1. Cell Biology
  2. Medicine
Download icon

Senotherapeutic drugs for human intervertebral disc degeneration and low back pain

  1. Hosni Cherif
  2. Daniel G Bisson  Is a corresponding author
  3. Matthew Mannarino  Is a corresponding author
  4. Oded Rabau  Is a corresponding author
  5. Jean A Ouellet
  6. Lisbet Haglund  Is a corresponding author
  1. Orthopaedic Research Lab, Department of Surgery, McGill University and the Research Institute of the McGill University Health Centre, Canada
  2. McGill Scoliosis and Spine Group, Department of Surgery, McGill University and the Research Institute of the McGill University Health Centre, Canada
  3. Shriner’s Hospital for Children, 1003 Decarie Blvd, Canada
Research Article
  • Cited 0
  • Views 483
  • Annotations
Cite this article as: eLife 2020;9:e54693 doi: 10.7554/eLife.54693

Abstract

Cellular senescence is a contributor to intervertebral disc (IVD) degeneration and low back pain. Here, we found that RG-7112, a potent mouse double-minute two protein inhibitor, selectively kills senescent IVD cells through apoptosis. Gene expression pathway analysis was used to compare the functional networks of genes affected by RG-7112, a pure synthetic senolytic with o-Vanillin a natural and anti-inflammatory senolytic. Both affected a functional gene network related to cell death and survival. O-Vanillin also affected networks related to cell cycle progression as well as connective tissue development and function. Both senolytics effectively decreased the senescence-associated secretory phenotype (SASP) of IVD cells. Furthermore, bioavailability and efficacy were verified ex vivo in the physiological environment of degenerating intact human discs where a single dose improved disc matrix homeostasis. Matrix improvement correlated with a reduction in senescent cells and SASP, supporting a translational potential of targeting senescent cells as a therapeutic intervention.

eLife digest

Pain in the lower back affects about four in five people during their lifetime. Over time, the discs that provide cushioning between the vertebrae of the spine can degenerate, which can be one of the major causes of lower back pain.

It has been shown that when the cells of these discs are exposed to different stress factors, they stop growing and become irreversibly dormant. Such ‘senescent’ cells release a range of proteins and small molecules that lead to painful inflammation and further degeneration of the discs. Moreover, it is thought that a high number of senescent cells may be linked to other degenerative diseases such as arthritis.

Current treatments can only reduce the severity of the symptoms, but they cannot prevent the degeneration from progressing. Now, Cherif et al. set out to test the effects of two different compounds on human disc cells grown in the laboratory. One of the molecules studied, RG-7112, is a synthetic drug that has been approved for safety by the US Food and Drug Administration and has been shown to remove senescent cells. The other, o-Vanillin, is a natural compound that has anti-inflammatory and anti-senescence properties.

The results showed that both compounds were able to trigger changes to that helped new, healthy cells to grow and at the same time kill senescent cells. They also reduced the production of molecules linked to inflammation and pain.

Further analyses revealed that the compounds were able to strengthen the fibrous matrix that surrounds and supports the discs. Cherif et al. hope that this could form the basis for a new family of drugs for back pain to slow the degeneration of the discs and reduce pain. This may also have benefits for other similar degenerative diseases caused by cell senescence, such as arthritis.

Introduction

Low back pain is a global health problem that is experienced by ~80% of individuals at some point in their lifetime (Vos et al., 2012). This problem is the number one single cause of years lived with disability with enormous personal and health system related costs (Institute of Medicine (US) Committee on Advancing Pain Research, Care, and Education, 2016; Hartvigsen et al., 2018). Intervertebral disc (IVD) degeneration is a major factor contributing to low back pain (Vos et al., 2012; Adams and Hutton, 1983; Vergroesen et al., 2015). The cellular pathogenesis of IVD degeneration and the mechanisms leading to pain are not fully understood. One novel approach to treat painful degeneration is to target cellular senescence, a state of irreversible growth arrest occurring in response to cellular stress (Tchkonia et al., 2013). Stress-induced premature senescence is caused by factors such as oxidative and genotoxic stresses (Toussaint et al., 2000; Campisi and d'Adda di Fagagna, 2007). Increasing evidence suggests that accumulation of senescent cells during tissue degeneration contributes directly to initiation and development of musculoskeletal degenerative diseases like osteoarthritis (Jeon et al., 2017) and IVD degeneration (Le Maitre et al., 2007; Feng et al., 2016; Wang et al., 2016; Patil et al., 2018; Cherif et al., 2019). Senescent cells secrete a range of cytokines, chemokines, growth factors, and proteases termed as the senescence-associated secretory phenotype (SASP) (Herbig et al., 2006; Kuilman et al., 2008; Coppé et al., 2008; Xu et al., 2015). These SASP factors are suggested to further induce senescence in a paracrine manner (Acosta et al., 2013), to promote matrix catabolism and sterile inflammation in IVDs, thereby accelerating the degenerative process (Parrinello et al., 2005; Tominaga, 2015). Elimination of senescent cells enhances disc tissue homeostasis in genetically modified progeroid Ercc1−/Δ22 and p16‐3MR (Patil et al., 2019) mice suggesting that senotherapeutic drugs have great potential to treat low back pain resulting from IVD degeneration. The effect could potentially be mediated by apoptotic (senoptosis) or nonapoptotic (senolysis) mechanisms (Schmitt, 2017; Soto-Gamez and Demaria, 2017; Kirkland et al., 2017; Niedernhofer and Robbins, 2018) or by modulating the SASP, indirectly suppressing senescence (senomorphics) (Zhu et al., 2015; Soto-Gamez and Demaria, 2017; Kirkland et al., 2017; Childs et al., 2017). Further, interest is growing toward the use of natural senotherapeutic compounds such as quercetin, fisetin and piperlongumine, curcumin and o-Vanillin (Cherif et al., 2019; Li et al., 2019); their key advantages being low toxicity and great potential to be translated into clinical applications.

Cell-cycle arrest of senescent disc cells is mainly mediated by the two pathways: p53-p21-Rb and p16-Rb. During disc degeneration, both pathways are activated simultaneously to induce senescence (Feng et al., 2016). The FDA-approved drug RG-7112 (RO5045337) (Weber, 2010; Laberge et al., 2018) is a highly potent and selective MDM2 antagonist (Weber, 2010; Tovar et al., 2013) that restores the physiological activity of p53. RG-7112 is the first nutlin family member to be assessed clinically (Weber, 2010; Laberge et al., 2018) showing evidence of acceptable safety (Ray-Coquard et al., 2012; Constantinidou et al., 2012). RG-7112 was reported to selectively kill senescent lung fibroblasts (IMR90) where senescence was induced by ionizing radiation (IR) (Weber, 2010; Laberge et al., 2018). The natural compound o-Vanillin, known for its antioxidant and anti-inflammatory effects (Santosh Kumar et al., 2002; Oliveira et al., 2014; Shah et al., 2019; Marton et al., 2016), has recently been described for its senotherapeutic activity in human IVD cells (Cherif et al., 2019).

One caveat in the field is that there are currently no large bipedal animal models that mimic naturally occurring IVD degeneration and human genetic background (Alini et al., 2008; Jin et al., 2018). One solution to this caveat is the use of an intact disc culture model (Gawri et al., 2011; Krock et al., 2014; Gantenbein et al., 2015; Rosenzweig et al., 2016) of human IVDs to test and develop new senotherapeutic drugs suitable for human disc degeneration.

Here, we utilized in vitro and ex vivo models, to assess the senotherapeutic effects of RG-7112 and o-Vanillin on naturally occurring senescent cells in degenerating human IVDs. We have previously shown that o-Vanillin has senolytic effects on senescent human IVD cells. o-Vanillin is a natural compound that in addition to its senolytic effect has antioxidant and anti-inflammatory properties. We compared the effect of o-Vanillin to RG-7112, a pure senolytic drug, that we found to efficiently kill senescent annulus fibrosus (AF) and nucleus pulposus (NP) cells. The objective was to evaluate if o-Vanillin with its dual function could further reduces inflammatory factors released by non-senescent cells thus enhancing the therapeutic effect. We also aimed to establish if one or both drugs could reach and kill senescent IVD cells in their native environment of intact human disc with naturally occurring degeneration.

Results

Determination of the senotherapeutic activity of RG-7112

We aimed to compare the senotherapeutic activity of the natural senolytic o-Vanillin a natural senolytics with antioxidant and antiinflammatory properties with a pure synthetic senolytic drug. We chose the commercially available FDA approved drug RG-7112 as a candidate. A concentration of 5 μM RG-7112, shown as a safe and effective dose for human IMR90 lung fibroblasts, was used to evaluate its effect on senescent and non-senescent human IVD cells (Weber, 2010; Laberge et al., 2018). Pellet cultures of IVD cells from degenerate NP and AF regions were exposed to RG-7112 for 4 days, the drug was removed, and the treated pellets were maintained for 21 days in standard media. Then, evaluated for cytotoxicity, senolytic and therapeutic activity. No cytotoxicity was observed following treatment. In contrast, both NP (9.87%; p<0.05) and AF (11.87%; p<0.05) cells showed a significant increase in metabolic activity (Figure 1—figure supplement 1A). Pellet cultures were treated as before and the presence of p16Ink4a, Ki-67 and caspase-3 were analyzed after 21 days (Figure 1A). RG-7112 significantly decreased the percentage of p16Ink4a positive NP (11.48%; p<0.001) and AF (20.03%; p<0.0001) cells compared to untreated control cultures (Figure 1B). Similarly, RG-7112 significantly increased the percentage of Ki-67-positive NP (23.07%; p<0.05) and AF (34.92%; p<0.0001) cells (Figure 1C). Caspase-3-positive AF cells increased significantly (17.88%; p<0.01) while a non-significant increase (2.94%; p=0.51) was observed in NP cells following treatment (Figure 1D). Caspase 3 and caspase 3/7 activity was confirmed by fluorescence microscopy and activity assays, respectively (Figure 1 —figure supplement 1B-C and d-f). Confocal immunofluorescence confirmed co-localization of p16Ink4a and caspase-3, whereas proliferating (Ki-67-positive) cells did not co-localize with p16Ink4a (Figure 1 —figure supplement 1E (a–c). RG-7112 selectively increased apoptosis (Figure 1 —figure supplement 1E) while it maintained comparable metabolic activity (Figure 1 —figure supplement 1F) in NP cells from both degenerate and non-degenerate IVDs. Finally, we investigated proteoglycan synthesis using the DMMB assay following treatment. A significant increase in proteoglycan release in conditioned media was observed after 14 days of treatment (Figure 1 —figure supplement 1G).

Figure 1 with 1 supplement see all
RG-7112 treatment of IVD pellet cultures.

(A) Representative images indicating how we deemed positive and negative p16INK4a (a), Ki-67 (d), and caspase-3 (g) staining. (b, e and h) Magnified images of (a, d and g). Arrow heads indicate positive (green) and negative (red) staining. (c, f and i) no secondary antibody control photomicrographs. Quantification of (B) p16INK4a (n = 7), (C) Ki-67, and (D) caspase-3 expression, (n = 8). Scale bars: 20 μm in (A). The cells were from degenerating IVDs as indicated in Table 2. Values are presented as mean ± SEM in (B–D). * Indicates a significant difference assessed by the two-tailed Student's t-test: p<0.05; **p<0.01 and ****p<0.0001.

Gene expression analysis revealed the potential pathways by which RG-7112 and o-Vanillin mediate their senolytic effect

To identify molecular pathways affected by RG-7112 and o-Vanillin in human disc cells, gene expression analysis of 96 selected cellular senescence genes was performed and 91 of the 96 genes were expressed at a detectable level. Pellet cultures were treated with RG-7112 (5 μM) or o-Vanillin (100 μM) for 4 days. The relationship between the differentially up- and downregulated genes are depicted in Figure 2A. Compared with the control group, the number of differentially expressed genes for o-Vanillin were 40 (30 upregulated and 10 downregulated) and eight for RG-7112 (6 upregulated and two downregulated). In upregulated DEGs, three are common to both drugs, mitogen-activated protein kinase 14 (MAPK14), cell division cycle 25 c (CDC25c) and cyclin dependant kinase 2D (CDKN2D or p19ARF). No down-regulated DEGs were common to the drugs. Although cyclin B1 (CCNB1) is significantly expressed following the treatment with the two compounds, it is upregulated in RG-7112 and downregulated in o-Vanillin (Figure 2A). Of the 91 genes identified, 44 were differentially expressed with a p<0.05 in one or both treatments (Figure 2B). The 47 genes that did not meet the significance criteria of p<0.05 are shown in (Figure 2—figure supplement 1A). Next, we compared gene expression profiles of the o-Vanillin group with the RG-7112 group. Of the 91 evaluated, only eight genes were significantly affected by RG-7112 treatment, four were common to o-Vanillin (MAPK14, CCNB1, CDC25c and CDKN2D (p19ARF)), and four were exclusive to RG-7112 (MDM2, CDKN1A (p21ARF), E2F1 and RBL1). In contrast, 40 genes were significantly affected by o-Vanillin treatment. Cell cycle and senescence genes were significantly downregulated including cyclin dependent kinase 2A (CDK2A or p16Ink4a), cyclin dependent kinase 2C (p18ARF), Cyclin A2, CCNB1, CDC25c, Vimentin, Mitogen-activated protein kinase 6 (MAPK6) and Checkpoint kinase 1(CHEK1) (Figure 2C). Examples of apoptotic and proliferative genes significantly upregulated by o-Vanillin include B-cell lymphoma 2 (Bcl-2), Bcl-2-like 1, Bcl-2-like 2, interferon regulatory factor 5 (IRF5), IRF7 and receptor tyrosine-protein kinase ERBB2. Interestingly, MAPK14, CDC25c and CDKN2D (p19ARF) were the only genes significantly upregulated after the treatment with both compounds, while CCNB1 showed opposite regulation patterns. In conjunction, apoptotic pathways are significantly upregulated to kill senescent cells while proliferation related pathways are activated in non-senescent cells. Detailed fold change difference of differentially expressed genes and respective p value are included in Supplementary file 3 (a-d).

Figure 2 with 1 supplement see all
Differentially expressed senescence related genes in NP pellets.

(A) Venn diagrams of the differentially up and downregulated genes among the different groups. O-Vanillin, RG-7112-treated NP cells in pellets culture. For Upregulated genes odds ratio (OR) = 2.13 and p=0.39; for Downregulated genes: OR = 0 and p=1. (B) Heatmap of the top 44 over and under expressed genes in control (CTRL), RG-7112 and o-Vanillin-treated NP cells. All genes shown were first normalized to the housekeeping gene GAPDH. Data shown are relative to the calculated Z scores across the samples (see Materials and methods) and ranked by significance adjusted to p<0.05. Red represents relatively high levels of expression; blue represents relatively low levels of expression. Significantly differentially expressed genes are indicated with green lines for o-Vanillin and in orange lines for RG-7112. Each column represents one individual (for a total of n = 5 per group) and each row represents expression of a single gene. Donor ID and gender are indicated for each subject. (C) Volcano plots of mRNA expression of o-Vanillin and RG-7112 treated NP pellets: Plotted along the x-axis is the mean of log2 fold-change, along the y-axis the negative log10 of the p-values. Blue circles refer to downregulated genes, red circles refer to upregulated genes and grey circles to non-DEGs in o-Vanillin and RG-7112-treated NP pellets. The horizontal grey line is the negative logarithm of the t-test-adjusted p-value threshold (-log10 of p=0.05). (D) IPA diagrams of differentially expressed genes in RG-7112 and (E–G) o-Vanillin-treated NP pellets within the selected set of 91 genes. Direct and indirect interactions are shown by solid lines and dashed lines respectively. Green indicates gene downregulation; red depicts upregulation and molecules found by the data mining tools of IPA (build tools) are shown in gray. Color intensity represents the average of log2 fold change with brighter colors representing a more significant difference between treated and controls. Symbols for each molecule are presented according to molecular functions and type of interactions. Functional assignations attributed by IPA software. Significant difference set at p<0.05 was assessed by repeated measures Analysis of Variance (ANOVA) with Turkey’s post hoc test for multiple pairwise comparison in (B–C) and Fisher’s exact test in (A, D–G). The cells were from degenerating IVDs as indicated in Table 2.

To gain a further insight into a potential mechanism of action, the differentially expressed genes were mapped to networks in the Ingenuity Pathways Analysis (IPA) database. The scores take into account the number of focus genes and the size of the network to approximate the relevance of the network to the original list of focus genes. The IPA core analysis features allowed identification and determination of one network connecting the cell cycle, cell death and survival, connective tissue development and function pathways (RG-7112, network 1) of NP cells treated with RG-7112 (Figure 2D). In the o-Vanillin-treated pellets, the highest-scoring network revealed a significant link with cell death and survival, neurological disease and organismal injury and abnormalities (o-Vanillin, network 1) (Figure 2E). Furthermore, connective tissue development and function, cell cycle (o-Vanillin, network 2), cancer, cellular movement (o-Vanillin, network 3), were shown to be influenced in the other two networks (Figure 2F–G). All networks were identified and ranked by the score of the calculated p-value of the IPA assay within the selected set of 91 genes, the scores and molecules used to order these networks are shown in Supplementary file 4 (a-b). These results confirmed the expected mode of action for RG-7112 and provide new insights to the predicted pathways involved in senescence and the responses to treatment with o-Vanillin.

RG-7112 and o-Vanillin reduced inflammatory factors

Transcriptomic results following the treatment with the two senolytics predicted the activation of apoptotic (in senescent cells) and proliferative (in non-senescent cells) pathways which suggest a reduction in the senescence burden likeley affecting SASP factor release. To verify an effect on SASP, media of untreated NP cell pellets, RG-7112 and o-Vanillin-treated pellets were used. The media collected were from the same donors used for gene expression analysis in Figure 2. Samples were analyzed using an antibody array that simultaneously screen 80 factors. Out of the 80 factors present, 50 were detected in at least four donors and were included in further analysis (Figure 3 —figure supplement 1A). The 25 most affected SASP factors are divided into four classes: Cytokines (Figure 3A–a), chemokines CC (Figure 3A–b), chemokines CXC (Figure 3A–c), growth and neurotrophic factors (Figure 3A–d). The most reduced factors following RG-7112 treatment compared with control were for cytokines (TNF-α; −78.05 ± 4.69%) (p=0.71), CC-chemokines (CCL26; −66.69 ± 7.09%) (p=0.93), CXC-chemokines (CXCL13; −29.02 ± 17.26%) (p=0.68), growth and neurotrophic factors (HGF; −65.24 ± 5.9%) (p=0.87). The top four factors decreased by o-Vanillin treatment were: IL-7 (−60,37 ± 11.46%) (p=0.5), CCL26 (−40,14 ± 3.41%) (p=0.99), CXCL10 (−18,11 ± 6,15%) (p=0.62) and HGF (−52,42 ± 5,45%) (p=0.96) (Figure 3A (a-d)). To better visualize the overall effect of the two drugs on SASPs release, a scatter plot of average changes was generated demonstrating a similar overall effect of the two senolytics (Figure 3A–e). The trends for all 80 factors are visualized in (Figure 3—figure supplement 1B-C). The semi-quantitative cytokine array results were used to select 19 factor that were quantified with the Luminex assay. The 19 factors were selected based on the following criteria: only factors detected in all conditions and in at least four donors were chosen. These factors were selected to represent all the SASP classes and to include the most variably expressed cytokines, chemokines and growth factors. The 19 factors selected for further investigation were (INF-γ, TNF-α, IL1-α, IL1-b, IL-6, IL-8, CCL5, CCL7, CCL11, CCL24, CCL26, CXCL1, CXCL5, CXCL9, CXCL10, CXCL11, CX3CL1, Angiogenin and VEGF-A). The concentrations were measured and expression heatmap showed an overall decrease in the level of proinflammatory factors from both RG-7112 and o-Vanillin when compared to non-treated cultures. (Figure 3B). The levels of INF-γ, IL-6, IL-8 and CCL24 were significantly decreased following both treatments whereas the decrease in CCL7, CXCL1, CXCL5, CXCL9, CXCL10 and VEGF significance was reached only following o-Vanillin treatment (Figure 3C). However, significance was not reached for TNF-α, IL1-α, IL1-β, CCL5, CCL11, CCL26, CXCL11, CX3CL1 and Angiogenin in treated cultures compared to untreated cultures (Figure 3—figure supplement 1C). Although, o-Vanillin reduced more SASP factors than RG-7112 at a significant level when compared to untreated cultures, there was no significant difference in concentrations between o-Vanillin and RG-7112-treated cultures, validating the similar overall effect the two drugs have on SASP factor release observed by cytokine array in Figure 3A–f. These results demonstrate that both RG-7112 and o-Vanillin decreased the overall inflammatory environment in pellet cultures of degenerating tissue and suggest a broader anti-inflammatory effect of o-Vanillin treatment.

Figure 3 with 1 supplement see all
Profile of SASP factors released from pellet cultures following senolytic treatment.

(A) Culture media was analyzed by a RayBio Human Cytokine Array. Relative mean densitometry units of the 80 factors were normalized to untreated controls with the most 25 downregulated SASP factors presented: cytokines (A–a), CC-chemokines (A–b), CXC-chemokines (A–c), growth and neurotrophic factors (A–d). Scatter plot showing the distribution in average change of 50 cytokines quantified using cytokine array (A–e). (B) Heatmap displaying quantification of 19 selected cytokines (19-plex Luminex array). Each column represents one individual (n = 5). The rows represent expression of a single protein. Data shown are log2 (fold change) relative to the average expression level in each condition. Donors ID and gender are indicated for each subject. (C) Significantly downregulated factors are presented as mean fold difference ± SEM; (n = 5). Culture media was collected from the same NP cells used in Figure 2. *Indicates significant difference assessed by repeated measures Analysis of Variance (ANOVA) with Turkey’s post hoc test for multiple pairwise comparison: p<0.05 and **indicates p<0.01.

Senotherapeutic treatment improves disc matrix and reduces senescent cells in intact human disc

RG-7112 or o-Vanillin was injected into the central region of intact human IVDs to verify that the drugs can reach and kill the target cells in native tissue. Discs were pre-cultured for 4–6 days. The IVDs were treated with a single injection of vehicle, o-Vanillin, or RG-7112, delivered to the centre of the NP and were cultured for another 28 days as outlined in Figure 4A. An adapted MRI protocol (Rosenzweig et al., 2018) was used to assess T1ρ-weighted MRI signal that directly correlates with proteoglycan content (Rosenzweig et al., 2018; Mulligan, 2015). The method used to assess uniform NP regions of interest is shown in (Figure 2—figure supplement 1B-D). The same NP regions of interest were scanned in the same orientation pre- and post-treatment. Increased T1ρ values were found in RG-7112 and o-Vanillin-treated discs while vehicle-treated discs displayed decreased T1ρ values. (Figure 4B (a-d)) Quantification of pre- versus post- treatment intensity values showed a non-significant decrease of 13.2 ± 4.7% (p=0.058) in vehicle-treated control IVDs. In RG-7112 treated discs, there was a significant 6.8 ± 1.5% (p=0.024) increase in the T1ρ value post-treatment. Discs that were treated with o-Vanillin, displayed a significant increase of 11.1 ± 1.2% (p=0.001) in T1ρ values following treatment (Figure 4C). The red dye safranin-O, binds negatively charged molecules, which are primarily represented by proteoglycan in the NP. Histological evaluation post-treatment using Safranin-O/fast green staining showed strong (intense red) staining in treated discs (Figure 4D). Finally, p16Ink4a and Ki-67 immunohistochemistry were performed to verify senolytic activity of the two compounds (Figure 4E). Indeed, immunohistochemical assessment of p16Ink4a showed a significant decrease in the number of senescent cells of 17.38 ± 1.6% (p=0.0017) in RG-7112 and 22.65 ± 3.6% (p=0.008) in o-Vanillin-treated IVDs compared with vehicle-treated control discs (Figure 4F). Quantification of Ki-67 staining showed a non-significant increase in both RG-7112 and o-Vanillin 3.05 ± 3.4% (p=0.4) treated IVDs (Figure 4G). The data suggest that both senolytics can reach and kill naturally occurring senescent human IVDs cells situated in their native environment and at the same time promote tissue repair and regeneration.

RG-7112 and o-Vanillin effects in ex vivo human IVD culture.

(A) Schematic of the ex vivo organ culture experiment. Lumbar spines from organ donors were assessed radiographically for signs of degeneration. Three discs per experiment were isolated from the same spine, cultured for 4–6 days then scanned with MRI and injected with vehicle, o-Vanillin or RG-7112. Discs were then cultured for an additional 28 days, with media changes every 3–4 days. The discs were scanned by MRI again at day 28. Media and tissues were used for SASP factor release and histology respectively. (B) Representative images of mid-axial T1ρ slices pre-treatment (a–b, e–f, i–j) and the same location post treatment (c–d, g–h, k–l) with vehicle (CTRL), RG-7112 (5 μM) or o-Vanillin (100 μM). The heatmap correlates the red color with the highest and the blue color with the lowest T1ρ values. (C) Quantification for NP regions with the graph showing percentage change in T1ρ values post- compared to the pre-treatment scans. (D) Representative safranin O/fast green staining of histological sections. (E) Representative images of disc sections stained with antibodies against p16INK4a and Ki-67. Quantification of (F) p16INK4a and (G) Ki-67 expression. Scale bars = 150 µm in 4D, 25 µm in 4E (p16 INK4a) and 50 µm in 4E (ki-67); Error bars represent mean ± SEM, Statistical significance was assessed by two-tailed Student's t-test to compare pre and post disc groups (C) and by repeated measures Analysis of Variance (ANOVA) with Turkey’s post hoc test for multiple pairwise comparison in (F and G). *Indicates p<0.05 and **indicates p<0.01, n = 4 for each condition. The tissues were from degenerating IVDs as indicated in Tables 2 and 3.

RG-7112 and o-Vanillin decreased SASP factor release from intact human IVDs

Culture media from treated and untreated discs (in Figure 4) was analyzed using cytokine arrays and were compared to their respective pre-treatment media. Interestingly, a single injection with RG-7112 strongly decreased secretion of several proinflammatory cytokines and chemokines including IL-7 (−60.63% (p=0.01)), IL-6 (−59.39% (p=0.14)), CXCL1 (−36.72% (p=0.26)), GRO-abg (−32.84% (p=0.21)) and CCL24 (−30.57% (p=0.15)). o-Vanillin also strongly decreased IL-7 (−83.37% (p=0.09)), CXCL1 (−65.45% (p=0.12)), IL-6 (−58.68% (p=0.10)), CXCL6 (−52.47% (p=0.009)), IGFBP-2 (−44.51% (p=0.15)), GRO-abg (−41.83% (p=0.03)) and CCL2 (−39.91% (p=0.05)). Both compounds significantly decreased IL-8 by 18.72% (p=0.004) for RG-7112 and by 11.75% (p=0.04) for o-Vanillin. We also detected a moderate increase of CXCL9, CCL22, NT3 and EGF for o-Vanillin and only in CCL2 and OPN for RG-7112-treated media (Figure 5A). The level of the SASP factors released from vehicle-treated discs showed an increase in IL-6 (530.85% (p=0.15)), CXCL6 (196.89% (p=0.07)), CXCL1 (99.83% (p=0.18)), OPN (92.36% (p=0.01)), CCL2 (53.37% (p=0.18)), IGFBP-2 (39.26% (p=0.13)), IL-7 (23.52% (p=0.53), GRO-abg, (14.25% (p=0.91)), CCL24 (9.64% (p=0.97)), EGF (3.18% (p=0.85)), TNSF-14 (2.09% (p=0.66)), IL-8 (1.61% (p=0.82)) and a decrease in CXCL9 (−35.23% (p=0.26)), CCL22 (−11.95% (p=0.56)) and NT3 (−13.46% (p=0.25))(Figure 5B). The complete set of factors from all groups are presented in Figure 5—figure supplement 1 (A-C).

Figure 5 with 1 supplement see all
Profile of SASP factors released from ex vivo human IVDs cultures following senolytic treatment.

Culture media was analyzed by a RayBio Human Cytokine Array. Relative mean densitometry units of the 80 factors were normalized to pre-treatment media of the same IVD. The percentage change (post/pre) of the 15 most affected factors are shown for RG-7112 and o-Vanillin (A) and vehicle (B) treated discs. (C) Heatmap displaying quantification of 19 selected cytokines (19-plex Luminex array). Each column represents one individual and each row represent expression of a single protein. Data shown are log2 (fold change) in pre- or post-treated disc media relative to their respective expression level average. (D) Nine analytes (INF-γ, TNF-α, CCL11, CCL24, CXCL1, CXCL9, CXCL10, Angiogenin and VEGF) displayed statistically significant differences when measured in post compared to pretreated disc media. Error bars represent mean ± SEM. Statistical significance when comparing pre and post discs groups was assessed by two-tailed Student's t-test (A, B and D): * Indicates p<0.05 and **indicates p<0.01. Data was analyzed by repeated measures Analysis of Variance (ANOVA) with Turkey’s post hoc test for multiple pairwise comparison (D) where # Indicates significant difference (p<0.05) between treated and untreated groups, (n = 4). The analyzed media were collected from same donors used in Figure 4.

Luminex quantification of the same 19 selected SASP factors that were analyzed in pellet cultures showed an overall decrease in inflammatory mediators in culture media from both RG-7112 or o-Vanillin-treated discs compared with that of vehicle (Figure 5C). All factors decreased in RG-7112 and o-Vanillin-treated discs (Figure 5D and Figure 5—figure supplement 1D). Six factors in RG-7112 and five in o-Vanillin-treated discs were significantly decreased in the post-treatment media compared with their respective pre-treatment media. For RG-7112, these proteins were TNF-α (mean 12.4 pg/ml in pre, 6.8 pg/ml in post, p=0.03), CCL11 (mean 13.1 pg/ml in pre, 7.8 pg/ml in post, p=0.009), CCL24 (mean 201.7 pg/ml in pre, 101.1 pg/ml in post, p=0.04), CXCL1 (mean 261.8 pg/ml in pre, 148.2 pg/ml in post, p=0.001), CXCL10 (mean 197.1 pg/ml in pre, 158.7 pg/ml in post, p=0.04) and Angiogenin (mean 51.7 pg/ml in pre, 37.6 pg/ml in post, p=0.02). o-Vanillin significantly reduced the levels of INF-γ (mean 1.6 pg/ml in pre, 1.1 pg/ml in post, p=0.04), CCL24 (mean 232.7 pg/ml in pre, 73.7 pg/ml in post, p=0.02), CXCL1 (mean 313.9 pg/ml in pre, 130.2 pg/ml in post, p=0.01), Angiogenin (mean 47.4 pg/ml in pre, 35.5 pg/ml in post, p=0.02) and VEGF-A (mean 24802.1 pg/ml in pre, 20905.5 pg/ml in post, p=0.04) (Figure 5D). Finally, we found a significant increase in the release of four factors in vehicle treated discs: INF-γ (mean 1 pg/ml in pre, 1.45 pg/ml in post, p=0.02), CCL11 (mean 7 pg/ml in pre, 19.2 pg/ml in post, p=0.04), CXCL1 (mean 104.3 pg/ml in pre, 476.9 pg/ml in post, p=0.03) and CXCL9 (mean 60.4 pg/ml in pre, 162.4 pg/ml in post, p=0.01).

Discussion

We have previously demonstrated that curcumin and its metabolite o-Vanillin have senolytic activity toward senescent human IVD cells (Cherif et al., 2019). Treatment with curcumin and o-Vanillin reduced SASP factors released and enhanced matrix synthesis in a pellet culture model (Cherif et al., 2019). In this study, we aimed to compare the effects of a synthetic pure senolytic compound with a natural anti-inflammatory and senolytic compound, to determine a potential enhanced therapeutic effect with the latter. We used o-Vanillin instead of curcumin, as o-Vanillin has higher specificity and better bioavailability. For a synthetic and pure senolytic we chose RG-7112, a drug with documented senotherapeutic effects in fibroblasts (Weber, 2010; Laberge et al., 2018). Cells from degenerate discs or intact degenerate discs that correspond with the tissue targeted for treatment were used. Cells from degenrating tissue was used as it would be difficult to evaluate an effect of removing senescent cells in non-degenerate discs since they have have very few senescent cells. Here, we demonstrated that RG-7112 has a potent senotherapeutic and a strong proliferative effect on human IVD cells in vitro. Metabolic activity and extracellular matrix production were also increased in treated cultures. We verified that treatment specifically targets senescent cells by activating the caspase-3 apoptotic pathway. This is similar to the effect of UBX0101, an analogue of RG-7112 that triggers apoptosis of senescent chondrocytes in a murine osteoarthritis model (Jeon et al., 2017). Currently, phase I and II clinical trials are in progress to assess safety, tolerability and clinical effects of single dose (NCT03513016 and NCT04129944) and both single and repeated doses (NCT04229225) of intra-articular administration of UBX0101 in patients with moderate to severe painful knee osteoarthritis (van Deursen, 2014; Vassilev et al., 2004; Vu et al., 2013).

Cellular senescence can be induced by replicative senescence or stress-induced premature senescence (Wang et al., 2016). The pathway and resulting changes in the microenvironment surrounding the cells depends on inducing factor (Frolov and Dyson, 2004). In this study, we found gene expression modulation for a number of inflammatory and cell cycle genes following treatment with the two senolytics, o-Vanillin and RG-7112. We evaluated differential gene expression in 96 pre-selected genes to determine the mechanisms by which the compounds mediate their effects. Our data demonstrate that 91 of the genes were expressed at a detectable level and a significant effect was found on 50% of the expressed genes, supporting the senolytic activity of the two compounds in vitro. RG-7112 decreased gene expression of CDK1A and MDM2 and increased expression of the E2F1, RB factors, MAPK-14, CDK2D, CCNB1 and CDC25c. o-Vanillin modulated gene expression of 40 genes including upregulation of cell cycle genes such as CDK6, CDK2C, CDK2D, and CDC25c while expression levels of CDK2A, Cyclin A2, Cyclin D1 and CCNB1 were decreased.

Collectively, the data support the mechanistic action of RG-7112 to stabilize p53 and p21 by attenuating the MDM2-p53 interaction (Weber, 2010; Tovar et al., 2013; Henley and Dick, 2012; Che et al., 2020). Also, it confirms regulation of the RB–E2F1 pathway that releases E2F1 and activates genes involved in cell cycle regulation, DNA synthesis, and cell proliferation (Andreeff et al., 2016; Xu et al., 2018). Another example of a senolytic drug that interferes with the E3 ubiquitin ligase-MDM2-p53 mechanism is UBX0101, which triggers apoptosis of senescent cells in articular cartilage and synovium in a murine osteoarthritis model (Jeon et al., 2017). o-Vanillin treatment affected multiple pathways suggesting that o-Vanillin eliminates senescent cells both by apoptosis and non-apoptotic means. Recently, p16 deletion in NP cells from mouse discs with induced degeneration was shown to also upregulate the expression of cyclin-dependent kinases 4/6, phosphorylated retinoblastoma protein, and transcription factor E2F1/2 (Che et al., 2020).

Clinical trials with RG-7112 for cancer treatment were limited by the high incidence of hematological toxicities (Ray-Coquard et al., 2012; Jung et al., 2008). Here, we used a lower concentration (5 μM) and short exposure time of RG-7112, in contrast to the high and toxic doses used in cancer therapy (20–1400 mg/m2 administrated daily for 10 days). This may prevent these side effects in patients treated for IVD degeneration. This hypothesis was verified by the clinical study of the RG-7112 analogue UBX0101 in patients diagnosed with painful osteoarthritis of the knee. An administered dose of 4 mg was safe and well-tolerated, and it improved pain scores, reduced SASP factors and disease-related biomarkers after a single dose (Weber, 2010; Laberge et al., 2018). The activation of multiple pathways following o-Vanillin treatment and within the selected set of 91 genes implies that diverse biological processes are affected, which was also reported for other natural senolytics like Quercetin, Fisetin, and Dasatinib (Pezet and McMahon, 2006; Wang et al., 2008). Although it is difficult to determine how these natural senolytics eliminate senescent cells and drive their therapeutic effects, the activation of several mechanisms increases their capacity to target the heterogeneous cellular states acquired by senescent cells after the initial growth arrest (Frolov and Dyson, 2004).

We further sought to determine if the two senolytics had apparent effects on the SASP secretome. Conditioned media of pellet cultures was analyzed by cytokine array following treatment. Although donor variation was observed, notable changes between treatment groups were found indicating an overall decrease in the majority of the SASP factors analyzed. Similar decrease in the SASP factors was observed following p16 deletion in NP cells in mice with induced disc degeneration (Yousefzadeh et al., 2018). An antiinflammatory effect was expected for o-Vanillin from previous studies (Cherif et al., 2019; Santosh Kumar et al., 2002; Oliveira et al., 2014; Shah et al., 2019; Marton et al., 2016). However, our study documents, for the first time, antiinflammatory properties of RG-7112 in human IVD cells. We selected 19 factors from the initial screening associated with SASP and measured their concentrations using a Luminex immunoassay. In general, concentrations were decreased in response to treatment. RG-7112 significantly reduced the concentrations of IFN-γ, IL-6 and CCL24 while o-Vanillin in addition reduced factors IL-8, CCL7, CXCL1, CXCL5, CXCL9, CXCL10 and VEGF-A. These factors are known for their implication in painful IVD degeneration (Acosta and Gil, 2009; Acosta et al., 2008; Phillips et al., 2013) and for promoting senescence of surrounding cells (Coppé et al., 2010; Adams et al., 2015). For example, IL-8 and chemokines binding to the C-X-C motif chemokine receptors is needed for the establishment and maintenance of senescence. CCL7 expression has been reported to be concordant with IVD degeneration (Binch et al., 2014) and it has previously been described as a SASP factor involved in IVD cell senescence (Burke et al., 2002). Finally, the decrease of VEGF-A indicates a possible role of o-Vanillin to block the mechanism of neovascularization. VEGF and its receptors have been proposed to be closely correlated with inflammation, chronic back pain and accelerated IVD degeneration (Freemont et al., 1997; Lu et al., 2013; Peng et al., 2006; Daly et al., 2016; Gruber et al., 2009; Thompson et al., 1991). These results consolidate the senolytic effect of the two compounds and suggest a potential role in reducing degenerative factors in human discs with o-Vanillin having a possible stronger and broader antiinflammatory effect.

Animal models that replicate human disc pathology are limited due to the differences in anatomy, disc size, cell type, and loading (Jin et al., 2018). Moreover, notochord cells are retained longer in the majority of animal species (Grezella et al., 2018), further increasing the difference between humans and animals. Thus, we previously developed and validated an ex vivo intact human disc culture system to study the potential for biologic repair and regeneration (Gawri et al., 2011). Matrix differences were quantified pre- and post-treatment by T1ρ weighted MRI that directly correlates with proteoglycan content in the IVDs (Rosenzweig et al., 2016; Mulligan, 2015). When comparing the intensity in RG-7112 and o-Vanillin-treated IVDs, we observed an increased intensity post-treatment, while a decrease was observed in vehicle-treated control. This suggests that treatment with senolytics could increase proteoglycan content in human patients. It also validates our results from the pellet cultures where the proteoglycan content also increased significantly in the treated cultures. The improvement of T1ρ MRI values was validated by the consistent high levels of proteoglycan content in the treated IVDs. We further verified that the improved matrix was linked to removal of senescent cells. Indeed, both compounds significantly reduced the number of p16Ink4a-positive cells in treated discs. Moreover, we observed a slight but non-significant increase in proliferating cells. These findings correlate with the negative impact previously reported between the number of senescent cells and cell proliferation during IVD degeneration (Hwang et al., 2018). Furthermore, culture media from control discs, showed no change in cytokine release whereas both compounds decreased levels of IL-6, IL-7, IL-8, CXCL1, CXCL6, CCL2, CCL22, GROa/b/g, IGFBP-2, TNSF-14 and OPN. Moreover, we observed a significant increase of EGF in the media from o-Vanillin treated discs. The positive effects of EGF on proteoglycan synthesis were first reported by Thompson et al.; these effects were more pronounced in the NP (Zhu et al., 2017). Together the results demonstrate an overall decrease in SASP factors following treatment. The difference in SASP compounds affected by the two drugs could be explained by the difference in selectivity and the specificity of the drugs (Zhu et al., 2017; Zhu et al., 2016; Wilke et al., 2006; Thompson et al., 1990; Gawri et al., 2014).

Finally, we compared the SASP factors release in pellet and disc culture media. The common SASP factors that were downregulated include INF-γ, IL-6, CCL24, CXCL1, CXCL10 and Angiogenin. Interestingly, o-Vanillin also significantly reduced the levels of IL8, CCL7, CXCL5, CXCL9 and VEGF-A. This could be due to the effect on the neighboring non-senescent cells. Consistent with prior literature, this decrease highlights the antioxidant and antiinflammatory properties of o-Vanillin (Cherif et al., 2019; Santosh Kumar et al., 2002; Oliveira et al., 2014; Shah et al., 2019; Marton et al., 2016). Also, these decreases have been reported in several other natural compounds such as quercetin, fisetin, and piperlongumine (Wang et al., 2008; Thompson et al., 1990).

Our findings suggest that both o-Vanillin and RG-7112 have the potential to be translated to treatment of painful IVD degeneration. As well, this study can be used as a base for the development of additional senolytic agents for lower back pain.

Materials and methods

Key resources table
Reagent type
(species) or resource
DesignationSource or referenceIdentifiersAdditional
information
AntibodyRabbit polyclonal anti-caspase-3Sigma-AldrichC8487
RRID:AB_476884
IF (1:500)
IHC-Fr (1:500)
AntibodyRabbit polyclonal anti-Ki-67Novus BiologicalsNB500-170
RRID:AB_10001977
IF (1:300)
IHC-Fr (1:500) IHC-P (1:2000)
AntibodyMouse monoclonal Anti- p16Ink4aAbcamab54210
RRID:AB_2059963
IHC-P (1:500)
AntibodyPolyclonal Alexa Fluor 488 (Donkey anti-Rabbit)Invitrogen Thermo FisherA-21202 RRID:AB_141607IF (1:1000)
AntibodyPolyclonal Alexa Fluor 555 (Donkey anti-Mouse)Invitrogen Thermo FisherA-31570 RRID:AB_2536180IF (1:1000)
Antibody ArrayHuman Cytokine Antibody Array C5RayBiotech, IncAAH-CYT-5–2 RRID:AB_10185250
Chemical compound, drugo-VanillinSigma-Aldrich120804
Chemical compound, drugRG-7112Selleck ChemicalsS7030
Chemical compound, drugDMSOSigma-AldrichD8418
Commercial KitApoptosis detectionAbcamab176749 RRID:SCR_018379
Commercial KitCaspase 3/7 activityAAT Bioquest 13503Amplite Fluorimetric Catalog # 13503
Commercial KitqScript cDNA Synthesis KitQuanta Biosciences95047–500
Commercial KitGoat anti-Mouse and Rabbit specific HRP/DAB Detection IHC KitAbcamab64264
Commercial KitMouse monoclonal Anti- p16Ink4aRoche, Ventana LaboratoriesCINtec Plus (9531) RRID:SCR_018380Prediluted
Commercial KitMycoplasma PCR Detection KitZmTech ScientificM209001
Commercial AssayCustom Taqman (R) Gene Expression 96-Well Fast PlatesThermo Fisher4413255See Supplementary file 1 for each gene product ID
Commercial AssayLuminex immunoassay 19-PLEX plate - Human Cytokine PROCARTAPLEXLife TechnologiesMXMFXHX
SoftwareImageJImageJ http://imagej.nih.gov/ij/RRID:SCR_003070
SoftwareGraphPad Prism 8GraphPad Prism https://www.graphpad.comRRID:SCR_015807
SoftwareIngenuity Pathway Analysis (IPA)Ingenuity SystemsRRID:SCR_008653
SoftwareR package ggplot2Wickham, 2016ISBN 978-3-319-24277-4
SoftwareMIPAVNIH Center for Information TechnologyRRID:SCR_007371
SoftwareImage Quant TL array analysisGE HealthcareRRID:SCR_018374
SoftwareFlowCytomix Pro2.2.1eBioscienceRRID:SCR_018375
SoftwareAxioVision LE64ZeissRRID:SCR_002677
OtherDMMBSigma-Aldrich341088
OtherAlamar Blue reagentThermo FisherDAL1025
OtherDAPI stainInvitrogenD1306(1 µg/mL)
OtherSafranin-OSigma-AldrichS2255
OtherTaqMan Fast Universal PCR Master Mix (2×)Thermo Fisher4366073
Cell linePrimary Human Nucleus Pulposus (NP) and Annulus Fibrosis (AF)Primary CellsHuman Biological samples. See Table 1 for demographics.Cell line maintained in Haglund Lab.

Human disc collection

Request a detailed protocol

Human lumbar IVDs were harvested from organ donors through a collaboration with Transplant Quebec. All procedures are approved by and performed in accordance with the ethical review board at McGill University (IRB#s A04-M53-08B). Familial consent was obtained for each subject. Table 1 provides an overview of donor demographics and Table 2 provides detailed description of the used discs. Lumbar spinal columns were removed from organ donors, they were imaged radiographically and visually, and signs of degeneration were noted (Page et al., 1993; Mort and Roughley, 2007). Discs were then dissected from the spinal column and used for cell and organ cultures. Nucleus pulposus (NP) and annulus fibrosis (AF) cells were isolated separately as described previously (Cherif et al., 2019; Kirkland and Tchkonia, 2017). All cultures were mycoplasma free as verified by Mycoplasma PCR Detection Kit (ZmTech Scientific).

Table 1
Donors demographics.
Donor IDAgeSexCause of death
153FAnoxia
253Mbone; cerebral aneurysm rupture
376FAnoxia
452MIschemic cerebral vascular accident
517MBrain death
673FCerebrovascular hemorrhage
768FCerebral hemorrhage
839MGunshot to the neck
966FUnknown
1067MCardiac arrest
1169MCerebral hemorrhage
1261MCardiac arrest
1340FBrain hemorrhage
1453FMedical suicide
1528MSuicide (Hang)
1635MAnoxia
Table 2
Complete donors list per assays and results.
Donor
ID
DiscsThompson grade
and Degeneration
Cell typeType of cultureType of assaysResults
6L1-L53 - (D)NP
&
AF
PelletIHC
Metabolic activity
Figure 1 (A–D)
Donor # 6, 7, 10–15
Figure 1—figure supplement 1(A)
Donor # 10–15
Figure 1—figure supplement 1(G)
Donor # 10–15
7L2-L54 - (D)
8L3-S13 - (D)
10L4-L54 - (D)
11L5-S13 - (D)
12L5-S13 - (D)
13L4-S13 - (D)
14L5-S1
L1-L2
3 - (D)
15L5-S15 - (D)
11L5-S13 - (D)NPPelletRT-qPCR
Cytokine array
Luminex
Figure 2 (A–E)
Figure 3 (A–E)
Figure 2—figure supplement 1(A)
Figure 3—figure supplement 1(A-C)
All Donor # 11–15
12L5-S13 - (D)
13L4-S13 - (D)
14L5-S1
L1-L2
3 - (D)
15L5-S15 - (D)
9T11-L2Table 3Intact discEx vivoMRI
Luminex
IHC
Figure 4 (A–F)
Figure 5 (A–D)
Figure 5—figure supplement 1(A-D)
Donor # 9, 11, 13 and 14
11L1-L4Table 3
13L1-L4Table 3
14L2-L5Table 3
1L1-S14 - (D)NPMonolayerICC
Metabolic activity
Caspase 3/7 activity
Figure 1—figure supplement 1(B, C and D)
Donor # 1–3
Figure 1—figure supplement 1(E and F)
Donor # 1–3 and 11 (D)
Donor # 4, 5, 10 and 11 (ND)
2L4-S13 - (D)
3L2-S14 - (D)
4L1-L51 - (ND)
5T11-S11 - (ND)
10T12-L11 - (ND)
11T12-L1
L5-S1
1 - (ND)
3 - (D)
15T11-L5
L5-S1
1 - (ND)
5 - (D)
Intact discNP Area and position measureFigure 2—figure supplement 1(B-C)
Donor # 15 and 16
16L1-S11 - (ND)
Table 3
Characteristics of discs injected with each treatment.
Donor ID9111314
Age66694053
SexFMFF
CTRLDisc LevelT12/L1L1/2L1/2L2/3
 Grade2222
 Disc Height (cm)0.840.920.920.88
O-VanillinDisc LevelL1/2L2/3L3/4L4/5
 Grade2222
 Disc Height (cm)0.920.890.990.88
RG-7112Disc LevelT11/T12L3/4L2/3L3/4
 Grade2222
 Disc Height (cm)0.820.90.930.99

Alamar blue metabolic activity in monolayer cell culture

Request a detailed protocol

10,000 cells were seeded per well in 96-well tissue culture plates for 12 hr. Then, cells were incubated in the presence or absence of RG-7112 (5 μM) for 6 h. Metabolic activity was evaluated by the Alamar blue assay as previously described (Livak and Schmittgen, 2001). We measured the metabolic activity of treated and untreated NP cells cultured in monolayer or in pellet from degenerate and non-degenerate discs to verify a non cytotoxic window of RG-7112. Results are presented as a percentage of metabolic activity compared to their control. Experiments were performed (3–6) times in triplicate wells for each compound and concentration.

Pellet cell culture and immunohistochemistry

Request a detailed protocol

300,000 NP cells/tube were collected by centrifugation at 500 × g for 5 min. Pellets were incubated in 1 mL DMEM (2.25 g/L glucose, 5% FBS, ascorbic acid (5 μM) (Sigma-Aldrich, Oakville, ON, Canada)) at 37°C and 5% CO2. Culture media was changed every 3 days and collected for analysis. After 4 days, the pellet culture is stabilized and a single dose of the senolytics or vehicle was added to the culture media. Metabolic activity measures on pellet culture media from degenerate NP and AF cells, treated or not with RG-7112, was performed to evaluate a potential cytotoxic effect of RG-7112 by comparing, in the same pellet culture, the effect of the treatment (at day 21) to the control before treatment (at day 4). RNA was collected in TRIzol (Thermo Fisher Scientific) for gene expression experiments (as described in Krock et al., 2014). For immunohistochemistry, pellets were fixed in 4% paraformaldehyde and cryopreserved, 5 μm sections were prepared and stained overnight at 4°C for p16Ink4a and 1 hr at room temperature for Ki-67 and caspase-3 primary antibodies as described (Cherif et al., 2019). Coverslips were mounted using Aqua Polymount, and bright-field images were visualized. Ten fields, randomly distributed across the well, were analyzed, and the number of positive (brown stained) and total cells were counted to calculate the percentage of senescent, proliferative and apoptotic cells. Senescence, Apoptosis and proliferation assessments were performed on cells pellet cultured for 21 days by comparing, in the same subject, the senolytics-treated pellet to their respective controls.

Monolayer cell culture and immunofluorescence

Request a detailed protocol

Isolated cells were expanded to Passage 1 (P1) in monolayer cultures. P1 cells were then seeded at 20,000 or 10,000 cells per well in eight-well chamber slides (Nunc Lab-Tek II Chamber Slide System) and 96- well flat clear bottom black microplates (Corning, NY) respectively. Cells were serum-starved in DMEM with ITS (1X) (Thermo Fisher, Waltham, MA) for 2 hr prior to treatment with 5 μM RG-7112 (Selleck Chemicals, TX), 100 μM o-Vanillin (Sigma-Aldrich, Oakville, ON, Canada) or vehicle (DMSO (0.01%, (Sigma-Aldrich, Oakville, ON, Canada) for 6 hr. Immunocytochemistry was performed as previously described (Cherif et al., 2019). Apoptosis was detected using a commercial kit (ab176749, Abcam, Cambridge, MA) according to the manufacturer’s instructions. Photomicrographs were acquired with a fluorescent Olympus BX51 microscope equipped with an Olympus DP71 digital camera (Olympus, Tokyo, Japan).

Caspase 3/7 activity in monolayer cell culture

Request a detailed protocol

Caspase 3/7 activity of treated and untreated NP cells from degenerate and non-degenerate IVDs was measured using the Amplite Fluorimetric Caspase 3/7 Assay Kit (AAT Bioquest, Sunnyvale, CA) according to the manufacturer’s protocol. Cells were incubated with the caspase 3/7 assay solution, which contained caspase substrate (Z-DEVD-R110), at room temperature for 1 hr in the dark. Fluorescence intensity was then measured at 490 nm excitation and 525 nm emission. The results are expressed as a percentage of the mean of the control group (set at 100%). Each experiment was performed in triplicate and carried out three times from each round of cell isolation.

DMMB in pellet cell culture

Request a detailed protocol

Sulphated glycosaminoglycans (GAGs) were quantified using the DMMB assay in the media of degenerate NP and AF pellets with or without RG-7112 treatment, performed as previously described (Wickham, 2016). Chondroitin sulfate was used to generate the standard curve. Conditioned media samples from days 7, 10, 14 and 21 were evaluated separately in triplicate into clear 96-well plates (Costar, Corning, NY). sGAG release in media was normalized to the sGAG concentration in media at day 0 and then normalized to the untreated group.

Quantitative real-time PCR in pellet cell culture

Request a detailed protocol

Following treatment, RNA was extracted using the TRIzol chloroform extraction method, as previously described (Krock et al., 2014). Briefly, 500 ng of RNA was reverse transcribed using a qScript cDNA Synthesis Kit (Quanta Biosciences, Beverly, MA) with an Applied Biosystems Verti Thermocycler (Thermo Fisher, Waltham, MA). RT-qPCR was performed using an Applied Biosystems StepOnePlus machine with TaqMan Fast Universal PCR Master Mix (2×) and Custom TaqMan Array 96-Well Fast Plates (Thermo Fisher, Waltham, MA). The evaluated genes are senescent and apoptotic genes included in the Human TaqMan Array, for Human Cellular Senescence (Thermofisher scientific, Array ID: RPU62T7, Catalog number: 4413255). The Array Plate was customized to include 12 additional recently identified senescence and anti-apoptotic pathway genes84. The 96 included genes are described in Supplementary file 1. Each well of the TaqMan Array Plate was reconstituted using a mix of Fast Master Mix and a cDNA sample (20 ng) to a set final volume (10 μl). Five plates were used for each group (CTRL, RG-7112 and o-Vanillin), and fold-change in gene expression was calculated using the 2−ΔΔCt method 85 after normalizing to the housekeeping gene and vehicle-treated cells.

Bioinformatics analysis

Request a detailed protocol

We conducted a gene expression study of a pre-specified set of apoptotic and senescence-genes of interest (Custom Taqman 96-Well Fast Plates, Thermofisher scientific) in nucleus pulposus cells. This single-gene approach offers the advantage that highly relevant genes can be identified and tested first. The candidate genes examined with this approach allowed us also to identify the genes within the selected group that together carry out the drug response in disc cells.

In order to mine the feature genes from different datasets, a Venn diagram analysis was conducted using Venny version 2.1.0 software (Oliveros, 2007). Differences in the expression levels of DEGs for treated and untreated groups were obtained, and the number of DEGs upregulated and downregulated were calculated. The odds ratio (OR) was calculated according to OR = (DEGs_o-Vanillin * DEGs_RG-7112/DEGs_o-Vanillin * NonDEGs_RG-7112) / (NonDEGs_o-Vanillin * DEGs_RG-7112/nonDEGs_o-Vanillin * NonDEGs_RG-7112).

Heatmaps of gene expression pattern were constructed using unsupervised hierarchical clustering using Euclidean distance metric and complete linkage clustering method of the 43 differentially expressed genes in response to either RG-7112 or o-Vanillin (p<0.05) treatment of degenerate NP cells pellet. Differences in gene expression between o-Vanillin, RG-7112 and control groups were respectively compared via unpaired t-tests using the R package. Genes for which met the p<0.05 cut-off point were selected as DEGs, following which gene expression profiles of DEGs were visualized (heatmaps) via the ‘ggplots’ in R package 87 and were represented in the heatmaps as Z-scores, which is: (expression value - mean expression value across samples)/divided by the standard deviation. Colors, ranging from blue to grey then red, for each treatment represents the average fold change of each subject in that group.

Differentially expressed genes were subjected to Ingenuity Pathways Analysis (IPA) (Ingenuity Systems, Redwood City, CA) and used as a starting point for building biological networks. This analysis uses computational algorithms to identify networks consisting of focus genes (genes that were present in our list of 91 genes) and their interactions with other genes (‘non-focused’) in the knowledge base. Scores were calculated for each network according to the fit of the network to the set of focus genes and used to rank networks on the Ingenuity analysis. IPA uses the genes from the highest-scoring network to extract a connectivity pathway that relates candidate genes to each other based on their interactions. The involved function and disease significantly associated with these candidates’ genes were shown. To generate the networks, significant pathways were filtered by p-value (α)<0.05 and activation Z-score > −2 or >2, set as the cut-off values and representing a significant deactivation or activation, respectively.

Colors are based on log2 fold changes on these genes. To rank networks of the IPA, p-scores were calculated from p-values. For example, for n genes in the network and f of them are Focus Genes. The p-value is the probability of finding f or more Focus Genes in a set of n genes randomly selected from the Global Molecular Network calculated using Fisher’s exact test. Since interesting p-values are typically quite low, it is visually easier to concentrate on the exponent and the p-score is defined as p-score = -log10(p-value). Networks with a score ≥2 have at least 99% confidence that indicate a 1/100 chance that the focus genes are in a network because of random chance.

Ex-vivo organ culture and MRI analysis

Request a detailed protocol

Intact lumbar spines were x-rayed, and discs were selected for the study based on the grading system described by Wilke et al. (Page et al., 1993). Discs with a Wilke grade of 2 were included for this study. Three IVDs from the same spine (n = 4 spines, 12 discs) were isolated and cultured as previously described (Gawri et al., 2011; Krock et al., 2014; Rosenzweig et al., 2016). Disc characteristics are described in Table 3. Isolated discs were scanned by MRI before and after treatment at day 28 as described by Rosenzweig et al., 2018. Images were obtained on a 7T Bruker BioSpec 70/30 USR (Bruker Biospin, Milton, ON, Canada) with the high-performance mini-imaging kit gradient upgrade AVIII electronics (Bruker) and a Bruker-issued T1ρ-RARE pulse sequence, as previously established (Rosenzweig et al., 2018; Mulligan, 2015). Briefly, 3D images were acquired and T1ρ values were quantified using the MIPAV software (NIH Center for Information Technology, Bethesda, MD, USA). T1ρ values of the same region of interest (ROI) of ʹbeforeʹ and ʹafterʹ treatment scans of each disc were normalized to the surrounding culture medium using editing features in MIPAV software. After 4 days of culture and one media change, single injection of the discs with vehicle, RG-7112 (5 μM/g disc) or 100 μM o-Vanillin (100 μM/g disc) in a total volume of 200 μl PBS was performed as previously described (Krock et al., 2014; Rosenzweig et al., 2016). Discs were then cultured in DMEM supplemented with 1x Glutamax, 50 μg/ml gentamicin and 1% FBS for 28 days. Media was changed and collected every 4 days. On day 28 and after MRI imaging, discs were prepared for immunohistochemistry as described below (See Intact IVD Tissue Immunohistochemistry). Conditioned media was collected at each change and frozen as individual samples at −80°C for protein analysis.

Preliminary measures to delimitate the NP region of interest (ROI) were calculated using 11-disc images from random organ donors. A contour was drawn around the disc using a Wacom Intuos Pro tablet and stylus (Wacom, Japan). This polygon was used to measure total disc voxel intensity. The NP area was created by reshaping the original disc contour to 30% of its frontal size and 40% of its sagittal size centered around the middle of NP area, which was calculated to be 10% shift below the center of the disc (Figure 2—figure supplement 1B-D). The T1ρ images were manually cropped around the perimeter of the IVDs. The average of the T1ρ values was calculated within the ROIs for the vehicle and the injected discs slices per image. Heat maps representing signal intensity were created using the MIPAV software.

Age of an individual donor is specified in years. The level of the disc injected with either DMSO, O-Vanillin, and RG-7112 from the lumbar region is indicated. Discs were graded based on Wilke et al., 2006 (Page et al., 1993). Disc height was determined by averaging the dorsal, ventral and midsection disc height. No differences in grade or height were found between groups prior to treatment.

Human cytokine array of pellet and intact IVD culture media

Request a detailed protocol

Media from the Degenerate NP Pellet used in gene expression analysis was collected from day 4 to day 21 and pooled separately for each group (vehicle, o-Vanillin and RG-7112) and subject. Then, they were analyzed and compared to evaluate the effect of the two senolytics on SASP release. Disc media collected from day 4 (pre-treatment) and day 28 (post-treatment) were analyzed separately for each disc and each factor. The Human Cytokine Antibody Array C5 (RayBiotech, Inc) was used for semi quantitative detection of 80 proteins according to manufacturer’s instructions and as previously described (Krock et al., 2014). Intensity units were detected by the chemiluminescence using an ImageQuant LAS4000 Image Analyzer (GE Healthcare, Baie d'Urfe, QC, Canada) and analyzed with ImageQuant TL array analysis software (GE Healthcare). The relative quantity of each factor present in each media sample was normalized to the positive and negative controls included on the array. Mean relative concentration of each factor of treated and control groups were then calculated. Data was normalized to secretion of vehicle injected discs from the same donor spine. A list of included cytokines is provided in Supplementary file 2.

Luminex mulitplex assay of pellet and intact IVD culture media

Request a detailed protocol

Nineteen proteins were selected for analysis by Luminex multiplex assay according to manufacturer's instructions. A limitation in the number of factors we could measure was the incompatibility of some factors to be measured simultaneously as indicated by the supplier. Concentrations (pg/mL) (INF-γ, TNF-α, IL-1α, IL-1β, IL6, IL8, CCL5, CCL7, CCL11, CCL24, CCL26, CXCL1, CXCL5, CXCL9, CXCL10, CXCL11, CX3CL1, VEGF-A and Angiogenin) were measured in 40 μl media. Median fluorescence intensity (MFI) from microspheres was acquired with a BD FACSCanto II and analyzed in FlowCytomix Pro2.2.1 software (eBioscience). Concentration of each analyte was obtained by interpolating fluorescence intensity to a seven-point dilution standard curve supplied by the manufacturer.

Intact IVD tissue immunohistochemistry

Request a detailed protocol

For intact IVDs, post-MRI analysis, a 2 mm wide sagittal tissue segment from the center of the IVD was fixed in periodate lysine paraformaldehyde (PLP) fixative overnight at 4°C. Samples were then washed in PBS and decalcified using Shandon TBD1 Decalcifier solution (ThermoFischer Scientific) over 72 hr at 4°C, changing solution each day. Tissue segments were washed in PBS and placed in 70% ethanol prior to paraffin embedding. Sections of 5 µm were cut and mounted on glass slides. All sections were heated on a hot plate at 55°C for 45 min and deparaffinized and rehydrated. Next, sections were stained with safranin-O/fast green (Sigma-Aldrich, Oakville, ON, Canada) and with antibodies against p16Ink4a and Ki-67 and counter stained using the DAB detection IHC Kit (ab64264, Abcam, Cambridge, MA) following the manufacturer’s instructions. All images were acquired using a Zeiss Axioskop 40 and an AxioCam MR (Zeiss) and processed using AxioVision LE64 software (Zeiss).

Statistical analysis

Request a detailed protocol

The data was analyzed using Graph Prism 8 (Graph Pad, La Jolla, CA). Analysis was performed by two-tailed Student's t test for comparison between two groups and a multiple pairwise comparison (Analysis of Variance (ANOVA) was used to evaluate the variance between multiple groups with Turkey’s post hoc test. A p value < 0.05 was considered statistically significant.

References

  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8
  9. 9
  10. 10
  11. 11
  12. 12
  13. 13
  14. 14
  15. 15
  16. 16
  17. 17
  18. 18
  19. 19
  20. 20
  21. 21
  22. 22
  23. 23
  24. 24
  25. 25
  26. 26
  27. 27
  28. 28
  29. 29
  30. 30
  31. 31
  32. 32
  33. 33
  34. 34
  35. 35
  36. 36
  37. 37
  38. 38
  39. 39
  40. 40
  41. 41
  42. 42
  43. 43
  44. 44
  45. 45
  46. 46
  47. 47
  48. 48
  49. 49
  50. 50
  51. 51
  52. 52
  53. 53
  54. 54
  55. 55
  56. 56
  57. 57
  58. 58
  59. 59
  60. 60
    Senotherapy: growing old and staying young?
    1. R Schmitt
    (2017)
    Pflügers Archiv - European Journal of Physiology 469:1051–1059.
    https://doi.org/10.1007/s00424-017-1972-4
  61. 61
  62. 62
  63. 63
  64. 64
  65. 65
  66. 66
  67. 67
  68. 68
  69. 69
  70. 70
  71. 71
  72. 72
    Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010
    1. T Vos
    2. AD Flaxman
    3. M Naghavi
    4. R Lozano
    5. C Michaud
    6. M Ezzati
    7. K Shibuya
    8. JA Salomon
    9. S Abdalla
    10. V Aboyans
    11. J Abraham
    12. I Ackerman
    13. R Aggarwal
    14. SY Ahn
    15. MK Ali
    16. MA AlMazroa
    17. M Alvarado
    18. HR Anderson
    19. LM Anderson
    20. KG Andrews
    21. C Atkinson
    22. LM Baddour
    23. AN Bahalim
    24. S Barker-Collo
    25. LH Barrero
    26. DH Bartels
    27. M-G Basáñez
    28. A Baxter
    29. ML Bell
    30. EJ Benjamin
    31. D Bennett
    32. E Bernabé
    33. K Bhalla
    34. B Bhandari
    35. B Bikbov
    36. AB Abdulhak
    37. G Birbeck
    38. JA Black
    39. H Blencowe
    40. JD Blore
    41. F Blyth
    42. I Bolliger
    43. A Bonaventure
    44. S Boufous
    45. R Bourne
    46. M Boussinesq
    47. T Braithwaite
    48. C Brayne
    49. L Bridgett
    50. S Brooker
    51. P Brooks
    52. TS Brugha
    53. C Bryan-Hancock
    54. C Bucello
    55. R Buchbinder
    56. G Buckle
    57. CM Budke
    58. M Burch
    59. P Burney
    60. R Burstein
    61. B Calabria
    62. B Campbell
    63. CE Canter
    64. H Carabin
    65. J Carapetis
    66. L Carmona
    67. C Cella
    68. F Charlson
    69. H Chen
    70. AT-A Cheng
    71. D Chou
    72. SS Chugh
    73. LE Coffeng
    74. SD Colan
    75. S Colquhoun
    76. KE Colson
    77. J Condon
    78. MD Connor
    79. LT Cooper
    80. M Corriere
    81. M Cortinovis
    82. KC de Vaccaro
    83. W Couser
    84. BC Cowie
    85. MH Criqui
    86. M Cross
    87. KC Dabhadkar
    88. M Dahiya
    89. N Dahodwala
    90. J Damsere-Derry
    91. G Danaei
    92. A Davis
    93. D De Leo
    94. L Degenhardt
    95. R Dellavalle
    96. A Delossantos
    97. J Denenberg
    98. S Derrett
    99. DC Des Jarlais
    100. SD Dharmaratne
    101. M Dherani
    102. C Diaz-Torne
    103. H Dolk
    104. ER Dorsey
    105. T Driscoll
    106. H Duber
    107. B Ebel
    108. K Edmond
    109. A Elbaz
    110. SE Ali
    111. H Erskine
    112. PJ Erwin
    113. P Espindola
    114. SE Ewoigbokhan
    115. F Farzadfar
    116. V Feigin
    117. DT Felson
    118. A Ferrari
    119. CP Ferri
    120. EM Fèvre
    121. MM Finucane
    122. S Flaxman
    123. L Flood
    124. K Foreman
    125. MH Forouzanfar
    126. FGR Fowkes
    127. R Franklin
    128. M Fransen
    129. MK Freeman
    130. BJ Gabbe
    131. SE Gabriel
    132. E Gakidou
    133. HA Ganatra
    134. B Garcia
    135. F Gaspari
    136. RF Gillum
    137. G Gmel
    138. R Gosselin
    139. R Grainger
    140. J Groeger
    141. F Guillemin
    142. D Gunnell
    143. R Gupta
    144. J Haagsma
    145. H Hagan
    146. YA Halasa
    147. W Hall
    148. D Haring
    149. JM Haro
    150. JE Harrison
    151. R Havmoeller
    152. RJ Hay
    153. H Higashi
    154. C Hill
    155. B Hoen
    156. H Hoffman
    157. PJ Hotez
    158. D Hoy
    159. JJ Huang
    160. SE Ibeanusi
    161. KH Jacobsen
    162. SL James
    163. D Jarvis
    164. R Jasrasaria
    165. S Jayaraman
    166. N Johns
    167. JB Jonas
    168. G Karthikeyan
    169. N Kassebaum
    170. N Kawakami
    171. A Keren
    172. J-P Khoo
    173. CH King
    174. LM Knowlton
    175. O Kobusingye
    176. A Koranteng
    177. R Krishnamurthi
    178. R Lalloo
    179. LL Laslett
    180. T Lathlean
    181. JL Leasher
    182. YY Lee
    183. J Leigh
    184. SS Lim
    185. E Limb
    186. JK Lin
    187. M Lipnick
    188. SE Lipshultz
    189. W Liu
    190. M Loane
    191. SL Ohno
    192. R Lyons
    193. J Ma
    194. J Mabweijano
    195. MF MacIntyre
    196. R Malekzadeh
    197. L Mallinger
    198. S Manivannan
    199. W Marcenes
    200. L March
    201. DJ Margolis
    202. GB Marks
    203. R Marks
    204. A Matsumori
    205. R Matzopoulos
    206. BM Mayosi
    207. JH McAnulty
    208. MM McDermott
    209. N McGill
    210. J McGrath
    211. ME Medina-Mora
    212. M Meltzer
    213. ZA Memish
    214. GA Mensah
    215. TR Merriman
    216. A-C Meyer
    217. V Miglioli
    218. M Miller
    219. TR Miller
    220. PB Mitchell
    221. AO Mocumbi
    222. TE Moffitt
    223. AA Mokdad
    224. L Monasta
    225. M Montico
    226. M Moradi-Lakeh
    227. A Moran
    228. L Morawska
    229. R Mori
    230. ME Murdoch
    231. MK Mwaniki
    232. K Naidoo
    233. MN Nair
    234. L Naldi
    235. KMV Narayan
    236. PK Nelson
    237. RG Nelson
    238. MC Nevitt
    239. CR Newton
    240. S Nolte
    241. P Norman
    242. R Norman
    243. M O'Donnell
    244. S O'Hanlon
    245. C Olives
    246. SB Omer
    247. K Ortblad
    248. R Osborne
    249. D Ozgediz
    250. A Page
    251. B Pahari
    252. JD Pandian
    253. AP Rivero
    254. SB Patten
    255. N Pearce
    256. RP Padilla
    257. F Perez-Ruiz
    258. N Perico
    259. K Pesudovs
    260. D Phillips
    261. MR Phillips
    262. K Pierce
    263. S Pion
    264. GV Polanczyk
    265. S Polinder
    266. CA Pope
    267. S Popova
    268. E Porrini
    269. F Pourmalek
    270. M Prince
    271. RL Pullan
    272. KD Ramaiah
    273. D Ranganathan
    274. H Razavi
    275. M Regan
    276. JT Rehm
    277. DB Rein
    278. G Remuzzi
    279. K Richardson
    280. FP Rivara
    281. T Roberts
    282. C Robinson
    283. FR De Leòn
    284. L Ronfani
    285. R Room
    286. LC Rosenfeld
    287. L Rushton
    288. RL Sacco
    289. S Saha
    290. U Sampson
    291. L Sanchez-Riera
    292. E Sanman
    293. DC Schwebel
    294. JG Scott
    295. M Segui-Gomez
    296. S Shahraz
    297. DS Shepard
    298. H Shin
    299. R Shivakoti
    300. D Silberberg
    301. D Singh
    302. GM Singh
    303. JA Singh
    304. J Singleton
    305. DA Sleet
    306. K Sliwa
    307. E Smith
    308. JL Smith
    309. NJC Stapelberg
    310. A Steer
    311. T Steiner
    312. WA Stolk
    313. LJ Stovner
    314. C Sudfeld
    315. S Syed
    316. G Tamburlini
    317. M Tavakkoli
    318. HR Taylor
    319. JA Taylor
    320. WJ Taylor
    321. B Thomas
    322. WM Thomson
    323. GD Thurston
    324. IM Tleyjeh
    325. M Tonelli
    326. JA Towbin
    327. T Truelsen
    328. MK Tsilimbaris
    329. C Ubeda
    330. EA Undurraga
    331. MJ van der Werf
    332. J van Os
    333. MS Vavilala
    334. N Venketasubramanian
    335. M Wang
    336. W Wang
    337. K Watt
    338. DJ Weatherall
    339. MA Weinstock
    340. R Weintraub
    341. MG Weisskopf
    342. MM Weissman
    343. RA White
    344. H Whiteford
    345. ST Wiersma
    346. JD Wilkinson
    347. HC Williams
    348. SRM Williams
    349. E Witt
    350. F Wolfe
    351. AD Woolf
    352. S Wulf
    353. P-H Yeh
    354. AKM Zaidi
    355. Z-J Zheng
    356. D Zonies
    357. AD Lopez
    358. CJL Murray
    (2012)
    The Lancet 380:2163–2196.
    https://doi.org/10.1016/S0140-6736(12)61729-2
  73. 73
  74. 74
  75. 75
  76. 76
  77. 77
  78. 78
  79. 79
  80. 80
  81. 81
  82. 82
  83. 83
  84. 84

Decision letter

  1. Yousin Suh
    Reviewing Editor; Columbia University, United States
  2. Jessica K Tyler
    Senior Editor; Weill Cornell Medicine, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

This paper provides ex vivo experimental evidence to demonstrate the potential of senolytic drugs as a therapeutic strategy against intervertebral disc degeneration (IDD) in humans. It is a step forward for testing senolytic drugs against aging-related disease in humans.

Decision letter after peer review:

Thank you for submitting your article "Senotherapeutic drugs for human intervertebral discs degeneration and low back pain" for consideration by eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Jessica Tyler as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

The manuscript by Haglund and colleagues seeks to test the potential of senolytic drugs as a therapeutic strategy against intervertebral disc degeneration (IDD) in humans. Using two senolytic compounds, o-Vanillin and RG-7112, the authors demonstrated their abilities to selectively kill senescent cells and to reduce the senescence-associated secretory phenotype (SASP) in a cell culture model of IDD, and discovered the gene regulatory networks that are common and distinct between the two drugs by transcriptome analysis. Furthermore, using ex vivo model of IDD, the authors showed similar senolytic effects to those found in in vitro model. The study presents a well-designed work, particularly in using the ex vivo model of IDD to demonstrate the physiological relevance and the potential of senolytics against IDD in humans, which will certainly be of general interest to the community of researchers studying the biology of aging. Both reviewers agreed that the study provides interesting insights into senescence and senolytics in IDD, and should be of broad interest. However, as detailed below, a number of concerns were also raised, relating in large part to insufficient clarity in the current version with respect to the authors' methods and the limitations of their approach and data presentation/interpretation, and the rationale behind the choice of drugs.

Essential revisions:

1) The rationale on the choice of two drugs should be clarified, as it seems that they are "arbitrarily" included together in the same manuscript. In particular, since the authors have already demonstrated the senolytic effects of o-Vanillin in in vitro IVD model, what was the rational for showing the effects of yet another drug, RG-7112, in a descriptive way rather than delving deeper into the mechanisms of o-Vanillin? Is one better than the other? Would they be expected to target fundamentally unique and complementary pathways? In light of the recent studies showing that the combination of senolytic drugs, e.g. dasatinib and quercetin (D+Q), selectively targets a broader range of senescent cell types than alone (Xu et al., Nature Medicine, 2019), why not testing the potential of the combo treatment?

2) The relationship and rationale of comparing metabolic activities measured at day 4 of treatment vs. assessments of senescence measured at day 21 of treatment need to be clarified.

3) The descriptions on transcriptome data analysis are inadequate and need improvement as follows:

a) It needs more information as to how the transcriptome analysis was performed in the Materials and methods section. It is not even clear if it was genome-wide or candidate approach. Description of samples, materials, and basic workflows are needed, and information on data availability (GEO and accession number) should be provided.

b) It needs more reasoning and better interpretation to enhance readability to non-specialists in the field of cellular networks and bioinformatics.

c) While information on IVD samples are tabled, it would be important to provide a general logic and information on grouping of the different tissue/cell samples for each of the analysis. For examples, in each of the comparative analyses, were the same cells used in the control and tested experiments? If they were different, how would this affect the interpretation and statistical analyses? In Figure 1—figure supplement 1, it is difficult to locate data generated from different sources and compared with n=6, 3 and 8, and the degenerative score of the cells and tissues etc.

d) The presentation of the genes and gene numbers from each of the treatments with RG-7112 or o-Vanillin were complex and the information provided is not easy to follow. The authors concluded that distinct senescence pathways are affected based on 8 genes affected by RG-7112 treatment and 40 genes by o-Vanillin, but only 2 genes common to both. They need to provide more information to better understand the results. For example, in Figure 2, a volcano plot and a venn diagram will be useful with statistical test to assess the overlaps. In Figure 2A, how are the genes in heat map ordered vertically; from hierarchical clustering or just shown as a dendrogram? Further, how was the clustering performed; using Spearman or Euclidean, and the linkage (average or complete)? The information provided in subsection “Bioinformatics Analysis” is too vague.

e) It was also unclear which gene sets were used for the IPA, the entire RNA-seq data or a selection that also included the differentially expressed genes. From the data and networks provided, the authors need to state clearly what the new insights, as they appear to be the expected pathways, were. The information provided in the Results section on the IPA pathways are superficial. IPA is very generic, it does not consider species, tissue, development stage, disease/control etc.; thus cautions need to be taken in not over-interpreting the IPA. Importantly, statistics is needed to support the conclusions. For example, what are the P-values of the networks in Figure 2 (B)-(E)? Are they the most significant ones? Were there others that you didn't show? The authors need to clarify these points in the Materials and methods.

f) Finally, it should be avoided to make conclusive statements when there is only a trend without statistical significance.

4) The descriptions on the SASP analysis need clarifications as follows:

a) Logic of the data presentation in Figure 3 is confusing and inconsistent. The authors should provide a clear picture as to the number of factors tested, affected, and chosen to be further followed-up and with proper statistical analysis. Please provide a scatter-plot of the SASPs' average changes per drug in Figure 3A, in order to show potential of co-linearity in the responses. Explain why the Luminex assay was used to reassess the 19 factors: Is this more accurate and quantitative, and how are the two assay correlated?

b) The heat map of the 19 gene panel in Figure 3B suggests similar and different patterns of change between the two treatments. Provide information on age, sex, degenerative score, and other IVD characteristics on the two individuals in the RG-7112 treatment that seem to be different. Are the same set of cells used for the respective control and treatments? Such information seem to be missing and nor discussed in the manuscript.

c) The gene information obtained seem not have contributed to additional analyses, as the authors then moved specifically to an analysis of SASPs which were not implicated in the network data. Since they indicate "To verify the effect of RG-7112 and o-Vanillin on SASP factor release…", perhaps the authors could make some links here to SASP to present a better flow and logic. Again, please state whether the same cells were used in the control, RG7112 and o-Vanillin assessments. This would be important in the interpretation of the gene differences.

5) For the T1p-weighted MRI and disc matrix data, more description and clarity on the organ culture and data analysis need to be provided. For example, are the MRI and immunostaining data from a single injection? Further, were the medium collected from these discs for the measurement of SASP shown in Figure 5? Statistical analysis should be performed to support the conclusions made.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Senotherapeutic drugs for human intervertebral disc degeneration and low back pain" for further consideration by eLife. Your revised article has been evaluated by Jessica Tyler (Senior Editor) and a Reviewing Editor.

The authors have made concerted effects to constructively address the concerns and criticisms raised by two reviewers who agree that the revised manuscript has improved. However, reviewer #2 still has questions and concerns which are mostly pertinent to gene expression analysis. Before publication, these should be addressed to further improve the clarity and transparency of the manuscript. I would like to encourage the authors to revise the manuscript to address the specific suggestions and questions raised by reviewer 2. My recommendation is as follows:

1) Clarify gene expression variability vs genetic variation: In the methodology under the "bioinformatics analysis", the author indicated they "conducted a gene association study that focus at the genetic variation associated with degenerate nucleus pulposus cell". This is not a genetic study but a study in gene expression?

2) Describe selection criteria and provide the full list of 91 genes in supplement: It is also stated that it is "a pre-specified set of apoptotic and senescence-genes of interest". How were the 91 genes selected and a need to show the full list? As this are preselected, there would be a bias on the potential pathway networks, and thus the differentially expressed genes are confined within this gene set and one would expect specific pathways to be within this set. Therefore, it would be appropriate to disclose the existing pathways within this set of 91 genes, and estimate the selectivity of this pathway in the DEGs for RG-7112 or O-Vanillin.

3) Provide a line or two justification, e.g transcriptome analysis provides candidate gene sets and shorten the discussion around the DEGs: I do not think the transcriptome data added value but rather confusion as the gene set were pre-selected for apoptosis and senescence pathways, and the need to study SASP does not seem to be directly linked to this dataset. Therefore, I would be rather careful in placing a lot of weight on this data, as much of the discussion evolved around the DEGs.

4) Rephrase the sentences not to mislead: I still have concern that the authors continue to stretch the boundary of suggestive results that have not reach statistical significance. Suggesting a trend in the absence of statistical support is not scientific. Thus statements such as "approaching significance" and "slightly non-significant increase" should be avoided, as the data presented indicated there is not difference. Once can never predict how the values will changed with more data point.

5) Indicate that they were selected as candidates from the transcriptome analysis: In the main text, it is described as "RNA-Seq analysis". This is not correct, as the authors should clearly indicate they are not testing RNA-Seq genome-wide, but only 91 selected genes. Very confusing.

6) Describe in the text the nature of the 4 overlapped genes and their directional impact: In Figure 2A, is the venn diagram included both up and down-regulated DEGs? May consider providing separate Venn diagrams, for up- and down-regulated genes. Also, need to provide an odds-ratio for the overlaps, and a p-value for the Chi-square test of co-occurrence. A crude estimate is that by chance alone, if you randomly sample 8 genes from 91, and another 40 from 91, you will get 8*40/91=3.51, and in reality you detect 4 genes in overlap, which is not much more than by chance. Further, should indicate the identity of these 4 overlapping genes.

7) Clarify if FDR or p values were used for cut-off for DEGs: It was indicated a p<0.05 was used as cutoff for DEGs, "Of the 91 genes tested, 44 were differentially expressed with a P<0.05 in one or both treatments (Figure 2B)". But in Figure 2C, the author indicated using "logFDR". Therefore, which measure was used for the cutoff?

8) In the figure legend, Y axis is indicated as log10 of the p-values while in the Figure 2C, log (FDR). Clarify which one: Remake the Figure 2C so that two drug effects can be easily differentiated: For the vertical axis in Figure 2C, is it log(FDR) or log10FDR ? as typically, "log10FDR" is used. In Figure 2C, genes can be better illustrated with different colours for gene with logFC >0 and those <0, and different shape for the drugs, or separate volcano plots. For insignificant genes, use grey. Further, this figure is problematic to me; as it does not seem to reflect a typical volcano plot. If FDR used, then many of the non-significant p-values would be 1.0, and the Log of 1 is zero. So, many more of the values should be around zero. Some explanation needed.

9) It is OK to leave as they are: For Figure 2(D) to (G), for each of these networks, how unusual is it to observe this? Given the bias, you are bound to have some networks, even by randomly selecting genes into IPA. Can the authors provide the p values or FDR to address this, and label them on the figures directly?

Reviewer #1:

I have no further comments. The authors admirably addressed my concerns and should be commended for a careful and thorough revision.

Reviewer #2:

This revised manuscript has improved in logic and additional information provided have addressed most of my concerns. However, I am still uncertain on the bioinformatics approach for the transcription data that remained unclear to me, and the potential bias in the approach and interpretation.

In the methodology under the "bioinformatics analysis", the author indicated they "conducted a gene association study that focus at the genetic variation associated with degenerate nucleus pulposus cell". This is not a genetic study but a study in gene expression?

It is also stated that it is "a pre-specified set of apoptotic and senescence-genes of interest". How were the 91 genes selected and a need to show the full list? As this are preselected, there would be a bias on the potential pathway networks, and thus the differentially expressed genes are confined within this gene set and one would expect specific pathways to be within this set. Therefore, it would be appropriate to disclose the existing pathways within this set of 91 genes, and estimate the selectivity of this pathway in the DEGs for RG-7112 or O-Vanillin.

I do not think the transcriptome data added value but rather confusion as the gene set were pre-selected for apoptosis and senescence pathways, and the need to study SASP does not seem to be directly linked to this dataset. Therefore, I would be rather careful in placing a lot of weight on this data, as much of the discussion evolved around the DEGs.

I still have concern that the authors continue to stretch the boundary of suggestive results that have not reach statistical significance. Suggesting a trend in the absence of statistical support is not scientific. Thus statements such as "approaching significance" and "slightly non-significant increase" should be avoided, as the data presented indicated there is not difference. Once can never predict how the values will changed with more data point.

In the main text, it is described as "RNA-Seq analysis". This is not correct, as the authors should clearly indicate they are not testing RNA-Seq genome-wide, but only 91 selected genes. Very confusing.

In Figure 2A, is the venn diagram included both up and down-regulated DEGs? May consider providing separate Venn diagrams, for up- and down-regulated genes. Also, need to provide an odds-ratio for the overlaps, and a p-value for the Chi-square test of co-occurrence. A crude estimate is that by chance alone, if you randomly sample 8 genes from 91, and another 40 from 91, you will get 8*40/91=3.51, and in reality you detect 4 genes in overlap, which is not much more than by chance. Further, should indicate the identity of these 4 overlapping genes.

It was indicated a p<0.05 was used as cutoff for DEGs, "Of the 91 genes tested, 44 were differentially expressed with a P<0.05 in one or both treatments (Figure 2B)". But in Figure 2C, the author indicated using "logFDR". Therefore, which measure was used for the cutoff?

For the vertical axis in Figure 2C, is it log(FDR) or log10FDR ? as typically, "log10FDR" is used. In Figure 2C, genes can be better illustrated with different colours for gene with logFC >0 and those <0, and different shape for the drugs, or separate volcano plots. For insignificant genes, use grey. Further, this figure is problematic to me; as it does not seem to reflect a typical volcano plot. If FDR used, then many of the non-significant p-values would be 1.0, and the Log of 1 is zero. So, many more of the values should be around zero. Some explanation needed.

For Figure 2(D) to (G), for each of these networks, how unusual is it to observe this? Given the bias, you are bound to have some networks, even by randomly selecting genes into IPA. Can the authors provide the p values or FDR to address this, and label them on the figures directly?

https://doi.org/10.7554/eLife.54693.sa1

Author response

Essential revisions:

1) The rationale on the choice of two drugs should be clarified, as it seems that they are "arbitrarily" included together in the same manuscript. In particular, since the authors have already demonstrated the senolytic effects of o-Vanillin in in vitro IVD model, what was the rational for showing the effects of yet another drug, RG-7112, in a descriptive way rather than delving deeper into the mechanisms of o-Vanillin?

We appreciate the reviewer’s insight. It is proposed that, the inflammatory environment triggered by senescent cells prevents adjacent cells from maintaining tissue homeostasis (1-3) and it is proposed to induce senescence in a paracrine manner thus exacerbating tissue deterioration (4). The rational for comparing the two drugs was to evaluate if natural senolytics with anti-oxidant or anti-inflammatory properties (like o-Vanillin) in addition to their senolytic effect further reduces inflammatory factors released by non-senescent cells thus enhancing the therapeutic effect (5-8). RG-7112, a pure senolytic without documented anti-inflammatory effects, was used for comparison. Another reason to test the two different drugs was to evaluate bioavailability to cells in their native environment. It was not known if either of the drugs could reach and kill senescent cells embedded in their native environment. A reason to select RG7112 instead of other available senolytic agents was that it is an analogue of UBX0101, a nutilin-3a inhibitor that also targets the p53/MDM2 interaction (9-10). UBX0101, is currently in clinical trials to treat knee osteoarthritis, it promotes clearance of senescent cells in cartilage from patients with knee osteoarthritis (9, 11). Articular cartilage and IVD tissue share many characteristics both at the environmental, cellular and molecular level and we hypothesized that a drug effective in articular cartilage would have a good chance of also working in the IVD. RG-7112 is an FDA approved drug (12) and was the first MDM2 inhibitor to be advanced into Phase I human clinical trials (NCT01164033, NCT01143740, NCT00623870, NCT00559533), to treat a wide range of cancers (13-16). Altogether, the similarities to UBX0101, the safety and promising clinical properties of RG-7112 as well as repurposing this approved drug may offer an efficient drug development pathway for treatments of intervertebral disc degeneration and low back pain with few or no therapeutic options. We clarified the rationale for choosing the two drugs in the Introduction.

Is one better than the other? Would they be expected to target fundamentally unique and complementary pathways?

The pathway affected by RG7112 was known while the mode of action was unknown for o-Vanillin. The two major pathways mediating cell-cycle arrest during disc degeneration are p53-p21-Rb and p16-Rb (17). Although both drugs activate cyclin-dependent-kinases (CDKs: cdk4 and cdk6) directing senescent cells to apoptosis and nonsenescent cells to proliferation, they act on two different upstream pathways. RG-7112 act on the MDM2-p53 interaction to reduce p21 accumulation leading to the suppression of its inhibitory effect of CDKs. This was confirmed in the pathway analysis. The data presented here is showing that o-Vanillin act by reducing p16 and suppressing the inhibitory effect on CDKs. Our findings suggest that the compounds result in a similar end result that can be explained by the activation of the same downstream effectors: CDKs-E2F-RB (please see Author response image 1). At this level of the study, our first and most important goal was to determine that the drugs could reach the target cells and remove senescent cells and associated inflammatory factors in native intact human IVD tissue.

Author response image 1

In light of the recent studies showing that the combination of senolytic drugs, e.g. dasatinib and quercetin (D+Q), selectively targets a broader range of senescent cell types than alone (Xu et al., Nature Medicine, 2019), why not testing the potential of the combo treatment?

It is possible that the combination would have a stronger effect. However, we found that the drugs removed both senescent NP and AF cells. Xu et al. used Dasatanib and Quercetin since the senescent cell associated pathways (SCAPs) of senescent preadipocytes differed considerably from those of senescent endothelial cells and that one drug could not remove both types (18). Similar navitoclax, a BCL‐2 prosurvival pathway inhibitor, lacks senolytic effect in adipose tissue, but displays a strong effect on senescent endothelial cells and fibroblasts (19-20). Combining drugs allowed targeting of heterogenous senescent cell populations in conditions where they are spread across multiple organs (6). For example, ongoing clinical trials using systemic administration of D+Q include diseases with senescent cells in multiple tissues such as pulmonary fibrosis, chronic kidney disease and hematopoietic stem cells. For local osteoarthritis treatment, a single drug, UBX0101, was used to target senescent chondrocytes (8). In our study we aimed to target localized natural occurring senescent disc cells using local administration of o-Vanillin or RG-7112. We chose to initially test them separately as the supply of live intact human IVDs are very limited. Further, single treatment may reduce side effects like those frequently reported for D+Q therapy, including respiratory symptoms and gastrointestinal discomfort or heartburn (21). We agree that it will be important to evaluate the combination especially in vivo. Preclinical animal experiments will be performed to optimize frequency of treatment as well as to verify safety and tolerability, and determine specific, senolytic effects of single or combination treatments for intervertebral disc degeneration and low back pain.

2) The relationship and rationale of comparing metabolic activities measured at day 4 of treatment vs. assessments of senescence measured at day 21 of treatment need to be clarified.

We apologize for the lack of clarity regarding the relationship between metabolic activity and the assessments of senescence. No measure was performed at 4 days, both metabolic activity and assessments of senescence were performed at 21 days for disc cell cultured in pellet. Metabolic activity, measured in pellet cultures, was used to determine a safe dose without cytotoxicity to non-senescent cells (Figure 1—figure supplement 1A). We treated the cells for a four-day period. Cytotoxicity and senolytic effect of the treatment, was then evaluated at day 21 to allow for the drugs to take effect and also to verify a sustained effect Figure 1(A-D). In Monolayer culture, the rationale behind the comparison of the effect of the treatment in the cells from degenerate and non-degenerate discs is to confirm the senolytic activity (Figure 1—figure supplement 1E) and the safety of RG-7112 (Figure 1—figure supplement 1F). We modified the text accordingly in the Results and the Materials and methods sections to clarify.

3) The descriptions on transcriptome data analysis are inadequate and need improvement as follows:

a) It needs more information as to how the transcriptome analysis was performed in the Materials and methods section. It is not even clear if it was genome-wide or candidate approach. Description of samples, materials, and basic workflows are needed, and information on data availability (GEO and accession number) should be provided.

We thank the reviewers for the constructive comment. We have now further described the samples, materials and workflow in the Materials and methods section of the revised manuscript and in the legend of Figure 2. This small dataset of a candidate approach might not need GEO submission as all data related to Figure 2 is now available in the attached source data files of Figure 2: Figure 2—source-data 1, Figure 2—figure supplement 1—source-data 1 and the Supplementary File 3 (a-d). In brief, we conducted a candidate gene association study to evaluate the variation in gene expression profiles associated with treated and non-treated cells within a pre-selected set of apoptotic and senescence-genes of interest. This approach offers the advantage that highly relevant genes can be identified and tested first. The candidate genes examined in this pathway approach allowed us also to identify the genes and aggregate of genes that together carry out the drug response of disc cells to the senolytic treatment.

b) It needs more reasoning and better interpretation to enhance readability to non-specialists in the field of cellular networks and bioinformatics.

We agree with the reviewers comment and have modified the text in the Results section accordingly.

c) While information on IVD samples are tabled, it would be important to provide a general logic and information on grouping of the different tissue/cell samples for each of the analysis.

We acknowledge this concern. In Table 2, we linked each result of the assays, cultures and cell type used as well as disc level and the donor from which the samples were obtained. To more precisely clarify the link between the tissue and cells used with the results, we indicated in Table 2 the degeneration score (according to Thompson grading scale) and state (degenerate and non-degenerate) of each of the intact discs. We evaluated cells from degenerate discs as well as intact degenerate disc as this would correspond with the tissue targeted for treatment. In addition, it would be difficult to evaluate an effect of removing senescent cells in non-degenerate discs as they have very few senescent cells (5). The effect of the two senolytics on cells from non-degenerate discs was done to confirms their safety for healthy cells (Figure 1—figure supplement 1F). Also, we clarified the logic behind grouping different samples in the figure’s legends and our revised text and we added the donor ID number and gender for each of the heatmaps in Figure 2B, Figure 3B, Figure 5C and Figure 2—figure supplement 1-A.

For examples, in each of the comparative analyses, were the same cells used in the control and tested experiments? If they were different, how would this affect the interpretation and statistical analyses?

In the comparative analyses, we always used cells from the same donor for control and experimental conditions. the number (n) in each experiment represent the number of donors used to replicate the experiment with the same conditions (biological replicates).

In Figure 1—figure supplement 1, it is difficult to locate data generated from different sources and compared with n=6, 3 and 8, and the degenerative score of the cells and tissues etc.

The data generated in Figure 1—figure supplement 1 is obtained from independent experiments, using complementary assays, to demonstrate the senolytic activity of RG-7112. Thus, they cannot be directly compared. Figure 1—figure supplement 1A is verifying that the RG-7112 concentration selected from the literature (22-23) has no cytotoxic effect on human IVD cells. Metabolic activity is compared between RG7112 treated and untreated cells from 6 donors (Table 2). Figure 1—figure supplement 1B and 1C demonstrate the apoptotic effect RG-7112 in NP cells from 3 donors. Figure 1—figure supplement 1D illustrates the senolytic activity of RG-7112 by immunostaining, where apoptotic caspase 3 positive cells are also p16ink4a positive while proliferating ki-67 positive cells are not p16ink4a positive (NP cells from 3 different donors). Figure 1—figure supplement 1E confirm the apoptotic effect of RG-7112 observed in Figure 1—figure supplement 1B and 1C by an increase of caspase3/7 activity in degenerate NP cells, where there are more senescent cells compared to non-degenerate cells. Figure 1—figure supplement 1F validate the safety of RG-7112 in both degenerate and non-degenerate cells by showing similar metabolic activity. In Figure 1—figure supplement 1E-1F, NP cells used were from 4 degenerate and 4 nondegenerate discs for a total number n = 8. In Figure 1—figure supplement 1G we used pellet culture media of NP and AF cells from 6 donors to demonstrate the beneficial effect of the treatment with RG-7112 on proteoglycan synthesis (sGAG release). The degenerative state for each disc used in each of the assays presented in Figure 1—figure supplement 1 are included in Table 2 in the revised manuscript. We modified the text accordingly in the legend of Figure 1—figure supplement 1, the Results and the Materials and methods sections in the revised version of the manuscript.

d) The presentation of the genes and gene numbers from each of the treatments with RG-7112 or o-Vanillin were complex and the information provided is not easy to follow. The authors concluded that distinct senescence pathways are affected based on 8 genes affected by RG-7112 treatment and 40 genes by o-Vanillin, but only 2 genes common to both. They need to provide more information to better understand the results. For example, in Figure 2, a volcano plot and a venn diagram will be useful with statistical test to assess the overlaps.

We fully agree with this request and accordingly we included new figures and tables to clarify the information provided. Please see Figure 2A, Figure 2C and the Supplementary File 3 (a-d). Better interpretation of the results is now included in the new version of the manuscript (please see response in 3. 2).

In Figure 2A, how are the genes in heat map ordered vertically; from hierarchical clustering or just shown as a dendrogram? Further, how was the clustering performed; using Spearman or Euclidean, and the linkage (average or complete)? The information provided in subsection “Bioinformatics Analysis” is too vague.

In Figure 2B, (Figure 2A in the previous version), the significant differentially expressed genes in the heat map were ordered vertically from hierarchical clustering using Euclidean distance metric and complete linkage clustering method. The text of the Materials and methods section was modified accordingly in the revised version of the manuscript.

e) It was also unclear which gene sets were used for the IPA, the entire RNA-seq data or a selection that also included the differentially expressed genes. From the data and networks provided, the authors need to state clearly what the new insights, as they appear to be the expected pathways, were. The information provided in the Results section on the IPA pathways are superficial. IPA is very generic, it does not consider species, tissue, development stage, disease/control etc.; thus cautions need to be taken in not over-interpreting the IPA. Importantly, statistics is needed to support the conclusions. For example, what are the P-values of the networks in Figure 2 (B)-(E)? Are they the most significant ones? Were there others that you didn't show? The authors need to clarify these points in the Materials and methods.

We fully accept this as a valid comment. in Figure 2 (D)-(G) (B-E in the old version), only the differentially expressed genes (P < 0.05) were included in the network analysis. We clarified the experimental procedures of signaling pathway analysis using IPA in the revised version of the manuscript. We also included new supplemental tables in the Supplementary File 3 (a-d) for the average fold change of all the genes (differentially and non-differentially expressed) in the o-Vanillin and RG-7112 treated NP pellets as well as their respective p values and tables in the Supplementary File 4 (a-b) for the significance scores, focus molecules used to reorder the networks and determine the possible physical function of each compound. In accordance with the reviewer’s suggestion, we have modified the IPA results, legends and discussion in the new version of the manuscript to highlight the new insights and better interpret our findings.

f) Finally, it should be avoided to make conclusive statements when there is only a trend without statistical significance.

As suggested, we modified the conclusive statements where statistical significance was not reached.

4) The descriptions on the SASP analysis need clarifications as follows:

a) Logic of the data presentation in Figure 3 is confusing and inconsistent. The authors should provide a clear picture as to the number of factors tested, affected, and chosen to be further followed-up and with proper statistical analysis. Please provide a scatter-plot of the SASPs' average changes per drug in Figure 3A, in order to show potential of co-linearity in the responses.

We agree the reviewer’s comments. Therefore, we added in Figure 3—figure supplement 1A, in our revised manuscript, a descriptive schematic to summarize the number of factors tested, factors detectable by the cytokine array and factors selected for Luminex assay. We modified the text accordingly in the new version of the manuscript to better describe the analysis and discuss the results. The complete list of the factors analyzed by cytokine array and Luminex assay in Figure 3, Figure 5, Figure 3—figure supplement 1 and Figure 4—figure supplement 1are included in the corresponding source data files uploaded with the manuscript. As suggested, we revised Figure 3—figure supplement 1A and classified the SASP factors in 4 groups: cytokines, chemokines (CC and CXC series), growth and neurotrophic factors and we showed the most downregulated factors in each class. Also, to better visualize the average effect of the two drugs on SASPs release, a scatter plot representing the average change for 50 factors is included in the new version of the manuscript in Figure 3A (e) and subsection “RG-7112 and o-Vanillin reduced inflammatory factors”. Statistical analysis was performed for both cytokine array and Luminex analysis in pellets and discs cultures. Significances, is highlighted in the figures and their respective legends when applicable.

Explain why the Luminex assay was used to reassess the 19 factors: Is this more accurate and quantitative, and how are the two assay correlated?

Both used the same media from the same donors. Cytokine array was used to semi-quantitative screen 80 factors. Most factors were modulated by the two senolytics (in both pellets and intact disc). Luminex assay is more sensitive and it allowed us to measure the concentrations of selected factors in all conditions. The choice of the 19 factors were based on the cytokine array as follows: only factors detected in all conditions and in at least four donors were chosen. These factors were selected to represent a variety of SASP factors and to include the most variably expressed cytokines, chemokines and growth factors. A limitation in the number of factors we could measure was the incompatibility of some factors to be measured simultaneously (as indicated by the supplier). The Luminex analysis reproduced the overall changes observed by the cytokine array. However, more significant differences were measured in treated vs. untreated groups. Also, Luminex results allowed us to better visualize and correlate the observed similarities and differences in amplitude and significance between the two compounds for each factor analyzed.

b) The heat map of the 19 gene panel in Figure 3B suggests similar and different patterns of change between the two treatments. Provide information on age, sex, degenerative score, and other IVD characteristics on the two individuals in the RG-7112 treatment that seem to be different. Are the same set of cells used for the respective control and treatments? Such information seem to be missing and nor discussed in the manuscript.

We agree with the reviewer comment on the variability pattern between the donors. This is expected due to the heterogenic nature of human patients including, age, sex, degeneration level, diseases, physical activity, cause of death…. However, it is important to highlight the similar average effect (downregulation of SASP factors) observed in Figure 3C and Figure 3—figure supplement 1C following RG-7112 and o-Vanillin treatment. Cells from the same donor were used for control and treatment, as now indicated by an ID number and the gender of each subject. Demographic and disc characteristics information for each donor used is provided in Table 1 and 2, and in the figure’s legends and the text of the revised version of the manuscript.

c) The gene information obtained seem not have contributed to additional analyses, as the authors then moved specifically to an analysis of SASPs which were not implicated in the network data. Since they indicate "To verify the effect of RG-7112 and o-Vanillin on SASP factor release…", perhaps the authors could make some links here to SASP to present a better flow and logic. Again, please state whether the same cells were used in the control, RG7112 and o-Vanillin assessments. This would be important in the interpretation of the gene differences.

The gene analysis was performed to verify the senolytic activity of the two compounds and the prediction of the potential pathway(s) activated. All the focus genes selected were senescence and cell cycle genes which explain the absence of SASP genes in the network data. However, the cytokine array analysis was performed to evaluate and measure, at the protein level, the effect the two compounds have on SASP factor release. In accordance with the reviewer’s request, we modified the Result and Materials and method sections in the revised version of the manuscript to better highlight the complementarity nature of the analysis. The media from the pellets used for cytokine array and Luminex assay in Figure 3 and Figure 3—figure supplement 1 were the same pellets used for gene expression analysis as mentioned in the legend of Figure 3.

5) For the T1p-weighted MRI and disc matrix data, more description and clarity on the organ culture and data analysis need to be provided. For example, are the MRI and immunostaining data from a single injection? Further, were the medium collected from these discs for the measurement of SASP shown in Figure 5? Statistical analysis should be performed to support the conclusions made.

We acknowledge these valid suggestions. Accordingly, we added Figure 4A to better describe the organ culture and data analysis. All the ex vivo analysis (in Figure 4, Figure 5 and Figure 4—figure supplement 1) was performed on the discs, and their respective media, following a single injection of the senolytic drug or vehicle at day 4. The discs used for the 3 conditions (CTRL, o-Vanillin and RG-7112) were from the same donor and the experiment was performed on IVDs from four different donors (Table 3). We modified the ex vivo organ culture and MRI analysis section in the new version of the manuscript for more clarity. Statistical tests (two tailed student t-test to compare pre and post treatment groups and ANOVA Kruskal-Wallis nonparametric test with Turkey’s post hoc test was performed for multiple pairwise comparison between treated and untreated groups. This information is highlighted when applicable in the legends of the figures and in the Materials and methods.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

The authors have made concerted effects to constructively address the concerns and criticisms raised by two reviewers who agree that the revised manuscript has improved. However, reviewer #2 still has questions and concerns which are mostly pertinent to gene expression analysis. Before publication, these should be addressed to further improve the clarity and transparency of the manuscript. I would like to encourage the authors to revise the manuscript to address the specific suggestions and questions raised by reviewer 2. My recommendation is as follows:

1) Clarify gene expression variability vs genetic variation: In the methodology under the "bioinformatics analysis", the author indicated they "conducted a gene association study that focus at the genetic variation associated with degenerate nucleus pulposus cell". This is not a genetic study but a study in gene expression?

Indeed, it is a gene expression study. We modified the text of the revised version of the manuscript.

2) Describe selection criteria and provide the full list of 91 genes in supplement: It is also stated that it is "a pre-specified set of apoptotic and senescence-genes of interest". How were the 91 genes selected and a need to show the full list? As this are preselected, there would be a bias on the potential pathway networks, and thus the differentially expressed genes are confined within this gene set and one would expect specific pathways to be within this set. Therefore, it would be appropriate to disclose the existing pathways within this set of 91 genes, and estimate the selectivity of this pathway in the DEGs for RG-7112 or O-Vanillin.

A complete list of the genes is found in Supplementary File 1. The genes were provided in a configurable human TaqMan array for human cellular senescence, fast 96-well plate from Thermofisher scientific (Array ID: RPU62T7, Catalog number: 4413255). The plate selected was customized to also include recently described genes implicated in senescent cells anti- apoptotic pathways (SCAPs) (24). The assay includes 82 pre-selected genes involved in cellular senescence and 2 housekeeping genes (GAPDH and 18s). We selected an additional 12 SCAPs genes for a total of 96 gene. Out of the 96 genes analyzed, 91 were detectable and are represented in the results of Figure 2. The Assay ID is provided in the key resources table. We included this clarification in the Materials and methods section of the revised manuscript.

The Network analysis feature in Ingenuity pathway analysis (IPA) (Ingenuity Systems, Redwood City, CA) suite is a broad analysis of interactions among identified proteins mined from various literature sources: The Ingenuity pathway knowledge base (IPKB). The program does not generate potential networks as it takes into account any possible combination of up or down regulated gene combinations. It is therefore not possible to disclose potentially existing pathways within the set. IPA is used to further analyze functional associations and networks of interconnected proteins. We agree that the discovered pathways are restricted to the set of 91 genes analyzed and they reflect the specific pattern of DEGs for each compound. Using IPA, we identified genes that fell within IPA-generated pathways at a higher frequency than those expected to occur for a randomly selected set of genes. This analysis identified three interacting networks for o-Vanillin and one for RG-7112 within the selected genes list. The number of networks obtained for each compound is correlated with their respective number of DEGs (8 DEGs were involved in one network for RG-7112 (Figure 2D) while 40 DEGs for o-Vanillin were involved in three networks (Figure 2 (E-G)). Interaction networks were limited to 35 molecules per network and 25 networks per analysis and excluded endogenous chemicals. We focused on algorithmically derived interaction networks, which are assigned a score on the basis of their relevance to the genes in the input dataset, the number of focus genes (i.e. dysregulated genes in our data that are in that network), and their connectivity (25). The score is calculated as –log10(p value), where p is generated using a Fisher’s exact test (26). Networks with a score ≥2 have at least 99% confidence that it is not generated by chance (27). Studies have found scores greater than 3 to be significant, with a score of 3 indicating a 1/1000 chance that the focus genes are in a network because of random chance (28-30). Other studies have opted to utilize more stringent criteria and higher scores to ensure that their discovered networks are highly significant (31-32); the network score for RG-7112 was 21 and for the three o-Vanillin networks were 37, 24 and 13. The score is not an indication of the quality or biological relevance of the network; it simply calculates the approximate "fit" between each network and our Network Eligible Molecules. Moreover, the single network pathway affected following the treatment with RG-7112 in disc cells validate the expected MDM2-p53 pathway previously reported in other cell types. The complete list of networks, their scores, the focus molecules and associated diseases and functions are provided in the Supplementary File 4. We modified the text in the revised version of the manuscript to disclose the obtained pathways within the selected set of 91 genes and estimate their selectivity.

3) Provide a line or two justification, e.g transcriptome analysis provides candidate gene sets and shorten the discussion around the DEGs: I do not think the transcriptome data added value but rather confusion as the gene set were pre-selected for apoptosis and senescence pathways, and the need to study SASP does not seem to be directly linked to this dataset. Therefore, I would be rather careful in placing a lot of weight on this data, as much of the discussion evolved around the DEGs.

We agree, gene expression analysis of known senescence associated genes was performed to determine how the compounds carry out their senolytic activity. We reduced the discussion around the DEGs in the revised version.

4) Rephrase the sentences not to mislead: I still have concern that the authors continue to stretch the boundary of suggestive results that have not reach statistical significance. Suggesting a trend in the absence of statistical support is not scientific. Thus statements such as "approaching significance" and "slightly non-significant increase" should be avoided, as the data presented indicated there is not difference. Once can never predict how the values will changed with more data point

The higher variability between individual human specimens prompt us to report any trend in the results. However, we agree with the reviewer’s recommendation and modified the text of the revised manuscript to exclude any statements such as approaching significance /slightly non-significant for better scientific accuracy.

5) Indicate that they were selected as candidates from the transcriptome analysis: In the main text, it is described as "RNA-Seq analysis". This is not correct, as the authors should clearly indicate they are not testing RNA-Seq genome-wide, but only 91 selected genes. Very confusing.

We thank the reviewers for the comment. We have now corrected in the revised manuscript text.

6) Describe in the text the nature of the 4 overlapped genes and their directional impact: In Figure 2A, is the venn diagram included both up and down-regulated DEGs? May consider providing separate Venn diagrams, for up- and down-regulated genes. Also, need to provide an odds-ratio for the overlaps, and a p-value for the Chi-square test of co-occurrence. A crude estimate is that by chance alone, if you randomly sample 8 genes from 91, and another 40 from 91, you will get 8*40/91=3.51, and in reality you detect 4 genes in overlap, which is not much more than by chance. Further, should indicate the identity of these 4 overlapping genes.

The Venn diagram in Figure 2A of the previous version include both up- and down-regulated DEGs and the 4 overlapping genes are the 4 significantly DEGs: MAPK14, CDC25c, CCNB1 and CDKN2D (p19ARF) as mentioned in the Results. In the revised version of the manuscript, two separate Venn diagrams for up and down regulated DEGs is provided in Figure 2A viewer. The total number of overlapping genes is now three since CCNB1 is upregulated in RG-7112 and downregulated in o-Vanillin. In upregulated DEGs, the three overlapping genes between o-Vanillin and RG-7112 are MAPK14, CDC25c and CDKN2D (p19ARF). No down-regulated DEGs was shared between RG-7112 and o-Vanillin. Please see the modified legend and Results section. The detailed list of up- or down-regulated DEGs and their respective p values are provided in the Supplementary file 3. Odds ratios (33) and p values were calculated using Fisher exact test.

7) Clarify if FDR or p values were used for cut-off for DEGs: It was indicated a p<0.05 was used as cutoff for DEGs, "Of the 91 genes tested, 44 were differentially expressed with a P<0.05 in one or both treatments (Figure 2B)". But in Figure 2C, the author indicated using "logFDR". Therefore, which measure was used for the cutoff?

We acknowledge this concern. p<0.05 was used as a cut-off for DEGs in both Figure 2B and 2C. We corrected this mistake in the two new volcano plots of Figure 2C.

8) In the figure legend, Y axis is indicated as log10 of the p-values while in the Figure 2C, log (FDR). Clarify which one: Remake the Figure 2C so that two drug effects can be easily differentiated: For the vertical axis in Figure 2C, is it log(FDR) or log10FDR ? as typically, "log10FDR" is used. In Figure 2C, genes can be better illustrated with different colours for gene with logFC >0 and those <0, and different shape for the drugs, or separate volcano plots. For insignificant genes, use grey. Further, this figure is problematic to me; as it does not seem to reflect a typical volcano plot. If FDR used, then many of the non-significant p-values would be 1.0, and the Log of 1 is zero. So, many more of the values should be around zero. Some explanation needed.

We used the negative log10 (p values) in Figure 2C. We included in the revised version of the manuscript two separate volcano plots for each drug as recommended. We modified the respective legend accordingly. p values were used, and significance was set at 0.05. Significance in the volcano plots is indicated by a grey line that correspond to -log10 (0.05) = 1.3 in the new Figure 2C.

9) It is OK to leave as they are: For Figure 2(D) to (G), for each of these networks, how unusual is it to observe this? Given the bias, you are bound to have some networks, even by randomly selecting genes into IPA. Can the authors provide the p values or FDR to address this, and label them on the Figures directly?

As mentioned in response 2, the gene network identification in Figure 2D to 2G was based on each set of the differentially expressed genes observed between the treated and control groups. IPA used the Ingenuity Knowledge Base to screen for reported interactions involving these genes. The identified networks were scored based on the number of network eligible molecules they contain. The IPA network score for each gene network displayed as the negative log of the p-value of that specific network, gives the likelihood that the set of genes in this network could be explained by chance alone. Therefore, networks with a score ≥ 2 have at least 99% confidence that it is not generated by chance (27-30). Although a potential bias, the high network scores obtained for o-Vanillin (37, 24 and 13) and for RG-7112 (21) showed a strong degree of relevance to the Network Eligible Molecules in our dataset. The p-value, calculated using Fisher’s exact test, are typically quite low, it is visually easier to concentrate on the exponent and the p-score is defined as p-score = -log10(p-value). p scores (=network scores) are provided in Supplementary file 4. As recommended, we labeled the p scores of each network directly on the figures.

References

1) J. Campisi, Aging, cellular senescence, and cancer, Annual review of physiology 75 (2013) 685-705.

2) S. Parrinello, J.P. Coppe, A. Krtolica, J. Campisi, Stromal-epithelial interactions in aging and cancer: senescent fibroblasts alter epithelial cell differentiation, J Cell Sci 118(Pt 3) (2005) 485-96.

3) K. Tominaga, The emerging role of senescent cells in tissue homeostasis and pathophysiology, Pathobiology of Aging & Age Related Diseases 5 (2015).

4) J.C. Acosta, A. Banito, T. Wuestefeld, A. Georgilis, P. Janich, J.P. Morton, D. Athineos, T.W. Kang, F. Lasitschka, M. Andrulis, G. Pascual, K.J. Morris, S. Khan, H. Jin, G. Dharmalingam, A.P. Snijders, T. Carroll, D. Capper, C. Pritchard, G.J. Inman, T. Longerich, O.J. Sansom, S.A. Benitah, L. Zender, J. Gil, A complex secretory program orchestrated by the inflammasome controls paracrine senescence, Nature cell biology 15(8) (2013) 978-90.

5) H. Cherif, D.G. Bisson, P. Jarzem, M. Weber, J.A. Ouellet, L. Haglund, Curcumin and o-Vanillin Exhibit Evidence of Senolytic Activity in Human IVD Cells in vitro, J Clin Med 8(4) (2019).

6) Xu M, Pirtskhalava T, Farr JN, Weigand BM, Palmer AK, Weivoda MM, Inman CL, Ogrodnik MB, Hachfeld CM, Fraser DG et al. (2018) Senolytics improve physical function and increase lifespan in old age. Nature Med 24, 1246–1256.

7) Yousefzadeh MJ, Zhu Y, McGowan SJ, Angelini L, Fuhrmann‐Stroissnigg H, Xu M, Ling YY, Melos KI, Pirtskhalava T, Inman CL et al. (2018) Fisetin is a senotherapeutic that extends health and lifespan. EBioMedicine 36, 18–28.

8) Li, W., Qin, L., Feng, R., Hu, G., Sun, H., He, Y., & Zhang, R. (2019, Jul). Emerging senolytic agents derived from natural products. Mech Ageing Dev, 181, 1-6. A

9) O.H. Jeon, C. Kim, R.M. Laberge, M. Demaria, S. Rathod, A.P. Vasserot, J.W. Chung, D.H. Kim, Y. Poon, N. David, D.J. Baker, J.M. van Deursen, J. Campisi, J.H. Elisseeff, Local clearance of senescent cells attenuates the development of post-traumatic osteoarthritis and creates a pro-regenerative environment, Nature medicine 23(6) (2017) 775-781.

10) Tovar, C., Graves, B., Packman, K., Filipovic, Z., Higgins, B., Xia, M., Tardell, C., Garrido, R., Lee, E., Kolinsky, K., To, K. H., Linn, M., Podlaski, F., Wovkulich, P., Vu, B., & Vassilev, L. T. (2013, Apr 15). MDM2 smallmolecule antagonist RG7112 activates p53 signaling and regresses human tumors in preclinical cancer models. Cancer Res, 73(8), 2587-2597. https://doi.org/10.1158/0008-5472.CAN-12-2807

11) B.G. Childs, M. Gluscevic, D.J. Baker, R.M. Laberge, D. Marquess, J. Dananberg, J.M. van Deursen, Senescent cells: an emerging target for diseases of ageing, Nature reviews. Drug discovery 16(10) (2017) 718-735

12) Weber, L. (2010, Feb). Patented inhibitors of p53-Mdm2 interaction (2006 – 2008). Expert Opin Ther Pat, 20(2), 179-191. https://doi.org/10.1517/13543770903514129

13) A. Calcinotto, J. Kohli, E. Zagato, L. Pellegrini, M. Demaria, A. Alimonti, Cellular Senescence: Aging, Cancer, and Injury, Physiol Rev 99(2) (2019) 1047-1078.

14) B. Vu, P. Wovkulich, G. Pizzolato, A. Lovey, Q. Ding, N. Jiang, J.J. Liu, C. Zhao, K. Glenn, Y. Wen, C. Tovar, K. Packman, L. Vassilev, B. Graves, Discovery of RG7112: A Small-Molecule MDM2 Inhibitor in Clinical Development, ACS Med Chem Lett 4(5) (2013) 466-9.

15) M. Andreeff, K.R. Kelly, K. Yee, S. Assouline, R. Strair, L. Popplewell, D. Bowen, G. Martinelli, M.W. Drummond, P. Vyas, M. Kirschbaum, S.P. Iyer, V. Ruvolo, G.M. Gonzalez, X. Huang, G. Chen, B. Graves, S. Blotner, P. Bridge, L. Jukofsky, S. Middleton, M. Reckner, R. Rueger, J. Zhi, G. Nichols, K. Kojima, Results of the Phase I Trial of RG7112, a Small-Molecule MDM2 Antagonist in Leukemia, Clin Cancer Res 22(4) (2016) 868-76.

16) Feng, C., et al., Disc cell senescence in intervertebral disc degeneration: Causes and molecular pathways. Cell Cycle, 2016. 15(13): p. 1674-84

17) Abdulhameed Al-Ghabkari and Aru Narendran. Cancer Biotherapy and Radio pharmaceuticals.May 2019.252-257.http://doi.org/10.1089/cbr.2018.2732

18) Zhu Y, Tchkonia T, Pirtskhalava T, et al. The Achilles' heel of senescent cells: from transcriptome to senolytic drugs. Aging Cell. 2015; 14:644–58.

19) Zhu Y, Tchkonia T, Fuhrmann-Stroissnigg H, et al. Identification of a novel senolytic agent, navitoclax, targeting the BCl-2 family of anti-apoptotic factors. Aging Cell. 2016; 15:428–35.

20) Chang J, Wang Y, Shao L, Laberge RM, Demaria M, Campisi J, Janakiraman K, Sharpless NE, Ding S, Feng W et al. (2016) Clearance of senescent cells by ABT263 rejuvenates aged hematopoietic stem cells in mice. Nature Med 22, 78–83.

21) Jamie N. Justice, Anoop M. Nambiar, Tamar Tchkonia, Nathan K. LeBrasseur, Rodolfo Pascual, Shahrukh K. Hashmi, Larissa Prata, Michal M. Masternak, Stephen B. Kritchevsky, Nicolas Musi, James L. Kirkland. Senolytics in idiopathic pulmonary fibrosis: Results from a first-in-human, open-label, pilot study. EBioMedicine, 2019; DOI: 10.1016/j.ebiom.2018.12.052.

22) Laberge, 2018. Unit Dose of an MDM2 Inhibitor that Provides Long-Lasting Relief from Idiopathic Pulmonary Fibrosis and other Pulmonary Conditions by Selectively Removing Senescent Cells from the Lung Publication number: 20180303828 https://patents.justia.com/patent/20180303828.

23) Laberge, 2019. Therapy For Removing Senescent Cells And Treating Senescenceassociated Disease Using An Mdm2 Inhibitor Publication number: 20190343832

https://patents.justia.com/patent/20190343832.

24) Kirkland JL, Tchkonia T. Cellular Senescence: A Translational Perspective. EBioMedicine. 2017;21:21-28. doi:10.1016/j.ebiom.2017.04.013

25) Savli H, Szendröi A, Romics I, Nagy B. Gene network and canonical pathway analysis in prostate cancer: a microarray study. Exp Mol Med 2008; 40:176–185.

26) Li Y, Carrillo JA, Ding Y, He Y, Zhao C, Zan L, Song J. Ruminal transcriptomic analysis of grass-fed and grain-fed angus beef cattle. PLoS One 2015; 10:e0116437.

27) Yick, C. Y. (2013). The airway smooth muscle in asthma: More than meets the eye.

28) Baranzini SE, Galwey NW, Wang J, Khankhanian P, Lindberg R, Pelletier D, et al. Pathway and network-based analysis of genome-wide association studies in multiple sclerosis. Hum Mol Genet 2009; 18:2078–2090.

29) Yan-Fang T, Dong W, Li P, Wen-Li Z, Jun L, Na W, et al. Analyzing the gene expression profile of pediatric acute myeloid leukemia with real-time PCR arrays. Cancer Cell Int 2012; 12:40

30) Naito Y, Kuroda M, Mizushima K, Takagi T, Handa O, Kokura S, et al. Transcriptome analysis for cytoprotective actions of rebamipide against indomethacin-induced gastric mucosal injury in rats. J Clin Biochem Nutr 2007; 41:202–210.

31) Reyes-Gibby CC, Yuan C, Wang J, Yeung SC, Shete S. Gene network analysis shows immune-signaling and ERK1/2 as novel genetic markers for multiple addiction phenotypes: alcohol, smoking and opioid addiction. BMC Syst Biol 2015; 9:25.

32) Jia P, Kao CF, Kuo PH, Zhao Z. A comprehensive network and pathway analysis of candidate genes in major depressive disorder. BMC Syst Biol 2011; 5 (Suppl 3):S12.

33) https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/fisher.test

https://doi.org/10.7554/eLife.54693.sa2

Article and author information

Author details

  1. Hosni Cherif

    1. Orthopaedic Research Lab, Department of Surgery, McGill University and the Research Institute of the McGill University Health Centre, Montreal, Canada
    2. McGill Scoliosis and Spine Group, Department of Surgery, McGill University and the Research Institute of the McGill University Health Centre, Montreal, Canada
    Contribution
    Conceptualization, Data curation, Formal analysis, Validation, Investigation, Methodology, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0703-3898
  2. Daniel G Bisson

    1. Orthopaedic Research Lab, Department of Surgery, McGill University and the Research Institute of the McGill University Health Centre, Montreal, Canada
    2. McGill Scoliosis and Spine Group, Department of Surgery, McGill University and the Research Institute of the McGill University Health Centre, Montreal, Canada
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Visualization, Writing - review and editing
    For correspondence
    daniel.g.bisson@gmail.com
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4812-059X
  3. Matthew Mannarino

    1. Orthopaedic Research Lab, Department of Surgery, McGill University and the Research Institute of the McGill University Health Centre, Montreal, Canada
    2. McGill Scoliosis and Spine Group, Department of Surgery, McGill University and the Research Institute of the McGill University Health Centre, Montreal, Canada
    Contribution
    Validation, Investigation, Writing - review and editing
    For correspondence
    matthew.mannarino@mail.mcgill.ca
    Competing interests
    No competing interests declared
  4. Oded Rabau

    1. McGill Scoliosis and Spine Group, Department of Surgery, McGill University and the Research Institute of the McGill University Health Centre, Montreal, Canada
    2. Shriner’s Hospital for Children, 1003 Decarie Blvd, Montreal, Canada
    Contribution
    Resources, Validation, Writing - review and editing
    For correspondence
    odedrabau@gmail.com
    Competing interests
    No competing interests declared
  5. Jean A Ouellet

    1. McGill Scoliosis and Spine Group, Department of Surgery, McGill University and the Research Institute of the McGill University Health Centre, Montreal, Canada
    2. Shriner’s Hospital for Children, 1003 Decarie Blvd, Montreal, Canada
    Contribution
    Resources, Funding acquisition, Validation, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
  6. Lisbet Haglund

    1. Orthopaedic Research Lab, Department of Surgery, McGill University and the Research Institute of the McGill University Health Centre, Montreal, Canada
    2. McGill Scoliosis and Spine Group, Department of Surgery, McGill University and the Research Institute of the McGill University Health Centre, Montreal, Canada
    3. Shriner’s Hospital for Children, 1003 Decarie Blvd, Montreal, Canada
    Contribution
    Conceptualization, Supervision, Funding acquisition, Validation, Visualization, Methodology, Project administration, Writing - review and editing
    For correspondence
    lisbet.haglund@mcgill.ca
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1288-2149

Funding

Canadian Institutes of Health Research (CIHR MOP-119564)

  • Lisbet Haglund

Arthritis Society (TPF-19-0513)

  • Hosni Cherif

Réseau de Recherche en Santé Buccodentaire et Osseuse

  • Hosni Cherif

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This research was funded by the Canadian Institutes of Health Research (CIHR), grant CIHR MOP-119564, a major infrastructure grant and two postdoctoral fellowships to Dr. Cherif from Arthritis Society (AS) and Réseau de Recherche en Santé Buccodentaire et Osseuse (RSBO). The authors would like to acknowledge the contribution of Kai Sheng in the experimental work and Alain Pacis from the Canadian Centre for Computational Genomics (C3G) in the figure’s preparation.

Ethics

Human subjects: All procedures are approved by and performed in accordance with the ethical review board at the RI-MUHC (IRB#sTissue Biobank2019-4896Extracellular Matrix 2020-5647) . Familial consent was obtained for each subject.

Senior Editor

  1. Jessica K Tyler, Weill Cornell Medicine, United States

Reviewing Editor

  1. Yousin Suh, Columbia University, United States

Publication history

  1. Received: December 22, 2019
  2. Accepted: August 3, 2020
  3. Version of Record published: August 21, 2020 (version 1)

Copyright

© 2020, Cherif et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 483
    Page views
  • 74
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Cell Biology
    Shufen Li et al.
    Research Article

    Heterodimeric motor organization of kinesin-II is essential for its function in anterograde IFT in ciliogenesis. However, the underlying mechanism is not well understood. In addition, the anterograde IFT velocity varies significantly in different organisms, but how this velocity affects ciliary length is not clear. We show that in Chlamydomonas motors are only stable as heterodimers in vivo, which is likely the key factor for the requirement of a heterodimer for IFT. Second, chimeric CrKinesin-II with human kinesin-II motor domains functioned in vitro and in vivo, leading to a ~2.8-fold reduced anterograde IFT velocity and a similar fold reduction in IFT injection rate that supposedly correlates with ciliary assembly activity. However, the ciliary length was only mildly reduced (~15%). Modelling analysis suggests a nonlinear scaling relationship between IFT velocity and ciliary length that can be accounted for by limitation of the motors and/or its ciliary cargoes, e.g. tubulin.

    1. Cell Biology
    2. Developmental Biology
    Danielle Yi et al.
    Research Article

    Brown adipose tissue is a metabolically beneficial organ capable of dissipating chemical energy into heat, thereby increasing energy expenditure. Here, we identify Dot1l, the only known H3K79 methyltransferase, as an interacting partner of Zc3h10 that transcriptionally activates the Ucp1 promoter and other BAT genes. Through a direct interaction, Dot1l is recruited by Zc3h10 to the promoter regions of thermogenic genes to function as a coactivator by methylating H3K79. We also show that Dot1l is induced during brown fat cell differentiation and by cold exposure and that Dot1l and its H3K79 methyltransferase activity is required for thermogenic gene program. Furthermore, we demonstrate that Dot1l ablation in mice using Ucp1-Cre prevents activation of Ucp1 and other target genes to reduce thermogenic capacity and energy expenditure, promoting adiposity. Hence, Dot1l plays a critical role in the thermogenic program and may present as a future target for obesity therapeutics.