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. Author manuscript; available in PMC: 2019 May 1.
Published in final edited form as: J Orthop Res. 2018 Jan 10;36(5):1356–1369. doi: 10.1002/jor.23834

RNA sequencing identifies gene regulatory networks controlling extracellular matrix synthesis in intervertebral disk tissues

Scott M Riester 1,2, Yang Lin 1,3, Wei Wang 1,4, Lin Cong 1,5, Abdel-Moneim Mohamed Ali 1, Sun H Peck 6,7, Lachlan J Smith 6,7, Bradford L Currier 1, Michelle Clark 8, Paul Huddleston 1, William Krauss 8, Michael J Yaszemski 1, Mark E Morrey 1, Matthew P Abdel 1, Mohamad Bydon 8, Wenchun Qu 9,10,11, A Noelle Larson 1, Andre J van Wijnen 1,*, Ahmad Nassr 1,*
PMCID: PMC5990467  NIHMSID: NIHMS960248  PMID: 29227558

Abstract

Degenerative disk disease of the spine is a major cause of back pain and disability. Optimization of regenerative medical therapies for degenerative disk disease requires a deep mechanistic understanding of the factors controlling the structural integrity of spinal tissues. In this investigation, we sought to identify candidate regulatory genes controlling extracellular matrix synthesis in spinal tissues. To achieve this goal we performed high throughput next generation RNA sequencing on 39 annulus fibrosus and 21 nucleus pulposus human tissue samples. Specimens were collected from patients undergoing surgical discectomy for the treatment of degenerative disk disease. Our studies identified associations between extracellular matrix genes, growth factors, and other important regulatory molecules. The fibrous matrix characteristic of annulus fibrosus was associated with expression of the growth factors platelet derived growth factor beta (PDGFB), vascular endothelial growth factor C (VEGFC), and fibroblast growth factor 9 (FGF9). Additionally we observed high expression of multiple signaling proteins involved in the NOTCH and WNT signaling cascades. Nucleus pulposus extracellular matrix related genes were associated with the expression of numerous diffusible growth factors largely associated with the transforming growth signaling cascade, including transforming factor alpha (TGFA), inhibin alpha (INHA), inhibin beta A (INHBA), bone morphogenetic proteins (BMP2, BMP6), and others.

Keywords: RNA sequencing, nucleus pulposus, annulus fibrosus, intervertebral disk, extracellular matrix

Introduction

Back pain is among the leading global causes of disability1, 2, with degenerative disk disease and osteoarthritis being important causes of disease. Disk degeneration is caused by a dysregulation of extracellular matrix homeostasis, characterized by dehydration of the central nucleus, reduced proteoglycan content, decreased cellularity, diminished endplate density, and disruption of the annulus35. Environmental exposures, as well as genetic and epigenetic factors have been associated with disk degeneration and altered extracellular matrix synthesis in disk tissues6,7. Novel molecular approaches that can target molecular factors regulating extracellular matrix synthesis in disk tissue have the potential to be used as therapeutic agents to slow or reverse disk degeneration in patients.

The molecular phenotype of intervertebral spinal disk tissue, including the annulus fibrosus (AF) and nucleus pulposus (NP), has been studied extensively in non-human animal models for degenerative disk disease814. Studies evaluating transcriptome data using microarrays have provided us with an initial understanding of the molecular mechanisms underlying disk biology and have played a major role in helping to identify important biologic markers specific for AF and NP disk tissues1519. However knowledge regarding the regulatory role of molecular factors and how they contribute to tissue homeostasis still requires further study.

In this investigation we seek to identify molecular regulatory factors whose transcriptional profiles correlate with the expression of extracellular matrix proteins important for the structural phenotype of human AF and NP tissues. To achieve this objective we evaluated transcriptome profiles of a cohort of human cervical disk tissue samples utilizing high throughput next generation RNA sequencing. We obtained complete gene expression profiles for 60 surgically harvested cervical disk specimens (AF and NP), and evaluated the main molecular landscapes of these two principal disc tissues. The large cohort of samples analyzed in this study allowed us to successfully perform weighted gene correlation analysis to identify gene regulatory clusters in disk tissues and assess gene relationships.

The molecular regulators that show relationships with extracellular matrix gene expression represent promising candidates for future study and therapeutic validation. The findings in this investigation also serve to support regenerative medicine therapies currently under development for the treatment of intervertebral disk disease, including stem cell therapies and tissue engineering strategies to regrow disk tissue for surgical transplantation and disk replacement procedures20,21. Both of these strategies require a comprehensive definition of the molecular phenotype of the human intervertebral disk to evaluate the efficacy of strategies to differentiate stem cells or engineer tissue disk tissue in vivo. The transcriptional signatures and gene relationships identified in this study have broad applicability in both the stem cell and tissue engineering fields.

Methods

Surgical tissue collection

A total of 60 tissue specimens were collected for research use from 48 adult patients undergoing cervical discectomy. Patients ranged in age from 32 to 77 years of age and included a balanced distribution of male and female patients (Supplemental Table 1). Patients in this study underwent surgery for the treatment of symptomtic degenerative disk disease presenting with or without myelopathy. Subjects were enrolled in the study in the period between January 2011 and April 2015. Cases in which discectomy was performed in the setting of acute trauma or infection were excluded from this study. At the time of tissue collection, the AF and NP were carefully dissected from one another in the operating room by the staff surgeon. In cases where disc degeneration was severe, NP tissue could not always be readily identified and distinguished from the AF tissue and therefore could not be collected for some patients. At the time of surgical harvest, tissues were snap frozen in liquid nitrogen and stored at −80°C until ready for RNA extraction. All samples were frozen within 40 minutes of removal from the patient. Grade of disk degeneration was evaluated on preoperative lateral radiographs and was characterized using the classification described by Lane et al.22,23. Clinical data available for each disk sample is provided in Supplemental Table 1. The specimens used in this investigation were collected under institutional review board approved protocols (IRB#10–005713). Written informed consent was obtained for all biospecimens that were analyzed.

RNA extraction from intervertebral disk tissue

Frozen tissue biopsies were ground into a powder using a mortar and pestle and homogenized in Qiazol reagent (Qiagen, Hilden, Germany) and homogenized. Total RNA was extracted from research biopsies based on previous methods24, 25 using the miRNeasy minikit (Qiagen, Hilden, Germany) and quantified using the NanoDrop 2000 spectrophotometer (Thermo Fischer Scientific, Wilmington, Delaware). For samples selected for next generation sequencing, RNA integrity was assessed using the Agilent Bioanalyzer DNA 1000 chip (Invitrogen, Carlsbad, CA).

Next generation mRNA sequencing, statistics and bioinformatics

RNA sequencing and bioinformatics analyses were performed as previously described2629. In brief, library preparation was performed using the TruSeq RNA library preparation kit (Illumina, San Diego, CA). Polyadenylated mRNAs were selected using oligo dT magnetic beads. TruSeq Kits (12-Set A and 12-Set B) were used for indexing to permit multiplex sample loading on the flow cells. Paired-end sequencing reads were generated on the Illumina HiSeq 2000 sequencer. Quality control for concentration and library size distribution was performed using an Agilent Bioanalyzer DNA 1000 chip and Qubit fluorometry (Invitrogen, Carlsbad, CA). Sequence alignment of reads and determination of normalized gene counts were performed using the MAP-RSeq (v.1.2.1) workflow30, utilizing TopHat 2.0.631, 32, and HTSeq33.

RNA sequencing data were analyzed to assess relevant genes that differ between AF and NP specimens. Genes with a minimal expression value (RPKM > 0.01) were included in subsequent computational analysis. Fold-change differences in gene expression were evaluated using the Mann-Whitney U test with a 1% false discovery rate (FDR), and statistical significance was set at p < 0.05. Unsupervised hierarchical clustering was performed using the Pearson correlation method. Weighted gene correlation analysis was performed using the R package WGCNA (Weighted Gene Correlation Analysis)34. Genes with an average RPKM expression > 0.01 across all specimens were included in the computational analysis. Functional gene annotation classification of WGCNA clusters was performed using DAVID Bioinformatics Resources 6.7 database (DAVID 6.7)35.

Results

RNA sequencing was performed using 60 unique cervical spine disk tissue samples (39 AF and 21 NP specimens). High quality sequencing reads were obtained for 57 of the 60 samples. The 3 samples with abnormally low read counts were excluded from further analysis. To detect sample outliers, an unbiased assessment of transcriptome data using unsupervised hierarchical clustering was performed. This analysis revealed 10 disk samples that clustered independently from the majority of the disk specimens (Supplemental Figure 1). A comparison of these samples with specimens in the primary cluster show that outlier samples express higher levels of blood related genes including genes linked to the erythroid, lymphoid, and myeloid lineages. A selective evaluation of the blood specific hemoglobin genes, hemoglobin subunit beta (HBB), hemoglobin subunit alpha 1 (HBA1), and hemoglobin subunit alpha 2 (HBA2), confirmed that these 10 samples express the highest levels of blood related genes (Supplemental Table 2). To ensure the comparability of samples in our study these outliers were removed from subsequent analysis. Repeated unsupervised hierarchical clustering after removal of outliers showed an expected trend toward independent clustering of AF and nucleus puplosus tissue samples (Figure 1a). Genes that are not strictly linked to a tissue phenotype such as hematopoietic and inflammation related genes, as well as tissue heterogeneity, played a role in defining the clustering dendrogram. This observation explains why the clustering dendrogram showed a trending, but not completely independent clustering of the AF and NP specimens, despite the distinct biological phenotypes of AF and NP tissues.

Figure 1.

Figure 1

(a) Unsupervised hierarchical clustering of RNA sequencing data after removal of sample outliers. In this clustering scheme there is a trend for AF and NP samples to preferentially cluster separately. These findings suggest that there are tissue specific differences contained within the transcriptome data, representing the known biological differences that exist between these two tissue types. Our unbiased approach also incorporates various factors that are not directly related to the disc phenotype such as tissue heterogeneity, blood content, and inflammation, which can drive some of the biological variation between specimens, thus precluding a perfect clustering dendogram in which AF and NP specimens cluster as completely independent groups. (b) Genes expressed > 100 RPKM in surgically isolated AF and NP tissue with equal expression levels (Fold change <1.5 between AF and NP). This analysis shows that AF and NP both share common expression of a large number of housekeeping genes as well as a small number of extracellular matrix proteins and growth factor binding associated proteins. (c) Gene ontology analysis reveals enrichment in pathways that promote cellular adhesion including genes linked to notch signaling (vasculature development, GTPase regulator activity) in AF tissue. (d) The NP shows enrichment in genes linked to extracellular matrix protein synthesis, including in genes controlling the extracellular matrix protein synthesis machinery (golgi complex and endoplasmic reticulum).

An examination of the most highly expressed genes (expression > 100 RPKM) commonly expressed in AF and NP expectedly showed common enrichment of genes associated with housekeeping functions (i.e. translation, protein ubiquitination) (Figure 1b). The AF and NP samples share many ECM related genes in common among their highest expressed genes, however the abundance of each gene and their ratios are quite different between AF and NP samples. Of the genes that are commonly enriched in AF and NP that are not associated with house-keeping functions, the AF samples showed higher expression of mRNAs encoding ECM proteins associated with a fibrous matrix including type I collagen (COL1A2), and type VI collagen (COL6A1, COL6A2, COL6A3) (Table 1). In contrast, the NP samples showed increased mRNA levels of genes encoding extracellular matrix proteins associated with a proteoglycan rich chondrogenic matrix, including cartilage oligomeric protein (COMP), lumican (LUM), type II collagen (COL2A1), cartilage intermediate layer protein (CILP), biglycan (BGN), aggrecan (ACAN), type III collagen (COL3A1), chondroadherin (CHAD), and others (Table 2)

Table 1.

Extracellular matrix related genes highly expressed in annulus fibrosus

GeneID Average expression (RPKM) in
annulus fibrosus
GeneID Average expression (RPKM) in
annulus fibrosus
COMP 3990.88 SERPINA1 247.96
FN1 3374.09 TIMP2 246.71
CLU 2562.49 CALR 242.68
TPT1 2502.16 CRTAC1 242.53
DCN 2434.52 DPT 204.77
MGP 2270.18 SERPINF1 198.73
LUM 2039.40 SERPING1 186.09
COL1A1 1393.37 CILP2 185.94
SPARC 1386.58 APOE 179.84
FMOD 1325.29 MMP14 175.88
COL1A2 1255.57 TGFBI 174.91
COL2A1 1161.98 POSTN 173.01
CI LP 1126.00 IBSP 168.53
COL3A1 1042.38 IGFBP4 165.21
CST3 972.51 COL9A3 154.93
HTRA1 871.24 SOD3 152.31
BGN 865.06 IGFBP6 145.98
FGFBP2 860.36 SPARCL1 142.91
LGALS1 711.77 SOD1 137.06
SPP1 681.08 BGLAP 135.65
PRELP 640.12 PLA2G2A 134.42
SCRG1 629.02 APOD 131.80
CHAD 614.98 CHI3L2 130.44
COL6A2 567.11 ANGPTL2 127.09
ACAN 564.31 TIMP3 126.93
CTSK 534.82 FSTL1 126.74
TIMP1 469.09 SERPINE2 125.99
GPX3 450.88 ALDOA 125.16
CTGF 433.32 PRDX4 123.56
MMP9 416.09 CCDC80 121.68
IGFBP7 352.81 COL11A2 117.92
PSAP 349.13 COL5A2 112.32
COL6A1 313.96 NUCB1 111.91
ASPN 308.16 A2M 111.41
MFGE8 292.56 COL6A3 106.22
CYTL1 277.74 LGALS3 105.95
GSN 277.71 FXYD6 104.61
OGN 259.14 ANXA2 100.74

Table 2.

Extracellular matrix related genes highly expressed in nucleus pulposus

GeneID Average expression (RPKM) in
nucleus pulposus
GeneID Average expression (RPKM) in
nucleus pulposus
FN1 6385.17 TIMP2 262.80
COMP 6014.23 CILP2 261.34
CLU 4378.49 CALR 261.25
DCN 3295.38 PLA2G2A 254.91
LUM 3093.90 COL9A3 245.78
MGP 2855.43 TGFBI 242.75
TPT1 2341.70 GSN 242.39
FMOD 2199.02 SERPING1 233.51
COL2A1 1840.27 SERPINE2 226.51
CILP 1626.80 SOD3 201.91
HTRA1 1482.74 IBSP 184.82
FGFBP2 1220.04 CHI3L1 184.64
BGN 1155.23 COL11A2 181.05
SPARC 1065.99 TIMP3 180.53
ACAN 1030.66 CCDC80 175.94
SCRG1 1028.47 COL9A2 168.77
PRELP 989.54 IGFBP6 160.61
COL3A1 940.80 POSTN 158.17
CHAD 835.31 SOD1 155.23
GPX3 643.54 FSTL1 153.32
CTGF 567.45 SPP1 152.31
CHI3L2 541.51 PRDX4 150.92
COL1A2 534.43 FXYD6 147.34
TIMP1 529.60 ANGPTL2 145.53
LGALS1 516.12 IGFBP4 138.17
CRTAC1 475.35 RBP4 137.83
CYTL1 452.88 NUCB1 124.30
CST3 437.51 ALDOA 122.41
COL6A2 430.48 APOE 121.20
SERPINA1 411.25 COL11A1 118.90
OGN 411.25 LGALS3 118.41
PSAP 402.65 APOD 116.58
MFGE8 337.14 COL5A2 116.18
COL1A1 322.44 COL6A3 111.84
ASPN 314.72 CTSK 111.52
DPT 306.39 CRLF1 110.49
COL6A1 285.52 ANXA2 103.52
IGFBP7 278.01 MIA 103.03

To determine genes that are differentially expressed between AF and NP tissues irrespective of their overall abundance, a fold-change comparison of gene expression data in AF and NP was performed. We observed statistically significant enrichment of 1399 genes in AF tissue and 373 genes with enrichment in NP tissue (Supplemental Table 3). Analysis revealed differential gene expression consistent with the biological properties and function of each tissue type. The AF showed enrichment in genes linked to adhesion and regulation of cell contact, consistent with its fibrous structural properties (Figure 1c). In contrast, the NP samples showed enrichment in mRNAs associated with proteoglycan extracellular matrix synthesis, including genes associated with the endoplasmic reticulum and Golgi apparatus (Figure 1d). These findings are consistent with the functional role of the NP, which acts as a hydrostatic cushion to reduce contact pressure between the bony vertebral bodies of the spine. We also observed preferential expression of the notochord specific transcription factor brachyury (T) in NP tissues at low, but detectable levels in about half of the samples. This indicates that residual notochord cell populations, detectable when highly sensitive molecular techniques are applied, may be present in degenerative adult disc tissue.

The AF and NP specimens both showed statistically significant enrichment in known, as well as novel, extracellular matrix proteins and signaling molecules. The AF specimens showed expression of phenotypically important genes such as type IV collagen (COL4A1), multiple laminins important for cell adhesion (LAMA3, LAMA4, LAMA5), and genes linked to NOTCH signaling (DLL1, JAG1, JAG2, NOTCH3, NOTCH4). In NP specimens we observed expression of genes promoting a proteoglycan rich ECM including aggrecan (ACAN), type XI collagen (COL11 A1), glypican 6 (GPC6), lumican (LUM), among others in NP specimens (Table 3).

Table 3.

Significant extracellular matrix related genes enriched in annulus fibrosus and nucleus pulposus

Genes enriched in nucleus pulposus Genes enriched in nucleus pulposus
CCBE1 collagen and calcium binding EGF domains 1 ACAN aggrecan
CNTN1 contactin 1 CHI3L1 chitinase 3 like 1
CNTNAP3B contactin associated protein-like 3B CHRD chordin
COL14A1 collagen type XIV alpha 1 chain COL10A1 collagen type X alpha 1 chain
COL17A1 collagen type XVII alpha 1 chain COL11A1 collagen type XI alpha 1 chain
COL18A1 collagen type XVIII alpha 1 chain COL8A2 collagen type VIII alpha 2 chain
COL21A1 collagen type XXI alpha 1 chain COL9A2 collagen type IX alpha 2 chain
COL24A1 collagen type XXIV alpha 1 chain CRTAC1 cartilage acidic protein 1
COL4A1 collagen type IV alpha 1 chain FMOD fibromodulin
DLL1 delta like canonical Notch ligand 1 FN1 fibronectin 1
DTX1 deltex E3 ubiquitin ligase 1 GPC6 glypican 6
DTX4 deltex E3 ubiquitin ligase 4 HHIPL1 HHIP like 1
EGFLAM EGF like, fibronectin type III and laminin G domains HHIPL2 HHIP like 2
JAG1 jagged 1 LAMC3 laminin subunit gamma 3
JAG2 jagged 2 LTBP2 latent transforming growth factor beta binding protein 2
LAMA3 laminin subunit alpha 3 LUM lumican
LAMA4 laminin subunit alpha 4 OGN osteoglycin
LAMA5 laminin subunit alpha 5 PRG4 proteoglycan 4
NOTCH3 notch 3 SDC4 syndecan 4
NOTCH4 notch 4 SRPX2 sushi repeat containing protein, X-linked 2
PDGFB platelet derived growth factor subunit B WISP3 WNT1 inducible signaling pathway protein 3

Given the heterogeneous nature of spinal tissues, statistical methods used to assess simple fold-change analyses may not always be able to identify all important biological gene relationships. To overcome this challenge and identify novel gene regulatory networks with a functional role in regulating extracellular matrix production, we performed weighted gene correlation network analysis for spine tissues using the R package WGCNA34. Gene correlation analysis identified 46 regulatory gene clusters present in our intervertebral disk samples (Figure 2). We observed gene regulatory clusters associated with housekeeping functions (i.e. translation, transcription, mitochondrion, nuclear homeostasis), cellular infiltration including blood and inflammatory cells. We also observed gene regulatory clusters associated with non-disk tissue including processes related to muscle, bone, and adipogenesis, which likely represent small quantities of tissue mixed in with disk tissue at the time of surgical harvesting.

Figure 2.

Figure 2

Gene correlation networks predicted using weighted genes correlation analysis (WGCNA). Gene networks are associated with a variety of cellular activities including cellular housekeeping, mitosis, tissue heterogeneity, extracellular matrix synthesis as well as numerous others. Gene clusters “paleturquoise”, “darkorange2”, and “darkslateblue” are enriched in known extracellular matrix protein markers in AF, while the clusters “black”, “grey60”, and “lightyellow” are associated with extracellular matrix protein markers characteristic of NP.

To identify novel extracellular matrix proteins and regulatory molecules that control tissue specific phenotypes, we examined clusters containing genes associated extracellular matrix synthesis. The related clusters “paleturquoise”, “darkorange2”, and “darkslateblue” each show enrichment in extracellular matrix proteins and adhesive proteins associated with a fibrous matrix, which is typically characteristic of AF tissue. These clusters contain genes that promote a strong fibrous matrix, including collagens, fibulins, integrins, lamamins, elastin, and others (Table 4). These gene clusters were notably associated with a three diffusible growth factors, fibroblast growth factor 9 (FGF9), platelet-derived growth factor beta polypeptide (PDGFB), and vascular endothelial growth factor C (VEGFC). These findings suggest that these growth factors may play a regulatory roles in maintenance of the AF phenotype and warrant further investigation. Additionally, these clusters also exhibited strong enrichment in genes linked to cell-cell signaling interactions, including the Wnt signaling and NOTCH signaling pathways. Both of these pathways are known to be involved in mediating cell-cell interactions and cellular adhesion in various tissues outside of intervertebral disk36, 37. Given the paucity of diffusible growth factors and the fact that AF cells are in close contact with one another, these data suggest that AF ECM production may be regulated or strongly influenced by direct cell-cell signaling mechanisms, possibly mediated through the Wnt and NOTCH signaling pathways.

Table 4.

Annulus fibrosus co-regulatory gene networks

ECM and cell adhesion related genes Signaling Associated Genes
Gene
symbol
Gene name Function Cluster Gene symbol Gene name Function Cluster
ADAM12 ADAM metallopeptidase domain 12 ECM "paleturquoise" FGF9 fibroblast growth factor 9 Growth factor "paleturquoise"
COL5A1 collagen, type V, alpha 1 ECM "paleturquoise" KREMEN1 kringle containing transmembrane protein 1 Wnt signaling "paleturquoise"
COL5A2 collagen, type V, alpha 2 ECM "paleturquoise" PDGFRA platelet-derived growth factor receptor, alpha polypeptide Growth factor "paleturquoise"
COL6A3 collagen, type VI, alpha 3 ECM "paleturquoise" WISP1 WNT1 inducible signaling pathway protein 1 Wnt signaling "paleturquoise"
CDH5 cadherin 5, type 2 Adhesion "darkorange2" WNT5A wingless-type MMTV integration site family, member 5A Wnt signaling "paleturquoise"
CDH6 cadherin 6, type 2, K-cadherin Adhesion "darkorange2" WNT6 wingless-type MMTV integration site family, member 6 Wnt signaling "paleturquoise"
CDH24 cadherin-like 24 Adhesion "darkorange2" WNT9B wingless-type MMTV integration site family, member 9B Wnt signaling "paleturquoise"
COL17A1 collagen type XVII alpha 1 chain ECM "darkorange2" CDH6 cadherin 6 NOTCH signaling "darkorange2"
COL18A1 collagen, type XVIII, alpha 1 ECM "darkorange2" CDKN1B cyclin dependent kinase inhibitor 1B NOTCH signaling "darkorange2"
COL21A1 collagen, type XXI, alpha 1 ECM "darkorange2" DNER delta/notch like EGF repeat containing NOTCH signaling "darkorange2"
COL4A1 collagen type IV alpha 1 chain ECM "darkorange2" HES5 hes family bHLH transcription factor 5 NOTCH signaling "darkorange2"
COL4A2 collagen type IV alpha 2 chain ECM "darkorange2" HEYL hes related family bHLH transcription factor with YRPW motif-like NOTCH signaling "darkorange2"
COL4A5 collagen type IV alpha 5 chain ECM "darkorange2" HHEX hematopoietically expressed homeobox NOTCH signaling "darkorange2"
CNTN4 contactin 4 Adhesion "darkorange2" HOXD3 homeobox D3 NOTCH signaling "darkorange2"
ELN elastin ECM "darkorange2" IGFBP4 insulin-like growth factor binding protein 4 Wnt signaling "darkorange2"
FBLN1 fibulin 1 ECM "darkorange2" IGFBP6 insulin-like growth factor binding protein 6 Wnt signaling "darkorange2"
FBLN5 fibulin 5 ECM "darkorange2" IGFBP7 insulin-like growth factor binding protein 7 Wnt signaling "darkorange2"
ICAM1 intercellular adhesion molecule 1 Adhesion "darkorange2" JAG1 jagged 1 NOTCH signaling "darkorange2"
ICAM2 intercellular adhesion molecule 2 Adhesion "darkorange2" JAG2 jagged 2 NOTCH signaling "darkorange2"
ITGA3 integrin subunit alpha 3 Adhesion "darkorange2" KCNA5 potassium voltage-gated channel subfamily A member 5 NOTCH signaling "darkorange2"
ITGA6 integrin subunit alpha 6 Adhesion "darkorange2" MAML3 mastermind like transcriptional coactivator 3 NOTCH signaling "darkorange2"
ITGA7 integrin subunit alpha 7 Adhesion "darkorange2" NEURL1B neuralized E3 ubiquitin protein ligase 1B NOTCH signaling "darkorange2"
ITGA8 integrin subunit alpha 8 Adhesion "darkorange2" NOTCH3 notch 3 NOTCH signaling "darkorange2"
ITGA9 integrin subunit alpha 9 Adhesion "darkorange2" NOTCH4 notch 4 NOTCH signaling "darkorange2"
ITGB4 integrin subunit beta 4 Adhesion "darkorange2" NRARP NOTCH-regulated ankyrin repeat protein NOTCH signaling "darkorange2"
JAM2 junctional adhesion molecule 2 Adhesion "darkorange2" PDGFB platelet-derived growth factor beta polypeptide Growth factor "darkorange2"
LAMA3 laminin subunit alpha 3 Adhesion "darkorange2" PTP4A3 protein tyrosine phosphatase type IVA, member 3 NOTCH signaling "darkorange2"
LAMA4 laminin subunit alpha 4 Adhesion "darkorange2" VEGFC vascular endothelial growth factor C Growth factor "darkorange2"
LAMA5 laminin subunit alpha 5 Adhesion "darkorange2" WISP2 WNT1 inducible signaling pathway protein 2 Wnt signaling "darkorange2"
LAMB1 laminin subunit beta 1 Adhesion "darkorange2" WISP3 WNT1 inducible signaling pathway protein 3 Wnt signaling "darkorange2"
LAMB1 laminin subunit beta 1 Adhesion "darkorange2" ZNF423 zinc finger protein 423 NOTCH signaling "darkorange2"
MYH9 myosin heavy chain 9 Adhesion "darkorange2"
PCDH1 protocadherin 1 Adhesion "darkorange2"
PCDH12 protocadherin 12 Adhesion "darkorange2"
PCDH17 protocadherin 17 Adhesion "darkorange2"
PCDH19 protocadherin 19 Adhesion "darkorange2"
TINAGL1 tubulointerstitial nephritis antigen like 1 Adhesion "darkorange2"
ADAMTS7 ADAM metallopeptidase with thrombospondin type 1 motif, 7 ECM "darkslateblue"
CNTN1 contactin 1 Adhesion "darkslateblue"
COL5A3 collagen, type V, alpha 3 ECM "darkslateblue"
COL6A1 collagen, type VI, alpha 1 ECM "darkslateblue"
LAMA1 laminin, alpha 1 Adhesion "darkslateblue"

The clusters “black”, “grey60”, and “lightyellow” show enrichment in genes associated with a proteoglycan rich extracellular matrix. Genes included in these clusters include the known NP markers type II collagen (COL2A1), type IX collagens (COL9A2, COL9A3), type XI collagen (COL11A2), aggrecan (ACAN), as well as other genes associated with a proteoglycan rich ECM that have not previously been associated with NP phenotype (Table 5). In contrast to the gene clusters previously discussed that were associated with synthesis of a fibrous matrix, these gene clusters express a diverse array of diffusible growth factors, with many being associated with the TGFβ signaling cascade. Associated growth factors include transforming growth factor alpha (TGFA), inhibin beta A (INHBA), inhibin alpha (INHA), growth differentiation factors (GDF5, GDF6), and bone morphogenetic proteins (BMP2, BMP6) and others (Table 5). The reliance on diffusible growth factors to mediate ECM homeostasis in a proteoglycan rich matrix such as that observed in the NP is logical since cells are usually separated by a thick matrix and have limited direct cell to cell contact. A comprehensive list of the genes associated with each regulatory cluster showing enrichment in either AF (fibrous) or NP (proteoglycan) markers are shown in Supplemental Table 4.

Table 5.

Nucleus pulposus co-regulatory gene network

ECM and cell adhesion related genes Signaling associated genes
Gene symbol Gene name Function Cluster Gene symbol Gene name Function Cluster
CDH26 cadherin-like 26 Adhesion "black" CTGF connective tissue growth factor Growth factor "black"
CDHR5 mucin-like protocadherin Adhesion "black" FGFR2 fibroblast growth factor receptor 2 Growth factor "black"
CELSR3 cadherin, EGF LAG seven-pass G-type receptor 3 Adhesion "black" FGFR3 fibroblast growth factor receptor 3 Growth factor "black"
CILP cartilage intermediate layer protein, nucleotide pyrophosphohydrolase ECM "black" BMP2 bone morphogenetic protein 2 Growth factor "grey60"
COL27A1 collagen, type XXVII, alpha 1 ECM "black" BMP6 bone morphogenetic protein 6 Growth factor "grey60"
COL2A1 collagen, type II, alpha 1 ECM "black" FGF1 fibroblast growth factor 1 (acidic) Growth factor "grey60"
CRTAP cartilage associated protein ECM "black" FGF2 fibroblast growth factor 2 (basic) Growth factor "grey60"
CTGF connective tissue growth factor ECM "black" GDF6 growth differentiation factor 6 Growth factor "grey60"
DSCAML1 Down syndrome cell adhesion molecule like 1 Adhesion "black" HHIP hedgehog interacting protein Growth factor "grey60"
GPC6 glypican 6 ECM "black" IGFBP3 insulin-like growth factor binding protein 3 Growth factor "grey60"
ITGA10 integrin, alpha 10 Adhesion "black" INHBA inhibin, beta A Growth factor "grey60"
LMLN leishmanolysin-like Adhesion "black" NGF nerve growth factor (beta polypeptide) Growth factor "grey60"
PCDH20 protocadherin 20 Adhesion "black" NOG noggin Growth factor "grey60"
TESK2 testis-specific kinase 2 Adhesion "black" PDGFC platelet derived growth factor C Growth factor "grey60"
ADAMTS6 ADAM metallopeptidase with thrombospondin type 1 motif, 6 ECM "grey60" TGFA transforming growth factor, alpha Growth factor "grey60"
CCDC80 coiled-coil domain containing 80 ECM "grey60" TGFBR1 transforming growth factor, beta receptor 1 Growth factor "grey60"
CDH19 cadherin 19, type 2 Adhesion "grey60" TSHB thyroid stimulating hormone, beta Growth factor "grey60"
CHI3L1 chitinase 3-like 1 ECM "grey60" VEGFA vascular endothelial growth factor A Growth factor "grey60"
FN1 fibronectin 1 ECM "grey60" WNT1 wingless-type MMTV integration site family, member 1 Wnt signaling "grey60"
HAPLN1 hyaluronan and proteoglycan link protein 1 ECM "grey60" WNT16 wingless-type MMTV integration site family, member 16 Wnt signaling "grey60"
IMPG2 interphotoreceptor matrix proteoglycan 2 ECM "grey60" WNT9A wingless-type MMTV integration site family, member 9A Wnt signaling "grey60"
ITGB5 integrin, beta 5 Adhesion "grey60" GDF5 growth differentiation factor 5 Growth factor "lightyellow"
LAMB3 laminin, beta 3 Adhesion "grey60" HHIPL1 HHIP-like 1 Hedgehog signaling "lightyellow"
LUM lumican ECM "grey60" HHIPL2 HHIP-like 2 Hedgehog signaling "lightyellow"
PRG4 proteoglycan 4 ECM "grey60" INHA inhibin, alpha Growth factor "lightyellow"
SERPINE1 serpin peptidase inhibitor, clade E member 1 ECM "grey60" NRG4 neuregulin 4 Growth factor "lightyellow"
SERPINE2 serpin peptidase inhibitor, clade E member 2 ECM "grey60" NRTN neurturin Growth factor "lightyellow"
SMOC1 SPARC related modular calcium binding 1 ECM "grey60"
TIMP2 TIMP metallopeptidase inhibitor 2 ECM "grey60"
TIMP3 TIMP metallopeptidase inhibitor 3 ECM "grey60"
VCAN versican ECM "grey60"
ACAN aggrecan ECM "lightyellow"
ADAMTSL2 similar to ADAMTS-like 2; ADAMTS-like 2 ECM "lightyellow"
BGN biglycan ECM "lightyellow"
CHAD chondroadherin Adhesion "lightyellow"
CILP2 cartilage intermediate layer protein 2 ECM "lightyellow"
COL11A2 collagen, type XI, alpha 2 ECM "lightyellow"
COL9A2 collagen, type IX, alpha 2 ECM "lightyellow"
COL9A3 collagen, type IX, alpha 3 ECM "lightyellow"
COMP cartilage oligomeric matrix protein ECM "lightyellow"
EMILIN3 elastin microfibril interfacer 3 ECM "lightyellow"
PRELP proline/arginine-rich end leucine-rich repeat protein ECM "lightyellow"
SERPINA1 serpin peptidase inhibitor, clade A member 1 ECM "lightyellow"
SPINT2 serine peptidase inhibitor, Kunitz type, 2 ECM "lightyellow"

Discussion

The molecular phenotype of intervertebral spinal disk tissue, including the AF and NP, has been studied extensively over the past several years, primarily in animal models. The disk periphery is comprised of the fibrous annulus, derived from the scleroderm, while NP is derived from the notochord. However, notochordal cells in humans decrease in abundance with age, and are largely absent after adolescence38, 39, although visible notochord tissue is present at maturity in other species. NP cells make predominantly type II collagen, whereas AF cells make both type I and type II collagen40. The findings in our investigation utilizing high throughput RNA sequencing approaches are consistent with these findings in previous investigations, and also identify associations with other novel extracellular matrix proteins and associated regulatory factors.

Our initial clustering analysis performed using AF and NP specimens (Figure 1) demonstrates that blood content is an important consideration in the evaluation of surgically collected spinal disk tissues. Disk tissues have a very low density of cells, and the few cells that are present are usually encased in a thick extracellular matrix that makes RNA extraction technically challenging. Even the presence of small quantities of blood, from which RNA is much more easily extracted, can profoundly impact RNA content and resulting transcriptome data analyses if not carefully considered.

Our analysis reveals increased expression of known AF and NP markers within corresponding tissue types including enrichment of type I collagen in AF and a proteoglycan associated extracellular matrix enriched in genes such as ACAN, COMP, LUM, and others in the NP. We note that there is some overlap in mRNA expression between annulus and nucleus specimens. This overlap may reflect similarities in the developmental origin of these tissues or could be due to technical issues, for example, because there is some intermixing of annulus and nucleus cells during tissue harvest (e.g., in degenerative disk tissues with altered structural morphology).

These studies also implicate the WNT and NOTCH signaling pathway as a potentially important regulators of cell adhesion and matrix synthesis in AF tissue. These pathways are mediated by direct cell to cell interactions and have been shown to impact cellular adhesion and tissue integrity in various tissue types41. Golgi and ER related genes enriched in NP tissue may contribute to the production of the proteogylcan rich matrix associated with the NP environment. Therapeutic strategies that can increase protein output and upregulate the expression of NP specific genes have the potential to help disk tissue retain fluid and appropriate hydrostatic pressure, thus preventing disk space degeneration and associated disk space narrowing and osteoarthritis.

Recent studies have identified several novel AF and NP markers, our study shows support for many of these markers42. In our analyses, the proposed NP markers desmocollin 2 (DSC2)18, lubricin (PRG4)43, and paired box 1 (PAX1)20, showed co-regulation with networks enriched in NP related genes supporting their classification as NP markers. The novel AF markers brain abundant membrane attached signal protein 1 (BASP1), sclerostin domain containing 1 (SOSTDC1)18, glypican 3 (GPC3), and pleiotrophin (PTN)44 also showed co-regulation with AF related ECM gene networks. Our study did not show a clear link to either AF or NP phenotypes for several published markers including CD24 antigen (CD24), keratin 8 (KRT8), keratin 18 (KRT18), keratin 19 (KRT19), cadherin 2 (CDH2)17, carbonic anhydrase 12 (CA12)45, and hypoxia inducible factor 1 alpha subunit (HIF1A)13, 14, 46, all of which showed co-regulation with gene networks unrelated to disk phenotype. Protein levels do not always correlate with mRNA expression, which could explain some of the differences between our study and previous investigations. Discrepancies could also be related to interspecies differences, as many of these published studies were carried out using non-human tissues. In addition, our study focused on evaluation of degenerative disc tissue, and it is possible that many of these markers may be present during early disk development and are gradually lost over time with aging and degeneration.

It is important to note that the gene relationships defined by network analyses in this study may exclude important functional/regulatory genes when a gene has a stronger relationship to another network. This was observed for the known AF related gene type I collagen (COL1 A1, COL1A2), which showed stronger co-regulation with bone related genes (gene cluster “royalblue”) rather than AF related genes. Despite this limitation, we were still able to identify large gene regulatory networks associated with ECM production in AF and NP tissues. Our analysis also does not take in account the numerous regulatory mechanisms that act in coordination with transcriptional mechanisms including protein phosphorylation and acetylation, histone modifications, microRNAs, and others. Future studies that integrate intervertebral disk transcriptomic profiles with various types of molecular data including microRNA profiles, and mass spectroscopy data may further help to elucidate novel molecular pathways involved intervertebral disk homeostasis.

This investigation provides a comprehensive overview of mRNA expression in annulus fibrosus and nucleus pulposus intervertebral disk tissue, including extracellular matrix components. By applying computational analyses to our large dataset of human clinical specimens, we have been able to identify candidate gene regulatory networks that act in AF and NP tissues to regulate extracellular matrix synthesis, an important determinant of intervertebral disk integrity. The transcriptome data generated in this study also serves as an important reference data set and has the potential to help solve many biological questions related to disk tissues. For example, our data can be used to evaluate the efficacy of tissue engineering strategies for intervertebral disk development. The data can also be applied to optimize stem cell differentiation strategies for therapeutic disk regeneration, as a variety of stem cell therapies are just beginning to be investigated in new clinical trials. Information generated in this study can also potentially be applied to identify novel therapeutic targets to enhance extracellular matrix synthesis and restore the normal mechanical properties of intervertebral disk tissue.

Supplementary Material

Supplemental Figure 1. Supplemental Figure 1.

Unsupervised hierarchical clustering of spine samples was performed to detect sample outliers. In total 10 samples were found to cluster independent from the majority of samples. These 10 samples expressed high levels of blood related mRNAs compared with the remaining samples.

Supplemental Table 1. Supplemental Table 1.

Clinical data associated with each disk specimen

Supplemental Table 2. Supplemental Table 2.

Hemoglobin expression in sample outliers

Supplemental Table 3. Supplemental Table 3.

Differential gene expression between AF and NP

Supplemental Table 4. Supplemental Table 4.

WGCNA gene clusters and associated genes

Clinical Significance.

This investigation provides important data on extracellular matrix gene regulatory networks in disk tissues. This information can be used to optimize pharmacologic, stem cell, and tissue engineering strategies for regeneration of the intervertebral disk and the treatment of back pain.

Acknowledgments

This publication was made possible by grants from the Cervical Spine Research Society (21st Century grant; to AN) and the generous philanthropic support of William and Karen Eby. Additional support was provided by the National Institutes of Health, including the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS, R03 AR066342, to ANL; R01 AR049069, to AvW) and the National Center for Advancing Translational Sciences (NCATS), a component of the National Institutes of Health (CTSA Grant Number UL1 TR000135, to ANL). Funding was also provided by the Mayo Graduate School (to SMR) through CTSA Grant Number UL1 TR000135 from the National Center for Advancing Translational Sciences. Additional support was also provided by the Doctor Research Startup Fund’ of Liaoning Province (Grant#201601114; to LC). We also thank the members of our research groups, including Emily Camilleri and Amel Dudakovic for stimulating discussions, as well as the Mayo Clinic Bioinformatics Core, including Jared Evans and Asha Nair for their assistance with high-throughput RNA sequencing and bioinformatics support. Dr. Bradford Currier owns stock in Spinology and Tenex, and is on the Board of Directors for the Lumbar Spine Research Society.

Footnotes

This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record.

Additional Supporting Information may be found in the online version of this article.

Author Contributions:

Scott M. Riester - Carried out experiments, interpreted data, and helped draft and prepare final manuscript.

Yang Lin - Carried out experiments, and approved final manuscript.

Wei Wang - Carried out experiments, and approved final manuscript.

Sun H. Peck - assisted with technical design of study

Lachlan Smith - assisted with technical design of study, shared unpublished data and provided conceptual advice

Lin Cong - Carried out experiments, and approved final manuscript.

Abdel-Moneim Mohamed Ali - Interpreted data, and approved final manuscript.

Bradford Currier - Provided surgical specimens, and approved final manuscript.

Michelle Clark - Provided surgical specimens, and approved final manuscript.

Paul Huddleston - Provided surgical specimens, and approved final manuscript.

William Krauss - Provided surgical specimens, and approved final manuscript.

Michael J. Yaszemski - Provided surgical specimens, provided conceptual input and approved final manuscript.

Mark E. Morrey - Provided conceptual input, helped revise and approve final manuscript.

Matthew P. Abdel - Designed the study, provided conceptual input, helped revise and approve final manuscript.

Mohamad Bydon - Provided conceptual input, helped revise and approve final manuscript.

Wenchun Qu - Provided conceptual input, helped revise and approve final manuscript.

A. Noelle Larson - Assisted with design of the study, helped obtain funding, wrote introduction, helped revise and approve final manuscript.

Andre J. van Wijnen - Assisted with design of the study, provided funding for this project, helped revise and approve final manuscript.

Ahmad Nassr - Designed the study, provided funding for this project, provided surgical specimens for the study, helped draft revise and approve final manuscript.

The remaining authors have no conflicts relevant to this publication.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Figure 1. Supplemental Figure 1.

Unsupervised hierarchical clustering of spine samples was performed to detect sample outliers. In total 10 samples were found to cluster independent from the majority of samples. These 10 samples expressed high levels of blood related mRNAs compared with the remaining samples.

Supplemental Table 1. Supplemental Table 1.

Clinical data associated with each disk specimen

Supplemental Table 2. Supplemental Table 2.

Hemoglobin expression in sample outliers

Supplemental Table 3. Supplemental Table 3.

Differential gene expression between AF and NP

Supplemental Table 4. Supplemental Table 4.

WGCNA gene clusters and associated genes

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