-M475 branch was also associated with synapse-related functions and enriched Network-specific functional annotation of PD GWAS genes. for neuron-specific markers (Supplementary Table 2; Supple- The links between the genes in the MEGENA network suggest mentary Data 4). This is not surprising as multiple PD risk genes high correlation and potential co-regulation in similar biological are associated with synaptic transmission, such as LRRK2 and processes. Therefore, the network neighborhoods can be used to SYNJ16. The M32 and M6-M160/M165 branches, although not annotate biological functions of the known PD risk genes. The PD showing any cell-type specificity, were significantly enriched for MEGENA network includes 125 known PD GWAS genes curated the downregulated PD DEGs and associated with electron in the GWAS catalog (https://www.ebi.ac.uk/gwas/). For each transport chain and complex metabolic process, respectively, GWAS gene, we assigned the top pathway enriched in its two- suggesting shared disease mechanisms across different cell types. layer network neighborhood as the putative annotation (Supple- On the other hand, M16 was most significantly enriched for the mentary Data 8). For example, AAK1 was predicted to be asso- upregulated PD DEGs (Fig.1c) and the oligodendrocyte-specific ciated with synaptic signaling (cFET = 0.023, 4.4 FE), and indeed markers (Fig. 1d; Supplementary Table 2), suggesting that it was reported to regulate clathrin-mediated endocytosis41. Such oligodendrocytes play a significant role in the disease progression. contextual annotation of the PD GWAS genes can be applied to The KEGG PD pathway (hsa05012) comprised 142 genes was any gene in the network to understand their disease-specific enriched for the downregulated genes in PD (cFET p = 2.1E-07, function. For example, STMN2, one top hub in M4 and a known 3.8 FE) and most enriched in the M2-M32 and M165-M628- – regulator of microtubule dynamics42 44, was predicted to be M1010 branches (Supplementary Table 2). highly associated with synaptic signaling according to our net- work based annotation (cFET p = 1.9E-04, 4.4 FE; Supplementary Key regulators of the top module are dysregulated in PD.To Data 7). better understand the PD network, we investigated the underlying In addition, we examined how the network neighborhood of network topological structures of the top module, the neuron- each PD GWAS gene was enriched for the PD DEG signature specific M4 that was comprised of 947 genes and was enriched for (Fig. 2a; Supplementary Data 9). Strong enrichment indicates the downregulated DEGs (Fig. 1f). As the links in the MEGENA more functional involvement in PD pathogenesis beyond the network were undirected and represented gene co-expression or implicated genetic association with the disease. The top GWAS co-regulation, we further constructed a module-based BN using genes whose network neighborhoods were most significantly the same set of gene expression profiles from the PD patients to enriched for the genes downregulated in PD versus control were

NATURE COMMUNICATIONS | (2019) 10:5234 | https://doi.org/10.1038/s41467-019-13144-y | www.nature.com/naturecommunications 3 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-13144-y

ab–log10 corrected p 10

M4 M2

M16 M30 M116

M562 M1001M475 M114 M557 M160 M615 M117M32 M628 5 M165 M1010M37 M1146 M733 M2 M4 M486 M972 0 M6 M688 M31 M492 M33 M35 M161 M34 M16 M786

M557

M562

M572 c M576

M114 M116

M118 M119

M117

M1001

M29

M26 M4 M128

M22 M21 M615 PD-UP

M140 PD-DN

M20 M1010 M463

M16 M160 Ranking M628 Modules

M13

M461 M5

M945 M28 M165 KEGG-PD

M639 PD-GWAS PD-Overall

M460 M169 M917

M27

M428 M161 NPTX2 TMPRSS3 M25 BMP5

M6 SMURF1 M425 GPR39 M168 TRAPPC3

GSTCD SH3BP2 M424 MOXD1

M24 M173 ZSCAN18 DDX18 MAML3

M19 M1 M355 JPH3 UNKL

M186 LYPLA1 CNOT7 PTX3

M15 BPTF ZNF426 M305 SPINK1 NEIL3 M193 f RNF2 SLC25A36

M7 HIGD1B

FAM175B M50 SOX4 ZNF592

M210 ANXA2P1 AKAP3 REN

M9 TKTL1 RAB14 ANG M37 M688 LGALS3 M213 FAM110B CCDC64 BAX

ORAI2 HSD17B3

M34 M701

TKT M2 M214 COMT KLF7 CALU CYP2C8 ZMIZ2 MCM7 CYP27A1 KCNQ1DN

CCKAR CSRNP3

M32 AQR UNC13A PCLO M10 M217 GTDC1 CHEK2 C1orf95 PURG NPY GTF2H2B

CCNL2 SLIT2 LXN TARDBP M31 GTF3C2

M14 M228 HECTD3

M11 PRKACG ATP2A3 KCNB1 ARHGAP5 HAS2 M12 SATB2

GRIN2A SCN3A CCR9 M35 M231 PCNX BCL9 TMEM135 SLC16A2 IL11RA KIAA0319

LONRF3 M33 STAG3L4 M229 M733 CBLN1 COL11A1 RIMBP2 CACNB3 JMJD4 SLC35D1

PEG10 MYL5

M30 M234 VAT1 RPS6KA6 POMGNT1 SHANK2

RPP38 FAM155A DSG2 M492 M277 CALB2 M239 RIMS1 KCNA2 GPR19CNTN6 ABCA3

M260 M747 CNTN1 M250 INSM1 M259 BNC2 NELL2 CHRNB3 SEMA3A

GABRA5 RGS4 ARHGAP29 NEDD9 M486 TRPC6 DENR SAR1A TFB2M MPPED2 CTDSP2 DCBLD2

TFDP2 NR4A3 SRRD PDE1A RBM26 M475 CAD ZNF419 PBX1 ATP1A3 CSNK1E ARID1A PRRG3 SSBP3 SCN2A CBS SMPX LDLRAP1 CBX6 ZPBP SLC6A3 SYN1 LPHN1

CUX2 ATP2B3 M972 M786 SLC18A1 HR

M797 CLDN1 PIK3CA CLSTN2 SLC18A2 ITGB1BP1 ST6GALNAC5 SMAD7 SEMA6D EN1NCAPG ELAVL2 PTPRR TMEM35 CLOCK CHMP7 PDE3B ANKRD34C ZNF385D

PCDHA2 LTBP1 NUDT11 ANAPC13 PMF1 M1146 ALCAM HPN FLJ21369 SLITRK5 GPC4 KCNJ6 PAFAH1B2 THY1 OPRK1 M1089 KANK2 CCNA1 DLG4SLC29A1 LRRN3 CIB1 TH CDH8 SLC24A3 INA NPTX1 FJX1 RAB40B NXF1 EEF1A2FAM178A NOL4 VPS33A EMCN CDH18 HPCAL4 MTL5 NFKB1 BICD1 NTNG1 PDLIM2 CADPS2 RALYL SERPINB6 SYT17 SLIT1 OR7E47P SEZ6L2 JUN CHD5 SLC8A1 DDC KCNA3 ERCC6L FLNC PRKCZ GRM5 SCN8A DLK1 EFNA1 PCIF1 PIK3C2G SCO2 FRMPD4 CCDC25 PCDH8 CLSPN GABRB1 IGSF3 LOC100127972 DPP6 PTPN4 HTR2A MAGEB4 OSBPL3 LPO BASP1 ATP8A2 SLC25A16 CALB1 PTHLH GABRB2 KCTD14 NKG7 ZHX2 RASL11B TRAM2 ZNF532 DIDO1 HPGDTM9SF4 PAK3 GZMB SUB1 DGKI CALCR NCDN RGS17 OVOL2 DNAJB12 PRKAR2B SDC1 GALT NOP10 ATP1B1 CLCN2 GRP P2RX7 RAB15 RIMS2 GLS2 BBS4 TIPRL COBL TOX4 WDR12 TIMM9 ASTE1 DLGAP2 GPR161 ARL5A STYK1 KCNJ13 CDC16 AGTR1 FGF13 SCG2 HN1 ENC1 GTF2F1 GFRA1 RGS7 SMARCA1 CABP1 GAP43 MBTPS2 PID1 CD24 SLC12A1SLCO3A1 ARHGEF9 FBXW4 NR4A2 ST3GAL1 SGSH OCLN IGF1 BMP2K SLCO4A1 FKBP1B SUSD4 NUP85 CHST3 ACIN1 DEG SCRN1 REEP1 PCP4 ZNF211 UAP1 RTN2 CDH12 GCH1 PCSK1 WARS CCNB1 APOOL ATP7A ALDH1A1 CAT ATG7 ERGIC3 PIP5K1B CNKSR1 PIAS2 TEP1 FBXO11 –20 –10 0 HMP19 HGSNAT MATN3 FGF12 RBM4B SRPRB SLC4A5 E2F3 ZNF226 ZDHHC11 ZMYND10 SV2C CDH3 SPOCK2 KCND3 VPS45 FBXW4P1 PYGL GRIA1 NEFH SYT1 GOLT1B TFB1M COQ10B CTBP1 INPP5F KCNQ2NRXN3 TXNDC15 MCFD2 TRIM36 SUPT3H RNF44 FTO HYAL4 DOK5 SNCG RFK OR7A5 AZGP1 STMN2 UBE2J1 UCP1 AHCYL2 AKAP5 CDKN2C GNAS SCN9A CASC3 CENPA NRIP3 NMNAT2 ACHE FUCA1 HCRT CACNG3 COPG2IT1 LBH ALOX5 MINA LILRB2 ECSIT CCK CDH13 ARHGDIG IL13RA2 DUS4L BCAS1 SCN11A RUVBL2 CNIH3 RNMT GRSF1 SV2B PAM AGK SHOC2 CNNM4 SDC2 CDK5RAP2 NUBPL d M557 CITED2 FAM63A CCL27 M562 TIMM22 CAMK1G ANP32B LPCAT4 GBE1 PGF ETS1 M572 CXCL1 PAPPA C9orf116 MXRA7 MKRN1 M576 PTGER3 CXCL2 EPS15L1 NDEL1 VAV3 DAB1 SIX3 KCNMA1 LSM14A KLF8 DDX10 SCN3B NOL3 KRT5 FAM3C SLC1A1 M114 PNLIPRP2 PTP4A2 ARMCX5 M116 CACNA2D2SYNGR3 CRYBB2 LMBRD1 RNASEH1 PTPRO M118 ATXN2 SLC44A1 RRS1 ATP9A M119 FAM107A B3GNTL1 DCC TROVE2 PTH1R FKBP1A TRPV2 SAP18 HGF ERC2 PCSK2 M117 POP5 PTTG1IP SYNGR1 EPCAM MTMR9 M1001 RPS6KA2 POLDIP3 PDK4 TRAK2 C12orf4

BMP6 M29 M26 M128 ARHGEF2

M4 FRMD4B NUP160 M22 EXOSC4 SAMM50

FANCE M21 M615 M140 ZNF24 LMO4

M20 M1010 USP32 SPOP DGKQ COMMD9

M463 CDC42SE1 DPYSL3 M16 SET AMDHD2

M160 M628 PFDN5 MAP1S

M13 APRT CORO7

CDC7

M461 M5 UBE2D1 SULT4A1 SLC16A1

RAP2A TNFAIP1 NHP2 M945 M28 M165 IFI44L ERCC2 NDUFA3

M639 TTC38 B3GALT4 APOF M460 TXNRD2 TBL3

MAGEC3 SLC27A6 PCBP3

M169 NOS1AP MEA1 M917 ISG20L2 OTOF NAAA C11orf48

M27 IL26 M428 DPYSL4 CD36 HOXB9 SLC16A7 PER2 SFMBT1

NECAP2 CHERP M161 PNPO GSK3B M25 NPY5R KIAA0355 MARK4 SLC25A22 SF3B5

M6

EYA4 M425 CNTNAP2 RPL3 ELF2 CACNB4 MEPE EFR3B CACNA1A SOX3 M168 MRE11A DDA1

QSOX1 FBL AMBRA1

RFPL3 TRPM2 PPFIA3 M424 ARID3A

M24 BRSK2 OTOR CHMP2A M173 ABCB7

GPR176 KCNC1 CHGA AKAP10 RPL27A

CST1

M19 M1 ARHGAP19 M355 RFPL2 RAB3A RPL8 TRIP13 TRAF3IP1 DGCR9 EIF3D

M186 NAT6 TIMM13 M15 CCNL1 SHFM1 PKN1

SYT13 CAMK4 KLHDC8A NAV3 GSTP1 M305 ABHD14A CLSTN3 PAFAH1B3 M193 C8B PCSK1N UXT

PPAN ARHGEF12 ZYX DBNDD1

M7 PRKAR2A CD320 COPB2 PRKAG1 POLDIP2 SETDB1 RNASEH2A M50 STAT4 TMEM214 COBLL1 CSDC2 STK4 M210 QRSL1 FAM96B

KCNA4 FNDC4 LAMB2

MRPL24 CLDND1 RAB27B

M9 FNBP4

ZSCAN12 ACOX3 PDE6H CNTNAP1 BCL7B M37 M688 PITPNM3 SLC2A6 TAF1D FAM186A NFKBIL1 PALLD USP5 M213 AEN TIE1 ACSL6 DPYS DCTPP1 ZNF576

YES1 MCHR1 CACNA2D1 EIF2B4

GOLGA4 HOOK2 CHM LRRFIP1 M34 LGI2 HIST1H2BC

M701 BTRC ZNF212 KPNA6 M2 M214 MAMLD1 GGNBP2 PHIP TCF12 ARF3

ROCK1 SCLY

USP25 DNAH7 LIMCH1 P4HTM M32 MAST2 ACTN2

CACNA1E CD209 M10 ABCG4 M217 DUSP26 ASAP3

UFM1

PRKACA BCAS4 RAP1GAP ARNTL2 HMG20B M31 USP11

M14 M228 ASB1 MAPK8 TMEM115

M11 BTN3A2 PADI1 CCDC51 M12 CLDN14

GH2 GPRC5A SIRT3 M35 DRP2 M231 MYO7A RUSC1 APBA1 NCKIPSD

PKNOX1 EEF2 ALDOAP2 M33 DNAJA4 DDX5 PELI1 CRELD2 EXT1 M229 M733 SLC7A4 KIF5A COG4

STIM1 TAOK2

M30 M234 RIC3 TUB UBE2O ERAP1

M492 M277 SRPK2 NOSIP M239 PDCD11

M260 M747 TRAK1 TWF2 M250 RNMTL1 M259 ACTR1BPTPN3 ADD2 TP63

KRT2 M486 KLF5 BRAF AJAP1 ZNF415 PORCN PAOX LAGE3 NPAS1 SCGB1A1

ANKRD2 PLK4 M475 ST6GALNAC2 BBS9 GPR173 GALNT8 NRBP1 MAGOHB MYL3 DLC1 FXYD6 C2CD2L SWAP70

HYOU1 CAMKV M972 M786 AKR1B10 M797 DSP JAK1 RRP1 TGFBRAP1 CRMP1 USP19 GLRA1 B4GALT4 SUPT6H ABCB8 APOC2 WNT10B ALDH1A2

TNNC2 TBC1D22B M1146 FOXRED2 FAT4 SAV1 M1089 C1orf115 KCNJ12 CLTCL1 PPP2R5B SPATA20 TNFAIP8 FGD1 POLR1C PIP5K1C RHOF MC2R INTS6 DRD2 HCFC2 KRCC1 BRD7 GIMAP6 CHRNB2 SERPINB10 RNF123 SLC7A8 USP34 ARHGAP26 PPP2R3C MC5R CXADR IFNGR1 REEP2 ZNF219 HSD17B4 NASP DPY19L4 EVI5 HNRNPC ST6GAL1 CFH GABBR1 GPR12 CCNF CAMTA2 CEP57 AASDHPPT CYP46A1 ZNF217 ACSL3 RREB1 ZFC3H1 BAP1 DENND4B SPTAN1 C6orf25 RNF103 ast mic oli LYST WDR5B SULT1A2 GPR87 ADRA1D PPP2R4 DNAI2 NRL KLC2 COPA GAK ADORA2B UBAP2L KIAA1661 SUPT5H STX1A CellType LPPR2 LRRC61 ADAM30 ST6GALNAC4 FLII BCHE FSD1 PSD FOLR3 ABCB1 BTN2A1 USP48 IL12RB1 CALML5 end NA SULT1A1 PCGF1 neu METTL8 GRIP2 CEACAM8 CD2AP IFI16 SHMT2 EPM2AIP1 MKL1 TOP3A GNMT MAPK3 GNG11 ZNF271 PPEF2 SEC31B SHPK HTR5A TMEM106C GIMAP4 GDF11 POLD4 ARVCF BCL7C PHKB PARVA MKI67 HEXIM1 CYBA TSPAN31 OS9 SBNO1 ALPI MLNR ABCC3 PRSS22 FUT5 HBQ1 ABCG2

GOLGA1 CAV1 RPS27A ARNT NGB PRR3 ACAP1 THTPA CCDC57 CRYBA2 IGHG1 NHEJ1 VPREB3 M557 SLC7A9 A2M CD79A e M562 ATXN7L1 FZD8 M572 CCBL1 WISP3 DTL PMFBP1 MUC1 MKRN2 M576 SLC35C2 GRWD1 ANXA2P3 NOS3 NUBP2 RPS6KB2 M114 MYOG ERICH1 M116 DNAH6 ITIH3 M118 OSR2

M119 TECTA RNF32 BARX1 M117

M1001 BGN HDAC7

M29 M128

M26 M4 FBXL8

M22 M21 M615 SLC10A3

M140

M20 M1010 INSL5

M463 M16 ZNF696

M160 M628 TMPRSS11D

M13

M461 M5 Major.pathway

M945 M28 M165 M639 GRB7

M460 M169 ARSE M917 M27

M428 Electron_transport_chain M161 TAF13 M25

M6

M425 M168 M424

M24 M173 Immune_response M19 M1

M355 M186 M15

M305 M193 Protein_folding M7

M50 M210 M688 Key hub M9 DEG

M37 M213 M701 RNA_splicing GWAS M34

M2 M214 NA M32

M10 M217

M31 M14 M228 M11 No

M12 Synaptic_transmission M35 DN

M231

M33 M229 M733

M30 M234 M492

M277 M239 M747

M260 M250

M259 Yes UP

M486 Transmembrane_transport

M475

M972 M786

M797

M1146 Metabolic_process M1089 Others

Fig. 1 Gene co-expression modules associated with PD. a A global MEGENA network. The modules at one particular compactness scale are represented by different colors. Four parental modules (M4, M2, M6, and M16) most associated with PD are highlighted. b The top 30 MEGENA modules most enriched for the PD-related gene signatures including the PD DEGs, the PD GWAS genes, and the genes in the KEGG PD pathway. c Enrichment of the DEGs of PD in all the MEGENA modules. Color intensity is proportional to −log10(corrected FET p-value). d Enrichment of cell-type-specific gene signatures in all the MEGENA modules. Color intensity is proportional to −log10(corrected FET p-value). e Major pathways associated with the MEGENA modules. f The top-ranked module M4 is highly enriched for the downregulated DEGs in PD and the neuron-specific gene signatures. Key regulators are highlighted in burgundy circles. The pie chart of each node indicates whether it is a DEG or GWAS gene. The size of a node is proportional to the node connectivity in the MEGENA network

4 NATURE COMMUNICATIONS | (2019) 10:5234 | https://doi.org/10.1038/s41467-019-13144-y | www.nature.com/naturecommunications NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-13144-y ARTICLE

INPP5F

a ns b MINA UBE2J1 ATG7 30 Significant SYNGR3 BASP1 MCFD2

CITED2 DOK5 COQ10B RGS7

RNF44 SMARCA1 KCNJ13 BCAS1 CHGA FAM63A GCH1 RALYL CNKSR1 ANKRD34C TEP1 RIT2 RUSC1 APOOL NRXN3 GRIA1 ARHGDIG 20 LPCAT4 GNAS KCND3 DDX10 CAMK1G RAB15 INPP5F SV2C GBE1 RFK NMNAT2SHOC2 SCN3A CNTNAP1 ERC2 HMP19TMEM35 NRIP3 HN1 C9orf116 KCNQ2 VAV3 SYT1

–log10(p.BH) PBX1 SCG2 SNCG ACHE STXBP1 CNIH3 SYT13 NEFL ALAS1 SULT4A1 STMN2 TM9SF4 10 SH3GL2 SCN3B NUDT11 LBH SYT17 COPG2IT1 NEFH GAP43 IL13RA2 AAK1 CACNA2D2 ATP8A2 BAG3 THY1 REEP1 PPFIBP1 CCNT2 CACNG3 INA CHD5 OGFOD2 BDNF SV2B MRPS30 PAK3 FGF12 POR NISCH SYN1 ATP1A3 NCDN DGKQ DLG2 CDH12 GPNMB BST1 RTN2 ARHGEF9 HTR2A COL5A2 GLS2 PCP4 0 BCKDK

10 0 10 20 FGFR1 SEC61A2 PNMAL1 TUBA4A DN FE UP ATP6V0E2 PFDN1 SLITRK5 ENO2 SDHA RAB11FIP5 cdHPCAL4 FJX1 GOT2 KIAA1045 CADPS2 OGDHL UQCRC1 SSBP3 MDH2 VGLL3 EN1 TH PACSIN2 LRRC49 KIF3C PTDSS1 CALCR

SLC6A 3 KCNJ 6 PPM1E

DLK1 ATP6V1B2 DEF8 PSME3 RCAN2 CLSTN2 AMPH HARS SLC9A6 CDH8 SLC18A 2 C10orf76 GRP BSCL2 ATP1A1 RET SDC1 PCSK1 EXTL2 TUSC2 CD24 DDC MAP4K4 SNAP91 CHGB CADPS MAGI1 TRIM37 RIT2 UCHL1 SLC4A5 NUP85 COPG2IT1 UAP1 PCDH8 BEX1 P2RX5 SLC25A15 BBS7 SCG3 STAT2 GCH1 SCG2 RNMT SLC12A5 RNF41 NOL4 SRD5A1 E2F3 PIP5K1B STXBP1 GNG3 HMP19 TRIM36 ATG4B TTC9 HYAL4 RUNDC3A RPH3A PVALB NRG1 BRCA1 AGTR1 PRKAR2B ZMYND10 FLNC FGF13 PCDHB11 ZFYVE26 SNCB CALY PID1 TTC39A ALDH1A 1 C10orf10 SUB 1 SCN1B PHTF1 PHF14 ANKZF1 SYT2 PYG L FXN ZNF226 CLCN2

NR4A 2 OSBPL 3 LY6H ARL5A

ASMT EIF5 e SNAPC4 f ADAMTS6 DNAJB4 PCID2 PAK2 USP39 C9orf114 WWC3 HNRNPA2B1 MUM1 RAB6B ST13 SNRNP27 UBE2D3 SIRT7 BHMT2 ZNF223 SH3BGR ATF4 TBC1D22A PIR HSPE1 SCCPDH DDIT3 KLHL21 PTGES3 NUDT9 ENOSF1 GNG3 CACNA1G CRYAB CSNK2A2 TPD52L2 MRPL18 PAN2 PLOD1 HSPA1L CDK2AP2 DNAJB2 CACYBP ZNF692 PCNXL2 RASGRF1 BDNF HSPB1 HNRNPH3 SERPINH1 HSPA5 NTS CLIC2 CCT3 HIST2H2BE ADCYAP1 NQO1 SPR HDAC1 RBM10 AHSA1 PDIA6 DNAJB1 NCK2 RPS19 HIST1H2BK APOM EFNB3 BAG3 HECW1 PHF10 G0S2 HSPH1 LMAN2L PCBD1 NFIL3 INTS8 STIP1 HSPA8 PPFIA2 FXYD7 IER5 JTB HIST1H2AC DNAJA1 HSP90AB1 ZNF365 NME3 DNAJB6 RELN FRS3 UPF2 ALB SPRED2 PRKAG2 CNNM1 CRY1 THAP11 MID2 TEX15 HSP90AA1 TAS2R14 GTF3A EIF4G2 Key hub SLC5A3 CTTN DEG IL17RB GWAS DLEU1 SVEP1 NA CA4 IRX5 TPD52L1 CLEC3B No DN Yes UP SLC27A2

Fig. 2 The subnetworks of the top-ranked PD GWAS genes. a Enrichment of the PD DEGs in the network neighborhoods of a subset of the PD GWAS genes. b–e The network neighborhoods of INPP5F, GCH1, RIT2, BAG3, and BDNF, respectively. The pie chart of each node indicates whether it is a DEG or GWAS gene. The size of a node is proportional to the node connectivity in the MEGENA network

NATURE COMMUNICATIONS | (2019) 10:5234 | https://doi.org/10.1038/s41467-019-13144-y | www.nature.com/naturecommunications 5 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-13144-y

INPP5F, GCH1, and RIT2 (Fig. 2b–d) and the latter two were immune-related genes is likely to be secondary. Specifically, downregulated in PD (Supplementary Data 1). The top two knockdown of Stmn2 upregulated nine PD GWAS genes GWAS genes, whose network neighborhoods were highly (GPNMB, SREBF1, STAB1, LHFPL2, PRRG4, CTSB, FNDC3B, enriched for the genes upregulated in PD, were BAG3 (Fig. 2e), PPFIBP1, and COL5A2), and downregulated one PD GWAS gene which was upregulated in PD, and BDNF (Fig. 2f), which was (SYT4) at the mRNA level. While STAB1, LHFPL2, PRRG4, downregulated in PD (Supplementary Data 1). STMN2 fell into FNDC3B, PPFIBP1, and COL5A2 were not reported to be func- the neighborhood of INPP5F (Fig. 2b). Although STMN2 is not a tionally related to PD, we predicted that they were associated with known genetic risk factor for PD, our results indicate that STMN2 immune response, oxidoreductase activity, and EGF pathway is functionally linked to some PD risk genes and potentially plays annotated based on their network neighborhoods. Indeed, a critical role in mediating molecular alterations in PD. GPNMB was elevated in the SN of human PD brains and played an anti-inflammatory role in a CD44-dependent manner49. SREBF1, a key regulator of the lipogenesis pathway, was found to Stmn2 knockdown causes presynaptic dysfunction in DA stabilize PINK1 during mitophagy initiation50,51. CTSB, known to neurons. Stmn2 is known to regulate microtubule dynamics, axon – encode cysteine cathespsin B, was essential in lysosomal degra- formation and neurite outgrowth during development42 44 and dation of α-synuclein52. Upregulation of these PD GWAS genes regeneration after injury45, while we predict that Stmn2 may suggested a compensatory mechanism in response to Stmn2 regulate presynaptic activity in PD (Supplementary Data 7). To deficiency, while SYT4-mediated somatodendritic release of DA test the hypothesis, we employed a live cell imaging assay utilizing was a key mechanism for the autoregulatory control of DA release pHluorin (described in the Methods section) in cultured mid- in the brain53. brain DA neurons where Stmn2 was highly expressed39 (Sup- We then examined how Stmn2 knockdown impacted STMN2- plementary Fig. 5a, b). pHluorin is a variant of green fluorescence correlated genes identified in the PD patients (Supplementary protein (GFP) sensitive to pH. When targeted to the acidic lumen Data 11). After mapping mouse genes to the human homologues, of synaptic vesicles, pHluorin is quenched but fluoresces upon we found a significant overlap between the genes positively exocytosis when exposed to the extracellular buffer (pH 7.4). correlated with STMN2 in PD patients and those downregulated Conjugating pHluorin to vesicular transporters, such as vesicular in Stmn2 knockdown mice (cFET p = 1.4E-27, 5.7 FE) and glutamate transporter 1 (vGLUT1) or vesicular monoamine between the genes negatively correlated with STMN2 in the PD transporter-2 (vMAT2) was used to examine synaptic vesicle – patients and the genes upregulated in Stmn2 knockdown mice kinetics in a quantitative manner46 48. DA neurons expressing (cFET p = 0.04, 1.6 FE). The DEGs downregulated in Stmn2 CMV-vMAT2-pHluorin were recorded at 9–11 days after Stmn2- knockdown mice were significantly enriched in the module M4 shRNA transfection. Stmn2-shRNA-treated DA neurons (FET p = 0.015, 2.0 FE, Fig. 4b) as well as in the two-layer demonstrated impaired SV endocytosis after and during stimu- MEGENA network neighborhood of STMN2 (FET p = 4.3E-06, lation compared with scrambled shRNA-treated DA neurons 8.6 FE; Fig. 4c), while the upregulated DEGs in Stmn2 knock- (Fig. 3a, c), while the exocytosis was not significantly affected down mice were not significantly enriched in either M4 or (Fig. 3a, b). We also observed enlarged boutons in Stmn2 STMN2’s MEGENA network neighborhood. As the majority of knockdown DA neurons where the pHluorin assay was recorded the DEGs in Stmn2 knockdown mice were upregulated and (Fig. 3d, e), while no obvious degeneration of the soma or cell involved in immune response, which was absent in the meta- death was seen at the time of recording. Interestingly, bouton size analysis of the data sets from the postmortem PD brains, we was not significantly affected in the cultured TH-negative neu- posited that this Stmn2 knockdown mouse model might partially rons (Supplementary Fig. 5d, e). Our data thus corroborate the recapitulate the PD pathogenic processes in the human brain. network based prediction and points to a key role of STMN2 in regulating presynaptic activity. Knockdown of Stmn2 impairs locomotor functions in mice.We next examined whether knockdown of Stmn2 could induce PD- Knockdown of Stmn2 perturbs STMN2-centered subnetworks. like behavioral and pathological changes. We performed a uni- We then sought to validate in vivo whether the perturbation of lateral injection of AAV2/1 carrying either the Stmn2-targeting STMN2, one of the top key regulators in the top-ranked module shRNA or a scrambled sequence into the SN of 2-month-old male M4 would preferentially induce changes of the gene expression in mice. We assessed the locomotor activity by performing Rota-rod STMN2’s network neighborhoods as well as STMN2-correlated and open field tests in the fourth week after injection (Fig. 5a). The genes in PD. To mimic the decreased STMN2 expression in PD mice with Stmn2-shRNA injection spent significantly less time on brains (Supplementary Fig. 4), we performed genetic knockdown the rod than the mice with scrambled shRNA injection (Student’s in mouse brains with a red-fluorescent-protein (RFP)-tagged t test p = 0.008, Fig. 5b). In open field, the Stmn2-shRNA-treated Adeno-associated virus (AAV2/1) containing the validated Stmn2 mice showed fewer vertical episode counts than scrambled shRNA sequence. After confirmation of the knockdown efficiency shRNA-treated ones (Student’s t test p = 0.038, Fig. 5c), while in N2A cells and wild-type mouse brains (Supplementary Fig. 6), there was no difference in the total distance between the two we performed a unilateral injection of the virus into the right SN groups (Fig. 5d) and a trend toward shorter center distance tra- of 2-month-old male C57BL/6J mice. The injected mice were veled (Fig. 5e) in the Stmn2 knockdown mice. As the injection was sacrificed at 1 month post injection, and the infected SN was given in mice unilaterally, administration of amphetamine could isolated for RNA sequencing. DEG analysis identified 527 upre- induce rotational asymmetry in mice, which was commonly used gulated and 115 downregulated genes caused by Stmn2 knock- for testing DA-dependent progression in locomotor function down (fold change > 1.2 and FDR < 0.05; Fig. 4a; Supplementary lesion54. The amphetamine treatment indeed induced a significant Data 10). Stmn2 itself was most downregulated, indicating the increase in contralateral rotation in the Stmn2-knockdown mice knockdown efficiency. The genes downregulated by Stmn2 (Fig. 5f). Taken together, our data suggested that Stmn2 knock- knockdown were associated with sterol/lipid biosynthesis and down caused DA related locomotor dysfunction. metabolism (cFET p = 1.4E-10, 59.2 FE), while those upregulated by Stmn2 knockdown were associated with immune/inflamma- tory response (cFET p = 3.9E-44, 3.6 FE). As Stmn2 is not known Stmn2 knockdown reduces striatal DA content and DA term- as a transcription factor or cofactor, the upregulation of these inals. We further examined the striatal DA content by performing

6 NATURE COMMUNICATIONS | (2019) 10:5234 | https://doi.org/10.1038/s41467-019-13144-y | www.nature.com/naturecommunications NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-13144-y ARTICLE

a p = 0.016 p = 0.39 0.30 10 Hz,10 s 60 Scrambled 0.25 Stmn2KD 50 0.20 40 0.15 30 0.10 20

Fraction of exocytosis 0.05

0.05 total pool 10

10 s Endocytosis time constant (s) 10 11 10 11 0 0.00

Stmn2KD Stmn2KD Scrambled Scrambled

b c p = 0.005 p = 0.14 80 0.35 10 Hz,120 s 10 Hz, 120 s 0.30 +bafilomycin +bafilomycin 60 0.25

0.20 40 0.15

0.10 20 0.4 recycling pool Exocytosis time constant (s) 0.2 total pool 0.05 25 s 10 Hz, 30 s 20 s 5 6 56

0 Fraction of endocytosis during stimulus 0.00 No bafilomycin

Stmn2KD Stmn2KD Scrambled Scrambled d e VMAT2-pHluorin RFP TH Merge p = 0.008 2.0

1.5 Scrambled μ 20 m 1.0

0.5 Normalized bouton size

8 10 0.0 Stmn2KD

Stmn2KD Scrambled

Fig. 3 pHluorin assay in cultured DA neurons with Stmn2 knockdown. a SV endocytosis and exocytic fraction in midbrain DA neurons with or without Stmn2 knockdown. Two-sided Student’s t test for two-group comparison. t = −2.6572, df = 19, p-value = 0.01556 for endocytosis time constant, which is the τ of the fitted one-phase exponential decay indicated by the blue dash line; t = 0.87198, df = 19, p = 0.3941 for exocytic fraction, which is the normalized peak height. b SV exocytosis in midbrain DA neurons with or without Stmn2 knockdown. The exocytosis time constant is the τ of the fitted one- phase exponential decay indicated by the blue dash line. This is a subset of neurons recorded in (a). Two-sided Student’s t test for two group comparison. t = −1.6213, df = 9, p-value = 0.1394. c SV endocytosis during stimulation in midbrain DA neurons with or without Stmn2 knockdown as indicated by the arrows in c. This is a subset of neurons recorded in (a) and the same neurons in (b). Two-sided Student’s t test for two group comparison. t = 3.6414, df = 9, p-value = 0.005389. N indicated in each experiment. d Immunostaining of VMAT2-pHluorin, RFP tag of Stmn2-shRNA plasmid, and TH of recorded DA neurons treated with scrambled or Stmn2-shRNA. Arrows pointed to representative boutons. e Quantification of bouton sizes in scrambled and Stmn2- shRNA-treated TH + neurons. Two-sided Student’s t test for two group comparison. t = 3.2224, df = 16, p-value = 0.008433. All data are present as mean ± SEM. Source data are provided as a Source Data file

NATURE COMMUNICATIONS | (2019) 10:5234 | https://doi.org/10.1038/s41467-019-13144-y | www.nature.com/naturecommunications 7 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-13144-y

MATN3 a FKBP1B c ST6GALNAC5 CAT RGS17 ZDHHC11 DLGAP2 MPPED2 LSM14A HMP19 GFRA1 SCO2 15 C9orf116 BASP1 SHANK2 SGSH GALT PTHLH FAM155A SCN9A CABP1 HGSNAT SLIT1 NDEL1 MKRN1 PCIF1 ANAPC13 PIAS2 PIN1 CITED2 STYK1 CAMK1G CCNA1 CNKSR1 FTO NRIP3 ZNF532 EFNA1 CACNB3 CHD5 FBXW4 ANKRD34C TMEM35 NELL2 PTPRR SPOCK2 SCN3B MBTPS2 RIMS2 KCNQ2 RTN2 CALB1 SCG2 TM9SF4 HN1 NRXN3 PAM ZHX2 CCNB1 SYT13 RAB15 CDH13 ANP32B SV2C KCND3 PAK3 CACNA2D2 Sig NUDT11 ATP8A2 ATP1B1 MXRA7 CACNG3 ns ENC1 DOK5 10 Significant PTPN4 NOP10 STMN2 SUSD4 FRMPD4 GRIA1 REEP1CDH18 LRRN3 PCP4 SULT4A1 SYNGR3 SYT1 RALYL NMNAT2 GAP43 HTR2A FGF12 SMARCA1 CDH12 INPP5F NEFH SNCG LBH THY1 ARHGEF9 ATP1A3 INA RGS7 ERC2 ACHE BCAS1 OPRK1 NCDN APOOL SV2A GLS2 TEP1 RFK GNAS PRPF19 –log10(FDR) RUVBL2 UBE2J1 LPCAT4 GBE1 RNF44 STXBP1 MCFD2 ARHGDIG IL13RA2 SYN1 SV2B RUSC1 CNIH3 SLC12A5 5 NDUFS7 LOC100127972 SHOC2 GRM5 COPG2IT1 DGKI FAM63A VAV3 SLC1A1 NEFL DDX10 ATG7 CNTNAP1CHGA SIX3 ARMCX5 MINA RNASEH1 PTPRO

KLF8

In human 0 Key hub STMN2.correlated NA NA DN –2 0 2 46 Negative UP Positive logFC In mouse NPTX2 TMPRSS3 UP BMP5 SMURF1 GPR39 TRAPPC3 NA

GSTCD SH3BP2 MOXD1 ZSCAN18 DDX18 MAML3 DN

JPH3 b UNKL LYPLA1 CNOT7 PTX3 BPTF ZNF426

SPINK1 NEIL3 RNF2

SLC25A36 HIGD1B FAM175B SOX4 ZNF592 ANXA2P1 AKAP3 REN

RAB14 ANG TKTL1 LGALS3 FAM110B CCDC64 BAX ORAI2 HSD17B3

COMT TKT KLF7 CALU CYP2C8 ZMIZ2 MCM7 CYP27A1 KCNQ1DN CCKAR CSRNP3 AQR UNC13A PCLO CHEK2 C1orf95 GTDC1 PURG NPY GTF2H2B

CCNL2 GTF3C2 SLIT2 LXN TARDBP HECTD3 PRKACG SATB2 ATP2A3 KCNB1 ARHGAP5 HAS2 GRIN2A SCN3A CCR9 PCNX BCL9 TMEM135 SLC16A2 IL11RA KIAA0319 LONRF3 STAG3L4 JMJD4 COL11A1 RIMBP2 CACNB3 SLC35D1 PEG10 CBLN1 MYL5 VAT1 RPS6KA6 POMGNT1 SHANK2 RPP38 CALB2 FAM155A DSG2 ABCA3 RIMS1 KCNA2 GPR19CNTN6 CNTN1 CHRNB3 BNC2 INSM1 NELL2 GABRA5 RGS4 SEMA3A NEDD9 ARHGAP29 TRPC6 DENR SAR1A TFB2M MPPED2 CTDSP2 DCBLD2 TFDP2 NR4A3 PDE1A RBM26 CAD ZNF419 SRRD PBX1 ATP1A3 CSNK1E ARID1A PRRG3 SSBP3 SCN2A CBS SMPX LDLRAP1 CBX6 ZPBP SLC6A3 LPHN1 CUX2 SYN1 ATP2B3 CLDN1 SLC18A1 HR PIK3CA CLSTN2 SLC18A2 ITGB1BP1 ST6GALNAC5 SMAD7 SEMA6D EN1NCAPG ELAVL2 PTPRR TMEM35 CLOCK CHMP7 PDE3B ZNF385D PCDHA2 PMF1 LTBP1 HPN ANAPC13 ANKRD34CFLJ21369 ALCAM NUDT11 SLITRK5 GPC4 KCNJ6 PAFAH1B2 THY1 OPRK1 KANK2 CCNA1 DLG4SLC29A1 LRRN3 CIB1 TH CDH8 SLC24A3 INA NPTX1 FJX1 RAB40B NXF1 EEF1A2FAM178A VPS33A EMCN CDH18 HPCAL4 NOL4 MTL5 NFKB1 BICD1 NTNG1 PDLIM2 CADPS2 SLIT1 SERPINB6 OR7E47P SYT17 SEZ6L2 JUN CHD5 SLC8A1 RALYLPRKCZ KCNA3 ERCC6L FLNC DDC GRM5 SCN8A EFNA1 DLK1 PIK3C2G PCIF1 CCDC25 PCDH8 SCO2 FRMPD4 GABRB2 GABRB1 IGSF3 LOC100127972 CLSPN DPP6 PTPN4 HTR2A MAGEB4 OSBPL3 LPO ATP8A2 SLC25A16 NKG7 CALB1 PTHLH KCTD14 TRAM2 BASP1 ZNF532 ZHX2 RASL11B DIDO1 TM9SF4 GZMB SUB1 HPGD DGKI PAK3 CALCR NCDN RGS17 OVOL2 DNAJB12 PRKAR2B NOP10 CLCN2 GRP SDC1 P2RX7 RAB15 GALT RIMS2 ATP1B1 GLS2 BBS4 TIPRL COBL TOX4 WDR12 TIMM9 ASTE1 DLGAP2 GPR161 ARL5A KCNJ13 CDC16 AGTR1 FGF13 SCG2 STYK1 GFRA1 HN1 ENC1 GTF2F1 CABP1 GAP43 MBTPS2 RGS7 SMARCA1 PID1 CD24 SLC12A1SLCO3A1 ARHGEF9 FBXW4 ST3GAL1 SGSH OCLN IGF1 BMP2K SLCO4A1 NR4A2 SUSD4 NUP85 CHST3 FKBP1B SCRN1 REEP1 PCP4 ACIN1 GOLT1B ZNF211 UAP1 RTN2 CDH12 ATP7A GCH1 PCSK1 WARS CCNB1 APOOL ALDH1A1 CAT ATG7 ERGIC3 PIP5K1B CNKSR1 PIAS2 TEP1 FBXO11 HGSNAT MATN3 FGF12 RBM4B SRPRB HMP19 SLC4A5 E2F3 ZMYND10 ZNF226 ZDHHC11 SV2C CDH3 SPOCK2 KCND3 VPS45 PYGL FBXW4P1 GRIA1 NEFH SYT1 TFB1M CTBP1 INPP5F COQ10B KCNQ2NRXN3 TXNDC15 MCFD2 TRIM36 SUPT3H RNF44 FTO HYAL4 OR7A5 AZGP1 DOK5 SNCG UBE2J1 RFK UCP1 AHCYL2 STMN2AKAP5 CDKN2C GNAS SCN9A CASC3 CENPA NRIP3 ACHE FUCA1 HCRT NMNAT2 CACNG3 LILRB2 COPG2IT1 LBH ALOX5 MINA ECSIT CCK CDH13 ARHGDIG IL13RA2 DUS4L BCAS1 SCN11A RNMT RUVBL2 CNIH3 GRSF1 SV2B AGK CNNM4 SDC2 PAM SHOC2 CDK5RAP2 NUBPL CITED2 CCL27 TIMM22 CAMK1G FAM63A LPCAT4 ANP32B ETS1 PAPPA GBE1 PGF CXCL1 C9orf116 MXRA7 MKRN1 PTGER3 CXCL2 EPS15L1 NDEL1 VAV3 DAB1 SIX3 KCNMA1 LSM14A KLF8 DDX10 KRT5 SCN3B SLC1A1 NOL3 PNLIPRP2 PTP4A2 FAM3C ARMCX5 LMBRD1 RNASEH1 CRYBB2 SYNGR3 PTPRO ATXN2 RRS1 SLC44A1 ATP9A FAM107A B3GNTL1 DCC TROVE2 PTH1R FKBP1A TRPV2 SAP18 HGF ERC2 POP5 PCSK2 CACNA2D2 MTMR9 SYNGR1 EPCAM POLDIP3 PDK4 RPS6KA2 C12orf4 BMP6 TRAK2 PTTG1IP ARHGEF2 NUP160 FRMD4B EXOSC4 SAMM50 ZNF24 LMO4 NDUFA3 SULT4A1 USP32 SPOP Key hub DGKQ COMMD9 CDC42SE1 DPYSL3 SET FANCE In human In mouse PFDN5 MAP1S APRT CORO7 SLC16A1 CDC7 B3GALT4 CLSTN3 RAP2A TNFAIP1 NHP2 ERCC2 NA UP TTC38 TBL3 MAGEC3 PCBP3 PPFIA3 MEA1 NAAA ISG20L2 C11orf48 DN NA IL26 CD36 HOXB9 SLC16A7 SFMBT1 CHERP GSK3B UP DN ARHGAP19 SF3B5 ELF2 MEPE SOX3

AMBRA1

ARID3A

RPL27A CST1

TRIP13

Fig. 4 Validation of the PD MEGENA network by Stmn2 perturbation. a Volcano plot shows genes that are significantly differentially expressed in the midbrain between the mice received scrambled AAV and Stmn2-shRNA AAV. b The genes in M4 are significantly enriched for the downregulated DEGs identified in the midbrain of the Stmn2 knockdown mice. Nodes with burgundy labels are key hubs of M4. The left-side half circle of each node indicates the DEGs in human between the PD and control groups while the right-side represents the DEGs in mice by Stmn2 knockdown. c The two-layer network neighborhood of STMN2 is significantly enriched for the DEG downregulated by Stmn2 knockdown. Nodes with burgundy labels are key hubs in the MEGENA network. The left upper part of the circle represents the DEG in human between the PD and control groups while the right upper part of the circle represents the correlation with STMN2 in PD patients. The bottom part of the circle indicates the DEGs in mice by Stmn2 knockdown high-performance liquid chromatography (HPLC). We found a shRNA injection (Fig. 6a). We also examined the density of 50% reduction of DA as well as the three major metabolites dopamine transporter positive (DAT+) terminals in the dorsal (DOPAC, 3-MT, and HVA) in the ipsilateral (right) striatum and ventral striatum based on DAT fluorescence intensity. We compared with the contralateral (left) side in the mice with Stmn2 noticed a reduction of DAT+ terminal density in both the dorsal

8 NATURE COMMUNICATIONS | (2019) 10:5234 | https://doi.org/10.1038/s41467-019-13144-y | www.nature.com/naturecommunications NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-13144-y ARTICLE

acb p = 0.008 p = 0.038

800 250

Striatal DA 200 600 content/DAT TH+ cell staining 150 counting 400 100 200 Rotarod 50 Vertical episode counts

open field Duration on the rod (s) 15 15 15 15 0 0 Day 0 Day 21–28 Day 30

Stmn2KD Stmn2KD Scrambled Scrambled

def60 20,000 ** 6000 50 Scrambled ** 5000 ** 15,000 40 Stmn2KD * 4000 30 10,000 3000 Saline amph 2000 20

Total distance (cm) 5000 1000 10 Counter-clockwise revolution Counter-clockwise revolution 15 15 Center distance travelled (cm) 15 15 0 0 0 10 30 50 70 90110 130 150 Time (min) Stmn2KD Stmn2KD Scrambled Scrambled

Fig. 5 Locomotor behavioral analysis in mice with AAV-mediated Stmn2 knockdown. a The schematic of the experimental design. AAV carrying scrambled shRNA or Stmn2-targeting shRNA was injected in the right SN. Behavioral tests were performed from day 22 to day 27 post injection. b The injected mice were examined on the rotarod. The average of three trials was used to represent the performance of each individual mouse. N = 15 in each group. Student’s t test, two-sided, t = 2.873, df = 28, p-value = 0.008. c–e The injected mice were examined in open field. The vertical episode count (c t = 2.1851, df = 28, p-value = 0.038), total distance (d t = −0.36436, df = 28, p-value = 0.3592), and center distance traveled (e t = 1.7304, df = 28, p-value = 0.094) were compared between the control and Stmn2 knockdown mice. N = 15 in each group, two-sided Student’s t test for two group comparison. f The injected mice were also examined in open field at baseline, under saline injection, and under amphetamine injection. The counter-clockwise revolution was compared between the control group and Stmn2 knockdown group. N = 15 in each group, two-way ANOVA, df = 1, F = 75.951, p = 2E-16 between control and Stmn2 knockdown; df = 14, F = 13.669, p = 2e-16 between time points and df = 14, F = 6.244, p = 2.27E-11 for interacting terms; post hoc comparison was performed using Tukey HSD test *<0.05, **<0.01. All data are present as mean ± SEM. Source data are provided as a Source Data file and ventral striatum of the ipsilateral side in the mice with Stmn2 which was pathologically associated with aggregated or modified shRNA injection compared with that in the mice with scrambled α-synuclein, we examined the Ser129 phosphorylation of α- shRNA injection (Fig. 6b, c), suggesting the knockdown of Stmn2 synuclein, a hallmark of synucleinopathy lesions in human55 via led to the terminal degeneration of DA neurons in both SN and immunofluorescence. We observed puncta of phosphorylated α- the ventral tegmental area (VTA). By tracing the AAV RFP tag, we synuclein proteins detected with anti-pSer129 α-synuclein anti- confirmed the viral spreading into the SN-adjacent brain regions, body only in the Stmn2 shRNA mice, but not in the scrambled including VTA (Supplementary Fig. 6h). shRNA mice (Fig. 7g, h), indicating that reduced Stmn2 expres- sion caused an increase of Ser129 phosphorylation of α-synuclein, fi α Stmn2 knockdown leads to DA neuron loss.Wethenaskedif a critical toxic modi cation of -synuclein related to impaired locomotor activity and reduced striatal DA content were idiopathic PD. associated with the loss of DA neurons by performing stereological + counting of TH neurons of the substantia nigra compacta (SNc) Discussion and VTA in the injected mice. We found a striking (~70%) + Our multiscale network analysis of the gene expression data from reduction of TH neurons in the SNc of the mice with Stmn2- a large number of postmortem PD brains has revealed network shRNA injection compared with the mice with scrambled shRNA + structures and key regulators functionally connected to known injection (Fig. 7a, b). A 40% reduction of TH neurons in the VTA PD risk genes. Functional validation of one top-ranked key reg- was also observed (Fig. 7c). Increased cleaved caspase-3 immunos- ulator STMN2 demonstrates its critical role in controlling DA taining was found in the Stmn2-shRNA injected midbrain (Fig. 7d), neuron function/viability and regulating α-synuclein modifica- confirming that the reduction of TH+ cell number was likely due to + tion, which are highly relevant to PD pathogenesis. apoptotic cell death. We also examined the TH-/NeuN neurons in The emergence of systems/network biology has revolutionized the substantia nigra reticulata (SNr) and found no significant cell + the discovery of novel mechanisms and promising drug targets loss in this region (Fig. 7e, f), suggesting that TH neurons were for complex diseases, such as cancer, diabetes, and AD16,56,57. particularly vulnerable when Stmn2 was depleted. The lack of large-scale molecular profiling data from PD post- mortem brains has impeded the application of such systems Stmn2 knockdown increases α-synuclein Ser129 phosphoryla- biology approaches to PD. Although many gene expression stu- tion. To investigate further the link between Stmn2 and PD, dies of PD postmortem brains have been reported, each

NATURE COMMUNICATIONS | (2019) 10:5234 | https://doi.org/10.1038/s41467-019-13144-y | www.nature.com/naturecommunications 9 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-13144-y

a c 2.0 p = 0.023 p = 0.0008 p = 0.015 p = 0.003 p = 0.01 p = 0.008 1.5 1.5

content 1.0 1.0

Normalized 0.5

0.5 Normalized DAT intensity DAT monoamine 7 7 7 77777 7777 0.0 0.0 Stmn2KD Stmn2KD Stmn2KD Stmn2KD Stmn2KD Stmn2KD Scrambled Scrambled Scrambled Scrambled Scrambled Scrambled 3-MT DA DOPAC HVA Dorsal Ventral b DAT RFP

1000 μm

Fig. 6 Striatal alterations in mice with AAV-shRNA-mediated Stmn2 knockdown. a The content of dopamine (DA) and its major metabolites (3-MT, DOPAC, and HVA) were measured in the contralateral and ipsilateral striatal tissues isolated from the mice that received scrambled AAV and Stmn2- shRNA AAV injections, N = 7 in each group. The biogenic monoamine levels of the ipsilateral striatum were normalized to those of the contralateral side in each individual mouse, and the comparison between mice with scrambled and Stmn2-shRNA AAV injection was analyzed by two-sided Student’s t test. DA: t = 4.4367, df = 12, p-value = 0.0008; DOPAC: t = 2.8365, df = 12, p-value = 0.01499; 3-MT: t = 2.614, df = 12, p-value = 0.02263; HVA: t = 3.6415, df = 12, p-value = 0.003379. b Anti-DAT antibody was used to detect DAT + terminals in the striatum in mice that received scrambled AAV and Stmn2- shRNA AAV injections. RFP serves as an indicator of transfected terminals. The yellow line separated the dorsal and ventral striatum. c The quantitative analysis of DAT + immunofluorescent intensity in the two groups of mice. The DAT fluorescence intensity of ventral striatum without injection was used as the baseline to normalize the DAT intensity of the rest regions. N = 7 in each group. Two-way ANOVA, F = 25.689, df = 25, p = 3.12E-05. All data are present as mean ± SEM. Source data are provided as a Source Data file individual study involves a small number of samples and thus synaptic transmission, oxidative phosphorylation, mitochon- each alone is insufficient to derive a holistic picture or detailed drion, myelination, and response to unfolded protein. STMN2 is signaling circuits of PD-related pathways. Here, we sought to one key regulator of the top PD-associated module. STMN2 has address the above unmet challenge in PD research by assembling an overall reduced expression in the PD brains, and is predicted the gene expression profiles of the postmortem human brains to play a role in synaptic transmission. STMN2, also known as from eight published PD studies into one gene expression data superior cervical ganglion 10 (SCG10), is a ~20 kDa phospho- set, which enabled co-expression and causal network inference to protein of the stathmin family. Overexpression of Tau systematically uncover intrinsic network structures and key reg- downregulated Stmn2 in PC12 cell line58; administration of 1- ulators of PD. The comparison between the MEGENA modules methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) also caused and those from the traditional Weighted Gene Co-expression downregulation of Stmn2 level and the rescue of MPTP-induced Network Analysis (WGCNA) has shown that MEGENA could PD-like phenotype restored Stmn2 expression in mice59, impli- provide consistent yet complementary information at a higher cating a potential association between Stmn2 and PD. Despite its resolution in the PD context (See Supplementary Note 1 and known functions in regulating microtubule dynamics during Supplementary Data 12). Different from the simple clustering development and after injury, knockdown of Stmn2 led to analysis offered by WGCNA, MEGENA can identify more impaired SV endocytosis in cultured DA neurons. Furthermore, coherent and functionally relevant modules with high-resolution the direct perturbation of Stmn2 in the mouse SN recapitulated topological network structures and clearly defined key regulators the gene expression pattern and the network structure identified for downstream analyses. By investigating the neighborhood of a from the SN of the PD subjects. Moreover, perturbation of Stmn2 gene within the gene co-expression and causal networks, we were expression in mouse midbrain led to PD-like pathologies and able to annotate and predict biological functions for each gene in behavioral abnormality, though the underlying mechanisms a disease-specific manner, thus complementing the current remain to be characterized. GWAS studies with functional contexts. Unlike monogenic form of PD, idiopathic PD is polygenic and The network modules that are most associated with PD contain has a complex etiology with interaction with environmental fac- genes involved in a number of diverse pathways, including tors. In addition to STMN2, which we provide proof-of-principle

10 NATURE COMMUNICATIONS | (2019) 10:5234 | https://doi.org/10.1038/s41467-019-13144-y | www.nature.com/naturecommunications NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-13144-y ARTICLE

a b p = 0.006 c p = 0.003 Control side Injected side Control side Injected side 15,000 20,000

15,000 10,000 500 μm Scrambled Stmn2 KD 10,000

5000 5000

55 Estimated TH+ neuron in SN

5 5 Estimated TH+ neuron in VTA 0 0 100 μm

Stmn2KD Stmn2KD Scrambled Scrambled d RFP Cleaved Cas3 Cleaved Cas3 RFP TH Merge Scrambled 200 μm 10 μm Stmn2 KD

e f TH NeuN RFP p = 0.20 1.5

1.0 Scrambled

0.5 Normalized NeuN counts

66 0.0 Stmn 2KD

200 μm Stmn2KD Scrambled Scrambled Stmn2 KD g h RFP RFP p = 0.004

15

10

α -syn pS129 counts pS129-α-Syn pS129- -Syn α 5

Normalized 0 66 200 μm50 μm

Stmn2KD Scrambled validation of PD relevance in our study, we also predict many rescued by overexpression of Gap43, suggesting a functional other putative regulators that could contribute to sporadic PD similarity between the two genes60. Interestingly, Stmn2 knock- pathogenesis. For example, BASP1 belongs to the family of down in the mouse SN led to a decrease in Gap43 but not Basp1 growth-associated proteins, which also includes GAP43/BASP2. at the mRNA level, implicating a functional difference between Basp1−/− mice exhibited high postnatal mortality but can be Basp1 and Gap43. RALYL, another top-ranked regulator, is

NATURE COMMUNICATIONS | (2019) 10:5234 | https://doi.org/10.1038/s41467-019-13144-y | www.nature.com/naturecommunications 11 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-13144-y

Fig. 7 Pathological characterization in the SN of mice with Stmn2 knockdown. a Immunohistochemistry staining with anti-TH antibody in the SN from mice injected with AAV carrying scrambled or Stmn2 shRNAs. b Stereological counting of TH+ neurons in SN of mice injected with AAV carrying scrambled or Stmn2 shRNAs and the result was analyzed by two-sided Student’s t test. N = 5 in each group. t = 3.7256, df = 8, p-value = 0.005824. c Stereological counting of TH+ neurons in VTA of mice injected with AAV carrying scrambled or Stmn2 shRNAs and the result was analyzed by two-sided Student’s t test. N = 5 in each group. t = 4.1854, df = 8, p-value = 0.003057. d Immunofluorescent staining with anti-cleaved caspase-3 and TH antibody in the SN of mice injected with AAV carrying scrambled or Stmn2 shRNA. Red dash squares were zoomed in with higher magnification. Representative images from N = 6 in each group. e, f Immunofluorescent staining with anti-NeuN and anti-TH and quantification of TH−/NeuN+ cells in the SNr of mice injected with AAV carrying scrambled or Stmn2 shRNA. SNr highlighted by yellow dash circle and SNc highlighted between two yellow dash lines. Two-sided Student’s t test, N = 6 in each group, t = 1.362, df = 10, p = 0.2031. g, h Immunofluorescent staining with anti-pS129 of α-synuclein (pS129-α-syn) antibody and quantification of pS129-α-syn puncta in the SN of mice injected with AAV carrying scrambled or Stmn2 shRNA. Red dash squares were zoomed in with higher magnification. Representative images from N = 6 in each group. Two-sided Student’s t test p = 0.004, t = −3.7825, df = 10, p = 0.004. All data are present as mean ± SEM. Source data are provided as a Source Data file

reported to be an interacting protein of LRRK2 in a yeast 2 hybrid downloaded from GEO using two key words, including Parkinson’s disease and analysis61. RALYL was downregulated by Stmn2 knockdown human substantia nigra. Only studies with sample size >= 5 in each group were included. Altogether we obtained eight data sets that focused specifically on SN, as in vivo. Reduced expression of RALYL has been implicated in fi 62 listed in Supplementary Table 1. The downloaded gene expression pro le of each worse prognosis in clear-cell renal cell carcinoma but its data set was already preprocessed using either RMA or MAS. Out of the eight data function in the human brain remains unclear. Thus, our study sets we collected, only one (GSE8397) has two regions (lateral and medial SN) begins to reveal a global landscape of gene interaction and reg- assayed for the same subject and both of them were from SN. We used the lateral ulatory circuits underlying PD pathogenesis. SN to represent the gene expression within the SN as the original publication claimed that lateral SN is more vulnerable in PD29. Thus we ensured that each Distinct from the causal genetic variants in PD, many of the sample in our analysis was from different individuals. The data were log2 trans- hub genes identified in our PD network have not been reported to formed, quantile normalized, and corrected for covariates, such as gender and age be genetically linked to the disease, including our top hub gene of etc. using a linear regression model lm(expression~as.factor(gender) + as.numeric interest, STMN2. However, these key hub genes are predicted (age) + as.numeric(RIN)) in R/3.4.3, if the information was available. As the fi samples were profiled from different platforms with unique probe sets, we inves- and/or con rmed to govern the expressions of many other genes, tigated the expression on the gene level. For genes with multiple probes measured, including disease-associated GWAS genes in the disease-specific we chose the probe with the largest variance to represent the gene expression. networks and therefore are considered as drivers that can modify disease onset and progression. Indeed, we previously identified 64 TYROBP as a key driver in AD pathogenesis16, and yet no SNPs DEG analysis and Braak correlation analysis. Metafor 2.0.0 was used to per- form meta-analysis to identify differential gene expression across the eight data sets are known to be associated with sporadic AD in the TYROBP in our collection. For each gene profiled in all the eight data sets, we calculated the locus. TYROBP has been identified as a binding partner for standardized mean difference (SMD) between the PD and control groups across all TREM2, CD33, and CR3 with increased expression in AD the data sets and then tested for heterogeneity using metafor package in R. If the 16 data are significantly heterogeneous (p < 0.05), a random effect model was fitted; if patients . Knockout of Tyrobp was neuroprotective in a mouse fi fi ’ 63 not, a xed effect model was tted. BH correction was used to control multiple- model of early Alzheimer s pathology . Therefore, they may testing errors. Genes with FDR < 0.05 and mean SMD > 0.5 were considered as serve as novel drug targets in addition to the disease-causing differentially expressed genes (DEGs). We chose the cutoff based on the guideline genetic variants. Our validation experiments provide strong evi- by Cohen et al.65 that SMD = 0.5 represented a medium effect size. In one specific dence supporting a role for the key regulator Stmn2 in mod- data set GSE49036, the gene expression data came together with complete Braak stage assessment. Therefore, we performed Spearman correlation analysis between ulating PD-related pathogenesis. Our future experiments will the gene expression and the Braak score to identify BCGs (BH-corrected p-value < include assessment of other predicted key regulators of PD and 0.05). Note that in this data set, samples from patients with incidental Lewy Body discovery of novel compounds that can restore the expression or Disease (iLBD) were excluded for the DEG analysis, but included for Braak cor- activity of key regulators of PD. relation analysis. In summary, our integrative network biology approach has systematically uncovered a number of gene subnetworks and key Network construction and key regulator identification. Each QCed data set was regulators of PD. Our study of postmortem PD brains, however, split into disease and control groups. Z-score transformation was applied for each offers only a snapshot at the late stage of the disease where the gene across all the samples (both disease (PD) and control samples) within each majority of DA neurons degenerated. This limitation poses hur- data set to control possible biases from different platforms and processing methods. We then merged the transformed data into a global disease data set (n = 83) or a dles to directly reconstruct molecular processes of the disease control (n = 70) data set. Module detection and key hub identification were done in progression. The incomplete clinical assessment and limited the disease-specific expression profile using MEGENA20. Modules were ranked number of samples in the published cohorts also prevent us from based on enrichment with PD DEGs. conducting stringent association study of the molecular data with BN was constructed from genome-wide gene expression profile through a Monte Carlo Markov Chain (MCMC) simulation-based procedure by using clinical and pathophysiological traits. Our approach is expected to software RIMBANET35,36. We made use of known transcription factor (TF)-target be improved with comprehensive longitudinal studies, increased relationships derived from brain tissue-related cell types from the ENCODE sample size, and better diagnosis of PD. Nonetheless, our study project66,67. The TFs were allowed to be parental nodes of their targets, but the provides a proof of principle of an integrated systems approach to targets were not allowed to be parental nodes of the TFs. Such relationships were used as structural priors to assist with the topological computation. We followed a a complex disease. It is imperative that future investigation should network averaging strategy with which 1000 networks were generated from the develop a large and coherent multi-Omics cohort from PD brains MCMC procedure starting with different random structure, and links that shared with high quality genetic, epigenetic, transcriptomic, and pro- by more than 30% of the networks were used to define a final consensus network teomic data, which will ultimately enable the discovery of more structure. An iterative de-loop procedure was executed to ensure that the consensus network was a directed acyclic graph, removing the most-weakly supported link of robust regulators for idiopathic PD. all links involved in any loop. Following Zhang et al.16, we performed KDA on the consensus Bayesian network to identify key hub genes which regulated a large number of downstream nodes. Methods To identify WGCNA modules, we first determined the soft power beta to be 4.5 Data collection and analysis. Publicly available data sets comparing the expres- as it gave the optimal fit for a scale free topology (R2 = 0.98, slope = −2.67). We sional difference between Parkinson’s disease patients and controls were then determined the module membership by the dynamic cutting tree algorithm

12 NATURE COMMUNICATIONS | (2019) 10:5234 | https://doi.org/10.1038/s41467-019-13144-y | www.nature.com/naturecommunications NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-13144-y ARTICLE implemented in the R package WGCNA. This process yielded 24 well-separated exocytosis. We stimulated the neurons with a short train of 100 pulses (10 Hz 10 s) modules with a minimal size of 20. to measure the endocytosis time constant after the short stimulation (the τ of a The hub genes were first evaluated for the enrichment of the PD DEGs in their fitted one-phase exponential decay after the peak) and the fraction of exocytosis network neighborhoods in the MEGENA and BN networks separately, and the two (The peak height of 100 pulses trace divided by the peak height of NH4Cl trace). fi = enrichment scores for each hub were then combined into a nal ranking score Gj We also stimulated the neurons with a long train of 300 pulses (10 Hz, 30 s). ∏ fi fi igji, where gji is the discriminant value of a key hub gene j in the network i, Ba lomycin (1 µM) was used to block vesicle re-acidi cation and a train of 1200 fi + − ∑ de ned as (maxj(rji) 1 rji)/ jrji and rji is the ranking of the enrichment score pulses (10 Hz, 120 s) was given for measurement of recycling pool and exocytosis 16 τ fi for the key hub gene j in the network i . A smaller rji indicates a higher rank of a time constant (the of a tted one-phase exponential decay since the stimulation fi gene with a larger enrichment score. A gene with a larger nal score Gj is starts until the plateau is reached). The peak height difference between 300 pulses considered as a more important key hub gene in both networks. and 1200 pulses w/ Bafilomycin traces normalized by the recycling pool refers to the endocytosis fraction during stimulation. Only the neurons co-transfected with the CMV-VMAT2-pHluorin and U6-Stmn2 shRNA-CMV-RFP or U6-scrambled Gene set enrichment analysis. Modules and network neighborhood genes of key shRNA-CMV-RFP plasmids were recorded for the pHluorin assay. The staging fi hubs were intersected with GO terms, signaling pathways, and cell-type speci c information for each recorded neuron was registered to ensure the re-identification fi expression gene signatures. The signi cance level of the overlap between two gene of the same neuron after immunostaining. After recording, the coverslips were ’ sets was determined by Fisher s exact test. For all the module-related enrichment fixed and stained with anti-TH antibody to confirm whether the recorded neurons analyses, the genes in all the modules were used as the background. For the net- were TH positive. work neighborhood enrichment analysis, all the genes in a given network (i.e., the MEGENA or Bayesian network in PD) were taken as the background. For GO analysis, the genes shared by a query gene set (e.g., the genes in all the modules, all In vivo Stmn2 knockdown mouse model and behavioral tests. Thirty 2-month- the genes in the MEGENA network and so on) and the set of all the genes present old male C57BL/6J mice were purchased from Jackson Laboratory (ME, USA). The in the GO database were used as the background. The size of the background used IACUC guidelines were strictly followed during animal maintenance and proce- for the abovementioned enrichment analyses can be found in the associated dure. AAVs carrying either scrambled shRNA or Stmn2-targeting shRNA were Supplementary Tables, i.e., the column background size. The p-values were com- packaged by the Boston Children’s Hospital Viral Core. In total, 2 μl of virus with a puted based on the hypergeometric distribution followed by BH correction for titer of 1 × 10E13 gc per mL were injected into the right SN (injection coordinates multiple testing. based on Bregma: anterior–posterior: −3.28 mm; medial–lateral: −1.5 mm; dorsal–ventral: −4.1 mm). Behavioral tests were performed during the fourth week after injection. Cell cultures and shRNA transfection. Mouse N2A neuroblastoma cells (ATCC The locomotor function was tested using a rotarod setting with incremental CCL-131) were cultured in DMEM supplemented with 10% fetal bovine serum speed from 4 to 40 RPM. The duration of time an individual mouse remained on (FBS) at 37 °C with 5% CO2. Approximately 80,000 cells were seeded into 12-well the rotarod served as the readout of the experiment. The average duration of three plates 24 h before lipofectamine 3000 (Life Technology, CA, USA) transfection. trials was used to represent the performance of the mice. Seventy-two hours after transfection, cells were collected in RIPA buffer for The overall behavior features were examined by open field test. Individual mice western blot. were placed into the 16″ ×16″ animal cage of a Versamax monitor system The Institutional Animal Care and Use Committee (IACUC) of Icahn School of (Accuscan, NY, USA) in a quiet dark room and allowed to move freely for an hour. Medicine at Mount Sinai approved all the animal-related experiments. We have The mouse horizontal and vertical movement was monitored and recorded by a complied with all relevant ethical regulations for animal testing and research. grid of 32 infrared beams at ground level and 16 elevated (3″) beams. Then saline Mouse midbrain neurons were cultured from P0-P1 wild-type C57BL/6J mice was administrated by intraperitoneal (IP) injection, and the mice were placed back (Jackson Laboratory, ME, USA). Typically, four P0-P1 mouse brains were required into the same cage for 30 min. Finally, amphetamine was administrated by IP for a midbrain culture. Only the ventral midbrain containing SN and VTA was injection at a dose of 2.5 mg per kg, and the mice were monitored for another hour collected to enrich the TH-positive population (~30% of the total neurons per in the cage. A series of parameters including total distance, vertical movement, and culture dish based on our estimate). The brain tissues were then incubated with rotational behavior were analyzed. papain (Worthington Biochemical Corp., NJ, USA) for 10 min with humidified oxygenation. Cells were then disassociated, washed twice, and plated on the poly- ornithine-coated coverslips at a density of 75,000 cells per cm2 in the culture media Immunostaining and stereological counting. Cells were fixed by 4% PFA for 10 containing (v/v) 60% Neurobasal-A, 20% Basal Media Eagle, 10% FBS (Atlanta min and permeabilized in PBS with 0.2% Triton X-100 for 10 min. Then cells were Biologicals, GA, USA) supplemented with 1× B27 and 2 mM GlutaMAX (Thermo blocked in PBS with 5% BSA for 1 h and incubated with primary antibody diluted Fisher Scientific, MA, USA)46,47. The calcium phosphate transfection method was in the blocking buffer at 37 °C for 1 h. Cells were then incubated with secondary employed to achieve sparse expression and to enable the analysis of a single neuron antibody diluted in the blocking buffer at room temperature for 1 h. The cells were during the imaging experiments. Following transfection at DIV 3–5, the growth washed four times with PBS between each step, and were incubated in PBS medium was replaced with fresh medium supplemented with an antimitotic agent, overnight at 4 °C before mounting to reduce background. After behavioral test, ARA-C (Sigma-Aldrich, MO, USA) and glial cell line-derived neurotrophic factor mice were anesthetized by ketamine (Vedco Inc. MI, USA, KetaVed) and then (GDNF) (Millipore, MA, USA). The Stmn2 shRNA plasmids were purchased from perfused with PBS and 4% PFA. The whole brain was taken out and subjected to Origene, MD, USA (TF511127), in which the Stmn2 shRNA expression was driven post fixation in 4% PFA for 24 h and then incubated in 15% and 30% sucrose until by a universal U6 promoter with an RFP tag driven by CMV promoter on the same fully dehydrated. The brain was cryo-sectioned at 40 μm in thickness using a Leica construct. CM3050s cryostat (Germany). The immunohistochemistry (DAB) was performed according to the manual provided by VECTOR LABORATORIES (Burlingame, CA, USA). For stereology counting, one in every five sections was selected with a Phluorin-based optical assay for endo-/exocytic kinetics. The phluorin-based random start, and a total of 5–8 brain slices were counted for each mouse using the 46–48 – optical assay was adopted from and performed on DIV12 16 for midbrain Stereo Investigator software. Primary antibodies used included Rabbit polyclonal fl cultures. For live cell imaging, cells were mounted on a custom-made laminar- ow anti-STMN2 (Invitrogen, CA, USA #720178; 1:1000 for western blot and 1:250 for – stimulation chamber with constant perfusion (at a rate of ~0.2 0.3 mL per min) of immunostaining); mouse monoclonal anti-beta-actin (Cell Signaling, MA, USA ’ a Tyrode s salt solution containing (in mM) 119 NaCl, 2.5 KCl, 2 CaCl2, 2 MgCl2, #3700; 1:5000 for western blot); mouse monoclonal anti-TH and rabbit polyclonal 25 HEPES, 30 glucose, 10 µM 6-cyano-7-nitroquinoxaline-2,3-dione (CNQX), and anti-TH (Sigma-Aldrich, MO, USA T2928, T8700; 1:1000 for immunostaining); 50 µM D, L-2-amino-5-phosphonovaleric acid (AP5) and buffered to pH 7.40. All rabbit monoclonal anti-cleaved caspase-3 (Cell Signaling, MA, USA #9579; 1:500 fi chemicals were purchased from Sigma-Aldrich, except for ba lomycin A1 (1 µM, for immunostaining); rat monoclonal anti-DAT (EMD-Millipore, MA, USA Calbiochem/Sigma-Aldrich, MO, USA, 196000-1SET). Temperature was clamped MAB369; 1:500 for immunostaining), and chicken polyclonal anti-GFP tag (Life at 30.0 °C at the objective throughout the experiment. Field stimulations were Technology, CA, USA A10262; 1:1000 for immunostaining); rabbit monoclonal delivered at 10 V per cm by A310 Accupulser and A385 stimulus isolator (World anti-Ser129 phosphorylated α-synulein (Abcam, UK, #51253; 1:250 for Precision Instruments, FL, USA). Images were acquired using a highly sensitive, immunostaining). back-illuminated EM-CCD camera (iXon + Model # DU-897E-BV, Andor Corp., CT, USA). An Olympus IX73 microscope was modified for laser illumination building a solid-state 488 nm OPSL smart laser at 50 mW (used at 10% and output Sample preparation for DA measurement and RNA sequencing. After beha- at ~2 mW at the back aperture) into a laser combiner system for millisecond on/off vioral test, mice were decapitated and the whole brain was taken out for quick switching and camera blanking control (Andor Corp, NY, USA). An Olympus dissection on ice. Striatal samples were sliced and punched to ensure equal amount PLAPON 60XO 1.42 NA objective with a 525/50 m emission filter and 495LP of tissues. Midbrain samples were carefully isolated, and both samples were dichroic filters was used for pHluorin fluorescence excitation and collection separated into the left and right, and were kept at −80 °C until use. Striatal DA (Chroma, VT, USA, 49002). Images were sampled at 2 Hz with an Andor Imaging content measurement was carried out in neurochemistry core in Vanderbilt Uni- Workstation driven by Andor iQ-CORE-FST (ver 2.x) iQ3.0 software. NH4Cl versity using HPLC. The total RNA were extracted from midbrain samples using μ solution containing (in mM) 50 NH4Cl, 70 NaCl, 2.5 KCl, 2 CaCl2, 2 MgCl2,25 Qiagen RNeasy mini kit (Cat No. 74104) and we sent 2.5 g of each sample to HEPES, 30 glucose, 10 µM CNQX, and 50 µM AP5 and buffered to pH 7.40 was Novogene, CA, USA for pair-end RNA sequencing using 250–300 bp insert cDNA used to reveal the total pHluorin expression (total vesicle pool) for normalizing library (rRNA depleted by Ribo-ZeroTM Magnetic Kit) on Illumina (CA, USA)

NATURE COMMUNICATIONS | (2019) 10:5234 | https://doi.org/10.1038/s41467-019-13144-y | www.nature.com/naturecommunications 13 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-13144-y

HiSeq4000 platform. We applied FastQC (http://www.bioinformatics.babraham.ac. 4. Orenstein, S. J. et al. Interplay of LRRK2 with chaperone-mediated autophagy. uk/projects/fastqc/) for quality check and then STAR68 for alignment followed by Nat. Neurosci. 16, 394–406 (2013). edgeR package69 for differential gene expression analysis. 5. Niu, J., Yu, M., Wang, C. & Xu, Z. Leucine-rich repeat kinase 2 disturbs mitochondrial dynamics via Dynamin-like protein. J. Neurochem. 122, – Fractionation of midbrain samples. Right midbrain samples were homogenized 650 658 (2012). ’ in fresh-made brain homogenization buffer (20 mM HEPES pH 7.4, 0.32 M 6. Pan, P. Y. et al. Parkinson s disease-associated LRRK2 hyperactive kinase fi sucrose, 1 mM NaHCO , 2.5 mM CaCl , 1 mM MgCl , protease, and phosphatase mutant disrupts synaptic vesicle traf cking in ventral midbrain neurons. J. 3 2 2 – inhibitor cocktail (Thermo Fisher Scientific, MA, USA)). The homogenate was Neurosci. 37, 11366 11376 (2017). centrifuged at 500 rcf for 10 min at 4 °C and supernatant was collected as the 7. Seaman, M. N., McCaffery, J. M. & Emr, S. D. A membrane coat complex starting point. After BCA quantification, the supernatant containing ~250 µg essential for endosome-to-Golgi retrograde transport in yeast. J. Cell. Biol. 142, protein was taken from each sample and lysed with 2× lysis buffer (final con- 665–681 (1998). centration: 50 mM Tri-HCl pH 7.4, 150 mM NaCl, 1% Triton X, protease, and 8. Blauwendraat, C., Nalls, M. A. & Singleton, A. B. The genetic architecture of phosphatase inhibitor). The lysate was incubated on ice for 30 min with gentle Parkinson’s disease. Lancet Neurol. https://doi.org/10.1016/S1474-4422(19) shaking. Then the lysate was centrifuged at 16,000 rcf for 30 min at 4 °C. The 30287-X (2019). supernatant was collected as the Triton-soluble fraction. The pellet was washed 9. Betarbet, R. et al. Chronic systemic pesticide exposure reproduces features of four times with PBS containing 1% Triton X and centrifuged at 16,000 rcf for 10 Parkinson’s disease. Nat. Neurosci. 3, 1301–1306 (2000). min at 4 °C. Then the pellet was resuspended and incubated in 50 µl PBS con- 10. Ross, G. W. et al. Association of coffee and caffeine intake with the risk of taining 2% SDS, 1%Triton X, and protease and phosphatase inhibitor at 60 °C for 1 Parkinson disease. JAMA 283, 2674–2679 (2000). h. The samples were centrifuged again at 16,000 rcf for 30 min at 4 °C. The 11. Chang, D. et al. A meta-analysis of genome-wide association studies identifies supernatant was collected as the Triton-insoluble fraction. For Triton-soluble 17 new Parkinson’s disease risk loci. Nat. Genet. 49, 1511–1516 (2017). fraction, we quantified the concentration by BCA assay and loaded 25 µg protein; 12. Nalls, M. A. et al. Large-scale meta-analysis of genome-wide association data for Triton-insoluble fraction, we added 6x sample buffer and load 25 µl of samples. identifies six new risk loci for Parkinson’s disease. Nat. Genet. 46, 989–993 (2014). Western blot. Sample concentration was determined by BCA (Pierce, MA, USA). 13. Nalls, M. A. et al. Imputation of sequence variants for identification of genetic Then samples were boiled in 3× sample buffer at 95 °C for 10 min and briefly risks for Parkinson’s disease: a meta-analysis of genome-wide association centrifuged. In all, 30 µg protein samples were loaded into 4–12% gradient gels studies. Lancet 377, 641–649 (2011). (Invitrogen, CA, USA) and run at 120 V in 1× MOPS running buffer. Transfer was 14. Zhang, B., Tran, L., Emilsson, V. & Zhu, J. Characterization of genetic done at 4 °C in transfer buffer with 10% methanol for 2 h at 90 V. The membrane networks associated with Alzheimer’s disease. Methods Mol. Biol. 1303, was blocked in LI-COR blocking buffer for 1 h and then overnight incubation with 459–477 (2016). primary antibody at 4 °C. Then the membrane was washed three times in TBST 15. Zhang, B. & Horvath, S. A general framework for weighted gene co-expression buffer and incubated in fluorescence-conjugated secondary antibody (1:10000, LI- network analysis. Stat. Appl. Genet. Mol. Biol. 4, Article17 (2005). COR, Germany) for 1 h. The membrane was washed three times in TBST buffer 16. Zhang, B. et al. Integrated systems approach identifies genetic nodes and with 0.01% SDS followed by imaging in LI-COR imaging system. networks in late-onset Alzheimer’s disease. Cell 153, 707–720 (2013). 17. Bagot, R. C. et al. Circuit-wide transcriptional profiling reveals brain region- fi Quantification and statistical analysis. The data analysis was performed by speci c gene networks regulating depression susceptibility. Neuron 90, – Bioconductor R 3.4.3. Images were processed by ImageJ. Data were represented as 969 983 (2016). mean ± SEM or mean ± 95% confidence interval (CI) as specified. Student’s t test 18. Emilsson, V. et al. Co-regulatory networks of human serum proteins link – was used for between two-group comparisons. ANOVA and Tukey post hoc genetics to disease. Science 361, 769 773 (2018). comparison were applied in multi-group/factorial design. Fisher’s exact test was 19. Labonte, B. et al. Sex-specific transcriptional signatures in human depression. used to examine whether two sets were significantly overlapped/enriched. BH- Nat. Med. 23, 1102–1111 (2017). corrected P < 0.05 was considered as statistically significant unless stated otherwise. 20. Song, W. M. & Zhang, B. Multiscale embedded gene co-expression network The details, including the N number, the statistic tests used and the p-values, are analysis. PLoS Comput. Biol. 11, e1004574 (2015). indicated in each figure and figure legend. 21. Wang, M. et al. Integrative network analysis of nineteen brain regions identifies molecular signatures and networks underlying selective regional vulnerability to Alzheimer’s disease. Genome Med. 8 104 (2016). Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article. 22. Margolin, A. A. et al. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinforma. 7,S7 (2006). Data availability 23. Wang, K. et al. Genome-wide identification of post-translational modulators The human SN expression profiles are downloaded from the gene expression omnibus of transcription factor activity in human B cells. Nat. Biotechnol. 27, 829–839 (GEO) (https://www.ncbi.nlm.nih.gov/geo/) with accession number GSE8397, GSE7621, (2009). GSE24378, GSE20292, GSE20141, GSE20163, GSE20164, GSE49036. RNA sequencing 24. Audrain, M. et al. Integrative approach to sporadic Alzheimer’s disease: data from Stmn2-knockdown mouse brains are deposited to GEO with accession number deficiency of TYROBP in a tauopathy mouse model reduces C1q and GSE114840. The source data underlying Figs. 3a–c, e, 5b–f, 6a, c, 7b, c, f, h and normalizes clinical phenotype while increasing spread and state of supplementary Figs. 4, 5c, e, 6a, b, d–g are provided as a Source Data file. All the other phosphorylation of tau. Mol. Psychiatry 24, 1383–1397 (2019). data are contained in the article and its supplementary information or available upon 25. Haure-Mirande, J. V. et al. Integrative approach to sporadic Alzheimer’s request. disease: deficiency of TYROBP in cerebral Abeta amyloidosis mouse normalizes clinical phenotype and complement subnetwork molecular – Code availability pathology without reducing Abeta burden. Mol. Psychiatry 24, 431 446 (2018). The R packages MEGENA and Metafor are available from https://cran.r-project.org/ and 26. Huang, F. et al. CDT2-controlled cell cycle reentry regulates the pathogenesis the code for Bayesian network inference can be found through RIMBANet (https://icahn. of Alzheimer’s disease. Alzheimers Dement. 15, 217–231 (2019). mssm.edu/research/genomics/about/resources). 27. Kajiwara, Y. et al. GJA1 (connexin43) is a key regulator of Alzheimer’s disease pathogenesis. Acta Neuropathol. Commun. 6, 144 (2018). Received: 19 January 2019; Accepted: 21 October 2019; 28. Dijkstra, A. A. et al. Evidence for immune response, axonal dysfunction and reduced endocytosis in the substantia nigra in early stage Parkinson’s disease. PLoS. One 10, e0128651 (2015). 29. Duke, D. C., Moran, L. B., Pearce, R. K. & Graeber, M. B. The medial and lateral substantia nigra in Parkinson’s disease: mRNA profiles associated with References higher brain tissue vulnerability. Neurogenetics 8,83–94 (2007). 1. Hernandez, D. G., Reed, X. & Singleton, A. B. Genetics in Parkinson disease: 30. Lesnick, T. G. et al. A genomic pathway approach to a complex disease: axon Mendelian versus non-Mendelian inheritance. J. Neurochem. 139,59–74 guidance and Parkinson disease. PLoS Genet. 3, e98 (2007). (2016). 31. Zhang, Y., James, M., Middleton, F. A. & Davis, R. L. Transcriptional analysis 2. Fortin, D. L. et al. Lipid rafts mediate the synaptic localization of alpha- of multiple brain regions in Parkinson’s disease supports the involvement of synuclein. J. Neurosci. 24, 6715–6723 (2004). specific protein processing, energy metabolism, and signaling pathways, and 3. Burre, J. et al. Alpha-synuclein promotes SNARE-complex assembly in vivo suggests novel disease mechanisms. Am. J. Med. Genet. Part B, Neuropsychiatr. and in vitro. Science 329, 1663–1667 (2010). Genet. 137b,5–16 (2005).

14 NATURE COMMUNICATIONS | (2019) 10:5234 | https://doi.org/10.1038/s41467-019-13144-y | www.nature.com/naturecommunications NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-13144-y ARTICLE

32. Zheng, B. et al. PGC-1alpha, a potential therapeutic target for early 60. Frey, D., Laux, T., Xu, L., Schneider, C. & Caroni, P. Shared and unique roles intervention in Parkinson’s disease. Sci. Transl. Med. 2, 52ra73 (2010). of CAP23 and GAP43 in actin regulation, neurite outgrowth, and anatomical 33. Darmanis, S. et al. A survey of human brain transcriptome diversity at the plasticity. J. Cell. Biol. 149, 1443–1454 (2000). single cell level. Proc. Natl Acad. Sci. USA 112, 7285–7290 (2015). 61. Zheng, X. Y. et al. Screening of LRRK2 interactants by yeast 2-hybrid analysis. 34. Zhang, Y. et al. Purification and characterization of progenitor and mature Zhong. Nan. Da. Xue. Xue. Bao. Yi. Xue. Ban. 33, 883–891 (2008). human astrocytes reveals transcriptional and functional differences with 62. Cui, Z. W. et al. RALY RNA binding protein-like reduced expression is mouse. Neuron 89,37–53 (2016). associated with poor prognosis in clear cell renal cell carcinoma. Asian Pac. J. 35. Zhu, J. et al. Stitching together multiple data dimensions reveals interacting Cancer Prev. 13, 3403–3408 (2012). metabolomic and transcriptomic networks that modulate cell regulation. PLoS 63. Haure-Mirande, J. V. et al. Deficiency of TYROBP, an adapter protein for Biol. 10, e1001301 (2012). TREM2 and CR3 receptors, is neuroprotective in a mouse model of early 36. Zhu, J. et al. Increasing the power to detect causal associations by combining Alzheimer’s pathology. Acta Neuropathol. 134, 769–788 (2017). genotypic and expression data in segregating populations. PLoS Comput. Biol. 64. Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. 3, e69 (2007). Stat. Softw. 36, 48 (2010). 37. Glaab, E. & Schneider, R. Comparative pathway and network analysis of brain 65. Cohen, J. Statistical Power Analysis for the Behavioral Sciences. 2nd Edition. transcriptome changes during adult aging and in Parkinson’s disease. Routledge (1988). Neurobiol. Dis. 74,1–13 (2015). 66. Dunham, I. et al. An integrated encyclopedia of DNA elements in the human 38. Oerton, E. & Bender, A. Concordance analysis of microarray studies identifies genome. Nature 489,57–74 (2012). representative gene expression changes in Parkinson’s disease: a comparison 67. Davis, C. A. et al. The encyclopedia of DNA elements (ENCODE): data portal of 33 human and animal studies. BMC Neurol. 17, 58 (2017). update. Nucleic Acids Res. 46, D794–d801 (2018). 39. Saunders, A. et al. Molecular diversity and specializations among the cells of 68. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, the adult mouse brain. Cell 174, 1015–1030.e1016 (2018). 15–21 (2013). 40. McInnes, J. et al. Synaptogyrin-3 mediates presynaptic dysfunction induced by 69. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor Tau. Neuron 97, 823–835.e828 (2018). package for differential expression analysis of digital gene expression data. 41. Conner, S. D. & Schmid, S. L. Identification of an adaptor-associated kinase, Bioinformatics 26, 139–140 (2010). AAK1, as a regulator of clathrin-mediated endocytosis. J. Cell. Biol. 156, 921–929 (2002). 42. Antonsson, B. et al. Identification of in vitro phosphorylation sites in the Acknowledgements growth cone protein SCG10. Effect of phosphorylation site mutants on We thank Dr. Zhigang He and Chen Wang from the viral core of Boston Children’s microtubule-destabilizing activity. J. Biol. Chem. 273, 8439–8446 (1998). Hospital for packaging AAV for us. This study is supported in part by NIH grants 43. Li, Y. H. et al. Rnd1 regulates axon extension by enhancing the microtubule P50NS0947331 (Udall, Z.Y.), U01AG046170 (B.Z., Z.Y.), RF1AG057440 (B.Z.), and destabilizing activity of SCG10. J. Biol. Chem. 284, 363–371 (2009). R01NS060809 (Z.Y.). 44. Xu, H., Dhanasekaran, D. N., Lee, C. M. & Reddy, E. P. Regulation of neurite outgrowth by interactions between the scaffolding protein, JNK-associated Author contributions leucine zipper protein, and neuronal growth-associated protein superior B.Z. and Z.Y. conceived the study. B.Z. designed the network analyses. Z.Y. and Q.W. cervical ganglia clone 10. J. Biol. Chem. 285, 3548–3553 (2010). designed the validation experiments. Q.W., M.W., and B.Z. performed the data analyses. 45. Shin, J. E. et al. SCG10 is a JNK target in the axonal degeneration pathway. W.S., Q.S., X.Z., and A.M. provided critical tools and data. Q.W. conducted all the Proc. Natl Acad. Sci. USA 109, E3696–E3705 (2012). primary validation experiments. Y.Z., I.C., and P.P. contributed to the validation 46. Mani, M. & Ryan, T. A. Live imaging of synaptic vesicle release and retrieval experiments. Q.W., B.Z., and Y.Z. wrote and edited the paper. All authors reviewed and in dopaminergic neurons. Front. Neural Circuits 3, 3 (2009). edited the paper. All authors read and approved the paper. 47. Pan, P. Y. & Ryan, T. A. Calbindin controls release probability in ventral tegmental area dopamine neurons. Nat. Neurosci. 15, 813–815 (2012). 48. Sankaranarayanan, S. & Ryan, T. A. Calcium accelerates endocytosis of Competing interests vSNAREs at hippocampal synapses. Nat. Neurosci. 4, 129–136 (2001). The authors declare no competing interests. 49. Neal, M. L., Boyle, A. M., Budge, K. M., Safadi, F. F. & Richardson, J. R. The fl glycoprotein GPNMB attenuates astrocyte in ammatory responses through Additional information the CD44 receptor. J. Neuroinflammation 15, 73 (2018). Supplementary information 50. Ivatt, R. M. et al. Genome-wide RNAi screen identifies the Parkinson disease is available for this paper at https://doi.org/10.1038/s41467- GWAS risk locus SREBF1 as a regulator of mitophagy. Proc. Natl Acad. Sci. 019-13144-y. USA 111, 8494–8499 (2014). Correspondence 51. Ivatt, R. M. & Whitworth, A. J. SREBF1 links lipogenesis to mitophagy and and requests for materials should be addressed to Z.Y. or B.Z. sporadic Parkinson disease. Autophagy 10, 1476–1477 (2014). Peer review information 52. McGlinchey, R. P. & Lee, J. C. Cysteine cathepsins are essential in lysosomal Nature Communications thanks Louis-Eric Trudeau and the degradation of alpha-synuclein. Proc. Natl Acad. Sci. USA 112, 9322–9327 other, anonymous, reviewer(s) for their contribution to the peer review of this work. (2015). Reprints and permission information is available at http://www.nature.com/reprints 53. Mendez, J. A., Bourque, M. J., Fasano, C., Kortleven, C. & Trudeau, L. E. Somatodendritic dopamine release requires synaptotagmin 4 and 7 and the Publisher’s note participation of voltage-gated calcium channels. J. Biol. Chem. 286, Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 23928–23937 (2011). 54. Wang, C. et al. Synaptotagmin-11 is a critical mediator of parkin-linked neurotoxicity and Parkinson’s disease-like pathology. Nat. Commun. 9,81 (2018). Open Access This article is licensed under a Creative Commons 55. Fujiwara, H. et al. alpha-Synuclein is phosphorylated in synucleinopathy Attribution 4.0 International License, which permits use, sharing, lesions. Nat. Cell Biol. 4, 160–164 (2002). adaptation, distribution and reproduction in any medium or format, as long as you give 56. Bailey, P. et al. Genomic analyses identify molecular subtypes of pancreatic appropriate credit to the original author(s) and the source, provide a link to the Creative cancer. Nature 531,47–52 (2016). Commons license, and indicate if changes were made. The images or other third party 57. Chen, Y. et al. Variations in DNA elucidate molecular networks that cause material in this article are included in the article’s Creative Commons license, unless disease. Nature 452, 429–435 (2008). indicated otherwise in a credit line to the material. If material is not included in the 58. Vega, I. E., Hamano, T., Propost, J. A., Grenningloh, G. & Yen, S. H. Taxol article’s Creative Commons license and your intended use is not permitted by statutory and tau overexpression induced calpain-dependent degradation of the regulation or exceeds the permitted use, you will need to obtain permission directly from microtubule-destabilizing protein SCG10. Exp. Neurol. 202, 152–160 (2006). the copyright holder. To view a copy of this license, visit http://creativecommons.org/ 59. Sconce, M. D., Churchill, M. J., Moore, C. & Meshul, C. K. Intervention with licenses/by/4.0/. 7,8-dihydroxyflavone blocks further striatal terminal loss and restores motor deficits in a progressive mouse model of Parkinson’s disease. Neuroscience 290, 454–471 (2015). © The Author(s) 2019

NATURE COMMUNICATIONS | (2019) 10:5234 | https://doi.org/10.1038/s41467-019-13144-y | www.nature.com/naturecommunications 15