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1 Supplemental Material Comprehensive Transcriptome Analysis of Cerebral Cavernous Malformation Across Multiple Species and Genotypes Janne Koskimäki *,1 ; Romuald Girard *,1 ; Yan Li 2 ; Laleh Saadat 1 ; Hussein A. Zeineddine 1 ; Rhonda Lightle 1 ; Thomas Moore 1 ; Seán Lyne 1 ; Kenneth Avner 1 ; Robert Shenkar 1 ; Ying Cao 1 ; Changbin Shi 1 ; Sean P. Polster 1 ; Dongdong Zhang 1 ; Julián Carrión-Penagos 1 ; Sharbel Romanos 1 ; Gregory Fonseca 3 ; Miguel A. Lopez-Ramirez 4 ; Eric M. Chapman 5 ; Evelyn Popiel 5 ; Alan T. Tang 6 ; Amy Akers 7 ; Pieter Faber 8 ; Jorge Andrade 2 ; Mark Ginsberg 4 ; W. Brent Derry 5,9 ; Mark L. Kahn 6 ; Douglas A. Marchuk 10 ; Issam A. Awad 1 From: 1 Neurovascular Surgery Program, Section of Neurosurgery, The University of Chicago Medicine and Biological Sciences, Chicago, IL 2 Center for Research Informatics, The University of Chicago, Chicago, IL 3 Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 4 Department of Medicine, University of California San Diego, La Jolla, CA 5 Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada 6 Department of Medicine and Cardiovascular Institute, University of Pennsylvania, Philadelphia, PA 7 Angioma Alliance Norfolk, Virginia 8 University of Chicago Genomics Facility, The University of Chicago, Chicago, IL 9 Program in Developmental and Stem Cell Biology, Hospital for Sick Children, Toronto, ON, Canada. 10 The Molecular Genetics and Microbiology Department, Duke University Medical Center, Durham, NC *Equal Contribution Correspondence to: Issam A. Awad, MD, MSc, FACS, MA (hon), Section of Neurosurgery, University of Chicago Medicine, 5841 S. Maryland, MC3026/Neurosurgery J341, Chicago, IL 60637. Telephone +1 773-702-2123; Fax +1 773-702-3518. E-mail [email protected]

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Page 1: Supplemental Material...Telephone +1 773-702-2123; Fax +1 773-702-3518. E-mail iawad@uchicago.edu 2 Supplemental Material and Methods Patient demographics The descriptive data of the

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Supplemental Material

Comprehensive Transcriptome Analysis of Cerebral Cavernous

Malformation Across Multiple Species and Genotypes

Janne Koskimäki *,1; Romuald Girard*,1; Yan Li2; Laleh Saadat1; Hussein A. Zeineddine1;

Rhonda Lightle1; Thomas Moore1; Seán Lyne1; Kenneth Avner1; Robert Shenkar1; Ying Cao1; Changbin Shi1; Sean P. Polster1; Dongdong Zhang1; Julián Carrión-Penagos1; Sharbel

Romanos1; Gregory Fonseca3; Miguel A. Lopez-Ramirez4; Eric M. Chapman5; Evelyn Popiel5; Alan T. Tang6; Amy Akers7; Pieter Faber8; Jorge Andrade2; Mark Ginsberg4; W. Brent Derry5,9;

Mark L. Kahn6; Douglas A. Marchuk10; Issam A. Awad1 From: 1Neurovascular Surgery Program, Section of Neurosurgery, The University of Chicago Medicine and Biological Sciences, Chicago, IL 2Center for Research Informatics, The University of Chicago, Chicago, IL 3Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 4Department of Medicine, University of California San Diego, La Jolla, CA 5Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada 6Department of Medicine and Cardiovascular Institute, University of Pennsylvania, Philadelphia, PA 7Angioma Alliance Norfolk, Virginia 8University of Chicago Genomics Facility, The University of Chicago, Chicago, IL 9Program in Developmental and Stem Cell Biology, Hospital for Sick Children, Toronto, ON, Canada. 10The Molecular Genetics and Microbiology Department, Duke University Medical Center, Durham, NC *Equal Contribution Correspondence to: Issam A. Awad, MD, MSc, FACS, MA (hon), Section of Neurosurgery, University of Chicago Medicine, 5841 S. Maryland, MC3026/Neurosurgery J341, Chicago, IL 60637. Telephone +1 773-702-2123; Fax +1 773-702-3518. E-mail [email protected]

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Supplemental Material and Methods

Patient demographics

The descriptive data of the subjects for whom 5 human cerebral cavernous malformation (CCM) lesions were surgically resected are presented in Table S1. Neurovascular unit (NVU) controls were collected from the brains of autopsy subjects free of neurological diseases.

Laser capture microdissection

NVUs from CCMs and normal capillaries were collected using laser capture microdissection at 40x magnification, and cut NVUs were subsequently stored at -80°C (Leica LMD 6500, Leica Biosystems Inc.) (1).

Bioinformatics analyses

The quality of raw sequencing reads was assessed by FastQC (v0.11.2; http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The post-alignment quality control was evaluated using RSeQC(2), and Picard tools (v1.117; http://broadinstitute.github.io/picard/). Reads were mapped to University of California Santa Cruz (UCSC) human genome model (hg19), GENCODE mouse model (GRCm38), Ensembl Caenorhabditis elegans genome model (WBcel235) separately with respect to different species with STAR v2.5.3a (3). Gene transcripts were assembled and quantified using featureCounts (4) with respect to corresponding reference genome gene annotations.

Differentially expressed genes (DEGs) analyses were conducted using DESeq2(5), with an additive model for batch effect correction if necessary. The human homologous genes of DEGs from mouse and C. elegans animal models were annotated based on the National Center for Biotechnology Information (NCBI) HomoloGene database. Out of 18,038 corresponding human genes in the database, 16,200 (around 90%) genes from mouse and 3013 (~17%) genes from C. elegans have corresponding human homologous genes. Because the homologous genes limitation from C. elegans, we implemented different threshold to DEGs from C. elegans (p≤0.25, false discovery rate (FDR) corrected; and |fold change (FC)|≥1.2) compared to human lesional ECs and the 2 murine models (p≤0.05, FDR corrected; and |FC|≥1.2) when comparing all the models together.

Flow chart of the study is presented in Figure S1. We implemented 2 approaches to study the transcriptome of CCM models using sub-network analysis and gene ontology (GO) enrichment analyses. In the first, DEGs of engineered animal models were compared with the DEGs from human lesional NVUs to identify common DEGs across species (Ccm1/Krit1 or Ccm3/Pdcd10). In the second approach, genotype specific associated DEGs from mouse and C. elegans were compared separately.

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Both approaches were followed with GO enrichment analyses, network essential genes identification, network clustering analysis, and where sub-network analysis, network essential genes identification, and network clustering analysis. GO enrichment analysis was conducted with R bioconductor package clusterProfiler. Network analyses were performed with ReactomeFIViz (6) in Cytoscape (http://www.cytoscape.org/) based on a highly reliable Reactome function interaction network (7) (version 2016). The entire FI network was constructed by merging interactions extracted from human curated pathways with interactions predicted using a machine learning approach.

The individual CCM gene expression profiles for each human patient have been generated following trimmed mean of M-values (TMM) normalization implemented into the raw count reads after low expressed gene features removal. The TMM normalized expression values can be compared directly across different patients and genes.

Supplemental Results

RNA quality

For each sample of laser-captured NVUs from human surgically resected CCM lesion and normal capillaries the concentration of RNA was between 145 and 3360 pg/µl (RNA integrity numbers = [1.8 - 5.1]). For each sample of BMEC models and their respective controls, the concentration of RNA was between 322 and 89 ng/µl (RNA integrity numbers = [6.0 - 8.7]). Similarly, for C. elegans models and their respective controls the concentration of RNA was between 55 and 309 ng/µl (RNA integrity numbers = [9.8 - 10]).

Definition of neurovascular unit and restrictions of laser capture microdissection in humans

NVUs in capillaries are composed of endothelial cells, pericytes, astrocytes and neurons (8). As we acknowledge the technical limitations of laser capture microdissection to isolate pure capillary endothelial cells (Figure S7, video), we determined the proportion of DEGs that may be attributed to other cell types than endothelial cells.

We first defined the top 500 human homologous DEGs derived for each of the following analyses: neurons versus endothelial cell (ECs), astrocytes versus ECs, new oligodendrocytes versus ECs, myelinating oligodendrocytes versus ECs and microglia versus ECs (https://web.stanford.edu/group/barres_lab/brain_rnaseq.html) (9). We then cross-compared each top 500 DEGs to the 915 DEGs from human lesional NVUs. The results showed that 655 of the 915 DEGs (72%) in the human lesional NVUs represented EC DEGs versus other non-EC cell types. Among the 260 non-EC DEGs, 108 were common DEGs with neurons, 50 with astrocytes, 70 with new oligodendrocytes, 72 with myelinating and 108 with microglia.

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Supplementary References

1. Espina V, Milia J, Wu G, Cowherd S, Liotta LA. Laser capture microdissection. Methods Mol Biol. 2006;319:213-29. 2. Wang L, Wang S, Li W. RSeQC: quality control of RNA-seq experiments. Bioinformatics. 2012;28(16):2184-5. 3. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15-21. 4. Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30(7):923-30. 5. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. 6. Wu G, Dawson E, Duong A, Haw R, Stein L. ReactomeFIViz: a Cytoscape app for pathway and network-based data analysis. F1000Res. 2014;3:146. 7. Wu G, Feng X, Stein L. A human functional protein interaction network and its application to cancer data analysis. Genome Biol. 2010;11(5):R53. 8. Iadecola C. The Neurovascular Unit Coming of Age: A Journey through Neurovascular Coupling in Health and Disease. Neuron. 2017;96(1):17-42. 9. Zhang Y, Chen K, Sloan SA, Bennett ML, Scholze AR, O'Keeffe S, et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J Neurosci. 2014;34(36):11929-47. 10. Berman JR, Kenyon C. Germ-cell loss extends C. elegans life span through regulation of DAF-16 by kri-1 and lipophilic-hormone signaling. Cell. 2006;124(5):1055-68. 11. Ito S, Greiss S, Gartner A, Derry WB. Cell-nonautonomous regulation of C. elegans germ cell death by kri-1. Curr Biol. 2010;20(4):333-8.

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Supplementary Figures and Tables

Figure S1. Flowchart of the study. 1. We analyzed differentially expressed genes (DEGs) from human lesional neurovascular units, mouse brain microvascular endothelial cell Ccm1/Krit1ECKO and Ccm3/Pdcd10 ECKO, as well as C. elegans ccm1/kri-1 and ccm3/pdcd10 compared with the appropriate controls for each model. 2. Genes from the animal models were annotated based on the National Center for Biotechnology Information (NCBI) HomoloGene database to allow direct comparison with human genes. 3. We compared DEGs across species and genotypes. 4. We finally performed gene set ontology enrichment, clustering, and network analyses.

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Figure S2. Volcano plots of differentially expressed genes (DEGs) in human lesional neurovascular units (NVUs), mouse brain microvascular endothelial cell (BMEC) Ccm1/Krit1 and Ccm3/Pdcd10, as well as C. elegans ccm1/kri-1 and ccm3/pdcd10 models. The transcriptome analyses identified (A) 915 DEGs in human lesional NVUs, (B) 1932 DEGs in BMEC Ccm1/Krit1ECKO, (C) 1643 DEGs in C. elegans ccm1/kri-1 (D) 524 DEGs in BMEC Ccm3/Pdcd10ECKO, and (E) 1581 DEGs in C. elegans ccm3/pdcd10. All DEGs were considered at a fold change (FC)≥1.2; significance level p<0.05, false discovery rate (FDR) corrected. Each dot represents an individual DEG with respect to the control. Red dots represent up-regulated DEGs and blue dots are down-regulated DEGs. Results are presented as x-axis log2 FC and y-axis log10 FDR corrected p-value.

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697 DEGs 884 DEGs

0 .0

2 .5

5 .0

7 .5

1 0 .0

− 3 .0 0 .0 3 .0log 2 (FC)

−lo

g 10

(FD

R)

E

Human microdissected lesional NVUs

Mouse BMEC Ccm1/Krit1ECKO

Mouse BMEC Ccm3/Pdcd10ECKO

C. elegans ccm1/kri-1

C. elegans ccm3/pdcd10

Page 7: Supplemental Material...Telephone +1 773-702-2123; Fax +1 773-702-3518. E-mail iawad@uchicago.edu 2 Supplemental Material and Methods Patient demographics The descriptive data of the

7

Figure S3. Individual gene expression profiles for CCM1, CCM2 and CCM3 of each patients

with cerebral cavernous malformation (CCM) and autopsy controls. The results suggest that

at least one of the CCM genes was down-regulated in the human microdissected lesional

neurovascular units (NVUs). Particularly, patient 4 has been clinically genotyped and identified

with a familial CCM1 mutation. Her gene expression profile supports this genotype by showing a

down-regulation of CCM1 (darkest blue box). CPM = counts per million.

CCM3CCM2CCM1

−1.5 −0.5 0.5 1.5Scaled log2 (CPM)

Patien

t 2Pati

ent 3

Patien

t 4Pati

ent 5

Patien

t 1

Contro

l 1Con

trol 2

Contro

l 3

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Figure S4. Common gene ontology (GO) terms across species. One GO term (GO:0051656 establishment of organelle localization) was common across the 5 models studied. In addition, 24 enriched GO terms were present in 4 of the 5 models; and 149 GOs were commonly identified in at least 3 (p<0.1, false discovery rate corrected for all comparisons). These identified GO terms were related to cell-cell adhesion, vesicle transportation, cell organelle localization and movement, immunity and inflammation, ion transmembrane transporter activity, MAPK signaling and responses to oxidative stress. A GO term was defined as common between species if it was identified in both genotypes of the same species in order to exclude GOs that may be specific for either genotype.

regulation of homotypic cell−cell adhesionnegative regulation of MAPK cascade

morphogenesis of embryonic epitheliumregulation of integrin−mediated signaling pathway

extrinsic component of membranenegative regulation of protein kinase activity

regulation of cell shapecell−cell junction organization

microtubuleplatelet dense tubular network

cell junction organizationregulation of insulin secretion

cellular response to iron ionepithelial cell proliferation

regulation of MAP kinase activitymorphogenesis of a branching structure

positive regulation of protein transportresponse to oxygen levels

muscle cell migrationpeptidyl−tyrosine modification

regulation of developmental growthbone remodeling

response to inorganic substancenegative regulation of leukocyte differentiation

regulation of extrinsic apoptotic signaling pathwayneuron spine

dendritic spinemorphogenesis of a branching epithelium

cell projection membranetube morphogenesis

positive regulation of MAPK cascaderegulation of apoptotic signaling pathway

negative regulation of phosphorylationnegative regulation of cell−cell adhesion

response to metal ioncell cortex part

actin filament bundle organizationmicrotubule binding

regulation of B cell proliferationpositive regulation of cellular component biogenesis

actin filament bundle assemblyresponse to lipopolysaccharide

positive regulation of protein serine/threonine kinase activitynegative regulation of protein phosphorylation

membrane regionconnective tissue development

cell−cell junctionregulation of long−term neuronal synaptic plasticity

regulation of protein polymerizationregulation of actin filament length

heart developmentregulation of actin polymerization or depolymerization

kinase regulator activitymembrane microdomain

protein polymerizationmembrane raft

regulation of protein complex assemblycartilage development

negative regulation of intracellular signal transductionregulation of cellular component size

response to molecule of bacterial origintubulin binding

positive regulation of neurogenesisactin filament organization

homotypic cell−cell adhesionpositive regulation of protein kinase activity

regulation of protein serine/threonine kinase activityresponse to mechanical stimulus

positive regulation of neuron differentiationregulation of protein secretionregulation of peptide secretion

growth conecell leading edge

hemostasiscoagulation

regulation of cytoskeleton organizationblood coagulation

positive regulation of cell developmentpositive regulation of cell projection organization

cell adhesion molecule bindingleukocyte chemotaxis

side of membraneregulation of supramolecular fiber organization

small GTPase mediated signal transductionadherens junction

cell−matrix adhesionregulation of actin cytoskeleton organization

negative regulation of cell adhesionregulation of actin filament−based process

skeletal system developmentcellular metal ion homeostasis

protein secretionregulation of lymphocyte activation

positive regulation of cell motilitypositive regulation of locomotion

regulation of T cell activationpositive regulation of cell migration

leukocyte proliferationleukocyte migrationT cell differentiation

regulation of leukocyte differentiationpositive regulation of cellular component movement

lymphocyte differentiationregulation of mononuclear cell proliferation

integrin−mediated signaling pathwayregulation of lymphocyte proliferation

regulation of leukocyte cell−cell adhesionregulation of cell−cell adhesion

blood vessel morphogenesisleukocyte differentiation

leukocyte cell−cell adhesionT cell activation

axonextracellular structure organization

extracellular matrix organizationresponse to hydrogen peroxide

response to reactive oxygen speciesestablishment of protein localization to plasma membrane

response to oxidative stresscadherin binding

carbohydrate catabolic processglycoprotein biosynthetic process

Golgi subcompartmentglycoprotein metabolic process

P−P−bond−hydrolysis−driven transmembrane transporter activityprimary active transmembrane transporter activity

phosphoric ester hydrolase activityvesicle lumen

cytoplasmic vesicle lumensecretory granule lumen

hydrolase activity, acting on acid anhydrides, catalyzing transmembrane movement of substancesATPase activity, coupled to transmembrane movement of substances

organic anion transportATPase activity, coupled to movement of substances

vacuolar lumenactive ion transmembrane transporter activity

response to drugcell−substrate adherens junction

focal adhesioncell−substrate junction

neutrophil activationneutrophil degranulation

neutrophil activation involved in immune responseneutrophil mediated immunity

granulocyte activationorganelle localization

establishment of vesicle localizationvesicle localization

establishment of organelle localization

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Gene Count ● ● ●30 60 90

Human

Mouse B

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Mouse B

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ECKO

C. elega

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Figure S5. Venn diagrams of human homologous differentially expressed genes (DEGs) in Ccm1/Krit1 and Ccm3/Pdcd10 genotype models. A total number of 915 DEGs were identified in human lesional neurovascular units, 1852 and 506 in respectively brain microvascular endothelial cell (BMEC) Ccm1/Krit ECKO and Ccm3/Pdcd10 ECKO, as well as 1104 and 1084 in C. elegans ccm1/kri-1 and ccm3/pdcd10 respectively (for human and mouse BMEC FC≥1.2; p<0.05 false discovery rate (FDR) corrected, for C. elegans FC>1.0; p<0.25 FDR corrected). (A) Eight DEGs were common across species in Ccm1/Krit1 models: SPARCL1, PDGRFA, FAXC, UNC13A, FAT1, PLXDC2, PLCD3, and GNAO1. (B) One DEG was common across species in Ccm3/Pdcd10 models: FAT1.

A B

ccm1/kri-1Ccm1/Krit1ECKO Ccm3/Pdcd10ECKO ccm3/pdcd10

8 1

1011

837736

401579

137 34

934128 420

45 32

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Figure S6. Venn diagram of human homologous differentially expressed genes (DEGs) between human micro-dissected lesional neurovascular units (NVUs) and mouse brain microvascular endothelial cell (BMEC) Ccm1/Krit1ECKO and Ccm3/Pdcd10ECKO models. The transcriptomes analyses identified 36 common DEGs between the human microdissected lesional neurovascular units (NVUs), mouse BMECs Ccm1/Krit1ECKO and Ccm3/Pdcd10ECKO models. In addition, further analyses showed that 109 and 10 DEGs were common between human NVUs and respectively Ccm1/Krit1ECKO and Ccm3/Pdcd10ECKO (FC≥1.2, p<0.05; FDR corrected).

Ccm1/Krit1ECKO Ccm3/Pdcd10ECKO

36

760

1432

109 10

185275

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Figure. S7. Laser capture microdissection of a cerebral cavernous malformation in vivo (video).

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Table S1: Characteristics of CCM subjects used in neurovascular unit RNAseq.

Patient Age Sex Sporadic/Familial Indication of resection 1 13 Female Sporadic Hemorrhagic expansion 2 31 Male Sporadic Seizures 3 34 Female Sporadic Size of the lesion 4 4 Female Familial (CCM1) Hemorrhagic expansion

5 40 Female Sporadic Hemorrhagic expansion/Seizures

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Table S2. List of identified differentially expressed genes (DEGs) in all models. (For human and mouse BMEC FC≥1.2; p<0.05 false discovery rate corrected (FDR), for C. elegans FC>1.0; p<0.25 FDR corrected). Table S3. List of identified GO terms in all models (p<0.10, false discovery rate corrected). Table S4. List of 95 enriched gene ontology terms using the 16 genes with more than 20 edges in the analysis (p<0.01, false discovery rate corrected). Table S5. Identified differentially expressed genes between human microdissected lesional neurovascular units and mouse BMEC Ccm1/Krit1ECKO and Ccm3/Pdcd10ECKO models. Table S6. List of 71 differently expressed genes (DEGs) common across mouse brain microvascular endothelial cells (BMECs) and C. elegans that were identified only in the Ccm1/Krit1 mutant models regardless of the direction of the fold change. Mouse BMEC DEGs were considered at a fold change (FC)≥1.2; significance level p<0.05, false discovery rate (FDR) corrected. C. elegans DEGs were considered at a FC≥1.2; significance level p<0.25, FDR corrected.

Table S7. List of 11 differently expressed genes (DEGs) common across mouse brain microvascular endothelial cells (BMECs) and C. elegans that were identified only in the Ccm3/Pdcd10 mutant models regardless of the direction of the fold change. Mouse BMEC DEGs were considered at a fold change (FC)≥1.2; significance level p<0.05, false discovery rate (FDR) corrected. C. elegans DEGs were considered at a FC≥1.2; significance level p<0.25, FDR corrected. Table S8. List of genes with a documented role in CCM disease for each model. We identified a list of 54 genes with a documented role in CCM disease using DisGeNet (disease ID C2919945, Cavernous Hemangioma of Brain). Among the 54 documented genes, 42 were found in the human lesional neurovascular units transcriptome, 32 human homologues in mouse BMEC Ccm1/Krit1ECKO, 32 in mouse BMEC Ccm3/Pdcd10ECKO, 6 in C. elegans ccm1/kri1 and 6 in C. elegans ccm3/pdcd10. C. elegans ccm1/kri1 mRNA levels are upregulated but the transcripts have been shown to be non-functional(10, 11). Table S9. Full list of enriched gene ontology terms present only within the Ccm1/Krit1 genotype models (p<0.1, false discovery rate (FDR) corrected). Table S10. Full list of enriched gene ontology terms present only within the Ccm3/Pdcd10 genotype models (p<0.1, false discovery rate (FDR) corrected).