Rats and axolotls share a common molecular signature after spinal cord injury enriched in collagen-1 Athanasios Didangelos1, Katalin Bartus1, Jure Tica1, Bernd Roschitzki2, Elizabeth J. Bradbury1 1Wolfson CARD King’s College London, United Kingdom. 2Centre for functional Genomics, ETH Zurich, Switzerland. Running title: spinal cord injury in rats and axolotls Correspondence: A Didangelos: [email protected] SUPPLEMENTAL FIGURES AND LEGENDS Supplemental Fig. 1: Rat 7 days microarray differentially regulated transcripts. A-B: Protein-protein interaction networks of upregulated (A) and downregulated (B) transcripts identified by microarray gene expression profiling of rat SCI (4 sham versus 4 injured spinal cord samples) 7 days post-injury. Microarray expression data and experimental information is publicly available online (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45006) and is also summarised in Supplemental Table 1. Protein-protein interaction networks were performed in StringDB using the full range of protein interaction scores (0.15 – 0.99) to capture maximum evidence of proteins’ interactions. Networks were then further analysed for betweeness centrality and gene ontology (GO) annotations (BinGO) in Cytoscape. Node colour indicates betweeness centrality while edge colour and thickness indicate interaction score based on predicted functional links between nodes (green: low values; red: high values). C-D: The top 10 upregulated (C) or downregulated (D) transcripts sorted by betweeness centrality score in protein-protein interaction networks shown in A & B. E-F: Biological process GO analysis was performed on networks of upregulated and downregulated genes using BinGO in Cytoscape. Graphs indicate the 20 most significant GO categories and the number of genes in each category. G: MSigDB-Transfac was used to identify potential transcription factor binding sites in differentially regulated genes. The ten transcription factors that might regulate the highest number of upregulated (black) or downregulated (grey) genes are shown. The ubiquitous transcription factor SP1 is predicted to have binding sites for the greatest number of differentially regulated genes. H: The transcription factors MAZ, SP1, JUN and NFATC1 which are predicted to regulate a large number of differentially regulated genes (G) are significantly increased 7 days post-SCI in the rat microarray dataset. Graph displays their adjusted p value (T-Test) and fold-change upregulation after rat SCI (see Supplemental Table 1). Supplemental Fig. 2: Axolotl 7 days microarray differentially regulated transcripts. A-B: Protein-protein interaction networks of upregulated (A) and downregulated (B) transcripts identified by microarray gene expression profiling of axolotl SCI (3 sham versus 3 injured spinal cord sample replicates; each replicate is a pool of 10 axolotl spinal cords) 7 days post-injury. Microarray expression data and experimental information is publicly available online (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71934) and is also summarised in Supplemental Table 2. Protein-protein interaction networks were performed using StringDB using the full range of protein interaction scores (0.15 – 0.99). Networks were then further analysed for betweeness centrality and gene ontology annotations (BinGO) in Cytoscape. Node colour indicates betweeness centrality while edge colour indicates interaction score based on the predicted functional links between nodes (green: low values; red: high values). C-D: The top 10 upregulated (C) or downregulated (D) transcripts sorted by their betweeness centrality score in protein-protein interaction networks shown in A & B. Note that 4 of the most central downregulated (D) transcripts after axolotl SCI namely TSPO, ELAVL1, MYC (cMYC) and ISG15 are 4 of the most central upregulated transcripts after rat SCI (see Supplemental Fig. 1D). E-F: Biological process GO analysis was performed on the networks of upregulated and downregulated genes using BinGO in Cytoscape. Graphs indicate the 20 most significant GO categories and the number of genes in each category. G: MSigDB-Transfac was used to identify potential transcription factor binding sites in differentially regulated genes. The ten transcription factors that might regulate the highest number of upregulated (black) or downregulated (grey) genes are shown. Note SP1 is again likely regulating the highest number of differentially regulated genes but unlike rats (see Supplemental Fig. 1H) none of the transcription factors identified was differentially regulated in the axolotl gene expression array. Supplemental Fig. 3: Visualization of intersecting rat and axolotl transcripts and gene ontology (GO) analysis. The 4 different groups of shared differentially regulated genes found in rat and axolotl microarrays 7 days post-SCI are visualised as protein-protein interaction networks made in StringDB v10 and CytoScape. Overrepresented biological process GO terms in each group and number of genes in each GO term are depicted in graphs. GO terms were retrieved using BinGO in CytoScape. A: Group 1; 153 genes upregulated in axolotls but downregulated in rats. B: Group 2; 156 genes upregulated in both axolotls and rats. C: Group 3; 77 genes downregulated in axolotls but upregulated in rats. D: Group 4; 228 genes downregulated both in axolotls and rats. Smaller networks (pointed by arrows) depict the genes that make up the indicated GO terms. Zoom-in to browse genes. Note that Group 1 (A) returned only 2 terms. Supplemental Fig. 4: Comparison of rat proteomics and transcriptomics. A-B: Comparison of shot-gun high-throughput proteomics and microarray transcriptomics in rats identifies 110 and 90 molecules that were upregulated (A) or downregulated (B) both at the mRNA and protein levels 7 days post-SCI. C-D: The 10 most significant biological process GO terms retrieved using BinGO in the 110 upregulated (C) and 90 downregulated (D) proteins. Note that “extracellular matrix” and “collagen” categories are the most significant GO terms in the group of 110 upregulated proteins after SCI (C). Proteomics identifications and protein spectral counts for each sample can be browsed in Supplementary Tables 3 & 4. Supplemental Fig. 5: Type I collagen staining in advanced 12 week-old lesions. A-B: Collagen-1 (COL1A red; the antibody recognises both COL1A1 and COL1A2 chains) and CD34 (green) were costained in 30µm thick T10 injured spinal cord cryosections. Spinal cord tissue was collected 12 weeks post-SCI. (B) is a magnified area from (A). C-D: COL1A (red) was costained in 30µm intact T10 spinal cord cryosections with CD34 (green). (D) is a magnified area from (C). Supplemental Fig. 6: CD34+ cells are distinct from CD45+ cells in lesions 2 weeks post-SCI. A-B: CD45 (red) and CD34 (green) were costained in 30µm T10 injured spinal cord cryosections. Spinal cord tissue was collected 2 weeks post-SCI. (B) is a magnified area from (A). CD34 and CD45 are proximal but there is no cellular colocalisation. CD45 (pan leukocyte marker) will likely stain leukocytes including monocytes. The marker is retained in macrophages and is also expressed by activated microglia (albeit less than infiltrating monocytes and tissue macrophages). CD45 staining pattern resembles activated macrophages and microglia. C-D: CD45 (red) was costained in 30µm intact T10 spinal cord cryosections with CD34 (green). (D) is a magnified area from (C). Supplementary Figure 1 UPregulated Genes RAT 7D UPregulated Genes BIOLOGICAL PROCESS A GOLM1 SERF2 LIMD2 SLFN13 KLHL6 LYRM5 MFSD11 TMEM181 ARMC10 BRI3 CR1L TMEM206 NHLRC3 SIDT2 CCDC115 FAM76A FAM219B GUCD1 SLC25A45TMEM39B ESYT1 TMEM168 OGFOD3 NTMT1 SPATS2L FRMD4B SMPDL3A FOLR2 OAF PLEKHA4 NT5DC2 KLHL5 POC1B SOGA1 S100A3 FILIP1L C5orf43 CCDC125 TMEM208 NKAIN1 FAM198B ZMYM5 HRCT1 C E INMT RARRES1 MXRA8 PTPLAD2 PSCA SFT2D1 MFSD12 ALPK3 FAM193A ZFAND3 MPV17L TMEM37 GINM1 SCAMP4 PLAC9 FAM108C1 FKBP15 VGF COA4 IAH1 ABRACL TMEM131 NT5DC1 PARP12 TMEM140 APOOL SLAMF9 GSDMD CEP128 ZDHHC6 SLFN5 RPP25L FAM105A FAM76B TMEM123 GPR84 ZDHHC7 EMP3 FEZ2 ATP8B2 CHADL LRRC8B GPR34 ZNFX1 IER5 EMP1 SLC35A4 CALHM2 ATP10D ADAP2 SEPN1 PSMG2 OSBPL11 CCDC93 MTPAP LRRC40 ABHD2 SCAND1 PION TOR4A LEPROT LRRC8D ZFP36L2 RNF169 SCPEP1 LMAN2 C20orf111 REXO2 Sep-09 OMA1 CRYM PLGRKT CDCA3 LRP10 DNAJC13 LRRC41 RAB20 OTUD1 RNF217 TTPAL ISLR ALDH2 ZCCHC6 ARL4A DNASE1L1 KCNN4 SWAP70 WSB1 TFPT RNF122 SLC12A9 TRAFD1 STK19 PAPD4 SLC25A24 SBF2 NPEPL1 NLN IRGM PTTG1IP TBC1D23 TBC1D2B CDK2AP2 HILPDA TMEM14C PDCD2 XYLT2 UBTD1 EPB41L2 OST4 CRIP1 TM9SF1 KCTD12 NAV1 HMGN2 PPFIBP1 EXTL3 TATDN1 SULF2 FCRL2 CLEC12A GNPTAB TIMM22 MS4A6A LRRC59 TNFAIP8L2 QSOX1 EIF2D HLX LILRB4 F8 SMIM3 WDR83OS MYO7A PRRC1 KDELC2 JTB PTPLA ARL11 NCLN PGAP2 EBPL DSCR3 SERTAD2 ADAMTS9 CENPW METRNL CCZ1B MTX1 SLC35E3 FBXO6 SLC25A39 DLGAP4 ZNRF2 TFEC NPL RENBP RTP4 IFI44 PHLDA1 SLC15A3 SLC15A4 CYP20A1 DDAH2 TEX264 ILDR2 RNF213 TEX261 SLC10A3 SLC35B1 LMF2 LAMA4 Most central Genes NUDT4 SASH3 DCAF11 RELL1 COX16 VPS13C FAM107B DHRSX DENND6A DENND3 TMEM248 CD276 WDR81 MPV17L2 CLIC1 FNDC3B ACOT1 PRSS23 RNF149 PDK3 FBXO46 BCKDK RNLS PCED1B TMEM86A RCBTB2 TMEM176A ATL3 BTG3 ETFDH FAM110A APBA3 TTC7A SPSB1 SYNGR1 ARHGAP42 LRRC42 CGGBP1 EVA1B ACOT9 C2orf18 CPNE8 BZW2 cellular process TTC13 EML3 CTHRC1 ANKRD49 NXPE4 SOWAHC EPSTI1 FBXL6 DRAM1 LMCD1 TMEM150ATMEM223 PXDC1 LYSMD3 RCN3 FABP5 DNAJC14 FAM49B SBNO2 SLC38A10 EEPD1 SP140 TMEM176B SZRD1 MOSPD3 VNN1 KCNE4 FRMD8 KANK2 RAI14 FAM111A OLFML3 CAPN5 CCDC90A ZFYVE26 RDH10 MFSD5 MYADM TMEM134 SLC44A2 CD300A AVPI1