RNA Sequencing Reveals a Role of TonEBP Transcription Factor in Regulation of Pro-inflammatory Genes in Response to Hyperosmolarity in Healthy Nucleus Pulposus Cells: A HOMEOSTATIC RESPONSE?

Transcription factor tonicity-responsive enhancer-binding protein (TonEBP/NFAT5) is critical for osmo-adaptation and extracellular matrix homeostasis of nucleus pulposus (NP) cells in their hypertonic tissue niche. Recent studies implicate TonEBP signaling in inflammatory disease and rheumatoid arthritis pathogenesis. However, broader functions of TonEBP in the disc remain unknown. RNA sequencing was performed on NP cells with TonEBP knockdown under hypertonic conditions. 1140 TonEBP-dependent genes were identified and categorized using Ingenuity Pathway Analysis. Bioinformatic analysis showed enrichment of matrix homeostasis and cytokine/chemokine signaling pathways. C-C motif chemokine ligand 2 (CCL2), interleukin 6 (IL6), tumor necrosis factor (TNF), and nitric oxide synthase 2 (NOS2) were studied further. Knockdown experiments showed that TonEBP was necessary to maintain expression levels of these genes. Gain- and loss-of-function experiments and site-directed mutagenesis demonstrated that TonEBP binding to a specific site in the CCL2 promoter is required for hypertonic inducibility. Despite inhibition by dominant-negative TonEBP, IL6 and NOS2 promoters were not hypertonicity-inducible. Whole-disc response to hypertonicity was studied in an ex vivo organ culture model, using wild-type and haploinsufficient TonEBP mice. Pro-inflammatory targets were induced by hypertonicity in discs from wild-type but not TonEBP-haploinsufficient mice. Mechanistically, NF-κB activity increased with hypertonicity and was necessary for hypertonic induction of target genes IL6, TNF, and NOS2 but not CCL2 Although TonEBP maintains transcription of genes traditionally considered pro-inflammatory, it is important to note that some of these genes also serve anabolic and pro-survival roles. Therefore, in NP cells, this phenomenon may reflect a physiological adaptation to diurnal osmotic loading of the intervertebral disc

Understanding nucleus pulposus cell phenotype: a prerequisite for stem cell based therapies to treat intervertebral disc degeneration.

Intervertebral disc (IVD) degeneration and associated low back pain (LBP) remains a major burden to our society without significant improvements in treatment strategies or patient\u27s quality of life. While the recent cell-transplantation studies for treatment of degenerative disc disease have shown promising results, to better gauge the success and functional outcomes of these therapies, it is crucial to understand if transplanted cells give rise to healthy nucleus pulposus (NP) tissue. NP cell phenotype is unique and is defined by expression of a characteristic set of markers that reflect specialized physiology and function. This review summarizes phenotypic markers that mirror the unique physiology and function of NP cells and their progenitors and should be considered to when measuring outcomes of cell-based therapies to treat disc degeneration

Lack of evidence for involvement of TonEBP and hyperosmotic stimulus in induction of autophagy in the nucleus pulposus.

Nucleus pulposus (NP) cells reside in a physiologically hyperosmotic environment within the intervertebral disc. TonEBP/NFAT5 is an osmo-sensitive transcription factor that controls expression of genes critical for cell survival under hyperosmotic conditions. A recent report on NP and studies of other cell types have shown that hyperosmolarity triggers autophagy. However, little is known whether such autophagy induction occurs through TonEBP. The goal of this study was to investigate the role of TonEBP in hyperosmolarity-dependent autophagy in NP. Loss-of-function studies showed that autophagy in NP cells was not TonEBP-dependent; hyperosmolarity did not upregulate autophagy as previously reported. NP tissue of haploinsufficient TonEBP mice showed normal pattern of LC3 staining. NP cells did not increase LC3-II or LC3-positive puncta under hyperosmotic conditions. Bafilomycin-A1 treatment and tandem mCherry-EGFP-LC3B reporter transfection demonstrated that the autophagic flux was unaffected by hyperosmolarity. Even under serum-free conditions, NP cells did not induce autophagy with increasing osmolarity. Hyperosmolarity did not change the phosphorylation of ULK1 by mTOR and AMPK. An ex vivo disc organ culture study supported that extracellular hyperosmolarity plays no role in promoting autophagy in the NP. We conclude that hyperosmolarity does not play a role in autophagy induction in NP cells

COX-2 expression mediated by calcium-TonEBP signaling axis under hyperosmotic conditions serves osmoprotective function in nucleus pulposus cells.

The nucleus pulposus (NP) of intervertebral discs experiences dynamic changes in tissue osmolarity because of diurnal loading of the spine. TonEBP/NFAT5 is a transcription factor that is critical in osmoregulation as well as survival of NP cells in the hyperosmotic milieu. The goal of this study was to investigate whether cyclooxygenase-2 (COX-2) expression is osmoresponsive and dependent on TonEBP, and whether it serves an osmoprotective role. NP cells up-regulated COX-2 expression in hyperosmotic media. The induction of COX-2 depended on elevation of intracellular calcium levels and p38 MAPK pathway, but independent of calcineurin signaling as well as MEK/ERK and JNK pathways. Under hyperosmotic conditions, both COX-2 mRNA stability and its proximal promoter activity were increased. The proximal COX-2 promoter (-1840/+123 bp) contained predicted binding sites for TonEBP, AP-1, NF-κB, and C/EBP-β. While COX-2 promoter activity was positively regulated by both AP-1 and NF-κB, AP-1 had no effect and NF-κB negatively regulated COX-2 protein levels under hyperosmotic conditions. On the other hand, TonEBP was necessary for both COX-2 promoter activity and protein up-regulation in response to hyperosmotic stimuli

Novel Roles of Transcription Factor TonEBP in Nucleus Pulposus Cells of the Intervertebral Disc

The intervertebral discs are essential for upright posture and flexibility of the spine. The inner nucleus pulposus (NP) of these discs distributes loads axial applied to the spine. This mechanical function depends on the abundant extracellular matrix of the NP, which is rich in proteoglycans and collagens that attract and hold water. Thus, resident NP cells must withstand an environment of high osmolarity, which fluctuates with diurnal water changes. The transcription factor Tonicity enhancer binding protein (TonEBP/NFAT5) is known to promote transcription of genes involved in transport and synthesis of non-ionic osmolytes to prevent apoptosis in NP and other cells exposed to hyperosmotic challenges. However, understanding of the broader role of TonEBP in the disc is limited. There is evidence that TonEBP may also play a role in inflammatory conditions, such as intervertebral disc degeneration (IDD). In severe IDD cases, matrix deterioration contributes to loss of biomechanical function and subsequent pain. While many factors contribute to pathology of IDD, inflammatory cytokines, chemokines, and downstream matrix catabolism, cellular senescence, and apoptosis play an important role. In immune and other cell types, TonEBP is activated by non-osmotic inflammatory stimuli and promotes production of cytokines and inflammatory disease progression. We combined unbiased RNA-sequencing, molecular methods, and animal models to better elucidate the role of TonEBP in the healthy and degenerative disc environments. In NP cells, analysis of sequencing results and ex vivo organ culture disc models revealed that several genes associated with inflammation were induced by transient hyperosmolarity in a TonEBP-dependent manner. For a subset of genes, osmotic induction also required activity of NF-κB, but we were unable to detect binding between Rel family members TonEBP and p65. Treatment with a cytokine commonly increased in IDD, TNF-α, activated TonEBP via increased nuclear abundance. This activation of TonEBP had no effect on its ability to modulate osmo-regulatory targets but did promote the pro-inflammatory response to TNF-α. Evaluating the role of TonEBP in NP tissue development, we were surprised to find that gross morphology and cellularity of the tissue did not depend on TonEBP activity up to E18.5. Together, the results herein provide a more complete understanding of the roles of TonEBP under various stimuli including regulation of inflammation-related genes under hyperosmotic and inflammatory conditions

Multipotent stromal cells/mesenchymal stem cells and fibroblasts combine to minimize skin hypertrophic scarring

Abstract Background Transplantation of mesenchymal stem cells (MSC) has been proposed to improve wound healing. However, as these cells only transiently survive in the implantation site, the mechanisms underlying this beneficial healing response are associated with restorative paracrine effects of MSC matricellular factors on resident stromal cells. However, this requires that the recipient has a robust reservoir of viable cells. Here, we examine the influence of MSCs on the behavior of cotransplanted fibroblasts, in a manner to provide augmented cellular reserve to debilitated individuals, specifically focusing on matrix remodeling following in-vivo wounding. Methods Using a Hylan-A dermal filler hydrogel containing collagen I and tenascin-C for delivery and increased survival of transplanted cells, we find that cotransplantation of MSCs with fibroblasts reduces scarring. Results Transplanted xenogeneic MSCs augmented fibroblast proliferation, migration, and extracellular matrix deposition critical for wound closure, and reduced inflammation following wounding. MSCs also corrected matrix remodeling by CXCR3-deficient fibroblasts which otherwise led to hypertrophic scarring. This effect was superior to MSC or fibroblast transplantation alone. Conclusions Taken together, these data suggest that MSCs, even if eventually rejected, transplanted with fibroblasts normalize matrix regeneration during healing. The current study provides insight into cellular therapies as a viable method for antifibrotic treatment and demonstrates that even transiently engrafted cells can have a long-term impact via matrix modulation and education of other tissue cells

Prediction of severity and subtype of fibrosing disease using model informed by inflammation and extracellular matrix gene index.

Fibrosis is a chronic disease with heterogeneous clinical presentation, rate of progression, and occurrence of comorbidities. Systemic sclerosis (scleroderma, SSc) is a rare rheumatic autoimmune disease that encompasses several aspects of fibrosis, including highly variable fibrotic manifestation and rate of progression. The development of effective treatments is limited by these variabilities. The fibrotic response is characterized by both chronic inflammation and extracellular remodeling. Therefore, there is a need for improved understanding of which inflammation-related genes contribute to the ongoing turnover of extracellular matrix that accompanies disease. We have developed a multi-tiered method using Naïve Bayes modeling that is capable of predicting level of disease and clinical assessment of patients based on expression of a curated 60-gene panel that profiles inflammation and extracellular matrix production in the fibrotic disease state. Our novel modeling design, incorporating global and parametric-based methods, was highly accurate in distinguishing between severity groups, highlighting the importance of these genes in disease. We refined this gene set to a 12-gene index that can accurately identify SSc patient disease state subsets and informs knowledge of the central regulatory pathways in disease progression

Novel classification for global gene signature model for predicting severity of systemic sclerosis

<div><p>Progression of systemic scleroderma (SSc), a chronic connective tissue disease that causes a fibrotic phenotype, is highly heterogeneous amongst patients and difficult to accurately diagnose. To meet this clinical need, we developed a novel three-layer classification model, which analyses gene expression profiles from SSc skin biopsies to diagnose SSc severity. Two SSc skin biopsy microarray datasets were obtained from Gene Expression Omnibus. The skin scores obtained from the original papers were used to further categorize the data into subgroups of low (<18) and high (≥18) severity. Data was pre-processed for normalization, background correction, centering and scaling. A two-layered cross-validation scheme was employed to objectively evaluate the performance of classification models of unobserved data. Three classification models were used: support vector machine, random forest, and naive Bayes in combination with feature selection methods to improve performance accuracy. For both input datasets, random forest classifier combined with correlation-based feature selection (CFS) method and naive Bayes combined with CFS or support vector machine based recursive feature elimination method yielded the best results. Additionally, we performed a principal component analysis to show that low and high severity groups are readily separable by gene expression signatures. Ultimately, we found that our novel classification prediction model produced global gene signatures that significantly correlated with skin scores. This study represents the first report comparing the performance of various classification prediction models for gene signatures from SSc patients, using current clinical diagnostic factors. In summary, our three-classification model system is a powerful tool for elucidating gene signatures from SSc skin biopsies and can also be used to develop a prognostic gene signature for SSc and other fibrotic disorders.</p></div

Summary of patient information for microarray biopsy samples used in models.

<p>Summary of patient information for microarray biopsy samples used in models.</p

Principal component analyses (PCA) of gene expression separation between low and high severity groups.

<p>Results based on CFS feature selection method are shown in A (Dataset 1) and B (Dataset 2). Results based on SVM-RFE feature selection method are shown in C (Dataset 1) and D (Dataset 2).</p