Genes uniquely expressed in human growth plate chondrocytes uncover a distinct regulatory network
© The Author(s). 2017
Received: 29 May 2017
Accepted: 11 December 2017
Published: 20 December 2017
Chondrogenesis is the earliest stage of skeletal development and is a highly dynamic process, integrating the activities and functions of transcription factors, cell signaling molecules and extracellular matrix proteins. The molecular mechanisms underlying chondrogenesis have been extensively studied and multiple key regulators of this process have been identified. However, a genome-wide overview of the gene regulatory network in chondrogenesis has not been achieved.
In this study, employing RNA sequencing, we identified 332 protein coding genes and 34 long non-coding RNA (lncRNA) genes that are highly selectively expressed in human fetal growth plate chondrocytes. Among the protein coding genes, 32 genes were associated with 62 distinct human skeletal disorders and 153 genes were associated with skeletal defects in knockout mice, confirming their essential roles in skeletal formation. These gene products formed a comprehensive physical interaction network and participated in multiple cellular processes regulating skeletal development. The data also revealed 34 transcription factors and 11,334 distal enhancers that were uniquely active in chondrocytes, functioning as transcriptional regulators for the cartilage-selective genes.
Our findings revealed a complex gene regulatory network controlling skeletal development whereby transcription factors, enhancers and lncRNAs participate in chondrogenesis by transcriptional regulation of key genes. Additionally, the cartilage-selective genes represent candidate genes for unsolved human skeletal disorders.
Chondrogenesis is the earliest stage of skeletal development and begins during embryogenesis with the proliferation and condensation of mesenchymal cells, followed by lineage commitment and differentiation, to yield the chondrocytes which populate and synthesize the scaffold for patterning the early skeletal elements. Subsequently, growth and mineralization of endochondral bones proceeds by establishment of the growth plates followed by the ordered differentiation, proliferation and hypertrophy of growth plate chondrocytes. Most of the terminally differentiated hypertrophic chondrocytes undergo apoptosis, while a subset can transdifferentiate to osteoblasts [1, 2]. Finally, the cartilage matrix is mineralized to form bone. This highly dynamic developmental process is regulated by transcription factors that include SOX9, RUNX2 and the GLI family of proteins, with synergistic regulation by multiple cell signaling pathways including the fibroblast growth factor (FGF), hedgehog, bone morphogenetic protein (BMP), transforming growth factor beta (TGF-beta) and WNT signaling pathways [3, 4]. Disruption of the genes that participate in chondrogenesis can lead to inherited skeletal disorders, as observed in humans and mice, which are characterized by abnormalities in the formation, linear growth and/or maintenance of the skeleton. The skeletal disorders comprise a broad range of severity from embryonic lethal phenotypes with extensive skeletal abnormalities to mild disorders in which short stature and early onset osteoarthritis are the main manifestations [5, 6].
Systematic identification of genes expressed in cartilage can uncover gene regulatory networks that are important for chondrogenesis and chondrocyte biology, providing fundamental insight into cartilage development and function. Indeed high-throughput gene expression profiling followed by data mining has been used to identify tissue-specific/selective genes in multiple human tissues, including cartilage [7–11]. Studies in cartilage have discovered genes that are highly and/or primarily expressed in chondrocytes and have been used as markers to investigate the differentiation potential of mesenchymal stem cells toward chondrocytes and other cell lineages . Not surprisingly, a large number of the genes selectively expressed in chondrocytes are associated with human skeletal disorders , and the published gene expression data have been used as a resource to facilitate studies that have led to identification of additional genes associated with human skeletal phenotypes. For example, TRPV4, COL11A1 and ACAN gene mutations were identified as the causes of autosomal dominant brachyolmia, fibrochondrogenesis and an autosomal recessive form of spondyloepimetaphyseal dysplasia, respectively, based in part on their cartilage-selective (CS) expression patterns [13–15].
Microarray technology has been widely used for the high-throughput detection of gene expression. However, microarray gene expression applications are limited by low signal-to-noise ratios for genes expressed at low levels, signal saturation for highly expressed genes, and detection restricted to a predefined gene set. The development of high throughput sequencing methods has facilitated measuring most cellular transcript levels by RNA sequencing (RNA-Seq) . In contrast to microarray technology, RNA-seq provides more sensitive and accurate measurement of gene expression, and is able to discover novel transcripts and genes. To provide a deeper measure of both quantitative and qualitative gene expression in human growth plate chondrocytes, we used RNA-seq to acquire a comprehensive gene expression profile of the tissue. These data were complemented by publically available RNA-Seq and ChIP-seq data sets, also generated on high throughput sequencing platforms, to reveal regulatory mechanisms that underlie chondrogenesis and linear growth. The data have provided a comprehensive understanding of the transcriptional network in chondrocytes and suggested novel regulatory networks and mechanisms underlying chondrogenesis and skeletal development.
Identification of cartilage-selective (CS) genes in human fetal cartilage
We sought to identify CS genes using RNA-seq data from human tissues and cells. Cartilage gene expression profiles were generated from 14 to 18 week distal femur growth plate chondrocytes. RNA-seq data from other tissues/cells were generated by previously described studies [17–23] and accessed from public databases as detailed in Methods. There were 111 RNA-seq gene expression datasets representing 22 tissues or cell types in the comparison data set (Additional file 1: Dataset S1). With the exception of the data from CD4+ and CD8+ primary cells, which were obtained from adults, the remaining data were generated from human fetal tissues/cells. Therefore, the gene expression data mainly reflected human fetal transcription. Data quality were assessed by calculating the Pearson’s correlation coefficients between datasets, requiring that gene expression patterns be similar in the same or closely related tissues, which would be reflected by high Pearson’s correlation coefficients. As shown in Additional file 2: Figure S1, tissues/cells with same origins had high Pearson’s correlation coefficients and formed unique clusters, indicating that the data were of high quality and therefore represented the intrinsic gene expression properties of the tissues/cells studied.
Using the PaGeFinder algorithm, we identified 403 transcripts, representing 332 protein coding genes, which were selectively expressed in cartilage (Additional file 3: Dataset S2). A boxplot of the selective gene expression showed that there was a set of genes that were highly expressed in cartilage as compared with other tissues/cells (Additional file 2: Figure S2). Many well-studied cartilage marker genes were identified and listed as top CS genes with high specificity measure (SPM) values, such as the known CS genes encoding types II, IX and XI collagens, aggrecan and matrix metalloproteinase 13 (Additional file 3: Dataset S2). We found that 63 (42%) of the CS genes previously discovered in our similar microarray-based study  were also identified as CS in the current study (Additional file 4: Dataset S3). Another 25 genes from the previous study  had high SPM values (between 0.7 and 0.9) in the current study, indicating they were preferentially, but not selectively, expressed in cartilage. This comparison indicates that the RNA-seq approach was capable of recapitulating the prior findings  and able to reveal known and newly identified genes selectively expressed in human fetal cartilage. There were also 57 CS genes from the microarray study that showed expression in non-cartilage samples in the RNA-seq dataset, reflecting the differences between the microarray and RNA-seq techniques. RNA-seq has a broader dynamic range that is able to better detect low-abundance transcripts . Thus genes expressed at low levels on microarrays have signals close to background and are set as non-expressed while by RNA-seq the low level expression can be measured, revealing that they may not be selectively expressed.
Cartilage-selective genes are required for skeletal development
As the GO analysis of the CS genes indicated that they have roles in skeletal development, it would be expected that disruption of their functions in mice and/or humans would lead to skeletal abnormalities. To assess this, we retrieved mouse phenotypes from the Mouse Genome Informatics Database (MGI, http://www.informatics.jax.org/) and found that mutations in 153/332 (46%) of the CS genes were associated with mouse skeletal defects (Fig. 1c; Additional file 3: Dataset S2). Using the MGI descriptors, the 153 genes were represented across a number of overlapping categories, with 128 genes associated with growth/size/body phenotypes, 92 genes associated with skeletal phenotypes, 52 genes associated with craniofacial phenotypes, and 60 genes associated with limb/digit/tail phenotypes. For the remaining 179 genes, 94 genes were not present in the MGI phenotype list and 85 genes did not show a skeletal phenotype when targeted in mice. Despite the latter observation, a review of the literature revealed that many of these genes do have roles in skeletogenesis, including human skeletal disorders due to mutations in SHOX, FBLN7 and VIT as examples [25–27]. In addition, although knockout mutations targeting Trpv4 or Comp do not show skeletal phenotypes in mice, dominant negative structural mutations in these genes result in skeletal disorders in humans [14, 28–30]. In this context, we compared the CS genes with the genes associated with human genetic skeletal disorders, expecting that many of the genes would be associated with skeletal dysplasias . There were 32 of the CS genes (Additional file 3: Dataset S2) associated with a broad spectrum of 62 human skeletal disorders, 26 of which were also associated with mouse skeletal phenotypes. The human and mouse skeletal phenotypes highlight the essential roles of the CS genes in skeletal development but also reveal that loss-of-function mouse models do not identify the essential roles of all genes important for skeletogenesis.
Cartilage-selective gene products interact
Transcriptional regulation of cartilage-selective genes
Cartilage-selective genes are regulated by active enhancers
With DNA motif analysis, we found that the chondrocyte unique enhancers were enriched for consensus-binding sites for transcription factors that have pivotal roles in chondrogenesis, including RUNX2, SOX8/9, SMAD3, STAT1/4 and FOSL1 (Fig. 6b), in agreement with the fact that they regulate transcription in part through binding to enhancers [52, 54, 57–60]. Furthermore, binding sites for additional transcription factors were also found to be enriched among the chondrocyte unique enhancers, suggesting roles in transcriptional regulation in chondrocytes that could be further explored experimentally (Additional file 6: Dataset S5).
Identification of cartilage-selective lncRNAs
LINC00673 and SOX9 were highly expressed in chondrocytes, and also had low expression in brain, kidney, intestine, lung, spinal cord and stomach (Fig. 7b). Among all tissues, the expression levels of LINC00673 and SOX9 were positively correlated with each other, yielding a Pearson’s correlation coefficient 0.66. When we only considered the tissues/cells that express SOX9 (FPKM > 1), the Pearson’s correlation coefficient reached 0.8, indicating LINC00673 and SOX9 have orchestrated expression patterns across tissues (Fig. 7c). To validate the finding, we analyzed gene expression data using the UCLA Gene Expression Tool (UGET) , which reported the Pearson’s correlation coefficient for every pair of 54,675 probe sets on Affymetrix HG U133 Plus 2.0 arrays from the ∼12,000 human expression profiles. Among the 38,500 genes detected by the Affymetrix arrays, SOX9 was ranked as the gene with the second highest correlation with LINC00673 expression (the most highly correlated gene was LINC00673 itself), with a Pearson’s correlation coefficient 0.43. Reciprocally, LINC00673 was ranked as the gene that had the nineteenth highest correlation with SOX9 expression, with a Pearson’s correlation coefficient 0.39. The high Pearson’s correlation coefficients suggest a gene expression co-regulatory mechanism between SOX9 and LINC00673 (Additional file 7: Dataset S6).
Because SOX9 positively regulates transcription through binding to gene promoters and enhancers , we also considered the possibility that SOX9 could directly bind to LINC00673 and activate its transcription. To assess this, we examined SOX9 binding sites in mouse chondrocytes  and found that SOX9 bound to the promoter and structural gene for 2610035D17Rik, the orthologue of LINC00673 in mice, strongly suggesting that expression of the gene is regulated by SOX9 (Additional file 2: Figure S6). Because LINC00673 was selectively expressed in chondrocytes and was embedded in the enhancer region downstream of SOX9 gene, it is also possible that there is reciprocal regulation in which LINC00673 could work with enhancers to activate SOX9 and possibly other genes, and therefore participate in regulation of chondrogenesis.
Organogenesis is a process of proliferation and differentiation of progenitor cells into diverse types of cells with distinct functions. Their unique functions are largely acquired through expression of tissue-specific/selective genes. Studies of gene expression patterns and regulatory mechanisms in particular tissues thus inform our understanding of tissue development and mechanisms of disease. Tissue-specificity refers to genes whose expression is restricted to one particular tissue or cell type, while tissue-selective expression includes genes that are expressed in one or a few generally biologically similar tissue types . The data presented in this study described 332 cartilage-selective genes expressed in human fetal cartilage, providing unique insights into chondrocyte gene expression and regulation.
The significance of cartilage-selective gene identification
High expression of the CS genes in chondrocytes suggests they may have essential roles in skeletal development and skeletal disorders. In support of this hypothesis, 32 CS genes were linked to human skeletal disorders that result from mutations in these genes. Gene-targeting mice for 153 genes showed skeletal development defects. Among the list, there were well-known genes whose products have pivotal roles in chondrogenesis, such as cartilage collagens, cell signaling molecules and transcription factors. Base on this observation, we suggest that some of the newly identified CS genes may be good candidate genes for skeletal disorders in which the associated genes have yet to be identified. As new skeletal dysplasia loci are defined, the candidate genes can be compared with the CS genes to promote rapid identification of the mutated gene. Gene network analysis revealed complex interactions of CS gene products, data compatible with the hypothesis that CS genes are functionally related in the initiation and progression of complex diseases such as osteoarthritis. It may thus be useful to correlate gene mutations and dysregulation of gene expression among the chondrodysplasias with the CS gene interactome for further understanding of the pathogenesis of cartilage disorders . In addition, we identified 94 genes that are less studied and for which knockout mice phenotypes have not been annotated by MGI (Additional file 3: Dataset S2). According to GO analysis, 39 of these genes are involved in cell signaling pathways and 27 genes encode secreted proteins. Their unique cartilage expression pattern suggests the genes might participate in chondrogenesis. For example, C2orf82 is highly expressed in human cartilage with an average FPKM value >1500 and encodes a type I single-pass transmembrane chondroitin sulfate proteoglycan. The gene showed a CS expression pattern with the highest expression in proliferating and prehypertrophic zones in embryonic and postnatal growth plate. Its expression was restricted to the uncalcified zone in adult articular cartilage, including chondrocyte clusters in human osteoarthritic cartilage. Its expression pattern was similar to the highly expressed CS genes Sox9, Acan and Col2a1, and could be induced by BMP-2, suggesting its functions may be associated with the development and maintenance of the chondrocyte phenotype . As a second example, zinc-finger 385 family genes including ZNF385B/C/D were also selectively expressed in cartilage and another family member, ZNF385A, was also highly expressed in the tissue (Additional file 3: Dataset S2). Fluorescence-labeled Znf385C protein was highly expressed in maturing chondrocytes in the pharyngeal arches as well as in the cartilage associated with the ear and ventral fins in zebrafish . Loss of Znf385C in zebrafish led to craniofacial defects and premature ossification of the Meckel’s cartilage, potentially through disturbance of p53-dependent cell cycle regulation . Therefore, targeting some of the 94 understudied CS genes is likely to reveal new biology related to cartilage function.
Regulation of chondrogenesis by epigenetic mechanisms
Epigenetic mechanisms such as histone modifications and enhancers regulate transcription in skeletal development and disease [70, 71]. Genome-wide profiling of SOX9 indicated that it functions in part by binding to enhancers. Disruption of upstream distal intergenic regions especially the regions between -50 kb and -350 kb 5′ of SOX9 by deletion/translocation/inversion are associated with severe campomelic dysplasia [72, 73]. Systematically screening the 350 kb region upstream of SOX9 using histone modification markers and DNase I hypersensitivity assays identified ten potential enhancers, with three enhancers primarily active in chondrocytes and that activate SOX9 expression in the chondrocyte lineage . SOX9, in complex with SOX5 and SOX6, reciprocally activate the enhancers, which provides a potential mechanism for autoregulation of its own expression in chondrocytes . In the current study, genome-wide mapping of histone marks led to identification of 11,334 enhancers that were uniquely active in chondrocytes and associated with 176 of the CS genes. The identified active enhancers could be used as a resource to explore epigenetic regulation and higher-order chromatin organization in chondrocytes. The data also indicated that these enhancers were enriched in the consensus binding motifs for well-known transcription factors such as SOX9 and RUNX2, suggesting that transcription factor-bound enhancers contribute to the selective gene expression pattern in chondrocytes.
lncRNAs and chondrogenesis
lncRNAs are non-protein coding transcripts with lengths of more than 200 nucleotides, which are proposed to regulate transcription through both trans- and cis-regulatory mechanisms. Some lncRNAs tether to their target gene loci and affect transcriptional activity of neighboring genes through association with regulatory proteins. They could also bind to regions on other chromosomes and also control gene activity through recruiting regulatory proteins . The roles for lncRNAs in skeletal development are emerging with a growing number of lncRNAs identified that participate in skeletal development and disease . In our study, 34 lncRNAs were identified to be selectively expressed in human cartilage. Eight lncRNAs are adjacent to loci of protein coding genes that participate in skeletal development, suggesting the cartilage-selective lncRNAs may be involved in chondrogenesis through regulation of protein coding genes. We found that LINC00673 and SOX9 are co-regulated in human tissues, and that LINC00673 is embedded within a potential enhancer region for SOX9, suggesting LINC00673 may regulate SOX9 expression through a distal enhancer. In support of this hypothesis, by Circularized Chromosome Conformation Capture sequencing (4C–seq), the LINC00673 locus was found to be involved in long-range enhancer-promoter interactions at the SOX9 locus in SOX9-expressed melanoma cells but not in SOX9-inactive cells, consistent with the enhancer-promoter interactions driving SOX9 activation . In addition, LINC00673 regulates target gene expression in gastric cancer cells by recruiting histone methyltransferase EZH2 and demethylase LSD1 to target gene promoters, suggesting that it can function as a transcriptional regulator through epigenetic mechanisms . While the exact role of LINC00673 in SOX9 gene regulation is unknown, loss- and gain-of-function experiments could be used to directly address this question.
Cartilage-selective genes and mouse phenotypes
There were 85 CS genes that did not show a skeletal phenotype when targeted in mice (Figure 1C). Some of these genes may have overlapping functions with paralogous genes. For example, NKX3.1 is a transcription factor selectively expressed in cartilage. Inactivation of the Nkx3.1 gene produced no apparent skeletal phenotype, while knockout of its paralogue Nkx3.2 in mice led to a lethal skeletal dysplasia, with abnormalities of the vertebral column and craniofacial bones [77–80]. Nkx3.1 and Nkx3.2 double knockout mice showed enhanced vertebral defects compared with Nkx3.2 knockout mice, suggesting that Nkx3.1 and Nkx3.2 regulate skeletal development coordinately . In addition, although knockout of some CS genes did not produce skeletal defects in mice, such mutations in humans produced skeletal disorders. As an example, knockout of Wisp3 in mice did not yield skeletal abnormalities, while human loss-of-function mutations in WISP3 produce the autosomal recessive skeletal disease progressive pseudorheumatoid dysplasia in humans (OMIM 208230). Depletion of the gene in zebrafish affected mandibular and pharyngeal cartilage development and over-expression Wisp3 in zebrafish indicated that it might function as a BMP and Wnt signaling modulator [82, 83]. As another example, COMP encodes a noncollagenous extracellular matrix protein that interacts with cartilage collagens and promotes formation of well-defined collagen fibrils . In contrast to COMP knockout mice, which showed normal skeletal development, dominant missense mutations of human COMP result in autosomal dominant pseudoachondroplasia and multiple epiphyseal dysplasia type 1 [29, 85, 86]. Furthermore, pseudoachondroplasia knock-in mice carrying the COMP p.Thr583Met mutation that causes a skeletal phenotype in humans exhibited short-limb dwarfism and severe degeneration of articular cartilage . COMP p.Asp469del knock-in mice developed a progressive short-limb dwarfism and hip dysplasia, with disorganized growth plate . Finally, dominant structural mutations in TRPV4, which encodes a Ca2+ permeable ion channel that responds to chemical and physical stimuli, result in a broad spectrum of inherited skeletal disorders in humans [14, 28, 89–91]. Trpv4 knockout mice showed bladder dysfunction and hearing loss without obvious skeletal defects . However, Trpv4 transgenic mice expressing the known pathogenic missense mutation p.Arg594His showed a severe skeletal dysplasia that recapitulated abnormalities of metatropic dysplasia in humans . In the latter examples, dominant negative or gain-of-function mutations interrupt normal chondrocyte activities and lead to abnormal skeletal phenotypes that mouse knockouts did not reveal.
Limitations and future directions
The goal of this study was to identify key genes and transcriptional regulatory mechanisms in chondrogenesis using total RNA isolated from fetal distal femur cartilage, including all types of chondrocytes from the growth plate. As a consequence, we were not able to determine if the selective genes are uniformly expressed throughout the growth plate or are preferentially expressed in certain chondrocyte cell type(s). For example, it is known that SOX9 is expressed in reserve and proliferating chondrocytes and COL10A1 is expressed in hypertrophic chondrocytes [94, 95]. Laser capture microdissection followed by RNA-seq could further define the expression patterns of the cartilage-selective genes. In addition, due the limited number of tissue types and samples used in this study, and objective but arbitrary criteria for identifying cartilage selectivity, the CS gene list could change if the criteria for identifying CS selective genes are changed, or as additional fetal tissue expression profiles are added. Curation of additional RNA-seq data from human tissues and developmental stages will help to better define tissue selective genes and promote functional studies at spatial and temporal scales.
In summary, the current study identified 332 protein coding genes and 34 lncRNA genes that are selectively expressed in human fetal cartilage. Proteins transcribed from the cartilage-selective genes formed a complex interaction network and participate in multiple processes in skeletal development. The data further indicate that transcription factors, enhancers and lncRNAs work cooperatively to mediate transcriptional regulation in cartilage. In addition to known regulatory molecules, our study also discovered novel protein coding genes and lncRNAs that are likely to regulate chondrogenesis. Together, these findings help us to better understand the mechanisms underlying chondrogenesis and will be instrumental in further dissecting gene regulatory mechanisms in skeletal pattern formation, endochondral ossification and chondrocyte function.
Cartilage specimen collection and RNA isolation
Under a UCLA IRB-approved human subjects protocol, five independent 14–18 week distal human femur growth plate cartilage samples were obtained with informed consent. RNA was isolated and purified from the tissue as previously described . Briefly, the fetal cartilage was digested in 0.3% bacterial collagenase, the chondrocytes were collected by centrifugation and total RNA was isolated with TRIZol (Thermo Fisher Scientific). Genomic DNA contamination was removed by DNase I (Thermo Fisher Scientific) digestion and the RNA was further purified with the RNeasy Mini Kit (Qiagen). All of the RNA samples had RIN (RNA Integrity Number) ≥8 (measured with the Agilent 2100 BioAnalyzer).
Gene expression analysis
PolyA-tailed mRNA was isolated from total RNA and used as the input for sequencing library construction with the TruSeq RNA Preparation Kit (Illumina). Sequencing was performed on the Illumina HiSeq 2000 instrument (Additional file 1: Dataset S1). The sequencing data analysis pipeline was described previously . Briefly, RNA-seq reads were mapped to the human genome (hg19) using TopHat (v2.0.9), allowing up to 2-bp mismatches per read . The mapped reads were analyzed using the Cufflinks (v2.2.1) package . The transcript abundance in each sample was quantified with Cuffquant. The Cuffquant outputs for all samples were then joined and normalized with Cuffnorm. Gene expression level is represented as FPKM (fragments per kilobase of exon per million fragments mapped) values. Refseq gene annotation was obtained from the UCSC genome website (https://genome.ucsc.edu/). LncRNA gene annotation was downloaded from GENCODE . Low abundance genes with FPKM values less than 1 were filtered before downstream analysis.
Identification of cartilage-selective (CS) genes
The PaGeFinder program has been used to define gene expression patterns from high-throughput transcriptomic data, which applies cosine similarity measurements to discover housekeeping and tissue-specific/selective genes . The specificity measure (SPM) value produced by PaGeFinder was used as a criterion to estimate the relative expression specificity of a gene in a given sample. To identify CS genes, gene expression FPKM values from multiple samples within the same tissue/cell type were averaged, yielding a table with average FPKM values for all measured genes. The data were then processed with PaGeFinder and SPM values were generated. CS genes were defined as the subset of genes with average FPKM values ≥1 and SPM ≥0.9 in cartilage samples. For the cartilage-selective lncRNAs, to eliminate false-positives, lncRNAs that had sense strand exonic overlap with expressed protein-coding genes or that were embedded in gene introns for which there was intron-retention, were excluded from the cartilage-selective lncRNA list.
External data sources
The data used in this study are summarized in Additional file 1: Dataset S1. Some of the RNA-seq data from normal human tissues/cells have been reported by others [17–23]. Histone modification ChIP-seq data were downloaded from the NCBI epigenome roadmap project (http://www.ncbi.nlm.nih.gov/geo/roadmap/epigenomics/) . Processed alignment files for histone ChIP-seq in human fetal tissues were also downloaded from the epigenome roadmap project website (http://egg2.wustl.edu/roadmap/data/byFileType/alignments/consolidated/). Genome-wide SOX9 binding data in mouse chondrocytes were described previously . All downloaded data were processed using the UCSC hg19 build.
ChIP-seq data processing
Bowtie 0.12.7 was used to align sequenced reads to the human (hg19, for histone modifications) and murine genomes (mm9, for SOX9 ChIP-seq). Reads that aligned to more than one genomic location or that had more than 2 mismatches were discarded. The human genome was divided into 100 bp windows and peak calling was performed with MACS (v18.104.22.168) using its default P-value .
Prediction of active enhancers
Active enhancers were predicted based on H3K27ac ChIP-seq peaks that were >3 kb away from gene transcription start sites (TSS) and H3K4me3 peaks (a marker for active promoters) . Enhancers within 1 kb of each other were merged. Chondrocyte-unique enhancers were defined as the enhancers only detected in chondrocytes in comparison with other tissues.
Gene ontology (GO) and network analysis
CS genes were submitted to DAVID for GO and KEGG pathway analysis . GO analysis for chondrocyte-unique enhancers was performed with the Genomic Regions Enrichment of Annotations Tool (GREAT) using default settings . Genome coordinates for CS lncRNAs were submitted to GREAT and protein coding genes flanking lncRNA loci were identified. Gene network analysis was performed with Cytoscape 3.2.1, using plug-ins ClueGO and GeneMANIA [31, 104, 105].
Enrichment analysis of known transcription factor binding motifs was performed with SeqPos  using DNA sequences from gene promoter regions (±2 kb from TSS) or enhancers.
Data processing and visualization
Heatmaps, boxplots and scatter plots were generated with R. Sequencing data were visualized with the Integrated Genome Browser .
We thank Min Chen, Jer-Young Lin and Bob Goldberg (UCLA) for their assistance with the RNA-seq experiment.
This study was supported in part by NIH grants RO1AR062651 and RO1AR066124.
Availability of data and materials
The RNA-seq data sets generated in this study have been deposited in the Gene Expression Omnibus (GEO) under accession GSE107649.
BL performed most of the experiments and analyses including data generation, computational analysis, methodological application and writing the original draft; KB participated in data generation, as well as review and editing of the manuscript; DK participated in conceptualizing the project, supervising the work, and reviewing and editing of the manuscript; DHC participated in conceptualizing the project, supervising the work, and reviewing and editing of the manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Under a UCLA IRB-approved human subjects protocol, five independent 14-18 week distal human femur growth plate cartilage samples were obtained with informed consent. (Protocol ID: IRB#14-000177).
Consent for publication
The authors declare that they have no competing interests.
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